diff --git a/simulator/config/dasprotocol.cfg b/simulator/config/dasprotocol.cfg
index 53125240..9fcdece0 100644
--- a/simulator/config/dasprotocol.cfg
+++ b/simulator/config/dasprotocol.cfg
@@ -1,30 +1,30 @@
-# ::::::::::::::::::::::::::::::::::::::::::::::::::::::
+# :::::::::::::::::::::::::::::::::`:::::::::::::::::::::
 # :: Kademlia Default Configuration
 # ::::::::::::::::::::::::::::::::::::::::::::::::::::::
 
 # ::::: GLOBAL ::::::
 
 # Network size
-SIZE 5000
+SIZE 1000
 
 # Random seed
 K 5
 
-MINDELAY  100
-MAXDELAY  100
+MINDELAY  5
+MAXDELAY  200
 
 #Simulation time in ms
-SIM_TIME 1000*60*7
+SIM_TIME 11999
 
-CHURN_RATE_HOUR 5
 
 #Traffic generator is executed every TRAFFIC_STEP
-TRAFFIC_STEP  300000 #10000000/SIZE
+TRAFFIC_STEP  12000 #10000000/SIZE
 #Tracing module is executed every OBSERVER_STEP
-OBSERVER_STEP 100000
+OBSERVER_STEP 10000
 #Turbulence module is executed every TURBULENCE_STEP enabling churning
-TURBULENCE_STEP  3600000/(4*SIZE*CHURN_RATE_HOUR/100)  #100000000/SIZE
-
+TURBULENCE_STEP  1000
+TURBULENCE_STEP_NONVAL 1000
+REFRESH_STEP 10000000
 
 # add network config parameters to simulation
 random.seed 24680
@@ -39,14 +39,16 @@ network.size SIZE
 protocol.0link peersim.core.IdleProtocol
 
 #A protocol that stores links. It does nothing apart from that. Use by default
-protocol.1uniftr peersim.transport.UniformRandomTransport
-protocol.1uniftr.mindelay MINDELAY
-protocol.1uniftr.maxdelay MAXDELAY
+protocol.1pairwiselattr peersim.transport.PairwiseFixedLatencyTransportFile
+protocol.1pairwiselattr.mindelay MINDELAY
+protocol.1pairwiselattr.maxdelay MAXDELAY
+protocol.1pairwiselattr.size SIZE
+protocol.1pairwiselattr.file /home/sergi/workspace/kademlia-simulator/simulator/python/latencies.csv
 
 #transport layer that reliably delivers messages with a random delay, emulating TCP
 protocol.2unreltr peersim.transport.UnreliableTransport
 protocol.2unreltr.drop 0
-protocol.2unreltr.transport 1uniftr
+protocol.2unreltr.transport 1pairwiselattr
 
 #Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
 #Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
@@ -61,18 +63,32 @@ protocol.3kademlia.FINDMODE 1
 protocol.4dasprotocol peersim.kademlia.das.DASProtocolBuilder
 protocol.4dasprotocol.transport 2unreltr
 protocol.4dasprotocol.kademlia 3kademlia
+protocol.4dasprotocol.reportDiscovery false
+protocol.4dasprotocol.reportMsg true
 
 protocol.5dasprotocol peersim.kademlia.das.DASProtocolValidator
 protocol.5dasprotocol.transport 2unreltr
 protocol.5dasprotocol.kademlia 3kademlia
+protocol.5dasprotocol.reportDiscovery false
+protocol.5dasprotocol.reportMsg true
 
 protocol.6dasprotocol peersim.kademlia.das.DASProtocolNonValidator
 protocol.6dasprotocol.transport 2unreltr
 protocol.6dasprotocol.kademlia 3kademlia
+protocol.6dasprotocol.reportDiscovery false
+protocol.6dasprotocol.reportMsg true
 
-protocol.7evildasprotocol peersim.kademlia.das.EvilDASProtocol
+protocol.7evildasprotocol peersim.kademlia.das.DASProtocolEvilValidator
 protocol.7evildasprotocol.transport 2unreltr
 protocol.7evildasprotocol.kademlia 3kademlia
+protocol.7evildasprotocol.reportDiscovery false
+protocol.7evildasprotocol.reportMsg true
+
+protocol.8evildasprotocol peersim.kademlia.das.DASProtocolEvilNonValidator
+protocol.8evildasprotocol.transport 2unreltr
+protocol.8evildasprotocol.kademlia 3kademlia
+protocol.8evildasprotocol.reportDiscovery false
+protocol.8evildasprotocol.reportMsg true
 
 # ::::: INITIALIZERS :::::
 #Class that initializes nodes with kademlia protocol and generates uniform ids
@@ -81,8 +97,9 @@ init.1uniqueNodeID.protocolkad 3kademlia
 init.1uniqueNodeID.protocoldasbuilder 4dasprotocol
 init.1uniqueNodeID.protocoldasvalidator 5dasprotocol
 init.1uniqueNodeID.protocoldasnonvalidator 6dasprotocol
-init.1uniqueNodeID.protocolEvildas 7evildasprotocol
-init.1uniqueNodeID.validator_rate 0.5
+init.1uniqueNodeID.protocolEvilValDas 7evildasprotocol
+init.1uniqueNodeID.protocolEvildas 8evildasprotocol
+init.1uniqueNodeID.validator_rate 0.2
 init.1uniqueNodeID.evilNodeRatioValidator 0.0
 init.1uniqueNodeID.evilNodeRatioNonValidator 0.0
 
@@ -99,33 +116,44 @@ control.0traffic peersim.kademlia.das.TrafficGeneratorSample
 control.0traffic.step TRAFFIC_STEP
 control.0traffic.mapping_fn 2
 control.0traffic.sample_copy_per_node 2
-control.0traffic.block_dim_size 512
+control.0traffic.block_dim_size 100
+control.0traffic.num_samples 75
 control.0traffic.kadprotocol 3kademlia
+
+#TrafficGenerator class sends and initial 
+control.1refresh peersim.kademlia.das.RefreshSearchTable
+control.1refresh.step REFRESH_STEP
+
 # turbulence non-validator
-# control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
-# control.2turbolenceAdd.protocol 3kademlia
-# control.2turbolenceAdd.protocoldas 4dasprotocol
-# control.2turbolenceAdd.protocolevildas 7evildasprotocol
-# control.2turbolenceAdd.transport 2unreltr
-# control.2turbolenceAdd.step TURBULENCE_STEP
-# control.2turbolenceAdd.p_idle 0.5
-# control.2turbolenceAdd.p_rem 0.25
-# control.2turbolenceAdd.p_add 0.25
-
-# # turbulence validators
-# control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
-# control.3turbolenceAdd.protocol 3kademlia
-# control.3turbolenceAdd.protocoldas 4dasprotocol
-# control.3turbolenceAdd.protocolevildas 7evildasprotocol
-# control.3turbolenceAdd.transport 2unreltr
-# control.3turbolenceAdd.step TURBULENCE_STEP
-# control.3turbolenceAdd.p_idle 0.5
-# control.3turbolenceAdd.p_rem 0.25
-# control.3turbolenceAdd.p_add 0.25
+
+#control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
+#control.2turbolenceAdd.protocolkad 3kademlia
+#control.2turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.2turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.2turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.2turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.2turbolenceAdd.transport 2unreltr
+#control.2turbolenceAdd.step TURBULENCE_STEP_NONVAL
+#control.2turbolenceAdd.p_idle 0.1
+#control.2turbolenceAdd.p_rem 0.45
+#control.2turbolenceAdd.p_add 0.45
+
+# turbulence validators
+#control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
+#control.3turbolenceAdd.protocolkad 3kademlia
+#control.3turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.3turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.3turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.3turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.3turbolenceAdd.transport 2unreltr
+#control.3turbolenceAdd.step TURBULENCE_STEP
+#control.3turbolenceAdd.p_idle 0.9
+#control.3turbolenceAdd.p_rem 0.05
+#control.3turbolenceAdd.p_add 0.05
 
 # ::::: OBSERVER :::::
 #The observer is executed every OBSERVER_STEP and will generate data traces 
 control.4 peersim.kademlia.KademliaObserver
 control.4.protocol 3kademlia
 control.4.step OBSERVER_STEP
-control.4.logfolder logsDas
+control.4.logfolder logs
diff --git a/simulator/config/dasprotocol1000.cfg b/simulator/config/dasprotocol1000.cfg
new file mode 100644
index 00000000..b02b7fa2
--- /dev/null
+++ b/simulator/config/dasprotocol1000.cfg
@@ -0,0 +1,159 @@
+# :::::::::::::::::::::::::::::::::`:::::::::::::::::::::
+# :: Kademlia Default Configuration
+# ::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+# ::::: GLOBAL ::::::
+
+# Network size
+SIZE 1000
+
+# Random seed
+K 5
+
+MINDELAY  5
+MAXDELAY  125
+
+#Simulation time in ms
+SIM_TIME 11999
+
+
+#Traffic generator is executed every TRAFFIC_STEP
+TRAFFIC_STEP  12000 #10000000/SIZE
+#Tracing module is executed every OBSERVER_STEP
+OBSERVER_STEP 10000
+#Turbulence module is executed every TURBULENCE_STEP enabling churning
+TURBULENCE_STEP  1000
+TURBULENCE_STEP_NONVAL 1000
+REFRESH_STEP 10000000
+
+# add network config parameters to simulation
+random.seed 24680
+simulation.experiments 1
+simulation.endtime SIM_TIME
+network.size SIZE
+
+
+# Peersim  protocols enabled in each node
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.0link peersim.core.IdleProtocol
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.1pairwiselattr peersim.transport.PairwiseFixedLatencyTransport
+protocol.1pairwiselattr.mindelay MINDELAY
+protocol.1pairwiselattr.maxdelay MAXDELAY
+protocol.1pairwiselattr.size SIZE
+
+
+#transport layer that reliably delivers messages with a random delay, emulating TCP
+protocol.2unreltr peersim.transport.UnreliableTransport
+protocol.2unreltr.drop 0
+protocol.2unreltr.transport 1pairwiselattr
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.3kademlia peersim.kademlia.KademliaProtocol
+protocol.3kademlia.transport 2unreltr
+protocol.3kademlia.BITS 256
+protocol.3kademlia.NBUCKETS 17
+protocol.3kademlia.FINDMODE 1
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.4dasprotocol peersim.kademlia.das.DASProtocolBuilder
+protocol.4dasprotocol.transport 2unreltr
+protocol.4dasprotocol.kademlia 3kademlia
+protocol.4dasprotocol.reportDiscovery false
+protocol.4dasprotocol.reportMsg true
+
+protocol.5dasprotocol peersim.kademlia.das.DASProtocolValidator
+protocol.5dasprotocol.transport 2unreltr
+protocol.5dasprotocol.kademlia 3kademlia
+protocol.5dasprotocol.reportDiscovery false
+protocol.5dasprotocol.reportMsg true
+
+protocol.6dasprotocol peersim.kademlia.das.DASProtocolNonValidator
+protocol.6dasprotocol.transport 2unreltr
+protocol.6dasprotocol.kademlia 3kademlia
+protocol.6dasprotocol.reportDiscovery false
+protocol.6dasprotocol.reportMsg true
+
+protocol.7evildasprotocol peersim.kademlia.das.DASProtocolEvilValidator
+protocol.7evildasprotocol.transport 2unreltr
+protocol.7evildasprotocol.kademlia 3kademlia
+protocol.7evildasprotocol.reportDiscovery false
+protocol.7evildasprotocol.reportMsg true
+
+protocol.8evildasprotocol peersim.kademlia.das.DASProtocolEvilNonValidator
+protocol.8evildasprotocol.transport 2unreltr
+protocol.8evildasprotocol.kademlia 3kademlia
+protocol.8evildasprotocol.reportDiscovery false
+protocol.8evildasprotocol.reportMsg true
+
+# ::::: INITIALIZERS :::::
+#Class that initializes nodes with kademlia protocol and generates uniform ids
+init.1uniqueNodeID peersim.kademlia.das.CustomDistributionDas
+init.1uniqueNodeID.protocolkad 3kademlia
+init.1uniqueNodeID.protocoldasbuilder 4dasprotocol
+init.1uniqueNodeID.protocoldasvalidator 5dasprotocol
+init.1uniqueNodeID.protocoldasnonvalidator 6dasprotocol
+init.1uniqueNodeID.protocolEvilValDas 7evildasprotocol
+init.1uniqueNodeID.protocolEvildas 8evildasprotocol
+init.1uniqueNodeID.validator_rate 0.2
+init.1uniqueNodeID.evilNodeRatioValidator 0.0
+init.1uniqueNodeID.evilNodeRatioNonValidator 0.0
+
+
+#Adds initial state to the routing tables
+init.2statebuilder peersim.kademlia.StateBuilder
+init.2statebuilder.protocol 3kademlia
+init.2statebuilder.transport 2unreltr
+
+# ::::: CONTROLS :::::
+
+#TrafficGenerator class sends and initial 
+control.0traffic peersim.kademlia.das.TrafficGeneratorSample
+control.0traffic.step TRAFFIC_STEP
+control.0traffic.mapping_fn 2
+control.0traffic.sample_copy_per_node 2
+control.0traffic.block_dim_size 512
+control.0traffic.num_samples 75
+control.0traffic.kadprotocol 3kademlia
+
+#TrafficGenerator class sends and initial 
+control.1refresh peersim.kademlia.das.RefreshSearchTable
+control.1refresh.step REFRESH_STEP
+
+# turbulence non-validator
+
+#control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
+#control.2turbolenceAdd.protocolkad 3kademlia
+#control.2turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.2turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.2turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.2turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.2turbolenceAdd.transport 2unreltr
+#control.2turbolenceAdd.step TURBULENCE_STEP_NONVAL
+#control.2turbolenceAdd.p_idle 0.1
+#control.2turbolenceAdd.p_rem 0.45
+#control.2turbolenceAdd.p_add 0.45
+
+# turbulence validators
+#control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
+#control.3turbolenceAdd.protocolkad 3kademlia
+#control.3turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.3turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.3turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.3turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.3turbolenceAdd.transport 2unreltr
+#control.3turbolenceAdd.step TURBULENCE_STEP
+#control.3turbolenceAdd.p_idle 0.9
+#control.3turbolenceAdd.p_rem 0.05
+#control.3turbolenceAdd.p_add 0.05
+
+# ::::: OBSERVER :::::
+#The observer is executed every OBSERVER_STEP and will generate data traces 
+control.4 peersim.kademlia.KademliaObserver
+control.4.protocol 3kademlia
+control.4.step OBSERVER_STEP
+control.4.logfolder logs1000
diff --git a/simulator/config/dasprotocol10000.cfg b/simulator/config/dasprotocol10000.cfg
new file mode 100644
index 00000000..3f28a5b3
--- /dev/null
+++ b/simulator/config/dasprotocol10000.cfg
@@ -0,0 +1,159 @@
+# :::::::::::::::::::::::::::::::::`:::::::::::::::::::::
+# :: Kademlia Default Configuration
+# ::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+# ::::: GLOBAL ::::::
+
+# Network size
+SIZE 10000
+
+# Random seed
+K 5
+
+MINDELAY  5
+MAXDELAY  125
+
+#Simulation time in ms
+SIM_TIME 11999
+
+
+#Traffic generator is executed every TRAFFIC_STEP
+TRAFFIC_STEP  12000 #10000000/SIZE
+#Tracing module is executed every OBSERVER_STEP
+OBSERVER_STEP 10000
+#Turbulence module is executed every TURBULENCE_STEP enabling churning
+TURBULENCE_STEP  1000
+TURBULENCE_STEP_NONVAL 1000
+REFRESH_STEP 10000000
+
+# add network config parameters to simulation
+random.seed 24680
+simulation.experiments 1
+simulation.endtime SIM_TIME
+network.size SIZE
+
+
+# Peersim  protocols enabled in each node
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.0link peersim.core.IdleProtocol
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.1pairwiselattr peersim.transport.PairwiseFixedLatencyTransport
+protocol.1pairwiselattr.mindelay MINDELAY
+protocol.1pairwiselattr.maxdelay MAXDELAY
+protocol.1pairwiselattr.size SIZE
+
+
+#transport layer that reliably delivers messages with a random delay, emulating TCP
+protocol.2unreltr peersim.transport.UnreliableTransport
+protocol.2unreltr.drop 0
+protocol.2unreltr.transport 1pairwiselattr
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.3kademlia peersim.kademlia.KademliaProtocol
+protocol.3kademlia.transport 2unreltr
+protocol.3kademlia.BITS 256
+protocol.3kademlia.NBUCKETS 17
+protocol.3kademlia.FINDMODE 1
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.4dasprotocol peersim.kademlia.das.DASProtocolBuilder
+protocol.4dasprotocol.transport 2unreltr
+protocol.4dasprotocol.kademlia 3kademlia
+protocol.4dasprotocol.reportDiscovery false
+protocol.4dasprotocol.reportMsg true
+
+protocol.5dasprotocol peersim.kademlia.das.DASProtocolValidator
+protocol.5dasprotocol.transport 2unreltr
+protocol.5dasprotocol.kademlia 3kademlia
+protocol.5dasprotocol.reportDiscovery false
+protocol.5dasprotocol.reportMsg true
+
+protocol.6dasprotocol peersim.kademlia.das.DASProtocolNonValidator
+protocol.6dasprotocol.transport 2unreltr
+protocol.6dasprotocol.kademlia 3kademlia
+protocol.6dasprotocol.reportDiscovery false
+protocol.6dasprotocol.reportMsg true
+
+protocol.7evildasprotocol peersim.kademlia.das.DASProtocolEvilValidator
+protocol.7evildasprotocol.transport 2unreltr
+protocol.7evildasprotocol.kademlia 3kademlia
+protocol.7evildasprotocol.reportDiscovery false
+protocol.7evildasprotocol.reportMsg true
+
+protocol.8evildasprotocol peersim.kademlia.das.DASProtocolEvilNonValidator
+protocol.8evildasprotocol.transport 2unreltr
+protocol.8evildasprotocol.kademlia 3kademlia
+protocol.8evildasprotocol.reportDiscovery false
+protocol.8evildasprotocol.reportMsg true
+
+# ::::: INITIALIZERS :::::
+#Class that initializes nodes with kademlia protocol and generates uniform ids
+init.1uniqueNodeID peersim.kademlia.das.CustomDistributionDas
+init.1uniqueNodeID.protocolkad 3kademlia
+init.1uniqueNodeID.protocoldasbuilder 4dasprotocol
+init.1uniqueNodeID.protocoldasvalidator 5dasprotocol
+init.1uniqueNodeID.protocoldasnonvalidator 6dasprotocol
+init.1uniqueNodeID.protocolEvilValDas 7evildasprotocol
+init.1uniqueNodeID.protocolEvildas 8evildasprotocol
+init.1uniqueNodeID.validator_rate 0.2
+init.1uniqueNodeID.evilNodeRatioValidator 0.0
+init.1uniqueNodeID.evilNodeRatioNonValidator 0.0
+
+
+#Adds initial state to the routing tables
+init.2statebuilder peersim.kademlia.StateBuilder
+init.2statebuilder.protocol 3kademlia
+init.2statebuilder.transport 2unreltr
+
+# ::::: CONTROLS :::::
+
+#TrafficGenerator class sends and initial 
+control.0traffic peersim.kademlia.das.TrafficGeneratorSample
+control.0traffic.step TRAFFIC_STEP
+control.0traffic.mapping_fn 2
+control.0traffic.sample_copy_per_node 2
+control.0traffic.block_dim_size 512
+control.0traffic.num_samples 75
+control.0traffic.kadprotocol 3kademlia
+
+#TrafficGenerator class sends and initial 
+control.1refresh peersim.kademlia.das.RefreshSearchTable
+control.1refresh.step REFRESH_STEP
+
+# turbulence non-validator
+
+#control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
+#control.2turbolenceAdd.protocolkad 3kademlia
+#control.2turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.2turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.2turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.2turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.2turbolenceAdd.transport 2unreltr
+#control.2turbolenceAdd.step TURBULENCE_STEP_NONVAL
+#control.2turbolenceAdd.p_idle 0.1
+#control.2turbolenceAdd.p_rem 0.45
+#control.2turbolenceAdd.p_add 0.45
+
+# turbulence validators
+#control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
+#control.3turbolenceAdd.protocolkad 3kademlia
+#control.3turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.3turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.3turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.3turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.3turbolenceAdd.transport 2unreltr
+#control.3turbolenceAdd.step TURBULENCE_STEP
+#control.3turbolenceAdd.p_idle 0.9
+#control.3turbolenceAdd.p_rem 0.05
+#control.3turbolenceAdd.p_add 0.05
+
+# ::::: OBSERVER :::::
+#The observer is executed every OBSERVER_STEP and will generate data traces 
+control.4 peersim.kademlia.KademliaObserver
+control.4.protocol 3kademlia
+control.4.step OBSERVER_STEP
+control.4.logfolder logs10000
diff --git a/simulator/config/dasprotocol5000.cfg b/simulator/config/dasprotocol5000.cfg
new file mode 100644
index 00000000..8ad1b89c
--- /dev/null
+++ b/simulator/config/dasprotocol5000.cfg
@@ -0,0 +1,159 @@
+# :::::::::::::::::::::::::::::::::`:::::::::::::::::::::
+# :: Kademlia Default Configuration
+# ::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+# ::::: GLOBAL ::::::
+
+# Network size
+SIZE 5000
+
+# Random seed
+K 5
+
+MINDELAY  5
+MAXDELAY  125
+
+#Simulation time in ms
+SIM_TIME 11999
+
+
+#Traffic generator is executed every TRAFFIC_STEP
+TRAFFIC_STEP  12000 #10000000/SIZE
+#Tracing module is executed every OBSERVER_STEP
+OBSERVER_STEP 10000
+#Turbulence module is executed every TURBULENCE_STEP enabling churning
+TURBULENCE_STEP  1000
+TURBULENCE_STEP_NONVAL 1000
+REFRESH_STEP 10000000
+
+# add network config parameters to simulation
+random.seed 24680
+simulation.experiments 1
+simulation.endtime SIM_TIME
+network.size SIZE
+
+
+# Peersim  protocols enabled in each node
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.0link peersim.core.IdleProtocol
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.1pairwiselattr peersim.transport.PairwiseFixedLatencyTransport
+protocol.1pairwiselattr.mindelay MINDELAY
+protocol.1pairwiselattr.maxdelay MAXDELAY
+protocol.1pairwiselattr.size SIZE
+
+
+#transport layer that reliably delivers messages with a random delay, emulating TCP
+protocol.2unreltr peersim.transport.UnreliableTransport
+protocol.2unreltr.drop 0
+protocol.2unreltr.transport 1pairwiselattr
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.3kademlia peersim.kademlia.KademliaProtocol
+protocol.3kademlia.transport 2unreltr
+protocol.3kademlia.BITS 256
+protocol.3kademlia.NBUCKETS 17
+protocol.3kademlia.FINDMODE 1
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.4dasprotocol peersim.kademlia.das.DASProtocolBuilder
+protocol.4dasprotocol.transport 2unreltr
+protocol.4dasprotocol.kademlia 3kademlia
+protocol.4dasprotocol.reportDiscovery false
+protocol.4dasprotocol.reportMsg true
+
+protocol.5dasprotocol peersim.kademlia.das.DASProtocolValidator
+protocol.5dasprotocol.transport 2unreltr
+protocol.5dasprotocol.kademlia 3kademlia
+protocol.5dasprotocol.reportDiscovery false
+protocol.5dasprotocol.reportMsg true
+
+protocol.6dasprotocol peersim.kademlia.das.DASProtocolNonValidator
+protocol.6dasprotocol.transport 2unreltr
+protocol.6dasprotocol.kademlia 3kademlia
+protocol.6dasprotocol.reportDiscovery false
+protocol.6dasprotocol.reportMsg true
+
+protocol.7evildasprotocol peersim.kademlia.das.DASProtocolEvilValidator
+protocol.7evildasprotocol.transport 2unreltr
+protocol.7evildasprotocol.kademlia 3kademlia
+protocol.7evildasprotocol.reportDiscovery false
+protocol.7evildasprotocol.reportMsg true
+
+protocol.8evildasprotocol peersim.kademlia.das.DASProtocolEvilNonValidator
+protocol.8evildasprotocol.transport 2unreltr
+protocol.8evildasprotocol.kademlia 3kademlia
+protocol.8evildasprotocol.reportDiscovery false
+protocol.8evildasprotocol.reportMsg true
+
+# ::::: INITIALIZERS :::::
+#Class that initializes nodes with kademlia protocol and generates uniform ids
+init.1uniqueNodeID peersim.kademlia.das.CustomDistributionDas
+init.1uniqueNodeID.protocolkad 3kademlia
+init.1uniqueNodeID.protocoldasbuilder 4dasprotocol
+init.1uniqueNodeID.protocoldasvalidator 5dasprotocol
+init.1uniqueNodeID.protocoldasnonvalidator 6dasprotocol
+init.1uniqueNodeID.protocolEvilValDas 7evildasprotocol
+init.1uniqueNodeID.protocolEvildas 8evildasprotocol
+init.1uniqueNodeID.validator_rate 0.2
+init.1uniqueNodeID.evilNodeRatioValidator 0.0
+init.1uniqueNodeID.evilNodeRatioNonValidator 0.0
+
+
+#Adds initial state to the routing tables
+init.2statebuilder peersim.kademlia.StateBuilder
+init.2statebuilder.protocol 3kademlia
+init.2statebuilder.transport 2unreltr
+
+# ::::: CONTROLS :::::
+
+#TrafficGenerator class sends and initial 
+control.0traffic peersim.kademlia.das.TrafficGeneratorSample
+control.0traffic.step TRAFFIC_STEP
+control.0traffic.mapping_fn 2
+control.0traffic.sample_copy_per_node 2
+control.0traffic.block_dim_size 512
+control.0traffic.num_samples 75
+control.0traffic.kadprotocol 3kademlia
+
+#TrafficGenerator class sends and initial 
+control.1refresh peersim.kademlia.das.RefreshSearchTable
+control.1refresh.step REFRESH_STEP
+
+# turbulence non-validator
+
+#control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
+#control.2turbolenceAdd.protocolkad 3kademlia
+#control.2turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.2turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.2turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.2turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.2turbolenceAdd.transport 2unreltr
+#control.2turbolenceAdd.step TURBULENCE_STEP_NONVAL
+#control.2turbolenceAdd.p_idle 0.1
+#control.2turbolenceAdd.p_rem 0.45
+#control.2turbolenceAdd.p_add 0.45
+
+# turbulence validators
+#control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
+#control.3turbolenceAdd.protocolkad 3kademlia
+#control.3turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.3turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.3turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.3turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.3turbolenceAdd.transport 2unreltr
+#control.3turbolenceAdd.step TURBULENCE_STEP
+#control.3turbolenceAdd.p_idle 0.9
+#control.3turbolenceAdd.p_rem 0.05
+#control.3turbolenceAdd.p_add 0.05
+
+# ::::: OBSERVER :::::
+#The observer is executed every OBSERVER_STEP and will generate data traces 
+control.4 peersim.kademlia.KademliaObserver
+control.4.protocol 3kademlia
+control.4.step OBSERVER_STEP
+control.4.logfolder logs5000
diff --git a/simulator/config/dasprotocoldht.cfg b/simulator/config/dasprotocoldht.cfg
index 9ed7afb7..9db480c8 100644
--- a/simulator/config/dasprotocoldht.cfg
+++ b/simulator/config/dasprotocoldht.cfg
@@ -5,7 +5,7 @@
 # ::::: GLOBAL ::::::
 
 # Network size
-SIZE 5000
+SIZE 20000
 
 # Random seed
 K 5
@@ -14,16 +14,16 @@ MINDELAY  100
 MAXDELAY  100
 
 #Simulation time in ms
-SIM_TIME 1000*60*7
+SIM_TIME 1000*60*1
 
 CHURN_RATE_HOUR 5
 
 #Traffic generator is executed every TRAFFIC_STEP
-TRAFFIC_STEP  300000 #10000000/SIZE
+TRAFFIC_STEP  12000 #10000000/SIZE
 #Tracing module is executed every OBSERVER_STEP
-OBSERVER_STEP 100000
+OBSERVER_STEP 10000
 #Turbulence module is executed every TURBULENCE_STEP enabling churning
-TURBULENCE_STEP  3600000/(4*SIZE*CHURN_RATE_HOUR/100)  #100000000/SIZE
+TURBULENCE_STEP  120000000  #100000000/SIZE
 
 
 # add network config parameters to simulation
@@ -102,27 +102,28 @@ control.0traffic.mapping_fn 1
 control.0traffic.sample_copy_per_node 1
 control.0traffic.block_dim_size 512
 control.0traffic.kadprotocol 3kademlia
+
 # turbulence non-validator
-# control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
-# control.2turbolenceAdd.protocol 3kademlia
-# control.2turbolenceAdd.protocoldas 4dasprotocol
-# control.2turbolenceAdd.protocolevildas 7evildasprotocol
-# control.2turbolenceAdd.transport 2unreltr
-# control.2turbolenceAdd.step TURBULENCE_STEP
-# control.2turbolenceAdd.p_idle 0.5
-# control.2turbolenceAdd.p_rem 0.25
-# control.2turbolenceAdd.p_add 0.25
-
-# # turbulence validators
-# control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
-# control.3turbolenceAdd.protocol 3kademlia
-# control.3turbolenceAdd.protocoldas 4dasprotocol
-# control.3turbolenceAdd.protocolevildas 7evildasprotocol
-# control.3turbolenceAdd.transport 2unreltr
-# control.3turbolenceAdd.step TURBULENCE_STEP
-# control.3turbolenceAdd.p_idle 0.5
-# control.3turbolenceAdd.p_rem 0.25
-# control.3turbolenceAdd.p_add 0.25
+control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
+control.2turbolenceAdd.protocol 3kademlia
+control.2turbolenceAdd.protocoldas 4dasprotocol
+control.2turbolenceAdd.protocolevildas 7evildasprotocol
+control.2turbolenceAdd.transport 2unreltr
+control.2turbolenceAdd.step TURBULENCE_STEP
+control.2turbolenceAdd.p_idle 0.5
+control.2turbolenceAdd.p_rem 0.25
+control.2turbolenceAdd.p_add 0.25
+
+# turbulence validators
+control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
+control.3turbolenceAdd.protocol 3kademlia
+control.3turbolenceAdd.protocoldas 4dasprotocol
+control.3turbolenceAdd.protocolevildas 7evildasprotocol
+control.3turbolenceAdd.transport 2unreltr
+control.3turbolenceAdd.step TURBULENCE_STEP
+control.3turbolenceAdd.p_idle 0.5
+control.3turbolenceAdd.p_rem 0.25
+control.3turbolenceAdd.p_add 0.25
 
 # ::::: OBSERVER :::::
 #The observer is executed every OBSERVER_STEP and will generate data traces 
diff --git a/simulator/config/malicious/dasprotocolevil.cfg b/simulator/config/malicious/dasprotocolevil.cfg
index 311b216d..7f061503 100644
--- a/simulator/config/malicious/dasprotocolevil.cfg
+++ b/simulator/config/malicious/dasprotocolevil.cfg
@@ -1,28 +1,30 @@
-# ::::::::::::::::::::::::::::::::::::::::::::::::::::::
+# :::::::::::::::::::::::::::::::::`:::::::::::::::::::::
 # :: Kademlia Default Configuration
 # ::::::::::::::::::::::::::::::::::::::::::::::::::::::
 
 # ::::: GLOBAL ::::::
 
 # Network size
-SIZE 5000
+SIZE 10000
 
 # Random seed
 K 5
 
-MINDELAY  100
+MINDELAY  5
 MAXDELAY  100
 
 #Simulation time in ms
-SIM_TIME 1000*60*12
+SIM_TIME 60001
+
 
 #Traffic generator is executed every TRAFFIC_STEP
-TRAFFIC_STEP  300000 #10000000/SIZE
+TRAFFIC_STEP  12000 #10000000/SIZE
 #Tracing module is executed every OBSERVER_STEP
-OBSERVER_STEP 100000
+OBSERVER_STEP 1000
 #Turbulence module is executed every TURBULENCE_STEP enabling churning
-TURBULENCE_STEP  (SIM_TIME*20)/SIZE   #100000000/SIZE
-
+TURBULENCE_STEP  1000
+TURBULENCE_STEP_NONVAL  1000
+REFRESH_STEP 1000
 
 # add network config parameters to simulation
 random.seed 24680
@@ -37,14 +39,16 @@ network.size SIZE
 protocol.0link peersim.core.IdleProtocol
 
 #A protocol that stores links. It does nothing apart from that. Use by default
-protocol.1uniftr peersim.transport.UniformRandomTransport
-protocol.1uniftr.mindelay MINDELAY
-protocol.1uniftr.maxdelay MAXDELAY
+protocol.1pairwiselattr peersim.transport.PairwiseFixedLatencyTransport
+protocol.1pairwiselattr.mindelay MINDELAY
+protocol.1pairwiselattr.maxdelay MAXDELAY
+protocol.1pairwiselattr.size SIZE
+
 
 #transport layer that reliably delivers messages with a random delay, emulating TCP
 protocol.2unreltr peersim.transport.UnreliableTransport
 protocol.2unreltr.drop 0
-protocol.2unreltr.transport 1uniftr
+protocol.2unreltr.transport 1pairwiselattr
 
 #Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
 #Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
@@ -56,23 +60,43 @@ protocol.3kademlia.FINDMODE 1
 
 #Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
 #Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
-protocol.4dasprotocol peersim.kademlia.das.DASProtocol
+protocol.4dasprotocol peersim.kademlia.das.DASProtocolBuilder
 protocol.4dasprotocol.transport 2unreltr
 protocol.4dasprotocol.kademlia 3kademlia
+protocol.4dasprotocol.reportDiscovery false
+
+protocol.5dasprotocol peersim.kademlia.das.DASProtocolValidator
+protocol.5dasprotocol.transport 2unreltr
+protocol.5dasprotocol.kademlia 3kademlia
+protocol.5dasprotocol.reportDiscovery false
+
+protocol.6dasprotocol peersim.kademlia.das.DASProtocolNonValidator
+protocol.6dasprotocol.transport 2unreltr
+protocol.6dasprotocol.kademlia 3kademlia
+protocol.6dasprotocol.reportDiscovery false
 
-protocol.5evildasprotocol peersim.kademlia.das.EvilDASProtocol
-protocol.5evildasprotocol.transport 2unreltr
-protocol.5evildasprotocol.kademlia 3kademlia
-#init.1uniqueNodeID.protocolEvildas 5evildasprotocol
+protocol.7evildasprotocol peersim.kademlia.das.DASProtocolEvilValidator
+protocol.7evildasprotocol.transport 2unreltr
+protocol.7evildasprotocol.kademlia 3kademlia
+protocol.7evildasprotocol.reportDiscovery false
+
+protocol.8evildasprotocol peersim.kademlia.das.DASProtocolEvilNonValidator
+protocol.8evildasprotocol.transport 2unreltr
+protocol.8evildasprotocol.kademlia 3kademlia
+protocol.8evildasprotocol.reportDiscovery false
 
 # ::::: INITIALIZERS :::::
 #Class that initializes nodes with kademlia protocol and generates uniform ids
 init.1uniqueNodeID peersim.kademlia.das.CustomDistributionDas
 init.1uniqueNodeID.protocolkad 3kademlia
-init.1uniqueNodeID.protocoldas 4dasprotocol
-init.1uniqueNodeID.protocolEvildas 5evildasprotocol
-init.1uniqueNodeID.validator_rate 1.0
-init.1uniqueNodeID.evilNodeRatio 0.5
+init.1uniqueNodeID.protocoldasbuilder 4dasprotocol
+init.1uniqueNodeID.protocoldasvalidator 5dasprotocol
+init.1uniqueNodeID.protocoldasnonvalidator 6dasprotocol
+init.1uniqueNodeID.protocolEvilValDas 7evildasprotocol
+init.1uniqueNodeID.protocolEvildas 8evildasprotocol
+init.1uniqueNodeID.validator_rate 0.5
+init.1uniqueNodeID.evilNodeRatioValidator 0.5
+init.1uniqueNodeID.evilNodeRatioNonValidator 0.5
 
 
 #Adds initial state to the routing tables
@@ -84,25 +108,46 @@ init.2statebuilder.transport 2unreltr
 
 #TrafficGenerator class sends and initial 
 control.0traffic peersim.kademlia.das.TrafficGeneratorSample
-control.0traffic.kadprotocol 3kademlia
-control.0traffic.dasprotocol 4dasprotocol
 control.0traffic.step TRAFFIC_STEP
 control.0traffic.mapping_fn 2
-control.0traffic.sample_copy_per_node 2
-control.0traffic.block_dim_size 512
-
-# turbulence
-#control.2turbolenceAdd peersim.kademlia.Turbulence
-#control.2turbolenceAdd.protocol 3kademlia
-#control.2turbolenceAdd.transport 2unreltr
-#control.2turbolenceAdd.step TURBULENCE_STEP
-#control.2turbolenceAdd.p_idle 0.5
-#control.2turbolenceAdd.p_rem 0.25
-#control.2turbolenceAdd.p_add 0.25
+control.0traffic.sample_copy_per_node 3
+control.0traffic.block_dim_size 100
+control.0traffic.num_samples 75
+control.0traffic.kadprotocol 3kademlia
+
+#TrafficGenerator class sends and initial 
+control.1refresh peersim.kademlia.das.RefreshSearchTable
+control.1refresh.step REFRESH_STEP
+
+# turbulence non-validator
+control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
+control.2turbolenceAdd.protocolkad 3kademlia
+control.2turbolenceAdd.protocoldasbuilder 4dasprotocol
+control.2turbolenceAdd.protocoldasvalidator 5dasprotocol
+control.2turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+control.2turbolenceAdd.protocolEvildas 7evildasprotocol
+control.2turbolenceAdd.transport 2unreltr
+control.2turbolenceAdd.step TURBULENCE_STEP_NONVAL
+control.2turbolenceAdd.p_idle 0.1
+control.2turbolenceAdd.p_rem 0.45
+control.2turbolenceAdd.p_add 0.45
+
+# turbulence validators
+control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
+control.3turbolenceAdd.protocolkad 3kademlia
+control.3turbolenceAdd.protocoldasbuilder 4dasprotocol
+control.3turbolenceAdd.protocoldasvalidator 5dasprotocol
+control.3turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+control.3turbolenceAdd.protocolEvildas 7evildasprotocol
+control.3turbolenceAdd.transport 2unreltr
+control.3turbolenceAdd.step TURBULENCE_STEP
+control.3turbolenceAdd.p_idle 0.9
+control.3turbolenceAdd.p_rem 0.05
+control.3turbolenceAdd.p_add 0.05
 
 # ::::: OBSERVER :::::
 #The observer is executed every OBSERVER_STEP and will generate data traces 
-control.3 peersim.kademlia.KademliaObserver
-control.3.protocol 3kademlia
-control.3.step OBSERVER_STEP
-control.3.logfolder logsTest0.5
\ No newline at end of file
+control.4 peersim.kademlia.KademliaObserver
+control.4.protocol 3kademlia
+control.4.step OBSERVER_STEP
+control.4.logfolder logsDasEvil0.5Test
diff --git a/simulator/config/malicious/dasprotocolevil0.25.cfg b/simulator/config/malicious/dasprotocolevil0.25.cfg
new file mode 100644
index 00000000..ecd9b35d
--- /dev/null
+++ b/simulator/config/malicious/dasprotocolevil0.25.cfg
@@ -0,0 +1,154 @@
+# :::::::::::::::::::::::::::::::::`:::::::::::::::::::::
+# :: Kademlia Default Configuration
+# ::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+# ::::: GLOBAL ::::::
+
+# Network size
+SIZE 5000
+
+# Random seed
+K 5
+
+MINDELAY  5
+MAXDELAY  100
+
+#Simulation time in ms
+SIM_TIME 60001
+
+
+#Traffic generator is executed every TRAFFIC_STEP
+TRAFFIC_STEP  12000 #10000000/SIZE
+#Tracing module is executed every OBSERVER_STEP
+OBSERVER_STEP 1000
+#Turbulence module is executed every TURBULENCE_STEP enabling churning
+TURBULENCE_STEP  1000
+TURBULENCE_STEP_NONVAL  1000
+REFRESH_STEP 1000
+
+# add network config parameters to simulation
+random.seed 24680
+simulation.experiments 1
+simulation.endtime SIM_TIME
+network.size SIZE
+
+
+# Peersim  protocols enabled in each node
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.0link peersim.core.IdleProtocol
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.1pairwiselattr peersim.transport.PairwiseFixedLatencyTransport
+protocol.1pairwiselattr.mindelay MINDELAY
+protocol.1pairwiselattr.maxdelay MAXDELAY
+protocol.1pairwiselattr.size SIZE
+
+
+#transport layer that reliably delivers messages with a random delay, emulating TCP
+protocol.2unreltr peersim.transport.UnreliableTransport
+protocol.2unreltr.drop 0
+protocol.2unreltr.transport 1pairwiselattr
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.3kademlia peersim.kademlia.KademliaProtocol
+protocol.3kademlia.transport 2unreltr
+protocol.3kademlia.BITS 256
+protocol.3kademlia.NBUCKETS 17
+protocol.3kademlia.FINDMODE 1
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.4dasprotocol peersim.kademlia.das.DASProtocolBuilder
+protocol.4dasprotocol.transport 2unreltr
+protocol.4dasprotocol.kademlia 3kademlia
+protocol.4dasprotocol.reportDiscovery false
+
+protocol.5dasprotocol peersim.kademlia.das.DASProtocolValidator
+protocol.5dasprotocol.transport 2unreltr
+protocol.5dasprotocol.kademlia 3kademlia
+protocol.5dasprotocol.reportDiscovery false
+
+protocol.6dasprotocol peersim.kademlia.das.DASProtocolNonValidator
+protocol.6dasprotocol.transport 2unreltr
+protocol.6dasprotocol.kademlia 3kademlia
+protocol.6dasprotocol.reportDiscovery false
+
+protocol.7evildasprotocol peersim.kademlia.das.DASProtocolEvilValidator
+protocol.7evildasprotocol.transport 2unreltr
+protocol.7evildasprotocol.kademlia 3kademlia
+protocol.7evildasprotocol.reportDiscovery false
+
+protocol.8evildasprotocol peersim.kademlia.das.DASProtocolEvilNonValidator
+protocol.8evildasprotocol.transport 2unreltr
+protocol.8evildasprotocol.kademlia 3kademlia
+protocol.8evildasprotocol.reportDiscovery false
+
+# ::::: INITIALIZERS :::::
+#Class that initializes nodes with kademlia protocol and generates uniform ids
+init.1uniqueNodeID peersim.kademlia.das.CustomDistributionDas
+init.1uniqueNodeID.protocolkad 3kademlia
+init.1uniqueNodeID.protocoldasbuilder 4dasprotocol
+init.1uniqueNodeID.protocoldasvalidator 5dasprotocol
+init.1uniqueNodeID.protocoldasnonvalidator 6dasprotocol
+init.1uniqueNodeID.protocolEvilValDas 7evildasprotocol
+init.1uniqueNodeID.protocolEvildas 8evildasprotocol
+init.1uniqueNodeID.validator_rate 0.5
+init.1uniqueNodeID.evilNodeRatioValidator 0.25
+init.1uniqueNodeID.evilNodeRatioNonValidator 0.25
+
+
+#Adds initial state to the routing tables
+init.2statebuilder peersim.kademlia.StateBuilder
+init.2statebuilder.protocol 3kademlia
+init.2statebuilder.transport 2unreltr
+
+# ::::: CONTROLS :::::
+
+#TrafficGenerator class sends and initial 
+control.0traffic peersim.kademlia.das.TrafficGeneratorSample
+control.0traffic.step TRAFFIC_STEP
+control.0traffic.mapping_fn 2
+control.0traffic.sample_copy_per_node 3
+control.0traffic.block_dim_size 100
+control.0traffic.num_samples 75
+control.0traffic.kadprotocol 3kademlia
+
+#TrafficGenerator class sends and initial 
+control.1refresh peersim.kademlia.das.RefreshSearchTable
+control.1refresh.step REFRESH_STEP
+
+# turbulence non-validator
+
+#control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
+#control.2turbolenceAdd.protocolkad 3kademlia
+#control.2turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.2turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.2turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.2turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.2turbolenceAdd.transport 2unreltr
+#control.2turbolenceAdd.step TURBULENCE_STEP_NONVAL
+#control.2turbolenceAdd.p_idle 0.1
+#control.2turbolenceAdd.p_rem 0.45
+#control.2turbolenceAdd.p_add 0.45
+
+# turbulence validators
+#control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
+#control.3turbolenceAdd.protocolkad 3kademlia
+#control.3turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.3turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.3turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.3turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.3turbolenceAdd.transport 2unreltr
+#control.3turbolenceAdd.step TURBULENCE_STEP
+#control.3turbolenceAdd.p_idle 0.9
+#control.3turbolenceAdd.p_rem 0.05
+#control.3turbolenceAdd.p_add 0.05
+
+# ::::: OBSERVER :::::
+#The observer is executed every OBSERVER_STEP and will generate data traces 
+control.4 peersim.kademlia.KademliaObserver
+control.4.protocol 3kademlia
+control.4.step OBSERVER_STEP
+control.4.logfolder logsDasEvil0.25
diff --git a/simulator/config/malicious/dasprotocolevil0.5.cfg b/simulator/config/malicious/dasprotocolevil0.5.cfg
new file mode 100644
index 00000000..df35a025
--- /dev/null
+++ b/simulator/config/malicious/dasprotocolevil0.5.cfg
@@ -0,0 +1,154 @@
+# :::::::::::::::::::::::::::::::::`:::::::::::::::::::::
+# :: Kademlia Default Configuration
+# ::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+# ::::: GLOBAL ::::::
+
+# Network size
+SIZE 50000
+
+# Random seed
+K 5
+
+MINDELAY  5
+MAXDELAY  100
+
+#Simulation time in ms
+SIM_TIME 11999
+
+
+#Traffic generator is executed every TRAFFIC_STEP
+TRAFFIC_STEP  12000 #10000000/SIZE
+#Tracing module is executed every OBSERVER_STEP
+OBSERVER_STEP 10000
+#Turbulence module is executed every TURBULENCE_STEP enabling churning
+TURBULENCE_STEP  1000
+TURBULENCE_STEP_NONVAL 1000
+REFRESH_STEP 10000000
+
+# add network config parameters to simulation
+random.seed 24680
+simulation.experiments 1
+simulation.endtime SIM_TIME
+network.size SIZE
+
+
+# Peersim  protocols enabled in each node
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.0link peersim.core.IdleProtocol
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.1pairwiselattr peersim.transport.PairwiseFixedLatencyTransport
+protocol.1pairwiselattr.mindelay MINDELAY
+protocol.1pairwiselattr.maxdelay MAXDELAY
+protocol.1pairwiselattr.size SIZE
+
+
+#transport layer that reliably delivers messages with a random delay, emulating TCP
+protocol.2unreltr peersim.transport.UnreliableTransport
+protocol.2unreltr.drop 0
+protocol.2unreltr.transport 1pairwiselattr
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.3kademlia peersim.kademlia.KademliaProtocol
+protocol.3kademlia.transport 2unreltr
+protocol.3kademlia.BITS 256
+protocol.3kademlia.NBUCKETS 17
+protocol.3kademlia.FINDMODE 1
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.4dasprotocol peersim.kademlia.das.DASProtocolBuilder
+protocol.4dasprotocol.transport 2unreltr
+protocol.4dasprotocol.kademlia 3kademlia
+protocol.4dasprotocol.reportDiscovery false
+
+protocol.5dasprotocol peersim.kademlia.das.DASProtocolValidator
+protocol.5dasprotocol.transport 2unreltr
+protocol.5dasprotocol.kademlia 3kademlia
+protocol.5dasprotocol.reportDiscovery false
+
+protocol.6dasprotocol peersim.kademlia.das.DASProtocolNonValidator
+protocol.6dasprotocol.transport 2unreltr
+protocol.6dasprotocol.kademlia 3kademlia
+protocol.6dasprotocol.reportDiscovery false
+
+protocol.7evildasprotocol peersim.kademlia.das.DASProtocolEvilValidator
+protocol.7evildasprotocol.transport 2unreltr
+protocol.7evildasprotocol.kademlia 3kademlia
+protocol.7evildasprotocol.reportDiscovery false
+
+protocol.8evildasprotocol peersim.kademlia.das.DASProtocolEvilNonValidator
+protocol.8evildasprotocol.transport 2unreltr
+protocol.8evildasprotocol.kademlia 3kademlia
+protocol.8evildasprotocol.reportDiscovery false
+
+# ::::: INITIALIZERS :::::
+#Class that initializes nodes with kademlia protocol and generates uniform ids
+init.1uniqueNodeID peersim.kademlia.das.CustomDistributionDas
+init.1uniqueNodeID.protocolkad 3kademlia
+init.1uniqueNodeID.protocoldasbuilder 4dasprotocol
+init.1uniqueNodeID.protocoldasvalidator 5dasprotocol
+init.1uniqueNodeID.protocoldasnonvalidator 6dasprotocol
+init.1uniqueNodeID.protocolEvilValDas 7evildasprotocol
+init.1uniqueNodeID.protocolEvildas 8evildasprotocol
+init.1uniqueNodeID.validator_rate 0.5
+init.1uniqueNodeID.evilNodeRatioValidator 0.5
+init.1uniqueNodeID.evilNodeRatioNonValidator 0.5
+
+
+#Adds initial state to the routing tables
+init.2statebuilder peersim.kademlia.StateBuilder
+init.2statebuilder.protocol 3kademlia
+init.2statebuilder.transport 2unreltr
+
+# ::::: CONTROLS :::::
+
+#TrafficGenerator class sends and initial 
+control.0traffic peersim.kademlia.das.TrafficGeneratorSample
+control.0traffic.step TRAFFIC_STEP
+control.0traffic.mapping_fn 2
+control.0traffic.sample_copy_per_node 2
+control.0traffic.block_dim_size 512
+control.0traffic.num_samples 75
+control.0traffic.kadprotocol 3kademlia
+
+#TrafficGenerator class sends and initial 
+control.1refresh peersim.kademlia.das.RefreshSearchTable
+control.1refresh.step REFRESH_STEP
+
+# turbulence non-validator
+
+#control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
+#control.2turbolenceAdd.protocolkad 3kademlia
+#control.2turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.2turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.2turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.2turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.2turbolenceAdd.transport 2unreltr
+#control.2turbolenceAdd.step TURBULENCE_STEP_NONVAL
+#control.2turbolenceAdd.p_idle 0.1
+#control.2turbolenceAdd.p_rem 0.45
+#control.2turbolenceAdd.p_add 0.45
+
+# turbulence validators
+#control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
+#control.3turbolenceAdd.protocolkad 3kademlia
+#control.3turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.3turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.3turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.3turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.3turbolenceAdd.transport 2unreltr
+#control.3turbolenceAdd.step TURBULENCE_STEP
+#control.3turbolenceAdd.p_idle 0.9
+#control.3turbolenceAdd.p_rem 0.05
+#control.3turbolenceAdd.p_add 0.05
+
+# ::::: OBSERVER :::::
+#The observer is executed every OBSERVER_STEP and will generate data traces 
+control.4 peersim.kademlia.KademliaObserver
+control.4.protocol 3kademlia
+control.4.step OBSERVER_STEP
+control.4.logfolder logsDasEvil0
diff --git a/simulator/config/malicious/dasprotocolevil0.75.cfg b/simulator/config/malicious/dasprotocolevil0.75.cfg
new file mode 100644
index 00000000..628285af
--- /dev/null
+++ b/simulator/config/malicious/dasprotocolevil0.75.cfg
@@ -0,0 +1,154 @@
+# :::::::::::::::::::::::::::::::::`:::::::::::::::::::::
+# :: Kademlia Default Configuration
+# ::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+# ::::: GLOBAL ::::::
+
+# Network size
+SIZE 5000
+
+# Random seed
+K 5
+
+MINDELAY  10
+MAXDELAY  100
+
+#Simulation time in ms
+SIM_TIME 11999
+
+
+#Traffic generator is executed every TRAFFIC_STEP
+TRAFFIC_STEP  12000 #10000000/SIZE
+#Tracing module is executed every OBSERVER_STEP
+OBSERVER_STEP 10000
+#Turbulence module is executed every TURBULENCE_STEP enabling churning
+TURBULENCE_STEP  1000
+TURBULENCE_STEP_NONVAL 1000
+REFRESH_STEP 10000000
+
+# add network config parameters to simulation
+random.seed 24680
+simulation.experiments 1
+simulation.endtime SIM_TIME
+network.size SIZE
+
+
+# Peersim  protocols enabled in each node
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.0link peersim.core.IdleProtocol
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.1pairwiselattr peersim.transport.PairwiseFixedLatencyTransport
+protocol.1pairwiselattr.mindelay MINDELAY
+protocol.1pairwiselattr.maxdelay MAXDELAY
+protocol.1pairwiselattr.size SIZE
+
+
+#transport layer that reliably delivers messages with a random delay, emulating TCP
+protocol.2unreltr peersim.transport.UnreliableTransport
+protocol.2unreltr.drop 0
+protocol.2unreltr.transport 1pairwiselattr
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.3kademlia peersim.kademlia.KademliaProtocol
+protocol.3kademlia.transport 2unreltr
+protocol.3kademlia.BITS 256
+protocol.3kademlia.NBUCKETS 17
+protocol.3kademlia.FINDMODE 1
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.4dasprotocol peersim.kademlia.das.DASProtocolBuilder
+protocol.4dasprotocol.transport 2unreltr
+protocol.4dasprotocol.kademlia 3kademlia
+protocol.4dasprotocol.reportDiscovery false
+
+protocol.5dasprotocol peersim.kademlia.das.DASProtocolValidator
+protocol.5dasprotocol.transport 2unreltr
+protocol.5dasprotocol.kademlia 3kademlia
+protocol.5dasprotocol.reportDiscovery false
+
+protocol.6dasprotocol peersim.kademlia.das.DASProtocolNonValidator
+protocol.6dasprotocol.transport 2unreltr
+protocol.6dasprotocol.kademlia 3kademlia
+protocol.6dasprotocol.reportDiscovery false
+
+protocol.7evildasprotocol peersim.kademlia.das.DASProtocolEvilValidator
+protocol.7evildasprotocol.transport 2unreltr
+protocol.7evildasprotocol.kademlia 3kademlia
+protocol.7evildasprotocol.reportDiscovery false
+
+protocol.8evildasprotocol peersim.kademlia.das.DASProtocolEvilNonValidator
+protocol.8evildasprotocol.transport 2unreltr
+protocol.8evildasprotocol.kademlia 3kademlia
+protocol.8evildasprotocol.reportDiscovery false
+
+# ::::: INITIALIZERS :::::
+#Class that initializes nodes with kademlia protocol and generates uniform ids
+init.1uniqueNodeID peersim.kademlia.das.CustomDistributionDas
+init.1uniqueNodeID.protocolkad 3kademlia
+init.1uniqueNodeID.protocoldasbuilder 4dasprotocol
+init.1uniqueNodeID.protocoldasvalidator 5dasprotocol
+init.1uniqueNodeID.protocoldasnonvalidator 6dasprotocol
+init.1uniqueNodeID.protocolEvilValDas 7evildasprotocol
+init.1uniqueNodeID.protocolEvildas 8evildasprotocol
+init.1uniqueNodeID.validator_rate 0.5
+init.1uniqueNodeID.evilNodeRatioValidator 0.75
+init.1uniqueNodeID.evilNodeRatioNonValidator 0.75
+
+
+#Adds initial state to the routing tables
+init.2statebuilder peersim.kademlia.StateBuilder
+init.2statebuilder.protocol 3kademlia
+init.2statebuilder.transport 2unreltr
+
+# ::::: CONTROLS :::::
+
+#TrafficGenerator class sends and initial 
+control.0traffic peersim.kademlia.das.TrafficGeneratorSample
+control.0traffic.step TRAFFIC_STEP
+control.0traffic.mapping_fn 2
+control.0traffic.sample_copy_per_node 2
+control.0traffic.block_dim_size 512
+control.0traffic.num_samples 75
+control.0traffic.kadprotocol 3kademlia
+
+#TrafficGenerator class sends and initial 
+control.1refresh peersim.kademlia.das.RefreshSearchTable
+control.1refresh.step REFRESH_STEP
+
+# turbulence non-validator
+
+#control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
+#control.2turbolenceAdd.protocolkad 3kademlia
+#control.2turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.2turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.2turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.2turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.2turbolenceAdd.transport 2unreltr
+#control.2turbolenceAdd.step TURBULENCE_STEP_NONVAL
+#control.2turbolenceAdd.p_idle 0.1
+#control.2turbolenceAdd.p_rem 0.45
+#control.2turbolenceAdd.p_add 0.45
+
+# turbulence validators
+#control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
+#control.3turbolenceAdd.protocolkad 3kademlia
+#control.3turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.3turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.3turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.3turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.3turbolenceAdd.transport 2unreltr
+#control.3turbolenceAdd.step TURBULENCE_STEP
+#control.3turbolenceAdd.p_idle 0.9
+#control.3turbolenceAdd.p_rem 0.05
+#control.3turbolenceAdd.p_add 0.05
+
+# ::::: OBSERVER :::::
+#The observer is executed every OBSERVER_STEP and will generate data traces 
+control.4 peersim.kademlia.KademliaObserver
+control.4.protocol 3kademlia
+control.4.step OBSERVER_STEP
+control.4.logfolder logsDasEvil0.75
diff --git a/simulator/config/malicious/dasprotocolevil0.9.cfg b/simulator/config/malicious/dasprotocolevil0.9.cfg
new file mode 100644
index 00000000..cb9c93f7
--- /dev/null
+++ b/simulator/config/malicious/dasprotocolevil0.9.cfg
@@ -0,0 +1,154 @@
+# :::::::::::::::::::::::::::::::::`:::::::::::::::::::::
+# :: Kademlia Default Configuration
+# ::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+# ::::: GLOBAL ::::::
+
+# Network size
+SIZE 10000
+
+# Random seed
+K 5
+
+MINDELAY  5
+MAXDELAY  100
+
+#Simulation time in ms
+SIM_TIME 60001
+
+
+#Traffic generator is executed every TRAFFIC_STEP
+TRAFFIC_STEP  12000 #10000000/SIZE
+#Tracing module is executed every OBSERVER_STEP
+OBSERVER_STEP 1000
+#Turbulence module is executed every TURBULENCE_STEP enabling churning
+TURBULENCE_STEP  1000
+TURBULENCE_STEP_NONVAL  1000
+REFRESH_STEP 1000
+
+# add network config parameters to simulation
+random.seed 24680
+simulation.experiments 1
+simulation.endtime SIM_TIME
+network.size SIZE
+
+
+# Peersim  protocols enabled in each node
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.0link peersim.core.IdleProtocol
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.1pairwiselattr peersim.transport.PairwiseFixedLatencyTransport
+protocol.1pairwiselattr.mindelay MINDELAY
+protocol.1pairwiselattr.maxdelay MAXDELAY
+protocol.1pairwiselattr.size SIZE
+
+
+#transport layer that reliably delivers messages with a random delay, emulating TCP
+protocol.2unreltr peersim.transport.UnreliableTransport
+protocol.2unreltr.drop 0
+protocol.2unreltr.transport 1pairwiselattr
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.3kademlia peersim.kademlia.KademliaProtocol
+protocol.3kademlia.transport 2unreltr
+protocol.3kademlia.BITS 256
+protocol.3kademlia.NBUCKETS 17
+protocol.3kademlia.FINDMODE 1
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.4dasprotocol peersim.kademlia.das.DASProtocolBuilder
+protocol.4dasprotocol.transport 2unreltr
+protocol.4dasprotocol.kademlia 3kademlia
+protocol.4dasprotocol.reportDiscovery false
+
+protocol.5dasprotocol peersim.kademlia.das.DASProtocolValidator
+protocol.5dasprotocol.transport 2unreltr
+protocol.5dasprotocol.kademlia 3kademlia
+protocol.5dasprotocol.reportDiscovery false
+
+protocol.6dasprotocol peersim.kademlia.das.DASProtocolNonValidator
+protocol.6dasprotocol.transport 2unreltr
+protocol.6dasprotocol.kademlia 3kademlia
+protocol.6dasprotocol.reportDiscovery false
+
+protocol.7evildasprotocol peersim.kademlia.das.DASProtocolEvilValidator
+protocol.7evildasprotocol.transport 2unreltr
+protocol.7evildasprotocol.kademlia 3kademlia
+protocol.7evildasprotocol.reportDiscovery false
+
+protocol.8evildasprotocol peersim.kademlia.das.DASProtocolEvilNonValidator
+protocol.8evildasprotocol.transport 2unreltr
+protocol.8evildasprotocol.kademlia 3kademlia
+protocol.8evildasprotocol.reportDiscovery false
+
+# ::::: INITIALIZERS :::::
+#Class that initializes nodes with kademlia protocol and generates uniform ids
+init.1uniqueNodeID peersim.kademlia.das.CustomDistributionDas
+init.1uniqueNodeID.protocolkad 3kademlia
+init.1uniqueNodeID.protocoldasbuilder 4dasprotocol
+init.1uniqueNodeID.protocoldasvalidator 5dasprotocol
+init.1uniqueNodeID.protocoldasnonvalidator 6dasprotocol
+init.1uniqueNodeID.protocolEvilValDas 7evildasprotocol
+init.1uniqueNodeID.protocolEvildas 8evildasprotocol
+init.1uniqueNodeID.validator_rate 0.5
+init.1uniqueNodeID.evilNodeRatioValidator 0.9
+init.1uniqueNodeID.evilNodeRatioNonValidator 0.9
+
+
+#Adds initial state to the routing tables
+init.2statebuilder peersim.kademlia.StateBuilder
+init.2statebuilder.protocol 3kademlia
+init.2statebuilder.transport 2unreltr
+
+# ::::: CONTROLS :::::
+
+#TrafficGenerator class sends and initial 
+control.0traffic peersim.kademlia.das.TrafficGeneratorSample
+control.0traffic.step TRAFFIC_STEP
+control.0traffic.mapping_fn 2
+control.0traffic.sample_copy_per_node 3
+control.0traffic.block_dim_size 512
+control.0traffic.num_samples 75
+control.0traffic.kadprotocol 3kademlia
+
+#TrafficGenerator class sends and initial 
+control.1refresh peersim.kademlia.das.RefreshSearchTable
+control.1refresh.step REFRESH_STEP
+
+# turbulence non-validator
+
+#control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
+#control.2turbolenceAdd.protocolkad 3kademlia
+#control.2turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.2turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.2turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.2turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.2turbolenceAdd.transport 2unreltr
+#control.2turbolenceAdd.step TURBULENCE_STEP_NONVAL
+#control.2turbolenceAdd.p_idle 0.1
+#control.2turbolenceAdd.p_rem 0.45
+#control.2turbolenceAdd.p_add 0.45
+
+# turbulence validators
+#control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
+#control.3turbolenceAdd.protocolkad 3kademlia
+#control.3turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.3turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.3turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.3turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.3turbolenceAdd.transport 2unreltr
+#control.3turbolenceAdd.step TURBULENCE_STEP
+#control.3turbolenceAdd.p_idle 0.9
+#control.3turbolenceAdd.p_rem 0.05
+#control.3turbolenceAdd.p_add 0.05
+
+# ::::: OBSERVER :::::
+#The observer is executed every OBSERVER_STEP and will generate data traces 
+control.4 peersim.kademlia.KademliaObserver
+control.4.protocol 3kademlia
+control.4.step OBSERVER_STEP
+control.4.logfolder logsDasEvil0.9Test
diff --git a/simulator/config/malicious/dasprotocolevil0.cfg b/simulator/config/malicious/dasprotocolevil0.cfg
new file mode 100644
index 00000000..83d5b2d1
--- /dev/null
+++ b/simulator/config/malicious/dasprotocolevil0.cfg
@@ -0,0 +1,159 @@
+# :::::::::::::::::::::::::::::::::`:::::::::::::::::::::
+# :: Kademlia Default Configuration
+# ::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+# ::::: GLOBAL ::::::
+
+# Network size
+SIZE 1000
+
+# Random seed
+K 5
+
+MINDELAY  5
+MAXDELAY  200
+
+#Simulation time in ms
+SIM_TIME 11999
+
+
+#Traffic generator is executed every TRAFFIC_STEP
+TRAFFIC_STEP  12000 #10000000/SIZE
+#Tracing module is executed every OBSERVER_STEP
+OBSERVER_STEP 10000
+#Turbulence module is executed every TURBULENCE_STEP enabling churning
+TURBULENCE_STEP  1000
+TURBULENCE_STEP_NONVAL 1000
+REFRESH_STEP 10000000
+
+# add network config parameters to simulation
+random.seed 24680
+simulation.experiments 1
+simulation.endtime SIM_TIME
+network.size SIZE
+
+
+# Peersim  protocols enabled in each node
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.0link peersim.core.IdleProtocol
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.1pairwiselattr peersim.transport.PairwiseFixedLatencyTransport
+protocol.1pairwiselattr.mindelay MINDELAY
+protocol.1pairwiselattr.maxdelay MAXDELAY
+protocol.1pairwiselattr.size SIZE
+
+
+#transport layer that reliably delivers messages with a random delay, emulating TCP
+protocol.2unreltr peersim.transport.UnreliableTransport
+protocol.2unreltr.drop 0
+protocol.2unreltr.transport 1pairwiselattr
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.3kademlia peersim.kademlia.KademliaProtocol
+protocol.3kademlia.transport 2unreltr
+protocol.3kademlia.BITS 256
+protocol.3kademlia.NBUCKETS 17
+protocol.3kademlia.FINDMODE 1
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.4dasprotocol peersim.kademlia.das.DASProtocolBuilder
+protocol.4dasprotocol.transport 2unreltr
+protocol.4dasprotocol.kademlia 3kademlia
+protocol.4dasprotocol.reportDiscovery false
+protocol.4dasprotocol.reportMsg true
+
+protocol.5dasprotocol peersim.kademlia.das.DASProtocolValidator
+protocol.5dasprotocol.transport 2unreltr
+protocol.5dasprotocol.kademlia 3kademlia
+protocol.5dasprotocol.reportDiscovery false
+protocol.5dasprotocol.reportMsg true
+
+protocol.6dasprotocol peersim.kademlia.das.DASProtocolNonValidator
+protocol.6dasprotocol.transport 2unreltr
+protocol.6dasprotocol.kademlia 3kademlia
+protocol.6dasprotocol.reportDiscovery false
+protocol.6dasprotocol.reportMsg true
+
+protocol.7evildasprotocol peersim.kademlia.das.DASProtocolEvilValidator
+protocol.7evildasprotocol.transport 2unreltr
+protocol.7evildasprotocol.kademlia 3kademlia
+protocol.7evildasprotocol.reportDiscovery false
+protocol.7evildasprotocol.reportMsg true
+
+protocol.8evildasprotocol peersim.kademlia.das.DASProtocolEvilNonValidator
+protocol.8evildasprotocol.transport 2unreltr
+protocol.8evildasprotocol.kademlia 3kademlia
+protocol.8evildasprotocol.reportDiscovery false
+protocol.8evildasprotocol.reportMsg true
+
+# ::::: INITIALIZERS :::::
+#Class that initializes nodes with kademlia protocol and generates uniform ids
+init.1uniqueNodeID peersim.kademlia.das.CustomDistributionDas
+init.1uniqueNodeID.protocolkad 3kademlia
+init.1uniqueNodeID.protocoldasbuilder 4dasprotocol
+init.1uniqueNodeID.protocoldasvalidator 5dasprotocol
+init.1uniqueNodeID.protocoldasnonvalidator 6dasprotocol
+init.1uniqueNodeID.protocolEvilValDas 7evildasprotocol
+init.1uniqueNodeID.protocolEvildas 8evildasprotocol
+init.1uniqueNodeID.validator_rate 0.5
+init.1uniqueNodeID.evilNodeRatioValidator 0.0
+init.1uniqueNodeID.evilNodeRatioNonValidator 0.0
+
+
+#Adds initial state to the routing tables
+init.2statebuilder peersim.kademlia.StateBuilder
+init.2statebuilder.protocol 3kademlia
+init.2statebuilder.transport 2unreltr
+
+# ::::: CONTROLS :::::
+
+#TrafficGenerator class sends and initial 
+control.0traffic peersim.kademlia.das.TrafficGeneratorSample
+control.0traffic.step TRAFFIC_STEP
+control.0traffic.mapping_fn 2
+control.0traffic.sample_copy_per_node 2
+control.0traffic.block_dim_size 100
+control.0traffic.num_samples 75
+control.0traffic.kadprotocol 3kademlia
+
+#TrafficGenerator class sends and initial 
+control.1refresh peersim.kademlia.das.RefreshSearchTable
+control.1refresh.step REFRESH_STEP
+
+# turbulence non-validator
+
+#control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
+#control.2turbolenceAdd.protocolkad 3kademlia
+#control.2turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.2turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.2turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.2turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.2turbolenceAdd.transport 2unreltr
+#control.2turbolenceAdd.step TURBULENCE_STEP_NONVAL
+#control.2turbolenceAdd.p_idle 0.1
+#control.2turbolenceAdd.p_rem 0.45
+#control.2turbolenceAdd.p_add 0.45
+
+# turbulence validators
+#control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
+#control.3turbolenceAdd.protocolkad 3kademlia
+#control.3turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.3turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.3turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.3turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.3turbolenceAdd.transport 2unreltr
+#control.3turbolenceAdd.step TURBULENCE_STEP
+#control.3turbolenceAdd.p_idle 0.9
+#control.3turbolenceAdd.p_rem 0.05
+#control.3turbolenceAdd.p_add 0.05
+
+# ::::: OBSERVER :::::
+#The observer is executed every OBSERVER_STEP and will generate data traces 
+control.4 peersim.kademlia.KademliaObserver
+control.4.protocol 3kademlia
+control.4.step OBSERVER_STEP
+control.4.logfolder logsDasEvil0
diff --git a/simulator/config/scale/dasprotocol20000.cfg b/simulator/config/scale/dasprotocol20000.cfg
new file mode 100644
index 00000000..03a8202a
--- /dev/null
+++ b/simulator/config/scale/dasprotocol20000.cfg
@@ -0,0 +1,159 @@
+# :::::::::::::::::::::::::::::::::`:::::::::::::::::::::
+# :: Kademlia Default Configuration
+# ::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+# ::::: GLOBAL ::::::
+
+# Network size
+SIZE 20000
+
+# Random seed
+K 5
+
+MINDELAY  5
+MAXDELAY  125
+
+#Simulation time in ms
+SIM_TIME 11999
+
+
+#Traffic generator is executed every TRAFFIC_STEP
+TRAFFIC_STEP  12000 #10000000/SIZE
+#Tracing module is executed every OBSERVER_STEP
+OBSERVER_STEP 10000
+#Turbulence module is executed every TURBULENCE_STEP enabling churning
+TURBULENCE_STEP  1000
+TURBULENCE_STEP_NONVAL 1000
+REFRESH_STEP 10000000
+
+# add network config parameters to simulation
+random.seed 24680
+simulation.experiments 1
+simulation.endtime SIM_TIME
+network.size SIZE
+
+
+# Peersim  protocols enabled in each node
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.0link peersim.core.IdleProtocol
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.1pairwiselattr peersim.transport.PairwiseFixedLatencyTransport
+protocol.1pairwiselattr.mindelay MINDELAY
+protocol.1pairwiselattr.maxdelay MAXDELAY
+protocol.1pairwiselattr.size SIZE
+
+
+#transport layer that reliably delivers messages with a random delay, emulating TCP
+protocol.2unreltr peersim.transport.UnreliableTransport
+protocol.2unreltr.drop 0
+protocol.2unreltr.transport 1pairwiselattr
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.3kademlia peersim.kademlia.KademliaProtocol
+protocol.3kademlia.transport 2unreltr
+protocol.3kademlia.BITS 256
+protocol.3kademlia.NBUCKETS 17
+protocol.3kademlia.FINDMODE 1
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.4dasprotocol peersim.kademlia.das.DASProtocolBuilder
+protocol.4dasprotocol.transport 2unreltr
+protocol.4dasprotocol.kademlia 3kademlia
+protocol.4dasprotocol.reportDiscovery false
+protocol.4dasprotocol.reportMsg true
+
+protocol.5dasprotocol peersim.kademlia.das.DASProtocolValidator
+protocol.5dasprotocol.transport 2unreltr
+protocol.5dasprotocol.kademlia 3kademlia
+protocol.5dasprotocol.reportDiscovery false
+protocol.5dasprotocol.reportMsg true
+
+protocol.6dasprotocol peersim.kademlia.das.DASProtocolNonValidator
+protocol.6dasprotocol.transport 2unreltr
+protocol.6dasprotocol.kademlia 3kademlia
+protocol.6dasprotocol.reportDiscovery false
+protocol.6dasprotocol.reportMsg true
+
+protocol.7evildasprotocol peersim.kademlia.das.DASProtocolEvilValidator
+protocol.7evildasprotocol.transport 2unreltr
+protocol.7evildasprotocol.kademlia 3kademlia
+protocol.7evildasprotocol.reportDiscovery false
+protocol.7evildasprotocol.reportMsg true
+
+protocol.8evildasprotocol peersim.kademlia.das.DASProtocolEvilNonValidator
+protocol.8evildasprotocol.transport 2unreltr
+protocol.8evildasprotocol.kademlia 3kademlia
+protocol.8evildasprotocol.reportDiscovery false
+protocol.8evildasprotocol.reportMsg true
+
+# ::::: INITIALIZERS :::::
+#Class that initializes nodes with kademlia protocol and generates uniform ids
+init.1uniqueNodeID peersim.kademlia.das.CustomDistributionDas
+init.1uniqueNodeID.protocolkad 3kademlia
+init.1uniqueNodeID.protocoldasbuilder 4dasprotocol
+init.1uniqueNodeID.protocoldasvalidator 5dasprotocol
+init.1uniqueNodeID.protocoldasnonvalidator 6dasprotocol
+init.1uniqueNodeID.protocolEvilValDas 7evildasprotocol
+init.1uniqueNodeID.protocolEvildas 8evildasprotocol
+init.1uniqueNodeID.validator_rate 0.2
+init.1uniqueNodeID.evilNodeRatioValidator 0.0
+init.1uniqueNodeID.evilNodeRatioNonValidator 0.0
+
+
+#Adds initial state to the routing tables
+init.2statebuilder peersim.kademlia.StateBuilder
+init.2statebuilder.protocol 3kademlia
+init.2statebuilder.transport 2unreltr
+
+# ::::: CONTROLS :::::
+
+#TrafficGenerator class sends and initial 
+control.0traffic peersim.kademlia.das.TrafficGeneratorSample
+control.0traffic.step TRAFFIC_STEP
+control.0traffic.mapping_fn 2
+control.0traffic.sample_copy_per_node 2
+control.0traffic.block_dim_size 512
+control.0traffic.num_samples 75
+control.0traffic.kadprotocol 3kademlia
+
+#TrafficGenerator class sends and initial 
+control.1refresh peersim.kademlia.das.RefreshSearchTable
+control.1refresh.step REFRESH_STEP
+
+# turbulence non-validator
+
+#control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
+#control.2turbolenceAdd.protocolkad 3kademlia
+#control.2turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.2turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.2turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.2turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.2turbolenceAdd.transport 2unreltr
+#control.2turbolenceAdd.step TURBULENCE_STEP_NONVAL
+#control.2turbolenceAdd.p_idle 0.1
+#control.2turbolenceAdd.p_rem 0.45
+#control.2turbolenceAdd.p_add 0.45
+
+# turbulence validators
+#control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
+#control.3turbolenceAdd.protocolkad 3kademlia
+#control.3turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.3turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.3turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.3turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.3turbolenceAdd.transport 2unreltr
+#control.3turbolenceAdd.step TURBULENCE_STEP
+#control.3turbolenceAdd.p_idle 0.9
+#control.3turbolenceAdd.p_rem 0.05
+#control.3turbolenceAdd.p_add 0.05
+
+# ::::: OBSERVER :::::
+#The observer is executed every OBSERVER_STEP and will generate data traces 
+control.4 peersim.kademlia.KademliaObserver
+control.4.protocol 3kademlia
+control.4.step OBSERVER_STEP
+control.4.logfolder logs20000
diff --git a/simulator/config/scale/dasprotocol30000.cfg b/simulator/config/scale/dasprotocol30000.cfg
new file mode 100644
index 00000000..0ffb78cf
--- /dev/null
+++ b/simulator/config/scale/dasprotocol30000.cfg
@@ -0,0 +1,159 @@
+# :::::::::::::::::::::::::::::::::`:::::::::::::::::::::
+# :: Kademlia Default Configuration
+# ::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+# ::::: GLOBAL ::::::
+
+# Network size
+SIZE 30000
+
+# Random seed
+K 5
+
+MINDELAY  5
+MAXDELAY  125
+
+#Simulation time in ms
+SIM_TIME 11999
+
+
+#Traffic generator is executed every TRAFFIC_STEP
+TRAFFIC_STEP  12000 #10000000/SIZE
+#Tracing module is executed every OBSERVER_STEP
+OBSERVER_STEP 10000
+#Turbulence module is executed every TURBULENCE_STEP enabling churning
+TURBULENCE_STEP  1000
+TURBULENCE_STEP_NONVAL 1000
+REFRESH_STEP 10000000
+
+# add network config parameters to simulation
+random.seed 24680
+simulation.experiments 1
+simulation.endtime SIM_TIME
+network.size SIZE
+
+
+# Peersim  protocols enabled in each node
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.0link peersim.core.IdleProtocol
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.1pairwiselattr peersim.transport.PairwiseFixedLatencyTransport
+protocol.1pairwiselattr.mindelay MINDELAY
+protocol.1pairwiselattr.maxdelay MAXDELAY
+protocol.1pairwiselattr.size SIZE
+
+
+#transport layer that reliably delivers messages with a random delay, emulating TCP
+protocol.2unreltr peersim.transport.UnreliableTransport
+protocol.2unreltr.drop 0
+protocol.2unreltr.transport 1pairwiselattr
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.3kademlia peersim.kademlia.KademliaProtocol
+protocol.3kademlia.transport 2unreltr
+protocol.3kademlia.BITS 256
+protocol.3kademlia.NBUCKETS 17
+protocol.3kademlia.FINDMODE 1
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.4dasprotocol peersim.kademlia.das.DASProtocolBuilder
+protocol.4dasprotocol.transport 2unreltr
+protocol.4dasprotocol.kademlia 3kademlia
+protocol.4dasprotocol.reportDiscovery false
+protocol.4dasprotocol.reportMsg true
+
+protocol.5dasprotocol peersim.kademlia.das.DASProtocolValidator
+protocol.5dasprotocol.transport 2unreltr
+protocol.5dasprotocol.kademlia 3kademlia
+protocol.5dasprotocol.reportDiscovery false
+protocol.5dasprotocol.reportMsg true
+
+protocol.6dasprotocol peersim.kademlia.das.DASProtocolNonValidator
+protocol.6dasprotocol.transport 2unreltr
+protocol.6dasprotocol.kademlia 3kademlia
+protocol.6dasprotocol.reportDiscovery false
+protocol.6dasprotocol.reportMsg true
+
+protocol.7evildasprotocol peersim.kademlia.das.DASProtocolEvilValidator
+protocol.7evildasprotocol.transport 2unreltr
+protocol.7evildasprotocol.kademlia 3kademlia
+protocol.7evildasprotocol.reportDiscovery false
+protocol.7evildasprotocol.reportMsg true
+
+protocol.8evildasprotocol peersim.kademlia.das.DASProtocolEvilNonValidator
+protocol.8evildasprotocol.transport 2unreltr
+protocol.8evildasprotocol.kademlia 3kademlia
+protocol.8evildasprotocol.reportDiscovery false
+protocol.8evildasprotocol.reportMsg true
+
+# ::::: INITIALIZERS :::::
+#Class that initializes nodes with kademlia protocol and generates uniform ids
+init.1uniqueNodeID peersim.kademlia.das.CustomDistributionDas
+init.1uniqueNodeID.protocolkad 3kademlia
+init.1uniqueNodeID.protocoldasbuilder 4dasprotocol
+init.1uniqueNodeID.protocoldasvalidator 5dasprotocol
+init.1uniqueNodeID.protocoldasnonvalidator 6dasprotocol
+init.1uniqueNodeID.protocolEvilValDas 7evildasprotocol
+init.1uniqueNodeID.protocolEvildas 8evildasprotocol
+init.1uniqueNodeID.validator_rate 0.333
+init.1uniqueNodeID.evilNodeRatioValidator 0.0
+init.1uniqueNodeID.evilNodeRatioNonValidator 0.0
+
+
+#Adds initial state to the routing tables
+init.2statebuilder peersim.kademlia.StateBuilder
+init.2statebuilder.protocol 3kademlia
+init.2statebuilder.transport 2unreltr
+
+# ::::: CONTROLS :::::
+
+#TrafficGenerator class sends and initial 
+control.0traffic peersim.kademlia.das.TrafficGeneratorSample
+control.0traffic.step TRAFFIC_STEP
+control.0traffic.mapping_fn 2
+control.0traffic.sample_copy_per_node 2
+control.0traffic.block_dim_size 512
+control.0traffic.num_samples 75
+control.0traffic.kadprotocol 3kademlia
+
+#TrafficGenerator class sends and initial 
+control.1refresh peersim.kademlia.das.RefreshSearchTable
+control.1refresh.step REFRESH_STEP
+
+# turbulence non-validator
+
+#control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
+#control.2turbolenceAdd.protocolkad 3kademlia
+#control.2turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.2turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.2turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.2turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.2turbolenceAdd.transport 2unreltr
+#control.2turbolenceAdd.step TURBULENCE_STEP_NONVAL
+#control.2turbolenceAdd.p_idle 0.1
+#control.2turbolenceAdd.p_rem 0.45
+#control.2turbolenceAdd.p_add 0.45
+
+# turbulence validators
+#control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
+#control.3turbolenceAdd.protocolkad 3kademlia
+#control.3turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.3turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.3turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.3turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.3turbolenceAdd.transport 2unreltr
+#control.3turbolenceAdd.step TURBULENCE_STEP
+#control.3turbolenceAdd.p_idle 0.9
+#control.3turbolenceAdd.p_rem 0.05
+#control.3turbolenceAdd.p_add 0.05
+
+# ::::: OBSERVER :::::
+#The observer is executed every OBSERVER_STEP and will generate data traces 
+control.4 peersim.kademlia.KademliaObserver
+control.4.protocol 3kademlia
+control.4.step OBSERVER_STEP
+control.4.logfolder logs30000
diff --git a/simulator/config/scale/dasprotocol50000.cfg b/simulator/config/scale/dasprotocol50000.cfg
new file mode 100644
index 00000000..971bfc86
--- /dev/null
+++ b/simulator/config/scale/dasprotocol50000.cfg
@@ -0,0 +1,159 @@
+# :::::::::::::::::::::::::::::::::`:::::::::::::::::::::
+# :: Kademlia Default Configuration
+# ::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+# ::::: GLOBAL ::::::
+
+# Network size
+SIZE 50000
+
+# Random seed
+K 5
+
+MINDELAY  5
+MAXDELAY  125
+
+#Simulation time in ms
+SIM_TIME 11999
+
+
+#Traffic generator is executed every TRAFFIC_STEP
+TRAFFIC_STEP  12000 #10000000/SIZE
+#Tracing module is executed every OBSERVER_STEP
+OBSERVER_STEP 10000
+#Turbulence module is executed every TURBULENCE_STEP enabling churning
+TURBULENCE_STEP  1000
+TURBULENCE_STEP_NONVAL 1000
+REFRESH_STEP 10000000
+
+# add network config parameters to simulation
+random.seed 24680
+simulation.experiments 1
+simulation.endtime SIM_TIME
+network.size SIZE
+
+
+# Peersim  protocols enabled in each node
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.0link peersim.core.IdleProtocol
+
+#A protocol that stores links. It does nothing apart from that. Use by default
+protocol.1pairwiselattr peersim.transport.PairwiseFixedLatencyTransport
+protocol.1pairwiselattr.mindelay MINDELAY
+protocol.1pairwiselattr.maxdelay MAXDELAY
+protocol.1pairwiselattr.size SIZE
+
+
+#transport layer that reliably delivers messages with a random delay, emulating TCP
+protocol.2unreltr peersim.transport.UnreliableTransport
+protocol.2unreltr.drop 0
+protocol.2unreltr.transport 1pairwiselattr
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.3kademlia peersim.kademlia.KademliaProtocol
+protocol.3kademlia.transport 2unreltr
+protocol.3kademlia.BITS 256
+protocol.3kademlia.NBUCKETS 17
+protocol.3kademlia.FINDMODE 1
+
+#Kademlia protocol with 256 bits identifiers and 17 buckets in the routing table.
+#Use FINDMODE 1 to send FINDMODE messages looking for distance to specific node instead of sending the id of the node like in DEVP2P
+protocol.4dasprotocol peersim.kademlia.das.DASProtocolBuilder
+protocol.4dasprotocol.transport 2unreltr
+protocol.4dasprotocol.kademlia 3kademlia
+protocol.4dasprotocol.reportDiscovery false
+protocol.4dasprotocol.reportMsg true
+
+protocol.5dasprotocol peersim.kademlia.das.DASProtocolValidator
+protocol.5dasprotocol.transport 2unreltr
+protocol.5dasprotocol.kademlia 3kademlia
+protocol.5dasprotocol.reportDiscovery false
+protocol.5dasprotocol.reportMsg true
+
+protocol.6dasprotocol peersim.kademlia.das.DASProtocolNonValidator
+protocol.6dasprotocol.transport 2unreltr
+protocol.6dasprotocol.kademlia 3kademlia
+protocol.6dasprotocol.reportDiscovery false
+protocol.6dasprotocol.reportMsg true
+
+protocol.7evildasprotocol peersim.kademlia.das.DASProtocolEvilValidator
+protocol.7evildasprotocol.transport 2unreltr
+protocol.7evildasprotocol.kademlia 3kademlia
+protocol.7evildasprotocol.reportDiscovery false
+protocol.7evildasprotocol.reportMsg true
+
+protocol.8evildasprotocol peersim.kademlia.das.DASProtocolEvilNonValidator
+protocol.8evildasprotocol.transport 2unreltr
+protocol.8evildasprotocol.kademlia 3kademlia
+protocol.8evildasprotocol.reportDiscovery false
+protocol.8evildasprotocol.reportMsg true
+
+# ::::: INITIALIZERS :::::
+#Class that initializes nodes with kademlia protocol and generates uniform ids
+init.1uniqueNodeID peersim.kademlia.das.CustomDistributionDas
+init.1uniqueNodeID.protocolkad 3kademlia
+init.1uniqueNodeID.protocoldasbuilder 4dasprotocol
+init.1uniqueNodeID.protocoldasvalidator 5dasprotocol
+init.1uniqueNodeID.protocoldasnonvalidator 6dasprotocol
+init.1uniqueNodeID.protocolEvilValDas 7evildasprotocol
+init.1uniqueNodeID.protocolEvildas 8evildasprotocol
+init.1uniqueNodeID.validator_rate 0.2
+init.1uniqueNodeID.evilNodeRatioValidator 0.0
+init.1uniqueNodeID.evilNodeRatioNonValidator 0.0
+
+
+#Adds initial state to the routing tables
+init.2statebuilder peersim.kademlia.StateBuilder
+init.2statebuilder.protocol 3kademlia
+init.2statebuilder.transport 2unreltr
+
+# ::::: CONTROLS :::::
+
+#TrafficGenerator class sends and initial 
+control.0traffic peersim.kademlia.das.TrafficGeneratorSample
+control.0traffic.step TRAFFIC_STEP
+control.0traffic.mapping_fn 2
+control.0traffic.sample_copy_per_node 2
+control.0traffic.block_dim_size 512
+control.0traffic.num_samples 75
+control.0traffic.kadprotocol 3kademlia
+
+#TrafficGenerator class sends and initial 
+control.1refresh peersim.kademlia.das.RefreshSearchTable
+control.1refresh.step REFRESH_STEP
+
+# turbulence non-validator
+
+#control.2turbolenceAdd peersim.kademlia.das.TurbulenceDas
+#control.2turbolenceAdd.protocolkad 3kademlia
+#control.2turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.2turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.2turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.2turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.2turbolenceAdd.transport 2unreltr
+#control.2turbolenceAdd.step TURBULENCE_STEP_NONVAL
+#control.2turbolenceAdd.p_idle 0.1
+#control.2turbolenceAdd.p_rem 0.45
+#control.2turbolenceAdd.p_add 0.45
+
+# turbulence validators
+#control.3turbolenceAdd peersim.kademlia.das.TurbulenceDasValidator
+#control.3turbolenceAdd.protocolkad 3kademlia
+#control.3turbolenceAdd.protocoldasbuilder 4dasprotocol
+#control.3turbolenceAdd.protocoldasvalidator 5dasprotocol
+#control.3turbolenceAdd.protocoldasnonvalidator 6dasprotocol
+#control.3turbolenceAdd.protocolEvildas 7evildasprotocol
+#control.3turbolenceAdd.transport 2unreltr
+#control.3turbolenceAdd.step TURBULENCE_STEP
+#control.3turbolenceAdd.p_idle 0.9
+#control.3turbolenceAdd.p_rem 0.05
+#control.3turbolenceAdd.p_add 0.05
+
+# ::::: OBSERVER :::::
+#The observer is executed every OBSERVER_STEP and will generate data traces 
+control.4 peersim.kademlia.KademliaObserver
+control.4.protocol 3kademlia
+control.4.step OBSERVER_STEP
+control.4.logfolder logs50000
diff --git a/simulator/python/analysis_vs_evil.ipynb b/simulator/python/analysis_vs_evil.ipynb
index 6d9410dd..80c70e85 100644
--- a/simulator/python/analysis_vs_evil.ipynb
+++ b/simulator/python/analysis_vs_evil.ipynb
@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "f2905ced",
    "metadata": {},
@@ -49,19 +48,17 @@
     "## Simulation parameters\n",
     "\n",
     "* Number of nodes: 5000\n",
-    "* Number of attackers: Range from 0% to 50%\n",
+    "* Number of attackers: Range from 0% to 75%\n",
     "* Number of validators: All nodes are validators\n",
-    "* Overhead parameter: 2\n",
-    "* Latency between nodes: 100ms\n",
+    "* Redundancy parameter: 2\n",
+    "* Latency between nodes: 5ms - 100ms\n",
     "* Number of samples per block : 512x512\n",
     "* Sample size: 512 bytes\n",
-    "* Blocks: Single block simulation.\n",
-    "* Number of random samples fetch: 75. \n",
-    "* Alpha: 3"
+    "* Blocks: 5 blocks simulation.\n",
+    "* Number of random samples fetch: 75. \n"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "edd080e7",
    "metadata": {},
@@ -71,33 +68,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 24,
    "id": "2983fa0b",
-   "metadata": {
-    "vscode": {
-     "languageId": "python"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from matplotlib import pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
-    "ops_path = {'DAS': '../logsTest/operation.csv', \n",
-    "            'Evil-0.1': '../logsEvil0.1/operation.csv',\n",
-    "            'Evil-0.2': '../logsEvil0.2/operation.csv',\n",
-    "            'Evil-0.3': '../logsEvil0.3/operation.csv',\n",
-    "            'Evil-0.4': '../logsEvil0.4/operation.csv',\n",
-    "            'Evil-0.5': '../logsEvil0.5/operation.csv'\n",
-    "           }\n",
-    "msgs_path = {'DAS': '../logsTest/messages.csv', \n",
-    "            'Evil-0.1': '../logsEvil0.1/messages.csv',\n",
-    "            'Evil-0.2': '../logsEvil0.2/messages.csv',\n",
-    "            'Evil-0.3': '../logsEvil0.3/messages.csv',\n",
-    "            'Evil-0.4': '../logsEvil0.4/messages.csv',\n",
-    "            'Evil-0.5': '../logsEvil0.5/messages.csv'\n",
+    "ops_path = {'DAS': '../logsDasEvil0/operation.csv', \n",
+    "            'Evil-0.2': '../logsDasEvil0.2/operation.csv',\n",
+    "            'Evil-0.4': '../logsDasEvil0.5/operation.csv',\n",
+    "            'Evil-0.6': '../logsDasEvil0.6/operation.csv',\n",
+    "            'Evil-0.8': '../logsDasEvil0.8/operation.csv'\n",
     "           }\n",
+    "msgs_path = {'DAS': '../logsDasEvil0/messages.csv', \n",
+    "            'Evil-0.2': '../logsDasEvil0.2/messages.csv',\n",
+    "            'Evil-0.4': '../logsDasEvil0.5/messages.csv',\n",
+    "            'Evil-0.6': '../logsDasEvil0.6/messages.csv',\n",
+    "            'Evil-0.8': '../logsDasEvil0.8/messages.csv'\n",
+    "          }\n",
     "\n",
     "\n",
     "builder_address = '83814183170291850251680823880522715558189094423550585243365458794131648333116'\n",
@@ -111,17 +102,15 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "fd77860f",
    "metadata": {},
    "source": [
-    "In this graph we observe the CDF of the time required to complete the validator sampling of the DAS protocol, with and without attackers in the simulation, from 0\\% to 50\\% of attackers in the simulation.\n",
-    "We observe in all cases the validator sampling is completed on time, with small differences on the time required."
+    "In this graph we observe the CDF of the time required by validators to fetch 2 rows and 2 columnns using the DAS protocol, with and without attackers in the simulation, from 0\\% to 75\\% of malicious in the simulation.\n",
+    "We observe in all cases the validator sampling is completed on time, although when the number of malicious nodes in the network is 75\\% the time required to fetch the rows and columns is increased substantially and is close to the time limit of 4 seconds for some nodes."
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "c6cc3d58",
    "metadata": {},
@@ -129,13 +118,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 25,
    "id": "0c418d95",
-   "metadata": {
-    "vscode": {
-     "languageId": "python"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -143,13 +128,13 @@
        "Text(0.5, 0, 'Operation complete time (ms)')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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D399fPierZs2amDx5ssH+F+c7AOh/tiqVSuc/EoCCE/4BlOgaNI/2Byj4Xj3c7uXLl/XeWwAGy8qKoffJzMxMPuz4cPnDfT179ixOnjyJmjVr6jy071FKSkq59ZlKj+fUKJCNjQ3c3Nxw4sSJMtumdltl+Y9Q06ZN4e/vDwDo2bMnsrKyEB4ejrZt2+r9A/0kbm5uqFOnDnbt2gUvLy8IIeDr64uaNWti9OjRuHz5Mnbv3o3WrVvrBKfNmzfDy8sLjRs3lssaNmwIoODaNY+ef1NR8vPzDZYXNvvEULkwcOJnYesbqvuooKAgfPzxx7h58yasra2xceNGDBgwQGc67ciRI7F8+XKMGTMGvr6+sLW1hSRJ6N+/f7lO1+7Xrx/27t2LCRMmoEWLFrCysoJGo0FgYGCx2n14L9jjFGem06effoqwsDBs2LABv/76K0aNGoWoqCjs27cPtWrVwvnz5+Hn54eGDRti7ty58PDwgKmpKTZv3ozPPvtMr//F+Q4ARftsS6Oy2n0SQ/0qSl81Gg2aNm1q8BIWgH54pKqFe2oU6tVXX8X58+eRkJBQ6m1lZmZi/fr18PDwQKNGjcqgd4bNnDkT9+/fx8cffwygYDe2hYWFfEjpYX/99RdUKpXOPzDaQ027d+9GixYtYG1tjebNm8PW1haxsbE4cuSIfDKg1qZNm9CtWzedsu7duwMAvvvuuyf22dPTEwAK7aOjoyMsLS0LXd/e3l5vNk9OTg6uX7/+xLYrWlBQEPLy8vDTTz9hy5YtyMjIQP/+/XXqrF27FqGhofj000/Rp08fdO7cGW3bti3Rxe48PT1x/fp1eS+h1qPv9e3bt7F9+3a89957mDZtGnr16oXOnTvr7Y0ACg8tnp6e0Gg0OHv2rE55cnIy0tLS5M+5pJo2bYoPPvgAu3btwu7du3Ht2jVER0cDKDipPzs7Gxs3bsSbb76Jbt26wd/fv9ymiGs0Gr1DUtqLSZblSe0P8/T0xLlz5/TKDZUZUtSwWRbq1auH1NRU+Pn56e2F9vf3L/SkcKoaGGoUauLEibC0tMTQoUORnJyst/z8+fOYP3/+E7dz7949hISEIDU1Fe+//365/uNSr1499O7dGytWrEBSUhKMjIzQpUsXbNiwQWe3eHJyMlavXo22bdvKh4qAglBz6dIlxMTEyHtYVCoVWrdujblz5yI3N1dnz0tycjKOHDmic+gJAHx9fREYGIivv/7a4EyHnJwcjB8/HkDB+T0tWrTAN998o/PDfeLECfz66696gcnQmHft2qVTtmTJkkL31FSmRo0aoWnTpoiJiUFMTAxcXV31QqKRkZHef6EvWLCgROPp1q0b8vLydKY25+fnY8GCBXptAvp7BubNm6e3TW3AfDRkaT+nR9fR/tf6o9+RosrIyEBeXp5OWdOmTaFSqeTpwYb6n56ejuXLl5eozaL44osv5OdCCHzxxRcwMTGBn59fubQXEBCAhIQEHDt2TC5LTU3FqlWrirR+YZ9beejXrx+uXbuGr776Sm/ZvXv3yvRwMpU9Hn5SqHr16mH16tXytMaHryi8d+9e/PjjjzrXQQEKpl1q905kZmbi1KlT+PHHH5GUlIR33nkHb775Zrn3e8KECfjhhx8wb948zJw5Ex999BHi4uLQtm1bvP322zA2NsbixYuRnZ2NWbNm6ayrDSyJiYn45JNP5PL27dtjy5Yt8nVZtDZv3gwzMzN06tRJrx8rV65Ely5d8Prrr6N79+7w8/ODpaUlzp49izVr1uD69evytWpmz56Nrl27wtfXF0OGDJGndNva2j7x0u5Dhw7F8OHD0bt3b3Tu3Bl//PEHtm7dqnfcv6oICgrClClTYGZmhiFDhugcygMK9hB+++23sLW1RePGjZGQkIBt27ahRo0axW6re/fuaNOmDd577z1cunQJjRs3xrp16/TOMbGxsUH79u0xa9Ys5Obmwt3dHb/++isuXryot01vb28AwPvvv4/+/fvDxMQE3bt3R/PmzREaGoolS5YgLS0NHTp0wIEDB/DNN9+gZ8+eBr8jRbFjxw5ERESgb9++ePbZZ5GXl4dvv/0WRkZG8jWQunTpAlNTU3Tv3h1vvvkmMjMz8dVXX8HJyalc9tiZmZkhNjYWoaGh8PHxwZYtW7Bp0yZMnjxZnt5e1iZOnIjvvvsOnTt3xsiRI+Up3bVr10ZqauoT/2NJ+7mNGjUKAQEBMDIy0ttLWFZCQkLwww8/YPjw4di5cyfatGmD/Px8/PXXX/jhhx/k6xNRFVUpc66owpw5c0aEh4cLLy8vYWpqKqytrUWbNm3EggULxP379+V62qmZAIQkScLGxkY899xzIjw8XOzfv9/gtgGIESNGFLtP2um8hU1z7dixo7CxsRFpaWlCCCGOHDkiAgIChJWVlbCwsBCdOnXSmdL6MCcnJwFAJCcny2V79uwRAES7du106vbp00d069at0H5mZWWJOXPmiBdffFFYWVkJU1NT8cwzz4iRI0eKc+fO6dTdtm2baNOmjTA3Nxc2Njaie/fu4tSpUzp1DE2Jzc/PF++++65wdHQUFhYWIiAgQJw7d67QKd0HDx7U2aZ2+urDU3GFKJi6a2lpKb/WTumePXu23jhRyLRyQ86ePSt/T/bs2aO3/Pbt22Lw4MHC0dFRWFlZiYCAAPHXX3/pjacoU7qFKJj2GxISImxsbIStra0ICQmRp0k/PKX777//Fr169RJ2dnbC1tZW9O3bV/zzzz8GxzZjxgzh7u4uVCqVzueRm5srpk2bJurUqSNMTEyEh4eHmDRpks7fEyGefPmDh124cEG88cYbol69esLMzEw4ODiITp06iW3btunU27hxo2jWrJkwMzMTXl5e4r///a9YtmyZwSnUhto29HfR0Geu/V6cP39edOnSRVhYWAhnZ2cRGRkp8vPz9bZZlCndhvrToUMHnUsKCFEwvb1du3ZCrVaLWrVqiaioKPH5558LACIpKamwt1AIUTAle+TIkaJmzZpCkiSd6d2P9rOofyce7utzzz2nU5aTkyP++9//iueee06o1Wphb28vvL29xbRp00R6evpj+0qVSxKiks/mIqoEeXl5qFGjBqKiouSLlREpXVhYGNauXat3nlJlGTNmDBYvXozMzMwqeQsGqn54Tg09lVJTUzF27Fj06tWrsrtC9FS4d++ezutbt27h22+/Rdu2bRloqMzwnBp6Kjk5OT3xfBciKju+vr7o2LEjGjVqhOTkZCxduhQZGRn48MMPK7trpCAMNUREVO66deuGtWvXYsmSJZAkCS+88AKWLl2qN4OOqDSKfU7Nrl27MHv2bBw+fBjXr1/H+vXr0bNnz8euEx8fj3HjxuHkyZPw8PDABx98oDfzhoiIiKg0in1Ozd27d9G8eXP5VvBPcvHiRbzyyivo1KkTjh07hjFjxmDo0KHYunVrsTtLREREVJhSzX6SJOmJe2reffddbNq0SeeS/f3790daWhpiY2NL2jQRERGRjnI/pyYhIUG+v49WQEAAxowZU+g62dnZ8tU2gYLLeqempqJGjRoVerlsIiIiKjkhBO7cuQM3Nze9i3WWh3IPNUlJSXB2dtYpc3Z2RkZGBu7du2fw/iZRUVGYNm1aeXeNiIiIKsDVq1dRq1atcm+nSs5+mjRpEsaNGye/Tk9PR+3atXH16lWde/2Qrr9mvYyG+WfKtY22td2RV457yxpcs0GbvycBUvklek1+BvKyYiE0WQ9KxEN/GnguClteWNm/zM1rwMHWCoD2pnzSg+fyUwBSwVNJAJKkrQFtxX+raev9WyNfo4GZmdlDLT7y2ZT0o3rcZ6yzTOgtk4pR99/n8v89huEj5VIRB2lklA+1aU6R6j5Wkd+bqqCE/amMcZRxm0/8XpS4uQp+b8xsABPTim2zDN29dx+9x0TC2tq6Qtor91Dj4uKid0PF5ORk2NjYFHoXWrVaDbVarVduY2PDUPMY9moBm3wJ1+oPgPuz3v/+ckoqnecFP8MSNBIgJAkQEoQEaCTp359oSSqog4I6AhIgAb7fTsXz5wVaODaHicoE0oMffEn+XRcFZeLBX33xoOzBcwkANBqddbT1AYE7961xuIY1VJo8eDRzLuiy9kdc+1zS/vsnFQztwUJ5iHjoubY+AKgK/pm7eWU/Lh+7XcJLT0qP/Fk4IxMT9Jw4AV7Nni9JQ0RE1V5GRgYwJrLCTh0p91Dj6+uLzZs365TFxcXB19e3vJt+6qy3zsdPtrWQq9kPJB6AQEFYEBDQCE3B61L6dpMG6jwAOFbqbRkiWXkg12wbkJ+COykOgBAQQiOPQ2gKYpcQBQ888qf8HACE5sFrQAjNgx0uAjlZBXtoajVugo4hQyE9OM4rSdKDECRBepCWHi37N0xJ8p8StMsfrivB2NQUJmozQ8MkIqJyUOxQk5mZiXPnzsmvL168iGPHjsHBwQG1a9fGpEmTcO3aNaxcuRIAMHz4cHzxxReYOHEi3njjDezYsQM//PADNm3aVHajIADAXgsNslQqQOQVtqe+1AoCDVBjxNswsrJ+aG+IvGuk4KGSDJSj4AdfpdKt+9A27v+dhvzffgAA/JOYVD6DeMD1mYZwrlu/XNsgIqKKU+xQc+jQIXTq1El+rT33JTQ0FCtWrMD169dx5coVeXmdOnWwadMmjB07FvPnz0etWrXw9ddfIyAgoAy6T4YMc3gNfV8eCUB7KEYy+KdKemgPBQyXqSTVgz0RBds6G9UUAOAwcCCMa9Qo876b7z0O/PYDJJUZuo8Z9yDwqOTDS/IeFDwUmh7aSyLvKcGDYKW3F6VgG0YmJnD08Czz/hMRUeUpdqjp2LEjHndpmxUrVhhc5+jRo8VtikrIRmUBF0uXyu5GqUiSCZ7xaV3Z3SCiKio/Px+5ubmV3Y2nnomJSZW6IWmVnP1EVdfZer1w07E5Ds45DahU8jkrACA0QntesDxjqOB0GO15LXhw7ov2vGDxoAzydvJzy/eQExFVb0IIJCUlIS0trbK7Qg/Y2dnBxcWlSlxHjqGGiuVqrZcLZlPdLoOpsQZodwKqjMv/Ik1EVP1oA42TkxMsLCyqxA/p00oIgaysLKSkpAAAXF1dK7lHDDWK0uK0BuFH8uGi+gUXovfLs4bwYEYQ5JlBAtAYKNPW1Wj0y7SP5h8BALoOqgMLV4cH56r8O+363xlD+Pfclwf55OHp2PI0bZXudO0bVy7gp49XwczSpELfOyKq+vLz8+VAU6Mczumj4tNemiUlJQVOTk6VfiiKoUZBWh/RwCMZANKQjbRybcu5oRMsHSzLfLuZqQ/CDP/ji4geoT2HxsLCopJ7Qg/Tfh65ubkMNVR2tDngatfmaNNvtN50akmSANXDF+J7aDq2zjTrwuvu+PRqQVvG3JNCRJWDh5yqlqr0eTDUKFCWuwMsy+3ihlfLabtERESlw7MxiYiISBG4p4aKJT/nL+TnXsL2pYdhZCw9/nYFT7qdAQCh0RTU0xScmJxz716ljo+IqDyEhYXhm2++AQAYGxvDwcEBzZo1w4ABAxAWFgaVSncfQ0BAALZt24Z9+/bhxRdf1Fl248YNTJkyBZs2bUJycjLs7e3RvHlzTJkyBW3atKmwMVVFDDVULLl3twLIx5l95duOtT1nNhCRsgQGBmL58uXIz89HcnIyYmNjMXr0aKxduxYbN26EsXHBT/KVK1ewd+9eREREYNmyZXqhpnfv3sjJycE333yDunXrIjk5Gdu3b8etW7cqY1hVCkMNFVM+AKBl936wtLP+9yaPj9yi4OEbPgKP3MLgkec6ZQ/WqdWoSSWOkYio7KnVari4FFzt3d3dHS+88AJeeukl+Pn5YcWKFRg6dCgAYPny5Xj11Vfx1ltv4aWXXsLcuXPlqdNpaWnYvXs34uPj0aFDBwCAp6cnWrVqVTmDqmIYaqhEnusYCMdaTpXdDSJ6ygkhcC83v8LbNTcxKpNZPy+//DKaN2+OdevWYejQoRBCYPny5Vi4cCEaNmyI+vXrY+3atQgJCQEAWFlZwcrKCj///DNeeuklqNXqUvdBSRhqFCbPSA1Nvgnu3y24noP29gNCeysCFNy6oOB8F93l2jLtbQvk5w/f5oCIqAq5l5uPxlO2Vni7p6YHwMK0bH5CGzZsiOPHjwMAtm3bhqysLPmmz//5z3+wdOlSOdQYGxtjxYoVCA8PR3R0NF544QV06NAB/fv3R7NmzcqkP9UZQ42C3Kj5Gs406gJcBJa+s7tc26pClyUgIqrWhBDyXp9ly5YhKChIPr9mwIABmDBhAs6fP4969eoBKDin5pVXXsHu3buxb98+bNmyBbNmzcLXX3+NsLCwyhpGlcBQoyBZlg0fX6HgGnry7QrkWxoAgOrBnw/f5uChutrzZu7fLijnbQyIqCowNzHCqekBldJuWTl9+jTq1KmD1NRUrF+/Hrm5uVi0aJG8PD8/H8uWLcPHH38sl5mZmaFz587o3LkzPvzwQwwdOhSRkZEMNZXdASp7Di6/o9+H7+sEmLK64uOnQQV/VqUrSBLR00uSpDI7DFQZduzYgT///BNjx47FqlWrUKtWLfz88886dX799Vd8+umnmD59eqG3IWjcuLHeek+j6vtNoEJJkoCREa+rSERUlWRnZyMpKUlnSndUVBReffVVDBo0CN7e3ujTpw+aNNGd/enh4YFJkyYhNjYWL730Evr27Ys33ngDzZo1g7W1NQ4dOoRZs2ahR48elTSyqoOhhoiIqALExsbC1dUVxsbG8gXzPv/8c4SGhuLo0aP4448/8NVXX+mtZ2trCz8/PyxduhT+/v7w8fHBZ599hvPnzyM3NxceHh4IDw/H5MmTK2FUVQtDDRERUTlbsWIFVqxYUehyb29veYaqIZs3b5afR0VFISoqqiy7pxg8RkFERESKwFBDREREisBQQ0RERIrAUENERESKwFBDREREisBQQ0RERIrAUENERESKwOvUKEhefjJyMv/Aras38PPsGQ/uwC10/vz3Tt0a/Hunbs1Dd+Z+UBcC0Dz485F1iYiIqiKGGgW5n3cKGnEL93OB84f2l1s7agtLmJpblNv2iYiISoKhRlE0AABLeye07tsPgPTgjtsFt9l+9Dke3KFbUqke1AUkSaW9lTckSfXgbt0SJJUE6cHtuh1re8LY1LTSRklE9LTp2LEjWrRogXnz5gEAvLy8MGbMGIwZM6ZS+1XVMNQokJmVHZr5BVZ2N4iI6IGwsDB88803euUBAQGIjY194vrr1q2DiYlJsdpMTU3FyJEj8b///Q8qlQq9e/fG/PnzYWVlVeg6S5YswerVq3HkyBHcuXMHt2/fhp2dXbHarUw8UZiIiKgCBAYG4vr16zqP77//vkjrOjg4wNrauljtBQcH4+TJk4iLi8Mvv/yCXbt2YdiwYY9dJysrC4GBgdX25pjcU0NERFQB1Go1XFxc9MoHDhyI/Px8xMTEyGW5ublwdXXF3LlzMWjQIL3DT09y+vRpxMbG4uDBg2jZsiUAYMGCBejWrRvmzJkDNzc3g+tpD2fFx8cXa2xVBUMNERFVX0IAuVkV366JBR6cdFhqwcHB6Nu3LzIzM+VDQ1u3bkVWVhZ69epVom0mJCTAzs5ODjQA4O/vD5VKhf3795d4u1UdQw0REVVfuVnAJ4b3OpSryf8AppbFWuWXX37RO59l8uTJmDhxIiwtLbF+/XqEhIQAAFavXo3XXnut2IectJKSkuDk5KRTZmxsDAcHByQlJZVom9UBQw0REVEF6NSpExYtWqRT5uDgAGNjY/Tr1w+rVq1CSEgI7t69iw0bNmDNmjVF2u7w4cPx3Xffya8zMzPLtN/VCUMNERFVXyYWBXtNKqPdYrK0tET9+vUNLgsODkaHDh2QkpKCuLg4mJubIzCwaLNYp0+fjvHjx+uUubi4ICUlRacsLy8PqampBs/rUQqGGiIiqr4kqdiHgaqi1q1bw8PDAzExMdiyZQv69u1b5CncTk5OeoeafH19kZaWhsOHD8Pb2xsAsGPHDmg0Gvj4+JR5/6sKhhoiIqIKkJ2drXc+i7GxMRwdHQEUzIKKjo7GmTNnsHPnzlK11ahRIwQGBiI8PBzR0dHIzc1FREQE+vfvL898unbtGvz8/LBy5Uq0atUKQMG5OElJSTh37hwA4M8//4S1tTVq164NBweHUvWpIvA6NURERBUgNjYWrq6uOo+2bdvKy4ODg3Hq1Cm4u7ujTZs2pW5v1apVaNiwIfz8/NCtWze0bdsWS5YskZfn5uYiMTERWVn/zh6Ljo7G888/j/DwcABA+/bt8fzzz2Pjxo2l7k9FkEQ1uEthRkYGbG1tkZ6eDhsbm8ruTpU1r38I8sVt1PB4FmFz5lZ2d4iIytT9+/dx8eJF1KlTB2ZmZpXdHXrgcZ9LRf9+c08NERERKQJDDRERESkCQw0REREpAkMNERERKQJDDRERESkCQw0REREpAkMNERERKQJDDRERESkCQw0REREpAkMNERFRFdexY0eMGTNGfu3l5YV58+ZVWn+qKoYaIiKichYWFgZJkvQegYGBRVp/3bp1mDFjRrHaTE1NRXBwMGxsbGBnZ4chQ4YgMzPzsfVHjhyJBg0awNzcHLVr18aoUaOQnp5erHYrE+/STUREVAECAwOxfPlynTK1Wl2kdUtyh+zg4GBcv34dcXFxyM3NxeDBgzFs2DCsXr3aYP1//vkH//zzD+bMmYPGjRvj8uXLGD58OP755x+sXbu22O1XBoYaIiKiCqBWq+Hi4qJXPnDgQOTn5yMmJkYuy83NhaurK+bOnYtBgwahY8eOaNGiRZEPOZ0+fRqxsbE4ePAgWrZsCQBYsGABunXrhjlz5sDNzU1vnSZNmuCnn36SX9erVw8ff/wx/vOf/yAvLw/GxlU/MlT9HhIRERVCCIF7efcqvF1zY3NIklQm2woODkbfvn2RmZkJKysrAMDWrVuRlZWFXr16lWibCQkJsLOzkwMNAPj7+0OlUmH//v1F3q727trVIdAADDVERFSN3cu7B5/VPhXe7v6B+2FhYlGsdX755Rc5tGhNnjwZEydOhKWlJdavX4+QkBAAwOrVq/Haa6/B2tq6RP1LSkqCk5OTTpmxsTEcHByQlJRUpG3cvHkTM2bMwLBhw0rUh8rAUENERFQBOnXqhEWLFumUOTg4wNjYGP369cOqVasQEhKCu3fvYsOGDVizZk2Rtjt8+HB899138uvHnQxcVBkZGXjllVfQuHFjTJ06tdTbqygMNUREVG2ZG5tj/8D9ldJucVlaWqJ+/foGlwUHB6NDhw5ISUlBXFwczM3Nizwzavr06Rg/frxOmYuLC1JSUnTK8vLykJqaavC8nofduXMHgYGBsLa2xvr162FiYlKkflQFDDVERFRtSZJU7MNAVVHr1q3h4eGBmJgYbNmyBX379i1ymHByctI71OTr64u0tDQcPnwY3t7eAIAdO3ZAo9HAx6fww3UZGRkICAiAWq3Gxo0bYWZmVvJBVQKGGiIiogqQnZ2tdz6LsbExHB0dARTMgoqOjsaZM2ewc+fOUrXVqFEjBAYGIjw8HNHR0cjNzUVERAT69+8vz3y6du0a/Pz8sHLlSrRq1QoZGRno0qULsrKy8N133yEjIwMZGRkAgJo1a8LIyKhUfaoIDDVEREQVIDY2Fq6urjplDRo0wF9//QWg4BDUxx9/DE9PT7Rp06bU7a1atQoRERHw8/ODSqVC79698fnnn8vLc3NzkZiYiKysLADAkSNHsH9/waG8Rw+TXbx4EV5eXqXuU3mThBCisjvxJBkZGbC1tZWnlpFh8/qHIF/cRg2PZxE2Z25ld4eIqEzdv38fFy9eRJ06dardYREle9znUtG/37xNAhERESkCQw0REREpAkMNERERKUKJQs3ChQvh5eUFMzMz+Pj44MCBA4+tP2/ePPmunx4eHhg7dizu379fog4TERERGVLsUBMTE4Nx48YhMjISR44cQfPmzREQEKB3kR+t1atX47333kNkZCROnz6NpUuXIiYmBpMnTy5154mIiIi0ih1q5s6di/DwcAwePBiNGzdGdHQ0LCwssGzZMoP19+7dizZt2mDgwIHw8vJCly5dMGDAgCfu3SEiIiIqjmKFmpycHBw+fBj+/v7/bkClgr+/PxISEgyu07p1axw+fFgOMRcuXMDmzZvRrVu3QtvJzs6WL/rz8MV/iIiIiApTrIvv3bx5E/n5+XB2dtYpd3Z2li8e9KiBAwfi5s2baNu2LYQQyMvLw/Dhwx97+CkqKgrTpk0rTteIiIjoKVfus5/i4+PxySef4Msvv8SRI0ewbt06bNq0CTNmzCh0nUmTJiE9PV1+XL16tby7SURERNVcsUKNo6MjjIyMkJycrFOenJxc6F0/P/zwQ4SEhGDo0KFo2rQpevXqhU8++QRRUVHQaDQG11Gr1bCxsdF5EBERPa06duyIMWPGyK+9vLwwb968SutPVVWsUGNqagpvb29s375dLtNoNNi+fTt8fX0NrpOVlQWVSrcZ7U2xqsEdGoiIiEotLCwMkiTpPQIDA4u0/rp16x57hMOQ1NRUBAcHw8bGBnZ2dhgyZAgyMzMfu86bb76JevXqwdzcHDVr1kSPHj0KPb2kKir2DS3HjRuH0NBQtGzZEq1atcK8efNw9+5dDB48GAAwaNAguLu7IyoqCgDQvXt3zJ07F88//zx8fHxw7tw5fPjhh+jevXu1uOMnERFRWQgMDMTy5ct1ytRqdZHWdXBwKHZ7wcHBuH79OuLi4pCbm4vBgwdj2LBhWL16daHreHt7Izg4GLVr10ZqaiqmTp2KLl264OLFi9XiN7vYoSYoKAg3btzAlClTkJSUhBYtWiA2NlY+efjKlSs6e2Y++OADSJKEDz74ANeuXUPNmjXRvXt3fPzxx2U3CiIioipOrVYbPFVj4MCByM/PR0xMjFyWm5sLV1dXzJ07F4MGDULHjh3RokWLIh9yOn36NGJjY3Hw4EG0bNkSALBgwQJ069YNc+bMgZubm8H1hg0bJj/38vLCRx99hObNm+PSpUuoV69eMUZbOYodagAgIiICERERBpfFx8frNmBsjMjISERGRpakKSIiokIJISDu3avwdiVzc0iSVCbbCg4ORt++fZGZmQkrKysAwNatW5GVlYVevXqVaJsJCQmws7OTAw0A+Pv7Q6VSYf/+/UXa7t27d7F8+XLUqVMHHh4eJepHRStRqCEiIqoKxL17SHzBu8LbbXDkMCQLi2Kt88svv8ihRWvy5MmYOHEiLC0tsX79eoSEhAAouBr/a6+9Bmtr6xL1LykpCU5OTjplxsbGcHBwQFJS0mPX/fLLLzFx4kTcvXsXDRo0QFxcHExNTUvUj4rGG1oSERFVgE6dOuHYsWM6j+HDh8PY2Bj9+vXDqlWrABTsIdmwYQOCg4OLtN3hw4fDyspKfpRWcHAwjh49it9++w3PPvss+vXrV23u18g9NUREVG1J5uZocORwpbRbXJaWlqhfv77BZcHBwejQoQNSUlIQFxcHc3PzIs+Mmj59OsaPH69T5uLiondPxry8PKSmphZ6CRYtW1tb2Nra4plnnsFLL70Ee3t7rF+/HgMGDChSfyoTQw0REVVbkiQV+zBQVdS6dWt4eHggJiYGW7ZsQd++fWFiYlKkdZ2cnPQONfn6+iItLQ2HDx+Gt3fB4bkdO3ZAo9HAx8enyP0SQkAIgezs7KIPphIx1BAREVWA7OxsvfNZjI2N4ejoCKBgFlR0dDTOnDmDnTt3lqqtRo0aITAwEOHh4YiOjkZubi4iIiLQv39/eebTtWvX4Ofnh5UrV6JVq1a4cOECYmJi0KVLF9SsWRN///03Zs6cCXNz88fer7Eq4Tk1REREFSA2Nhaurq46j7Zt28rLg4ODcerUKbi7u6NNmzalbm/VqlVo2LAh/Pz80K1bN7Rt2xZLliyRl+fm5iIxMRFZWVkAADMzM+zevRvdunVD/fr1ERQUBGtra+zdu1dvT1BVJYlqcFnfjIwM2NraIj09nbdMeIx5/UOQL26jhsezCJszt7K7Q0RUpu7fv4+LFy+iTp06MDMzq+zu0AOP+1wq+vebe2qIiIhIERhqiIiISBEYaoiIiEgRGGqIiIhIERhqiIiISBEYaoiIiEgRGGqIiIhIERhqiIiISBEYaoiIiEgRGGqIiIiquI4dO2LMmDHyay8vL8ybN6/S+lNVMdQQERGVs7CwsII7ij/yCAwMLNL669atw4wZM4rVZmpqKoKDg2FjYwM7OzsMGTIEmZmZRVpXCIGuXbtCkiT8/PPPxWq3MvEu3URERBUgMDAQy5cv1ylTq9VFWtfBwaHY7QUHB+P69euIi4tDbm4uBg8ejGHDhmH16tVPXHfevHmQJKnYbVY27qkhIiKqAGq1Gi4uLjoPe3t7DBw4EEFBQTp1c3Nz4ejoiJUrVwLQP/z0JKdPn0ZsbCy+/vpr+Pj4oG3btliwYAHWrFmDf/7557HrHjt2DJ9++imWLVtW7DFWNu6pISKiaksIgbwcTYW3a2yqKrM9GcHBwejbty8yMzNhZWUFANi6dSuysrLQq1evEm0zISEBdnZ2aNmypVzm7+8PlUqF/fv3F7rdrKwsDBw4EAsXLoSLi0uJ2q5MDDVERFRt5eVosGT0bxXe7rD5HWCiNirWOr/88oscWrQmT56MiRMnwtLSEuvXr0dISAgAYPXq1XjttddgbW1dov4lJSXByclJp8zY2BgODg5ISkoqdL2xY8eidevW6NGjR4narWwMNURERBWgU6dOWLRokU6Zg4MDjI2N0a9fP6xatQohISG4e/cuNmzYgDVr1hRpu8OHD8d3330nvy7qycCP2rhxI3bs2IGjR4+WaP2qgKGGiIiqLWNTFYbN71Ap7RaXpaUl6tevb3BZcHAwOnTogJSUFMTFxcHc3LzIM6OmT5+O8ePH65S5uLggJSVFpywvLw+pqamFHlbasWMHzp8/Dzs7O53y3r17o127doiPjy9SfyoTQw0REVVbkiQV+zBQVdS6dWt4eHggJiYGW7ZsQd++fWFiYlKkdZ2cnPQONfn6+iItLQ2HDx+Gt7c3gILQotFo4OPjY3A77733HoYOHapT1rRpU3z22Wfo3r17CUZV8RhqiIiIKkB2drbe+SzGxsZwdHQEAAwcOBDR0dE4c+YMdu7cWaq2GjVqhMDAQISHhyM6Ohq5ubmIiIhA//794ebmBgC4du0a/Pz8sHLlSrRq1UqekfWo2rVro06dOqXqT0XhlG4iIqIKEBsbC1dXV51H27Zt5eXBwcE4deoU3N3d0aZNm1K3t2rVKjRs2BB+fn7o1q0b2rZtiyVLlsjLc3NzkZiYiKysrFK3VVVwTw0REVE5W7FiBVasWPHYOo0aNYIQwuCyR89nuXTp0hPbdHBweOyF9ry8vAptT+tJy6sa7qkhIiIiRWCoISIiIkVgqCEiIiJFYKghIiIiRWCoISIiIkVgqCEiIiJFYKghIiIiRWCoISIiIkVgqCEiIiJFYKghIiKq4jp27IgxY8bIr728vDBv3rxK609VxVBDRERUzsLCwiBJkt4jMDCwSOuvW7cOM2bMKFabqampCA4Oho2NDezs7DBkyBBkZmY+dp2OHTvq9XH48OHFarcy8d5PREREFSAwMBDLly/XKVOr1UVa18HBodjtBQcH4/r164iLi0Nubi4GDx6MYcOGPfZ+UAAQHh6O6dOny68tLCyK3XZlYaghIqJqSwiBvOzsCm/XWK2GJEnFWketVsPFxUWvfODAgcjPz0dMTIxclpubC1dXV8ydOxeDBg1Cx44d0aJFiyIfcjp9+jRiY2Nx8OBBtGzZEgCwYMECdOvWDXPmzIGbm1uh61pYWBjsZ3XAUENERNVWXnY2Pg/tU+HtjvpmLUzMzMpkW8HBwejbty8yMzNhZWUFANi6dSuysrLQq1evEm0zISEBdnZ2cqABAH9/f6hUKuzfv/+x2121ahW+++47uLi4oHv37vjwww+rzd4ahhoiIqIK8Msvv8ihRWvy5MmYOHEiLC0tsX79eoSEhAAAVq9ejddeew3W1tYlaispKQlOTk46ZcbGxnBwcEBSUlKh6w0cOBCenp5wc3PD8ePH8e677yIxMRHr1q0rUT8qGkMNERFVW8ZqNUZ9s7ZS2i2uTp06YdGiRTplDg4OMDY2Rr9+/bBq1SqEhITg7t272LBhA9asWVOk7Q4fPhzfffed/PpJJwM/zrBhw+TnTZs2haurK/z8/HD+/HnUq1evxNutKAw1RERUbUmSVGaHgcqbpaUl6tevb3BZcHAwOnTogJSUFMTFxcHc3LzIM6OmT5+O8ePH65S5uLggJSVFpywvLw+pqanFOl/Gx8cHAHDu3DmGGiIiInqy1q1bw8PDAzExMdiyZQv69u0LExOTIq3r5OSkd6jJ19cXaWlpOHz4MLy9vQEAO3bsgEajkYNKURw7dgwA4OrqWuR1KhNDDRERUQXIzs7WO5/F2NgYjo6OAArOZ4mOjsaZM2ewc+fOUrXVqFEjBAYGIjw8HNHR0cjNzUVERAT69+8vz3y6du0a/Pz8sHLlSrRq1Qrnz5/H6tWr0a1bN9SoUQPHjx/H2LFj0b59ezRr1qxU/akovPgeERFRBYiNjYWrq6vOo23btvLy4OBgnDp1Cu7u7mjTpk2p21u1ahUaNmwIPz8/dOvWDW3btsWSJUvk5bm5uUhMTERWVhYAwNTUFNu2bUOXLl3QsGFDvPPOO+jduzf+97//lbovFUUSQojK7sSTZGRkwNbWFunp6bCxsans7lRZ8/qHIF/cRg2PZxE2Z25ld4eIqEzdv38fFy9eRJ06dWBWTc6jeRo87nOp6N9v7qkhIiIiReA5NRXk4KVUTP/fKdzNyYMQBVfBFACEADRCQLu/TPtcQEAj8KBc/FvvoXVQ8D95nTdR5Xe6ERERlRuGmgqy4dg1/HktvULaMjXmDjgiInr6MNRUEM2DnSj9WtZC35YekABIUsE1FgqeS1BJgATpQfm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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -173,48 +158,102 @@
     "\n",
     "    ax3.plot(x, y,label=key)\n",
     "\n",
-    "ax3.set_xlim([0,4000])\n",
+    "ax3.set_xlim([0,8000])\n",
     "ax3.legend()\n",
     "ax3.set_ylim([0,1])\n",
     "\n",
-    "ax3.set_title(\"CDF Row/Column validator sampling time\")\n",
+    "ax3.set_title(\"CDF row/column fetching time\")\n",
     "ax3.set_xlabel(\"Operation complete time (ms)\")"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "3debb1ed",
    "metadata": {},
    "source": [
-    "In this graph we observe the CDF of the hops (nodes contacted) required to complete the validator sampling of the DAS protocol, with and without attackers in the simulation, from 0\\% to 50\\% of attackers in the simulation.\n",
-    "We observe in all cases the validator sampling is completed on time, with small differences on the nodes contacted.\n",
-    "We cans ee the maximum  number of hops required is 5, even with 50% malicious nodes."
+    "In this graph we observe the CDF of the number of requests required to complete the row and column fetching using the DAS protocol, with and without attackers in the simulation, from 0\\% to 75\\% of attackers in the simulation.\n",
+    "As we can see in the required time, we see a substantially increase when the number of malicious nodes in the network is very high (75\\%)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 26,
    "id": "707322b0",
-   "metadata": {
-    "vscode": {
-     "languageId": "python"
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'num_messages'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'num_messages'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[26], line 7\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m op_df:\n\u001b[1;32m      5\u001b[0m     vsdf \u001b[38;5;241m=\u001b[39m op_df[key]\u001b[38;5;241m.\u001b[39mloc[(op_df[key][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mValidatorSamplingOperation\u001b[39m\u001b[38;5;124m'\u001b[39m)]\n\u001b[0;32m----> 7\u001b[0m     x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msort(\u001b[43mvsdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnum_messages\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[1;32m      8\u001b[0m     N \u001b[38;5;241m=\u001b[39m vsdf[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnum_messages\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mcount()\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;66;03m# get the cdf values of y\u001b[39;00m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'num_messages'"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-   },
+   ],
+   "source": [
+    "fig8, ax8 = plt.subplots()\n",
+    "\n",
+    "for key in op_df:\n",
+    "\n",
+    "    vsdf = op_df[key].loc[(op_df[key]['type'] == 'ValidatorSamplingOperation')]\n",
+    "\n",
+    "    x = np.sort(vsdf['num_messages'])\n",
+    "    N = vsdf['num_messages'].count()\n",
+    "    # get the cdf values of y\n",
+    "    y = np.arange(N) / float(N)\n",
+    "\n",
+    "    ax8.plot(x, y,label=key)\n",
+    "\n",
+    "ax8.legend()\n",
+    "#ax8.set_xlim([0,10])\n",
+    "ax8.set_ylim([0,1])\n",
+    "\n",
+    "ax8.set_title(\"CDF number of requests per row/column fetching process\")\n",
+    "ax8.set_xlabel(\"# Messages\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "95bea67e",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0.5, 0, '# hops')"
+       "Text(0.5, 0, '#MBs')"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -226,51 +265,198 @@
    "source": [
     "fig8, ax8 = plt.subplots()\n",
     "\n",
-    "for key in op_df:\n",
+    "for key in msg_df:\n",
     "\n",
-    "    vsdf = op_df[key].loc[(op_df[key]['type'] == 'ValidatorSamplingOperation')]\n",
+    "    vsdf = msg_df[key].loc[(msg_df[key]['nodeType'] == 'validator')]\n",
     "\n",
-    "    x = np.sort(vsdf['hops'])\n",
-    "    N = vsdf['hops'].count()\n",
+    "    x = np.sort(vsdf['bytesIn'])/ 1024 / 1024\n",
+    "    N = vsdf['bytesIn'].count()\n",
     "    # get the cdf values of y\n",
     "    y = np.arange(N) / float(N)\n",
     "\n",
     "    ax8.plot(x, y,label=key)\n",
     "\n",
     "ax8.legend()\n",
-    "ax8.set_xlim([0,10])\n",
+    "#ax8.set_xlim([0,10])\n",
     "ax8.set_ylim([0,1])\n",
     "\n",
-    "ax8.set_title(\"CDF Row/Column sampling hops\")\n",
-    "ax8.set_xlabel(\"# hops\")"
+    "ax8.set_title(\"CDF bytes received per sampling process validator\")\n",
+    "ax8.set_xlabel(\"#MBs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "64a9d6f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '#MBs')"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig8, ax8 = plt.subplots()\n",
+    "\n",
+    "for key in msg_df:\n",
+    "\n",
+    "    vsdf = msg_df[key].loc[(msg_df[key]['nodeType'] == 'regular')]\n",
+    "\n",
+    "    x = np.sort(vsdf['bytesIn'])/ 1024 / 1024\n",
+    "    N = vsdf['bytesIn'].count()\n",
+    "    # get the cdf values of y\n",
+    "    y = np.arange(N) / float(N)\n",
+    "\n",
+    "    ax8.plot(x, y,label=key)\n",
+    "\n",
+    "ax8.legend()\n",
+    "#ax8.set_xlim([0,10])\n",
+    "ax8.set_ylim([0,1])\n",
+    "\n",
+    "ax8.set_title(\"CDF bytes received per sampling process regular node\")\n",
+    "ax8.set_xlabel(\"#MBs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "0792c905",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Malicious')"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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jyk6X7du3R1BQEIKCgjB06FDs2bMHzZo1kz5QOmFhYfjll19w5coVAA/f+IWFhRg+fHiZ29C9ocPDw+Hk5KT38+WXXyI/Px93794FAHz66adITEyEu7s72rdvjw8++KBcXz46np6eRbYthEDTpk2LbPvcuXNSR71Lly4BAPz8/Epc98WLFyGEwIwZM4qsS/flqltfcQYNGoS8vDypf8m9e/ewd+9evPzyy9IXiu61euaZZ4ps4+DBg9L6dfuhadOmRbbz1FNPlf1C/T8vL68ivwS8vb0BQDpnnpycjP379xepJygoqNjn/Pg+KMvj7W/cuIHc3Nxin4evry+0Wq3U30f3Cyc2NhY5OTk4ffo0AgMD0bVrVymMxMbGws7ODq1atZLW8/vvv+PGjRt6YeTSpUuoV6+e3i+Gx125cgVKpbLIFSaurq5wcHCQ9ktlaNCgQZF9Y29vD3d39yLTAOCff/4pso6GDRvqPa5du3aJbctS3vdmacrzGpdUO/Cw/vLUXp5lr1y5UuyVQoZcPaRUKvUCM1D086Pz+Ptc914p6X2u+4Ptxo0byMrKKvW7CSj/53T8+PHw9vZGcHAwGjRogFGjRhXpizNnzhzcuXMH3t7eaNGiBd5++238+eefZbwa1UuVupomJycHrVq1wqhRoxAaGmrw8lOnTi3yl0vPnj1lvcxQqVSiR48eWLJkCZKTk9G8eXMAwCuvvIK33noLmzdvxvTp07Fp0ya0a9euXL/4dEc95s+fX+Ilv7a2tgCAgQMHIjAwEN9++y0OHjyI+fPn45NPPkF0dDSCg4PL3Ja1tXWRbSsUCuzbt6/YXve67ZaH7nlMnToVvXv3LrZNaV9kHTt2hIeHB7Zv344hQ4bghx9+QF5eHgYNGlRkG19//XWRDpcApI7FpqTVatGrVy+88847xc7XffnqPL4PymJo+0fVq1cPnp6eOHr0KDw8PCCEQKdOneDk5IRJkybhypUriI2NRUBAAJTK//2ts3fvXnh4eBh0FOlRFbn6pqRlHu24/KiSrhIpabp4pNNkRdqWxdTvzX9Te2U+78ryb97n5VHez6mzszMSEhJw4MAB7Nu3D/v27cP69esRFhaGr776CsDDoQkuXbqE7777DgcPHsSXX36Jzz77DKtWrcKYMWOM+jyeFFUqjAQHB5f6CzI/Px/vv/8+tm7dijt37sDPzw+ffPKJNCqjra2t3i/DP/74A2fPnsWqVauMXXqpHjx4AAB6pxPq1KmDPn36YPPmzRg6dCh++eUXLF68WG+5kr5sdYda7ezspJReGjc3N4wfPx7jx49HZmYm2rZti48++qhcYaS4bQsh4OnpWeSXZnE1JiYmllij7i+gGjVqlOt5FGfgwIFYsmQJsrKy8M0338DDwwMdO3YsUoezs3Op22jUqBEAFHsYVXfKojx0R3se3XcXLlwAAGkU1yZNmuDevXsVfs6GcnJygo2NTbHPIykpCUqlUu/oQGBgII4ePQpPT0+0bt0atWrVQqtWrWBvb4/9+/fj1KlTmD17tt569uzZg+eff15vWpMmTXDgwAHcvn27xL/cGzVqBK1Wi+TkZPj6+krTMzIycOfOHWm/FEd3VOLOnTt60yvzaIoxlfe9CZT+XVDWa2wqjRo1wsWLF4tML25aSbRaLS5fvqz33fL456e07QPFf16TkpLg6OiImjVrwtraGnZ2dkhMTCx1fYZ8Ti0tLdGvXz/069cPWq0W48ePx+rVqzFjxgzpD6o6depg5MiRGDlyJO7du4euXbvigw8+MJswUqVO05Rl4sSJiIuLw7Zt2/Dnn3/i5ZdfxnPPPVfiebgvv/wS3t7eeuc6Ta2wsBAHDx6EpaWl3pct8HCAobNnz+Ltt9+GSqXSO58NQDq99PiXrb+/P5o0aYIFCxboBRydGzduAHj4F6LudI2Os7Mz6tWrV+ZlkyUJDQ2FSqXC7Nmzi/xVJITArVu3AABt27aFp6cnFi9eXKR+3XLOzs7o3r07Vq9ejbS0tBKfR2kGDRqE/Px8fPXVV9i/fz8GDhyoN793796ws7PDvHnzUFhYWOI23Nzc0Lp1a3z11Vd6r9mhQ4dKvZT2cdevX9e7HDgrKwsbN25E69atpb9+Bw4ciLi4OBw4cKDI8nfu3JHCa2VRqVR49tln8d133+kd6s7IyMCWLVvQpUsX2NnZSdMDAwORmpqKb775RvrsKJVKBAQEYNGiRSgsLNT7TGVkZODUqVN6p2iAh+f0hRBFggvwv/eALsA8HsQXLVoEAEXW+SjdL/NH+9hoNBqDRzeVS3nfm0DJ3wXleY1NpXfv3oiLi9MbMfr27dt6fePKY9myZdL/hRBYtmwZatSogZ49e5a63KOf4Udfp8TERBw8eFB6rymVSoSEhOCHH34o9vJn3etW3s+p7jtPR6lUomXLlgAgfc8+3sbW1hZeXl4V/h6uiqrUkZHSXL16FevXr8fVq1dRr149AA8P7+/fvx/r16/HvHnz9Nrfv38fmzdvxnvvvWfSOvft24ekpCQAD88pbtmyBcnJyXjvvff0vvCBh1+0devWxY4dOxAcHAxnZ2e9+U2aNIGDgwNWrVqFWrVqoWbNmujQoQM8PT3x5ZdfIjg4GM2bN8fIkSNRv359XLt2DUeOHIGdnR1++OEHZGdno0GDBhgwYABatWoFW1tb/Pjjj/j999+xcOHCCj2/Jk2aYO7cuZg2bRpSU1MREhKCWrVqISUlBd9++y1effVVTJ06FUqlEitXrkS/fv3QunVrjBw5Em5ubkhKSsJff/0lfcCXL1+OLl26oEWLFhg7diwaN26MjIwMxMXF4b///S/++OOPUutp27YtvLy88P777yM/P1/vFA3w8OjRypUrMXz4cLRt2xavvPIKnJyccPXqVezZswedO3eWvvwiIyPRp08fdOnSBaNGjcLt27exdOlSNG/evNjQVxxvb2+MHj0av//+O1xcXLBu3TpkZGRg/fr1Upu3334b33//Pfr27YsRI0bA398fOTk5OHPmDKKiopCamgpHR0dDdkuZ5s6di0OHDqFLly4YP348LCwssHr1auTn5+PTTz/Va6sLGufPn9f7XHXt2hX79u2DWq3WO/W5d+9eWFlZoUePHnrr6dGjB4YPH47PP/8cycnJeO6556DVahEbG4sePXpg4sSJaNWqFcLDw7FmzRrcuXMH3bp1w4kTJ/DVV18hJCSkyDof1bx5c3Ts2BHTpk2Tjgxs27at0sOcsRjy3vT39wcAvPnmm+jdu7f0x0t5XmNTeeedd7Bp0yb06tULb7zxBmrWrIkvv/wSDRs2xO3bt8t1Ks7Kygr79+9HeHg4OnTogH379mHPnj2YPn16uTpxz58/H8HBwejUqRNGjx6NvLw8LF26FPb29npju8ybNw8HDx5Et27d8Oqrr8LX1xdpaWnYsWMHjh07BgcHh3J/TseMGYPbt2/jmWeeQYMGDXDlyhUsXboUrVu3lv4AbdasGbp37w5/f3/UqVMHJ0+eRFRUlEn3j+xMfPVOpQEgvv32W+nx7t27BQBRs2ZNvR8LCwsxcODAIstv2bJFWFhYiPT0dJPUW9ylvVZWVqJ169Zi5cqVepfZPWr8+PFFLjl81HfffSeaNWsmLCwsilxuePr0aREaGirq1q0r1Gq1aNSokRg4cKA4fPiwEEKI/Px88fbbb4tWrVqJWrVqiZo1a4pWrVqJFStWlPl8irsU8VE7d+4UXbp0kfaDj4+PmDBhgjh//rxeu2PHjolevXpJ22/ZsqXepYRCCHHp0iURFhYmXF1dRY0aNUT9+vVF3759RVRUlNSmuMs1dd5//30BQHh5eZX4fI4cOSJ69+4t7O3thZWVlWjSpIkYMWKEOHnyZJHn5evrK9RqtWjWrJmIjo4W4eHh5b60t0+fPuLAgQOiZcuWQq1WCx8fH7Fjx44ibbOzs8W0adOEl5eXsLS0FI6OjiIgIEAsWLBAFBQUCCH+dwnj/Pnzy9y2DoASL+U+deqU6N27t7C1tRU2NjaiR48e4vjx48W2dXZ2FgBERkaGNO3YsWMCgAgMDNRrO2DAAPH8888Xu54HDx6I+fPnCx8fH2FpaSmcnJxEcHCwiI+Pl9oUFhaK2bNnC09PT1GjRg3h7u4upk2bpne5txBFL+0V4uF7JygoSKjVamkcjEOHDhV7aW/z5s2L1KfbZ497/HUs6fNQ0uXFjyvt81Se9+aDBw/EG2+8IZycnIRCodC7zLc8r3FJ74tGjRqJ8PDwUp9PSa9Rcfvj9OnTIjAwUKjVatGgQQMRGRkpPv/8cwGgzO/i8PBwUbNmTXHp0iXx7LPPChsbG+Hi4iJmzZolNBqN1K6sz8WPP/4oOnfuLKytrYWdnZ3o16+fOHv2bJF2V65cEWFhYcLJyUmo1WrRuHFjMWHCBJGfny+1Kc/nNCoqSjz77LPC2dlZWFpaioYNG4px48aJtLQ0aT1z584V7du3Fw4ODsLa2lr4+PiIjz76SFqHOVAIIWMPo39BoVDg22+/RUhICICHl2wOHToUf/31V5HOVLa2tkU6gPXs2RN2dnYGjaAph7feegtr165Feno6bGxs5C6H/gUPDw/4+flh9+7dcpdiMg8ePEDdunURGRmJ8ePHy10OPYEmT56M1atX4969e6UONT9ixAhERUWV+ygkVS3V5jRNmzZtoNFokJmZWWYfkJSUFBw5cqTIkOJPmvv372PTpk146aWXGESoSrp9+zbeeustvbFVyHzl5eXpXeVy69YtfP311+jSpUuVvOcNVZ4qFUbu3bun1/M6JSUFCQkJqFOnDry9vTF06FCEhYVh4cKFaNOmDW7cuIHDhw+jZcuWeh3d1q1bBzc3twpdLWIKmZmZ+PHHHxEVFYVbt25h0qRJcpdEVCHOzs68tT1JOnXqhO7du8PX1xcZGRlYu3YtsrKyMGPGDLlLI5lVqTBy8uRJvQ5rulEZw8PDsWHDBqxfvx5z587FlClTcO3aNTg6OqJjx456d8PVarXYsGEDRowY8cQm8bNnz2Lo0KFwdnbG559/XuJYIUREVcnzzz+PqKgorFmzBgqFAm3btsXatWvRtWtXuUsjmVXZPiNERERUPVSrcUaIiIio6mEYISIiIllViT4jWq0W169fR61atSp0jwoiIiIyPSEEsrOzUa9ePb37VT2uSoSR69evF7lzJhEREVUNf//9Nxo0aFDi/CoRRmrVqgXg4ZN5fMh0IiIiejJlZWXB3d1d+j1ekioRRnSnZuzs7BhGiIiIqpiyuliwAysRERHJimGEiIiIZMUwQkRERLKqEn1GiIiIyPS0Wi0KCgpKnF+jRo1KubUKwwgREREVUVBQgJSUFGi12lLbOTg4wNXV9V+NA8YwQkRERHqEEEhLS4NKpYK7u3uxA5YJIZCbm4vMzEwAgJubW4W3xzBCREREeh48eIDc3FzUq1cPNjY2JbaztrYGAGRmZsLZ2bnCp2zYgZWIiIj0aDQaAIClpWWZbXVhpbCwsMLbYxghIiKiYpWnH0hl3DOOp2mIiAgajQaxsbFIS0uDm5sbAgMDK+UqCaLy4JERIiIzFx0dDS8vL/To0QNDhgxBjx494OXlhejoaLlLIzPBMEJEZMaio6MxYMAAtGjRAnFxccjOzkZcXBxatGiBAQMGMJCQSSiEEELuIsqSlZUFe3t73L17lzfKIyKqJBqNBl5eXmjRogV27dqld/mmVqtFSEgIEhMTkZyczFM2Zub+/ftISUmBh4eHdMVMSfLy8pCamgpPT09YWVnpzSvv728eGSEiMlOxsbFITU3F9OnTi4wjoVQqMW3aNKSkpCA2NlamCkkuuvBZ2uirOrm5uQAejsZaUezASkRkptLS0gAAfn5+xc7XTde1I/NhYWEBGxsb3LhxAzVq1Chz0DMHB4d/dfSMYYSIyEzpRsxMTExEx44di8xPTEzUa0fmQ6FQwM3NDSkpKbhy5UqpbXXDwf+r7bHPCBGReWKfESrLv71RHvuMEBFRqVQqFRYuXIjdu3cjJCRE72qakJAQ7N69GwsWLGAQMWNKpRJWVlYl/lTWe4OnaYiIzFhoaCiioqIwZcoUBAQESNM9PT0RFRWF0NBQGasjc2HwkZGjR4+iX79+qFevHhQKBXbt2lXuZX/55RdYWFigdevWhm6WiIiMJDQ0FBcvXsSRI0ewZcsWHDlyBMnJyQwiZDIGHxnJyclBq1atMGrUKIPeqHfu3EFYWBh69uyJjIwMQzdLRERGpFKp0L17d7nLIDNlcBgJDg5GcHCwwRt67bXXMGTIEKhUKoOOphAREVH1ZpIOrOvXr8fly5cxa9ascrXPz89HVlaW3g8RERFVT0YPI8nJyXjvvfewadMmWFiU70BMZGQk7O3tpR93d3cjV0lERERyMWoY0Wg0GDJkCGbPng1vb+9yLzdt2jTcvXtX+vn777+NWCURERHJyaiX9mZnZ+PkyZM4ffo0Jk6cCODhACpCCFhYWODgwYN45plniiynVquhVquNWRoRERE9IYwaRuzs7HDmzBm9aStWrMBPP/2EqKgoeHp6GnPzREREVAUYHEbu3buHixcvSo9TUlKQkJCAOnXqoGHDhpg2bRquXbuGjRs3QqlUFrkBk7OzM6ysrEq8MRMRERGZF4PDyMmTJ9GjRw/pcUREBAAgPDwcGzZsQFpaGq5evVp5FRIREVG1xhvlERERkVHwRnlERERUJTCMEBERkawYRoiIiEhWDCNEREQkK4YRIiIikhXDCBEREcmKYYSIiIhkxTBCREREsmIYISIiIlkxjBAREZGsGEaIiIhIVgwjREREJCuGESIiIpIVwwgRERHJimGEiIiIZMUwQkRERLJiGCEiIiJZMYwQERGRrBhGiIiISFYMI0RERCQrhhEiIiKSlYXcBRARkfw0Gg1iY2ORlpYGNzc3BAYGQqVSyV0WmQkeGSEiMnPR0dHw8vJCjx49MGTIEPTo0QNeXl6Ijo6WuzQyEwwjRERmLDo6GgMGDECLFi0QFxeH7OxsxMXFoUWLFhgwYAADCZmEQggh5C6iLFlZWbC3t8fdu3dhZ2cndzlERNWCRqOBl5cXWrRogZ07d+KXX36RTtN07twZL730EhITE5GcnMxTNlQh5f39zSMjRERmKjY2FqmpqQgICIC3t7feaRpvb2906tQJKSkpiI2NlbtUquYYRoiIzFRaWhoAYNq0acWeppk+fbpeOyJj4dU0RERmytnZGQDQpUsX7Nq1C0rlw79PO3bsiF27dqFr16745ZdfpHZExsIwQkREKCgowKpVq3Dp0iU0adIEr732GhQKhdxlkZlgGCEiMlOZmZkAgGPHjsHGxgaPXs8QEREhPda1IzIW9hkhIjJTbm5u0v8fPwqiO2XzeDsiY+CRESIiM9WhQwcAgKWlJe7cuYPffvtNurS3Q4cOcHBwQEFBgdSOyFh4ZISIyEytXr0aAFBYWIhBgwZBrVajb9++UKvVGDRoEAoLC/XaERkLwwgRkZm6dOkSAOCLL77AmTNnEBAQADs7OwQEBCAxMRFr1qzRa0dkLAwjRERmqkmTJgAAIQTOnz+Pzz77DBMnTsRnn32GpKQkaLVavXZExsLh4ImIzFRBQQFq1qyJmjVrwt7eHlevXpXmNWzYEHfv3kVOTg5ycnJgaWkpY6VUVXE4eCIiKpWlpSX69OmDu3fvIj09HYMHD8bChQsxePBgpKen4+7du+jTpw+DCBkdj4wQEZkp3Y3y8vLykJGRUWS+i4sLbGxseKM8qjAeGSEiolLpbpSXkZEBtVqtN0+tViMjI4M3yiOTYBghIjJT165dk/6fn5+vN+/Rx4+2IzIGg8PI0aNH0a9fP9SrVw8KhQK7du0qtX10dDR69eoFJycn2NnZoVOnTjhw4EBF6yUiokqSnp4u/d/JyQndu3dH165d0b17dzg5ORXbjsgYDA4jOTk5aNWqFZYvX16u9kePHkWvXr2wd+9exMfHo0ePHujXrx9Onz5tcLFERFR5bty4AeDhUPA3btxATEwMjh49ipiYGNy4cUMaIl7XjshYDB4OPjg4GMHBweVuv3jxYr3H8+bNw3fffYcffvgBbdq0MXTzRERUSU6dOgUAKOk6Bt10XTsiYzH5vWm0Wi2ys7NRp06dEtvk5+frna/MysoyRWlERGbl0U6rSqVSGuTs8cePd24lqmwmDyMLFizAvXv3MHDgwBLbREZGYvbs2SasiojI/Ny8eVP6/3PPPYfnn38e1tbWyMvLw969e7F3794i7YiMwaRhZMuWLZg9eza+++47ODs7l9hu2rRpiIiIkB5nZWXB3d3dFCUSEZmNW7duSf8/cuSIFD4AwNrauth2RMZgsjCybds2jBkzBjt27EBQUFCpbdVqNQ8LEhEZ2aPfs3l5eXrzHn3M72MyNpOMM7J161aMHDkSW7duRZ8+fUyxSSIiKsPw4cMrtR1RRRkcRu7du4eEhAQkJCQAAFJSUpCQkCDdYGnatGkICwuT2m/ZsgVhYWFYuHAhOnTogPT0dOmeB0REJJ833nijUtsRVZTBYeTkyZNo06aNdFluREQE2rRpg5kzZwIA0tLS9O78uGbNGjx48AATJkyAm5ub9DNp0qRKegpERFQRR48erdR2RBXFG+UREZmpoKAgHD58uMx2PXv2xI8//miCiqi64Y3yiIioVH///XeltiOqKIYRIiIzZWNjU6ntiCrK5IOeERHRk+HRs/Q1atSAvb09CgsLUaNGDdy9exeFhYVF2hEZA8MIEZGZysnJkf5fWFhY4kirj7YjMgaepiEiMlO6u/JWVjuiimIYISIyU40bN67UdkQVxTBCRGSmTp8+XantiCqKYYSIyEyVdyRsjphNxsYwQkRkpnRXy1RWO6KK4tU0JqDRaBAbG4u0tDS4ubkhMDAQKpVK7rKIiCRKpRJarVZ6rFKpoNFoZKyIzAnDiJFFR0djypQpSE1NlaZ5eHhg4cKFCA0Nla8wIqr2cnNzkZSUVOJ8Gxsb3Lt3DwCg1Wrh7OyM+vXr49q1a8jMzNRrd+rUqRLX4+Pjw4HR6F/hvWmMKDo6GgMGDECfPn0QHBwMa2tr5OXlYd++fdizZw+ioqIYSIjIaE6dOgV/f3+jbyc+Ph5t27Y1+nao6inv72+GESPRaDTw8vKCo6Mjbty4gStXrkjzGjVqBCcnJ9y6dQvJyck8ZUNERlHWkZFff/0VEyZMKHM9y5cvR8eOHUuczyMjVBKGEZnFxMSgR48eAIB+/fph+vTp8PPzQ2JiIubNm4cffvgBAHDkyBF0795dxkqJyFxpNBrUqVMHWVlZJbaxs7PD7du3+UcTVQjv2iuza9euAQCCg4Oxa9cudOzYEba2tujYsSN27dqF4OBgvXZkOhqNBjExMdi6dStiYmLYSY/Mlkqlwvr160tts379egYRMjqGESO5ceMGACA0NBRKpf7LrFQqERISoteOTCM6OhpeXl7o0aMHhgwZgh49esDLywvR0dFyl0Yki9DQUOzcuRMNGzbUm96oUSPs3LmT/drIJBhGjMTJyQnAw19+j14uBzzstb5r1y69dmR8ug7FGRkZetMzMjIwYMAABhIyW6Ghobh8+TJWr14NAFi9ejUuXbrEIEImwzBiJPXr1wcA7N+/HyEhIYiLi0N2djbi4uIQEhKC/fv367Uj49JoNHj99dchhChyO3TdtNdff52nbMhsqVQqtGvXDgDQrl07npohk2IYMZLAwEB4eHjA398fZ86cQUBAAOzs7BAQEIDExET4+/vD09MTgYGBcpdqFmJiYqRxE4KCgvTCYVBQEAAgMzMTMTExMlZJRGSeGEaMRKVSYeHChYiPj0d6errevLS0NMTHx2PBggX868NEfvrpJwBAp06d8N133+l1KP7uu+/QoUMHvXZERGQ6DCNGJoSAQqHQm6ZUKoucKiDjunr1KgBgyJAhEELoXU0jhMCQIUP02hERkelwOHgj0Wg0mDJlCvr164ft27dj1apVuHTpEpo0aYLXXnsNAwcOxNSpU9G/f38eHTEB3ZUCS5cuxYIFC4oMQqdWq/XaERGR6fDIiJHExsYiNTUVAQEB8PX1xVtvvYVly5bhrbfegq+vLzp16oSUlBTExsbKXapZeOaZZwAAFy5cwP3797FmzRpcv34da9aswf3793HhwgW9dkREZDo8MmIkaWlpAIBp06bB2tpab15GRgamT5+u146MKzAwULoraVZWFl599VVpnm4Ya6VSyQ7FREQy4JERI3F2dpb+37NnT72rN3r27FlsOzKe48ePS+O9FHdpL/Bw/Jfjx4+bvDYiInPHMGIkuvEq6tSpg6ioKNy/fx8//PAD7t+/j6ioKNSuXVuvHRmX7gjUpk2b4OLiojfPxcUFmzZt0mtHRESmw9M0RqLrC3L79m3Url0beXl50jxra2vpcWxsLJ599llZajQnbm5uAIAmTZrg0qVLiI2NRVpaGtzc3BAYGIgTJ07otSMiItPhkRETuH//fqmPyfh0g9DNmzcPhYWFSEhIwPHjx5GQkIDCwkJERkZyEDoiIpnwyIiRPPpLLTg4GH369JGOiOzZswd79+4t0o6MRzcI3UsvvQQbGxu9fiMREREQQmDnzp28zJqISAY8MmICCoVCuv9JcYOgkWn8+uuvAEruwKqbT0REpsUjI0by6Pghe/fuxZ49e6THj4YR9hkxjYKCAixcuBAAYGlpiYKCAmme7vHChQsxd+5cWFpaylUmEZFZYhgxgZL+EifTWbp0qXRpb69evfD8889Lp810YVGr1WLp0qWYMmWKzNUSEZkXhhEjebQvSEl/iT/ejoxHd6TKy8sLiYmJekeqGjVqpHeVDcMIEZFpsc+ICTwaRIp7TMaXk5MDALh48SJatmypNwhdy5YtcenSJb12RERkOgwjRvLzzz9Xajv6d9q2bQsAqFGjBqKiotCxY0fY2tqiY8eOiIqKQo0aNfTaERGR6fA0jZGkpqZK/7eystIbW+TRx4+2I+PRjbpaWFgId3d3NGvWDFqtFkqlEmfPnkVhYaFeOyIiMh2GESPJyMgA8LB/iKOjI/773/9K8xwdHZGZmYmCggKpHRnXoyEjMzMTmZmZZbYjIiLTYBgxEt1w7wUFBXpBBIDe40eHiSfjqV+/fqW2IyKiysM+I0bSqFGjSm1H/067du2k/yuV+m/7Rx8/2o6IiEzD4DBy9OhR9OvXD/Xq1YNCocCuXbvKXCYmJgZt27aFWq2Gl5cXNmzYUIFSq5aWLVtK/398xNVHHz/ajoznvffek/7v6OiIgQMHYuTIkRg4cCAcHR2LbUdERKZh8GmanJwctGrVCqNGjUJoaGiZ7VNSUtCnTx+89tpr2Lx5Mw4fPowxY8bAzc0NvXv3rlDRVcGff/5Z4jzd8PBltaPKc+HCBQCAu7s7rl+/ju3bt0vzVCoV3N3d8ffff0vtiIjIdAwOI8HBwQgODi53+1WrVsHT01MaitvX1xfHjh3DZ599VqXDSG5uLpKSkkqcf/bsWen/j4+4qhsJVNfu1KlTpW7Lx8cHNjY2FayUAKBmzZoAgL///rvIPI1GI03XtSMiItMxegfWuLg4BAUF6U3r3bs3Jk+ebOxNG1VSUhL8/f3/9Xr++OOPMtcTHx/P8S/+pRdeeKFcpxRfeOEF4xdDRER6jB5G0tPTi1wu6eLigqysLOTl5cHa2rrIMvn5+cjPz5ceZ2VlGbtMg/n4+CA+Pr7E+b/++ismTJgAAOjSpQuaNGmCr776CuHh4bh06RKOHTsGAFi+fDk6duxY5rbo37l161altiMiosrzRF7aGxkZidmzZ8tdRqlsbGxKPVrRqlUrTJs2DVlZWTh+/LgUPr766iuoVCoAgJ2dHcaNGyc9JuMp61SYoe2IiKjyGP3SXldX1yIDe2VkZMDOzq7YoyIAMG3aNNy9e1f6Ke48/5NOpVJh/fr1APT7iAAP+ygAwPr16xlETOTRkW5VKhXatGmDzp07o02bNnr7gCPiEhGZntHDSKdOnXD48GG9aYcOHUKnTp1KXEatVsPOzk7vpyoKDQ3Fzp070bBhQ73pjRo1ws6dO8t1NRJVjhs3bkj/b9CgAU6fPo1ffvkFp0+fhru7e7HtiIjINAwOI/fu3UNCQgISEhIAPLx0NyEhAVevXgXw8KhGWFiY1P61117D5cuX8c477yApKQkrVqzA9u3b8dZbb1XOM3jChYaG4vLly1i9ejUAYPXq1bh06RKDiIk9OtKtn58fli1bhrVr12LZsmVo3rx5se2IiMg0DO4zcvLkSfTo0UN6HBERAQAIDw/Hhg0bkJaWJgUTAPD09MSePXvw1ltvYcmSJWjQoAG+/PLLKn1Zr6FUKpU0sme7du14akYGDg4OuHbtGgBg37592LNnjzTv0f3h4OBg6tKIiMyewWGke/fuRcbNeFRxo6t2794dp0+fNnRTROVS1pgvANCjRw/89ddfAEruw6NrV1onVo75QkRU+Z7Iq2mIDFFZY74AwLJly7Bs2bIS53PMFyKiyscwQlVeWWO+6CxZsgQbN24scX5YWBgmTZpU5raIiKhyMYxQlVfWmC86X331FVxcXLBgwQK9U40KhQJTp07Fp59+aswyiYioBEa/tJfoSfLpp5/i/v37UsfriIgI3L9/n0GEiEhGDCNkdiwtLTF06FAAwNChQ2FpaSlzRURE5o1hhIiIiGTFMEJERESyYhghIiIiWTGMEBERkawYRoiIiEhWDCNEREQkKw56RkSVpjz3CXpcXl4eUlNT4eHhAWtr63Ivx/sEEVUfDCNEVGkq8z5BZeF9goiqD4YRIqo05b1P0KPOnTuHYcOGYdOmTfD19TVoW0RUPTCMEFGlKe99gorj6+vLIx1EZoodWImIiEhWDCNEREQkK56mISKqIpKTk5GdnW209Z87d07vX2OpVasWmjZtatRtUNXCMEJEVAUkJyfD29vbJNsaNmyY0bdx4cIFBhKSMIwQEVUBuiMihl51ZIiKjvliCN3VU8Y8wkNVD8MIEVEVYuyrjjp37my0dROVhB1YiYiISFYMI0RERCQrhhEiIiKSFcMIERERyYphhIiIiGTFMEJERESyYhghIiIiWTGMEBERkawYRoiIiEhWDCNEREQkK4YRIiIikhXDCBEREcmKN8p7RHJystHuJHnu3Dm9f42lVq1avC03ERFVKQwj/y85ORne3t5G386wYcOMvo0LFy4wkBARUZXBMPL/dEdENm3aBF9f30pff15eHlJTU+Hh4QFra+tKXz/w8KjLsGHDjHZ0h4iIyBgYRh7j6+uLtm3bGmXdnTt3Nsp6iYiIqjKGESKiKsLVVgHrOxeA61X32gPrOxfgaquQuwx6wjCMEBFVEeP8LeF7dBxwVO5KKs4XD58H0aMYRoiIqojV8QUYNHMDfH185C6lws4lJWH1wiF4Qe5C6IlSoTCyfPlyzJ8/H+np6WjVqhWWLl2K9u3bl9h+8eLFWLlyJa5evQpHR0cMGDAAkZGRsLKyqnDhRETmJv2eQJ6DN1CvtdylVFheuhbp94TcZdATxuAw8s033yAiIgKrVq1Chw4dsHjxYvTu3Rvnz5+Hs7NzkfZbtmzBe++9h3Xr1iEgIAAXLlzAiBEjoFAosGjRokp5ElT9GHPMF8A0475wzBciovIxOIwsWrQIY8eOxciRIwEAq1atwp49e7Bu3Tq89957RdofP34cnTt3xpAhQwAAHh4eGDx4MH777bd/WTpVV6Ya8wUw/rgvHPOFiKhsBoWRgoICxMfHY9q0adI0pVKJoKAgxMXFFbtMQEAANm3ahBMnTqB9+/a4fPky9u7di+HDh5e4nfz8fOTn50uPs7KyDCmTqjhjj/kCGH/cF475QkRUfgaFkZs3b0Kj0cDFxUVvuouLC5KSkopdZsiQIbh58ya6dOkCIQQePHiA1157DdOnTy9xO5GRkZg9e7YhpVE1ZMwxXwCO+0JE9KQw+sXqMTExmDdvHlasWIFTp04hOjoae/bswYcffljiMtOmTcPdu3eln7///tvYZRIREZFMDDoy4ujoCJVKhYyMDL3pGRkZcHV1LXaZGTNmYPjw4RgzZgwAoEWLFsjJycGrr76K999/H0pl0TykVquhVqsNKY2IiIiqKIPCiKWlJfz9/XH48GGEhIQAALRaLQ4fPoyJEycWu0xubm6RwKFSqQAAQjxZl3dV9dENObIhERFVRQZfTRMREYHw8HC0a9cO7du3x+LFi5GTkyNdXRMWFob69esjMjISANCvXz8sWrQIbdq0QYcOHXDx4kXMmDED/fr1k0LJk6Kqj27IkQ2JiKgqMjiMDBo0CDdu3MDMmTORnp6O1q1bY//+/VKn1qtXr+odCfnPf/4DhUKB//znP7h27RqcnJzQr18/fPTRR5X3LCpJVR/dkCMbEhFRVVShEVgnTpxY4mmZmJgY/Q1YWGDWrFmYNWtWRTZlUlV9dEOObEhERFVR1ewcQURERNUGwwgRERHJimGEiIiIZMUwQkRERLKqUAdWImPjmC9EROaDYYSeSBzzhUhfbm4uAODUqVNG24axbyAJPLyJJNHjGEboicQxX4j06W5GOnbsWJkrqRy1atWSuwR6gjCM0BOJY74Q6dPdgsPHxwc2NjZG2ca5c+cwbNgwbNq0Cb6+vkbZBvAwiDRt2tRo66eqh2GEiKgKcHR0lG44amy+vr5o27atSbZFBPBqGiIiIpIZwwgRERHJiqdp/p+xe6qzlzoREVHxGEb+X3Xqqc5e6kREVJUwjPw/Y/dUZy91IiKi4jGM/D9T9VRnL3UiIiJ97MBKREREsmIYISIiIlkxjBAREZGs2GeEnjjV4YZgvMyaiKj8GEboicPLrImIzAvDCD1xqssNwXiZNRFR+TCM0BOHNwQjIjIvDCNEVKrk5GRkZ2cbbf26/jXG7mfDI1VETy6GESIqUXJyMry9vU2yrWHDhhl9GxcuXGAgIXoCMYwQUYl0R0SM2bfGVDeRHDZsmFGP8BBVFo1Gg9jYWKSlpcHNzQ2BgYFQqVRyl2VUDCNEVCZj963p3Lmz0dZNVJVER0djypQpSE1NlaZ5eHhg4cKFCA0Nla8wI2MYISIiegJER0djwIAB6NOnD95++21YW1sjLy8P+/btw4ABAxAVFVVtAwnDCBERkcw0Gg2mTJkCf39/nDlzBrt375bmNWrUCP7+/pg6dSr69+9fLU/ZcDh4IiIimcXGxiI1NRUnT55Ey5YtERcXh+zsbMTFxaFly5Y4efIkUlJSEBsbK3epRsEwQkREJLNr164BAIKDg7Fz507cv38fP/zwA+7fv4+dO3ciODhYr111w9M0REREMrtx4waAh51Vvb29i3Rgfe655/TaVTc8MkJERCQzJycnAMDKlSvh5+end5rGz88Pq1at0mtX3TCMEBERyczV1VXvsRBC+imtXXXB0zRERERPCB8fHyQmJiIgIECa5unpCR8fH+mO5tURwwgREZHMMjMzAQDnz59Hnz59MHXqVGmckf3792PPnj167aobhhEiIiKZubm5AQDmzZuH1atX640z4unpiY8++gjTp0+X2lU37DNCREQks8DAQHh4eOD48eNITEzEhAkT8Oyzz2LChAk4c+YM4uLi4OnpicDAQLlLNQoeGSEiIpKZSqXCwoUL8dJLL6FWrVpSx9WDBw9ixYoVEEJg586d1XL0VYBHRoiIiJ4Iv/76KwBAoVDoTVcqlXrzqyOGESIiIpkVFBTgs88+g4uLC3Jzc3HkyBFs2bIFR44cQU5ODlxcXPDZZ5+hoKBA7lKNokJhZPny5fDw8ICVlRU6dOiAEydOlNr+zp07mDBhAtzc3KBWq+Ht7Y29e/dWqGAiIqLqZsWKFXjw4AHmzp0LCwv9HhQWFhaYM2cOHjx4gBUrVshUoXEZ3Gfkm2++QUREBFatWoUOHTpg8eLF6N27N86fPw9nZ+ci7QsKCtCrVy84OzsjKioK9evXx5UrV+Dg4FAZ9RMREVV5ly5dAvDwFI2Xl1eR4eDff/99vXbVjcFHRhYtWoSxY8di5MiRaNasGVatWgUbGxusW7eu2Pbr1q3D7du3sWvXLnTu3BkeHh7o1q0bWrVq9a+LJyIiqg6aNGkCABgzZgxatGihNxx8ixYtMHbsWL121Y1BYaSgoADx8fEICgr63wqUSgQFBSEuLq7YZb7//nt06tQJEyZMgIuLC/z8/DBv3jxoNJoSt5Ofn4+srCy9HyIioupq3LhxAABLS0tERUWhY8eOsLW1RceOHREVFQVLS0u9dtWNQWHk5s2b0Gg0cHFx0Zvu4uKC9PT0Ype5fPkyoqKioNFosHfvXsyYMQMLFy7E3LlzS9xOZGQk7O3tpR93d3dDyiQiIqpSfvvtNwAP/+hv2LAh1qxZg+vXr2PNmjVo2LCh1HFV1666Mfo4I1qtFs7OzlizZg1UKhX8/f1x7do1zJ8/H7NmzSp2mWnTpiEiIkJ6nJWVxUBCRERVVm5ubqn3ljl+/DgAYPDgwdi+fbveERCVSoVXXnkF27Ztw/Hjx2FnZ1fqtnx8fGBjY1M5hZuIQWHE0dERKpUKGRkZetMzMjJKvJOgm5sbatSooTdQi6+vL9LT01FQUCAdenqUWq2GWq02pDQiIqInVlJSEvz9/ctst3Xr1iLTNBoNtm3bBgB4//33pc6sJYmPj0fbtm0rVqhMDAojlpaW8Pf3x+HDhxESEgLg4ZGPw4cPY+LEicUu07lzZ2zZsgVarVYauOXChQtwc3MrNogQERFVNz4+PoiPjy9xvkajQUhICLy8vLBw4UKcP38ew4YNw6ZNm/DUU09hypQpuHTpEr799tsyR2H18fGp7PKNzuDTNBEREQgPD0e7du3Qvn17LF68GDk5ORg5ciQAICwsDPXr10dkZCQA4PXXX8eyZcswadIkvPHGG0hOTsa8efPw5ptvVu4zISIiekLZ2NiUebRi6dKlGDBgAObMmYPQ0FAAQGFhIebMmYPY2FhERUXh6aefNkW5JmdwGBk0aBBu3LiBmTNnIj09Ha1bt8b+/fulTq1Xr16VjoAAgLu7Ow4cOIC33noLLVu2RP369TFp0iS8++67lfcsiIiIqrjQ0FBERUVhypQp+OGHHwAAI0eOhKenJ6KioqSAUh1VqAPrxIkTSzwtExMTU2Rap06dqvWY+mUpKCjA5s2bAQCbN2+Gn58fT1EREVERoaGh6N+/P9auXYtx48Zh9erVGD16dLW9QZ4O701jZO+88w5q1qyJRYsWAXg4aFzNmjXxzjvvyFwZERE9iVQqFdq1awcAaNeuXbUPIoAJLu2trsq6TAsAlixZgo0bN6JOnTp48cUXsXbtWowePRrffvst5s+fj4yMDEyaNKnMbVXFy7SIiIjKi2Gkgsp7mRYA3L59G2vXrgUA6V8A2LhxIzZu3Fjm8lXxMi0iIqLyYhipoLIu09q8eTMWLVqEAQMG4JdffkFaWpo0z83NDQEBAdi5cyciIiIwdOjQMrdFRERUXTGMVFBZl2mtX78eABAVFQVra2u9eXfu3MHOnTsBPOzcyqMeRERkztiB1Ug8PDyk//fs2VPvDow9e/Ysth0REZE54pERI2nWrBkAoEaNGti5c6d0KW/Hjh2xc+dO2NraorCwUGpHFVeezsSPO3funN6/5cXOxERElY9hxEh0Nz0qLCxEw4YNMWfOHPTt2xe7d+/GzJkzUVhYKLULDg6Ws9Qqz5DOxI8bNmyYQe3ZmZiIqPIxjBjZwIEDER0drXcHRgsLC7z88svYsWOHjJVVH2V1Ji5OXl4eUlNT4eHhUaRPT1nbIiKiysUwYiTdu3fH3Llzcf36dWRnZ2PVqlW4dOkSmjRpgtdeew29evWS2tG/U557PhSnc+fORqiGqGriSNEkJ4YRI+nevTucnJxw7NgxvPzyy5g+fTpGjx6NxMREvPzyyzh27BicnZ0ZRojIaMrbn2rJkiXYvHkzNBoNgIcjRS9ZsgRDhw7lwIxkEgwjRqJSqbBq1Sq89NJLOHz4MHbv3i3N031oV65caRbD/BKRPP5NfyqNRsOBGclkGEaMKDQ0VBrY7MqVK9J0Z2dnLFy4sFrfgZGI5FdWf6qCggJ06dIF9vb22LdvHwoLC6W+VDVq1EBwcDDu3r2LY8eOlXrKhn2p6N9iGDEy3R0YY2NjkZaWBjc3NwQGBvKICBEZXVn9qRYvXgyNRoNPPvkE7du3B6DflyoyMhLjxo3Dr7/+ismTJxu7XDJjDCMmoFKp2DeEiJ44ly5dAgD07du32Pm66bp2RMbCEViJiMxUkyZNAECvT9ujdNN17YiMhUdGiIjM1Pjx4/H222/jP//5D0aMGAELi//9Snjw4AFmzpwJCwsLjB8/XsYqn0zJycnIzs422vorOkq0oWrVqoWmTZsadRvlwTBCRGSmLC0t8dZbb2H+/Plo0KABhg4disaNG+Py5cvYvHkzMjIy8Pbbb3O8kcckJyfD29vbJNsydJToirhw4YLsgYRhhIjIjH366ae4cOECvvvuOyxatEhvXv/+/fHpp5/KVNmTS3dEZNOmTfD19TXKNio6SrQhzp07h2HDhhn1CE95MYwQEZmx6OhofP/99+jTpw+8vLyQl5cHa2trXLx4Ed9//z2io6M5DEEJfH19jTq+ijmNEs0wQkRkpjQaDaZMmYK+ffti165dUCr/d02DVqtFSEgIpk6div79+3M4AjIqXk1DRGSmYmNjkZqaiunTp+sFEQBQKpWYNm0aUlJSEBsbK1OFZC4YRoiIzFRaWhoAwM/Pr9j5uum6dkTGwjBCRGSm3NzcAACJiYnFztdN17UjMhb2GSEiMlOBgYHw8PDAvHnziu0zEhkZCU9PTwQGBspY5ZPJ1VYB6zsXgOtV92966zsX4GqrkLsMAAwjRERmS6VSYeHChRgwYABCQkIwbdo0+Pn5ITExEZGRkdi9ezeioqLYebUY4/wt4Xt0HHBU7koqzhcPn8eTgGGEiMiMhYaGIioqClOmTEFAQIA03dPTE1FRUbystwSr4wswaOYG+FbhOxafS0rC6oVD8ILchYBhhIjI7PHu4oZLvyeQ5+AN1GstdykVlpeuRfo9IXcZABhGiIgIvLs4yavq9rwhIiKiaoFhhIiIiGTF0zREREQGyM3NBQCcOnXKaNsw1Y3ynhQMI0RERAZISkoCAIwdO1bmSipHrVq15C6BYYSIiMgQISEhAAAfHx/Y2NgYZRvnzp3DsGHDsGnTJvj6+hplG8DDINK0aVOjrb+8GEaIiIgM4OjoiDFjxphkW76+vmjbtq1JtiUndmAlIiIiWTGMEBERkawYRoiIiEhWDCNEREQkqwqFkeXLl8PDwwNWVlbo0KEDTpw4Ua7ltm3bBoVCIfVEJiIiIjI4jHzzzTeIiIjArFmzcOrUKbRq1Qq9e/dGZmZmqculpqZi6tSpCAwMrHCxREREVP0YHEYWLVqEsWPHYuTIkWjWrBlWrVoFGxsbrFu3rsRlNBoNhg4ditmzZ6Nx48b/qmAiIiKqXgwKIwUFBYiPj0dQUND/VqBUIigoCHFxcSUuN2fOHDg7O2P06NEVr5SIiIiqJYMGPbt58yY0Gg1cXFz0pru4uEjD4z7u2LFjWLt2LRISEsq9nfz8fOTn50uPs7KyDCmTiIiIqhCjjsCanZ2N4cOH44svvoCjo2O5l4uMjMTs2bONWBkREZHp5ObmlvhHe3F0N7GryM3sjDlMvbEYFEYcHR2hUqmQkZGhNz0jIwOurq5F2l+6dAmpqano16+fNE2r1T7csIUFzp8/jyZNmhRZbtq0aYiIiJAeZ2Vlwd3d3ZBSiYiInhhJSUnw9/c3eLlhw4YZvEx8fHyVG0LeoDBiaWkJf39/HD58WLo8V6vV4vDhw5g4cWKR9j4+Pjhz5ozetP/85z/Izs7GkiVLSgwYarUaarXakNKIiIieWD4+PoiPjy93+7y8PKSmpsLDwwPW1tYGb6uqMfg0TUREBMLDw9GuXTu0b98eixcvRk5ODkaOHAkACAsLQ/369REZGQkrKyv4+fnpLe/g4AAARaYTERFVVzY2NgYfrejcubORqnnyGBxGBg0ahBs3bmDmzJlIT09H69atsX//fqlT69WrV6FUcmBXIiIiKp8KdWCdOHFisadlACAmJqbUZTds2FCRTRIREVE1xUMYREREJCuGESIiIpIVwwgRERHJyqiDnhFR1edqq4D1nQvA9ar7t4v1nQtwtVXIXQYRlYBhhIhKNc7fEr5HxwFH5a6k4nzx8HkQ0ZOJYYSISrU6vgCDZm6AbxUcSEnnXFISVi8cghfkLoSIisUwQkSlSr8nkOfgDdRrLXcpFZaXrkX6PSF3GURUgqp7EpiIiIiqBYYRIiIikhXDCBEREcmKYYSIiIhkxTBCREREsmIYISIiIlkxjBAREZGsGEaIiIhIVgwjREREJCuGESIiIpIVwwgRERHJimGEiIiIZMUwQkRERLJiGCEiIiJZMYwQERGRrBhGiIiISFYMI0RERCQrhhEiIiKSFcMIERERyYphhIiIiGTFMEJERESyYhghIiIiWTGMEBERkawYRoiIiEhWDCNEREQkK4YRIiIikhXDCBEREcmKYYSIiIhkxTBCREREsmIYISIiIlkxjBAREZGsGEaIiIhIVgwjREREJKsKhZHly5fDw8MDVlZW6NChA06cOFFi2y+++AKBgYGoXbs2ateujaCgoFLbExERkXkxOIx88803iIiIwKxZs3Dq1Cm0atUKvXv3RmZmZrHtY2JiMHjwYBw5cgRxcXFwd3fHs88+i2vXrv3r4omIiKjqMziMLFq0CGPHjsXIkSPRrFkzrFq1CjY2Nli3bl2x7Tdv3ozx48ejdevW8PHxwZdffgmtVovDhw//6+KJiIio6jMojBQUFCA+Ph5BQUH/W4FSiaCgIMTFxZVrHbm5uSgsLESdOnVKbJOfn4+srCy9HyIiIqqeDAojN2/ehEajgYuLi950FxcXpKenl2sd7777LurVq6cXaB4XGRkJe3t76cfd3d2QMomIiKgKMenVNB9//DG2bduGb7/9FlZWViW2mzZtGu7evSv9/P333yaskoiIiEzJwpDGjo6OUKlUyMjI0JuekZEBV1fXUpddsGABPv74Y/z4449o2bJlqW3VajXUarUhpREREVEVZdCREUtLS/j7++t1PtV1Ru3UqVOJy3366af48MMPsX//frRr167i1RIREVG1Y9CREQCIiIhAeHg42rVrh/bt22Px4sXIycnByJEjAQBhYWGoX78+IiMjAQCffPIJZs6ciS1btsDDw0PqW2JrawtbW9tKfCpERERUFRkcRgYNGoQbN25g5syZSE9PR+vWrbF//36pU+vVq1ehVP7vgMvKlStRUFCAAQMG6K1n1qxZ+OCDD/5d9URERFTlGRxGAGDixImYOHFisfNiYmL0HqemplZkE0RERGQmeG8aIiIikhXDCBEREcmqQqdpiMg85ObmAgBOnTpltG3k5eUhNTUVHh4esLa2Nso2zp07Z5T1ElHlYBghohIlJSUBAMaOHStzJZWjVq1acpdARMVgGCGiEoWEhAAAfHx8YGNjY5RtnDt3DsOGDcOmTZvg6+trlG0AD4NI06ZNjbZ+Iqo4hhEiKpGjoyPGjBljkm35+vqibdu2JtkWET1Z2IGViIiIZMUwQkRERLJiGCEiIiJZMYwQERGRrBhGiIiISFYMI0RERCQrhhEiIiKSFcMIERERyYphhIiIiGTFEViJqNLk5uZK97MpL91N7Ay9mZ0xh6gnItNiGCGiSpOUlAR/f/8KLTts2DCD2sfHx3P4eKJqgmGEiCqNj48P4uPjDVomLy8Pqamp8PDwgLW1tUHbIqLqQSGEEHIXUZasrCzY29vj7t27sLOzk7scIiIiKofy/v5mB1YiIiKSFcMIERERyYphhIiIiGTFMEJERESyYhghIiIiWTGMEBERkawYRoiIiEhWDCNEREQkK4YRIiIikhXDCBEREcmKYYSIiIhkxTBCREREsmIYISIiIllZyF1AeehuLJyVlSVzJURERFReut/but/jJakSYSQ7OxsA4O7uLnMlREREZKjs7GzY29uXOF8hyoorTwCtVovr16+jVq1aUCgUcpdTIVlZWXB3d8fff/8NOzs7ucsxe9wfTw7uiycH98WTo7rsCyEEsrOzUa9ePSiVJfcMqRJHRpRKJRo0aCB3GZXCzs6uSr+xqhvujycH98WTg/viyVEd9kVpR0R02IGViIiIZMUwQkRERLJiGDERtVqNWbNmQa1Wy10KgfvjScJ98eTgvnhymNu+qBIdWImIiKj64pERIiIikhXDCBEREcmKYYSIiIhkxTBCREREsmIYMZHly5fDw8MDVlZW6NChA06cOCF3SdVeZGQknn76adSqVQvOzs4ICQnB+fPn9drcv38fEyZMQN26dWFra4uXXnoJGRkZMlVsPj7++GMoFApMnjxZmsZ9YTrXrl3DsGHDULduXVhbW6NFixY4efKkNF8IgZkzZ8LNzQ3W1tYICgpCcnKyjBVXTxqNBjNmzICnpyesra3RpEkTfPjhh3r3cTGbfSHI6LZt2yYsLS3FunXrxF9//SXGjh0rHBwcREZGhtylVWu9e/cW69evF4mJiSIhIUE8//zzomHDhuLevXtSm9dee024u7uLw4cPi5MnT4qOHTuKgIAAGauu/k6cOCE8PDxEy5YtxaRJk6Tp3Bemcfv2bdGoUSMxYsQI8dtvv4nLly+LAwcOiIsXL0ptPv74Y2Fvby927dol/vjjD/HCCy8IT09PkZeXJ2Pl1c9HH30k6tatK3bv3i1SUlLEjh07hK2trViyZInUxlz2BcOICbRv315MmDBBeqzRaES9evVEZGSkjFWZn8zMTAFA/Pzzz0IIIe7cuSNq1KghduzYIbU5d+6cACDi4uLkKrNay87OFk2bNhWHDh0S3bp1k8II94XpvPvuu6JLly4lztdqtcLV1VXMnz9fmnbnzh2hVqvF1q1bTVGi2ejTp48YNWqU3rTQ0FAxdOhQIYR57QuepjGygoICxMfHIygoSJqmVCoRFBSEuLg4GSszP3fv3gUA1KlTBwAQHx+PwsJCvX3j4+ODhg0bct8YyYQJE9CnTx+91xzgvjCl77//Hu3atcPLL78MZ2dntGnTBl988YU0PyUlBenp6Xr7wt7eHh06dOC+qGQBAQE4fPgwLly4AAD4448/cOzYMQQHBwMwr31RJW6UV5XdvHkTGo0GLi4uetNdXFyQlJQkU1XmR6vVYvLkyejcuTP8/PwAAOnp6bC0tISDg4NeWxcXF6Snp8tQZfW2bds2nDp1Cr///nuRedwXpnP58mWsXLkSERERmD59On7//Xe8+eabsLS0RHh4uPR6F/edxX1Rud577z1kZWXBx8cHKpUKGo0GH330EYYOHQoAZrUvGEbILEyYMAGJiYk4duyY3KWYpb///huTJk3CoUOHYGVlJXc5Zk2r1aJdu3aYN28eAKBNmzZITEzEqlWrEB4eLnN15mX79u3YvHkztmzZgubNmyMhIQGTJ09GvXr1zG5f8DSNkTk6OkKlUhW5KiAjIwOurq4yVWVeJk6ciN27d+PIkSNo0KCBNN3V1RUFBQW4c+eOXnvum8oXHx+PzMxMtG3bFhYWFrCwsMDPP/+Mzz//HBYWFnBxceG+MBE3Nzc0a9ZMb5qvry+uXr0KANLrze8s43v77bfx3nvv4ZVXXkGLFi0wfPhwvPXWW4iMjARgXvuCYcTILC0t4e/vj8OHD0vTtFotDh8+jE6dOslYWfUnhMDEiRPx7bff4qeffoKnp6fefH9/f9SoUUNv35w/fx5Xr17lvqlkPXv2xJkzZ5CQkCD9tGvXDkOHDpX+z31hGp07dy5yifuFCxfQqFEjAICnpydcXV319kVWVhZ+++037otKlpubC6VS/9ewSqWCVqsFYGb7Qu4etOZg27ZtQq1Wiw0bNoizZ8+KV199VTg4OIj09HS5S6vWXn/9dWFvby9iYmJEWlqa9JObmyu1ee2110TDhg3FTz/9JE6ePCk6deokOnXqJGPV5uPRq2mE4L4wlRMnTggLCwvx0UcfieTkZLF582ZhY2MjNm3aJLX5+OOPhYODg/juu+/En3/+Kfr3718tLyeVW3h4uKhfv750aW90dLRwdHQU77zzjtTGXPYFw4iJLF26VDRs2FBYWlqK9u3bi19//VXukqo9AMX+rF+/XmqTl5cnxo8fL2rXri1sbGzEiy++KNLS0uQr2ow8Hka4L0znhx9+EH5+fkKtVgsfHx+xZs0avflarVbMmDFDuLi4CLVaLXr27CnOnz8vU7XVV1ZWlpg0aZJo2LChsLKyEo0bNxbvv/++yM/Pl9qYy75QCPHIUG9EREREJsY+I0RERCQrhhEiIiKSFcMIERERyYphhIiIiGTFMEJERESyYhghIiIiWTGMEBERkawYRohINiNGjEBISIj0uHv37pg8eXK5lo2JiYFCoShyPxsiqnoYRojMwObNm+Hu7o7atWsjIiJCb15qaiq8vb2RlZVV6jpSU1OhUCigUqlw7do1vXlpaWmwsLCAQqFAampqheuMjo7Ghx9+WK62AQEBSEtLg729fYW3R0RPBoYRomru5s2bGDNmDBYsWICDBw9i06ZN2L17tzR//Pjx+Pjjj2FnZ1eu9dWvXx8bN27Um/bVV1+hfv36/7rWOnXqoFatWuVqa2lpCVdXVygUin+9XSKSF8MIUTV3+fJl2NvbY9CgQXj66afRo0cPnDt3DgCwdetW1KhRA6GhoeVeX3h4ONavX683bf369QgPD9ebptFoMHr0aHh6esLa2hpPPfUUlixZUuq6Hz9Nk5+fj3fffRfu7u5Qq9Xw8vLC2rVrARR/mmbnzp1o3rw51Go1PDw8sHDhQr31KxQK7Nq1S2+ag4MDNmzYAAAoKCjAxIkT4ebmBisrKzRq1Ei6nTsRGQ/DCFE117RpU+Tm5uL06dO4ffs2fv/9d7Rs2RL//PMPZsyYgWXLlhm0vhdeeAH//PMPjh07BgA4duwY/vnnH/Tr10+vnVarRYMGDbBjxw6cPXsWM2fOxPTp07F9+/ZybyssLAxbt27F559/jnPnzmH16tWwtbUttm18fDwGDhyIV155BWfOnMEHH3yAGTNmSEGjPD7//HN8//332L59O86fP4/NmzfDw8Oj3MsTUcVYyF0AERlX7dq18dVXXyEsLAx5eXkICwtD7969MXr0aEycOBEpKSl44YUXUFhYiA8++AADBgwodX01atTAsGHDsG7dOnTp0gXr1q3DsGHDUKNGjSLtZs+eLT329PREXFwctm/fjoEDB5ZZ94ULF7B9+3YcOnQIQUFBAIDGjRuX2H7RokXo2bMnZsyYAQDw9vbG2bNnMX/+fIwYMaLM7QHA1atX0bRpU3Tp0gUKhQKNGjUq13JE9O8wjBCZgRdffBEvvvii9Pjnn3/Gn3/+iaVLl8LLywtbt26Fq6sr2rdvj65du8LZ2bnU9Y0aNQoBAQGYN28eduzYgbi4ODx48KBIu+XLl2PdunW4evUq8vLyUFBQgNatW5er5oSEBKhUKnTr1q1c7c+dO4f+/fvrTevcuTMWL14MjUYDlUpV5jpGjBiBXr164amnnsJzzz2Hvn374tlnny3X9omo4niahsjM5OfnY/z48Vi9ejUuXryIBw8eoFu3bnjqqafg7e2N3377rcx1tGjRAj4+Phg8eDB8fX3h5+dXpM22bdswdepUjB49GgcPHkRCQgJGjhyJgoKCctVpbW1t8HMri0KhgBBCb1phYaH0/7Zt2yIlJQUffvgh8vLyMHDgwDKPFBHRv8cwQmRm5s6di+eeew5t27aFRqPRO6JRWFgIjUZTrvWMGjUKMTExGDVqVLHzf/nlFwQEBGD8+PFo06YNvLy8cOnSpXLX2aJFC2i1Wvz888/lau/r64tffvmlSA3e3t7SUREnJyekpaVJ85OTk5Gbm6u3jJ2dHQYNGoQvvvgC33zzDXbu3Inbt2+Xu24iMhxP0xCZkbNnz+Kbb77B6dOnAQA+Pj5QKpVYu3YtXF1dkZSUhKeffrpc6xo7dixefvllODg4FDu/adOm2LhxIw4cOABPT098/fXX+P333+Hp6Vmu9Xt4eCA8PByjRo3C559/jlatWuHKlSvIzMwsts/JlClT8PTTT+PDDz/EoEGDEBcXh2XLlmHFihVSm2eeeQbLli1Dp06doNFo8O677+r1dVm0aBHc3NzQpk0bKJVK7NixA66uriU+RyKqHDwyQmQmhBB49dVXsWjRItSsWRPAw1MhGzZswJw5czB69GgsW7as3OOFWFhYwNHRERYWxf9NM27cOISGhmLQoEHo0KEDbt26hfHjxxtU88qVKzFgwACMHz8ePj4+GDt2LHJycopt27ZtW2zfvh3btm2Dn58fZs6ciTlz5uh1Xl24cCHc3d0RGBiIIUOGYOrUqbCxsZHm16pVC59++inatWuHp59+Gqmpqdi7dy+USn5VEhmTQjx+ApWIiIjIhBj3iYiISFYMI0RERCQrhhEiIiKSFcMIERERyYphhIiIiGTFMEJERESyYhghIiIiWTGMEBERkawYRoiIiEhWDCNEREQkK4YRIiIikhXDCBEREcnq/wCqEJEThaYZmAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig18, ax18 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in msg_df:\n",
+    "\n",
+    "    vsdf = msg_df[key].loc[(msg_df[key]['nodeType'] == 'validator')]\n",
+    "    x = np.sort(vsdf['bytesIn']) \n",
+    "    data.append(x)\n",
+    "\n",
+    "ax18.boxplot(data)\n",
+    "\n",
+    "ax18.legend()\n",
+    "#ax18.set_xlim([0,2])\n",
+    "#ax18.set_ylim([0,1])\n",
+    "ax18.set_xticklabels([0,20,40,60,80])\n",
+    "ax18.set_title(\"Bytes received per row/column fetching process\")\n",
+    "ax18.set_xlabel(\"% Malicious\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "ddc5328e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '# MBs')"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig18, ax18 = plt.subplots()\n",
+    "\n",
+    "for key in msg_df:\n",
+    "\n",
+    "    vsdf = msg_df[key].loc[(msg_df[key]['nodeType'] == 'regular')]\n",
+    "\n",
+    "    vsdf = vsdf['bytesOut'] / 1024 / 1024\n",
+    "    vsdf = vsdf[~np.isnan(vsdf)]\n",
+    "\n",
+    "    x = np.sort(vsdf) \n",
+    "    N = vsdf.count()\n",
+    "    # get the cdf values of y\n",
+    "    y = np.arange(N) / float(N)\n",
+    "\n",
+    "    ax18.plot(x, y,label=key)\n",
+    "\n",
+    "ax18.legend()\n",
+    "#ax18.set_xlim([0,2])\n",
+    "ax18.set_ylim([0,1])\n",
+    "\n",
+    "ax18.set_title(\"CDF data sent per row/column fetching process\")\n",
+    "ax18.set_xlabel(\"# MBs\")"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "1f80f0a7",
    "metadata": {},
    "source": [
-    "In this graph we observe the CDF of the time required to complete the random sampling of the DAS protocol, with and without attackers in the simulation, from 0\\% to 50\\% of attackers in the simulation.\n",
-    "In this case we do observe some performance degradation with scenarios with more than 30\\% of malicious nodes.\n",
-    "When 40\\% of nodes are attackers, the samples obtained is close to 95\\%, and with 50\\% is below 70\\%.\n",
-    "Note that we have not evaluated DAS protocol with any complementary method to get samples when sampling fails. These methods could be:\n",
-    " - Getting missing samples directly from the builder.\n",
+    "In this graph we observe the CDF of the time required to complete the random sampling (fetching 75 random samples) of the DAS protocol, with and without attackers in the simulation, from 0\\% to 75\\% of attackers in the simulation.\n",
+    "In this case we do observe some performance degradation with scenarios with more than 50\\% of malicious nodes.\n",
+    "However all nodes manage to finish the random sampling within the deadline of 12 seconds.\n",
+    "The performance degradation is related to the necessity to add complementary method to get samples when sampling fails. These methods are be:\n",
     " - Getting missing samples from validators they downloaded the row or column for that sample.\n",
-    " - Trying to obtain 10 additional samples for each missing sample.\n",
-    "\n",
-    "In next reports we will include an evaluation of different methods could be applied to improve random sampling in the presence of attackers."
+    " - Trying to obtain 10 additional samples for each missing sample.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 29,
    "id": "a511ead6",
-   "metadata": {
-    "vscode": {
-     "languageId": "python"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -278,13 +464,13 @@
        "Text(0.5, 0, 'Operation complete time (ms)')"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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wwQcYjUY6dOjAv/71rxJfRDt37mTs2LFoNBr8/PyIjo5m5MiRjvuk1ISuXbvSr18/PvjgA6ZOncqtt97KJ598wquvvsrkyZNp3Lgxr732GsePH690qOnQoQMrVqxgypQpvPDCCzRq1IgXX3yRc+fOldjmxx9/TJMmTZgzZw6LFy8mPDycqVOnMm3atKrY3ev685//zLfffssPP/xAYWEhsbGx/OMf/+Dpp592LDN//nwef/xx3nvvPZRSDB48mOXLlxMZGVktNT333HPs2rWLGTNmkJOTw4ABA3j//ffx9vaulvbAfnQoPDycL774gsWLFzNw4EAWLlxIy5Ytb3inYoPBwGeffcbUqVN59NFHsVgszJ49u1pCDcCYMWOIjIzk1Vdf5Y033qCwsJCoqCj69OnDhAkTqqVNIcpLo1zZW00IIWqJtWvX0r9/f7766iunoS1cZefOnXTu3Jm5c+eSlJTk6nKEqBOkT40QQrjY1aPJF5s5cyZarbbEHY+FEGWT009CCOFir7/+Otu2baN///7o9XqWL1/O8uXLeeSRR0rcO0YIUTYJNUII4WI9e/Zk5cqVvPzyy+Tm5hITE8P06dP529/+5urShKhTKtyn5ueff+aNN95g27ZtnDt3jsWLF3PHHXdcd521a9cyZcoU9u7dS3R0NH//+99LXJUihBBCCHEzKtynJi8vj44dO/Lee++Va/ljx44xYsQI+vfvz86dO5k8eTIPP/xwiftlCCGEEELcjJu6+kmj0dzwSM2zzz7L999/73Q7+3vvvZfMzEzHQG5CCCGEEDer2vvUbNy4scTttIcMGcLkyZPLXKewsNBpUD+bzUZGRgYNGzas0VuCCyGEEKLylFLk5OQQGRlZ6mC+Va3aQ01qaiphYWFO08LCwsjOziY/Px8vL68S68yYMYMXX3yxuksTQgghRA04deoUjRo1qvZ2auXVT1OnTmXKlCmO91lZWcTExHDq1CkZ2l4IIapBdoGZnjNWA9AtrgF6nRatBnRaDVqNBp0WdBr7a61WY3+t1RDk68HEPk3w9zS4eA9EbZSdnU10dDR+fn410l61h5rw8PASgw2mpaXh7+9f6lEaAKPR6DRacTF/f38JNUIIUR08zGiN9qEg5j/WH6Ne5+KC6iZbQQGWCxexXryA5eJFLBcuYL14EcuFi+gaNCBk0mOuLrFaKaVQKGzKhlKKQqu9K0lNdR2p9lCTmJjIsmXLnKatXLmSxMTE6m5aCCHqrewCM7PXHScjrxCrUlhtYLMprEo5nq02he3yc5HFVn3F2GygrGA1w7Y5kJsKjmtU1FWvcZ7ueK9KzrvOcmnWfLJs5stfrgobYMOGrfgLF4UVhVLF08F2edni+fb17F/OFFoh14I2z4I214Lu8mtdrhVdnhV9rgWdyYoh14quqOxrb/KC9SwMWIQNeztWFIrLbYOjzSvTrkxXgPXy+xLzL69jVcXrXjUNLu/TVe1cNc161faKl79Sw7XrXLs9nObZANs12cWaby3z51EdKhxqcnNzOXz4sOP9sWPH2LlzJ0FBQcTExDB16lTOnDnD559/DsCjjz7K//3f//HMM8/w4IMPsnr1ar788ku+//77qtsLIYQQTpbsPMu/f0yp8HoBXgb05e3QeXA5JE8FswlsVntwsRU/LFfec3NDDCogX6PBpNVg0mgdz3laDSaNBpP2yrQVPt4cNHrcYIMKnwIIMEFAHgTmKQLyICBPEZgH/qYrrwPywGipWL1mHWT5QKY3ZPloyPKxv08PtLHKll7pn8MNyXU0FQ81W7dupX///o73xX1fxo0bx5w5czh37hwnT550zG/cuDHff/89Tz75JG+//TaNGjXi448/ZsiQIVVQvhBCiNKYCu3fxK3C/RjcNhzd5X4xxf1hrvSV0Vw1DbrENECnLee34+6v4NKxCtW1ydPIUYMBU1wvTMqCSVkdz3nKgklZyFdWTDbLVe8t5YpFWpuiYTa0TbURmAtR+Qb88xT+eQo/x7MN3zyFvoIHEMwGDSZfHSZfHfm+OvJ99Jj8dBT6Gijw1ZPvq6fQT0+RrwcWTy1arQ4NoNNo0WD/WQej5b7L/ZI0aNCisc/XaNBiX87eb+nKOlq09mdN8fzi5Z3nadHYpzvW0Trasa9jX0Z71XPxNJ22uL3L611eX4MGrVZ7TQ1adMXbLm5Lq7uyTTTotDp7dRoNuXn5RPK7iv2wb0KFQ02/fv243q1t5syZU+o6O3bsqGhTFWKz2SgqKqrWNkT5eHh41Mile0KIG2sbGcCUQS2qZ+PF3wW9JkOHe0CrA43O/ux4rb/8Wsuh7JNMXDHWvk7u/ko1GWT1IjbHSGS2johMDcGXbDTMMNPgYiG+GflorVd/PxWWuR0ArZ8f+qAgdMHB6Bs2RB/cEF3DhugbBl95fXme1tu7UvXWdyo7u0bbq5VXP1VUUVERx44dw2arxnPCoty0Wi2NGzfGw+MGh4CFELXLts/g+C8lTyWpy6eTbFZQtiuvLx6yr+cfCWFtbrj5jCz78r4GXwbGDsRb7423wdv5WeeJX5YZ7/QcjGmXMJzLQHvuPOrMOaynz2K9dAnIKbMNjcGAISoKfWjo5WByTWC5HFJ0DRuiLeWCFFG31flQo5Ti3Llz6HQ6oqOj5QiBi9lsNs6ePcu5c+eIiYmRmyUKUVcU5cHSyfbQUlEBV+4/YraZMZlN5FvyyTPnYTKbMFlMmMwmdl/YDUC0IZS/hYzFfOoURadOYT55iqLTpzCfOo359GmU2Xxle6U0p2vQAENMNB6Noq88RzfCIyYGfWgoGvkeqLfqfKixWCyYTCYiIyPxlsODtUJISAhnz57FYrFgMMi9K4SoE2yWK4Fm8CugN5ZyCumqU0taPRuzj/Dv0z+QtecdTDtfxWQ2UWQr2Q0gIFfR/riiw3HF+ycUwdkpHOP2smvR6zFERV4JK9ExjtBiaNQIna9vNf0QRF1X50ON1Wrv7SWnOmqP4s/CarVKqBGiLuo+0R5qbmDxz8+wP+dEienGIkX70zo6ndTS7qiVyLSSlw9p/f3xiI7GEB19+bnR5ecYDOFhaPR1/utJuIDb/NbIaY7aQz4LIeqH4otGxrd+gFHmtui37cP26w6KftsNZucjNsY2rfHt2RPvxES82rVDFxDgipKFm3ObUCOEEKJmBZzLYcoiK11Pz8eSV8DVx2P0kRH49OxpDzI9eqAPCnJZnaL+kFAjhBCiUjqsOELzgwooQOvnh0+PBLwTE/Ht2RNDbKwctRU1TrqIu8j48ePRXL55kcFgICwsjEGDBvHpp5+Wemn6kCFD0Ol0bNmypcS88+fP88c//pGYmBiMRiPh4eEMGTKE9evX18SuCCHqKZ3Z/rcq9a5etNi4gUbvvkvQmDF4xMVJoBEuIUdqXGjo0KHMnj0bq9VKWloaycnJPPHEE3z99dd8++236C93lDt58iQbNmxg0qRJfPrpp3Tr1s1pO3fddRdFRUV89tlnNGnShLS0NFatWsXFixddsVtCiDoqTadjj9ED07Fl5F++NDvP4nxZ9tWvh+SfpwlgbuArHXtFrSC/hS5UfFQFICoqii5dutCjRw8GDBjAnDlzePjhhwGYPXs2t912G3/84x/p0aMHb731lmOE88zMTH755RfWrl1L3759AYiNjaV79+6u2SkhRJ1ktlm4KyqcLJ0ONrxQrnUG2OxXnwZ5Sn8ZUTu4XahRSpFvrtlRQYt5GXQ3fcj11ltvpWPHjixatIiHH34YpRSzZ8/mvffeo1WrVjRr1oyvv/6asWPttxr39fXF19eXb775hh49emCUO2QKISqhwFJgDzRAQlg3fDz88DH4lLzj7+VnH4MPDTbNhv2baRLQxMXVC2HndqEm32ylzQsrXNL2vpeG4O1x8z/SVq1asWvXLgB+/PFHTCaTYwDQ+++/n08++cQRavR6PXPmzGHixInMmjWLLl260LdvX+699146dOhw07UIIeom34Jz/MfwFi2OFcLHnqUPc+AY/sAGygzB9v8Uvd//bTyMfjds44zXEmp2ZJ+6yVxkZffa0/gEGGmZEO7qctya24Uad6CUchzx+fTTT7nnnnsc/Wvuu+8+nn76aY4cOULTpk0Be5+aESNG8Msvv7Bp0yaWL1/O66+/zscff8z48eNdtRtCCBdqfH4VPXVbIR84XY4VNBoIjra/1spNM6uC1WJj//qzbFl2HFNWET6BRpp2CUFv0Lm6NLfldqHGy6Bj30tDXNZ2Vdi/fz+NGzcmIyODxYsXYzab+eCDDxzzrVYrn376Ka+88opjmqenJ4MGDWLQoEE8//zzPPzww0ybNk1CjRBuQilFanYBFqvCalNYlcJ2+dlqu/KwKYXVBll5BQAc9u5Ms1FPXzXcgbb0oQ+shbDmEXtjMnbSTbHZFCm/prJl6TGyL9g/B78gT7rd1hitVq4Kq05uF2o0Gk2VnAJyldWrV7N7926efPJJ5s2bR6NGjfjmm2+clvnhhx/417/+xUsvvYROV3qQatOmTYn1hBB11+SFO1my82y5l39Ed4lhBsj0CINWI268QlHZI1+L8lFKcXTneTZ/e4xL5/IA8PL3oOuwONr2jkRnkLBY3erut78bKCwsJDU11emS7hkzZnDbbbfxwAMPEB8fz+9//3vatWvntF50dDRTp04lOTmZHj16MHr0aB588EE6dOiAn58fW7du5fXXX+f2268zYJwQok7ZeSoTAA+9Fg+dFq0GdFoNOq0Grcb5WafV0NDsAYUQ29DHtYXXA0opTu3PYPOSo6SfsIdDo7eeLkNiad+vEQajnG6qKRJqXCg5OZmIiAj0ej0NGjSgY8eOvPPOO4wbN44dO3bw22+/8dFHH5VYLyAggAEDBvDJJ58wcOBAEhIS+Pe//82RI0cwm81ER0czceJEnnvuORfslRCiOn0xMYH42HJcQr3+N1gJIb5yRWR1Onc4k01LjnL2UCYAeqOOTgOi6TQwGqO39E2qaRJqXGTOnDnMmTOnzPnx8fGOweJKs2zZMsfrGTNmMGPGjKosTwghxHWcP5XD5m+PcmK3/SanWr2G9rc0osvQWLz9PVxcXf0loUYIIYQop0upefz63TEOb0sHQKPV0LpnBF2Hx+EX5Oni6oSEGiGEEOIGsi/ms/X74xzYeI7ig+jNu4XR/bbGBIZ5u7Y44SChRgghhCiDKbuIbcuPs+eXM9gs9jQT1yGYhFGNCW504xsUipoloUYIIYS4RkGemZ0rT/Lb6lNYiuyjkUe1DKTH7U0JbxLg4upEWSTUCCGEKDdlsVCwZw+5GzZg2r7D1eVUOXOhlV1rTrHjh5MUmiwAhMb50+OOJkS3koE7azsJNUIIIcqklMJ84gS5GzaQt2EDps2/Ysu56kZ9Wi3G5s1cV2AVsZpt7PnlDNuWHyc/xwxAUKQPCaOa0Lhj8E0PVixqhoQaIYQQTqyXLpH96zbyNm4kb/0GzGed72Ss9ffHJyEBn1498endG49GjVxU6c2zWW0c2JTKlu+PkZtRCIB/sCfdRzahebcwGdagjpFQI4QQAoBB220M+M3G8Vf7w9X3yTIY8O7UyR5ievbEs21bNGUM0VJXKJvi8PZ0fv3uGJlpJgB8Ao10HR5H614R6HQypEFdJKFGCCEENlM+D62wYf8qVxhbtMAnMRGfXj3x7toVrbd7XLaslOLEnots/vYoF07lAuDpYyB+WCztbolC71G3w1p9J6GmjurXrx+dOnVi5syZAMTFxTF58mQmT57s0rqEEHWU1UrxsYmYlcn4RMe6tJzqcPbQJTZ9c5RzR7IAMHjq6Dwoho63RuPhJV+H7kCOr7nI+PHj0Wg0JR5Dhw4t1/qLFi3i5ZdfrlCbGRkZJCUl4e/vT2BgIA899BC5ubnXXefDDz+kX79++Pv7o9FoyMzMrFCbQoi6Rx/c0NUlVKn0E9l8+85OFv9rB+eOZKEzaOk8KIYH/tGTbiMaS6BxI/JJutDQoUOZPXu20zSjsXyDzwUFVfzSwqSkJM6dO8fKlSsxm81MmDCBRx55hPnz55e5jslkYujQoQwdOpSpU6dWuE0hhHCVjLN5bP7uKEd3nAdAq9XQpk8kXYfF4RMoA326Iwk1LmQ0GgkPDy8xfcyYMVitVhYuXOiYZjabiYiI4K233uKBBx4ocfrpRvbv309ycjJbtmyha9euALz77rsMHz6cN998k8jIyFLXKz6dtXbt2grtmxBCuEr2hXx+XXqMlM2p9v7OGmiZEE63EY0JCPFydXmiGrlfqFEKzCbXtG3whiq4l0FSUhKjR48mNzcXX19fAFasWIHJZOLOO++s1DY3btxIYGCgI9AADBw4EK1Wy+bNmyu9XSGEqE2O777A8lm7sVntV2816RxC95GNaRjp6+LKRE1wv1BjNsE/Sz/qUO2eOwsePuVefOnSpY7Q4tjEc8/xzDPP4OPjw+LFixk7diwA8+fPZ9SoUfj5VW6skdTUVEJDQ52m6fV6goKCSE1NrdQ2hRCiNinMt7Bm7gFsVkVUy0B6/q4ZobH+ri5L1CD3CzV1SP/+/fnggw+cpgUFBaHX67n77ruZN28eY8eOJS8vjyVLlrBgwYJybffRRx9l7ty5jvc36gwshBDuYOPiI5iyiggM8+a2SR3RG+Ty7PrG/UKNwdt+xMRVbVeAj48PzZqVfnvxpKQk+vbtS3p6OitXrsTLy6vcV0a99NJLPPXUU07TwsPDSU9Pd5pmsVjIyMgotV+PEELUJWcPZbL35zMA9EtqKYGmnnK/UKPRVOgUUG3Vs2dPoqOjWbhwIcuXL2f06NEYDIZyrRsaGlriVFNiYiKZmZls27aN+Ph4AFavXo3NZiMhIaHK6xdCiJpiNdtYO+8AAG16RRDVooGLKxKu4n6hpg4pLCws0Z9Fr9cTHBwM2K+CmjVrFikpKaxZs+am2mrdujVDhw5l4sSJzJo1C7PZzKRJk7j33nsdVz6dOXOGAQMG8Pnnn9O9e3fA3hcnNTWVw4cPA7B79278/PyIiYmp1GXlQoiaYVE2Nnh5klaUimnvZ5jMJkwWE3nmPEwWk+O9yWx/WHKyecPVRVfS1uTjXEo14eXvQeLv6v7gmqLyJNS4UHJyMhEREU7TWrZsyYED9v9xJCUl8corrxAbG0uvXr1uur158+YxadIkBgwYgFar5a677uKdd95xzDebzRw8eBCT6crVY7NmzeLFF190vL/lllsAmD17NuPHj7/pmoQQ1WN5zhGeCw+FgsOw9c0bLu9VcGWsJ722fEeFa4OLZ3PZnnwCgFvuaYGnT92pXVQ9CTUuMmfOHObMmXPdZVq3bo26elC5q1x735jjx4/fsM2goKDr3mgvLi6uRHvTp09n+vTpN9y2EKJ2uWDNByBCYyS+8SC89d74GHzwMnjhrffG2+CNj94Hb4O3fV4h8O9xAGiq4NYUNUHZFGsvX+0U1yGYpl1CXF2ScDEJNUII4ca66QN4pc+MGy5nzckhpQbqqUp7fj5D6tFsDJ46+t7Xos6EMVF9ZOwnIYQQdU7upQI2fnMEgMQ7muLbwNPFFYnaQEKNEEKIOkUpxU9fpGAusBLexJ92t0S5uiRRS0ioEUIIUacc2X6e47suoNVp6Hd/KzRaOe0k7CTUCCGEqDMK8sz8vNDe+6fLkFgZ00k4kVAjhBCizti46DD52UU0CPem67A4V5cjahkJNUIIIeqEMymX2Lf+HAD97m+FziBfYcKZ/EYIIYSo9SxmK2vnHQSgbZ9IIpsFurYgUSvJfWqEEKKOMFKE1pwL+Rqw2UBZwWYBm/Xya+uV14U5ri63Sm1ddpzMNBPeATIUgiibhJo6ql+/fnTq1ImZM2cC9rsBT548mcmTJ7u0LiFE9Zhc+B/u9FwOc8u5QoAfBLnHwI4XTueyY8VJAG65twVGL/nqEqWT008uMn78eDQaTYnH0KFDy7X+okWLePnllyvUZkZGBklJSfj7+xMYGMhDDz1Ebm7udZd//PHHadmyJV5eXsTExPDnP/+ZrKysCrUrhLh53aw7SpmqAa0e9J5g8AGjP3g1AO9g8PCzLxIYW6N1VjWbTbFm7gFsNkXjjsE07Rzq6pJELSZx14WGDh3K7NmznaYZjcZyrVuZEbKTkpI4d+4cK1euxGw2M2HCBB555JEyx4M6e/YsZ8+e5c0336RNmzacOHGCRx99lLNnz/L1119XuH0hxM07OPQLWnYbBBodaK/z/9I9s2HbW+AfUfYydcDutadJP56Nh6eOW+5t6epyRC0nocaFjEYj4eHhJaaPGTMGq9XKwoULHdPMZjMRERG89dZbPPDAAyVOP93I/v37SU5OZsuWLXTt2hWAd999l+HDh/Pmm28SGRlZYp127drxv//9z/G+adOmvPLKK9x///1YLBb0evn1EaKmKa0BdPVnJOp9684CkHB7E3wblO8/faL+crtvJaUU+ZZ8l7TtpfeqkgHVkpKSGD16NLm5ufj62m8stWLFCkwmE3feeWeltrlx40YCAwMdgQZg4MCBaLVaNm/eXO7tZmVl4e/vL4FGCFEjLGYbACEx/i6uRNQFbvfNlG/JJ2F+gkva3jxmM94G73Ivv3TpUkdoKfbcc8/xzDPP4OPjw+LFixk7diwA8+fPZ9SoUfj5+VWqttTUVEJDnc9F6/V6goKCSE1NLdc2Lly4wMsvv8wjjzxSqRqEEEKI6uR2oaYu6d+/Px988IHTtKCgIPR6PXfffTfz5s1j7Nix5OXlsWTJEhYsWFCu7T766KPMnXvlEonrdQYur+zsbEaMGEGbNm2YPn36TW9PCCGEqGpuF2q89F5sHrPZZW1XhI+PD82alX6/haSkJPr27Ut6ejorV67Ey8ur3FdGvfTSSzz11FNO08LDw0lPT3eaZrFYyMjIKLVfz9VycnIYOnQofn5+LF68GIOh/pzPF0IIUXe4XajRaDQVOgVUW/Xs2ZPo6GgWLlzI8uXLGT16dLnDRGhoaIlTTYmJiWRmZrJt2zbi4+MBWL16NTabjYSEsk/XZWdnM2TIEIxGI99++y2enp6V3ykhhBCiGrldqKlLCgsLS/Rn0ev1BAcHA/aroGbNmkVKSgpr1qy5qbZat27N0KFDmThxIrNmzcJsNjNp0iTuvfdex5VPZ86cYcCAAXz++ed0796d7OxsBg8ejMlkYu7cuWRnZ5OdnQ1ASEgIOp3upmoSQgghqpKEGhdKTk4mIsL5HhItW7bkwIEDgP0U1CuvvEJsbCy9evW66fbmzZvHpEmTGDBgAFqtlrvuuot33nnHMd9sNnPw4EFMJhMA27dvZ/Nm+6m8a0+THTt2jLi4uJuuSQghhKgqEmpcZM6cOcyZM+e6y7Ru3RqlVKnz1q5d6/T++PHjN2wzKCiozBvtgX2ohavb69evX5ntCyGEELWNDJMghBBCCLcgoUYIIYQQbkFCjRBCCCHcQqVCzXvvvUdcXByenp4kJCTw66+/Xnf5mTNnOkZ6jo6O5sknn6SgoKBSBQshhBBClKbCoWbhwoVMmTKFadOmsX37djp27MiQIUNK3Nit2Pz58/nrX//KtGnT2L9/P5988gkLFy7kueeeu+nihRBCCCGKVTjUvPXWW0ycOJEJEybQpk0bZs2ahbe3N59++mmpy2/YsIFevXoxZswY4uLiGDx4MPfdd98Nj+4IIYQQQlREhUJNUVER27ZtY+DAgVc2oNUycOBANm7cWOo6PXv2ZNu2bY4Qc/ToUZYtW8bw4cPLbKewsNBxo7erb/gmhBBCCFGWCt2n5sKFC1itVsLCwpymh4WFOW4Yd60xY8Zw4cIFevfujVIKi8XCo48+et3TTzNmzODFF1+sSGlCCCGEqOeq/eqntWvX8s9//pP333+f7du3s2jRIr7//ntefvnlMteZOnUqWVlZjsepU6equ0whhBBC1HEVCjXBwcHodDrS0tKcpqelpZU50vPzzz/P2LFjefjhh2nfvj133nkn//znP5kxYwY2m63UdYxGI/7+/k4P4axfv35MnjzZ8T4uLo6ZM2e6rB4hhBDC1SoUajw8PIiPj2fVqlWOaTabjVWrVpGYmFjqOiaTCa3WuZnigRDr8y34x48fj0ajKfEYOnRoudZftGjRdY92lSYjI4OkpCT8/f0JDAzkoYceIjc397rr/OEPf6Bp06Z4eXkREhLC7bffXuapRiGEqEqZ6SYK88yuLkPUIRUe+2nKlCmMGzeOrl270r17d2bOnEleXh4TJkwA4IEHHiAqKooZM2YAMHLkSN566y06d+5MQkIChw8f5vnnn2fkyJH1fpTnoUOHMnv2bKdpRqOxXOsGBQVVuL2kpCTOnTvHypUrMZvNTJgwgUceeeS640HFx8eTlJRETEwMGRkZTJ8+ncGDB3Ps2LF6//kJIarP8V0XWDl7H0X5FnwCjQRH+7q6JFEHVDjU3HPPPZw/f54XXniB1NRUOnXqRHJysqPz8MmTJ52OzPz9739Ho9Hw97//nTNnzhASEsLIkSN55ZVXqm4v6iij0VjqabsxY8ZgtVpZuHChY5rZbCYiIoK33nqLBx54gH79+tGpU6dyn3Lav38/ycnJbNmyha5duwLw7rvvMnz4cN58800iIyNLXe+RRx5xvI6Li+Mf//gHHTt25Pjx4zRt2rQCeyuEKGa1Kd5ZdYiTGSasNoVVKWw2hcVmf7YqhdWmsBU/2+ANmwJN1ddiTk0lb/0Gctf9UvUbrwSbTbFl6TG2LjsOQHgTf4ZMbI/BQ/4TJW6sUqN0T5o0iUmTJpU679rRo/V6PdOmTWPatGmVaarClFKo/PwaaetaGi8vNJqb/6uTlJTE6NGjyc3NxdfX/r+TFStWYDKZuPPOOyu1zY0bNxIYGOgINAADBw5Eq9WyefPmcm03Ly+P2bNn07hxY6KjoytVhxACdp66xNurDlVoHeUBaKCBj8dNtW3NzcX066/krd9A3oYNFB075jTf2KY1GoPhptqorII8Mys/3cvJvRkAtO8bRa/RzdHpZUQfUT6VCjW1mcrP52CXeJe03XL7NjTe3uVefunSpY7QUuy5557jmWeewcfHh8WLFzN27FjAfmfmUaNG4efnV6naUlNTCQ0NdZqm1+sJCgoiNTX1uuu+//77PPPMM+Tl5dGyZUtWrlyJh8fN/WEVoj4rNNsvkgjxM/Jo36boNKDTatBqNeg0GnTaKw/t5fchyUbIgzA/zwq1pbHYMG3f7ggx+bt2gdV6ZQGtFs/27fDp2ROfxES8O3Wqkv+cVdT5kzkkf7ib7AsF6Axa+ie1pGWPiBqvQ9Rtbhdq6pL+/fvzwQcfOE0LCgpCr9dz9913M2/ePMaOHUteXh5LlixhwYIF5druo48+yty5cx3vb9QZ+EaSkpIYNGgQ586d48033+Tuu+9m/fr1eHpW7I+rEMJZkLcHD/VuXL6FV+sgr/zbNmTk8JdFVjqfWMqJgm+c58XG2ENMz574dO+OLiCg/BuuBgc2nWPtvINYzTb8gz0Z+of2hERX7j9won5zu1Cj8fKi5fZtLmu7Inx8fGjWrFmp85KSkujbty/p6emsXLkSLy+vcl8Z9dJLL/HUU085TQsPDy8xPpfFYiEjI6PMy/GLBQQEEBAQQPPmzenRowcNGjRg8eLF3HfffeWqRwhR8xpuOEjcQQVY0AUG4p3Y4/LRmJ54NIpydXkAWC021n11iD0/nQEgtl1DBk5og6ePa05/ibrP/UKNRlOhU0C1Vc+ePYmOjmbhwoUsX76c0aNHYyjnee7Q0NASp5oSExPJzMxk27ZtxMfbT8+tXr0am81GQkJCuetSSqGUorCwsPw7I4SocZrLp5hOxEcx5L8/oNHWrn4puZcKSf5wN2nH7MPgdBsRR7cRjdFoa/7Ul3Afbhdq6pLCwsIS/Vn0ej3BwcGA/SqoWbNmkZKSwpo1a26qrdatWzN06FAmTpzIrFmzMJvNTJo0iXvvvddx5dOZM2cYMGAAn3/+Od27d+fo0aMsXLiQwYMHExISwunTp3n11Vfx8vK67thdQojaw2LU1bpAcyblEis+2kN+jhmjt56BE9oQ1z7Y1WUJN1C7ftPrmeTkZCIiIpwevXv3dsxPSkpi3759REVF0atXr5tub968ebRq1YoBAwYwfPhwevfuzYcffuiYbzabOXjwICaTCQBPT09++eUXhg8fTrNmzbjnnnvw8/Njw4YNJY4ECSHEjSil2PnjSZbM3El+jpmGUb6MntpVAo2oMnKkxkXmzJnDnDlzrrtM69aty7zr8rWXzh8/fvyGbQYFBV33RntxcXFO7UVGRrJs2bIbblcIIW6kqMDCmv8e4PA2e9++Fglh9EtqJfefEVVKQo0QQohqdSk1j+X/2cOlc3lotRp6jW5O+35RLrl0XLg3CTVCCCGqzdGd5/lxzj7MBVa8AzwY+kh7Ipq69hJy4b4k1AghhKhyNpti87dH2Z58AoCIZgEMmdgOn4DyjW8nRGVIqBFCCFGl8nOLWPnJXk7tvwRAx1ujSbyrKTqdXJsiqpeEGiGEEFUm/UQ2y/+zm9yMQvQeWm4d25rm3cJcXZaoJyTUCCGEqBL71p/l5y9SsFpsBIR6MewP7WkY5XvjFYWoIhJqhBBC3BSr2cbPC1PYt+4sAHEdghk4oQ1GL/mKETVLfuOEEEJUWk5GAcn/2U36iRzQQMKoJsQPiZXhDoRLSKgRQghRKalHs/j+/V0U5Jox+ugZ/GBbYto2dHVZoh6Truh1VL9+/Zg8ebLjfVxcHDNnznRZPUKI+qWowMKKj/ZQkGsmJMaPu6d2k0AjXE5CjYuMHz/ePqL4NY+hQ4eWa/1Fixbx8ssvV6jNjIwMkpKS8Pf3JzAwkIceeojc3NxyrauUYtiwYWg0Gr755psKtSuEcD+blxwl91Ihfg09uWNKZ/yDvVxdkhBy+smVhg4dyuzZs52mGY3luzFVUFBQhdtLSkri3LlzrFy5ErPZzIQJE3jkkUeuOx5UsZkzZ8otzYUQgP200661pwHol9QSD0/5KhG1g/wmupDRaCQ8PLzE9DFjxmC1Wlm4cKFjmtlsJiIigrfeeosHHniAfv360alTp3Kfctq/fz/Jycls2bKFrl27AvDuu+8yfPhw3nzzTSIjI8tcd+fOnfzrX/9i69atREREVGwnhRBVIkuj+NXbi7xz68nLO0y+JR+T2YTJYiLPnOd4XfzcMfUYsdVQh9ViY83cA6CgZUI4MW3klJOoPdwu1CilsBTZXNK23kNbJUczkpKSGD16NLm5ufj62u/xsGLFCkwmE3feeWeltrlx40YCAwMdgQZg4MCBaLVaNm/eXOZ2TSYTY8aM4b333is1gAkhasYTPja2BYTAgdk3XhhobrH/HfTz8KvSOnb8cJKMs3l4+hroNbpZlW5biJvldqHGUmTjwyd+cknbj7zdF4NRV+7lly5d6ggtxZ577jmeeeYZfHx8WLx4MWPHjgVg/vz5jBo1Cj+/yv2BSk1NJTQ01GmaXq8nKCiI1NTUMtd78skn6dmzJ7fffnul2hVCVI30y/9f6ujflLAGTfE2eONj8MFb7423wRsvvZfTe/9Lq2HNFzQLbF5lNWSmmdi67DgAvUc3x8vXo8q2LURVcLtQU5f079+fDz74wGlaUFAQer2eu+++m3nz5jF27Fjy8vJYsmQJCxYsKNd2H330UebOnet4X97OwNf69ttvWb16NTt27KjU+kKIqvdUs9F0ap90w+Uu+h0kHaiqnnDKplgz9wBWi42YNkG06C5DHwhnVouZ7AvnyUpPI/t8GlnpaaSeOlmjNbhdqNF7aHnk7b4ua7sifHx8aNas9MO3SUlJ9O3bl/T0dFauXImXl1e5r4x66aWXeOqpp5ymhYeHk56e7jTNYrGQkZFR5mml1atXc+TIEQIDA52m33XXXfTp04e1a9eWqx4hRN23f8M5zh7KRO+hpe+YlnLhQD1ks1rJzbhIVnoqWefT7eGl+PX5NHIzLoJSTusUmM01WqPbhRqNRlOhU0C1Vc+ePYmOjmbhwoUsX76c0aNHYzAYyrVuaGhoiVNNiYmJZGZmsm3bNuLj4wF7aLHZbCQkJJS6nb/+9a88/PDDTtPat2/Pv//9b0aOHFmJvRJC1EV5WYVsWHQYsN8xWC7fdk/KZiMv85LTkZas81de51y8gM1qve429B5G/ENCCQgNwz8kDJ2vHyz+oYb2wA1DTV1SWFhYoj+LXq8nODgYsF8FNWvWLFJSUlizZs1NtdW6dWuGDh3KxIkTmTVrFmazmUmTJnHvvfc6rnw6c+YMAwYM4PPPP6d79+6Eh4eXehQnJiaGxo0b31Q9Qoi645eFKRSaLITE+NGhfyNXlyMqSSlFfk62/UhLehrZ59PJSk+9/JxG9oV0rDc4sqLV6fEPCSEgNNweXkLCHAEmIDQM74BAp6N42dnZ1b1bTiTUuFBycnKJS6RbtmzJgQMHAPspqFdeeYXY2Fh69ep10+3NmzePSZMmMWDAALRaLXfddRfvvPOOY77ZbObgwYOYTKabbksI4R6O/XaeI9vPo9Fq6D+2FVqd3LO1NivIy718Wsh+lOXqoy7Z59MxFxZcd32NRotfcDABIWH4h4ZdFVpCCQgNx7dBEBpt7f0dkFDjInPmzGHOnDnXXaZ169aoa85PFru2P8vx48dv2GZQUNB1b7QXFxdXZnvFbjRfCOE+ivIt/PRFCgCdBkYTEl21l4eLiisqyL8qsKSTfT6VrHR7n5bs9DQKTXk33IZvgyD8Q8MJCA0jICTUKbz4BgWj09fdaFB3KxdCCFGtNn1zhLzMQvyDPel2m5xyrgmWoiKyL6Q792u56nV+zo1P53j5B1wOLNcebbEfcdGXs39mXSShRgghRAmpR7PY/fMZAPoltcLgUfcvwKgNrBYLORcvOPVlcbw+n0bepYwbbsPTx9fRh6W4U+7VfVwMnp41sCe1k4QaIYQQTqwWG6v/ax8KoVViONGtKz7WXH1ls1nJzcgo2afl8uvcixdR6vp3vTcYPe2BJTSs1CMuRm+fGtqbukdCjRBCCCfbV5zg0rk8vPwM9Lqr6u5I7C5sNitpRw6TmZ5qDy+X79WSnZ5G9oXz2KyW666vMxgcR1oCQq50wi3u3+Ll5y/3AaokCTVCCCEcMs7lsXX5cQB6390cT1/37X9RWWs/+5gdyd+VOV+r0+EXHFLicufi1z4BgbX6CqK6TEKNEEIIwD4Uwtp5B7BZFDFtG9K8qwyFcC1Tdha7V60AILJlGxqER1x1pCUM/9BQfIMaotVKHyRXkFAjhBACgL3rznLucBZ6o46+Y1rIKZBS7F61Aou5iLAmzbj3xdfkZ1TLyPEvIYQQ2Kw2Nn1zBIAeo5rg31CGQriW1WJh54qlAHQZfrsEmlpIQo0QQgjMRTYKTfYOrm37RLq4mtopZfN6ci9l4BPYgJaJvV1djiiFhJo6ql+/fkyePNnxPi4ujpkzZ7qsHiGE+5AjECUppdj+/TcAdBw8HJ1eOlDXRhJqXGT8+PFoNJoSj6FDh5Zr/UWLFvHyyy9XqM2MjAySkpLw9/cnMDCQhx56iNzc3Ouu069fvxI1PvrooxVqVwgh6rpzhw6QeuQQOoOBjgOHubocUQbpKOxCQ4cOZfbs2U7TjEZjudYNCqr4zbCSkpI4d+4cK1euxGw2M2HCBB555JHrjgcFMHHiRF566SXHe29v7wq3LYQQddn2Zd8C0KpXX7wDAl1bjCiT24UapRSWwkKXtK03Git02NZoNBIeHl5i+pgxY7BarSxcuNAxzWw2ExERwVtvvcUDDzxAv3796NSpU7lPOe3fv5/k5GS2bNlC165dAXj33XcZPnw4b775JpGRZZ9D9/b2LrVOIYSoD7IvnCdl83oAugwb5eJqxPW4XaixFBbyzrjfu6TtP3/2dZWMuZGUlMTo0aPJzc3F19cXgBUrVmAymbjzzjsrtc2NGzcSGBjoCDQAAwcORKvVsnnz5utud968ecydO5fw8HBGjhzJ888/L0drhBD1xs4fvkfZbES37UBoXBNXlyOuw+1CTV2ydOlSR2gp9txzz/HMM8/g4+PD4sWLGTt2LADz589n1KhR+Pn5Vaqt1NRUQkNDnabp9XqCgoJITU0tc70xY8YQGxtLZGQku3bt4tlnn+XgwYMsWrSoUnUIIURdYi4sYPePyYAcpakL3C7U6I1G/vzZ1y5ruyL69+/PBx984DQtKCgIvV7P3Xffzbx58xg7dix5eXksWbKEBQsWlGu7jz76KHPnznW8v1Fn4Ot55JFHHK/bt29PREQEAwYM4MiRIzRt2rTS2xVCiLpg389rKMjLJSAsnCbx3VxdjrgBtws1Go2mzgy77uPjQ7NmzUqdl5SURN++fUlPT2flypV4eXmV+8qol156iaeeesppWnh4OOnp6U7TLBYLGRkZFeovk5CQAMDhw4cl1Agh3JpSiu3L7R2EuwwdKUMf1AFuF2rcRc+ePYmOjmbhwoUsX76c0aNHYzCU774IoaGhJU41JSYmkpmZybZt24iPjwdg9erV2Gw2R1Apj507dwIQERFR7nWEEKIuOrFrBxlnTuHh5UXbfoNcXY4oBwk1LlRYWFiiP4teryc4OBiw92eZNWsWKSkprFmz5qbaat26NUOHDmXixInMmjULs9nMpEmTuPfeex1XPp05c4YBAwbw+eef0717d44cOcL8+fMZPnw4DRs2ZNeuXTz55JPccsstdOjQ4abqEUKI2m77siUAtOs3CKNcHFEnyM33XCg5OZmIiAinR+/eV269nZSUxL59+4iKiqJXr1433d68efNo1aoVAwYMYPjw4fTu3ZsPP/zQMd9sNnPw4EFMJhMAHh4e/PjjjwwePJhWrVrxl7/8hbvuuovvvvvupmsRQoja7OKZUxzbuQ00GjoPHenqckQ5yZEaF5kzZw5z5sy57jKtW7dGKVXqvLVr1zq9P378+A3bDAoKuu6N9uLi4pzai46O5qeffrrhdoUQwt3sSLYPXNk0vjuB4XK6va6QIzVCCCHEVQpyc9n7048AdBl2u4urERUhoUYIIYS4yu7VK7AUFhISE0d02/auLkdUgJx+EkKIGqaxmZmun0ObvIvwuT8oK9guP5QVbJaS770soJM/2dXNZrWyY4X91FPn4aNkxPI6Rv6FCCFEDfO9sJPx+h/AAhwt50qNLvfr8A29/nLiphzespGcC+fx8vOnda9+ri5HVJDbhJqyOtSKmiefhRDXp7GZATivaUjIHTNAq7M/NDrQ6q96fdX0X5+H/PPgE+Li6t3btsujcXccPBy9h4eLqxEVVedDjU5nv8NjUVERXl5eLq5GgP2zgCufjRCidLkaX0I63lO+hbdX853S5T8jpB45xNmD+9Dq9HQcNNzV5YhKqPOhRq/X4+3tzfnz5zEYDGi10vfZlWw2G+fPn8fb2xu9vs7/eglRbxzZcR4AD08dGl397EdSPCRCy5598G0Q5OJqRGXU+W8djUZDREQEx44d48SJE64uRwBarZaYmBjpYCdEHZGXVciG/x0GoOuIxmi19e/fbu6lDA5u+AWA+OFyGXddVedDDdjvfNu8eXPHaQ/hWh4eHnLETIg65JeFhyg0WQiJ8aPjrY1cXY5L/LZyGTarhciWbQhrUvpAw6L2c4tQA/ajA551ZHRuIYSoLY7tusCR7elotBr6398Kra7+/YfEUlTEbz8sAyB++CgXVyNuRv377RVCCAFAUb6Fn784CECngdGExPi5uCLX2L9+Lfk52fgFh9CsW6KryxE3wW2O1AghhKiYTUuOknupEP9gT7rd1tjV5dS4ooJ8jm7fwubFXwLQechtaOWqzTpNQo0QQtRDqUez2P3TaQD6JbXC4FE/vswLTXkc3fYrKZvXc3zndixme19MT18/2t86xMXViZsloUYIIeoZq8XGmrkHQEGrHuFEt3bvy5fzc3M4snUzhzav58SuHVgtFse8wLAIWvToRftbh+Dp6+vCKkVVqFSoee+993jjjTdITU2lY8eOvPvuu3Tv3r3M5TMzM/nb3/7GokWLyMjIIDY2lpkzZzJ8uNzcSAghatr2FSfIOJuHl5+BXr9v7upyqoUpO4vDWzZxaPN6Tu75DZvV6pgXFNmIFj160TyhFyGxjeX2E26kwqFm4cKFTJkyhVmzZpGQkMDMmTMZMmQIBw8eJDS05JgkRUVFDBo0iNDQUL7++muioqI4ceIEgYGBVVG/EEKICriUmsfW5ccB6H13czx9Da4tqArlZV7i8JaNpGxaz6l9u1E2m2NecEwcLRJ60aJHLxo2inFhlaI6VTjUvPXWW0ycOJEJEyYAMGvWLL7//ns+/fRT/vrXv5ZY/tNPPyUjI4MNGzZgMNj/8cTFxd1c1UIIISpM2RRr5h7AZlHEtG1I865hri7ppuVkXODQ5o0c2rye0wf2Og33EBrX9PIRmZ4ERdbP++/UNxUKNUVFRWzbto2pU6c6pmm1WgYOHMjGjRtLXefbb78lMTGRxx57jCVLlhASEsKYMWN49tlnyxwbqLCwkMLCQsf77OzsipQphBCiFPvWn+Xc4Sz0Rh19x7Sos6ddsi+kc2jzBlI2redsyn6neeHNWtC8e09aJPQiMDzCRRUKV6lQqLlw4QJWq5WwMOd0HxYWxoEDB0pd5+jRo6xevZqkpCSWLVvG4cOH+dOf/oTZbGbatGmlrjNjxgxefPHFipQmhBDiOvIyC9mw6AgAPUY1wb9h3RoAODMtlZRN6zj06wZSD6c4zYts0dp+RKZ7T/xDSnaDEPVHtV/9ZLPZCA0N5cMPP0Sn0xEfH8+ZM2d44403ygw1U6dOZcqUKY732dnZREdHV3epQgjhtn5emEJRvoXQWD/a968bp2Iyzp7h0Ob1pGxaT/rxI1dmaDQ0at2W5t170TwhEb+gYNcVKWqVCoWa4OBgdDodaWlpTtPT0tIIDw8vdZ2IiAgMBoPTqabWrVuTmppKUVERHh4eJdYxGo0YjcaKlCaEEKIMR3ee5+iO82i1GvqPbVWrB6y8ePokKZvWk7J5PRdOHndM12i0RLdtT4sevWjWLRGfwAauK1LUWhUKNR4eHsTHx7Nq1SruuOMOwH4kZtWqVUyaNKnUdXr16sX8+fOx2WyOQQ5TUlKIiIgoNdAIIYSoOmalvzIUwuAYghvVrqEQlFKcP3HMcUQm4+xpxzytTkdMu440T+hFs2498PYPcGGloi6o8OmnKVOmMG7cOLp27Ur37t2ZOXMmeXl5jquhHnjgAaKiopgxYwYAf/zjH/m///s/nnjiCR5//HEOHTrEP//5T/785z9X7Z4IIYRAmc3k796Naes2APbmNyOvqIiAEC+6DY9zbXGXKaVIP3aElE3rSNm8nszUc455Or2e2A6dadGjN03jE+SGeKJCKhxq7rnnHs6fP88LL7xAamoqnTp1Ijk52dF5+OTJk44jMgDR0dGsWLGCJ598kg4dOhAVFcUTTzzBs88+W3V7IYQQ9ZRSiqKjR8nbsJG8DRsw/fortrw8ADL9m3CsyN4fsd/9rdC7cCgEZbNx7nAKKZvXc2jzBrLPX+nGoDd4ENepCy0SetEkvjtGbx+X1Snqtkp1FJ40aVKZp5vWrl1bYlpiYiKbNm2qTFNCCCGuYblwgbyNm8jbsIG8jRuxpKY6zdcFBmLs0ZOtuqGQB617RdCoZc33QVE2G2dS9nNo03pSft1A7sULjnl6o5EmnbvRokcvGnfuiodn3boaS9ROMvaTEELUAcEXzPRbZ8Xw5bMcOnLSaZ7GwwOv+C749OyJT8+eeLZuzZbvj5P9/XG8/D3o+btmNVanzWrl9P69pGxez+EtG8m7lOGYZ/D0oml8d1ok9CKuUxcMRs8aq0vUDxJqhBCiDrh/YTrRZxRgDzTGNq3xSUzEp2dPvOPj0XpeCQgZZ/PYlnwCgD53N8fTp3qHQrBaLJzat5tDm9ZzaMtG8rOzHPOM3j407ZpA84RexHXojF4uEBHVSEKNEELUAV759nGMLI/cS+vxj6MPKn1kbcdQCFZFXPuGNIuvnpvRWS1mTuzeyaHNGzi8ZRMFuTmOeZ6+fjTr1oMWCb2Iad8Rnd59xpcStZuEGiGEqENsHVuXGWgA9v5yhtSjWRiMOm65r2WVDoVgKSri+K4dHNq0jiPbfqXQlOeY5+UfQPNuiTTv0YvoNu3R6eXrRdQ8+a0TQgg3kXupkA2LLw+FcEcT/IJuvs+KubCA4zu3k7J5PUe2/Yq5IN8xzyewAc0T7OMsRbVqi7aM8fyEqCkSaoQQwg0opfh5wUHMBVbCGvvTrm/lh0IoKsjn6PYtHNq0nqM7t2K5aoBh34bBtOjek+Y9ehHVojWaq27hIYSrSagRQgg3cHTHeY79dgGtTkP/+ys+FEKhKY+j234lZfN6ju/cjsVc5JjnHxLmGDAyolkLCTKi1pJQI4QQdVyhyczPC+wjV3cZEkvDqPLdhTc/N4cjWzdzaPN6TuzagdViccwLDI+gRUIvWvToTWjjplXaN0eI6iKhRggh6rgNi49gyi4iMMyb+GGx113WlJ3F4S2bOLR5PSf3/IbNanXMC4psZD8ik9CLkNjGEmREnSOhRggh6rCzhy6x75ezAPS/vyV6Q8nOunmZlzi8ZSMpm9Zzat9ulM3mmBccE3f5iEwvGjaKqbG6hagOEmqEEKKOspitrJlrH4G7Te9IIptfGQohJ+MChzZv5NDm9Zw+sBeUcswLjWt6+YhMT4IiK9+hWIjaRkKNEELUUduWnyAzzYS3vwc9f9eU7AvpHNq8gZRN6zmbst9p2fBmLWiRYD+1FBgW7qKKhaheEmqEEKIOungml+3JJ7BZM4lonMPXrywi9XCK0zKRLVo7rlryD6meOwsLUZtIqBFCiDrm4pnTfPPml+Rn7kFZ09m79vIMjYZGrdvSIqEXzbon4hcU7MoyhahxEmqEEKIOKNDpORTmx7mvvyU343PHdI1WS3Sb9rTo0Ytm3RLxCWxwna0I4d4k1AghRC127vBBtn67iJSISNBoICMT0KLVx9CqV2/63j8Mb/8AV5cpRK0goUYIIWoZZbNxbOc2tnz7P07v32OfqNEQnGPC3HIkBXktiWgWxtBHu6Cp4J2DhXBnEmqEEKKWsFrM7F/3E1u/W8TF0ycB0Or0tO7dF83C/2K0tWZvfkd0BvtQCBJohHAmoUYIIVys0JTHbyuXs2P5t+ReygDAw8uLDgOH0WXYKPwaBrPmiwWkNB8NQPzQWIIifVxZshC1koQaIYRwEZNZx09zP2XXj8spys8HwLdBEJ2HjaLjoGEYva8El9TwEZg9/PHysxA/NM5FFQtRu0moEUKIGpZzMYPksy3Ylx2KSlkEQMNGMXS97U5a9e6H3mAosU6ub0sA4tqb0BlklGwhSiOhRgghaoBSitP7drPlu0Uc27EVCAOgUZt2dBt5F407xaPR3jisGIy2Gy4jRH0loUYIIaqRzWbl0OaNbPn2f6QdPeSY3sLvPJHBVuKnLXVhdUK4Fwk1QghRDcyFBexdu4qt3y8mKy0VAL3Bg7b9BuIX7kfC1kkc08a6uEoh3IuEGiGEqEKm7Cx2rvienSuWkp+TDYCnnz+dBo+g89Db8PYPYM+6b11cpRDuSUKNEEJUgczUc2z9/hv2rv0RS1EhAAGhYcSPuIN2/QZh8PREKYXFasMq3WKEqBYSaoQQ4iakHk5h6bz5ZO7bhgYFwAXPUHYHdeGoTxMs6zTYfl6NVSmUfTaJ2gN84eHCooVwUxJqhBCigpRSHNu5la3fLuLUvt0AaIDjXjFsD+jEGc/L4zRZFFwOOqXxMepqpmAh6gkJNUIIUU5Wi5kD639m63eLuHDqBABanY6syPZ8Z2vB3YO783jnKLQaDXqtBp1Wg1arQafRoNWCTmOfZjjpCQsg1M/TxXskhHuRUCOEEDdQaDKxa1Uy25ctITfjIgAGTy86DBxKl2GjeO6HU1zcdY4wPyMtwvxuvEEP+dMrRHWQf1lCCFGG3IyLbF/+Lb+tXE5RvgkAnwZBdBk2ig4Dh+Lp43t5yVOuK1II4SChRgghrnHh1Am2freY/evWYrNaAAiKiqbbyN+VOYyBEML1JNQIIQSXhzHYv4et3y3i6PYtjulRrdrSbdRdNOnctVzDGAghXEdCjRCiXrPZrBz+dSNbvltE6uEU+0SNhubdEuk68ndEtmhVc7UoGwWWAvLMeZgsJkxmk+O1EOLGJNQIIeolc1Ehe9euYtvSxWSmnQNAZzDQrt9A4kfcQYOIqGpru9Bm5vmQhhzzNGFaNMIRXPIt+WWu8zh/B0ArR4uEKJOEGiFEvWLKzuK3H5axI/m7K8MY+PrRacgIOg+5De+AwGqvYVf2cZb7+gA2yDlZYr5Wo8Vb7423wdvxrEEDQIRPeLXXJ0RdJaFGCFEvZKalsu37xexZc2UYA/8Q+zAG7fvbhzGoKTbs4yRE2TS8OuJzvPRejgDjY/DBqDOi0Wic1vl08f8A0CA37BOiLBJqhBBuLfXIIbZ8t4hDm9ajlD1MhDZuSrdRd9EioRdanetCghcaOoV2cln7QrgbCTVCCLejlOL4b9vZ8u3/OLV3l2N6XMcudBt1F9FtO5Q4EiKEqPsk1Agh3IZjGIOli7lw8jhgH8agVc9b6Dryd4TENnZtgUKIaiWhRghR5xWaTOxelcy25d+Se/ECcHkYgwFD6DL8dvyDQ1xcoRCiJkioEULUWbkZF9me/B27Vi6n0JQHgE9gAzoPG0XHQcOuGsZACFEfSKgRQtQ5F0+fZMt3i9j/y1XDGEQ2ouvI39G6T38ZxkCIekpCjRCiTlm34HM2L/7S8T6qVZvLwxh0k2EMhKjnJNQIIeqMrPQ0fv3mawCadUuk26jfEdmitYurEkLUFhJqhBB1xo4VS1HKRmyHztz+1N9cXY4QopaRY7VCiDqhKN/EntU/ANBl+CgXVyOEqI0k1Agh6oS9P62i0JRHg4goGneMd3U5QohaSEKNEKLWUzYbO5K/A6DzsJHSIVgIUSr5yyCEqPWO7dzGpXNnMXr70LbvAFeXI4SopSTUCCFqvW3LlgDQfsAQPDy9XFyNEKK2klAjhKjVLpw6wcndO9FotHQecpuryxFC1GISaoQQtdr25d8C0Kx7D/xDQl1cjRCiNpNQI4SotUzZWez/eQ0AXYbf7uJqhBC1nYQaIUSttXvVCizmIkIbNyWqZRtXlyOEqOUk1AghaiWrxcLOFUsBiB9+OxqNxsUVCSFqOwk1QohaKWXzenIvZeAT2IAWiX1cXY4Qog6QUCOEqJV2LLN3EO44aDh6g8HF1Qgh6gIJNUKIWudsygHOHT6ITq+n46Bhri5HCFFHSKgRQtQ62y/fbK9V7354BwS6thghRJ0hoUYIUavkXLxAyub1AHQZJqNxCyHKT0KNEKJW2bliKcpmI7pNe0Ljmri6HCFEHSKhRghRa5gLC9j1YzIgN9sTQlRcpULNe++9R1xcHJ6eniQkJPDrr7+Wa70FCxag0Wi44447KtOsEMLN7f9lLQV5uQSEhtEkvpuryxFC1DEVDjULFy5kypQpTJs2je3bt9OxY0eGDBlCenr6ddc7fvw4Tz31FH36yP0mhBAlKaUco3F3HjoKrVbn4oqEEHVNhUPNW2+9xcSJE5kwYQJt2rRh1qxZeHt78+mnn5a5jtVqJSkpiRdffJEmTeQcuRCipBO7dpBx5hQeXl606z/I1eUIIeqgCoWaoqIitm3bxsCBA69sQKtl4MCBbNy4scz1XnrpJUJDQ3nooYfK1U5hYSHZ2dlODyGEeysejbttv4EYvb1dXI0Qoi6qUKi5cOECVquVsLAwp+lhYWGkpqaWus66dev45JNP+Oijj8rdzowZMwgICHA8oqOjK1KmEKKOyTh7mmM7toJGQ+ehI11djhCijqrWq59ycnIYO3YsH330EcHBweVeb+rUqWRlZTkep06dqsYqhRCutn35dwA0je9Og/BIF1cjhKir9BVZODg4GJ1OR1pamtP0tLQ0wsPDSyx/5MgRjh8/zsiRV/7nZbPZ7A3r9Rw8eJCmTZuWWM9oNGI0GitSmhCijirIzWXvTz8CcrM9IcTNqdCRGg8PD+Lj41m1apVjms1mY9WqVSQmJpZYvlWrVuzevZudO3c6HqNGjaJ///7s3LlTTisJIdi95gcshYUEx8QR3baDq8uplSwXLoBSri5DiFqvQkdqAKZMmcK4cePo2rUr3bt3Z+bMmeTl5TFhwgQAHnjgAaKiopgxYwaenp60a9fOaf3AwECAEtOFEPWPzWplR7L91FOX4aPQaDQurqh2sOXnY9q6lbz1G8jbuJHCgwchYToAWqOMWC5EWSocau655x7Onz/PCy+8QGpqKp06dSI5OdnRefjkyZNotXKjYiHEjR3euomcC+fx8vOnda9+ri7HZZTVSsG+fY4Qk799O8psdlpGY7CHGUNMrCtKFKJOqHCoAZg0aRKTJk0qdd7atWuvu+6cOXMq06QQwg0Vj8bdcdAw9B4eLq6mZjXMVnQ/bOX0E5PJ27QJW1aW03x9ZAQ+PXvik5iIT2IiW/99EM7no5H/NApRpkqFGiGEuFlpRw9z5sA+tDo9HQcNd3U5NUqTnsXbs6x4WCGHFQBofX3x7pHgCDIecXFyOk6ICpJQI4RwieKjNC0Te+Mb1NDF1dQs7fksPKxQ4AGNHn0cn8REvNq3R6OXP8lC3Az5FySEqHG5lzI4sOEXwD1G49YqCz20+2h0/gIc2A/KCjYL2KygbFe9vjw97QAAmf4aOv/pTy6uXgj3IaFGCFHjflu5DJvVQmSL1oQ3be7qcm7ayAufMMhjAezE/riRSz5AQLXWJER9JKFGCFGjLEVF/LZyOeAeR2kAgiz2G5Lmeobj27ARaHWg1YNGa3/W6kBzeZpWC0fOAyft78shL6sQU3YRADq99LMRoiwSaoQQNerA+p/Iz87Cr2EIzbuXvGlnXbYndhw97nvuxgt+Pxt43R5wymHdl4ewFFoJjvYluJHvzRUphBuTawOFEDVGKeXoINxpyAi0Op2LK6r9ju+6wOFt6Wi0Gm4d2xqtTv5sC1EW+dchhKgxp/ft5vzJ4+iNRtoPGOLqcmq9ogILP31xEIBOA6IJifFzcUVC1G4SaoQQNWbbsm8BaHvLALx85Qv6RjYtOUrupUL8gz3pNrKxq8sRotaTUCOEqBGZqec4sm0zAJ2HjXRxNbVf6tEsdq89DUC/Ma0weMipOiFuREKNEKJG7FixFJQirlM8DaOiXV1OrWa12Fgz9wAoaNkjnOg2Qa4uSYg6QUKNEKLaFZpM7FnzAwDxw0a5uJrab8cPJ8g4m4eXn4Hev6/79/ERoqZIqBFCVLu9a1dSlJ9PUGQjYjt2cXU5tdql1Dy2LDsOQO/RzfH0Nbi2ICHqEAk1QohqZbNZ2ZG8FIAuw0fJII3XoWyKtfMOYrMoYtoG0bxbmKtLEqJOkVAjhKhWR7dvJTPtHJ4+vrTpc6ury6nV9q0/y9lDmeg9tPS9r6UEQCEqSEKNEKJa7Vhuv9le+wFDMHh6uria2isvq5ANi44AkDCqCf7BXi6uSIi6R0KNEKLanD9xjJN7dqHRauk0ZISry6nVflmQQlG+hdBYPzrcKleHCVEZEmqEENVm+3L7zfaad++Jf3Coi6upvY7uPM+RHefRaDX0H9sKrVZOOwlRGRJqhBDVwpSdxf51awH3GY27OhTlW/h5QQoAnQfFENxI7rQsRGVJqBFCVItdK5djNZsJb9qcyBatXF1OrbXxmyPkZRYSEOJFtxFxri5HiDpN7+oChBDux2oxs/OH7wHoMkwu4y6LySuO4z+fAaBfUkv0MhSCEDdFjtQIIapcysZ15GVewqdBEC0Se7u6nFrJptFzNvxuUNC6ZwSNWslQCELcLAk1QogqpZRyjMbdafAIdHq5I25pTsQMpsgYjpefgZ53NXN1OUK4BQk1QogqdfbgftKOHkJnMNBh4FBXl1MrFeXrOB47GIA+97TA00eCnxBVQUKNEKJKFV/G3bp3f7z9A1xcTe2Un2dAaQ14FJ2nWbxc6i5EVZFQI4SoMtnn0zm0eQNgH+dJXJ/GZpFO1EJUIQk1Qogqs2PFUpSyEdOuAyExca4uRwhRz0ioEUJUCXNBAbtXrwCg8zC52Z4QouZJqBFCVIm9P6+mMC+PwLAImnbp5upyhBD1kIQaIcRNUzYbOy53EO48bCQarfxpEULUPPnLI4S4acd37SDj7Gk8vLxp12+gq8sRQtRTEmqEEDdt+7IlALTrPwgPL28XVyOEqK8k1AghbsrF06c4/tt20GjoPHSkq8sRQtRjEmqEEDdlR7K9L02zrgkEhoW7uBohRH0moUYIUWn5uTns/Wk1YB+NWwghXElCjRCi0navWoGlqJCQ2MY0atPe1eUIIeo5CTVCiEqxWizsWLEUgC7Db5fb/QshXE5CjRCiUg5v2UjuxQt4+QfQquctri5HCCEk1AghKmf7MnsH4Y6DhqP38HBxNUIIIaFGCFEJ5w4f5GzKfrQ6PZ0GD3d1OUIIAUioEUJUQvFRmlY9++AT2MDF1QghhJ2EGiFEheRmXCRl0zrA3kFYCCFqCwk1QogK2fnDMmxWK1Gt2hDWpJmryxFCCAcJNUKIcjMXFbLrx+WAHKURQtQ+elcXIISoOw6s+4n8nGz8Q0Jp1rWHq8up1WzKRoGlAJPFRJ45D5PZhMliwmQ2cTrnFBDm6hKFcDsSaoQQ5aKUcozG3WnIbWh1OhdXVHvsM5j4V0QYaUXfYftypSPAlKXn2WZ0oGsNVihE/SChRghRLqf27uLCqRMYjJ607z/Y1eXUKj8bc9jtaQRyIT/XaZ5Wo8Vb721/GOyPaL9QyACdVoKhEFVJQo0Qoly2XT5K07bfADx9fV1cTe1iuzxCxEBtSx4d8Yo9vFwOMZ46zxJDSBw2r2fFiUK0GunWKERVklAjhLihS6lnObp9CwCdh450cTW1V7DGl5ZBLV1dhhD1lvw3QQhxQzuSvwOlaNy5K0GRjVxdjhBClEpCjRDiugpNeexZ8yMgl3ELIWo3CTVCiOvas2Yl5oJ8GjaKIbZ9J1eXI4QQZZJQI4Qok81mtZ96AroMG1Wiw6sQQtQmEmqEEGU6su1XstLT8PT1o3Wffq4uRwghrktCjRCiTMU32+swYAgGo6eLqxFCiOuTUCOEKJW5oIDT+/YA0HHQcBdXI4QQNyb3qRFClMpmszpeewc2cGElNe/I+Vx2nc7EagObTWFVCqtNYbv8fOU12JTCYrW5umQhBBJqhBDCSZHFxh3/t56cQku51+nZyB5q9HLsWwiXklAjhBBXKbBYHYGmT/NgDDotWo0GnRZ0Ws3l1xp0Gg3ay88ZGfY/peEBXq4sXYh6T0KNEEKU4eNxXTHqbzzo5NNzDWAFvVYueRfCleRgqRBCCCHcgoQaIYQQQrgFCTVCCCGEcAuVCjXvvfcecXFxeHp6kpCQwK+//lrmsh999BF9+vShQYMGNGjQgIEDB153eSGEEEKIyqhwqFm4cCFTpkxh2rRpbN++nY4dOzJkyBDS09NLXX7t2rXcd999rFmzho0bNxIdHc3gwYM5c+bMTRcvhBBCCFGswqHmrbfeYuLEiUyYMIE2bdowa9YsvL29+fTTT0tdft68efzpT3+iU6dOtGrVio8//hibzcaqVatuunghhBBCiGIVCjVFRUVs27aNgQMHXtmAVsvAgQPZuHFjubZhMpkwm80EBQWVuUxhYSHZ2dlODyGEEEKI66lQqLlw4QJWq5WwsDCn6WFhYaSmppZrG88++yyRkZFOwehaM2bMICAgwPGIjo6uSJlCCCGEqIdq9OqnV199lQULFrB48WI8Pcse8Xfq1KlkZWU5HqdOnarBKoUQQghRF1XojsLBwcHodDrS0tKcpqelpREeHn7ddd98801effVVfvzxRzp06HDdZY1GI0ajsSKlCSGEEKKeq9CRGg8PD+Lj4506+RZ3+k1MTCxzvddff52XX36Z5ORkunbtWvlqhRBCCCHKUOGxn6ZMmcK4cePo2rUr3bt3Z+bMmeTl5TFhwgQAHnjgAaKiopgxYwYAr732Gi+88ALz588nLi7O0ffG19cXX1/fKtwVIYQQQtRnFQ4199xzD+fPn+eFF14gNTWVTp06kZyc7Og8fPLkSbTaKweAPvjgA4qKivj973/vtJ1p06Yxffr0m6teCCGEEOKySo3SPWnSJCZNmlTqvLVr1zq9P378eGWaEEIIIYSoEBn7SQghhBBuQUKNEEIIIdyChBohhBBCuAUJNUIIIYRwCxJqhBBCCOEWJNQIIYQQwi1IqBFCCCGEW5BQI4QQNSj7Yj7nzlldXYYQbqlSN98TQghRPvk5RZw+eMn+OHCJ7PP5jnkGW4ELKxPC/UioEUKUKis9zdUl1ElFBRbOHsp0hJiLp3Od5ms0ikCVgf+JX4kzngMecE2hQrghCTVCiBLyMi+x5M1XAIhp1wG9weDiimovq9mGMSuWrhk9yDnYlU+++wWbTTktE2Aw0eDSQfyPbSIw8zB6q/0Ijf/wYa4oWQi3JaFGCOHEXFDA4tdeIvt8GoFhEYx44llXl1Sr2GyKC6dyOH3Afkrp3KFMIswTiADsPWUUPh5mgkzH8T+ygQYX9uNhznGsb2zTGp/ERHx69sSne3cX7YUQ7klCjRDCwWazsvSd10k7eghPP39+N3U63v4Bri6rxoWRQRvtCbQpCoUiM8PG6ZNaTp/Wc+aMgcJC52ssbLocDLkHaZJ6iOC0/XgVXHTM00dE4NNzkD3EJCaiDwqq6d0Rot6QUCOEAEApxZo5H3J026/oDAbuePp5GkREubqsmmc18z/9a+Sam/LTh4WcLmpPni3YaRGDxkSUxx4aeeymkccu1u0tpMUBHQBaX1+8BwzAp2ciPok98Wgch0ajccWeCFHvSKgRQgCwbelidq74HjQahk/6C1EtW7u6pBpTkGfmzOUrlE7uu0D2hXec5ms1FiL8ztIo8DSNAs8Q6p+BVqcFjRa0XdDv/w0oJPWO7vT7xydo9PKnVQhXkH95QggOblzHT3M/BaBv0gRa9Ojt4oqql7nQyrnDmY5+MedP5cBVfXs1WAkxHCXilr7EtgslomkAeg9d2Rv8dgBwFnNwAwk0QriQ/OsTop47c2Afy9/7FwCdhtxG/G13uriiqme12kg/lu24zDr1aBY2q/MVSg3CvWnUKoigaD3NV/TCqDVROPIcRk9vF1UthKgoCTVC1GMZZ8/wzZv/wGo207RrAv3HT3SL/h/Kprh4Ntd+JObAJc4eysRc6HwXX98gI41aBdGoZQMatWyAT6ARgOzMixhXmlxRthDiJkmoEaKeMmVnsejVaRTkZBPWpDkjHn8arfY6p1hqMaUU2RfyHSHm9MFLFOSanZbx9DEQ1bIBjVrZHwEhXm4R4IQQV0ioEaIeMhcV8s3rL5GVlop/SBh3PvsCBk9PV5dVIXlZhY4Ac/pABrkZhU7z9UYdkc0CHSEmOMoXjVZCjBDuTEKNEPWMzWZl+bv/4tyhg3j6+PK7qdPxCWzg6rJuqNBk5kzKleEHLp3Lc5qv1WkIa+xvP6XUqgFhcf7o9DJmrxD1iYQaIeqZn+d+yqFfN6DT67n9qb/TMCra1SWVylJk5dzRLMcppfMnslFX9+3VQHAjX0eIiWwWiMFYN0+fCSGqhoQaIeqR7cu/Zdv3SwAY8qcnadSmnYsrusJmtZF+onj4gQxSj2RjtdiclgkM87Z37G3VgKgWDfD0lTGphBBXSKgRop44tGUjaz77CIDe942jda++Lq1HKUXG2TxHv5izKZcoKnC+QsknwMNxJCaqZQP8gupWvx8hRM2SUCNEPXDu0EGWvfMmKEWHAUPpfvvvXVJH9oV8R5+Y0wcvkZ9d5DTf6K0nqsWVK5QCw7zlCiUhRLlJqBHCzWWmnmPx6y9hKSqkceeuDHjojzUWFPJziq6EmAMZZF8ocJqvN2iJaBbgOBoTHO2HVq5QEkJUkoQaIdxYfk42i16dTn52FqFxTblt8rNoddXXmbaowMLZQ5mOzr0Xz+Q6zddoNYTF+duPxLRsQHiTAHQGuUJJCFE1JNQI4aYsRUUsefMfXDp3Br+GIdz57At4eHpVaRtWs43Uo1mOe8WkHc9B2ZyHH2gY5es4nRTZPBAPT/mzI4SoHvLXRQg3pGw2lr//b84c2IfR24ffTZ2Ob1DDm96uzaa4cCrHcTrp3OEsLGbnK5T8gz2vdO5t0QBvf4+bblcIIcpDQo0QbuiXLz4jZeMvaHV6Rv3lOYKjYyu1HaUUmWkmx+mkMymXKDRZnJbx8vdwXGbdqGUD/IOr9mhQbaWsVgr27iVvwwZCj2S4uhwhBBJqhHA7v61cxpZv/wfA4D88Tky7jhVaP/dSwZUxlA5kkJflfIWSh6eOyKuuUAqK8KkXVyhZbVZyjh8me90vFGzchGXrTjQ59rsa+wI2DeRHB7u2SCHqOQk1QriRI9t+ZdUnswDoeXcSbfsOuOE6Bblme5+Yg5c4c/ASmWnOI1Tr9FrCmwY4QkxojB9anXt37n2rQSAbvDxRX91B7KFsmh8y0eaomdCsK8togDwj7InVsKuxht8aa/hDpxYuq1kIIaFGCLeRdvQwS99+DaVstOs/iB6/u7fU5cyFVs4eznQciblwOheu6tur0UBIrL8jxEQ0CUDvUX+GH8gzmzh91Jfxv9lonHqKq+ObRQspURoONjVyuIUvF2MC8PL0xdvgTXfvMAbE3DhECiGqj4QaIdxA9vl0Fr/2IpbCQmI7dGbgw485TglZrTbSjmU7QkzasWxsVucrlBpE+Dj6xES1CMToXX+HH7AVmHhohe1KmGkcg6FHV3x6JhKQ0JN2fg3qxek2IeoiCTVC1HEFubn8b8Y08jIvERwTx21P/JWMc/mOfjFnD2diKXQefsAvyNNxJCaqZQN8Aowuqr4WslgdgSZ86SIaNGvt0nKEEOUnoUaIOsxiNrPkX6+QceYURp9AgqLvY9707RTkmp2W8/Q1XLlCqZX9CiU52nBjuoY3fxm8EKLmSKgRog7Kyyrk1P4MNiz8gEtndwMeKO1ITu4rBMBg1BHZPNARYhpG+qKR4QeEEG5OQo0QdUChycyZlCudey+lmrDkb8RSsB3QYPQfSVSr5o5+MaGN/dG5+RVKQghxLQk1QtRCliIr545kOULM+ZM5qKv69iplwlL4KwDxt42n1913YDDWnyuUhBCiNBJqhKgFbFYb6SdyOH0gg9MHLnHuaBY2i/MVSoFh3o5+MedSfmTzYithTZrT9/7fSf8YIYRAQo0QLqGUIuNsnuNIzJlDmZgLnK9Q8gk0OvrENGrZAN8GngBYLWZWfpgMQPzwURJohBDiMgk1QtSQ7Av5jhBz+uAl8nOcr1AyeuuJatnAcTQmMMy71MCSsmk9eZcy8GkQRIvE3jVVvhBC1HoSaoSoJqbsIs4cvBJisi8UOM3XG7RENA90hJjgaD+0N7hCSSnFtu+XANBp0HB0+vp7kzwhhLiWhBohqkhRvoUzhzI5c+ASpw9mcPFMntN8rVZDaNyV4QfCGwegM1TsCqWzKQdIO3oIncFAh0HDqrJ8IYSo8yTUCFFJFrOV1KPZjs696SdyUDbnzr0NG/k6+sRENg/Ew/Pm/sltX/4tAK1798PbP+CmtiWEEO5GQo0Q5WSzKc6fvOoKpSNZWM02p2X8Q7wcIaZRywZ4+XlUWfvZF9I5tHk9AF2Gjaqy7dYXSilsCqw2hU0prDaFVSlstiuvrTbF+ZxCZNAIIeomCTVC3ICyKdb/7zAHNp6j0GRxmufl7+E8/EBDr2qrY+eK71E2GzHtOhAS27ja2nE3v53KZMKcLWTkFZVr+VDrWT6r5pqEENVDQo0QN7B33Vl+W3UKAA9PHZEtroSYoAifGrmk2lxQwO5VKwDoPOz2am/PnWw6erFcgUajAZ1Gg/7K+NwY9XJDQyHqEgk1QlxHXmYhGxcdBiDh9iZ0GRyD1gXDD+z7ZTUFebkEhkXQpEvXGm/fHdzWIYKXb2+HVqtBp9Wg02jQau1BRqfVOMJp1tmDnP3mTRdXK4SoDAk1QlzHzwtTKCqwEhrrR5chsTe85Lo6KJuN7cvsHYQ7DxuJVitHDyrDqNfRwOdKHydls2FJT6fo1CmKTp2m6NRJzKdOk3/0kAurFELcDAk1QpTh6M7zHN1xHq1WQ/+xrVwSaABO7NpBxtnTeHh50bbvQJfUUFfZ8vPxPnuChHN7abthF6kpSx3hxXz6NKqo7NNSJ0KgiUHuAyREXSKhRohSFOZb+PmLgwB0GhxDcCM/l9Wy7fJl3O36D8bo7e2yOmojpRSW8+cxnz6N+dQpik6ewnz6ypEX6/kLdAWKT9hdunYDWjAEeOARqMcQqMcjQI/V38aDwTmcaQibZQgKIeoUCTVClGLj4iPkZRUREOJFt+FxLqvj4plTHN+5DTQaOg8d6bI6qoKyWLAVFKKKClEFBdgKC1GFxa+LUIWXpxUUOr8uKrSvV1iIrbAAVVCINSsL86mTFJ06jSoouG67NqMBb+88DL5WPHwtGHwuP/taMXhb0VzTRSpHo+FEWLT9jVb+RApRl8i/WCGucfZwJnt/PgNAv/tbofdwXR+WHcu/A6BpfAKBYeFVsk1ltV4OCCVDhSosDhClhYqrQ8flgFEcRq6ddm1oKSoCi+XGxVWGVoshPBxDdDQeMdEYGl31HN2ILd+9Rdsjb3PKsyWhfceBRgfayw+Nzh5cHK91YDPDjn9c3rb0XxKiLpFQI8RVrGYba+ceAKB1rwgatWxQbW0ppa4bKkxZmexd8wMALf2CuLRgwVXzrxy1KO1IRonQUnQlaGA236Cy6qcxGNB4eqIxGtEajZdfe6A1XjXNaETjabwyzdOIxsO+rM7P1xFaDJGRaDxufJPDVGNjohMfu3FxRTlXQo0Qok6RUCPEVbYlH+dSqgkvXz3devhQcPAgttxc+yM//4ah4kZHLa6edr1OqgBHQgKxRDbEL78QNfNdUqtjhw0GtB4epYeKa6aVHjCMaD090XhcnmY0ojF6ojV6lAwtHh72ZY1GNNqavyz+amabGZPZRL4lH5PZhMliIs+ch8lsIqMgw6W1CSEqT0KNcAvKZsNmyseWl3slhOTlYc3NxZabZ39vsj87puVdfp9nf59t8WZrsz+CVk+Tzf/h9NLtNbcDWi0aT09HAMDDg5MB9n+eLX2D8O3T2Dl0lBIq7KHjcqhwBIwroeLK0Q9Pe5AxGtHo3f9PQJrK5YGIUE4ZDmFZ0AeT2USR7cY349Nr9DVyY0UhRNVx/79ootZSSqGKikoPIXm5V0LHNSHElpeL9er3l9e9qVrQsK/zkyitnoYXdhN6frv9y9/XF62PNzofXzTeXs6hwniDIxnFoeK6RzIuv77m0uGDG9eRP/NVvPwD6PXebPTlOL0iSvcb59jh6QmYoTDTaZ5Ba8Db4I2P3gdvgzfeem/Hc+9GvTFo5ZJuIeoSCTWiwpTFctVRjrwrIaS0oyN5eZdDyDXB5PK6Vd55VKdD6+uLzsfHHkh8fdH6+KD19UHn64vWp/i971XTfDh4wkDW+nwMHhqGvXkf/lF/KhE0atL2ZUsA6DhomASam6Swj5zesciXF0fPdQouBp2EFiHciYQaUYKyWrGkpl6+0+opzFfdbdV86hTWrKwqb1Pr7X1VALkqcHhfFU58fdD6XJ5+VUDR+V5ZRmM0VviUQe6lArYt2gxAjzubExAXWuX7VxGph1M4m7IfrU5Px0HDXVqLO/FVOpoGNnV1GUKIaiShpp6y5uZhPnOaopMnS4SWorNny3WFjMbD46oQcvnoiE8ZIcTnylGTq0OI1scHrbc3Gp1rLp1VSvHzghTMBVbCGvvTrm+US+q42vbLN9tr1bMPvg2CXFyNEELUHRJq3JSy2ex3Wj150mlcG/Ploy/WjBtc4WEw4BEZiSEmxn7ZbHTxczT6kBB7IHGD0yJHd5zn2G8X0Oo09L/fdUMhFMvNuMjBjb8A0GW4jMYthBAVUalQ89577/HGG2+QmppKx44deffdd+nevXuZy3/11Vc8//zzHD9+nObNm/Paa68xfLgcVq9qOWvXkvnFAvspoxuMawOgCwy037Ds6tBy+cZl+rAwlx09qSkFeWZ+XpACQJchsTSM8nVxRfDbymXYrFaiWrUhrEkzV5dTK+Wk72PrnrnkWQowWQswWYsw2QoxWYvIsxVhspmvPJSZM9YscO0V5EKIGlLhULNw4UKmTJnCrFmzSEhIYObMmQwZMoSDBw8SGlqyL8KGDRu47777mDFjBrfddhvz58/njjvuYPv27bRr165KdkLYpb/5JkWHj1yZoNNhiIwsNbQYGjVC5+/vumJrgY2Lj2DKLiIwzJv4YbGuLgdzUSG/rVwOuOdRGqUUFmXBarNiVVYsNovjucBSgMliwmS+fL+Yy6+vfc4z57HkyJKKNXw50DRUrg+tQojqpVFKqYqskJCQQLdu3fi///s/AGw2G9HR0Tz++OP89a9/LbH8PffcQ15eHkuXLnVM69GjB506dWLWrFnlajM7O5uAgACysrLwr+dfxNdzePAQzCdPEvb3v+Pb9xYMERH14j4klXEm5RLfvLUDgDv/0oXI5oE10q5SCquyf6lbbVanL/kDa9ewec5neAcFMeiV51EanL74S1vn6vfXPt9oPbPN7Hh9vWUtymKfV0qbZa3nmG+zYLm8vzZlq9KfZZBZQ1ihEYNNi4dNh96mRa906G3FDz06pUNn06Ms3nS+5UlG9+9WpTUIIa6vpr+/K/SNV1RUxLZt25g6dapjmlarZeDAgWzcuLHUdTZu3MiUKVOcpg0ZMoRvvvmm4tWKcvFs0waP6GhXl1FrHT3yEz9+lAt44dP0DBtP/YD5uBmzMlNkNWO2WTAri/358muLzUqRsmBWVsw2q/358qNI2eyvsWFRNszKRhE2Cm02rChsgBVlf5TVZUfBqHURBOHBT6FHeP+739XgT8S1dErhqRTeNhveSuFtu/q1/dnHZsPrqnnNzGZOd/+G2Kat0Wk16DQatFqueq1Br9Wg1WjQaTV4GXQ08Kn7fcCEENdXoVBz4cIFrFYrYWFhTtPDwsI4cOBAqeukpqaWunxqatk3fS8sLKSwsNDxPuvyJcTZ2dkVKbfeybFYsFitZOfmYpGfVZmWfLER68W2mAzn+MhrJkUp+a4uifCLRrwzNOTqCjkZdAmfXCs6QK8UOuxf/DoFOuzPehR6ZT+zYp9+zbJXPeuVQg9oi6dfXleHQnt5W8Xb1l/VxpX39m1pr2mj9GWVo56rp+svt3X1cjalQ4MGDZCND59ahmJDiwWt47kQLSalJR0dVrSXHxo+04XzfstmhPiV1u9LXX5cxWomO/v6o3kLIape8fd2BU8KVVqtPDcxY8YMXnzxxRLTo+XoQ/n0vcXVFYgK2g+sKX7ztQsLcZlLwMcVWqPZG5OrpRIhRNW7ePEiAQEB1d5OhUJNcHAwOp2OtLQ0p+lpaWmEh4eXuk54eHiFlgeYOnWq0ymrzMxMYmNjOXnyZI38UGqL7OxsoqOjOXXqVL3qSyT7LftdH8h+y37XB1lZWcTExBAUVDP33KpQqPHw8CA+Pp5Vq1Zxxx13APaOwqtWrWLSpEmlrpOYmMiqVauYPHmyY9rKlStJTEwssx2j0YjRaCwxPSAgoF79MhTz9/eX/a5HZL/rF9nv+qW+7rdWWzP3Vajw6acpU6Ywbtw4unbtSvfu3Zk5cyZ5eXlMmDABgAceeICoqChmzJgBwBNPPEHfvn3517/+xYgRI1iwYAFbt27lww8/rNo9EUIIIUS9VuFQc88993D+/HleeOEFUlNT6dSpE8nJyY7OwCdPnnRKZD179mT+/Pn8/e9/57nnnqN58+Z88803co8aIYQQQlSpSnUUnjRpUpmnm9auXVti2ujRoxk9enRlmgLsp6OmTZtW6ikpdyb7LftdH8h+y37XB7LfNbPfFb75nhBCCCFEbSQjogghhBDCLUioEUIIIYRbkFAjhBBCCLcgoUYIIYQQbqHWh5r33nuPuLg4PD09SUhI4Ndff3V1SRXy888/M3LkSCIjI9FoNCUG8lRK8cILLxAREYGXlxcDBw7k0KFDTstkZGSQlJSEv78/gYGBPPTQQ+Tm5jots2vXLvr06YOnpyfR0dG8/vrr1b1rZZoxYwbdunXDz8+P0NBQ7rjjDg4ePOi0TEFBAY899hgNGzbE19eXu+66q8Sdp0+ePMmIESPw9vYmNDSUp59+GovF4rTM2rVr6dKlC0ajkWbNmjFnzpzq3r0yffDBB3To0MFxc63ExESWL1/umO+O+1yaV199FY1G43TDTXfc9+nTp6PRaJwerVq1csx3x30udubMGe6//34aNmyIl5cX7du3Z+vWrY757vh3LS4ursTnrdFoeOyxxwD3/bytVivPP/88jRs3xsvLi6ZNm/Lyyy87jeVUqz5vVYstWLBAeXh4qE8//VTt3btXTZw4UQUGBqq0tDRXl1Zuy5YtU3/729/UokWLFKAWL17sNP/VV19VAQEB6ptvvlG//fabGjVqlGrcuLHKz893LDN06FDVsWNHtWnTJvXLL7+oZs2aqfvuu88xPysrS4WFhamkpCS1Z88e9cUXXygvLy/1n//8p6Z208mQIUPU7Nmz1Z49e9TOnTvV8OHDVUxMjMrNzXUs8+ijj6ro6Gi1atUqtXXrVtWjRw/Vs2dPx3yLxaLatWunBg4cqHbs2KGWLVumgoOD1dSpUx3LHD16VHl7e6spU6aoffv2qXfffVfpdDqVnJxco/tb7Ntvv1Xff/+9SklJUQcPHlTPPfecMhgMas+ePUop99zna/36668qLi5OdejQQT3xxBOO6e6479OmTVNt27ZV586dczzOnz/vmO+O+6yUUhkZGSo2NlaNHz9ebd68WR09elStWLFCHT582LGMO/5dS09Pd/qsV65cqQC1Zs0apZT7ft6vvPKKatiwoVq6dKk6duyY+uqrr5Svr696++23HcvUps+7Voea7t27q8cee8zx3mq1qsjISDVjxgwXVlV514Yam82mwsPD1RtvvOGYlpmZqYxGo/riiy+UUkrt27dPAWrLli2OZZYvX640Go06c+aMUkqp999/XzVo0EAVFhY6lnn22WdVy5Ytq3mPyic9PV0B6qefflJK2ffRYDCor776yrHM/v37FaA2btyolLKHQa1Wq1JTUx3LfPDBB8rf39+xn88884xq27atU1v33HOPGjJkSHXvUrk1aNBAffzxx/Vin3NyclTz5s3VypUrVd++fR2hxl33fdq0aapjx46lznPXfVbK/reld+/eZc6vL3/XnnjiCdW0aVNls9nc+vMeMWKEevDBB52m/e53v1NJSUlKqdr3edfa009FRUVs27aNgQMHOqZptVoGDhzIxo0bXVhZ1Tl27BipqalO+xgQEEBCQoJjHzdu3EhgYCBdu3Z1LDNw4EC0Wi2bN292LHPLLbfg4eHhWGbIkCEcPHiQS5cu1dDelC0rKwvAMaDZtm3bMJvNTvvdqlUrYmJinPa7ffv2jjtVg32fsrOz2bt3r2OZq7dRvExt+P2wWq0sWLCAvLw8EhMT68U+P/bYY4wYMaJEfe6874cOHSIyMpImTZqQlJTEyZMnAffe52+//ZauXbsyevRoQkND6dy5Mx999JFjfn34u1ZUVMTcuXN58MEH0Wg0bv159+zZk1WrVpGSkgLAb7/9xrp16xg2bBhQ+z7vWhtqLly4gNVqdfoFAAgLCyM1NdVFVVWt4v243j6mpqYSGhrqNF+v1xMUFOS0TGnbuLoNV7HZbEyePJlevXo5hsZITU3Fw8ODwMBAp2Wv3e8b7VNZy2RnZ5Ofn18du3NDu3fvxtfXF6PRyKOPPsrixYtp06aNW+8zwIIFC9i+fbtjzLerueu+JyQkMGfOHJKTk/nggw84duwYffr0IScnx233GeDo0aN88MEHNG/enBUrVvDHP/6RP//5z3z22WdA/fi79s0335CZmcn48eMd9bjr5/3Xv/6Ve++9l1atWmEwGOjcuTOTJ08mKSkJqH2fd6WGSRCivB577DH27NnDunXrXF1KjWjZsiU7d+4kKyuLr7/+mnHjxvHTTz+5uqxqderUKZ544glWrlyJp6enq8upMcX/UwXo0KEDCQkJxMbG8uWXX+Ll5eXCyqqXzWaja9eu/POf/wSgc+fO7Nmzh1mzZjFu3DgXV1czPvnkE4YNG0ZkZKSrS6l2X375JfPmzWP+/Pm0bduWnTt3MnnyZCIjI2vl511rj9QEBwej0+lK9B5PS0sjPDzcRVVVreL9uN4+hoeHk56e7jTfYrGQkZHhtExp27i6DVeYNGkSS5cuZc2aNTRq1MgxPTw8nKKiIjIzM52Wv3a/b7RPZS3j7+/vsi8VDw8PmjVrRnx8PDNmzKBjx468/fbbbr3P27ZtIz09nS5duqDX69Hr9fz000+888476PV6wsLC3HbfrxYYGEiLFi04fPiwW3/eERERtGnTxmla69atHafe3P3v2okTJ/jxxx95+OGHHdPc+fN++umnHUdr2rdvz9ixY3nyyScdR2Vr2+dda0ONh4cH8fHxrFq1yjHNZrOxatUqEhMTXVhZ1WncuDHh4eFO+5idnc3mzZsd+5iYmEhmZibbtm1zLLN69WpsNhsJCQmOZX7++WfMZrNjmZUrV9KyZUsaNGhQQ3tzhVKKSZMmsXjxYlavXk3jxo2d5sfHx2MwGJz2++DBg5w8edJpv3fv3u30D2HlypX4+/s7/qAmJiY6baN4mdr0+2Gz2SgsLHTrfR4wYAC7d+9m586djkfXrl1JSkpyvHbXfb9abm4uR44cISIiwq0/7169epW4RUNKSgqxsbGA+/5dKzZ79mxCQ0MZMWKEY5o7f94mkwmt1jkq6HQ6bDYbUAs/7wp1K65hCxYsUEajUc2ZM0ft27dPPfLIIyowMNCp93htl5OTo3bs2KF27NihAPXWW2+pHTt2qBMnTiil7JfCBQYGqiVLlqhdu3ap22+/vdRL4Tp37qw2b96s1q1bp5o3b+50KVxmZqYKCwtTY8eOVXv27FELFixQ3t7eLrv08Y9//KMKCAhQa9eudboE0mQyOZZ59NFHVUxMjFq9erXaunWrSkxMVImJiY75xZc/Dh48WO3cuVMlJyerkJCQUi9/fPrpp9X+/fvVe++959LLH//617+qn376SR07dkzt2rVL/fWvf1UajUb98MMPSin33OeyXH31k1Luue9/+ctf1Nq1a9WxY8fU+vXr1cCBA1VwcLBKT09XSrnnPitlv2xfr9erV155RR06dEjNmzdPeXt7q7lz5zqWcce/a0rZr8CNiYlRzz77bIl57vp5jxs3TkVFRTku6V60aJEKDg5WzzzzjGOZ2vR51+pQo5RS7777roqJiVEeHh6qe/fuatOmTa4uqULWrFmjgBKPcePGKaXsl8M9//zzKiwsTBmNRjVgwAB18OBBp21cvHhR3XfffcrX11f5+/urCRMmqJycHKdlfvvtN9W7d29lNBpVVFSUevXVV2tqF0sobX8BNXv2bMcy+fn56k9/+pNq0KCB8vb2Vnfeeac6d+6c03aOHz+uhg0bpry8vFRwcLD6y1/+osxms9Mya9asUZ06dVIeHh6qSZMmTm3UtAcffFDFxsYqDw8PFRISogYMGOAINEq55z6X5dpQ4477fs8996iIiAjl4eGhoqKi1D333ON0rxZ33Odi3333nWrXrp0yGo2qVatW6sMPP3Sa745/15RSasWKFQoosS9Kue/nnZ2drZ544gkVExOjPD09VZMmTdTf/vY3p0uva9PnrVHqqtsCCiGEEELUUbW2T40QQgghREVIqBFCCCGEW5BQI4QQQgi3IKFGCCGEEG5BQo0QQggh3IKEGiGEEEK4BQk1QgghhHALEmqEqAPGjx/PHXfc4eoyXKa27f+cOXNKjMhckz755BMGDx5cbdsvKioiLi6OrVu3VlsbQlQHCTWi3jh16hQPPvggkZGReHh4EBsbyxNPPMHFixddXZrD8ePH0Wg07Ny502n622+/zZw5c1xSU120du1aNBpNiQEGKyMuLo6ZM2c6TbvnnntISUm56W1XRkFBAc8//zzTpk2rtjY8PDx46qmnePbZZ6utDSGqg4QaUS8cPXqUrl27cujQIb744gsOHz7MrFmzHAOkZmRkVGv7RUVFN7V+QECAS48MCGdeXl6Ehoa6pO2vv/4af39/evXqVa3tJCUlsW7dOvbu3Vut7QhRlSTUiHrhsccew8PDgx9++IG+ffsSExPDsGHD+PHHHzlz5gx/+9vfHMvGxcXx8ssvc9999+Hj40NUVBTvvfee0/YyMzN5+OGHCQkJwd/fn1tvvZXffvvNMX/69Ol06tSJjz/+mMaNG+Pp6QlAcnIyvXv3JjAwkIYNG3Lbbbdx5MgRx3rFI5p37twZjUZDv379gJKnXwoLC/nzn/9MaOj/t3f/MVHXfxzAn8d5/LjjsOMWP9W7gCJQcDD6AUYqg45YTCqEaKuzMdoqIS9l/bJZ+OMcFE5L3WVK1gycYq0FXo4ccSE0TGTFbwhEy18haTckBF7fP9h9vny8QxGlDF+PjY3P++fn/ebDPq993u+7jxdcXV3xyCOPoK6uTsi3Pan47rvvEBUVBblcjpiYGLu3K1/t1KlTyMjIgKenJxQKBaKiovDjjz8K+du3b0dgYCCcnZ0RHByMzz//XFRfIpHAZDLhiSeegFwuR0hICGpqatDR0YFFixZBoVAgJiZGNGbbXJlMJsyePRtyuRxpaWm4ePHiuOc5MjICo9GIe+65B25ubpg/fz72798PYPRp1+LFiwEAKpUKEokEy5Ytu249RxYtWoQTJ07AYDBAIpFAIpEAsF9+so1h165dmDNnDtzd3fHyyy9jeHgY+fn58PHxgZeXF9avXy9q/3rXkSMlJSVITk4Wpdmujw0bNsDb2xt33XUX8vLyMDQ0hNzcXHh6emLWrFkoKioS6gwODmL58uXw9fWFq6srNBoNjEajkK9SqbBgwQKUlJRc83wYu61M5gVXjP2X9Pb2kkQioQ0bNjjMz8rKIpVKRSMjI0REpNFoSKlUktFopNbWVtqyZQtJpVLRyynj4+MpOTmZ6urqqK2tjVauXElqtZp6e3uJiGjNmjWkUCgoMTGRjh07Rg0NDUREtH//fiotLaX29naqr6+n5ORkCgsLo+HhYSIafQMyAKqoqKDTp08L7en1elqyZInQf05ODvn5+VF5eTk1NjaSXq8nlUollLe9SPWhhx6iyspKamxspNjYWIqJiRl3nv766y8KCAig2NhYslgs1N7eTnv37qUjR44QEdGBAwdIJpPR1q1bqbW1lT744AOSSqV0+PBhoQ0A5O/vT3v37qXW1lZKSUkhrVZLcXFxZDabqampiR5++GFKTEwU6tjmKi4ujurr6+n777+noKAgevbZZ4UyV49/3bp1dP/995PZbKbOzk4qKioiFxcXqqyspKGhISotLRVePHj69Gn6888/r1vPkd7eXpo1axbl5eUJb5snIioqKqKZM2eKxuDu7k6pqanU2NhIX3/9NTk7O5NOp6Ps7GxqaWmhXbt2EQDRS3mvdx05MnPmTCopKRGl6fV6UiqV9Morr1BLSwvt3LmTAJBOp6P169dTW1sbrV27lmQyGZ08eZKIiAoKCmj27NlUVVVF3d3dZLFY6IsvvhC1+/rrr9PChQvHPRfGbjcc1LBpr7a2lgDQl19+6TC/sLCQANDZs2eJaDSoGXvTJRp9I/Pjjz9OREQWi4U8PDxoYGBAVCYwMJBMJhMRjd7kZDIZnTt37prndv78eQJAP//8MxERdXV1EQCqr68XlRt7U7darSSTyWjPnj1C/uDgIPn5+VF+fj4R/T+oqaioEMqUlZURALp8+bLDczGZTKRUKse9ocbExFBWVpYobenSpZSUlCQcA6DVq1cLxzU1NQSAdu7cKaQVFxeTq6urcLxmzRqSSqV06tQpIe3gwYPk5OQkBBFjxz8wMEByuVwItmwyMzMpIyNDNP6+vj4hfyL1HNFoNLRp0yZRmqOgRi6X06VLl4Q0nU5HWq1WCFiJiIKDg8loNBLRxK6jq/X19REAqqqqEqXr9XrSaDR2fcXGxgrHQ0NDpFAoqLi4mIiIsrOzKS4uTgjmHdm8eTNptdpx8xm73fDyE7tj0A28kD46OtruuLm5GQDQ0NAAq9UKtVoNd3d34aerq0u0rKLRaHD33XeL2mlvb0dGRgYCAgLg4eEBrVYLAOjp6ZnwuXV2duLKlSuiPRUymQwPPvigcI424eHhwu++vr4AgHPnzjls9/jx44iIiICnp6fD/ObmZrt9HAsWLLhmn97e3gCAsLAwUdrAwAAuXbokpM2ZMwf+/v7CcXR0NEZGRhwul3V0dKC/vx8JCQmi+f/ss89E83+r6k2UVquFUqkUjTM0NBROTk6iNNv8T/Q6Guvy5csAICxnjjV37ly7vsbOu1QqhVqtFvpftmwZjh8/juDgYOTk5ODQoUN2bbq5uaG/v/9GpoGxf9WMf/sEGJtqQUFBkEgkaG5uxpNPPmmX39zcDJVKZReAjMdqtcLX1xeVlZV2eWP3WSgUCrv85ORkaDQa7NixA35+fhgZGcG8efNueiPxeGQymfC7bT/IyMiIw7Jubm5T1ueNnMf1WK1WAEBZWZkoEAIAFxeXW15vosaOERgdp6M027gneh2NpVarIZFI0NfXd9P9R0ZGoqurCwcPHkRFRQXS0tIQHx8v2mN04cKFCf9fMHY74KCGTXtqtRoJCQnYtm0bDAaD6OZ95swZ7NmzB88//7xwswWA2tpaURu1tbUICQkBMHozOHPmDGbMmCE8aZmI3t5etLa2YseOHYiNjQUA/PDDD6Iyzs7OAIDh4eFx27Ft1K2uroZGowEAXLlyBXV1dVixYsWEz+dq4eHh+OSTT3DhwgWHT2tCQkJQXV0NvV4vpFVXVyM0NHTSfdr09PTg999/h5+fH4DR+XZyckJwcLBd2dDQULi4uKCnpwcLFy502J6jeZxIvfHautbfY7Imcx05OzsjNDQUTU1Nt+R7ajw8PJCeno709HSkpqYiMTFR9Pf/5ZdfEBERcdP9MPZP4eUndkf46KOP8Pfff0On06GqqgonT56E2WxGQkIC/P397T6VUl1djfz8fLS1tWHr1q3Yt28fXn31VQBAfHw8oqOjkZKSgkOHDqG7uxtHjhzB22+/fc0vK1OpVFCr1fj444/R0dGBw4cP47XXXhOV8fLygpubG8xmM86ePevwE0AKhQIvvfQScnNzYTab0dTUhKysLPT39yMzM3PSc5SRkQEfHx+kpKSguroav/76K0pLS1FTUwMAyM3Nxaeffort27ejvb0dhYWFOHDgAFatWjXpPm1cXV2h1+vR0NAAi8WCnJwcpKWlwcfHx66sUqnEqlWrYDAYsHv3bnR2duLYsWP48MMPsXv3bgCjS38SiQTffPMNzp8/D6vVOqF6jmi1WlRVVeG3337DH3/8cdNjtZnsdaTT6eyC4ckoLCxEcXExWlpa0NbWhn379sHHx0f0lMhisUzpl/wxdqtxUMPuCPfeey+OHj2KgIAApKWlITAwEC+++CIWL16MmpoauycTK1euxNGjRxEREYF169ahsLAQOp0OwOgj/PLycjz66KN44YUXcN999+GZZ57BiRMnhD0kjjg5OaGkpAQ//fQT5s2bB4PBgIKCAlGZGTNmYMuWLTCZTPDz88OSJUsctrVx40Y8/fTTeO655xAZGYmOjg58++23UKlUk54j20fevby8kJSUhLCwMGzcuBFSqRQAkJKSgs2bN+P999/H3LlzYTKZUFRUJHzs/GYEBQXhqaeeQlJSEh577DGEh4dj27Zt45Zfu3Yt3nnnHRiNRoSEhCAxMRFlZWXCR+L9/f3x3nvv4Y033oC3tzeWL18+oXqO5OXlobu7G4GBgbd0KWay11FmZibKy8uv+ZH3iVAqlcjPz0dUVBQeeOABdHd3o7y8XNiXU1NTg4sXLyI1NfWm+mHsnyShG9k9ydgdQKvVYsWKFTe1lMMm7t1338VXX31l9y3KbHxLly5FZGQk3nzzzSnrIz09HfPnz8dbb701ZX0wdqvxkxrGGPuPKSgogLu7+5S1Pzg4iLCwMBgMhinrg7GpwBuFGWPsP0ar1SI7O3vK2nd2dsbq1aunrH3GpgovPzHGGGNsWuDlJ8YYY4xNCxzUMMYYY2xa4KCGMcYYY9MCBzWMMcYYmxY4qGGMMcbYtMBBDWOMMcamBQ5qGGOMMTYtcFDDGGOMsWmBgxrGGGOMTQv/AyfLqprK4ZPhAAAAAElFTkSuQmCC",
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P5IorriAzM5OdO3cyb948TjnlFP7xj39E/LxCNJUkOcLQLrjgAlatWsWTTz7Jp59+ysyZM7Hb7fTr14+nn376sBemFStWMGHCBEwmE0lJSeTn53P++eeH92lpDYMGDWLkyJHMnDmTqVOncsYZZ/Dqq6/y2GOPcdttt9GxY0cef/xxtm/f3uQkp1+/fixYsIApU6Zw//33065dOx588EH27dt32GO+8sordOrUidmzZzN37lxycnKYOnUq06ZNi8TTPaY//elPfPbZZ/z73//G7XbToUMH/va3v/GXv/wlfJt33nmHP/7xj7zwwgsoisLZZ5/Nl19+SV5eXovEdM8997Bq1SqmT59OVVUVZ555Ji+++CLx8fEtcj5QR49ycnJ49913mTt3LqNGjWLOnDl07979uDsp22w23njjDaZOncrNN9+Mz+fj9ddfb5EkB2DcuHHk5eXx2GOP8eSTT+J2u2nbti0jRoxg4sSJLXJOIZrKpGhZ/SaEEDqxePFiTj/9dD744IN6rTi0smLFCk444QTeeustxo8fr3U4QhiS1OQIIYTGamtrD7tuxowZmM3mw3ZkFkI0nExXCSGExp544gmWLVvG6aefjtVq5csvv+TLL7/kxhtvPGzvGiFEw0mSI4QQGhs2bBgLFy7k4Ycfprq6mvbt2/PAAw/w17/+VevQhDC0Rtfk/Pe//+XJJ59k2bJl7Nu3j7lz53LRRRcd8z6LFy9mypQprFmzhvz8fO69997DVr0IIYQQQkRSo2tyampq6N+/Py+88EKDbr9t2zbOPfdcTj/9dFasWMFtt93GDTfccNh+HUIIIYQQkdSs1VUmk+m4Izl33XUX8+bNq7f9/hVXXEF5eXm4MZ0QQgghRKS1eE3OkiVLDtv+e/To0dx2221HvY/b7a7XpDAQCFBaWkp6enqrbmEuhBBCiKZTFIWqqiry8vKO2Jy4pbV4klNYWEh2dna967Kzs6msrKS2than03nYfaZPn86DDz7Y0qEJIYQQohXs2rWLdu3atfp5dbm6aurUqUyZMiX8eUVFBe3bt2fXrl0kJyc3+/Fnfr2FFxbXb37XNSuRgR3akJYQR3ycBWecBZvFjAkwm0yYzerRZDJhNoUuAxz83Gwm+PXgdai3MQVvYzJDz9xk4uN0+W2PPEWBmgNQVw615VBXdvDoKoPaUnCVQm1Z8Bj8nMM7SzeeCSxxYLWrH5bghzXu4OfWuN8cQ5cdYLGB1RG8rRVMluCHGczB41EvW4KXTQfvYzKr8bQfEoHn1nDrStfxS9EvlLhKKKkroaS2hAN1B6h0V1LhqSCgNO17bTVbiTPFEWdRP5w2J06rE7vFjs1kw2K2YDFZwkezyYzFbMFqsmIxq5+bMEHw78OE+rdV7/jb60OXf/v5oY9zpMf4zXW2mjqSd1eQuKuUhF0l2HcUY9tVhMnrO+rztaSkYMnOxpKcjMlhx2y3Y4qzY3Y4MMfHY4qLw2S1YrLZMMXFgdUKZhMm9Z9C8HcAMAWvw4T6DyPYtiF8nRqn+s8leDz0uuDjHHzcI1wX+n6EHjd4nckcejxz+LEdXTqr8cYIV6WH4p1VuGs8eGr9eNw+vHV+vHV+PG4/pXtrKCus0TrMZjOFf/4E/2aCfx9mU/hrJrN6Q5vdQnK6naQ0J0npDvqd3g6zJXIjLpWVleTn55OUlBSxx2yMFn+1zcnJOax5YlFREcnJyUccxQGw2+31ujGHJCcnNzvJ+XTFHmYu2YvZHo/FbOKfVw1keNcMHDZLsx5XHMJbByvehu+fh7JG9hiyA+Y4yOwB2b0howvYU8DmVD+sDrA5wBYfTEKCiUj4GEpobMF/7rFnU9kmfiz8kdlrZlNYU3jkG5nB5DCRYEkgxZ6C0+rEYXXQKaUTPdJ6kO5MpyC5gKS4JOwWeziZiTOrR7NJ//uIKj4fnu3bqduwAff6DdRtWI97w0Z8R2jmCmBOSsLevTv27t2IKyjAlp2NrV074vLzsaSmtm7wotmUgELR9koqD9RSXepmy/JiindUHfd+zrgEnMlxxCfFEZ9sw5EYhy3OjCXOgtVqxhJnxmozY7VZsNjUy+Gj1YzJYjqYWISOwUTVFEw6OPRrh1x/+NcP/1o4gQk/7uHn0SOt4mrxJGfo0KF88cUX9a5buHAhQ4cObelTH2br/mrunasWQF89tAPTzu+NxazPXwjDOrAF3r0CSjYevM6RAs429T/i09UPZxrEpx38PDFbPVpiZLQrgn7Y9wMvr3qZHwt/rHd9h+QOjGw3ksz4TDKdmWQ4M0ixp5Acl0xWfBYWs/ETfH95OXXrN+DeuEE9rl+Pe/NmFI/niLe35edj794NR/ce2Ht0x9G9O7Z27YKjLMLI/N4AB/ZWs2TuFnavLzvs62l5CSSlOYhzWLA5rMQ5LMQ5rcQ5rNgTrBT0ycCRaNMgctESGv1KUl1dzebNB6d6tm3bxooVK0hLS6N9+/ZMnTqVPXv28OabbwJw8803849//IM777yT6667jq+++or333+fefPmRe5ZNNDMxVuocvvo2zaF+8/rJQlOJPnc8H+3wpq54KuDxBwYfjucOAHiErSOLqp5/B7+/M2fWbxrMQBmk5mTsk+iR1oPzut8Hj3SemgaXyQpPh+eHTuoW6+OyoRHZwqPPGJlio/H0a0b9u7dcfTojr17D+zdumJJTGzlyEVLqSl3s27JPravKqHqQB2uyvqJbW6XFJLSHSSmOug2OJv0tvKzjyWNTnJ+/vlnTj/99PDnodqZa665htmzZ7Nv3z527twZ/nrHjh2ZN28et99+O8899xzt2rXjlVdeYfTo0REIv3FKa9Rf/isG52ON4JxjzKsthzfOh8Jgd+usXjBhLiTlaBpWLPhm1zc88dMT7KxS/+ZOyTuFaUOnkZuYq3FkzaMoCt7du3Fv2oxn+3aqvlpEoMaFZ+tWlENWXh7K1q6dOirTrfvB0Zn8fBmdiSJet5/CbRVs+rGI8iIXFSW1uCoOH62Lc1jI65rKiaM7kNsltfUDFbrR6CRn5MiRHGtrndmzZx/xPr/88ktjTxVxFbVeAFKdsVNo1yoW/PVggjPiDjj9r8HiRtFSan21PPXTU7y/8X1ALQS+ZcAtXNv7WqxmY071+fbvp+b776lZ8gPV332Hv6TkiLczxcfj6NpVrZ/p0R1Hjx7Yu3WT0ZkoVl7s4qs311G4pYLQy4/ZBnHxZhwpZjLaJdLphEzSchNISI3DHm8L14DU1dVpGHn0s9lsWCz6nfI25n/DJvL61ZUkDpu8AEdM6VZY+a56eewb0PsiTcOJBYqicOc3d7J492IAJvSawPV9rifdma5tYE3kKytjx/ir8GzdWv8LNhv2zp2J61gQLATOIX7wYOIKOsjoTIwI+AN89/4m1ny3l4BfzW7ik+PodnoyiTkmLFYzZnNw5RguKj0uKou1jTkWpaamkpOTo8ui55hKcnwB9Y9EanEi6D8PguKHghHQ60Kto4l6iqLwwooXwgnO9BHTOa/TedoG1QxV//kPe++6m0CNumzXnJhI6qWXknj66TgH9Md8hFWWIropikJZoYvCrRWs/mYP+3eqK6Iy8hMZfUMfagMVlJeXk5WVRXx8vC5fWGOFoii4XC6Ki9XMMjdXf9PkMZXk+INJjlXeBUbG/g2w9hP18tl/i9kl263p8Z8e5+11bwNwx6A7DJ3glH3wAYX33Q+o9TRZd9xB8pjWr9UTrc9V6aGi2EV1mTv4UUd1uZvq0jqqSuuorfLWu/2oib3oNjibQCDAvo1qgpOebsyRy2gT2gqmuLiYrKws3U1dxWSSIyM5EfLTq+oxfwjkDdA0lFhQ6akMJzhX9byKq3tdrXFETRNwuyl+6mnK3noLgKQxY8h74nHMMbQpXbSrqXBTW+WhttpLXbWX2iovFftdHNhTQ+ne6sOSmN8yW03kdkohs30SHftnkNe1DQBer3q/+Pj4Fn8OouFCPw+v1ytJjpbCIzkWSXKareYA/PhP9fKwP2kbS4yYvnQ6APlJ+dw1+C6No2kaRVHYfctkar77DoDk884j74nHpcYmCgQCCpt/LuKHT7ZSVXqcYl8TJKU5SEpzkJBqJynNTkKqg8Q2dhLb2GmTk4DNfvQXS5mi0hc9/zxiKsnxBtTCY7OOfyCGsTHYQT65HfQ4V9tYYsDcTXP5fOvnAEweMFnjaJqu5Pl/hBOczNtuI/2mG3X9D1I03OK317Puf/sOXmGCtNwEHAk2nEk2EtMcpOclkt424bhJjBCRElNJTnWd2psm0R5TT7tlrA9u5tj7IqnFaWEur4unfn4KgG5tuvG7jr/TOKKmqfrPfyh58UUAMiZPJuPmmzSOSESCz+PnP6+vZcsv+wHod0Y7CvpkkN8rTePIhICYGiP2yXRVZBSthQ3BJKfn+drGEgNmrpxJpacSp9XJP8/6pyFHPtxbt7HnTnWKLfm888icfIvGEYlIKNpeyTsPLg0nOEMu6MiIy7pJgnME1157bbgflc1mIzs7m7POOovXXnuNQODwRrmjR4/GYrHw008/Hfa1/fv38/vf/5727dtjt9vJyclh9OjR/O9//2uNp2IoMTWkEQgVHhvwRUJXFj2kHtsOVIuORYt5e93bzF4zG4A/D/wzGc4MbQNqAiUQYM+tt6K4XDh69SL3kb9pHZKIgJWLdrHkky34vQESUu2MHN+dgr7G+/1sTWPGjOH111/H7/dTVFTE/PnzufXWW/nwww/57LPPsFrVl+SdO3fy/fffM3nyZF577TVOOumkeo9zySWX4PF4eOONN+jUqRNFRUUsWrSIAwcOaPG0dC2mkhy/Iqurms3vhR3Bdwsn3SBTVS1EURSeWfZMOMEZkjuEy7pfpm1QTXTg5Vdwb9qEKS6Ots/NkL1vDC7gD/DpjBXs3VQOQFaHJC64dQD2eGlqeTyhUReAtm3bcuKJJ3LyySdz5plnMnv2bG644QYAXn/9dc477zx+//vfc/LJJ/PMM8+El2qXl5fz7bffsnjxYk477TQAOnTowODBg7V5UjoXU9NVgdB24JLkNN2GL8FdqXYP7ztW62ii1mdbPgsnOFf2uJKXz3rZkNNUdWvXsv/55wHIuutO4vLzNY5INMfWFft5/c7/hROc9r3T+H93DtQ0wVEUBZfH1+ofx2pv1BhnnHEG/fv35+OPPw4/n9dff52rrrqKHj160KVLFz788MPw7RMTE0lMTOSTTz7BfZQ+buKgmBrJkemqCNj6tXrsdQFY5J1bS3B5XTyz7BkALu12KfcMuUfjiJomUFPDnjvvBJ+PhFNOoc24cVqHJJph+68lfDnrV0B9ozjo3AJOOrejxlFBrddPr/sXtPp51z40mvi4yLyE9ujRg1Wr1P5///nPf3C5XOEm1ldddRWvvvoqEyZMAMBqtTJ79mwmTZrErFmzOPHEEznttNO44oor6NevX0TiiSYxNZITmq6SLTmaYdeP6rFghLZxRClFUbj6y6sprSsF4I8n/FHjiJpG8fnYdctkPJu3YI6PJ/fhhww5EiUOWvPtXgC6Dc7m+qdH6CLBiRaKooT/Pl577TUuv/zycH3OlVdeyf/+9z+2bNkSvv0ll1zC3r17+eyzzxgzZgyLFy/mxBNPPGKD7FgXMyM5iqKEu9fKSE4T1RyA4rXq5XaDtI0lSn206SM2lG0A4PkznifNYcxVKuVz5+L64QdMTiftXvgHtrw8rUMSzeCu9bFrnZp4nzi6A3FO/bx0OG0W1j7U+u1AnLbI7fOzbt06OnbsSGlpKXPnzsXr9TJz5szw1/1+P6+99hqPPPJI+DqHw8FZZ53FWWedxX333ccNN9zAtGnTuPbaayMWVzTQz29qCwstHwcpPG6yVXNACUBOX2hToHU0Uccb8PLMz+o01c39b2Zk/khtA2qimiVLKHrkUQAybr6ZhKFDNY5INEdlSS1zn16O3xsgNTuetLwErUOqx2QyRWzaSAtfffUVv/76K7fffjtvv/027dq145NPPql3m3//+988/fTTPPTQQ0dtm9CrV6/D7idiKMmpcfvCl438B6Gp1R+px36XaxtHFAooAe797l6qvFUk2hK5oe8NWofUJAGXi733/BWlro74QYNIu8aY/bWEat+WCj77+wp8bj8AJ5zdXqYdm8HtdlNYWFhvCfn06dM577zzuPrqqxk4cCCXXnopffr0qXe//Px8pk6dyvz58zn55JMZO3Ys1113Hf369SMpKYmff/6ZJ554ggsvvFCjZ6ZfMfNqf8hADjbZDLDxqopgzzL1cs8LtI0lCn206SO+2PYFoHYXt1uMucz6wKuv4du3D0tGBu1mzcLscGgdkmiiuhovX8xchc/tJyM/kRPOak/XQdlah2Vo8+fPJzc3F6vVSps2bejfvz9///vfueaaa/jll19YuXIlL7/88mH3S0lJ4cwzz+TVV19l1KhRDBkyhGeffZYtW7bg9XrJz89n0qRJ3HOPMRcptKQYSnIOZjnyTqQJtv0XUCC7L7TpoHU0UeWbXd/w6FJ1emfKwClc0u0SjSNqGm9xMQdeegmA7L/cgSVRX9MaonHWL9lHXbUXe7yVC287AUeCrKZsjtmzZx+zMHjgwIHHXJb+xRdfhC9Pnz6d6dOnRzK8qBVzSY7kN020d7l6lILjiNpVtYtbv74Vv+KnZ1pPJvSaoHVITVbywosoXi/27t1JvkBG+4xMURTWBptt9jsjXxIcYVixs5g6tBGgZDmNpyiwJbg/Tt4J2sYSZd5e9zZ+xU+7xHa8PuZ1rGZjvu8o/+QTyufMASDztltltNTgdvx6gLJ9NZjMJnoOy9U6HCGaLGaSnPBux/K/t/HKtsH+derlrmdrG0sU2VS2iffWvwfAvSffS4LNmNM7devWse/e+wBIvfIKkk4/XeOIRHOt+XYPAF0GZpGUJnVVwriM+baxCQ5OV0mW02hrP1WPbQdCsryriwRFUfjbD3/Dr/g5s/2ZnNL2FK1DahJfSQm7brwJfD7ih55Mzl//qnVIohkURWHd9/vY/qva6PGEs9prHJEQzRNDIznBJEfjOAxp47/VYx9jFsTq0bPLn2V58XLsFjt3D75b63CaRPH52Hv3VHz792PNzCTv0UcxWWPmfVNU2r2hjK//tR6A9HaJZLZP0jgiIZonZv4jKVKT0zSemoNLxzsM0zaWKHH3t3czb+s8AC7ucjE5CTkaR9Q0++69j5rvvgOg7YxnseXKKJ/RrQsWG1vtFn53U1+NoxGi+WJmJEeRmpymWTMX/G5IzIYcaf7WXPO2zgsnOOd0PMewzTdrfviBiuDuqjkPPUj8wIHaBiQiomh7JQCjb+hNSqZT42iEaL6YGckJTVfJSE4jrVJXzDDwWjBHrldLLCqpLeHhHx4GYHzP8dx10l2GrBHzlZWx7777AUg+5xzaXHaZxhGJSDiwp5rK/bUApLdN1DgaISIjZkZyZJ+cJvB5YOdS9XL3c7SNxeBqfbXc8c0d1Hhr6JTSib8M+oshE5xATQ07xl+Fd9cuzPHxZN8rhcbRQFEU5r+0GoDU7HhZUSWiRgwlOerRiC8smtn1gzpVFZ+hNuUUTRJQAjy69FGWFS3DhIm7B9+NxaCjYoV/ewTP1q2YExNp/9qrWNOM2SVd1Ld3UznlRS4ARl3bS+NoxJGMHDmS2267Lfx5QUEBM2bM0Cweo4iZJEcJT1dpHIiR7N+gHnP7y1RVMzy3/Dk+2fwJAPcNvY+hecbsyn3gtdepmDsXgHbP/x3ngAHaBiQiwlPr4z+vrwWg+5AcsjsmaxxRdLr22msxmUyHfYwZM6ZB9//44495+OGHG3XO0tJSxo8fT3JyMqmpqVx//fVUV1cf8/Z//OMf6d69O06nk/bt2/OnP/2JioqKRp1XT2KmJqfGo3bRddrkxbrBioMbAGZ21zYOA1t3YB2vrX4NgPuH3s/YbmM1jqhpyj+eS/ETTwCQ8Yc/kDDUmImaONy+LRVUl7lxJtkYPrar1uFEtTFjxvD666/Xu85ub1gz3rQmjJqOHz+effv2sXDhQrxeLxMnTuTGG2/knXfeOeLt9+7dy969e3nqqafo1asXO3bs4Oabb2bv3r18+OGHjT6/HsRMkuMPBACwWWNm8Kr5tn2jHvMHaxuHgb2w4gUAeqf35pKuxtxnqPbXXyl84AEA0q65how/TtY2IBFRVaV1gFqL40iUHlUtyW63k5Nz+JYR48aNw+/3MyfYGgXA6/WSm5vLM888w9VXX83IkSMZMGBAg6eo1q1bx/z58/npp58YNEjtOfj8889zzjnn8NRTT5GXl3fYffr06cNHH30U/rxz58488sgjXHXVVfh8PqwG3AfLeBE3UWgJucxWNVDZDjiwGTBBp5FaR2NIv+7/lf/u/i8Ajwx/BLPJeAl29X//y+7Jf0TxeEg45RSy7rpT6tqizPol6t44Bf0yNI6kiRQFvK7WP68tPmIrWcaPH8/YsWOprq4mMVFd2bZgwQJcLhcXX3xxkx5zyZIlpKamhhMcgFGjRmE2m1m6dGmDH7eiooLk5GRDJjgQQ0lOQDYDbJy1n6jHguHgbKNpKEa09sBarpl/DQoKJ2adSOfUzlqH1CiKolD27rsUTX8MvF4c/fqR99STmMzGS9TE0dXVeCnapu6N06l/psbRNJHXBY8ePirR4u7ZC3GN6zf3+eefh5OY8MPccw933nknCQkJzJ07lwkTJgDwzjvvcMEFF5CU1LRdpwsLC8nKyqp3ndVqJS0tjcLCwgY9RklJCQ8//DA33nhjk2LQg5hJchRZQt44hb+qx06naRuHwSiKwqurX2Xmipl4A166pHbhudOf0zqsRvEdOEDxE09S8anasyxpzBjyHn8McwNrB4Rx7N1YDkBimp3U7Hhtg4kBp59+OjNnzqx3XVpaGlarlcsuu4y3336bCRMmUFNTw6effsp7773XoMe9+eabeeutt8KfH6u4uKEqKys599xz6dWrFw8Ep6uNKGaSHFlC3kih/XFyB2gahtF8uuVTnluuJjWn5J3CA8MeINWRqm1QjVC3YSM7r74af0UFmM1k/XkKadddJ383UchT6+P7uZsBaN/TwFsB2OLVURUtzttICQkJdOnS5YhfGz9+PKeddhrFxcUsXLgQp9PZ4JVXDz30EHfccUe963JyciguLq53nc/no7S09Ih1QYeqqqpizJgxJCUlMXfuXGw249ZqxUySoyBLyBusdBtU7ARM0O4kraMxjJ2VO5mxbAYA43qM4+7BdxsmOVACASrnzaP4iSfxV1Rgzc0l75G/kTBM+pVFq00/F1FRrO5wfOKYAm2DaQ6TqdHTRno0bNgw8vPzmTNnDl9++SVjx45tcHKRlZV12NTU0KFDKS8vZ9myZQwMtl356quvCAQCDBky5KiPVVlZyejRo7Hb7Xz22Wc4HMbeGDJ2kpxw4bExXnQ0teN79dhuEDhTNQ3FKGavns1zy5/Dp/hIsiXxhwF/MEyC4923j71/uRPXzz8DEFdQQIe3/oU1w6CFqKJBKoItHHqNyJM+Va3E7XYfVg9jtVrJCP6tjRs3jlmzZrFx40a+/vrrZp2rZ8+ejBkzhkmTJjFr1iy8Xi+TJ0/miiuuCK+s2rNnD2eeeSZvvvkmgwcPprKykrPPPhuXy8Vbb71FZWUllZVqzVZmZiYWi/G2YIm9JMcYrzvaCi0d73CKtnEYxMr9K3l62dMA5CXkMXPUTFLsKRpH1TCeHTvYfuU4/KWlYDKRPmkS6ZNuwNLEYkdhDEpAYfuqEgCyC2Tzv9Yyf/58cnNz613XvXt31q9fD6hTVo888ggdOnTglFOa///37bffZvLkyZx55pmYzWYuueQS/v73v4e/7vV62bBhAy6Xujpt+fLlLF2qlir8dlpt27ZtFBQUNDum1hYzSc7B3lWS5RyTosDG+eplWTp+XNWeah74/gEA+qT34c1z3sRmNsb8dcDtZvfkP+IvLcWWn0+7fzyPo7ts/BgLdqw5QFmh+sLWtluqtsHEiNmzZzN79uxj3qZnz57hRTK/tXjx4nqfb9++/bjnTEtLO+rGf6C2hjj0fCNHjjzq+Y0q5pIcqck5jtoyqAtu4S2bAB7XzJUz2Vy+meS4ZB479THDJDgApW+8iXvTJsxJSXR48w1sv3mHKaJXqBanbbdUUjJlVZWIXjGz6UUoN5WBnOPY/B/1mNEtKor5WlKlp5J31qvvkh465SE6JHfQOKKGc2/bRsk//gFA1p1/kQQnxuxaXwpAWl7icW4phLHFTpITHsmRLOeYfgnutdDrQm3jMICPNn6EL+CjY0pHzmx/ptbhNJiiKBQ+9BCKx0P8oEGkXmLMdhOiaXxeP3vWlwHQ/eRjLyUWwuhiKMlRj1KTcxxFa9RjZ+O8aGvhp8KfeHHFiwBM7D1R42gap/LzebiW/ABWK9n33ye7GMeYwi0V+LwB4lPiyOogBeYiusVQTY56lBTnGIrWgKsETBbI6at1NLoUUAL8a+2/eOrnpwDo3qY7F3YxzqiXoiiUvKgmZ+nXXYejWzeNIxKtbdtKdVVVfo80edMnol7MJDmKFB4fX3h/nJPALnP1v+Xxe5j67VT+vePfAHRt05VHRzxqqMabdStX4tm2DZPDQfqkG7QOR7Syom2VrPp6NwAdB8g+SCL6xUyS4/EHALDK0PzR7fifeuwiU1W/5Qv4uGLeFWwq2wTAyHYjeeb0Zwy1mgqg5KWXAUg66yzZCycGbfqpCIC8rqnGbcgpRCPETJJT51WTHGec8XZsbDX7VqnH7D7axqEzASXAHd/cEU5w7h1yL5f3uFzjqBrPu3cv1V99BSYTGTffpHU4opXVVnvY+JO6227fke0wybC2iAExk+RIF/LjqNgDpVvUy9Kvqp7PtnzGop2LALh/6P2M7TZW44iapvq/3wLgPPFE7J07axyNaG2L39pAbZUXm91CQd90rcMRolXEzNxNeJ8cTaPQsQ1fqMe8EyBRhrFDFEXhgw0fAOoqKqMmOABVX38FQPxgSWJjTU25m20r9wNw9vW9scqItuGMHDmS2267Lfx5QUEBM2bM0Cweo4idJEf2yTm2LcFmcN3P0TYOnfl86+esKlmF1WxlQq8JWofTZN7iYmq++S8AKeefr3E0orXt2ViGokBm+yQK+knBsRauvfZaTCbTYR9jxoxp0P0//vhjHn744Uads7S0lPHjx5OcnExqairXX3891dXVDbqvoij87ne/w2Qy8cknnzTqvHoSQ9NV6lFynCPweWD7d+rlzmdoG4uOlNeVh5eK39zvZjLjjTvCVfXvhQDYe/XE3qmTxtGI1lZVWgdAcoZ0G9fSmDFjeP311+tdZ7fbG3TftLS0Rp9v/Pjx7Nu3j4ULF+L1epk4cSI33njjMftZhcyYMSMqthiInZGc8CXj/9Aibuf34K6AhEzIO1HraHTjqZ+forSulC6pXbiuz3Vah9Ms1V+rI3XJY36ncSSitXk9flZ9pS4bb9c9VdtgYpzdbicnJ6feR5s2bRg3bhyXX15/MYPX6yUjI4M333wTOHy66njWrVvH/PnzeeWVVxgyZAjDhw/n+eef57333mPv3r3HvO+KFSt4+umnee211xr9HPVGRnIErJ+nHguGgyyxB2BZ0TI+3fIpJkxMGzoNm8VYS8UP5a+uxvXTTwAknnaqxtGI1rZnQxmuSg+ORBs9T8nTOpyIUxSFWl9tq5/XaXVGbKRj/PjxjB07lurqahIT1T3KFixYgMvl4uKLL27SYy5ZsoTU1FQGDRoUvm7UqFGYzWaWLl161Md1uVyMGzeOF154gZwc47f9iJkkJ9SFXHKcIwhtAtjjPG3j0JF5W9XE78z2ZzIga4C2wTST68efUDwebPn52GWH45gS8AdYPn8HAAV90rFYo+9NTK2vliHvDGn18y4dt5R4W+M6uH/++efhJCbknnvu4c477yQhIYG5c+cyYYJa+/fOO+9wwQUXkNTE/awKCwvJysqqd53VaiUtLY3CwsKj3u/2229n2LBhXHihcXZyP5aYSXJC01VSePwbtWVQtFq93FHe5YcsL1oOwNC8oRpH0ny1v6jPJf6kk6Jijl003C8Ld7JvSwVmi4nep7bVOpyYd/rppzNz5sx616WlpWG1Wrnssst4++23mTBhAjU1NXz66ae89957DXrcm2++mbfeeiv8eUOLi3/rs88+46uvvuKXX35p0v31KGaSHGSfnCPbs0w9praHxKxj3zZGlNSWsKVC3TPorA5naRxN89X+qiax8QOl3irW7Nmgdhs/6dwCcjqlaBxNy3BanSwdt1ST8zZWQkICXbp0OeLXxo8fz2mnnUZxcTELFy7E6XQ2eOXVQw89xB133FHvupycHIqLi+td5/P5KC0tPeo01FdffcWWLVtITU2td/0ll1zCiBEjWLx4cYPi0ZOYSXLC++RIklPfmrnqse1AbePQkW93q5vm9UzrSRtHG42jaR4lEKBujdpZ3t6tu8bRiNZWurcGgHY9Gr8yxyhMJlOjp430aNiwYeTn5zNnzhy+/PJLxo4di83WsFrArKysw6amhg4dSnl5OcuWLWPgQPX/+1dffUUgEGDIkCNP7919993ccEP9nnZ9+/bl2Wef5XyDbj0RM0lOIBCqyZEsJ8zngV8/Ui8PuErbWHTkf3vVHl6DcwZrHEnzuTdsIFBVhcnhwNGzh9bhiFbkqfVRU+EBICndoXE0AsDtdh9WD2O1WsnIUPcuGjduHLNmzWLjxo18HVwR2VQ9e/ZkzJgxTJo0iVmzZuH1epk8eTJXXHEFeXlqAfqePXs488wzefPNNxk8eHB4xddvtW/fno4dOzYrHq1EXxXaUYSXkEuOc9CBTeCrBbNV9scJ8ga8fLPrGwB+19H4y61dy9V6HOcJAzBZY+Y9jQDW/k9dJpyc4SA+OU7jaATA/Pnzyc3NrfcxfPjw8NfHjx/P2rVradu2Laecckqzz/f222/To0cPzjzzTM455xyGDx/OSy+9FP661+tlw4YNuFyuZp9Lr2Lmv15oCbkUHh9i3f+pxw7DZOl40LaKbdT560iyJdEzvafW4TSbe8NGAJz9+msciWhta79Tk5yew3Kl4FwHZs+ezezZs495m549e4Z35/+t39bDbN++/bjnTEtLO+bGfwUFBUc9X8jxvq53MfPKJkvIj2DNJ+pRlo6H/VKkriooSCnAbDL+n0fdunUAOLrL0vFY4vX4KStU350X9DPuTt1CNJfx/4s3kryhCao5APvVF0C6G39aJlLmb58PwOiC0RpH0nwBjyec5Nh7Gn9USjRc8fZKAOKcVtrkGr8oV4imipkkJ7zjsbZh6EfxWvWY3E5dPi6o9dWycv9KAE7KMX6n7rrVa8Dnw9KmDXEFBVqHI1pRxX51B+DM9klYLDHzb16IwzTpt/+FF16goKAAh8PBkCFD+PHHH495+xkzZtC9e3ecTif5+fncfvvt1NXVNSngplKQLuT17PxBPeb01TYOHdlcthlvwEtSXBI90oy/Esm9eRMAjl69pCYjxmxbWQJAdkHTdssVIlo0OsmZM2cOU6ZMYdq0aSxfvpz+/fszevTowzYdCnnnnXe4++67mTZtGuvWrePVV19lzpw53HPPPc0OvjEC4Y1yWvW0+rUz2MqhoPkV/NFiebG6EqlPep+oqMdxr98AgL277I8TKxRFYelnW9m+Sk1ycrukahuQEBpr9H/yZ555hkmTJjFx4kR69erFrFmziI+PP2q30u+//55TTjmFcePGUVBQwNlnn82VV1553NGfSDs4XSVZDgE/7P5ZvVww/Ni3jSGr9q8ComOqCsC9eTMAce1lOjJW7Pj1AD9/sR2AXiPy6NAnXduAhNBYo5Icj8fDsmXLGDVq1MEHMJsZNWoUS5YsOeJ9hg0bxrJly8JJzdatW/niiy8455xzjnoet9tNZWVlvY/mcvv8AMRZJclh90/grgR7MuT00zoa3dhRqTYy7J4WHSMf7o3q8nFHnz4aRyJay8afigDoelI2I8d1l2lKEfMatU9OSUkJfr+f7OzsetdnZ2ezfv36I95n3LhxlJSUMHz4cBRFwefzcfPNNx9zumr69Ok8+OCDjQntuLz+AAB2qyWij2tI6z9Xj93GgFm+HwC+gI+91eq+Itnx2ce5tf75Kyrwl5cDYO9YoGksonXUVnvY8as6TdX95BxJcISgFVZXLV68mEcffZQXX3yR5cuX8/HHHzNv3jwefvjho95n6tSpVFRUhD927drV7DjC01Xydw9b1R196XKmtnHoyIriFVR5q0ixp9AptZPW4TSbd88eACypqZgTEjSORrSGLcuK8dT5Sc500q6HsXuuCREpjUpyMjIysFgsFBUV1bu+qKjoqF1N77vvPiZMmMANN9xA3759ufjii3n00UeZPn06gUDgiPex2+0kJyfX+2iug3XHMZ7lFK+HwlVgMkOnkVpHoxtFLvV3uktqF2zmhjXF0zPXcnVTQyk6jh0bfwz+Dp+YKcvGo9DIkSO57bbbwp8XFBQwY8YMzeIxikb9JcTFxTFw4EAWLVoUvi4QCLBo0SKGDh16xPu4XC7Mv2kZYLGoUyStuV20jOQEbV6oHgtGQNKRE9NYtK1iGwAdkjtoHElkuDeoK6ucJ56gcSSiNVSXudm3pQKA7ifnahyNOJJrr70Wk8l02MeYMWMadP+PP/74mDMgR1JaWsr48eNJTk4mNTWV66+/nurq6uPeb8mSJZxxxhkkJCSQnJzMqaeeSm1tbaPOrReN7l01ZcoUrrnmGgYNGsTgwYOZMWMGNTU1TJw4EYCrr76atm3bMn36dADOP/98nnnmGU444QSGDBnC5s2bue+++zj//PPDyU5rCO2TE9M5jqLA8jfVy13P0jYWnQmtrOrWJjraH3iL1Xf1tmxJZGPBkk/UlXRZHZJIy5XpSb0aM2YMr7/+er3r7HZ7g+6blpbW6PONHz+effv2sXDhQrxeLxMnTuTGG288Zj+rJUuWMGbMGKZOncrzzz+P1Wpl5cqVhw1WGEWjk5zLL7+c/fv3c//991NYWMiAAQOYP39+uBh5586d9b4Z9957LyaTiXvvvZc9e/aQmZnJ+eefzyOPPBK5Z9EQMpIDO76Hko1gscMJV2kdjW4oisLqktUADMoepHE0keHdqdaxxbXP1zgS0dLKi11sXKomtYMvMH49WTSz2+1HLO0YN24cfr+fOXPmhK/zer3k5ubyzDPPcPXVVzNy5EgGDBjQ4CmqdevWMX/+fH766ScGDVL/rz3//POcc845PPXUU+Tl5R3xfrfffjt/+tOfuPvuu8PXdTfwtHeTupBPnjyZyZMnH/Frv+2UarVamTZtGtOmTWvKqSImXJMTy1nO939XjwPGgVMKE0NKakuo8lYB0DGlo8bRNJ+iKPiCdXPWXJm6iHY7fj0AQEZ+Ih16x96+OIqioGgwlWJyOiP2ejJ+/HjGjh1LdXU1iYmJACxYsACXy8XFF1/cpMdcsmQJqamp4QQHYNSoUZjNZpYuXXrExy0uLmbp0qWMHz+eYcOGsWXLFnr06MEjjzzC8OHG3FOtSUmOEQUCMT5dVb4LNi5QLw89coIaq0JTVWmONOIscRpH03z+khICLheYTNiO8m5NRAef18+K/+wEoKBvhsbRaEOprWXDiQNb/bzdly/DFN+45qeff/55OIkJueeee7jzzjtJSEhg7ty5TJgwAVC7BVxwwQUkJTWtNUdhYSFZWVn1rrNaraSlpVFYWHjE+2zduhWABx54gKeeeooBAwbw5ptvcuaZZ7J69Wq6du3apFi0ZMxJtiYIlzjHapaz7jNAgfZDIaOL1tHoysoStSnnybknaxxJZLg3qT2rbPn5mBs43y+MaePSIqrL3DgSbfQd2U7rcMRxnH766axYsaLex80334zVauWyyy7j7bffBqCmpoZPP/2U8ePHN+hxb775ZhITE8MfTRVa8XzTTTcxceJETjjhBJ599lm6d+9+1K4GehczIzkx39Zh/Tz12K1hlfyxZEOpuhJpUE501OO4t6jvxuxdJJmNZp46H99/rBYc9zolj/hk449CNoXJ6aT78mWanLexEhIS6HKUv8vx48dz2mmnUVxczMKFC3E6nQ1eefXQQw9xxx131LsuJyfnsJ6SPp+P0tLSo275khuc3u7Vq1e963v27MnOnTsbFIvexE6SE1pdFYs5zi9vwY7/ASboe6nW0ejOiuIVAPRoY/zO4wDe3aGiY+lZFa38vgCfPPMLbpcPR6KNQecUaB2SZkwmU6OnjfRo2LBh5OfnM2fOHL788kvGjh2LzdawPbuysrIOm5oaOnQo5eXlLFu2jIED1em8r776ikAgwJAhQ474OAUFBeTl5bEhuAVFyMaNG/nd737XhGelvdhJcsIjOTGmqhAWBFto9L4YUmRI+1DVnmpcPhcAnVM7axxNZLi3qXv+xBVEx54/4nA7Vh9g/84qbHYLZ03shc0u7VmMwO12H1YPY7VaychQ66nGjRvHrFmz2LhxI19//XWzztWzZ0/GjBnDpEmTmDVrFl6vl8mTJ3PFFVeEV1bt2bOHM888kzfffJPBgwdjMpn4y1/+wrRp0+jfvz8DBgzgjTfeYP369Xz44YfNikcrMZPkhMTcSM6ih6GuAnIHwEUztY5Gd/ZUq+0PUu2pxNuM/24QwFekDlHb2sny8Wi1bZXao6rnKbm0j8EVVUY1f/788JRQSPfu3cO9H8ePH88jjzxChw4dOOWUU5p9vrfffpvJkydz5plnYjabueSSS/j73/8e/rrX62XDhg24XK7wdbfddht1dXXcfvvtlJaW0r9/fxYuXEjnzsZ8ExgzSU5od+WYqslZ9T6sDG76NOYxsDm0jUeHQklONDTlBFD8/nDfKmtmpsbRiJZQXuRiU7CFQ9tushWEUcyePZvZs2cf8zY9e/Y8aieA327Psn379uOeMy0t7Zgb/xUUFBzxfHfffXe9fXKMLOZWV8XMSE7pVvjkD6AEoP+V0D46Vg5F2oE6dY+RvMToWGrtKywkUF0NNhv2zrIxXLTx1PmY+8xy/L4AyZlOOvSVURwhjiV2kpzwjscxkuX8+AoEvJDVCy58MYayu8bZV70PgHRndLxYuIP7XMS1a4fJGjMDtTHB71eLjV0VHhwJNs66rpc04hTiOGLmLySmelfVlMAKdb8Fht8OBu050hp2V+0GoF1idBRk1wXn9u09jLsNuziyjUsL2b+zCqvdwu9u7ktOxxStQxJC92Lm1S9mupB7auC9cVBXDsltoecFWkeka2tL1wLQM62nxpFEhmfHDgDsnYxZJCiOrKq0jp/mbQdg8Hkdyeuaqmk8QhhFzIxnh2tyon0sZ96fYddSsMTBxf+UYuNjUBSF0tpSALIToqPw2LdPXZ4q7Ryix89fbOenz7cRCCg4k+Poc2pbrUMSwjBiJ8lRYmAzwJXvwcp31ctXvAsdR2gbj87VeGvCjTlzE6KjkWWoJseWFx3PJ5YpAYUtv+xn6WfqzzSrQxKnT+ghe+II0QgxlOSox6jNcbZ8BZ9PUS+fMAG6jtI2HgOo9lYDYDVbcVobv0W73vira/DtUwupHb17axyNaK7vPtzEqq/UmrG8rqlc/OcTNY5ICOOJvSQnGrOcA1vgnSvA74bMHnDu01pHZAg7K9VeLNnx2VGx6s67U63HsaSmYklO1jga0Rzrf9gXTnC6npTNaeOkkFyIpoidJCfcu8r4L2b1VBXCB9eoCU67k2D8h2CVztMNsb1yOwDtk6Kjx5N7s9qs0SY9qwxtw9JCFs1eB0BulxRGTeyF2Rxl/7eEaCUxs7rK51eTHHM0JTl1lfDqWVD4K9iT1bYNzlStozKMjWUbAeiZHh0rq+rWq031HN3lXb9Rbf+1hP+8rq7463xiFhfefoIkOAKAkSNHctttt4U/LygoYMaMGZrFYxQxk+TU+fwAJERL0V7FHnjxZCjfCXGJcP2/IaOr1lEZyoFadbfjaCk69mzZAoC9Z3R0U481VaV1zHthFQD2eCtnXy+b/UWTa6+9Vu2Y/puPMWPGNOj+H3/8MQ8//HCjzllaWsr48eNJTk4mNTWV66+/nurq6mPep7CwkAkTJpCTk0NCQgInnngiH330UaPOqyexM1115HYgxlRVBK+Mgqq9YLHD+A8gKzpGI1pTsUttZJlqT9U2kAjx7NoFQFwH6T5uNDUVbt5/5CcALDYzV04bglkSnKgzZswYXn/99XrX2e0NKy9IS0tr9PnGjx/Pvn37WLhwIV6vl4kTJ3LjjTces5/V1VdfTXl5OZ999hkZGRm88847XHbZZfz888+ccMIJjY5BazHzVxQVq6tqDsDix+HpbmqCE5cEE7+EDsO0jsxwFEVhZ5VaeNwh2fhJQaC2Fk+wYZ+9Y0dtgxGNoigKi9/eQF2Nl4RUO2PvHkRCitTVRSO73U5OTk69jzZt2jBu3Dguv/zyerf1er1kZGTw5ptvAodPVx3PunXrmD9/Pq+88gpDhgxh+PDhPP/887z33nvs3bv3qPf7/vvv+eMf/8jgwYPp1KkT9957L6mpqSxbtqxJz1lrsTOSg0GXVxWvgx9fho0LoHL3weuT28HFM6HdQO1iM7Aydxnl7nJMmChIKdA6nGbzbN8OgQDmxETZCNBgDuypZvuqEsxmE+f/sT/pbRO1DslQFEXB5wm0+nmtceaILWQZP348Y8eOpbq6msRE9ee/YMECXC4XF198cZMec8mSJaSmpjJo0KDwdaNGjcJsNrN06dKjPu6wYcOYM2cO5557Lqmpqbz//vvU1dUxcuTIJsWhtdhJcow2kqMoMG8K/Pxa/evTOkG3MTDqAVlF1QyhnlWZ8ZlRsUdO3Zo1ADj79dU4EtFYO9equ27n90qTBKcJfJ4AL936Tauf98bnTmv0xoyff/55OIkJueeee7jzzjtJSEhg7ty5TJgwAYB33nmHCy64gKSkpCbFV1hYSFZWVr3rrFYraWlpFBYWHvV+77//Ppdffjnp6elYrVbi4+OZO3cuXbp0aVIcWoudJCd4NMxAzo7vDyY4Xc6Ck26A9ifL6qkIqfKoOx2nORo/z61H7q3bAIiTnlWGs3t9GQD5PaPjd1Ec3emnn87MmTPrXZeWlobVauWyyy7j7bffZsKECdTU1PDpp5/y3nvvNehxb775Zt56663w58crLj6W++67j/Lycv7zn/+QkZHBJ598wmWXXca3335L377GexMVO0lOeCTHIFnOgU3qsf0wuOpDbWOJQpWeSgDirfEaRxIZ7i3qHjlxHQu0DUQ0Sk2Fm13BkZycztJVvCmscWZufO40Tc7bWAkJCUcdERk/fjynnXYaxcXFLFy4EKfT2eCVVw899BB33HFHvetycnIoLi6ud53P56O0tJScnJwjPs6WLVv4xz/+werVq+kd3DW9f//+fPvtt7zwwgvMmjWrQfHoScwkOWCw3lUH1OXAZMly4JawqUxNIqOhHgfAvUHd88feWUZyjMLvC/DZcysASEiJIzNfpqqawmQyRUU/r2HDhpGfn8+cOXP48ssvGTt2LDabrUH3zcrKOmxqaujQoZSXl7Ns2TIGDlRrN7/66isCgQBDhgw54uO4XC4AzOb6CZzFYiEQaP26p0iImSTHcDU5pWpTPjK6aRtHlKpwVwBqSwej85WU4AvOsdtlI0DD2LayhNK9NVisZkbf2FeWjMcAt9t9WD2M1WolIyMDgHHjxjFr1iw2btzI119/3axz9ezZkzFjxjBp0iRmzZqF1+tl8uTJXHHFFeQFFyfs2bOHM888kzfffJPBgwfTo0cPunTpwk033cRTTz1Feno6n3zyCQsXLuTzzz9vVjxaiZm/KsPV5LjUjepIio6N6vQmtEdOG0cbjSNpvpoflgJqgmNtY/znEyvWfrcHgL6ntyNXpqpiwvz588nNza33MXz48PDXx48fz9q1a2nbti2nnHJKs8/39ttv06NHD84880zOOecchg8fzksvvRT+utfrZcOGDeERHJvNxhdffEFmZibnn38+/fr148033+SNN97gnHPOaXY8WoihkZzgdJVRxnJqy9WjXYawW8K6UrU3UJdUY64YOFTtqpUAOE8YoG0gosHKCmvYtU4tOO45TN7IxILZs2cze/bsY96mZ8+e4deq31q8eHG9z7cH98U6lrS0tGNu/FdQUHDY+bp27WroHY5/K+ZGcoyS41AR3BMn1fgb1emN2+8Oj+R0SumkcTTNV/fragAcwUJBoX+L3lCT7JRMJ21yoqP4XQg9ip0kx0g1OR4XBJc445Tph0jbXLYZBYU0R5rhl5AH6uqoXa0mOfEDBx3n1kIP3LU+irarq/tG39gnYhvKCSEOFztJTvBoiH8oVfvUo9UJ8enaxhKFDtSp9U7Z8dnG+H04BveWLeD1YklJkeXjBrF/RyUokJTuIDO/aRu9CSEaJnaSnHBNjgF4ghs5OZINVCltHKGpqnSn8RNI90Z1KXxcp06GT9hiRVVpHQBtsmWaSoiWFjNJToghXgcq1FUXMlXVMkLLx40+VQXg2bEdgLjOxq8tihXbVpYA6kiOaJqjFecKbej55xEzSY5ipP6cXnU5H4lZx76daJIKj5rkJNgSNI6k+UJFx3Zp52AIm34uCic5Bf0yNI7GeEKb44WWPAt9OHQJut7EzhJyDLSE3OdWjxZpwNkStldsB6BDsrFXrikeD65lywCIHyTd6PVu9X/38N93NwDQe0QeBX0lyWksi8VCampquF1BfHy8TNNqSFEUXC4XxcXFpKamYrHob+fp2ElyjDSS41ZXXkiX8Zaxo3IHAB2TO2ocSfPUrlqFUleHOSEBR58+WocjjqFiv4tv3lETnPxeaZx6pexM3VShvku/7csktJOamnrUflhai7kkxxCqg3+8icZvOaBH5e5ywPi7HVd/9x0ACcOGYTLHzMyzIRXvULeEaJMTz/mT+2MyG+Hdlj6ZTCZyc3PJysrC6/VqHU7Ms9lsuhzBCYmdJCfcoNMA/1xC01V2WV4aabW+Wkrr1K7PeYl5GkfTPLXLfwEg/iTZH0fvQp3G23ZrIwlOhFgsFl2/uAp9iJm3f4baDNAfTHJkuiriKoNTgSZMJMclaxxN0ykeD7W/hJKckzSORhzLgb3VrF+i7n3VrqexRw+FMJrYSXKCRyMM5ISnq2yyj0ak7atRX2xyEnKMMap3FHUbNqB4vZgcDuw9emgdjjiGzT8XoyjQvncanQZkah2OEDElZpIcwiM5BnhhCxUeJ+mzkMvIXMHl+UYexQGoWfIDoDblNHKyFgvKi9TfuXY90uRnJUQri5kk52BNjsaBNIQ/WEwn01URF2rpkBhn7O7u1V9/DUDSyJHaBiKOq7xYTXJSs5waRyJE7ImZJMfrV5McQ9T8hffJidM2jii0tWIrAF1Su2gcSdP5q2uoXbUKgIThwzWORhyLoihUFNcCkJIl089CtLaYSXLqvH4A4uMMsKAsNF0lSU7EhQqPjbx83PXzT+D3Y83Kwt5ZdjrWs93ry/C6/ZhMkJIhIzlCtLaYSXJCDDFdVaNu+y775ERekasIgEyncQtAq76cD0DCKadoHIk4ns0/q79ved3aYLHF3L9bITQXM391ipEKjwM+9Rhn/N5KelPlUTdlS7WnahtIEyk+H1WLFgGQfN65Gkcjjqdwmzpy2G9kO40jESI2xU6SY8TCY7MBptYMpsqrJjlGbc5Zt3YtgepqTA4HCUOGaB2OOIa6Gi+le2sAyOmconE0QsSm2ElyDLUZoEc9WvTX0dXoCqsLAchNyNU4kqap/ua/AMQPGoTJKkmwnu0PtnJIyXQSnyz1dUJoIXaSnNAFvWc5fi8oapG0FB5HltvvDo/kGLXw2LV0KQCJp56qcSTieA7srQYgLc+Yo4ZCRIPYSXKCQzm6r8mpVgsVMdvAmaZtLFGmqEb93jqtTkPW5ARcLlwrVwIQP2SwxtGI4ykK1uNkdzT2xpNCGFnsJDnBo+5rckJTVVYHSGfpiNpfux+AFHuKIXeerfn+e/B6sWZmYu/WTetwxDEoikLxDjXJyciXRrtCaCV2XkWNUpMTKjq2SL1FpIVGctontdc4kqap+o+6qirx9NMNmaTFktoqL5UldQBkd5CRHCG0EjNJzsGRHJ2/OISLjqUeJ9JKatX9h4xYj3Po0vGks8/WOBpxPAd2q/U4yZlOHImygEAIrcROkqMYZAl5jTqlgk12R420UJKTHW+8TRZdy5cTqKrCHB9PgtTj6F51uTqKkyqtHITQVOwkOcGj3nMc3Oo7QBKytI0jCoVqcozYgbz0jTcBSDrrLEw2GRnQO69bXSFps1s0jkSI2BY7SU6oJkfvWU4gVJMj01WRtq9mHwAdkjtoHEnjuLdtozo4VdVm3JUaRyMaoqpUbbLrlKkqITQVO0mOUcZyAqE9cqTwONJ2Ve0CIDPeWH2rKud9AYCzf3+c/ftrHI1oiH2bywHZ6VgIrcVOkmOYkZxg3ypp6RBRXr+XYlcxAF1Su2gcTcMpPh8Vc+cCkDRmjMbRiIbw1PnCux3ndU3VNhghYlzsJTnahnF84b5VMswdSRWeivDlRFuihpE0TsWnn+LdswdLSgqpF1+kdTiiAYq2VxIIKCSlOUhKc2gdjhAxLWaSnBDdLyGvUUcbZHVVZO2u2g1ATkIOFrMxikEDbjclL84EoM1VV2FJTdU2INEgVQfUlVVpbaWdgxBai70kR+sAjsfjUo+JxlvmrGcHag8AkOk0Tj3O/meexbtnD9bMTNKunqB1OKKBaqvUva4cCTIaK4TWYibJMcw+OeG2DrK6KpJCK6uM0n289tdfKX3jDQDSJ03CkiIFrEYR2uk4KV2mqoTQWuwkOcGj7ht0+mUJeUsIJTkZzgyNIzm+gMfD3jvvAsDRqxdtJlylcUSiMUI9q9JyZbpKCK01Kcl54YUXKCgowOFwMGTIEH788cdj3r68vJxbbrmF3Nxc7HY73bp144svvmhSwE1lmNVVvlr1KElORO13qRsBtktqp3Ekx1fx0Ud4tm3DnJBA/quv6L+OTIR56nzhlg65nVO1DUYIQaPXKc+ZM4cpU6Ywa9YshgwZwowZMxg9ejQbNmwgK+vwXXo9Hg9nnXUWWVlZfPjhh7Rt25YdO3aQ2spFlAf3ydG5KrWJJImy43Ek7aneA+h/j5yAy0XJyy8DkH7zTVjbGK/PViwr2VWFokBimp3ENnatwxEi5jU6yXnmmWeYNGkSEydOBGDWrFnMmzeP1157jbvvvvuw27/22muUlpby/fffYwtuR19QUNC8qJvAMCM54ZocWV0VSWXuMkD/fasqPvsM3959WPNyaXPlOK3DEY1UXqyOxLbJkakqIfSgUdNVHo+HZcuWMWrUqIMPYDYzatQolixZcsT7fPbZZwwdOpRbbrmF7Oxs+vTpw6OPPorf7z/qedxuN5WVlfU+msswNTnhzQCNsczZCPwBP3ur9wKQFa/fEbJAXR0l/3wJgLQJV2NJlBdKoykrVFdHJskojhC60Kgkp6SkBL/fT3Z2/XfD2dnZFBYWHvE+W7du5cMPP8Tv9/PFF19w33338fTTT/O3v/3tqOeZPn06KSkp4Y/8/PzGhHlEhhnJCbd1kOWnkVJaV4pfUb+vek5yyj/6CN++fVgyMmhz2VitwxFNsONXtdN9WlvjbDgpRDRr8dVVgUCArKwsXnrpJQYOHMjll1/OX//6V2bNmnXU+0ydOpWKiorwx65duyIQiUGWkIcadEpbh4gJ1ePkJuRi0+lO0r6SEvbPeA6A9InXYk6QURyjqSypDY/kdBmo32RaiFjSqFfSjIwMLBYLRUVF9a4vKioiJyfniPfJzc3FZrNhsRycfunZsyeFhYV4PB7i4g5fRWS327HbIzvc6wuoSY5Z71mOV91jQ5KcyDlQp24EmGpP1TaQYyh+8ikCVVXEdexImwmy8Z8RFW1Xp9XT2yaQkCLTVULoQaNGcuLi4hg4cCCLFi0KXxcIBFi0aBFDhw494n1OOeUUNm/eTCAQCF+3ceNGcnNzj5jgtBSXR52ucNp0XusSauvgTNM2jiiyvnQ9AF3bdNU4kiPz7N5Nxbx5AOQ+8jfMrfh3ISLH7VLr6ZLSZdGAEHrR6OmqKVOm8PLLL/PGG2+wbt06fv/731NTUxNebXX11VczderU8O1///vfU1payq233srGjRuZN28ejz76KLfcckvknkVDBGtyzGadj+SEVlc5krWNI4qsKVkDQJ+MPhpHcjhFUdj/zDPg8+E84QTiTzxR65BEExVtVZvASlNOIfSj0XMil19+Ofv37+f++++nsLCQAQMGMH/+/HAx8s6dOzGbD+ZO+fn5LFiwgNtvv51+/frRtm1bbr31Vu66667IPYsGCO2To/MU52DhsUxXRczWiq0AdE3V30hO8RNPUvnFl2AykfXnKVqHI5ph/y51E8D2vWUUVgi9aNIr6eTJk5k8efIRv7Z48eLDrhs6dCg//PBDU04VMYZZXeWXwuNIK6tT98jR00aASiDA/mefpfT11wHIvvsu4gcN0jgq0RxVpWo9XXKGTFcJoRcx80pqvH1yYuZH06IO1B7A5XNhwqSrDuRFf3uEsnfeASDz1j+Rds01GkckmsNd68NTq/7tJqRK0bEQehEzr6SG6EIe8MsS8ggrrSsFICkuiXhbvMbRqL+HxU88GU5wsu+/j7RxsrOx0RUHV1YlpTuwO+VvVwi9iMEu5DpWHVxZZTJDfLq2sUSJ3VW7AWib2FbjSFQVH88NT1GlXX+dJDhRonRfDQCZ7ZM0jkQIcajYSXKMkOWERnEscWCRd4ORsL9W7T6e7tQ+aaz+9juKHnkEgJRL/h9Zd9yhcUQiUkJTVY4EfW42KUSsirlXUl3X5Eg9TsSFela1T2rfqudV/H58JSX4CgvxFhZR88MSyue8D4EAzoEDyZ02DZOu505FY4SKjp1JkuQIoScx8WqqhIdxQNfb5Mjy8YjbXa1OV7Vkz6qA203lF19S/fXXeAsL8RUV4du/Hw7ZADMk5dJLyLn/fkyy4V9UKdqm1uRkdZD9rYTQk5h4NT0kx9H3u2cZyYm4TWWbAChILmiRx3ctW8aeO/6Cb9++w79osWDNzMSWnY2tbR7J559P4siR+v4dFE1SeUAdyUnLk55jQuhJTLyaHpLj6HmyCnxu9ShJTkR4/J7wRoD9MvtF/vF37WLX7/9AoLISk8NB2rXX4OzTB2t2tvqRno7JovM2IqLZPHU+fO5g25hEma4SQk9i4tX00OkqXb+JrlGLZLEnahtHlFi5fyWgNubMcGZE/PEPvPQSgcpK4jp3puDtt7Ckpkb8HEL/9u+oAiCxjR17vCQ5QuhJTKyuqj+So+MsxxfsQC7LxyNiedFyAIbkDon4FJG/uoaKz9Wmmjn3/lUSnBh2YK8sHxdCr2IiyQnUK8rRLo7jCrd0kHeDkVBSWwJAbkJuxB/b9dOPKLW12Nq2Jf7kkyP++MI46qrVprrOZCkmF0JvYiLJqV94rF0cxxVeXSV1HJGwtnQtAJ1TO0f8sasWLQIgYcRwKSSOcaX7XACkSM8qIXQnJpKcQ+n65Si8GaCM5DSXoigU1hQC0CG5Q8Qfv26NmkAlDBsW8ccWxlJepCY56e2klk4IvYmJJMcwS8hDNTmyuqrZtpRvodhVjN1ip3ub7hF97EBNDe6NGwFw9OoV0ccWxhPe7ViKjoXQndhIcjDIZoA1ag0JdtlQrLn+t/d/AJyYdWLEG3PWrl4Dfj/W7Gzi2rWL6GMLY/H7A9RWqTU59gR5cyKE3sRGklOv7ljHWY5f/WeJs422cUSB7/Z8B7TM/ji1K1YA4OzfP+KPLYxl76ZyfN4AziQbyekOrcMRQvxGbCQ5h1zW82zVwdVV8o6wOSrcFfyw7wcATm13asQf371lMwCOXj0j/tjCWFwV6huT9LaJmC0x8e9UCEOJib/KQzcD1LVQWwfpQN4si3aqK5/yEvLok9En4o/v3qwmOfYuXSL+2MJY6qrVNyZ2p/zNCqFHsZHkHHJZ1yM50qAzIr7e9TUAF3a5ELMpsr/iiqLg3bkLAFt+63Y2F/pTskvd7Vh6VgmhTzGR5Hh8B7tBW/Sc5UiDzohYU7IGgJNzI79Jn3fHDgLV1WCzEVcQ+aXpwlhqKtR+cymZskeOEHoUE0mOO5jkxFnNWPU8b+5W3xVikZ1Tm2p31W7216o9wLq26Rrxx6/bpHY1d3Ttitluj/jjC2PxedT/LdY42cBTCD3S8St+5IRqcnQ8hqOqLVOPiVnaxmFgH278EFD7VSXFRb6XkDuY5MgojgBwB/fIsdolyRFCj2IkyVGPep6pAkCRmpzmWl2yGoAxBWNa5PG9e/cCENexU4s8vjAORVGoKK4FIDVLpquE0KOYSHJCdL1HDkhNTjO5vC6WFi4FoG9G3xY5h2eTurIqrn1+izy+MA6/N4A/OBXuTJIpZiH0KCaSHMOM5EiDzmZZsH0BAMlxyS1Sj6MoCu5t2wCwd+sW8ccXxhKqxwGw2mLiX6kQhhMTf5mhtg56z3HCSY5Jkpym+GrnVwBc0vWSiC8dB/CXlxOorAQgrqAg4o8vjKXOpe6RY40zy0aAQuhUTPxlHhzJ0XmaI9NVTVZUU8Ti3YsBGNVhVIucw7tL3R/HmpWF2Sk1GLEutNtxQoqsshNCr2IjyQkedZ7iHJLkyEhOY/1r7b8A6N6me4v0qwLwhDcBlHocAXU1wd2OE6T7uBB6FRtJTngoR9s4jqu6SD0607SNw2ACSoD/2/p/AFzW/bIWO493924A6TwuAHAFNwJMSJGiYyH0KjaSnOBR7zlOuAu5PVHbOAzmo00fUVpXCsDvOv6uxc7j3bMHAFvbvBY7hzAOj1utoYtzyPSyEHoVG0lOaDNAw9TkyPB3Y8zfNh+AK7pf0SIbAIZ4CwsBsObmttg5hHHUVqnTVXHSnFMI3YqRJEc9mnWe48gS8sZzeV38XPQzAJd3v7xFz+UrKQHAlp3doucRxlC0rQKAzPYy8iqEXsVGkhM8GmckR94ZNtTXu74moATIcmbRpU2XFj2XLzSSkyVtN2Kd3xegeLvaay63c6q2wQghjio2khyD1B1LktM4Lq+LZ5c9C8Cl3S9t0XMF3G78ZWpvMUlyRHmRC78vgM1hIUVaOgihW7GR5IQ2A9RzluNxHSw8tjq0jcUg3tvwHkWuItomtmVi74ktei5/uTo1gdmMpU2bFj2X0L+ibeqmkJn5SfofIRYihsVGkhOar9LzWE5defCCCRLStYzEEMrrynlt9WsA/L+u/w9HCyeGvmJ1eb81I0Ne1AR7N5cDkNslRdtAhBDHFFNJjq5fm0JTVVbZPbUh/rb0b1S4K8hyZnFt72tb/Hy+/WrRsSVdElAB+4JJTl6XVE3jEEIcW2wkOUboXSX1OA22ZO+ScDPOy7pfRpyl5Tdj8+3fD8jKKqF2H68sqQMgs0PLbVkghGi+2EhyDDGSI8vHG+KjjR9x839uBqBnWk8m9ZvUKuf1Fu4DwCpJTswrL3YBYLVbcEhLByF0LSaSnBCTnsdyZCPA41pTsobHfnyMgBLglLxTmDlqZot0Gz8Sf3CPHGtmZqucT+hX4Va1CD27QIqOhdC7mJgbMcRITp26WgOLJDm/FVACzFg2g9fXvA5Av8x+zBw1s1VfYHylweXjGRmtdk6hTyW7qwHILkjWOBIhxPHERpJjhJqcWrX3EgnyInqosroybv36Vn4p/gWAHmk9eO7051r9HbSvuBgAS7o0T411B4JJTlK67I8jhN7FRpITHsnRcZoTqsmJ8T1y3H43m8o2MW/rPOZvn09JrTpN5LA4uH3g7VzZ40pNfo7+UjUJtaZJkhPrKvbXApCel6BxJEKI44mJJMflURMIu03HJUhKMMkxxVbhcVFNEW+sfYPVJavZUbkj3E38UJnOTJ47/Tn6ZvbVIEK1wWt4dVVOjiYxCH0o2V2Nq9IDJkjJitc6HCHEccREkuMPqEM5cRYdJzlRurqqwl3BVzu/otpbzZK9S7CZbbgDbup8deyt3su+mn2H3cdpdTIkdwiDsgdxcu7JdE7tjFXDpfX+8nIUj7obteyTE9tCTTlzO6cQn9zyWxcIIZonJpIcJdyiU8eUgHqMoiSnzlfHFZ9fwe7q3ce8Xe/03vTN6MuFXS6kXWI7Uuwpuppa9BWrozjmlBTMjtieTox1ezaWA9KUUwijiI0kx0g1OVE0XfVj4Y/hBGdMwRgSbAlUe6sZ3nY4DqsDp8VJVnwWPdN7ahzpsclUlQBwVXrYskwtQM/rmqptMEKIBomJJCcQzHLMOs5xDu6TEz1JzudbPwfgrA5n8eRpT2ocTdP5ig72rRKxa9vK/QQCCo4EG+17SwG6EEag4yKVyAlNVul5ICcaC4+XFS0D4NyO52ocSfP4y9RiaFk+Htt2rlV/Dwr6put7VFgIERYTSU4oy9H1jsfV6mgB8dFR2FpUU0SxSx3a75/VX+Nomscb3CPHlpWlcSRCK0pAoXiHumFn15OktYcQRhETSU54M0Ad5zj4veoxLjr23lh9YDUA+Un5ZDiNPc3jK1KTHGnpELsqSmqpLnVjtpjIlc7jQhhGbCQ54ZEcHYuyLuRby7cC0DW1q8aRNJ/vgLohoUVqcmJW1QG167gjwYbNHj1TykJEu5hKcnQ9lBNlhcdbKrYAaLaBXyT5Sw4AMpITy0r31gCQkZ+ocSRCiMaIjSQneNRxihN1mwGWBntxpTuMX2PkLy8HwJKSom0gQjOFh2wCKIQwjthIchQD1ORE2XRVaCfjdkntNI6keRSfL5zkyBLy2FW8owqA7I6S5AhhJDGR5AS7OmDWc5bjdanHKFhC7g142Vm1E4B2icZOcvxlZeoFkwlLcrK2wQhNeOp8VJWoTTnTpCmnEIYSE0lOaMJKxykO1KjFrSQYv+6jqKaIgBLAYrKQFW/sZdf+CnWawpKcjMlm0zgaoYWirZUoCiSlOUhIsWsdjhCiEWIiyTnY1kHbOI4pipaQbyjbAEDn1M5YDF5jFJqqMssoTszas0kdzcuRehwhDCc2kpzgUdebAYZqcizGHy0oqlE3NuyQ3EHjSJrPF5yuskr38Zi1baU6ytq+l+x4LYTRxEaSY4TlVVG0hLzOr+4p4rQ6NY6k+fwlwT1y0uQFLhbt2VAWXj6e1UFG84QwmthIcoxQkxNFq6vK3eVAlCQ5VdUAWFJTtQ1EtDpPrY9/v7oGgB5Dc6ToWAgDio0kxwg1OT63ejQbf7qqxKWOfrRNbKtxJM3nP6BuBCh75MSevZvLcVV6iE+O49QrumsdjhCiCWIjyQkedV2TE2rQmWjs1UgApXXqRoBJcUkaR9J84dVVaW00jkS0trJ96rYOuV1SpZWDEAbVpCTnhRdeoKCgAIfDwZAhQ/jxxx8bdL/33nsPk8nERRdd1JTTNlloM0CznlO60Ooqq0PbOCKgyKUmbNEwkuMLjuRY20iSE2t2b1CLztNy4zWORAjRVI1+2Z8zZw5Tpkxh2rRpLF++nP79+zN69GiKi4uPeb/t27dzxx13MGLEiCYH21QHG3TqeCRHCbV1MH5NTlmd+uKQHGf8Qs1Arfpu3pwoPYtiSU2Fm51r1QS3XU8pOhfCqBqd5DzzzDNMmjSJiRMn0qtXL2bNmkV8fDyvvfbaUe/j9/sZP348Dz74IJ06dWpWwE3hD0hbh9bi9Xs5UKe+OOQl5mkcTfMFXGqSY7LLJnCxZMfqA6CoDTmlX5UQxtWoJMfj8bBs2TJGjRp18AHMZkaNGsWSJUuOer+HHnqIrKwsrr/++gadx+12U1lZWe+jOVweNYFIiNNxAhFu0KnnObXjq/ZWhy9HRU1OqG+VLCGPKRt+KASgQ590TLp+dySEOJZGvaKWlJTg9/vJzs6ud312djaFhYVHvM93333Hq6++yssvv9zg80yfPp2UlJTwR35+fmPCPEyo8FjX+UOUjOTsrdkLQJojDavBnwuA4lJ7FpkTZPlwrCjcWsHeTeUA9Dg5V9tghBDN0qIv+1VVVUyYMIGXX36ZjEZ0cJ46dSoVFRXhj127djUrDt3X5AT8BwuPDZ4Y1PnUjQCjoR5H8fvxB0cRzUnGH5USxxcIKPz3vY0A5HZOITVbio6FMLJGvaJmZGRgsVgoKiqqd31RURE5OTmH3X7Lli1s376d888/P3xdIBBQT2y1smHDBjp37nzY/ex2O/YI1kAoB7McfXIdOFh4nGDsJeShJMduMX4Ni1JXB8HfV+lAHhvW/HcP+3dWYbaaOOPqnlqHI4RopkaN5MTFxTFw4EAWLVoUvi4QCLBo0SKGDh162O179OjBr7/+yooVK8IfF1xwAaeffjorVqxo9jRUQ4Wnq/Q6t+73qEezDSzGHskprFGnLdOdxu/1FKitDV+WwuPYsPZ/6nRrv5HtZBRHiCjQ6FfUKVOmcM011zBo0CAGDx7MjBkzqKmpYeLEiQBcffXVtG3blunTp+NwOOjTp0+9+6cGt8f/7fUtKaDzgZzwVFUUNOcMFR6nOYxfqOsPNuc0p6Rg0nVBl4gEr9tPyS7197fbkMNHpoUQxtPoJOfyyy9n//793H///RQWFjJgwADmz58fLkbeuXMnZp29IISmq/Q6kHOw6Nj4SU5JrdrSISpWVlUH+1bJVFVMWP3NHgDs8VYy2sm+SEJEgybNjUyePJnJkycf8WuLFy8+5n1nz57dlFNGhF5znHDfKoNPVcHB5pxZ8cauLYJD+lbJbsdRz+8PsOprdYFD95NzZNm4EFFCX0MuLeRgg06d/uOqCe4WbTf+iEGo8NhhMX57ikBNDQAWWVkV9Tb9WER1mRtHoo0hF7T+hqVCiJYRE0lOQO/TVaGRnHjj17GUudU6lnib8Ys2Q805paVDdFMCCsvm7wCg/xn5xDmMP6IqhFDFRJKj+y7kUbJHDkB5XTkA2fHZx76hAYRrclJkW/9otm7JPsqLXFisZrqfLAXHQkQT47+qNsDB6Spt4ziqKCo8rvMHp6uioJu6ElxCbnYa/7mII/v5i20s/b9tAPQ7ox1JafKzFiKaxEaSExzL0WuOc7BvlUXbOCKg0q3uEOy0OjWOpPl8paUAmGV1VVRa9fUuln6mJjjte6cz8HcF2gYkhIi42EhygiM5ut0MMDSSY/B9cgJKIFyTExWrq4I1ObK6Kvrs2VDGt3M2AdDv9HaMuLybxhEJIVpCbNTk6L3w2Kuu4sHgUzzegDd8ORpWV/lL1CXk1kb0XRP6pwQUvnl3AwD5vdIYPrarxhEJIVpKTI3k6DbJ8QSTHIMvIXf73eHL0dC7yl9dBcgS8mjiqvQw74WVlBW6ADj5wk6YzHr9xyCEaK7YSHLCl3T6z8yrFutiNXZicKBWHfmIM8dhjYKVYv5SderNEmxFIoztwJ5qFry8OpzgDP1/ncnqYOw3FkKIYzP+K1EDHKzJ0TaOowpNV9mMXaxbE3weGc4M/W682AhKnZp8muONv+dPrNux+gALX1uD2+XDardwzk19ye9l/H2phBDHFhNJju43A6xTVyThMPZ+LKGaHJvBC6hBreMKBJMck8P49UWxrLqsji//+St+b4D0tomc/6f+JKQYe9RUCNEwMZHk6H4zwEB0bAbo8XsAsEXBfj+BqioIBACZrjK6n+Ztx+8NkNk+iYumnCA7GgsRQ2JidZXbq+5DY7Po9OmG9skx+AhIlUct1I2zxGkcSfMFXGrdBjYbZru86zeqlYt2sfa7vQCcOLqDJDhCxBidvupHlsevviN32HT6dMNtHYyd5JTWqZvnRUVLh7Jg0bGsrDIsvz/Aiv/sBKD3qW3pfGKmxhEJIVqbTl/1I0v3S8iDnbuNPpITqsmJhuXjoZEcSXKMa93/9lFd5sYeb+WUS7tERTG8EKJxYiTJUbMc3e54XLNfPRq88DhUkxMN01WKW93zxyRTVYakKEp4mqrXKXnY4ozfMkUI0XixkeQEjzpNcSCYHOBI1TSM5gq1dIiGvlX+SnXFmywfN6byIhf7d1aBCQac1V7rcIQQGomNJCec5eg0zQl3ITf2u80Kt9rrKRpqcgLV1YCsrDKqNcFRnMz8JOKTjT+yKIRomthIcghNV2kcyNGEu5Abe+VHrbcWiI6RnFBNjskpe+QYTV2Nl41LCwHoO7KdxtEIIbQUE0lOIFR4rNcJq/BIjrGTnOLaYgCSDd6DC8BfXg6AJcXYdVKxRlEUFry8mtoqL/EpcbKiSogYFxNJju5XV0XJdFVon5wMh/G7dgeChcdmp9TkGMnyBTvYvV6tDTv3D/1kXxwhYlxMJDmh0mO95jjhBp0GH8kJdSG3G7zRKIDiVovBTXap5zCK/Tur+OHTrQD0O72dNN8UQsRGkhNu0KnXopzQEnJnG23jaKZQF/JEW6LGkTSfv1ItojYnJGgciWgIvy/AN+9uAAUy8hMZdkkXrUMSQuhATCQ5gfDyKh1SlIO9qwxcyxJQAlR71RVJGc4omK6SzQAN5Zt3NlC0TV32f9Z1vbFYY+JfmxDiOGLiP4Gua3JCK6vA0DU5od2OITp2PFbqgpsBSgdy3fN5/Wz4UV1NdcLZ7UnLldE3IYQqNpKc4FGXq6sOSQ6MXJNTF2pNQXQkOeHVVTKSo3slu6oJ+BRsDgtDL+6sdThCCB2JiSQnoOh4n5zQyiowdO+qYpe6fDzBloDVwMlaSKBO3fPHnGD8+qJot/UXtaYtp2Oy9KcSQtQTE0kOep6uCm6gBxh6JCe0sio5LjpeaELTVWaH8UelolnlgVpWf7sHgPa90zWORgihNzGR5Oh6uqpaHQHBnmLoJCdUk2MzG3c06lCh6SqzTFfplqfOx4KX1+Ct85ORnyi7GwshDhMbSU5wukqXAwzhlVWJOg2wYbx+9XlERQdyRSFQUwOAJdm4K96i3S8Ld1K8vRKz2cSpl3eTFVVCiMPExH+FcFsHPSYRUdK3qrSuFIB4q/F3CA5UV4eX5Mk+OfpUW+3hl3/vBOCUsV3J7ZKqbUBCCF2KiSTn4HSVDgVHQIye5FR61D1K0p3Gr4sIVKrPxRQXhzne+ElbNNr0UxF+b4DU7Hj6ntZW63CEEDoVE0lOnVcdLYnT43B2lDTn9PjVNggOi/H3lQn1rTI5jd9NPVptWa6uqOo9Ig+TLpdNCiH0QIev+pHn9QcAcNh0uNmeW21qicFrWWp96ioxm4GXwYeERnJkFEefCrdWsHdTOQAd+0uXcSHE0cVEkhOuydE2jCNzlajHxCxt42im0D450dDSwV+ltqewpKRoHIk4ki3BfXHa9WhDSqaMtgkhji4mkpzQ6iqzHp9tcJqHOGMXuIb2yUmKM/6S61DfKrNMV+lOdVkdq77aBUD3ITkaRyOE0Ds9vuxHXLgLuR5XV/mCSY7Bp6s8AfV5RMM+OeGWDjKSozvL5+8g4FdIb5tI95MlyRFCHFtMJDmB8D45OkxygiMgRk9yKt1qHUs0FB4rwcJjqcnRl7pqL+uW7AOg3xnt9Pn3LITQldhKcjSO44gq1X/aJBq7gLKsrgyArHhj1xYBBNxqs1GTXVo66MnKr3bh8wRwJtno2N/4tV9CiJYXE0mOrqerQjU5NmPX5ITaOkRDB/JARQUgGwHqiRJQ2LJcLW4/+aLOOBONPfIphGgdMZbkaBvHEYX3ydHh8vZGCNfkRMMSclewA3mSdCDXi9X/3UNZoQuTCdr3StM6HCGEQcREkhPQde+qYJJj8OTA7VPrWKKh8Dg0XWW2G7++KFqsCXYa7zWiLYlt5OcihGiYGEtydJjlRMGOx4qiUOQqAiAz3ti1RXDIZoCJMpKjB546Hwf2qA1TTzy7vcbRCCGMJCaSnFDvKl3W5HjVqREMPALiCXjwK2rrjJQ44y+7DtSqIzkWma7ShbXf7QUgMc1OUpqM4gghGi4mkhxd73hcrRZTEm/cOoPSWrUDudVsxWk1/gZ6oSXksrpKe4qisDK4+d9J53SUPlVCiEaJiSRH1zse+9RRAxypmobRHBUedTVSG3sbLAYvoAbwV6n9xEwOGTXQWm2Vl+pSN5ig2+BsrcMRQhiMHl/2Iy60ukqXNTmhJeRW4y6J9frV5ePRUHQMB2tyrGnGHV2LFuVFaouN5HQH1jjjJ9BCiNYVE0lOnVetF4mz6PDphkZyDLy/TDQtHwfwhwqPZZ8czYWSnNQs2X1aCNF4OnzVjzyPPwCAw6bDpxsFOx57/NHTt0oJBAhUB7uQt2mjcTRic3ADwPR2UgQuhGg8Hb7qR55up6sU5WDvKgN37y6pLQEg3ZGucSTNp3g84ctmKTzWVHmxi11r1aL2Pqe21TgaIYQRxUSSo9veVaE9cgAsxt0nx+VVpxSSDJyohQRqa8OXTXHGrZOKBns3lQOQ1SGJ5Azjr9oTQrS+mEhydNu7yn9w1MDIXchrfOpGbQ6r8Vcj+UvVkQNzUhImm/Gn34ysaJtaG5XXNVXbQIQQhhUjSY5O2zrUVQYvmAyd5BTVqLsdZziN3xk6EFw+Ln2rtFe4Vd2aICPf+COEQghtxEaSEzzqbiTHdUA9JmQYuneVO1hXFA3TVb6yMgCsqVJ0rCVFUSjbp44QykiOEKKpYiLJCdXk6E5ousrAy8fhYJJjN/jzgEN2O46XGhAtBXxKeJrZ7jRuvZoQQlsxkeQcXF2lbRyHCW6iZ+SiY4BydzlAVLR0CLjUwmPpQK4tj/tgUb4lLib+TQkhWkBM/PcI6LXwOBBKcoxbjwNQVqdO8WTHG3/bfX+ZWnhsSZfdjrVUuFWtV0tItWPR4yaeQghDiJH/HjotPA7V5MQZu8i1LrhrczSM5PiDNTmWlFRtA4lxRdvUouP2vSXZFEI0XUwkObodyQmtrkow7m7HAMW16q60KfYUjSNpPn+F+uJqTZPCYy1tWxncYDLP2G8AhBDaiokkR9HrZoDhwmPjrqzyBXxUedRl11nxWRpH03yBumDhscP4o1JGVVfjpXSvurKqy0Dj/04JIbQTG0lO8Ki7tg6hHY8NnOSEEhyABJvxG1r6K9WRHHO8NITUypZgv6qkdAfxKcauVxNCaCsmkpxAQKc1OaGRHAM3ttxfux+ANvY2xBm8gBogUK2OIFhSU7UNJIaFOo937JehvzcmQghDiYkkR7ebAVYVqsc4446ARFPRMUDApb7Amp2yhFwLiqKwNViPk9nB+JtLCiG01aQk54UXXqCgoACHw8GQIUP48ccfj3rbl19+mREjRtCmTRvatGnDqFGjjnn7lhDeJ6dVz9oAwcaWJBp36XW1txqIjr5VAP6KckDtXSVa3/6dVVTur8VqM9NpgLEL8oUQ2mt0kjNnzhymTJnCtGnTWL58Of3792f06NEUFxcf8faLFy/myiuv5Ouvv2bJkiXk5+dz9tlns2fPnmYH31ABvfauCk1XWY07zRPqW5WTkKNxJJGh1KojU5ZEWdWjhY1L1d+ngn4ZxDmMvUmmEEJ7jU5ynnnmGSZNmsTEiRPp1asXs2bNIj4+ntdee+2It3/77bf5wx/+wIABA+jRowevvPIKgUCARYsWNTv4hlAUBZfHD0B8nM7+afqNvxlgrU/dITjRFh1JQaBOTXJMjugYmTISRVHYtkqt8Srom65xNEKIaNCoJMfj8bBs2TJGjRp18AHMZkaNGsWSJUsa9Bgulwuv10ta2tE3+XK73VRWVtb7aKrAIW2rrGadDeW4gyuTDDzVU+yKnj1yAm43Sq2atFlkuqrVFe+oorKkDmucmU4nyNJxIUTzNSrJKSkpwe/3k51dv4YkOzubwsLCBj3GXXfdRV5eXr1E6bemT59OSkpK+CM/P78xYdZzaHNO3RUe16gFliTlahtHM4SWkGc4MzSOpPkC1dXhy+bkZA0jiU0bl6r/Qzr2y8Bmt2gcjRAiGrTq6qrHHnuM9957j7lz5+I4xnTA1KlTqaioCH/s2rWryec8tAG5SW9rycI1Ocbt3l3nV6d3oqEDeailgzkpCZNZb78s0W/fFnWPIhnFEUJESqOKVDIyMrBYLBQVFdW7vqioiJycYxeePvXUUzz22GP85z//oV+/fse8rd1ux26PzIumrkdyomDH40q3OpUYDUvIQ8vHLTKK0+pK99awf6c6KpjdUb7/QojIaNTb1bi4OAYOHFivaDhURDx06NCj3u+JJ57g4YcfZv78+QwaNKjp0TZBvZGcVj1zA4SWkBu48LjUrXbtzk4w7jL4kIBLrceRouPWt29LOQB5XVNJSpPvvxAiMhq93GjKlClcc801DBo0iMGDBzNjxgxqamqYOHEiAFdffTVt27Zl+vTpADz++OPcf//9vPPOOxQUFIRrdxITE0lshWW6uh7JqQ4uu3catxmkJzga5bAY/4XJX14OgCXF+EXURlO0XR0RTG8XHav0hBD60Ogk5/LLL2f//v3cf//9FBYWMmDAAObPnx8uRt65cyfmQ+oZZs6cicfj4dJLL633ONOmTeOBBx5oXvQNcMhAjv72yQnuFmzkJCe0hDwqWjrUqIXHsrKq9dVWqdsppOUad/dvIYT+NGnjmMmTJzN58uQjfm3x4sX1Pt++fXtTThExuh3JCfhBCaiXDZwgFNaoI3PR0IH80MJj0bpqytXu744E49anCSH0J+qXkITyCNDZSE6o6BgMW3js8rpw+9UXpzTH0fc9Mgp/cAm5TFe1Lq/bT8lu9XsvRcdCiEiK/iQHnY7kuA4cvGzQ5dd7qtXWHElxSVGx43GgRppzaqFoWwVKQCGxjV2KjoUQERX1Sc6hOx7rasPj2nL1GJ9h2N5VoY0AU+2pmPSUQDaR/4CaeFoyjL+xoZHs2VgOQE4nGUETQkRWDCQ5B7McXb0QhzcCNO471701e4HoqMcB8AWTHOsxWo6IyNsQ3Ok4t0uqtoEIIaJOzCQ5espvgKjYCDC0sio5LjrqKAJV6siUJTVV20BiiKfWR9UBdZVhj5Ojo5O9EEI/oj7J8Qfnq3RVjwPgqVGPBm7p4ApuZhgNe+SA2qATwGSPjudjBNXBVVVxDgtxziYt9hRCiKOK+iSnxu0HICFOZw3/qoOtMZLztI2jGaJp+Tgc3AzQnCh7tbSWmjI1yUmUgmMhRAuI+iRHCU5XWXRVdczBjQBt8drG0QwunzqSk2I3fsGooij4K9QGkVYpPG41FSXqlGdiG+OOaAoh9Cvqk5zQ6ir9JTnqO1gjbwR4oFYt1E2MM/7ycX95Ofh8AFjaGHcHaqMpK1SnbdvITsdCiBYQA0lOqPBYZ0lOlTrVg924CUKZW90hODve+M05ldpgc864OMxxxk08jSbUeTw9z7h/B0II/Yr6JOdg4bHGgfxWnTo1QnJbbeNohrrglFs0FB77K9UGkeZ4404fGo3P6w835sztbPwpTyGE/kR9khPaJkd3q6u86siBkWtyomm6yrtvHwDW3FyNI4kdRVsrCfgUnEk2UrKcWocjhIhCUZ/khKardJfkVKotEUgwbpFrpUd9F57pzNQ4kuYL7XZszTTuz8Nofl28G4AOfdL1N50shIgKMZPk6O5/qEdtSEh8urZxNJGiKHgDXgDiDFw8HRKoVafezAlSANsaFEVh68oSAPqdka9xNEKIaBUzSY7uRnLcwSTHZsxh+lD3cYiOJMdfphZRWxKTNI4kNhzYU40SUMAEKRnG/BsQQuhfDCQ56lF3S8ir1BoQnMbsk1TkUjcztFvsxFuNW1cU4iuV6arWtPWX/QC075UuOx0LIVpM9Cc5AR1OVynKwbYOCcasZ6n2qiNRqfZULGad7SbdBL4SderEmmnMn4fR7F6vjpx1GiBJpRCi5UR9kuP1B3c81lOW43MDwSEmmzGXX4dWViXFRcf0jr9UfdE1y3RVi/PU+ijcphat5/c05kimEMIYoj7JqfUGe1fZdTQkHlpZZYkDgy6/DiU5OQnR0TnaW6hOH9pyo+P56NmejWUoAYXkTCfJUo8jhGhBUZ/khDYD1FVNjlt9F0t8Bhh0qifUt8ppjY4XKaVGfT7S0qHl7dlUDsgojhCi5UV9kqMoOtzx2KWOghBn3ILdfdXqyEeG0/g1FYrHE97x2JIiO++2tFDn8VTZAFAI0cKiPsnx63EJeZW6MomUdtrG0Qw7q3YC0D6pvcaRNJ+vtFQtBjebZSSnhQUCSngkR6aqhBAtLeqTnIAe2zqE+lY5UjUNozlCNTltE43beyvEF9rtOCMDk8WY04dGsemnImorPdjjrXToa8yNMIUQxhH9SY4ea3IqdqnHJOMWuYY6kEdD3yp/eTkA5mRZWdWS9m2p4Kt/rQOg04BMLJao//cjhNBY1P+XCe94rKckp6pQPRo0yfEFfOyt3gtEx3SVP7hHji0rS+NIolfFfhef/X0FAZ9Cm5x4hl3SReuQhBAxIOqTnNDqKj3lOOEl5CnG7Nmzs2onfsWP0+qMisJjb3ExAJYM4z8Xvdr4YxE+tx97gpXz/zQAR4JN65CEEDEg6pMcJdTWQY81OfHGXEIbGsXJSciJit2OA6GVVamp2gYSpcqLXCz/t1qofvKFnUlKM+YGmEII49HRDnktwx/uQq6jJKdc/YdPUq62cTRRSa06vZOXmKdxJJHhLVRXu8l0VeSV7K7mu/c34nP7yemUTM+hxvydF0IYU9QnObUedcdju1Ung1buKvCqG88ZNckprysHINFm/KJjAN8+dc8fa7Yxa6T0yFPrY9n8HSxfsANQe8cNubAzFptO/g6FEDEh6pMcty8AgMOmk2mV8uDKKnsKOIy58VyoA3m0jOSENwJMkz1yImHvpnL+7x8r8bnVNxgZ+YmcekV3cjsb8/ddCGFcUZ/khFZX6Wa1apVaz0JChs5aozdcubscgOS4ZG0DiQBFUfDuVX8m0oG8+WqrPfzw6RZ8bj9xDgtnXNOTTv0zMemq8l8IESuiPsnRXe+q0PLxNh20jaMZQoXH0bARYKCigkB1NQBx7Y2/HL61KIqCu8ZHdXkdu9eXsW1lCWWFNdRWecO3GXvPSaRmGbd1iRDC+KI+yQnora1DjVq0i9OYK6sAKj3q9E5KnPGnH3zBPXJMTidmR2yu+lEUBSWgEPAr+P0KAX+AgE/BHzyq1wcI+BUCvgCl+2pY+tnWegnNodLyEug9oq0kOEIIzUV/kqO3kZzQbsdpHbWNo4kURWFXlfoc2iYZfyTHs0t9LtE+ilNb7eHbOZvYt7lcTWR8gXrJC0rTHteZZCOxjYPkDAcDRrUnLS+BOEfU/1sRQhhE1P830l2DzvDycWOu5ClyFeH2u7GYLGTHZ2sdTrN5w0mOMTdmbIhd60r59ytrqKs58sjLkZjMJiwWE2aLCbPVjNliwmJRjzaHhfweafQflU9Cir0FIxdCiOaJ/iRHXVyln5GcUE1OsjE7kG8s2whAh+QOOKzGn97x7FCTzriCAm0DaSHlxS4+/8dKAn6FOIeFs67rTVK6Q01egkmLJZjEHJrISKGwECIaRH2Sc3B1lU7+aZep+4YYdSSntK4UiJ7l455t2wCw5hjz53E8X/9rPQG/QnKmk8v/epJMJQkhYopeFla3mBq3DwCHHjYDrDkA7mBLB4PW5Ox37QcgxW78omMAz+7dQHSO5OxYfYC9m8oxm02cd0s/SXCEEDFHB6/8LcsT2gwwTgebAR7YrB4Tsw27EWCZuwwgKupxFEXBu0dtlhrXoUDbYFrA2v+pS/17Ds+jTU6CxtEIIUTri/okxxdcXWUz6+CplqlTI6R31TaOZqjz1QHgsBi/Hse7Zw/4/WCxYM2Kro0A3S4v239Vl8f3OdX4q+CEEKIpdPDK37J8elpCXrpVPSYbt54l1JwzKS5J40iaz71pEwC2dm0xx8VpHE1kHdhTQ8CnkNjGTka76OgxJoQQjRX1SY4/oE5XWS06SHL2b1CPmd21jaMZDtQeAKKj8Lh2xUoAnL37aBxJ5BXvCG7YKBvyCSFiWNQnOV6/OpJj1cN0VWgjwAxjTlcpisL2yu0A5CQYfzVS3fp1ADj69tU4ksgr2a22qpCmmEKIWKaDV/6W5fIEV1fZdPBUw9NVxtwjp8hVRKWnEqvJSqeUTlqH02yh6Sp7ly4aRxJZFftr2bysGIDsjsZvoiqEEE2lg1f+luX2qtNV8VqvrvLUQK26MsmozTlDozjZCdmG3wjQW1SMb+8+ABx9emscTWSt+noXfm+A9LaJtO+drnU4QgihmahPcrwBnUxXFatTIzjTIN6YLzyr9q8CoHe68ZOC2l+WA+r+ONY2bTSOJrJK99YA0HNYLmY9FNwLIYRGoj7J8QX7Oli0Ljzes0w9thsEeumj1Ui/7v8VgAFZA7QNJAJcP/0MQPxJgzSOJLLKi1zs26xuOJnXNVXbYIQQQmNRn+T49bJPTqW66RwpxqzHAdhUrtawdEk1fg1L3erVADgHnKBxJJHjqfPx4eM/4/cFSM50kpEvS8eFELEt6pMcr18nS8h3LFGPOf20jaOJNpZtZE/1HqxmK/0yjfkcQhSvl7p1wZVVvXpqHE3kbP1lP26XD5PZxNnX9cZk0BFDIYSIlKhPcirr1NVViXYN+/b4vbBvhXq5/VDt4miGH/b+AMDQ3KEk2IzdIsD1888oHg/mlBTsXY25nP9I9u+sAqDfGe1kVZUQQhADSY7b6wfAqeXqquK14PeAyQIZ3bSLoxlW7lc3zuudYfyi45rvvwcgYehQTNboaVpZeUBtuZGQbNc4EiGE0IeoT3I8wemqOIuGT3Xzf9RjwXDQujaoCTx+D9/t+Q6A4W2HaxxN81X9eyEAiaeeqnEkkVWyWx3JkVocIYRQGe8VtxF8/gDuYBdyu1abASoK/PqhernLKG1iaKZ317+Ly+ciKz7L8MvHvUVFeHbsACDx9JGaxhJJnjoftZVeAJLSjb2HkRBCREpUJzkurx9FXVxFitOmTRA7l6jTVWYb9L9Smxiaoc5Xxz9++QcA53c6H6vZ2NM7VYsWAWDv3j2q9sfZ+sv+8KqqlAyn1uEIIYQuRHWS4wmO4oCG01XL/6Ueu4+BxExtYmiGL7d9SZ2/DqfVyY39btQ6nGYLTVUlnX2WxpFEVlmhugFgfs80TLIBoBBCAFGe5ISWj9ssJm2W0/p9sP5z9fIJV7f++SNgS/kWAM7qcBbxNmN3tPbs2IFr6VIAkn93jsbRRI6nzsemn9VeVVkdkjSORggh9CO6kxxfcCNArUZxNn4J7kqIS4TOZ2gTQzP4A37+vePfAAzJHaJxNM2jKApFj04HRSF+yBDsnTpqHVLELH57A1UH6rAnWOkyMEvrcIQQQjeiOsmpqA0WYjo0qiNZNls99r0ULMarZdlSsYV9NfuIM8dxdoeztQ6nWSo++ZTqb74Bs5ms22/TOpyIObC3mk0/FQFw9vW9idPqd10IIXQoqpOc2uAeOQlxGvzjrzkA2/+nXu4/rvXP30x1vjqe+ukpAE7IPsHQXccr/u9zCqdNAyBtwgScAwZoG1AE/fR/2wDI7ZJC+17GbPwqhBAtJarf9rl9apITZ9Ugl5t3O/hqIbUDtD2x9c/fDJvLNnPTf26i2FVMnDmOKQOnaB1SoyiKgnvTJjzbtlM5/0uqvpwPQPxJJ5F5260aRxc565fsY8sv+wEYMKq9xtEIIYT+RHWSU1rjASC5tZePb/gS1n6qXj7nSbBotHy9CRZsX8B9/7uPWl8tAM+d8Ry90ns1+fHq1q+n5n//Q/EHIBAAJYCiKBBQIBBAUQLhyygBlIACfj9KIBA8+sEfUI8BBQL+4GMdPAZq6whUVeGvqiJQVYVv//7D4kgYNox2L76A2WHcEalD+f0BlsxVi8K7D8mhY/8MjSMSQgj9ieokp9qt9q1q1T1yvLWw8H71cp9LoNvo1jt3M5TUlvDo0kdZuENdYp0Vn8XzZzzfrASn/MMP2TftAfD7IxRlI1gs4PeTcuEFpF52GfEDB7Z+DC2ktsrDl//8FVelB3uClZFXdZdmnEIIcQRNSnJeeOEFnnzySQoLC+nfvz/PP/88gwcPPurtP/jgA+677z62b99O165defzxxznnnJZfwltarY7ktFqS43HBnPFQshHi0+Gcp5r8UHur9zJnwxxqvDUoioKCQkBRl8QHlMDRP1cgQCB8H0VRwl8/9HFCn9f6atlWsY0yd1n43MPyhvHEqU+QYk9pcvy1v/7KvvunQSCAo1cv7D17YDKbAZPa2sJswmQyq5dNJnVvF5MZLGb1dmYLJkv9I2YTJrMleJvg0WLBZHdgSUrEnJQcPCZhzc7GHBfX5Pj1pqbCzbaVJezdWMaWFfsJBFcO9j2tHVabhn3ZhBBCxxqd5MyZM4cpU6Ywa9YshgwZwowZMxg9ejQbNmwgK+vw5avff/89V155JdOnT+e8887jnXfe4aKLLmL58uX06dMnIk/iaLYfcAHQrk0r7ABbsRvm3gzbvwVbAlz2JsSnNemhNpRu4Navb2VP9Z4IB3ls6Y50RnUYxdTBU7GYm/7C6Vq+nD233gaBAM4TTqDD228FExzj8PsCuCo97NlQxoG9NSh+hUBwmi2ggBJQDn4oEAgEk8iAghIgfDlwyGX1SPD6g/etd9sjPE4goOCq8NSLL7N9En1HtqPHyTkafYeEEEL/TIoSanzQMEOGDOGkk07iH/9Qt/oPBALk5+fzxz/+kbvvvvuw219++eXU1NTw+eefh687+eSTGTBgALNmzWrQOSsrK0lJSaGiooLk5OQGx3rZP5fw47ZSnrtiABcOaNvg+zXK7p/hmydg0wL1c1s8TJgL7U9u9EMpisLff/k7r/z6CgBt7G0Y230sFpMFk8mECRNmkxkTpmN/HrxsMpkwE/z8KLc3mUy0T2pP1zZdSbAlNPnboCgKrqVLKX19trpUG7BmZlLw4YfYsvW/d4un1sfqb/ewcWkh1eVu3DU+rUM6ogFntafLiVlkd2z434EQQmilqa/fkdKokRyPx8OyZcuYOnVq+Dqz2cyoUaNYsmTJEe+zZMkSpkypvzpn9OjRfPLJJ42PthG2l9Twy051CqZzZhO7Mu9ZBuW7wOsCT03w6AJvjXosWg27lh68fXoXOPfpRic46w6sY/WB1Ty//PnwtNEJWSfw4LAH6Ziin03rvEXF1P26Cn9VNYHqagLVVfgrq6j+738J1Lrw7d0Xvm3yBeeT9ec7NE1wfB4/u9aV4vX48XkC+Dx+vG71cvg6tx93rY/d60vxeQL17m+2mEjJiie/ZxustmCyaFY/1Jk3E2ZzMGE0E/662Uz4diZT8HLotoded8hlc+gxwternx98fBPJGQ7ZB0cIIRqhUf8xS0pK8Pv9ZGdn17s+Ozub9evXH/E+hYWFR7x9YWHhUc/jdrtxu93hzysqKgA1I2yIyjov5z77De46Px0z4slPbPh961nwOGyaf/zb2RLginegXbC4tZHneuq7p1iy72CSOCBzAE8OeRKnydm0uFtI5Xffsu/uqce8Tcqll5A6diyOLl2oBWo1jN9V6eGj535o8O1Ts+PpdlI27XunEZ9kxx5v1UkfKAVQqPO4qPMc98ZCCKEbodewRk4aRYwu3xZOnz6dBx988LDr8/PzG/1Yu4DUeyMQ1DFVwcORa9uwjnW8y7sRe7xW9dhj6ocQQggRdODAAVJSmr6YpakaleRkZGRgsVgoKiqqd31RURE5OUcugMzJyWnU7QGmTp1ab4qrvLycDh06sHPnTk2+SeKgyspK8vPz2bVrlybzq+Ig+Vnoh/ws9EV+HvpRUVFB+/btSUtr2kKc5mpUkhMXF8fAgQNZtGgRF110EaAWHi9atIjJkycf8T5Dhw5l0aJF3HbbbeHrFi5cyNChQ496Hrvdjt1uP+z6lJQU+YXVieTkZPlZ6IT8LPRDfhb6Ij8P/TBrtMK20dNVU6ZM4ZprrmHQoEEMHjyYGTNmUFNTw8SJEwG4+uqradu2LdOnTwfg1ltv5bTTTuPpp5/m3HPP5b333uPnn3/mpZdeiuwzEUIIIYQ4RKOTnMsvv5z9+/dz//33U1hYyIABA5g/f364uHjnzp31MrZhw4bxzjvvcO+993LPPffQtWtXPvnkkxbfI0cIIYQQsa1JhceTJ08+6vTU4sWLD7tu7NixjB07timnAtTpq2nTph1xCku0LvlZ6If8LPRDfhb6Ij8P/dD6Z9HozQCFEEIIIYzAWHvtCyGEEEI0kCQ5QgghhIhKkuQIIYQQIipJkiOEEEKIqKT7JOeFF16goKAAh8PBkCFD+PHHH7UOydCmT5/OSSedRFJSEllZWVx00UVs2LCh3m3q6uq45ZZbSE9PJzExkUsuueSwXat37tzJueeeS3x8PFlZWfzlL3/B56vfuXvx4sWceOKJ2O12unTpwuzZs1v66RnaY489hslkqrdxpvwsWteePXu46qqrSE9Px+l00rdvX37++efw1xVF4f777yc3Nxen08moUaPYtGlTvccoLS1l/PjxJCcnk5qayvXXX091dXW926xatYoRI0bgcDjIz8/niSeeaJXnZxR+v5/77ruPjh074nQ66dy5Mw8//HC9/kfys2gZ//3vfzn//PPJy8vDZDId1ky7Nb/vH3zwAT169MDhcNC3b1+++OKLxj8hRcfee+89JS4uTnnttdeUNWvWKJMmTVJSU1OVoqIirUMzrNGjRyuvv/66snr1amXFihXKOeeco7Rv316prq4O3+bmm29W8vPzlUWLFik///yzcvLJJyvDhg0Lf93n8yl9+vRRRo0apfzyyy/KF198oWRkZChTp04N32br1q1KfHy8MmXKFGXt2rXK888/r1gsFmX+/Pmt+nyN4scff1QKCgqUfv36Kbfeemv4evlZtJ7S0lKlQ4cOyrXXXqssXbpU2bp1q7JgwQJl8+bN4ds89thjSkpKivLJJ58oK1euVC644AKlY8eOSm1tbfg2Y8aMUfr376/88MMPyrfffqt06dJFufLKK8Nfr6ioULKzs5Xx48crq1evVt59913F6XQq//znP1v1+erZI488oqSnpyuff/65sm3bNuWDDz5QEhMTleeeey58G/lZtIwvvvhC+etf/6p8/PHHCqDMnTu33tdb6/v+v//9T7FYLMoTTzyhrF27Vrn33nsVm82m/Prrr416PrpOcgYPHqzccsst4c/9fr+Sl5enTJ8+XcOooktxcbECKN98842iKIpSXl6u2Gw25YMPPgjfZt26dQqgLFmyRFEU9Y/AbDYrhYWF4dvMnDlTSU5OVtxut6IoinLnnXcqvXv3rneuyy+/XBk9enRLPyXDqaqqUrp27aosXLhQOe2008JJjvwsWtddd92lDB8+/KhfDwQCSk5OjvLkk0+GrysvL1fsdrvy7rvvKoqiKGvXrlUA5aeffgrf5ssvv1RMJpOyZ88eRVEU5cUXX1TatGkT/vmEzt29e/dIPyXDOvfcc5Xrrruu3nX/7//9P2X8+PGKosjPorX8Nslpze/7ZZddppx77rn14hkyZIhy0003Neo56Ha6yuPxsGzZMkaNGhW+zmw2M2rUKJYsWaJhZNGloqICINw8bdmyZXi93nrf9x49etC+ffvw933JkiX07ds3vMs1wOjRo6msrGTNmjXh2xz6GKHbyM/ucLfccgvnnnvuYd8v+Vm0rs8++4xBgwYxduxYsrKyOOGEE3j55ZfDX9+2bRuFhYX1vpcpKSkMGTKk3s8jNTWVQYMGhW8zatQozGYzS5cuDd/m1FNPJS4uLnyb0aNHs2HDBsrKylr6aRrCsGHDWLRoERs3bgRg5cqVfPfdd/zud78D5Gehldb8vkfq/5Zuk5ySkhL8fn+9f94A2dnZFBYWahRVdAkEAtx2222ccsop4TYbhYWFxMXFkZqaWu+2h37fCwsLj/hz+f/t3XtUTekbB/DvqdPpHqeoo+gchRQhGiaXn7tisWQGaSxzGMMat9LQMIPFuGVhmsGE3MrMRO5zcckt6aKMSzUu6UbCRKYkrUo4z+8Pqz1tnUwuFfV81mqtzvu++33f/b679rP23u/Z5XkvK1NYWIiSkpKa2J33Unh4OC5evCi8660inovadf36dWzYsAGtW7fG0aNHMWXKFPj4+GD79u0A/h3Pl/1Punv3LiwtLUX5UqkU5ubmrzRnDd3cuXMxZswYtG3bFnp6enBxccHMmTMxduxYADwXdaU2x72qMq86L6/1WgdWP0ybNg2XL19GbGxsXXelQbp16xZ8fX1x/PhxGBgY1HV3GjyNRgNXV1csX74cAODi4oLLly9j48aNUKvVddy7hmX37t0ICwvDjh070K5dOyQlJWHmzJmwtrbmuWCv5J29ktOkSRPo6upWWkly7949KBSKOupV/TF9+nQcPHgQp06dQvPmzYV0hUKBsrIyFBQUiMpXHHeFQqF1XsrzXlbGzMwMhoaGb3t33ksXLlxAbm4uOnfuDKlUCqlUitOnT2Pt2rWQSqWwsrLiuahFzZo1g5OTkyjN0dER2dnZAP4dz5f9T1IoFMjNzRXlP336FPn5+a80Zw2dv7+/cDXH2dkZ48aNg5+fn3DFk+eibtTmuFdV5lXn5Z0NcmQyGbp06YKTJ08KaRqNBidPnoSbm1sd9uz9RkSYPn06Dhw4gMjISLRs2VKU36VLF+jp6YnGPTU1FdnZ2cK4u7m54dKlS6ID+fjx4zAzMxNOEm5ubqI6ysvw3P2rf//+uHTpEpKSkoQfV1dXjB07Vvid56L29OjRo9LXKaSlpUGpVAIAWrZsCYVCIRrLwsJCnD17VjQfBQUFuHDhglAmMjISGo0G3bp1E8pER0fjyZMnQpnjx4/DwcEBcrm8xvbvfVJcXAwdHfHpSVdXFxqNBgDPRV2pzXF/a/+3Xukx5VoWHh5O+vr6FBoaSlevXqXJkydT48aNRStJ2KuZMmUKNWrUiKKioignJ0f4KS4uFsp88cUXZGtrS5GRkXT+/Hlyc3MjNzc3Ib982fKgQYMoKSmJIiIiqGnTplqXLfv7+1NKSgoFBQXxsuVqqLi6iojnojb9+eefJJVKadmyZZSenk5hYWFkZGREv/zyi1BmxYoV1LhxY/rtt9/or7/+ouHDh2tdPuvi4kJnz56l2NhYat26tWj5bEFBAVlZWdG4cePo8uXLFB4eTkZGRg162fKL1Go12djYCEvI9+/fT02aNKGvvvpKKMNzUTMePXpEiYmJlJiYSAAoMDCQEhMT6ebNm0RUe+MeFxdHUqmUVq9eTSkpKbRw4cL6t4SciGjdunVka2tLMpmMunbtSgkJCXXdpfcaAK0/ISEhQpmSkhKaOnUqyeVyMjIyohEjRlBOTo6onqysLBo8eDAZGhpSkyZNaNasWfTkyRNRmVOnTlGnTp1IJpORnZ2dqA2m3YtBDs9F7frjjz+offv2pK+vT23btqVNmzaJ8jUaDS1YsICsrKxIX1+f+vfvT6mpqaIyeXl55O3tTSYmJmRmZkYTJkygR48eicokJydTz549SV9fn2xsbGjFihU1vm/vk8LCQvL19SVbW1syMDAgOzs7mjdvnmjJMc9FzTh16pTWc4RarSai2h333bt3U5s2bUgmk1G7du3o0KFDr7w/EqIKXyHJGGOMMVZPvLPP5DDGGGOMvQkOchhjjDFWL3GQwxhjjLF6iYMcxhhjjNVLHOQwxhhjrF7iIIcxxhhj9RIHOYwxxhirlzjIYew9MH78eHh6etZ1N+rMu7b/oaGhld4OX5u2bt2KQYMG1Vj9ZWVlUKlUOH/+fI21wVht4CCHNRi3bt3CZ599Bmtra8hkMiiVSvj6+iIvL6+uuybIysqCRCJBUlKSKH3NmjUIDQ2tkz69j6KioiCRSCq93PR1qFQq/PDDD6I0Ly8vpKWlvXHdr6O0tBQLFizAwoULa6wNmUyG2bNnY86cOTXWBmO1gYMc1iBcv34drq6uSE9Px86dO5GRkYGNGzcKL3zNz8+v0fbLysreaPtGjRrV6ZUDJmZoaAhLS8s6aXvv3r0wMzNDjx49arSdsWPHIjY2FleuXKnRdhirSRzksAZh2rRpkMlkOHbsGHr37g1bW1sMHjwYJ06cwJ07dzBv3jyhrEqlwpIlS+Dt7Q1jY2PY2NggKChIVF9BQQE+//xzNG3aFGZmZujXrx+Sk5OF/EWLFqFTp07YsmULWrZsCQMDAwBAREQEevbsicaNG8PCwgJDhw5FZmamsF35W+FdXFwgkUjQp08fAJVv1zx+/Bg+Pj6wtLSEgYEBevbsiXPnzgn55VcyTp48CVdXVxgZGaF79+6V3rL9otu3b8Pb2xvm5uYwNjaGq6srzp49K+Rv2LAB9vb2kMlkcHBwwM8//yzaXiKRIDg4GEOHDoWRkREcHR0RHx+PjIwM9OnTB8bGxujevbton8vHKjg4GC1atICRkRFGjx6Nhw8fVtlPjUaDgIAAtGzZEoaGhujYsSP27t0L4PnVsL59+wIA5HI5JBIJxo8f/5/badOnTx/cvHkTfn5+kEgkkEgkACrfrirfh23btsHW1hYmJiaYOnUqnj17hpUrV0KhUMDS0hLLli0T1f9fx5E24eHhGDZsmCit/PhYvnw5rKys0LhxYyxevBhPnz6Fv78/zM3N0bx5c4SEhAjblJWVYfr06WjWrBkMDAygVCoREBAg5MvlcvTo0QPh4eEv7Q9j77RXftsVY++ZvLw8kkgktHz5cq35kyZNIrlcThqNhoiIlEolmZqaUkBAAKWmptLatWtJV1eXjh07JmwzYMAAGjZsGJ07d47S0tJo1qxZZGFhQXl5eUREtHDhQjI2NiYPDw+6ePEiJScnExHR3r17ad++fZSenk6JiYk0bNgwcnZ2pmfPnhHR8zdhA6ATJ05QTk6OUJ9arabhw4cL7fv4+JC1tTUdPnyYrly5Qmq1muRyuVC+/CV73bp1o6ioKLpy5Qr16tWLunfvXuU4PXr0iOzs7KhXr14UExND6enptGvXLjpz5gwREe3fv5/09PQoKCiIUlNT6bvvviNdXV2KjIwU6gBANjY2tGvXLkpNTSVPT09SqVTUr18/ioiIoKtXr9KHH35IHh4ewjblY9WvXz9KTEyk06dPU6tWreiTTz4Ryry4/0uXLqW2bdtSREQEZWZmUkhICOnr61NUVBQ9ffqU9u3bRwAoNTWVcnJyqKCg4D+30yYvL4+aN29OixcvppycHOHlqCEhIdSoUSPRPpiYmNDIkSPpypUr9Pvvv5NMJiN3d3eaMWMGXbt2jbZt20YARC8Z/q/jSJtGjRpReHi4KE2tVpOpqSlNmzaNrl27Rlu3biUA5O7uTsuWLaO0tDRasmQJ6enp0a1bt4iIaNWqVdSiRQuKjo6mrKwsiomJoR07dojqnTNnDvXu3bvKvjD2ruMgh9V7CQkJBIAOHDigNT8wMJAA0L1794joeZBT8SRMROTl5UWDBw8mIqKYmBgyMzOj0tJSURl7e3sKDg4moucnPT09PcrNzX1p3+7fv08A6NKlS0REdOPGDQJAiYmJonIVT/JFRUWkp6dHYWFhQn5ZWRlZW1vTypUriejfIOfEiRNCmUOHDhEAKikp0dqX4OBgMjU1rfIE2717d5o0aZIobdSoUTRkyBDhMwCaP3++8Dk+Pp4A0NatW4W0nTt3koGBgfB54cKFpKurS7dv3xbSjhw5Qjo6OkJQUXH/S0tLycjISAi+yk2cOJG8vb1F+//gwQMhvzrbaaNUKun7778XpWkLcoyMjKiwsFBIc3d3J5VKJQSwREQODg4UEBBARNU7jl704MEDAkDR0dGidLVaTUqlslJbvXr1Ej4/ffqUjI2NaefOnURENGPGDOrXr58Q3GuzZs0aUqlUVeYz9q7j21WswSCiapd1c3Or9DklJQUAkJycjKKiIlhYWMDExET4uXHjhug2jFKpRNOmTUX1pKenw9vbG3Z2djAzM4NKpQIAZGdnV7tvmZmZePLkieiZDD09PXTt2lXoY7kOHToIvzdr1gwAkJubq7XepKQkuLi4wNzcXGt+SkpKpedAevTo8dI2raysAADOzs6itNLSUhQWFgpptra2sLGxET67ublBo9Fovb2WkZGB4uJiDBw4UDT+P/30k2j839Z21aVSqWBqairaTycnJ+jo6IjSyse/usdRRSUlJQAg3P6sqF27dpXaqjjuurq6sLCwENofP348kpKS4ODgAB8fHxw7dqxSnYaGhiguLn6VYWDsnSKt6w4wVtNatWoFiUSClJQUjBgxolJ+SkoK5HJ5pYCkKkVFRWjWrBmioqIq5VV8TsPY2LhS/rBhw6BUKrF582ZYW1tDo9Ggffv2b/xgclX09PSE38ufJ9FoNFrLGhoa1libr9KP/1JUVAQAOHTokCgwAgB9ff23vl11VdxH4Pl+aksr3+/qHkcVWVhYQCKR4MGDB2/cfufOnXHjxg0cOXIEJ06cwOjRozFgwADRM0r5+fnV/rtg7F3EQQ6r9ywsLDBw4ECsX78efn5+opP53bt3ERYWhk8//VQ4+QJAQkKCqI6EhAQ4OjoCeH5yuHv3LqRSqXAlpjry8vKQmpqKzZs3o1evXgCA2NhYURmZTAYAePbsWZX1lD/4GxcXB6VSCQB48uQJzp07h5kzZ1a7Py/q0KEDtmzZgvz8fK1XcxwdHREXFwe1Wi2kxcXFwcnJ6bXbLJednY2///4b1tbWAJ6Pt46ODhwcHCqVdXJygr6+PrKzs9G7d2+t9Wkbx+psV1VdL5uP1/U6x5FMJoOTkxOuXr36Vr4nx8zMDF5eXvDy8sLIkSPh4eEhmv/Lly/DxcXljdthrK7w7SrWIPz44494/Pgx3N3dER0djVu3biEiIgIDBw6EjY1NpVUvcXFxWLlyJdLS0hAUFIQ9e/bA19cXADBgwAC4ubnB09MTx44dQ1ZWFs6cOYN58+a99MvT5HI5LCwssGnTJmRkZCAyMhJffvmlqIylpSUMDQ0RERGBe/fuaV1hZGxsjClTpsDf3x8RERG4evUqJk2ahOLiYkycOPG1x8jb2xsKhQKenp6Ii4vD9evXsW/fPsTHxwMA/P39ERoaig0bNiA9PR2BgYHYv38/Zs+e/dptljMwMIBarUZycjJiYmLg4+OD0aNHQ6FQVCpramqK2bNnw8/PD9u3b0dmZiYuXryIdevWYfv27QCe3yqUSCQ4ePAg7t+/j6Kiomptp41KpUJ0dDTu3LmDf/755433tdzrHkfu7u6VguPXERgYiJ07d+LatWtIS0vDnj17oFAoRFeRYmJiavRLBxmraRzksAahdevWOH/+POzs7DB69GjY29tj8uTJ6Nu3L+Lj4ytduZg1axbOnz8PFxcXLF26FIGBgXB3dwfw/JL/4cOH8b///Q8TJkxAmzZtMGbMGNy8eVN4BkUbHR0dhIeH48KFC2jfvj38/PywatUqURmpVIq1a9ciODgY1tbWGD58uNa6VqxYgY8//hjjxo1D586dkZGRgaNHj0Iul7/2GJUvsbe0tMSQIUPg7OyMFStWQFdXFwDg6emJNWvWYPXq1WjXrh2Cg4MREhIiLHN/E61atcJHH32EIUOGYNCgQejQoQPWr19fZfklS5ZgwYIFCAgIgKOjIzw8PHDo0CFhCb6NjQ2+/fZbzJ07F1ZWVpg+fXq1ttNm8eLFyMrKgr29/Vu9dfO6x9HEiRNx+PDhly6xrw5TU1OsXLkSrq6u+OCDD5CVlYXDhw8Lz/XEx8fj4cOHGDly5Bu1w1hdktCrPI3JWAOgUqkwc+bMN7r1w6pv0aJF+PXXXyt9yzOr2qhRo9C5c2d8/fXXNdaGl5cXOnbsiG+++abG2mCspvGVHMYYe8+sWrUKJiYmNVZ/WVkZnJ2d4efnV2NtMFYb+MFjxhh7z6hUKsyYMaPG6pfJZJg/f36N1c9YbeHbVYwxxhirl/h2FWOMMcbqJQ5yGGOMMVYvcZDDGGOMsXqJgxzGGGOM1Usc5DDGGGOsXuIghzHGGGP1Egc5jDHGGKuXOMhhjDHGWL3EQQ5jjDHG6qX/A7tMPdewS9isAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -308,7 +494,7 @@
     "    ax4.plot(x, y,label=key)\n",
     "\n",
     "ax4.legend()\n",
-    "ax4.set_xlim([0,8000])\n",
+    "ax4.set_xlim([0,10000])\n",
     "ax4.set_ylim([0,1])\n",
     "\n",
     "ax4.set_title(\"CDF Random sampling time\")\n",
@@ -316,38 +502,44 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "a3f8ed6f",
    "metadata": {},
    "source": [
-    "In this graph we observe the CDF of the nodes contacted required to complete the random sampling of the DAS protocol, with and without attackers in the simulation, from 0\\% to 50\\% of attackers in the simulation.\n",
-    "The hops required are between 50 and 75 for most of the cases , except for scenarios with 50\\%, that the nodes contacted can reach higher than 100, and without completing the sampling in some cases."
+    "In this graph we observe the CDF of the number of requests required to complete the random sampling process using the DAS protocol, with and without attackers in the simulation, from 0\\% to 75\\% of attackers in the simulation.\n",
+    "As we can see in the required time, we see a substantially increase when the number of malicious nodes in the network is very high (75\\%)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 30,
    "id": "666028c4",
-   "metadata": {
-    "vscode": {
-     "languageId": "python"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, '# hops')"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "KeyError",
+     "evalue": "'num_messages'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'num_messages'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[30], line 7\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m op_df:\n\u001b[1;32m      5\u001b[0m     rsdf \u001b[38;5;241m=\u001b[39m op_df[key]\u001b[38;5;241m.\u001b[39mloc[(op_df[key][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRandomSamplingOperation\u001b[39m\u001b[38;5;124m'\u001b[39m)]\n\u001b[0;32m----> 7\u001b[0m     x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msort(\u001b[43mrsdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnum_messages\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[1;32m      8\u001b[0m     N \u001b[38;5;241m=\u001b[39m rsdf[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnum_messages\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mcount()\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;66;03m# get the cdf values of y\u001b[39;00m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'num_messages'"
+     ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -363,30 +555,26 @@
     "\n",
     "    rsdf = op_df[key].loc[(op_df[key]['type'] == 'RandomSamplingOperation')]\n",
     "\n",
-    "    x = np.sort(rsdf['hops'])\n",
-    "    N = rsdf['hops'].count()\n",
+    "    x = np.sort(rsdf['num_messages'])\n",
+    "    N = rsdf['num_messages'].count()\n",
     "    # get the cdf values of y\n",
     "    y = np.arange(N) / float(N)\n",
     "\n",
     "    ax9.plot(x, y,label=key)\n",
     "\n",
     "ax9.legend\n",
-    "ax9.set_xlim([0,500])\n",
+    "#ax9.set_xlim([0,500])\n",
     "ax9.set_ylim([0,1])\n",
     "\n",
-    "ax9.set_title(\"CDF Random sampling hops\")\n",
-    "ax9.set_xlabel(\"# hops\")"
+    "ax9.set_title(\"CDF number of requests per random sampling process\")\n",
+    "ax9.set_xlabel(\"# Messages\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "1da370f8",
-   "metadata": {
-    "vscode": {
-     "languageId": "python"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -396,6 +584,18 @@
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/simulator/python/analysis_vs_evil_final.ipynb b/simulator/python/analysis_vs_evil_final.ipynb
new file mode 100644
index 00000000..27011113
--- /dev/null
+++ b/simulator/python/analysis_vs_evil_final.ipynb
@@ -0,0 +1,688 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f2905ced",
+   "metadata": {},
+   "source": [
+    "# DAS protocol\n",
+    "\n",
+    "* The network is formed of a single builder that is generating the block with all samples and `N` nodes distributed between validators and regular nodes.\n",
+    "* Builder knows all the validators\n",
+    "* All nodes have a unique 256 bits identifier, following the Kademlia procotol used in DEVP2P.\n",
+    "* We assume DAS protocol works on top of Kademlia DHT. We reuse identifiers and we can use the DHT to discover peers. \n",
+    "However it is not necessary to use DEVP2P or Kademlia as long as we have nodes with random uniformly distributed identifiers and a way to discover nodes and resolve nodes identifiers to connection parameters.\n",
+    "* Builder divides the hash space into 512*512 sample-specific regions, according to the block matrix.\n",
+    "* Builder transforms the 2D block matrix into a two 1D line of samples (1 line row-wise, and 1 line column-wise, i.e, 1 line row-wise means that after all samples in the first row we continue by the first sample of the second row. 1 line column-wise we mean we first start with all the samples of the first column and then we continue with the samples of the second column)\n",
+    "* We assign each sample with a 256 identifier in the hash space with the double 1D aligment. Every sample is defined by a double id in the hash space, one following row-wise and another column-wise order. This way when fetching rows we can find colocated samples, but also when fetching columns.\n",
+    "* Builder chooses the redundancy factor `redundancy` - the higher `redundancy` the higher overhead but the more resistant the scheme becomes to malicious validators.\n",
+    "* The region of samples is defined as all the validators such that `dict(c, v) <= ((2^256 -1) * redundancy * 2) / N_validators `\n",
+    "* The validator pushes each sample to all the validators within the sample's region (Note that it's done in batches - i.e., the builder connects to each validator and gives them all the samples they should hold).\n",
+    "\n",
+    "* When a new block is released, nodes got the samples from builder, according to the redundancy parameter and their identifier. \n",
+    "After getting the assigned samples, they start the sampling process.\n",
+    "* There are two types of sampling processes: validator rows/columns fetching and random sampling.\n",
+    "* Validator row/column consists in getting 2 rows and 2 columns of the block matrix, selected randomly.\n",
+    "* Validators perform rows/columns fetching and regular nodes random sampling only.\n",
+    "* The rows/columns fetching and random sampling follows the same process with the only difference of the selection of the samples to obtain. \n",
+    "We call sampling nodes, the nodes requesting samples, and serving nodes the nodes replying with samples:\n",
+    "    - Sampling nodes select a subset of replying nodes from a list of known nodes that are within the redundancy range following the id space for the samples requesting (how all nodes of the network are discovered is not included in this initial report, but will be in the following reports. Validators are supposed to know all other validators by default).\n",
+    "    - Sampling nodes send samples requests to the replying nodes selected. Samples requests include the wanted samples (all samples pending to be received are included in the requests).\n",
+    "    - Replying nodes reply with the samples requested they have, and also with known nodes to the same distance, from their nodes discovery table. This way nodes can easily discover kew nodes in the network.\n",
+    "    - Once a response is received from all replying nodes, or a configurable time out happened, the process is restarted selecting a new subset of replying nodes, in case there are still samples to be received. The number of replying nodes selected is increasing each round,  defined by an algorithm increasing an aggressiveness parameter each round.\n",
+    "    - Once all nodes known nodes are queried, extra nodes are added to the list and the process is repeated.\n",
+    "    - This extra nodes added consists of:\n",
+    "        - In case of row/column, validators in the range of the column id for each missing sample, in case of row fetching, or each row id in case of column sampling.\n",
+    "        - In case of random samplings, validators that requested in the range of the column or row id, even though not the specific sample.\n",
+    "    - For every increase of the aggressiveness params, validators increase the number of samples obtained from the builder, to protect from sybil attacks.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca444432",
+   "metadata": {},
+   "source": [
+    "## Malicious protocol (ignoring requests / sybil attack)\n",
+    "\n",
+    "* Some of both validator and regular nodes might be malicious. \n",
+    "* Malicious behaviour is just ignoring samples requests without sending any response.\n",
+    "\n",
+    "\n",
+    "## Simulation parameters\n",
+    "\n",
+    "* Number of nodes: 15000\n",
+    "* Number of attackers: Range from 0% to 90%\n",
+    "* Number of validators: 50% nodes are validators\n",
+    "* Redundancy parameter: 2\n",
+    "* Latency between nodes: 5ms - 125ms\n",
+    "* Number of samples per block : 512x512\n",
+    "* Sample size: 512 bytes\n",
+    "* Blocks: 1 blocks simulation.\n",
+    "* Number of random samples fetch: 75. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edd080e7",
+   "metadata": {},
+   "source": [
+    "The following cell is just loading traces files into a dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2983fa0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "ops_path = {'DAS': '../logsDasEvil0/operation.csv', \n",
+    "            'Evil-0.25': '../logsDasEvil0.25/operation.csv',\n",
+    "            'Evil-0.5': '../logsDasEvil0.5/operation.csv',\n",
+    "            'Evil-0.75': '../logsDasEvil0.75/operation.csv',\n",
+    "            'Evil-0.9': '../logsDasEvil0.9/operation.csv'\n",
+    "           }\n",
+    "msgs_path = {'DAS': '../logsDasEvil0/messages.csv', \n",
+    "            'Evil-0.25': '../logsDasEvil0.25/messages.csv',\n",
+    "            'Evil-0.5': '../logsDasEvil0.5/messages.csv',\n",
+    "            'Evil-0.75': '../logsDasEvil0.75/messages.csv',\n",
+    "            'Evil-0.9': '../logsDasEvil0.9/messages.csv'\n",
+    "          }\n",
+    "\n",
+    "\n",
+    "builder_address = '83814183170291850251680823880522715558189094423550585243365458794131648333116'\n",
+    "\n",
+    "op_df={}\n",
+    "msg_df={}\n",
+    "for key in ops_path:\n",
+    "    op_df[key] = pd.read_csv(ops_path[key],index_col=False,low_memory=False)\n",
+    "for key in msgs_path:\n",
+    "    msg_df[key] = pd.read_csv(msgs_path[key],index_col=False,low_memory=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6cc3d58",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0c418d95",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Malicious nodes')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "fig3, ax3 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in op_df:\n",
+    "\n",
+    "    vsdf = op_df[key].loc[(op_df[key]['type'] == 'ValidatorSamplingOperation')]\n",
+    "    data.append(vsdf['completion_time']/1000)\n",
+    "\n",
+    "ax3.boxplot(data)\n",
+    "\n",
+    "#ax3.legend()\n",
+    "#ax18.set_xlim([0,2])\n",
+    "ax3.set_ylim([0,12])\n",
+    "ax3.set_xticklabels([0,25,50,75,90])\n",
+    "ax3.set_title(\"Validator row/column fetching time (seconds)\")\n",
+    "ax3.set_xlabel(\"% Malicious nodes\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2af47752",
+   "metadata": {},
+   "source": [
+    "In this graph we observe the time required to complete the row/column fetching process in validators. All validators are able to complete the row/colum fetching in our simulations. We observe the time increaseas, as the number of sybil in the network increases, but without preventing validators to complete the process. In all cases the process is completed within the 4 seconds requirements, but in the case of 90% sybils, where 75% of processes are completed within 5 seconds. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "95bea67e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Malicious nodes')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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HncIB+F+3vXZbpe1bW9w4OTmV2P60sRLLli3D2LFj4eTkBB8fH/Tr1w9jxoxB8+bNpZhr167h3Xffxf79+4ttLyMjQ2dZe/73yVyaNWtW7JSJnZ1difk9+bpZW1vDwcGhUlNZS8qnXr16Ovu9evUqHBwcYGlpqRPn4eFR4f2VV0XeR41Gg4yMDNjb2yMpKQlCiBI/WwDKPdvnyc91eber/Tw++drUqlVL79fpiIyMxIcffojz58/rjBMo6dRbScdpWZ58/YGSPxfaIr6oosemt7c3rl69Cg8Pj2J5tWrVSmc5KSkJAMo8nZeRkYF69eqV67isiKlTp2LXrl0IDAxE06ZN8dJLL2HYsGF4+eWXdfL766+/ih0vWunp6QD+GS/n6OiI+vXrl7q/q1evwszMrNjnpEmTJqhbt+5T/64B5X8/nnyde/bsiSFDhmDJkiVYvXo1/P39ERQUhJEjRxb72y/+/ynHip7Ore5YfMigc+fO0jnIoKAgvPDCCxg5ciQuXbokfTvYvn07xo0bh6CgIMydOxeNGjWCubk5QkNDdQamapU2o0LIMGiptH1XNqdhw4bBz88Pe/fuxcGDB7F8+XJ88skniIiIQGBgINRqNV588UXcv38f8+fPh6enJ6ysrHDjxg2MGzeu2GAvfef3rKra7Betyr5OGo0GCoUCv/zyS4mxT37jLc2T58n1td2iSvuD/uRg35JER0dj0KBB6NGjBzZu3AgHBwfUrl0bW7duLTZQGSj+fJ7GGJ9H7bGyfPnyYuPItLSv89OOy9LY29ujsLAQWVlZOl9oGjVqhPPnz+PAgQP45Zdf8Msvv2Dr1q0YM2YMtm3bJuX34osvYt68eSVuu2XLlhV+zuX9T12f74dCoUB4eDhOnTqFH3/8EQcOHMCECROwcuVKnDp1SuezrC1uGjRoUOH9VGcsPmSmLSgCAgKwfv16LFiwAAAQHh6O5s2bIyIiQudgKalbsTxcXFwA/O+bTlGXLl0qFvvbb78V+2ORmJiosy1DcnBwwNSpUzF16lSkp6ejQ4cO+OijjxAYGIgLFy7g8uXL2LZtG8aMGSM9pqzu22eVlJSEgIAAaTk7OxtpaWno16+fQfbn4uKCI0eOIDc3V6f3Izk5uVyPl/Nbk7u7O4QQcHNzq9R/Bs+6Xe3nMTk5Wec9KiwsRGpqKp577jmpTdsj+PDhQ51tPPmttyR79uxBnTp1cODAAZ1vq1u3bi3X89EHFxeXYscrUPzYdHFxQXx8PIQQOp+FJx/r7u4OALC1tS1xNsqTyjouS+Pp6Qngn1kvRd8L4J9p3wMHDsTAgQOh0WgwdepUbNq0CYsWLYKHhwfc3d2RnZ391Nzc3d1x4MAB3L9/v9TeDxcXF2g0GiQlJekMvr59+zYePnxYqb9rLi4u5fqbqtW1a1d07doVH330Eb777juMGjUKO3fu1DktnJKSAuDpA8RNDcd8GIG/vz86d+6MNWvWSNcC0FbdRavs06dPIyYmplL7cHBwQPv27bFt2zad0xKHDh2SzhNr9evXD2q1GuvXr9dpX716NRQKRZl/aJ6VWq0udtqkUaNGcHR0lLq5S3pthBDFpujp0+bNm3VOfX322WcoLCw02GvRt29fPH78GFu2bJHaNBqNNEX4aaysrAAU/0/WEIKDg2Fubo4lS5YU+1YohMC9e/cMut2OHTvC3t4eW7ZskcahAMC3335b7BSa9j9b7VgB4J/P3ObNm5+aj7m5ORQKhU4vSWpqKvbt21fh51ZZ/fr1w5kzZ3T+DuTk5GDz5s1wdXWVxpj069cPN2/e1JkGnJubW+x5+vj4wN3dHStWrEB2dnax/d25cwdA+Y7L0nTr1g0AdKa8Aij2uTAzM5OKE+02hw0bhpiYGBw4cKDYdh8+fCi930OGDIEQAkuWLCkWp/3saL8oFJ0hAwCrVq0CgGKzlcqjX79+OHXqFM6cOSO13blzB99++61O3IMHD4p9hrU9TU++frGxsbCzs5Nm1NQU7Pkwkrlz52Lo0KEICwvD66+/jgEDBiAiIgKvvPIK+vfvj5SUFHz++edo3bp1iX8kyiM0NBT9+/fHCy+8gAkTJuD+/ftYt24d2rRpo7PNgQMHIiAgAP/+97+RmpqKdu3a4eDBg/jhhx8wc+ZM6Q+4IWRlZaFZs2YICQlBu3btYG1tjd9++w1nz57FypUrAfzzTcrd3R1vv/02bty4AVtbW+zZs+epY0meRUFBAXr37o1hw4bh0qVL2LhxI1544QUMGjTIIPsLCgpC586dMWfOHCQnJ8PT0xP79+/H/fv3ATy9Z6N9+/YwNzfHJ598goyMDCiVSvTq1QuNGjXSe67u7u748MMPsXDhQmmKq42NDVJSUrB3715MnjwZb7/9tsG2a2FhgcWLF+PNN99Er169MGzYMKSmpiIsLAzu7u46r1WbNm3QtWtXLFy4UPqWvHPnTp2ipTT9+/fHqlWr8PLLL2PkyJFIT0/Hhg0b4OHhgb/++qvCz68yFixYgB07diAwMBAzZsxA/fr1sW3bNqSkpGDPnj3SpcsnTZqE9evXY8yYMYiNjYWDgwO++eabYmOIzMzM8MUXXyAwMBBt2rTB+PHj0bRpU9y4cQNHjhyBra0tfvzxx3Idl6Vp3rw5vL298dtvv+kMXp44cSLu37+PXr16oVmzZrh69SrWrVuH9u3bS9/6586di/3792PAgAHSNNecnBxcuHAB4eHhSE1NRYMGDRAQEIBXX30Va9euRVJSEl5++WVoNBpER0cjICAA06dPR7t27TB27Fhs3rwZDx8+RM+ePXHmzBls27YNQUFBOr1m5TVv3jx88803ePnll/HWW29JU21dXFx0PhPbtm3Dxo0b8corr8Dd3R1ZWVnYsmULbG1ti/WeHjp0CAMHDqxxYz441daAtNP8SppupVarhbu7u3B3dxeFhYVCo9GIpUuXChcXF6FUKsXzzz8vIiMji03/007nXL58ebFt4olpo0L8M4XTy8tLKJVK0bp1axEREVHilMKsrCwxa9Ys4ejoKGrXri1atGghli9frjMdT7uPJ6fflZaTdjrj7t27S32N8vPzxdy5c0W7du2EjY2NsLKyEu3atRMbN27Uibt48aLo06ePsLa2Fg0aNBCTJk0Sf/75Z4lTW62srIrtp2fPnqJNmzbF2l1cXET//v2lZe17dvToUTF58mRRr149YW1tLUaNGiXu3btXbJvlmWpbUj7a6aZF3blzR4wcOVLY2NgIOzs7MW7cOHHixAkBQGeKcGm2bNkimjdvLszNzXWmV5Y21fbJ96W0z6s21zt37ui079mzR7zwwgvCyspKWFlZCU9PTzFt2jRx6dKlMvMsbXsV3e7atWul46Vz587ixIkTwsfHR7z88ss6cVeuXBF9+vQRSqVSNG7cWLzzzjvi0KFD5Zpq++WXX4oWLVoIpVIpPD09xdatW0t870o6Lsry5OdO68n3Spt/SEiIqFu3rqhTp47o3LmziIyMLPbYq1evikGDBglLS0vRoEED8dZbb4lff/21xCnYf/zxhwgODhb29vZCqVQKFxcXMWzYMPH7778LIcp/XJZm1apVwtraWmeqcnh4uHjppZdEo0aNhIWFhXB2dhZTpkwRaWlpOo/NysoSCxcuFB4eHsLCwkI0aNBA+Pr6ihUrVuhMDS8sLBTLly8Xnp6ewsLCQjRs2FAEBgaK2NhYKebx48diyZIlws3NTdSuXVs4OTmJhQsXikePHunssyLvx19//SV69uwp6tSpI5o2bSo++OAD8eWXX+pMtY2LixMjRowQzs7OQqlUikaNGokBAwaIc+fO6WwrISFBuhxDTaMQooZdVo2oDGFhYRg/fjzOnj0rDRI2pn379uGVV17B8ePH0b17d2OnU6VpNBo0bNgQwcHBOqevSH4ZGRlo3rw5li1bhtdee83Y6VRZM2fOxLFjxxAbG1vjej445oOoinjyUvFqtRrr1q2Dra1tmVeUrIkePXpU7Jz6119/jfv37+tcXp2Mw87ODvPmzcPy5ctLvPQ4/TMG5osvvsCHH35Y4woPgGM+iKqMN998E3l5eejWrRvy8/MRERGBkydPYunSpRWexmnqTp06hVmzZmHo0KGwt7dHXFwcvvzyS3h7e0v3uSHjmj9/PubPn2/sNKose3v7So/nMwUsPoiqiF69emHlypWIjIzEo0eP4OHhgXXr1mH69OnGTq3KcXV1hZOTE9auXSsNJB0zZgw+/vjjKn8HXyICOOaDiIiIZMUxH0RERCQrFh9EREQkqyo35kOj0eDmzZuwsbGpkSOAiYiIqiMhBLKysuDo6ChdAK80Va74uHnzZrG7ahIREVH1cP36dTRr1qzMmCpXfGhvbHb9+nXY2toaORsiIiLDUqvVOHnyJG7duoUmTZrA19e3yt4NuyyZmZlwcnLSuUFpaapc8aE91WJra8vig4iITFpERATmzJmD1NRUqc3V1RUrV65EcHCw8RJ7BuUZMsEBp0REREYQERGBkJAQtG3bFjExMcjKykJMTAzatm2LkJAQREREGDtFg6ly1/nIzMyEnZ0dMjIy2PNBREQmSa1Ww8PDA23btsW+fft0BmhqNBoEBQUhPj4eSUlJ1eYUTEX+/2bPBxERkcyio6ORmpqKd955p9jMEDMzMyxcuBApKSmIjo42UoaGxeKDiIhIZmlpaQAAb2/vEtdr27VxpobFBxERkcwcHBwAAPHx8VCr1YiKisKOHTsQFRUFtVqN+Ph4nThTwzEfREREMtOO+WjQoAHu3LmDq1evSutcXFzQsGFD3Lt3j2M+iIiISD/Mzc0xdOhQnDt3Do8ePcLmzZtx8+ZNbN68GY8ePcK5c+cQEhJSbQqPimLPBxERkcyK9nzcvXtX5zofbm5usLe3N+mejyp3kTEiIiJTp53tsmPHDnTq1AnR0dFIS0uDg4MD/Pz8cObMGfj6+iI6Ohr+/v7GTlfvWHwQERHJrOhsF3Nz82IFBme7EBERkV4Vne1SEs52kRnHfBARkakreoXTPXv24MSJE9Jpl+7du2PIkCEmfYVTnnYhIiKSmbm5OVauXImQkBDY2dkhLy9PWqdSqfDo0SOEh4dXm8KjonjahYiIyEhKOvmgUChKbDclPO1CREQks6KnXXbt2oXPP/8cV65cgbu7O15//XUMGzaMp12IiIhIf7RTbadMmQIvLy+d63x8+umnmDx5Mn788UeTnWrL0y5EREQy006hfeedd9C2bVvExMQgKysLMTExaNu2Lf7973/rxJmaChcfx44dw8CBA+Ho6AiFQoF9+/aVGvv6669DoVBgzZo1z5AiERGRaWnUqBEAoHv37ti3bx+6du0Ka2trdO3aFfv27UP37t114kxNhYuPnJwctGvXDhs2bCgzbu/evTh16hQcHR0rnRwREZGpKygowJo1a/Dmm29izZo1KCgoMPkBpxUe8xEYGIjAwMAyY27cuIE333wTBw4cQP/+/SudHBERkSlKT08HABw/fhwqlUpn3axZs4rFmRq9j/nQaDR49dVXMXfuXLRp00bfmyciIqr2ynvlUlO9wqneZ7t88sknqFWrFmbMmFGu+Pz8fOTn50vLmZmZ+k6JiIioSunSpYv0e+3atdGjRw84ODggLS0Nx44dw+PHj4vFmRK9Fh+xsbH49NNPERcXB4VCUa7HhIaGYsmSJfpMg4iIqEpbu3at9HutWrXw+++/S8sqlUoqPtauXYv58+fLnp+h6fW0S3R0NNLT0+Hs7IxatWqhVq1auHr1KubMmQNXV9cSH7Nw4UJkZGRIP9evX9dnSkRERFXO9u3bpd8fPXqks67octE4U6LXno9XX30Vffr00Wnr27cvXn31VYwfP77ExyiVSiiVSn2mQUREVKUVHW7w5MyWostF40xJhYuP7OxsJCcnS8spKSk4f/486tevD2dnZ9jb2+vE165dG02aNEGrVq2ePVsiIiIT0LJlSyQlJQEAGjZsiKVLl2LAgAGIjIzEO++8gzt37khxpqjCxce5c+cQEBAgLc+ePRsAMHbsWISFhektMSIiIlPl6emJn376CcA/s0QTEhJQUFCAK1euQKPR6MSZogoXH/7+/hW6+EnR69UTERER8Oeff0q/37t3D6tWrXpqnCnhvV2IiIhkZmVlpde46obFBxERkcy0927RV1x1w+KDiIhIZt7e3nqNq25YfBAREcns22+/1WtcdcPig4iISGYpKSkAAGtr6xLXa9u1caaGxQcREZHMtHeyzc7OLnZXW5VKhezsbJ04U6P3G8sRERFR2Xx8fKT7uVhbW2PAgAGwtrZGdnY2oqKikJeXJ8WZIhYfREREMmvYsKH0+507d7B79+6nxpkSnnYhIiKS2YMHD/QaV92w+CAiIpJZea8UXpErilcnLD6IiIhk9vDhQ73GVTcsPoiIiGTGng8iIiKSVdE71+ojrrph8UFERCSzy5cv6zWuumHxQUREJLPk5GS9xlU3LD6IiIhkZmtrq9e46obFBxERkcxcXFz0GlfdsPggIiKSWUZGhl7jqhsWH0RERDIrLCzUa1x1w+KDiIhIZhcuXJB+VygUOuuKLheNMyW8sRwREZHMtHetBQB7e3sEBATAysoKOTk5OHLkCO7evVsszpSw+CAiItKj3NxcJCYmlhmjUCikq5fevXu31LvaKhQKxMXFlbodT09PWFpaVj5ZI2HxQUREpEeJiYnw8fHRy7aEEGVuKzY2Fh06dNDLvuTE4oOIiEiPPD09ERsbW2ZMRkYGevXqJS1bWloiNzdX+lfr8OHDsLOzK3Nf1RGLDyIiIj2ytLQsV2+Eu7s7rly5AgBSwVG08HB3d0dAQIBhkjQyznYhIiIyguTkZLi7u5e4zt3d3WQvrQ6w+CAiIjKa5ORkPHz4EO3atQMAtGvXDg8fPjTpwgNg8UFERGRUdnZ2+OqrrwAAX331VZljPEwFiw8iIiKSFYsPIiIikhWLDyIiIpIViw8iIiKSFYsPIiIikhWLDyIiIpIViw8iIiKSFYsPIiIikhWLDyIiIpIViw8iIiKSFYsPIiIiklWFi49jx45h4MCBcHR0hEKhwL59+6R1jx8/xvz589G2bVtYWVnB0dERY8aMwc2bN/WZMxEREVVjFS4+cnJy0K5dO2zYsKHYutzcXMTFxWHRokWIi4tDREQELl26hEGDBuklWSIiIqr+alX0AYGBgQgMDCxxnZ2dHQ4dOqTTtn79enTu3BnXrl2Ds7Nz5bIkIiIik1Hh4qOiMjIyoFAoULdu3RLX5+fnIz8/X1rOzMw0dEpERERkRAYdcPro0SPMnz8fI0aMgK2tbYkxoaGhsLOzk36cnJwMmRIREREZmcGKj8ePH2PYsGEQQuCzzz4rNW7hwoXIyMiQfq5fv26olIiIiKgKMMhpF23hcfXqVRw+fLjUXg8AUCqVUCqVhkiDiIiIqiC9Fx/awiMpKQlHjhyBvb29vndBRERE1ViFi4/s7GwkJydLyykpKTh//jzq168PBwcHhISEIC4uDpGRkVCr1bh16xYAoH79+rCwsNBf5kRERFQtVbj4OHfuHAICAqTl2bNnAwDGjh2LxYsXY//+/QCA9u3b6zzuyJEj8Pf3r3ymREREZBIqXHz4+/tDCFHq+rLWEREREfHeLkRERCQrFh9EREQkKxYfREREJCsWH0RERCQrFh9EREQkKxYfREREJCsWH0RERCQrFh9EREQkKxYfREREJCsWH0RERCQrFh9EREQkKxYfREREJCsWH0RERCQrFh9EREQkKxYfREREJCsWH0RERCQrFh9EREQkq1rGToCIiKiqS0pKQlZWlsG2n5CQoPOvodjY2KBFixYG3Ud5sPggIiIqQ1JSElq2bCnLvkaPHm3wfVy+fNnoBQiLDyIiojJoezy2b98OLy8vg+wjLy8PqampcHV1hUqlMsg+EhISMHr0aIP24JQXiw8iIqJy8PLyQocOHQy2/e7duxts21UNB5wSERGRrFh8EBERkaxYfBAREZGsWHwQERGRrFh8EBERkaxYfBAREZGsWHwQERGRrFh8EBERkaxYfBAREZGsWHwQERGRrFh8EBERkaxYfBAREZGsWHwQERGRrFh8EBERkaxYfBAREZGsWHwQERGRrFh8EBERkaxqVfQBx44dw/LlyxEbG4u0tDTs3bsXQUFB0nohBN577z1s2bIFDx8+RPfu3fHZZ5+hRYsW+sybiIhINk2sFVA9vAzcrL7f2VUPL6OJtcLYaQCoRPGRk5ODdu3aYcKECQgODi62ftmyZVi7di22bdsGNzc3LFq0CH379sXFixdRp04dvSRNREQkpyk+FvA6NgU4ZuxMKs8L/zyPqqDCxUdgYCACAwNLXCeEwJo1a/B///d/GDx4MADg66+/RuPGjbFv3z7861//erZsiYiIjGBTbAGGvxsGL09PY6dSaQmJidi0ciQGGTsRVKL4KEtKSgpu3bqFPn36SG12dnbo0qULYmJiSiw+8vPzkZ+fLy1nZmbqMyUiIqJnditbIK9uS8CxvbFTqbS8WxrcyhbGTgOAngec3rp1CwDQuHFjnfbGjRtL654UGhoKOzs76cfJyUmfKREREVEVY/SRMwsXLkRGRob0c/36dWOnRERERAak1+KjSZMmAIDbt2/rtN++fVta9ySlUglbW1udHyIiIjJdei0+3Nzc0KRJE/z+++9SW2ZmJk6fPo1u3brpc1dERERUTVV4wGl2djaSk5Ol5ZSUFJw/fx7169eHs7MzZs6ciQ8//BAtWrSQpto6OjrqXAuEiIiIaq4KFx/nzp1DQECAtDx79mwAwNixYxEWFoZ58+YhJycHkydPxsOHD/HCCy/g119/5TU+iIiICEAlig9/f38IUfpUHYVCgffffx/vv//+MyVGREREpkmv1/kgIiIyNbm5uQCAuLg4g+0jLy8PqampcHV1hUqlMsg+EhISDLLdymDxQUREVIbExEQAwKRJk4yciX7Y2NgYOwUWH0RERGXRTpjw9PSEpaWlQfaRkJCA0aNHY/v27fDy8jLIPoB/Co+qcKNXFh9ERERlaNCgASZOnCjLvry8vNChQwdZ9mVMRr/CKREREdUsLD6IiIhIViw+iIiISFYsPoiIiEhWLD6IiIhIViw+iIiISFYsPoiIiEhWLD6IiIhIViw+iIiISFYsPoiIiEhWLD6IiIhIViw+iIiISFYsPoiIiEhWLD6IiIhIViw+iIiISFYsPoiIiEhWLD6IiIhIViw+iIiISFYsPoiIiEhWLD6IiIhIVrWMnQAREclPrVYjOjoaaWlpcHBwgJ+fH8zNzY2dFtUQ7PkgIqphIiIi4OHhgYCAAIwcORIBAQHw8PBARESEsVOjGoLFBxFRDRIREYGQkBC0bdsWMTExyMrKQkxMDNq2bYuQkBAWICQLFh9ERDWEWq3GnDlzMGDAAOzZswePHj3Cjz/+iEePHmHPnj0YMGAA3n77bajVamOnSiaOxQcRUQ0RHR2N1NRU+Pr6omXLljqnXVq2bIlu3bohJSUF0dHRxk6VTBwHnBIR1RBpaWkAgIULF2LAgAGYO3cuVCoV8vLy8Msvv+Cdd97RiSMyFBYfREQ1RKNGjQAAnp6eiI+PR2RkpLTO1dUVnp6eSExMlOKIDIWnXYiIapjExER4e3vrDDj19vZGYmKisVOjGoLFBxFRDXHr1i2dZSGE9FNWHJG+sfggIqoh7ty5AwB44403EB8fD19fX9ja2sLX1xf/+c9/8Prrr+vEERkKx3wQEdUQDRs2BACkpqbi8uXLOHHihHSF0+7du2Pw4ME6cUSGwp4PIqIaomnTpgCAX375BUOGDIFSqcSAAQOgVCoxZMgQ/PLLLzpxRIbCng8iohrCz88Prq6uaNCgAf7880/4+vpK61xcXNCxY0fcu3cPfn5+RsySagIWH0RENYS5uTlWrlyJIUOGQKVS6axLT0/H1atXsWfPHt5gjgxO76dd1Go1Fi1aBDc3N6hUKri7u+ODDz4oNpqaiIiM59GjR2UuExmS3ns+PvnkE3z22WfYtm0b2rRpg3PnzmH8+PGws7PDjBkz9L07IiIqJ7VajTfeeAMA0K9fP/Tr10+6wunPP/+Mn376CW+88QYGDx7M3g8yKL0XHydPnsTgwYPRv39/AP9cNW/Hjh04c+aMvndFREQVEBUVhfT0dLzwwgvYv38/zMz+1/n9+uuvo0ePHjhx4gSioqLQu3dvI2ZKpk7vp118fX3x+++/4/LlywCAP//8E8ePH0dgYKC+d0VERBUQFRUFAFiyZAny8/Mxffp09O3bF9OnT0d+fj4WL16sE0dkKHrv+ViwYAEyMzPh6ekJc3NzqNVqfPTRRxg1alSJ8fn5+cjPz5eWMzMz9Z0SEREVsWjRIpw8eVJaPnjwIDZs2KAz+4XIkPTe87Fr1y58++23+O677xAXF4dt27ZhxYoV2LZtW4nxoaGhsLOzk36cnJz0nRIREQHw9/cH8M/pcQsLCyxYsADJyclYsGABLCwspIJEG0fyUKvVOHfuHADg3LlzUKvVRs7I8BRCz9NQnJycsGDBAkybNk1q+/DDD7F9+/YSb1pUUs+Hk5MTMjIyYGtrq8/UiIhqtOzsbNjY2AAA+vbtC2trazx48AD16tVDdnY2Dhw4AADIysqCtbW1MVOt1nJzc8t9k77Dhw9j9erVuHnzptTm6OiIWbNmoVevXk99vKenJywtLSudqz5lZmbCzs6uXP9/6/20S25urs4gJuCfueUajabEeKVSCaVSqe80iIjoCQsWLJB+1xYapcWtX79ejpRMUmJiInx8fCr9+Js3b2Lu3Lnlio2NjUWHDh0qvS9j0XvxMXDgQHz00UdwdnZGmzZt8Mcff2DVqlWYMGGCvndFREQVkJSUpNc4KpmnpydiY2PLjFGr1QgKCoKHhwdWrlyJ/Px8pKamwtXVFUqlEnPmzMGVK1ewd+/eMqc9e3p66jt9Wei9+Fi3bh0WLVqEqVOnIj09HY6OjpgyZQreffddfe+KiIgqwMXFRa9xVDJLS8un9kZERUXh5s2b2LNnDzp27AgA6N69u7T+448/hq+vL3JyckxyDI7eiw8bGxusWbMGa9as0femiYjoGRQdyKhQKHSuPF10uSYMeDS2tLQ0AIC3t3eJ67Xt2jhTw7vaEhHVENHR0dLvT841KLpcNI4Mw8HBAQAQHx9f4nptuzbO1LD4ICKqIcp7HSVeb8nwtHcYXrp0abEJGRqNBqGhoXBzczPZOwyz+CAiqiHq168v/a5QKHTWFV0uGkeGob3DcGRkJIKCghATE4OsrCzExMQgKCgIkZGRWLFihcneY4fFBxFRDdG4cWPp97JOuxSNI8MJDg5GeHg4Lly4AF9fX9ja2sLX1xfx8fEIDw9HcHCwsVM0GL0POCUioqrpyd6OZ42jZxccHIzBgwcjOjoaaWlpcHBwgJ+fn8n2eGix+CAiqiGKXk1aH3GkH+bm5iY5nbYsPO1CRFRDWFhY6DWOqLJYfBAR1RAPHjzQaxxRZfG0CxFRDfHf//5Xr3GkHwUFBdi4cSOuXLkCd3d3TJ061eR7n1h8EBHVEI8fP9ZrHD27efPmYfXq1SgsLJTa5s6di1mzZmHZsmVGzMywWHwQEdUQT95x/Fnj6NnMmzcPy5cvR6NGjTBmzBg0b94cf//9N77++mssX74cAEy2AFGIJyd7G1lmZibs7OyQkZEBW1tbY6dDRGQyHB0dy3WvEAcHB9y8eVOGjGqugoICWFlZwcrKCnXr1sXVq1eldS4uLnj48CFycnKQk5NTbU7BVOT/b5a3REQ1RMOGDfUaR5W3ceNGFBYWIiMjA+np6Trr0tPTkZGRgcLCQmzcuNFIGRoWiw8iohoiJydHr3FUeUlJSdLveXl5OuuKLheNMyUsPoiIaoi7d+/qNY4qr+jN5J4cY1N0+cmbzpkKFh9ERDUEr3BadVhbW0u/9+3bV+fGcn379i0xzpRwtgsRkQnIzc1FYmJimTGWlpZ49OgRAKBWrVo60zuLLltaWiIuLq7U7Xh6esLS0lIPWddc58+fl36Pi4vDX3/9BWdnZ/z11186r33ROFPC4oOIyAQkJibCx8en3PFFC48nl+/fv1/mtmJjY9GhQ4eKJ0kS7bgOe3t73L17F1OmTJHWmZubo379+rh//36x8SCmgsUHEZEJ8PT0RGxsbJkx2dnZ6Nmz51O3dfTo0TK7+z09PSucH+lydXXFiRMncO/ePfTv3x8eHh7Iy8uDSqVCcnIyfvrpJynOFPE6H0RENUjnzp1x9uzZUtd36tQJZ86ckTGjmungwYPS2A6VSqXTw1F0+cCBA3jppZeMkmNF8TofRERUojNnzqBTp04lrmPhIZ/evXtL/0Frx+FoaZdtbW3Ru3dv2XOTA4sPIqIa5syZM8jKypJOwfTs2RNZWVksPGRkbm6OrVu3lhmzdetWmJuby5SRvFh8EBHVQNbW1li1ahUAYNWqVSY7pbMqCw4Oxp49e+Dk5KTT7uzsjD179iA4ONhImRkeB5wSEREZSXBwMAYPHozo6GikpaXBwcEBfn5+JtvjocXig4iIyIjMzc3h7+9v7DRkxdMuREREJCsWH0RERCQrFh9EREQkKxYfREREJCsWH0RERCQrFh9EREQkKxYfREREJCsWH0RERCQrFh9EREQkK17hlIiIyIjUanWNu7w6ez6IiIiMJCIiAh4eHggICMDIkSMREBAADw8PREREGDs1g2LxQUREZAQREREICQlB27ZtERMTg6ysLMTExKBt27YICQkx6QJEIYQQxk6iqMzMTNjZ2SEjIwO2trbGToeIyGTFxcXBx8cHsbGx6NChg7HTqVHUajU8PDzQtm1b7Nu3D2Zm/+sL0Gg0CAoKQnx8PJKSkqrNKZiK/P/Nng8iIiKZRUdHIzU1Fe+8845O4QEAZmZmWLhwIVJSUhAdHW2kDA3LIMXHjRs3MHr0aNjb20OlUqFt27Y4d+6cIXZFRERU7aSlpQEAvL29S1yvbdfGmRq9Fx8PHjxA9+7dUbt2bfzyyy+4ePEiVq5ciXr16ul7V0RERNWSg4MDACA+Pr7E9dp2bZyp0ftU208++QROTk7YunWr1Obm5qbv3RBRNVNQUICNGzfiypUrcHd3x9SpU2FhYWHstIiMws/PD66urli6dGmJYz5CQ0Ph5uYGPz8/I2ZpOHrv+di/fz86duyIoUOHolGjRnj++eexZcuWUuPz8/ORmZmp80NEpmXevHmwsrLCrFmzsH79esyaNQtWVlaYN2+esVMjMgpzc3OsXLkSkZGRCAoK0pntEhQUhMjISKxYsaLaDDatKL0XH3///Tc+++wztGjRAgcOHMAbb7yBGTNmYNu2bSXGh4aGws7OTvpxcnLSd0pEZETz5s3D8uXLodFodNo1Gg2WL1/OAoRqrODgYISHh+PChQvw9fWFra0tfH19ER8fj/DwcAQHBxs7RYPR+1RbCwsLdOzYESdPnpTaZsyYgbNnzyImJqZYfH5+PvLz86XlzMxMODk5caotkQkoKCiASqWCRqNB7dq18fjxY2mddtnMzAx5eXk8BWMEnGpbNZjKFU4rMtVW72M+HBwc0Lp1a502Ly8v7Nmzp8R4pVIJpVKp7zSIqApYt26d1ONRWFios067rNFosG7dOsyZM0f2/Kq6pKQkZGVlGWz7CQkJOv8ago2NDVq0aGGw7ZsCc3Nz+Pv7GzsNWem9+OjevTsuXbqk03b58mW4uLjoe1dEVMUVvUbBk52sRZejo6NZfDwhKSkJLVu2lGVfo0ePNuj2L1++zAKEdOi9+Jg1axZ8fX2xdOlSDBs2DGfOnMHmzZuxefNmfe+KiKq48n5rN+S3++pK+5ps374dXl5eBtlHXl4eUlNT4erqCpVKpfftJyQkYPTo0Xx/qRi9Fx+dOnXC3r17sXDhQrz//vtwc3PDmjVrMGrUKH3vioiquCcHmT5rXE3k5eVl0PEY3bt3N9i2iUqj9+IDAAYMGIABAwYYYtNEVI2kp6frLLds2RL169fH/fv3cfny5VLjiMi0GaT4ICICgJycHJ3logVHWXFEZNp4YzkiIiKSFYsPIjKYJk2a6DWOiEwDiw8iMhiFQqHXOCIyDRzzQUQGk5ubq9e4mqaJtQKqh5eBm9Xze6Lq4WU0sWZhScWx+CAig0lOTtZrXE0zxccCXsemAMeMnUnleOGf50D0JBYfRGQwRe/loo+4mmZTbAGGvxsGL09PY6dSKQmJidi0ciQGGTsRqnJYfBCRwahUqnIVFoa4uqYpuJUtkFe3JeDY3tipVEreLQ1uZev13qVkIqrniUQiqhbKez8P3veDqGZh8UFEBpOamqrXOCIyDSw+iMhgONuFiErCMR9k0tRqNaKjo5GWlgYHBwf4+fnB3Nzc2GnVGGZm5ft+U944IjINPOLJZEVERMDDwwMBAQEYOXIkAgIC4OHhgYiICGOnVmNYWlrqNY6ITAN7PsgkRUREICQkBP369cPgwYORl5cHlUqF5ORkhISEIDw8HMHBwcZO0+Tl5eXpNY6ITAOLDzI5arUac+bMQfPmzfHLL79Ao9FI68zMzNC8eXO8/fbbGDx4ME/BGNijR4/0GkdEpoHFB5mc6OjoUmdPaDQaXLlyRYrz9/eXL7EaqLCwUK9xNYl2EG5cXJzB9pGXl4fU1FS4uroa5ForCQkJet8mmQYWH2Ryrl+/rtc4ImNITEwEAEyaNMnImTw7GxsbY6dAVQyLDzI5x46V70YYx44dw6uvvmrgbExbbm6u9J/ks3raN3xPT88aNTA1KCgIgGGfd0JCAkaPHo3t27fDy8vLIPuwsbHhReSoGBYfZHIOHjyo1zgqXWJiInx8fPSyradtJzY2Fh06dNDLvqqDBg0aYOLEibLsy8vLq0a9tmR8LD7I5Ny+fVuvcVQ6T09PxMbGlrr+/PnzeO211566nS+//BLt27d/6r6IyDSw+CCTk5+fr9c4Kp2lpWWZ35g7dOhQruJjwoQJ+kyLiKo4XmSMiAxKiLLvavq09URkelh8EJHBCSFw+vRpnbbTp0+z8CCqoVh8EJEsOnfuLI0PiY2NRefOnY2cEREZC4sPIiIikhWLDyIiIpIViw8iIiKSFYsPIiIikhWLDyIiIpIViw8iIiKSFYsPIiIikhWLDyIiIpIViw8iIiKSFYsPIiIikhWLDyIiIpIViw8iIiKSFYsPIiIikhWLDyIiIpKVwYuPjz/+GAqFAjNnzjT0roiIiKgaMGjxcfbsWWzatAnPPfecIXdDRERE1YjBio/s7GyMGjUKW7ZsQb169Qy1GyIiIqpmDFZ8TJs2Df3790efPn3KjMvPz0dmZqbODxEREZmuWobY6M6dOxEXF4ezZ88+NTY0NBRLliwxRBpERERUBem9+Lh+/TreeustHDp0CHXq1Hlq/MKFCzF79mxpOTMzE05OTvpOi4jIpOXm5iIxMbFCj0lISND5t7w8PT1haWlZoccQFaX34iM2Nhbp6eno0KGD1KZWq3Hs2DGsX78e+fn5MDc3l9YplUoolUp9p0FEVKMkJibCx8enUo8dPXp0heJjY2N1/sYTVZTei4/evXvjwoULOm3jx4+Hp6cn5s+fr1N4EFHVk5SUhKysLINsu7LftCvKxsYGLVq0MOg+qhpPT0/ExsZW6DF5eXlITU2Fq6srVCpVhfZF9Cz0XnzY2NjA29tbp83Kygr29vbF2k2VWq1GdHQ00tLS4ODgAD8/PxZdVC0kJSWhZcuWBt9PRb9pV8bly5drVAFiaWlZqd6I7t27GyAborIZZMBpTRYREYE5c+YgNTVVanN1dcXKlSsRHBxsvMSIykHb47F9+3Z4eXnpffuV/aZdEQkJCRg9erTBem+I6NnJUnxERUXJsRuji4iIQEhICAYMGIAdO3bA29sb8fHxWLp0KUJCQhAeHs4CRA8qM7CuNHFxcWWur6kD67y8vAx2Tp/ftKsG9tCSMbHnQ0/UajXmzJmDAQMGYN++fTAz++cSKl27dsW+ffsQFBSEt99+G4MHD+YB/oyeZWDdk562HQ6sI1MUERGB2bNn4+rVq1Kbi4sLVq1axS9IJAsWH3oSHR2N1NRU7NixQyo8tMzMzLBw4UL4+voiOjoa/v7+xknSRDxtYF1FCpOnDdDjwDoyNRERERgyZEix9qtXr2LIkCHYs2cPCxAyOBYfepKWlgYApQ6q1bZr46jynjawTvsH9GmGDBnCXg2qUdRqNcaPH19mzPjx49lDSwZn8Lva1hQODg4AgPj4+BLXa9u1cWQ44eHheo0jMhW///77U29hkZmZid9//12mjKimYvGhJ35+fnB1dcXSpUuh0Wh01mk0GoSGhsLNzQ1+fn5GyrBmEUI803oiU/Tll19Kv5uZmaFFixZo2bIlWrRooXO6uGgckSGw+NATc3NzrFy5EpGRkQgKCkJMTAyysrIQExODoKAgREZGYsWKFezKlJEQoti57SFDhrDwoBrr6NGj0u8ajQZJSUm4fPkykpKSdL40FY0jMgQWH3oUHByM8PBwXLhwAb6+vrC1tYWvry/i4+M5zdZIwsPDpUGlsbGxPNVCNVp57xrOu4uToXHAqZ4FBwdj8ODBnD9fAYa8nDfAS3oTadWpUwd5eXnliiMyJBYfBmBubs7ptOUk1+W8AV7Sm8ja2hoPHjwoVxyRIbH4MIDs7Gy8+uqruHLlCtzd3fHNN9/wYC6FoS/nDfCS3kRaT16D6FnjiCqLxYeede7cGWfPnpWWL1y4ABsbG3Tq1AlnzpwxYmZVmyEv5w3wkt5EAKBUKvUaR1RZLD706MnCo6izZ8+ic+fOLEBK0MRaAdXDy8DN6vttS/XwMppYK4ydBlGZBg4ciJUrV5YrjsiQWHzoSXZ2dqmFh9bZs2eRnZ3NUzBPmOJjAa9jU4Bjxs6k8rzwz/MwBdW9GGQhWDpHR0fpd4VCgRYtWqBevXp48OABkpKSpGnoReOIDIHFh54EBQWVO+63334zbDLVSG5uLjbFFqDdsAUGu49Kfn4+bt68CUdHR4N1J6ekpGBT7L8xyCBbl1d1LwZNqRDUt8aNG0u/CyFw+fLlp8YRGQKLDz0p7+WIedliXYmJibiVLRA8bYmxU9ELGxsbY6fwzDbFFmD4u2HwqqY31UtITMSmlSNNohDUt6ZNm0q/K5VK5Ofnl7hcNI7IEFh8kFFpe4w8PT1haWlpkH1oZ6IYckYNYBrX+cjNzcWtbIETf2cjr67m6Q+oIFlmHqWpcSubV7EtifY2EA0aNEB6ejquXbsmrWvSpAkaNmyIe/fu8TYQZHAsPsioGjRogIkTJ8qyL0PPqDEFiYmJAIBJkyYZOZNnZwq9UPqmvQ1ESEgI+vfvj3nz5kGlUiEvLw+//vorfvrpJ4SHh/OiiGRwLD6ISGLonij2Qhmf9jYQc+bMQWRkpNTu5ubG20CQbFh8EJFErp4o9kIZF28DQcbG4oOIqAbibSDImFh8lFNubq50PvxZxcXFlbrOkAMvTUVF34tnubEc3w8iIv1j8VFOiYmJ8PHx0cu2ytpObGwsu6OforLvRWVuLMf3g4hI/1h8lJOnpydiY2NLXV+R/wzL2o6hLrRlSp72XhSlVqtx6tQpJCYmwtPTE127dq3QeW2+H0RE+sfio5wsLS3L/AYcEBCAI0eOPHU7AQEB/Cb9jJ72XmhFRERgzpw5SE1NldpcXV2xcuVKjugnIjKiGlt8JCUl6fX25ytWrChX78eKFSvKHPNRUZxSWLKIiAiEhIRgwIAB2LFjB7y9vREfH4+lS5ciJCSEUwqJiIxIIbR3EqoiMjMzYWdnh4yMDNja2hpkH0lJSejRoRUcTODmU2nZAsfiLrEAKUKtVsPDwwNt27bFvn37YGb2vxukaTQaBAUFIT4+HklJSZxaKLO4uDj4+PhwLA2RCarI/981sufj9u3bmOJjgcX+hrnJmJwWR+XrtQfHFERHRyM1NRU7duzQKTwAwMzMDAsXLoSvry+io6M51ZCIyAhqZPGRmJiITbEF2H/psbFTeWZp2QKjeBlpHWlpaQAAb2/vEtdr27VxREQkrxpZfBjqEtLlGfNR3lka5cUxH8U5ODgAAOLj49GpU6diV3GMj4/XiaPK4zVXiKgyauSYD0O4dOlSuaZlJiYmolWrVjJkVHNpx3w0aNAAd+/eLTbbpUGDBrh37x7HfOiBdgyHHDhOhKhq45gPA3jaN7xOnTpJv9eqVQuFhYUlLrdu3Rpnz54tdTv8dvfszM3NMXToUCxfvrzYmI9r164hNTUVc+fOZeGhBxW55goA5OXlITU1Fa6urlCpVBXeFxGZBvZ8lJNc3/D47e7ZqdVqODo6Ij09HXXq1MGjR4+kddrbhzdq1Ag3b95kAUJEpCfs+TCAilzh1NfXF7Vr18adO3fQsGFDPH78GCdPnpTW8wqnhhUVFYX09HS88MILOHz4ME6cOCGN+ejevTsCAgJw4sQJREVFoXfv3sZOl4ioxmHxUU5Pu6pmq1atcOnSJQDQKTRKimPPhmFFRUUBAJYsWYLCwkKEh4cjKSkJLVq0QJcuXbB48WK8+OKLLD6IiIyExYeetGvXTio+nhZH8li0aJFOIXjw4EFs2LABvr6+RsyKiIjMnh5C5eHs7KzXOKo87YXDSuuB0rbzAmNERMbB4kNPLl68qNc4qrwuXbroNY6IiPSLxYeenDp1Sq9xVHlz586Vfn9yqm3R2S1F44iISD56Lz5CQ0PRqVMn2NjYoFGjRggKCirXWIjq7sGDB3qNo8rTXkelefPmaNasmc66Zs2aoXnz5jpxREQkL70XH0ePHsW0adNw6tQpHDp0CI8fP8ZLL72EnJwcfe+qSinv5VKq2GVVTJL2Ne7cuTOSkpKwevVqTJ8+HatXr8bly5fRsWNHnTgiIpKX3me7/PrrrzrLYWFhaNSoEWJjY9GjRw99746omD59+iAuLg67d+/GyZMnce3aNWnd6tWrcePGDSmOiIjkZ/AxHxkZGQCA+vXrl7g+Pz8fmZmZOj9Ez+Kll14C8M+VTq9fv47Ro0cjLi4Oo0ePxvXr16FWq3XiiIhIXga9vLpGo8GgQYPw8OFDHD9+vMSYxYsXY8mSJcXaq9rl1Z/G3NwcGo3mqXFmZmbSf35kGAUFBVCpVGW+H2ZmZsjLy4OFhYWMmRERma6KXF7doD0f06ZNQ3x8PHbu3FlqzMKFC5GRkSH9XL9+3ZApGUx5C6XqVFBVVydPnpQKD6VSqbOuTp06AP4pjMu6Ei0RERmOwYqP6dOnIzIyEkeOHCk246AopVIJW1tbnZ/qyM7OTq9xVHlpaWkAgO3bt8PBwUFnnYODA7Zv364TR0RE8tL7gFMhBN58803s3bsXUVFRcHNz0/cuqqSuXbvi6tWr5Yojw9IWHO7u7khOTkZ0dLR0Yzk/Pz+cOXNGJ46IiOSl9zEfU6dOxXfffYcffvgBrVq1ktrt7OygUqme+viKnDOqSg4ePIi+ffs+Ne7AgQMc6GhgarUaHh4eaNu2Lfbt26dzoTGNRoOgoCDEx8cjKSlJ56JjRERUeUYd8/HZZ58hIyMD/v7+cHBwkH6+//57fe+qSundu/dTiyuVSsW7qMrA3NwcK1euRGRkJIKCghATE4OsrCzExMQgKCgIkZGRWLFiBQsPIiIjMchpl5rKwsICeXl5Za4neQQHByM8PBxz5szRuYutm5sbwsPDERwcbMTsiIhqNr0XHzVVVFQUMjIy4OnpiZycHJ1ZO87OzrC0tERiYiKioqLY+yGT4OBgDB48uNiYD/Z4EBEZF4sPPYmKigIAbNiwAd26dcPcuXORlJSEFi1aYPny5Thx4gRefPFFFh8yMzc3h7+/v7HTICKiIlh86NmmTZvQt29fFBYWAvhnIOqmTZvYzU9ERPT/Gfzy6jWF9tv1rl27YG9vjy1btiAtLQ1btmyBvb09du3apRNHRERUU7H40JOigxo7duyINm3awMrKCm3atJHuovpkHBERUU3E4kNPNm3aJP1++PBh+Pr6wtbWFr6+vjhy5EiJcURERDURiw89uXLlCgDgiy++QKNGjXTWNWrUCFu2bNGJIyIiqqlYfOiJu7s7gH+uc3LlyhUcOXIE3333HY4cOYLk5GTpRmfaOCIioppK75dXf1bV9fLqBQUFsLKygr29Pf773/+iVq3/TSQqLCxEs2bNcO/ePeTk5PBiY0REZHKMenn1msrCwgKzZs3C7du30axZM2zevBk3b97E5s2b0axZM9y+fRuzZs1i4UFERDUer/OhR8uWLQMArF69GlOmTJHaa9Wqhblz50rriYiIajKedjGAgoICbNy4EVeuXIG7uzumTp3KHg8iIjJpFfn/m8UHERERPTOO+SAiIqIqi8UHERERyYrFBxEREcmKxQcRERHJisUHERERyYrFBxEREcmKxQcRERHJisUHERERyYrFBxEREcmKxQcRERHJisUHERERyYrFBxEREcmKxQcRERHJisUHERERyYrFBxEREcmKxQcRERHJisUHERERyYrFBxEREcmKxQcRERHJisUHERERyYrFBxEREcmKxQcRERHJisUHERERyYrFBxEREcmKxQcRERHJymDFx4YNG+Dq6oo6deqgS5cuOHPmjKF2RURERNWIQYqP77//HrNnz8Z7772HuLg4tGvXDn379kV6erohdkdERETViEGKj1WrVmHSpEkYP348Wrdujc8//xyWlpb46quvDLE7IiIiqkb0XnwUFBQgNjYWffr0+d9OzMzQp08fxMTE6Ht3REREVM3U0vcG7969C7VajcaNG+u0N27cGImJicXi8/PzkZ+fLy1nZGQAADIzM/WdGhERERmI9v9tIcRTY/VefFRUaGgolixZUqzdycnJCNkQERHRs8jKyoKdnV2ZMXovPho0aABzc3Pcvn1bp/327dto0qRJsfiFCxdi9uzZ0rJGo8H9+/dhb28PhUKh7/Rkk5mZCScnJ1y/fh22trbGTqdG43tRdfC9qFr4flQdpvBeCCGQlZUFR0fHp8bqvfiwsLCAj48Pfv/9dwQFBQH4p6D4/fffMX369GLxSqUSSqVSp61u3br6TstobG1tq+0HydTwvag6+F5ULXw/qo7q/l48rcdDyyCnXWbPno2xY8eiY8eO6Ny5M9asWYOcnByMHz/eELsjIiKiasQgxcfw4cNx584dvPvuu7h16xbat2+PX3/9tdggVCIiIqp5DDbgdPr06SWeZqkplEol3nvvvWKnlEh+fC+qDr4XVQvfj6qjpr0XClGeOTFEREREesIbyxEREZGsWHwQERGRrFh8EBERkaxYfBAREZGsWHwYwIYNG+Dq6oo6deqgS5cuOHPmjLFTMnmhoaHo1KkTbGxs0KhRIwQFBeHSpUs6Mf7+/lAoFDo/r7/+upEyNm2LFy8u9lp7enpK6x89eoRp06bB3t4e1tbWGDJkSLGrIpN+uLq6FnsvFAoFpk2bBoDHhdyysrIwc+ZMuLi4QKVSwdfXF2fPnpXWCyHw7rvvwsHBASqVCn369EFSUpIRMzYMFh969v3332P27Nl47733EBcXh3bt2qFv375IT083dmom7ejRo5g2bRpOnTqFQ4cO4fHjx3jppZeQk5OjEzdp0iSkpaVJP8uWLTNSxqavTZs2Oq/18ePHpXWzZs3Cjz/+iN27d+Po0aO4efMmgoODjZit6Tp79qzO+3Do0CEAwNChQ6UYHhfymThxIg4dOoRvvvkGFy5cwEsvvYQ+ffrgxo0bAIBly5Zh7dq1+Pzzz3H69GlYWVmhb9++ePTokZEz1zNBetW5c2cxbdo0aVmtVgtHR0cRGhpqxKxqnvT0dAFAHD16VGrr2bOneOutt4yXVA3y3nvviXbt2pW47uHDh6J27dpi9+7dUltCQoIAIGJiYmTKsOZ66623hLu7u9BoNEIIHhdyys3NFebm5iIyMlKnvUOHDuLf//630Gg0okmTJmL58uXSuocPHwqlUil27Nghd7oGxZ4PPSooKEBsbCz69OkjtZmZmaFPnz6IiYkxYmY1T0ZGBgCgfv36Ou3ffvstGjRoAG9vbyxcuBC5ubnGSK9GSEpKgqOjI5o3b45Ro0bh2rVrAIDY2Fg8fvxY5zjx9PSEs7MzjxMDKygowPbt2zFhwgSdG3fyuJBHYWEh1Go16tSpo9OuUqlw/PhxpKSk4NatWzrHhp2dHbp06WJyx4bBrnBaE929exdqtbrYZeQbN26MxMREI2VV82g0GsycORPdu3eHt7e31D5y5Ei4uLjA0dERf/31F+bPn49Lly4hIiLCiNmapi5duiAsLAytWrVCWloalixZAj8/P8THx+PWrVuwsLAodgPJxo0b49atW8ZJuIbYt28fHj58iHHjxkltPC7kY2Njg27duuGDDz6Al5cXGjdujB07diAmJgYeHh7S57+k/0NM7dhg8UEmZ9q0aYiPj9cZYwAAkydPln5v27YtHBwc0Lt3b1y5cgXu7u5yp2nSAgMDpd+fe+45dOnSBS4uLti1axdUKpURM6vZvvzySwQGBurc8pzHhby++eYbTJgwAU2bNoW5uTk6dOiAESNGIDY21tipyYqnXfSoQYMGMDc3LzZq//bt22jSpImRsqpZpk+fjsjISBw5cgTNmjUrM7ZLly4AgOTkZDlSq9Hq1q2Lli1bIjk5GU2aNEFBQQEePnyoE8PjxLCuXr2K3377DRMnTiwzjseFYbm7u+Po0aPIzs7G9evXcebMGTx+/BjNmzeXPv814f8QFh96ZGFhAR8fH/z+++9Sm0ajwe+//45u3boZMTPTJ4TA9OnTsXfvXhw+fBhubm5Pfcz58+cBAA4ODgbOjrKzs3HlyhU4ODjAx8cHtWvX1jlOLl26hGvXrvE4MaCtW7eiUaNG6N+/f5lxPC7kYWVlBQcHBzx48AAHDhzA4MGD4ebmhiZNmugcG5mZmTh9+rTpHRvGHvFqanbu3CmUSqUICwsTFy9eFJMnTxZ169YVt27dMnZqJu2NN94QdnZ2IioqSqSlpUk/ubm5QgghkpOTxfvvvy/OnTsnUlJSxA8//CCaN28uevToYeTMTdOcOXNEVFSUSElJESdOnBB9+vQRDRo0EOnp6UIIIV5//XXh7OwsDh8+LM6dOye6desmunXrZuSsTZdarRbOzs5i/vz5Ou08LuT366+/il9++UX8/fff4uDBg6Jdu3aiS5cuoqCgQAghxMcffyzq1q0rfvjhB/HXX3+JwYMHCzc3N5GXl2fkzPWLxYcBrFu3Tjg7OwsLCwvRuXNncerUKWOnZPIAlPizdetWIYQQ165dEz169BD169cXSqVSeHh4iLlz54qMjAzjJm6ihg8fLhwcHISFhYVo2rSpGD58uEhOTpbW5+XlialTp4p69eoJS0tL8corr4i0tDQjZmzaDhw4IACIS5cu6bTzuJDf999/L5o3by4sLCxEkyZNxLRp08TDhw+l9RqNRixatEg0btxYKJVK0bt372LvmylQCCGEETteiIiIqIbhmA8iIiKSFYsPIiIikhWLDyIiIpIViw8iIiKSFYsPIiIikhWLDyIiIpIViw8iIiKSFYsPIpKMGzcOQUFB0rK/vz9mzpxZrsdGRUVBoVAUu2eLKQgLCyt2F14iqjwWH0RG8O2338LJyQn16tXD7NmzddalpqaiZcuWyMzMLHMbqampUCgUMDc3x40bN3TWpaWloVatWlAoFEhNTa10nhEREfjggw/KFevr64u0tDTY2dlVen9EVDOw+CCS2d27dzFx4kSsWLECBw8exPbt2xEZGSmtnzp1Kj7++GPY2tqWa3tNmzbF119/rdO2bds2NG3a9JlzrV+/PmxsbMoVa2FhgSZNmkChUDzzfonItLH4IJLZ33//DTs7OwwfPhydOnVCQEAAEhISAAA7duxA7dq1ERwcXO7tjR07Flu3btVp27p1K8aOHavTplar8dprr8HNzQ0qlQqtWrXCp59+Wua2nzztkp+fj/nz58PJyQlKpRIeHh748ssvAZR82mXPnj1o06YNlEolXF1dsXLlSp3tKxQK7Nu3T6etbt26CAsLAwAUFBRg+vTpcHBwQJ06deDi4oLQ0NBS89WeNlqxYgUcHBxgb2+PadOm4fHjx1LMgwcPMGbMGNSrVw+WlpYIDAxEUlKSznbCwsLg7OwMS0tLvPLKK7h3716xff3www/o0KED6tSpg+bNm2PJkiUoLCwE8M9dlhcvXgxnZ2colUo4OjpixowZpeZNVNOw+CCSWYsWLZCbm4s//vgD9+/fx9mzZ/Hcc8/hwYMHWLRoEdavX1+h7Q0aNAgPHjzA8ePHAQDHjx/HgwcPMHDgQJ04jUaDZs2aYffu3bh48SLeffddvPPOO9i1a1e59zVmzBjs2LEDa9euRUJCAjZt2gRra+sSY2NjYzFs2DD861//woULF7B48WIsWrRIKizKY+3atdi/fz927dqFS5cu4dtvv4Wrq2uZjzly5AiuXLmCI0eOYNu2bQgLC9PZ57hx43Du3Dns378fMTExEEKgX79+UoFy+vRpvPbaa5g+fTrOnz+PgIAAfPjhhzr7iI6OxpgxY/DWW2/h4sWL2LRpE8LCwvDRRx8B+KfoWr16NTZt2oSkpCTs27cPbdu2LffzJjJ5xr2vHVHNFBERIby9vYW7u7t47733hBBCTJgwQaxevVocPXpUtG/fXrRp00bs3r271G2kpKQIAOKPP/4QM2fOFOPHjxdCCDF+/Hgxa9Ys8ccffwgAIiUlpdRtTJs2TQwZMkRaHjt2rBg8eLC03LNnT/HWW28JIYS4dOmSACAOHTpU4raOHDkiAIgHDx4IIYQYOXKkePHFF3Vi5s6dK1q3bi0tAxB79+7VibGzs5PuRvzmm2+KXr16CY1GU+pzKGrs2LHCxcVFFBYWSm1Dhw4Vw4cPF0IIcfnyZQFAnDhxQlp/9+5doVKpxK5du4QQQowYMUL069dPZ7vDhw8XdnZ20nLv3r3F0qVLdWK++eYb4eDgIIQQYuXKlaJly5bSbdKJSBd7PoiM4JVXXsGFCxeQnJyMxYsX4+jRo/jrr78wefJk/Otf/8KaNWuwZ88evPbaa0hPT3/q9iZMmIDdu3fj1q1b2L17NyZMmFBi3IYNG+Dj44OGDRvC2toamzdvxrVr18qV8/nz52Fubo6ePXuWKz4hIQHdu3fXaevevTuSkpKgVqvLtY1x48bh/PnzaNWqFWbMmIGDBw8+9TFt2rSBubm5tOzg4CC9hgkJCahVqxa6dOkirbe3t0erVq2kU18JCQk66wGgW7duOst//vkn3n//fVhbW0s/kyZNQlpaGnJzczF06FDk5eWhefPmmDRpEvbu3SudkiEinnYhMrr8/HxMnToVmzZtQnJyMgoLC9GzZ0+0atUKLVu2xOnTp5+6jbZt28LT0xMjRoyAl5cXvL29i8Xs3LkTb7/9Nl577TUcPHgQ58+fx/jx41FQUFCuPFUqVYWf29MoFAoIIXTaio7P6NChA1JSUvDBBx8gLy8Pw4YNQ0hISJnbrF27drF9aDQa/SUNIDs7G0uWLMH58+elnwsXLiApKQl16tSBk5MTLl26hI0bN0KlUmHq1Kno0aOHznMjqslYfBAZ2YcffoiXX34ZHTp0gFqt1vmG/Pjx43L3EkyYMAFRUVGl9nqcOHECvr6+mDp1Kp5//nl4eHjgypUr5c6zbdu20Gg0OHr0aLnivby8cOLEiWI5tGzZUuqZaNiwIdLS0qT1SUlJyM3N1XmMra0thg8fji1btuD777/Hnj17cP/+/XLn/WROhYWFOgXdvXv3cOnSJbRu3VqKebLgO3XqlM5yhw4dcOnSJXh4eBT7MTP758+qSqXCwIEDsXbtWkRFRSEmJgYXLlyoVN5EpqaWsRMgqskuXryI77//Hn/88QcAwNPTE2ZmZvjyyy/RpEkTJCYmolOnTuXa1qRJkzB06NBSL4bVokULfP311zhw4ADc3NzwzTff4OzZs3BzcyvX9l1dXTF27FhMmDABa9euRbt27XD16lWkp6dj2LBhxeLnzJmDTp064YMPPsDw4cMRExOD9evXY+PGjVJMr169sH79enTr1g1qtRrz58/X6blYtWoVHBwc8Pzzz8PMzAy7d+9GkyZNKn3BrxYtWmDw4MGYNGkSNm3aBBsbGyxYsABNmzbF4MGDAQAzZsxA9+7dsWLFCgwePBgHDhzAr7/+qrOdd999FwMGDICzszNCQkJgZmaGP//8E/Hx8fjwww8RFhYGtVqNLl26wNLSEtu3b4dKpYKLi0ul8iYyNez5IDISIQQmT56MVatWwcrKCsA/35bDwsLw/vvv47XXXsP69evLfb2OWrVqoUGDBqhVq+TvFFOmTEFwcDCGDx+OLl264N69e5g6dWqFcv7ss88QEhKCqVOnwtPTE5MmTUJOTk6JsR06dMCuXbuwc+dOeHt7491338X777+PcePGSTErV66Ek5MT/Pz8MHLkSLz99tuwtLSU1tvY2GDZsmXo2LEjOnXqhNTUVPz8889S70JlbN26FT4+PhgwYAC6desGIQR+/vlnqejp2rUrtmzZgk8//RTt2rXDwYMH8X//93862+jbty8iIyNx8OBBdOrUCV27dsXq1aul4qJu3brYsmULunfvjueeew6//fYbfvzxR9jb21c6byJTohBPnnAlIiIiMiD2fBAREZGsWHwQERGRrFh8EBERkaxYfBAREZGsWHwQERGRrFh8EBERkaxYfBAREZGsWHwQERGRrFh8EBERkaxYfBAREZGsWHwQERGRrFh8EBERkaz+HwX/k+z4sKixAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig3, ax3 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in op_df:\n",
+    "\n",
+    "    vsdf = op_df[key].loc[(op_df[key]['type'] == 'RandomSamplingOperation')]\n",
+    "    data.append(vsdf['completion_time']/1000)\n",
+    "\n",
+    "ax3.boxplot(data)\n",
+    "\n",
+    "\n",
+    "ax3.set_ylim([0,15])\n",
+    "ax3.set_xticklabels([0,25,50,75,90])\n",
+    "ax3.set_title(\"Random sampling time regular nodes (seconds)\")\n",
+    "ax3.set_xlabel(\"% Malicious nodes\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92fe1b66",
+   "metadata": {},
+   "source": [
+    "In this graph we observe the time required for the random sampling to complete when increasing the number of sybils. We can observe that only in case of 90% sybils, a small number of nodes are slighly delayed and cannot complete within the 12 seconds block time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "64a9d6f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/sergi/seal/tmpo/ipykernel_2730530/1455769239.py:11: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax3.set_xticklabels([0,0,25,50,75,90])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Malicious nodes')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig3, ax3 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in msg_df:\n",
+    "\n",
+    "    vsdf = msg_df[key].loc[(msg_df[key]['nodeType'] == 'builder')]\n",
+    "    data.append(vsdf['bytesOut'].values[0]/1024/1024/1024)\n",
+    "\n",
+    "ax3.bar([1,2,3,4,5],data)\n",
+    "\n",
+    "ax3.set_xticklabels([0,0,25,50,75,90])\n",
+    "ax3.set_title(\"GBs sent by the builder per sampling process\")\n",
+    "ax3.set_xlabel(\"% Malicious nodes\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d56ea3a",
+   "metadata": {},
+   "source": [
+    "In this graph we observe the amount of data sent by the builder per block in GBs. We observe this amount is reduced with the increasing number of sybils, since there are less validators requesting samples from the builder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0726c136",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Malicious nodes')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig3, ax3 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in msg_df:\n",
+    "\n",
+    "    vsdf = msg_df[key].loc[(msg_df[key]['nodeType'] == 'validator')]\n",
+    "    data.append(vsdf['bytesOut']/1024/1024)\n",
+    "\n",
+    "ax3.boxplot(data)\n",
+    "\n",
+    "\n",
+    "ax3.set_xticklabels([0,25,50,75,90])\n",
+    "ax3.set_title(\"MBs sent per validator\")\n",
+    "ax3.set_xlabel(\"% Malicious nodes\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "685667c3",
+   "metadata": {},
+   "source": [
+    "In this case we observe the amount of data sent by validators. We observe this is not incremented in our simulations, although the cause is that the number of validators requesting is also reduced for the presence of sybils. In any case, we observe the number of data sent is not reduced in the same range, meaning honest validators need to do more work in case of sybils."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "40b59007",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Malicious nodes')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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IEejXrx+WLFmC1NRUPProo4iOjtY6j9HGjRvRv39/+Pv7Y+bMmWjfvj0yMzORkJCAmzdv4sKFCwb52agJkGVsElEDVRzWXJO6DGtWKpVC27ZthVmzZgmZmZli3LVr14Tnn39e8PHxESwtLYVWrVoJAwcOFH744Yca/8/63G/v3r1C//79BRsbG8HGxkbo3LmzMG/ePOHKlStizIABA4QuXbpUue/UqVMlQ2UFQRAOHDggPProo4KpqWmtQ5w1w2UvXbokhIeHC3Z2dkLLli2F+fPnCyUlJTrNVZs7d+4I8+bNEzp37izY2NgIDg4OQkBAgLBr1y5JXOVhzYIgCJmZmcL06dOF1q1bC+bm5oK/v7/Wn/XkyZNCz549BXNz81qHONf2NxUbGyuEhIQIDg4OgqWlpeDj4yNMmzZNOH36tCTuq6++Ejp37ixYWFgIfn5+wsGDB4UxY8YInTt3lsRlZ2cLY8aMEaytrYWWLVsKs2fPFhITE+s0rPngwYNC165dBUtLS8HLy0t49913hc8++0wAIKSkpIhx2v7+a1Kfv7W6Pgc5OTnC5MmTBXt7e8HBwUGYPHmyOPS/cnxycrIwZcoUwdXVVTAzMxPatGkjDB8+XNizZ0+dfwZq/hSCoMceVUTU6KxYsQIrV65EdnY2WrduLXc6zVr37t3h5OQkmZGZiBqGfViIiB7S/fv3q/RfiouLw4ULF7gAI5GOsA8LEdFDunXrFgYPHoxJkybB3d0df/75JzZv3gxXV1f84x//kDs9omaBBQsR0UNq2bIlevbsif/+97/Izs6GjY0Nhg0bhnfeeQeOjo5yp0fULLAPCxERETV67MNCREREjV69CpbIyEj07t0bdnZ2cHZ2RmhoaJWZC+/du4d58+bB0dERtra2GDNmDDIzM2t8XEEQ8MYbb8DNzQ1WVlYYPHiwuI4HERERUb0uCQ0ZMgTPPvssevfujfLycrz22mtITEzEpUuXxNk258yZg2+//RZbtmyBg4MD5s+fDxMTE5w4caLax3333XcRGRmJL774At7e3li2bBkuXryIS5cuwdLSsta81Go1bt++DTs7O51Ny01ERET6JQgCCgoK4O7uXmVRTW3BDZaVlSUAEI4fPy4IgiDk5uYKZmZmwu7du8WYy5cvCwCEhIQErY+hVqsFV1dXYc2aNWJbbm6uYGFhIezcubNOedy4caPKpGC88cYbb7zxxlvTuN24caPWz/qHGiWkmWa5VatWAIAzZ87g/v37kkWsOnfuDE9PTyQkJKBPnz5VHiMlJQUZGRmS+zg4OCAgIAAJCQlap+ouLS2VrDki/P9Johs3bohLvhMREVHjlp+fDw8PD3FZh5o0uGBRq9V48cUX0a9fP3GNjoyMDJibm6NFixaSWBcXF2RkZGh9HE27i4tLne8TGRmJlStXVmm3t7dnwUJERNTE1KU7R4NHCc2bNw+JiYn46quvGvoQDbZ06VLk5eWJtxs3bhg8ByIiIjKcBhUs8+fPR0xMDGJjY9G2bVux3dXVFWVlZVWWdc/MzISrq6vWx9K0Vx5JVNN9LCwsxLMpPKtCRETU/NWrYBEEAfPnz8e+ffvw448/Vlm+vmfPnjAzM8OxY8fEtitXriAtLQ19+/bV+pje3t5wdXWV3Cc/Px+//PJLtfchIiIi41KvgmXevHnYtm0bduzYATs7O2RkZCAjIwMlJSUAHnSWnTFjBhYtWoTY2FicOXMG06dPR9++fSUdbjt37ox9+/YBeHDd6sUXX8SqVatw8OBBXLx4EVOmTIG7uztCQ0N195MSERFRk1WvTrcfffQRAFRZffTzzz/HtGnTAADr16+HiYkJxowZg9LSUoSEhGDTpk2S+CtXrogjjADglVdeQVFREWbNmoXc3Fz0798fhw8frtMcLERERNT8NYu1hPLz8+Hg4IC8vDz2ZyEiImoi6vP5zbWEiIiIqNFjwUJERESNHgsWIiIiavQeamp+0p2ysjJs2rQJycnJ8PHxwdy5c2Fubi53WkRERI0CC5ZG4JVXXsH69etRXl4utr388st46aWXsHr1ahkzIyIiahx4SUhmr7zyCtasWQNHR0dERUUhPT0dUVFRcHR0xJo1a/DKK6/InSIREZHsOKxZRmVlZbCxsYGjoyNu3rwJU9P/nfAqLy9H27ZtkZOTg6KiIl4eIiKiZofDmpuITZs2oby8HKtWrYJCoUBcXBx27tyJuLg4KBQKvPnmmygvL68y8R4REZGxYR8WGSUnJwN4sDyBr68vUlNTxWNeXl7417/+JYkjIiIyVjzDIiMfHx8AwAsvvAB/f38kJCSgoKAACQkJ8Pf3x8yZMyVxRERExop9WGRUUlICa2trmJubo6CgQNJPpaysDHZ2digrK0NxcTGsrKxkzJSIiEj32Ielifjll18APChOPD098cknn+D27dv45JNP4OnpibKyMkkcERGRsWLBIqP09HQAwMKFC5GTk4PZs2ejTZs2mD17NnJycrBw4UJJHBERkbFiwSIjNzc3AICrqyvc3d0lx9zd3eHi4iKJIyIiMlYsWGQUFBQEZ2dnLF26FF27dpV0uu3atStee+01ODs7IygoSO5UiYiIZMWCRWYV+zwLgiDeiIiI6H9YsMgoPj4e2dnZiIyMxMWLFxEYGAh7e3sEBgYiMTERb7/9NrKyshAfHy93qkRERLJiwSIjTWdaDw8PKBSKKsc9PT0lcURERMaKBYuMNJ1pJ0+erHXiuMmTJ0viiIiIjBUnjpMRFz8kIiJjxonjmoiTJ0+ivLwcmZmZCAsLk5xhCQsLQ2ZmJsrLy3Hy5Em5UyUiIpIVCxYZafqmbNu2DWfPnpV0uj137hy2bdsmiSMiIjJWLFhkpOmb8uqrr+LWrVuSYzdv3sSrr74qiSMiIjJW7MMiI5VKBUtLS5SXl1cbY2pqinv37kGpVBowMyIiIv2rz+e3aY1HSa8KCwvFYsXc3BxjxoxBr169cPr0aezduxdlZWUoLy9HYWEhHBwcZM6WiIhIPjzDIqN+/frh5MmTMDExqTLDrYnJg6t1arUagYGBOHHihFxpEhER6QXPsDQRf/zxB4AHRUllFds0cURERMaKnW5lZGVlJW5rzqho268YR0REZIxYsMgoJCREsl1xHpbKx4iIiIwZLwnJ6N69e+L2d999B5VKhddffx2rVq3CkSNHtMYREREZIxYsMrp586Zk/8iRI5JCpbo4IiIiY8NLQjLy8vLSaRwREVFzxYJFRlOnTtVpHBERUXNV74Llp59+wogRI+Du7g6FQoH9+/dLjisUCq23NWvWVPuYK1asqBLfuXPnev8wTU337t11GkdERNRc1btgKSoqQrdu3bBx40atx9PT0yW3zz77DAqFAmPGjKnxcbt06SK5388//1zf1JqcgQMH6jSOiIiouap3p9uhQ4di6NCh1R53dXWV7B84cAADBw5E+/bta07E1LTKfZu7tLQ0AMCgQYMQFxcHlUolHlMqlXjiiScQGxsrxhERERkrvfZhyczMxLfffosZM2bUGpuUlAR3d3e0b98ezz33XI0f0qWlpcjPz5fcmiLN+kDHjh2TFCvAg4URY2NjJXFERETGSq8FyxdffAE7OzuEhYXVGBcQEIAtW7bg8OHD+Oijj5CSkoKgoCAUFBRojY+MjISDg4N48/Dw0Ef6evfhhx+K205OToiKikJ6ejqioqLg5OSkNY6IiMgY6bVg+eyzz/Dcc8/B0tKyxrihQ4di7Nix6Nq1K0JCQnDo0CHk5uZi165dWuOXLl2KvLw88Xbjxg19pK93FhYW4nZ2djaioqJw4cIFREVFITs7W2scERGRMdLbxHHx8fG4cuUKvv7663rft0WLFujYsSOuXr2q9biFhUWz+BDfsWOHZP/XX3/FkCFDtMbV1G+IiIioudPbGZZPP/0UPXv2RLdu3ep938LCQiQnJ8PNzU0PmTUehYWFAKp2VNbQtGviiIiIjFW9C5bCwkKcP38e58+fBwCkpKTg/Pnzkk6y+fn52L17N1544QWtjzFo0CBs2LBB3F+8eDGOHz+O1NRUnDx5EqNHj4ZSqcSECRPqm16T0r9/fwBARkYGFAoFnn76aURGRuLpp5+GQqFARkaGJI6IiMhY1btgOX36NHr06IEePXoAABYtWoQePXrgjTfeEGO++uorCIJQbcGRnJyMO3fuiPs3b97EhAkT0KlTJ4wbNw6Ojo44deqUpONpczR9+nRxOzg4GElJSVi9ejWSkpIQHBysNY6IiMgYKQRBEORO4mHl5+fDwcEBeXl5sLe3lzudOhs9enSVmYK1CQ0Nxb59+/SfEBERkQHV5/ObawnJKDk5WadxREREzRULFhm1bdtW63Ztx4iIiIyN3oY1U+1SU1PFbX9/fyxduhRWVlYoKSlBTEwMbt68WSWOiIjIGLEPi4wcHR3x999/1xrXqlUr5OTkGCAjIiIiw2EfliairsVVUyrCiIiI9IEFi4yWLFmi0zgiIqLmigWLjCrORQMASqUSERERUCqVNcYREREZG/ZhkZGTk1OdipHWrVtLFkMkIiJqDtiHpYmo65kTnmEhIiJjx4KFiIiIGj0WLDKqfPrrqaeeQmRkJJ566qka44iIiIwNJ46T0bBhw7Bz505x/+jRozh69KjWOCIiImPGMywyio+P12kcERFRc8WChYiIiBo9Fiwy6tu3r07jiIiImisWLDL6/vvvJfuenp4YOXIkPD09a4wjIiIyNux0K6P8/HzJflpaGtLS0mqNIyIiMjY8w0JERESNHgsWGTk7O+s0joiIqLliwSKj9u3bS/bNzMywZMkSmJmZ1RhHRERkbFiwyOiRRx6R7N+/fx/vvPMO7t+/X2McERGRsWHBIqPY2FidxhERETVXLFhkVPlMysPGERERNVcsWGTk5eWl0zgiIqLmigWLjLZu3arTOCIiouaKE8fJaMSIEXWOS0xM1HM2pKFSqRAfH4/09HS4ubkhKCgISqVS7rSIiIwaz7DISNustg8TRw8vOjoavr6+GDhwICZOnIiBAwfC19cX0dHRcqdGRGTUWLDIyNbWVqdx9HCio6MRHh4Of39/JCQkoKCgAAkJCfD390d4eDiLFiIiGbFgkVHnzp0l+2ZmZhgzZkyVieMqx5HuqVQqREREYPjw4di/fz/69OkDW1tb9OnTB/v378fw4cOxePFiqFQquVMlIjJKCkEQBLmTeFj5+flwcHBAXl4e7O3t5U6nzszNzes0ZNnMzAxlZWUGyMh4xcXFYeDAgUhISECfPn2qHE9ISEBgYCBiY2MRHBxs+ASJiJqh+nx+8wyLjDgPS+ORnp4OAPDz89N6XNOuiSMiIsNiwUIEwM3NDQCqHY2ladfEERGRYbFgaWQ6duwodwpGKSgoCF5eXnj77behVqslx9RqNSIjI+Ht7Y2goCCZMiQiMm71Llh++uknjBgxAu7u7lAoFNi/f7/k+LRp06BQKCS3IUOG1Pq4GzduhJeXFywtLREQEIBff/21vqk1OZaWllXa/vrrrzrFkW4plUqsW7cOMTExCA0NlYwSCg0NRUxMDNauXcv5WIiIZFLvgqWoqAjdunXDxo0bq40ZMmQI0tPTxdvOnTtrfMyvv/4aixYtwvLly3H27Fl069YNISEhyMrKqm96Tcq9e/d0GkcPJywsDHv27MHFixcRGBgIe3t7BAYGIjExEXv27EFYWJjcKRIRGa2HGiWkUCiwb98+hIaGim3Tpk1Dbm5ulTMvNQkICEDv3r2xYcMGAA9OwXt4eGDBggVYsmRJrfdvqqOEFApFnWObwWCuJoMz3RIRGUZ9Pr/1MjV/XFwcnJ2d0bJlSzz55JNYtWoVHB0dtcaWlZXhzJkzWLp0qdhmYmKCwYMHIyEhQet9SktLUVpaKu7n5+fr9gcgo6ZUKjl0mYiokdF5p9shQ4bgyy+/xLFjx/Duu+/i+PHjGDp0aLUTbt25cwcqlQouLi6SdhcXF2RkZGi9T2RkJBwcHMSbh4eHrn8Mgxg/frxO44iIiJornZ9hefbZZ8Vtf39/dO3aFT4+PoiLi8OgQYN08n8sXboUixYtEvfz8/ObZNESHx+v0zjSDV4SIiJqfPQ+rLl9+/Zo3bo1rl69qvV469atoVQqkZmZKWnPzMyEq6ur1vtYWFjA3t5ecmuK8vLydBpHD4+LHxIRNU56L1hu3ryJnJycaifcMjc3R8+ePXHs2DGxTa1W49ixY+jbt6++05OVjY2NTuPo4WgWP9RWPHPxQyIiedW7YCksLMT58+dx/vx5AEBKSgrOnz+PtLQ0FBYW4uWXX8apU6eQmpqKY8eOYdSoUfD19UVISIj4GIMGDRJHBAHAokWLEBUVhS+++AKXL1/GnDlzUFRUhOnTpz/8T9iI1fUyVlO83NXUqFQqzJkzB4IgYNCgQZJ5WAYNGgRBEDBnzhwufkhEJBehnmJjYwUAVW5Tp04ViouLhaefflpwcnISzMzMhHbt2gkzZ84UMjIyJI/Rrl07Yfny5ZK2Dz/8UPD09BTMzc2Fxx9/XDh16lSdc8rLyxMACHl5efX9cWTl5OSk9XdZ+ebk5CR3qs3eDz/8IAAQ+vfvL6hUKskxlUol9OvXTwAg/PDDDzJlSETU/NTn85urNcuI87A0HsuWLcOqVatw7NgxPPnkk1WO//DDD3jqqafw+uuv46233pIhQyKi5oerNRMREVGzwoJFRnUdKsshtfqnmShu+fLlWhc/XLlypSSOiIgMiwWLjD766COdxlHDBQcHw8nJCT///DNGjRol6XQ7atQo/Pzzz3B2dmbBQkQkE/ZhkZGPjw+uXbtWa1z79u2RnJxsgIyMW3R0NMaMGQMrKyuUlJSI7dbW1iguLsbevXu5ACIRkQ6xD0sTcf36dZ3G0cMJCwvDyy+/jLKyMkl7aWkpXn75ZRYrREQy0svih1Q3JiYmdZrXw8SEdaUhREdHY+3atRg2bBiGDh0qnmn57rvvsHbtWvTp04dFCxGRTHhJSEaenp64ceOGuO/l5YXw8HDs2bMHqampYruHhwfS0tJkyNB4qFQq+Pr6wt/fH/v375cUiWq1GqGhoUhMTERSUhI7QRMR6QgvCTURTk5Okv3U1FSsXbtWUqxoiyPdi4+PR2pqKl577TUIgoC4uDjs3LkTcXFxEAQBS5cuRUpKCheiJCKSCS8JyejSpUs6jaOGS09PBwAkJydjwoQJkqLRy8sLq1atksQREZFhsWAhAsTFOSdPngxLS0vJsczMTEyePFkSR0REhsVLQjKqfKlHoVDA3t6+ypT9vCSkf4GBgTAxMYEgCHjyyScl87A8+eSTEAQBJiYmCAwMlDtVIiKjxIJFRq6urpJ9QRCQn59fZd2gynGke/Hx8eIMtwqFAoIgiDdNAalWq9mHhYhIJixYZHT69GmdxlHDxcXFAQBWrFiBxMREBAYGwt7eHoGBgfjjjz+wfPlySRwRERkWCxYZ1XVEeTMYed5kBAUF4erVq4iNjcWOHTsQGxuLpKQk9O/fX+7UiIiMGgsWGVlbW+s0jhqu4uKHCoUCwcHBmDBhAoKDg6FQKLj4IRGRzFiwyGjOnDk6jaOG4+KHRESNG2e6lVGLFi2Ql5dXa5yDgwNyc3P1n5CR4+KHRESGxZlum4j8/HydxtHDCQsLw969e9G6dWtJe+vWrVmsEBHJjAWLjCrPt/KwcfTwTp06VWU229u3b+PUqVMyZURERAALFln5+vpK9q2trTFp0qQqnWwrx5F+vPLKK1izZg0cHR0RFRWF9PR0REVFwdHREWvWrMErr7wid4pEREaLfVhkNGLECMTExNQaN3z4cHzzzTcGyMh4lZWVwcbGBo6Ojrh58yZMTf+3akV5eTnatm2LnJwcFBUVwdzcXMZMiYiaD/ZhaSK+++47ncZRw23atAnl5eVYtWqVpFgBAFNTU7z55psoLy/Hpk2bZMqQiMi4sWCRkWYqeF3FUcMlJycDeHA2SxtNuyaOiIgMiwWLjKysrHQaRw3n4+MDANVeotO0a+KIiMiw2IdFRq1bt0ZOTk6tcY6Ojrhz544BMjJe7MNCRGR49fn8Nq3xKOlVXSeD46Rx+mdubo6XXnoJa9asQdu2bfHcc8+hffv2uHbtGrZv347MzEy8/PLLLFaIiGTCMywyMjU1hUqlqjVOqVSivLzcABlRaGgoDhw4UKV91KhR2L9/v+ETIiJqxjhKqIlwc3PTaRw9nOjoaBw8eBCWlpaSdktLSxw8eBDR0dEyZUZERCxYZDRo0CCdxlHDqVQqzJkzB4IgYPDgwZLFDwcPHgxBEDBnzpw6nREjIiLdY8Eio7S0NJ3GUcPFxcUhKysL/fv3x1dffYVt27ZhzJgx2LZtG7766iv069cPWVlZiIuLkztVIiKjxIJFRklJSTqNo4bTFCJqtRq2trbYuHEjjhw5go0bN8LW1lacC4cFCxGRPFiwyMjBwUGncfTwTp48qbU9ISHBwJkQEVFFLFhkNHLkSMl+r169MH78ePTq1avGONK9gIAAcXvo0KGSPixDhw7VGkdERIZT74Llp59+wogRI+Du7g6FQiEZ6nn//n28+uqr8Pf3h42NDdzd3TFlyhTcvn27xsdcsWIFFAqF5Na5c+d6/zBNzbZt2yT7p0+fxtdff43Tp0/XGEe6V3GGWxMTE5w5cwa7du3CmTNnYGJiojWOiIgMp94TxxUVFaFbt254/vnnERYWJjlWXFyMs2fPYtmyZejWrRvu3r2LhQsXYuTIkVU+hCvr0qULfvjhh/8lZtr857RLT0/XaRw13JkzZ8TtQ4cO4dtvvxX3FQqF1jgiIjKcelcFQ4cOlZwir8jBwQFHjx6VtG3YsAGPP/440tLS4OnpWX0ipqZwdXWtbzpNmqmpaZ0mhDOG4k1uLVq0ELcrz6VYcb9iHBERGY7e+7Dk5eVBoVDU+kaflJQEd3d3tG/fHs8991yNQ3lLS0uRn58vuTVF48aN02kcNdzChQvFbScnJyxevBibNm3C4sWL4eTkpDWOiIgMR68Fy7179/Dqq69iwoQJNU65GxAQgC1btuDw4cP46KOPkJKSgqCgIBQUFGiNj4yMhIODg3jz8PDQ14+gV08//bRkv1WrVti4cSNatWpVYxzpnpmZmbidm5uLmzdvoqSkBDdv3pSs5VQxjoiIDOeh1hJSKBTYt28fQkNDqxy7f/8+xowZg5s3byIuLq5ea/zk5uaiXbt2eO+99zBjxowqx0tLS1FaWiru5+fnw8PDo8mtJcTVmhuPKVOmYOvWrbXGTZ48GV9++aUBMiIiav5kX635/v37GDduHK5fv44ff/yx3kVEixYt0LFjR1y9elXrcQsLC1hYWOgiVVlpvrkrlUqtU75r2rlas/5VdzavoXFERKRbOr8kpClWkpKS8MMPP8DR0bHej1FYWIjk5ORmv+ifpjNtdevTaNrZ6Vb/AgMDxW1TU1NMnDgR7733HiZOnCj5/VeMIyIiw6l3wVJYWIjz58/j/PnzAICUlBScP38eaWlpuH//PsLDw3H69Gls374dKpUKGRkZyMjIQFlZmfgYgwYNwoYNG8T9xYsX4/jx40hNTcXJkycxevRoKJVKTJgw4eF/wkZs9OjRkn0vLy9ERETAy8urxjjSvYrz/jz11FMIDAyEg4MDAgMD8dRTT2mNIyIiw6n3V/fTp09j4MCB4v6iRYsAAFOnTsWKFStw8OBBAED37t0l94uNjUVwcDAAIDk5WdIn4+bNm5gwYQJycnLg5OSE/v3749SpU5LRGc2RUqmU7KempmLdunW1xpHuffjhh+L2d999h++++67auBEjRhgqLSIi+n8P1em2sahPp53GxMbGBsXFxbXGWVtbo6ioyAAZGa/evXvXOrkh8GD5hN9++80AGRERNX/1+fzmWkIyqq7vSkPjqOF69uyp0zgiItItFiwycnZ21mkcNVzl2ZuHDBmCkydPYsiQITXGERGRYbBgkVHl+Ws6deqEsLAwdOrUqcY40r33339fsn/48GEEBgbi8OHDNcYREZFhcLysjC5duiTZv3LlCq5cuVJrHOmeZlFDExMTqNXqKsc17Vz8kIhIHjzDIqPMzEydxlHDaUZiaStWKrZzxBYRkTxYsMjIzs5Op3HUcL1795bsV9eHpXIcEREZBi8Jyai2FazrG0cNp1AoJPuHDx+u0n9FWxwRERkGz7DI6Nq1azqNo4Y7efKkTuOIiEi3WLDI6Pr16zqNo4dnZmZWr3YiIjIMFiwyqmsHTnb01L+goCAADxbvbN26Nbp164bOnTujW7duaN26Ne7fvy+JIyIiw2LBIiNPT0/Jfs+ePTF+/Pgqs6lWjiPd27p1q7h9584d+Pn5Yfv27fDz85Ose1UxjoiIDIcFi4wWLlwo2T9z5gy+/vrrKnN9VI4j3fvyyy8l+9u3b0fPnj2xffv2GuOIiMgwWLDI6OLFizqNo4ZLTk4GALRr107rcU27Jo6IiAyLBYuMNAtlW1lZaT2uaW8GC2o3ej4+PgCq7+CsadfEERGRYbFgkVGHDh0AACUlJRgyZAj8/f3Rpk0b+Pv7Y8iQISgpKZHEkf7Mnj1b3HZyckJUVBTS09MRFRUFJycnrXFERGQ4LFhkNHfuXJiamsLBwQGXLl3CxYsXcevWLVy8eBGXL1+Gg4MDTE1NMXfuXLlTbfbi4+PFbYVCgcuXL2Pv3r24fPmyZLK4inFERGQ4nOlWRubm5njppZewZs0aFBQUSI7duHEDarUaL7/8MszNzWXK0HhoRv/4+/vj4sWLeO+99yTH/fz8kJiYiK1bt+Lpp5+WI0UiIqPGMywy69OnD4Cq/VQ0+5rjpF+FhYUAHnRwrlwgmpubIzExURJHRESGxYJFRiqVChERERgxYgSKi4uxfv16zJ8/H+vXr0dxcTFGjBiBxYsXQ6VSyZ1qsxcYGChuV56or+J+xTgiIjIcXhKSUXx8PFJTU7Fz505YWlrixRdflBxfunQpAgMDER8fj+DgYFlyNBb+/v7itp2dHd5//30MHz4cMTExeP3118UO0BXjiIjIcFiwyCg9PR3Ag/4R2mjaNXGkPxU702ZlZWHWrFnVxg0ZMsRQaRER0f/jJSEZubm5AYDYP6IyTbsmjvQnLS0NANC6dWutxzXtmjgiIjIsFiwyCgoKgpeXF95++22o1WrJMbVajcjISHh7e3PBPQPQrNdUcd2gijTtXNeJiEgeLFhkpFQqsW7dOsTExCA0NBQJCQkoKChAQkICQkNDERMTg7Vr13K1ZgPo16+fuO3k5IRPPvkEt2/fxieffCKZOK5iHBERGQ77sMgsLCwMe/bsQUREhGQEire3N/bs2YOwsDAZszMeBw8eFLdzc3MlfVjMzMwkcc8884xBcyMiIhYsjUJYWBgGDRqEYcOGIS0tDZ6envj222/h4OAgd2pGo+IK2ffv35ccq7hfeSVt0i+VSoX4+Hikp6fDzc0NQUFBPONIZKRYsDQCjz/+OH777Tdx/8aNG2jRogV69+6NX3/9VcbMjEeLFi10GkcPLzo6GhEREUhNTRXbvLy8sG7dOp55JDJC7MMiM02xolAoMHnyZFy4cAGTJ0+GQqHAb7/9hscff1zuFI3CnDlzdBpHDyc6Ohrh4eHw9/eX9O3y9/dHeHg4oqOj5U6RiAxMIVSeE74Jys/Ph4ODA/Ly8mBvby93OnVWWFgIOzs7KBQKFBcXw9LSUjx27949WFtbQxAEFBQUwNbWVsZMm78RI0YgJiYGwIPFDwcPHozg4GDExcXhhx9+EJdKGD58OL755hs5U232VCoVfH194e/vj/3798PE5H/fq9RqNUJDQ5GYmIikpCReHiJq4urz+c0zLDKaPHkyAGDSpEkwMzNDXFwcdu7cibi4OJiZmWHixImSONKf06dPi9uCIODo0aP417/+haNHj0rWeaoYR/qhmQH6tddekxQrAGBiYoKlS5ciJSWFK2cTGRn2YZFRcnIyAKB79+7w8fHB9evXxWPt2rXD/PnzsX37djGOyBhwBmgi0oZnWGTk4+MDAIiIiEBWVpbkWFZWFl5++WVJHOnPE088IW5r+1avLY70gzNAE5E2LFhktGXLFnF74MCBks6FAwcO1BpH+tGjRw9xW61WIyQkBPHx8QgJCZHMQlwxjvSDM0ATkTb1Llh++uknjBgxAu7u7lAoFNi/f7/kuCAIeOONN+Dm5gYrKysMHjwYSUlJtT7uxo0b4eXlBUtLSwQEBBjFcN6K/SEOHTqEDRs24K+//sKGDRtw6NAhrXGkH7///rtk//vvv0dQUBC+//77GuNI9zgDNBFpU++CpaioCN26dcPGjRu1Hl+9ejU++OADbN68Gb/88gtsbGwQEhKCe/fuVfuYX3/9NRYtWoTly5fj7Nmz6NatG0JCQqpcJmlu4uLiAACdOnUCAGzfvh09e/bE9u3bAQAdO3aUxJH+1HVRQy5+aBiaGaAvXryIwMBA2NvbIzAwEImJiZwBmshI1btgGTp0KFatWoXRo0dXOSYIAv7zn//g9ddfx6hRo9C1a1d8+eWXuH37dpUzMRW99957mDlzJqZPn45HH30UmzdvhrW1NT777LP6ptckdevWrcq3RaVSiW7dusmUkfHx8vIStysOL6+8XzGO9CssLAxXr15FbGwsduzYgdjYWCQlJbFYITJSOu3DkpKSgoyMDAwePFhsc3BwQEBAABISErTep6ysDGfOnJHcx8TEBIMHD672PqWlpcjPz5fcmqLg4GAAwK5du6BSqSTHVCoVdu/eLYkj/ak4dHzgwIHYsGEDPv30U2zYsEHSn4hDzA1LqVQiODgYEyZMQHBwMC8DERkxnQ5rzsjIAAC4uLhI2l1cXMRjld25cwcqlUrrff7880+t94mMjMTKlSt1kLG8Ki52aGpqisWLF2PGjBn49NNPsXbtWpSXl1eJI/0wNf3fS+G7777Dd999J+4rFAqtcUREZDhNcpTQ0qVLkZeXJ95u3Lghd0oNsmHDBnFbqVTinXfeQYcOHfDOO+9IvklWjCP9qKm/VMWJ45p7vyoiosZKpwWLq6srACAzM1PSnpmZKR6rrHXr1lAqlfW6j4WFBezt7SW3pujAgQMAAEdHR5SWlkqOlZaWolWrVpI40p+6zunBuT+IiOSh04LF29sbrq6uOHbsmNiWn5+PX375BX379tV6H3Nzc/Ts2VNyH7VajWPHjlV7n+YmJydHa/vff/9t4EyMV0BAgE7jiIhIt+pdsBQWFuL8+fM4f/48gAcdbc+fP4+0tDQoFAq8+OKLWLVqFQ4ePIiLFy9iypQpcHd3R2hoqPgYgwYNklzmWLRoEaKiovDFF1/g8uXLmDNnDoqKijB9+vSH/gEbs5CQEHG7devWiIqKQnp6OqKiotC6dWutcaQfmzZtErfNzMywZMkSJCUlYcmSJTAzM9MaR0REhlPvHoSnT5+WjJpYtGgRAGDq1KnYsmULXnnlFRQVFWHWrFnIzc1F//79cfjwYcnQ0OTkZNy5c0fcHz9+PLKzs/HGG28gIyMD3bt3x+HDh6t0xG1uKo6CUigUOHr0KE6cOIHi4mJJR8/qRkuR7vz0008AACsrKzg5OeGdd97BO++8A+DBuk5ZWVkoKSnBTz/9hIiICDlTJSIySgqhYo/CJqo+y1M3Jm5ubtWOnqrI1dWVC73pWa9evXDmzBm4u7vj9u3bVY5r2nv27MmZh4mIdKQ+n99NcpRQc1F5grKHjaOGa9OmDQBoLVYqtmviiIjIsFiwyOiFF14Qt1u3bo1x48Zh+vTpGDdunKQPS8U40o+KHbwVCgUmTZqEc+fOYdKkSZLLc8bSEZyIqLFhwSKjinOt3LlzB3///Td8fX3x999/S/r4cHZP/UtOTha3BUHAtm3b0KNHD2zbtk0yD0vFOCIiMhz2YZFR//79ceLEiVrj+vXrh59//tkAGRmvTp064a+//qo1rmPHjrhy5YoBMiIiav7Yh6WJqHipQRdx1HDm5uY6jSMiIt1iwSKjESNG6DSOGm7ixInidl5eHtavX4/58+dj/fr1yMvL0xpHRESGw4JFRh07dpTsu7m54Ysvvqgy/XvlONK9nj17itutW7dGeno6FixYgPT0dEkH6IpxpH8qlQpxcXHYuXMn4uLiqqxqTkTGg31YZNS+fXukpKTUGuft7Y1r164ZICPjtXPnzjqdPdmxYwcmTJhggIwoOjoaERERSE1NFdu8vLywbt06hIWFyZcYEekM+7A0ERXfiHURRw2nOav13HPPVRmVpVQqxWKGix8aRnR0NMLDw+Hv74+EhAQUFBQgISEB/v7+CA8PR3R0tNwpEpGB8QyLjOrTmbYZPE2Nmkqlgq+vL1q3bo2srCykpaWJxzw9PeHs7IycnBwkJSVxmLmeaZ4Lf39/7N27FydOnEB6ejrc3NzQr18/jBkzBomJiXwuiJoBnmFpIuo6aypnV9U/pVKJsWPH4vTp0ygtLcUnn3yC27dv45NPPkFpaSlOnz6N8PBwfkAaQHx8PFJTUxEYGIgOHTpg4MCBmDhxIgYOHIgOHTqgb9++SElJQXx8vNypEpEBsWCRkYeHh07jqOFUKhV2796NXr16wcrKCrNmzYK7uztmzZoFa2tr9OrVC3v27GGnTwPQrJu1dOlSZGVlSY5lZWXhtddek8QRkXFgwSKjU6dO6TSOGk7zrf7DDz/E+fPn0a9fP3h4eKBfv344d+4cPvjgA36rNxBnZ2edxhFR82AqdwJEjYHm2/q8efNw9uxZsf3GjRto0aIFHnvsMUkc6U/Fs1iV+25V3OfZLiLjwjMsRPjf6J+zZ89CoVBg8uTJuHDhAiZPngyFQiEWMRwlpH/Hjx8Xt+3s7BAREYGNGzciIiICdnZ2WuOIqPnjGRYZ9erVC6dPn65THOlXjx49xO28vDycOXMGf/zxB55//nls3LhR7L1eMY704/r16wAeTOD3999/Y926deIxU1NTODo6IicnR4wjIuPAgkVGdSlW6hNHDTdt2jRx28XFBSUlJeK+lZWVJG7fvn2GTM1o3blzB8OGDcMzzzwDKysrlJSU4NChQ/j222/lTo2IZMBLQkQAkpOTxe179+5JjlXcrxhH+lF5VFyPHj0QHh5e5ewWR88RGReeYSHCg+UPLl68CAB45plnqv1W7+3tLWeaRqHi2k0//vij5IxKxbNdFeOIqPljwSKjfv364cSJE+K+s7MzbG1tUVhYKJl/ol+/fnKkZ1RmzpyJgwcPAgB27doFa2tr8di0adNgY2MjxpF+ubq66jSOiJoHXhKS0Z9//inZz87Oxv3795GdnV1jHOneL7/8Im7b2tpi0qRJOHv2LCZNmgRbW1utcaQfFWd2rmlJCs4ATWRceIZFRnfv3pXsC4KAGzdu1BpH+tOpUydcuXIF27dvx/bt28X2jh074q+//pIxM+MRFBQELy8vres6OTs7w8nJCTk5OQgKCpIxSyIyNJ5hkZGJSd1+/XWNo4YLDg4GADg5OSEvLw+hoaHw9/dHaGgo8vLyxFlVNXGkP0qlEuvWrcOZM2fQtWtXbNiwAZ9++ik2bNgAf39/nDlzBmvXruW6TkRGhqs1y6hXr144c+ZMrXE9e/bk0GY9U6lUcHNzQ3Z2NoYPH47XXnsNfn5+SExMxNtvv42YmBg4Ozvj9u3b/KA0kOjoaERERCA1NVVs8/b2xtq1axEWFiZfYkSkM1ytuYlo27atZN/W1hZ+fn6SPhPa4kj3lEolNm/eDAA4duwYAgMDYW9vj8DAQPz4448AgI8++ojFioFV/j6lVqtlyoSI5MaCRUYHDhyQ7BcWFiIxMRGFhYU1xpF+hIWFYe/evXBycpK0Ozk5Ye/evfxWb0DR0dEIDw9H165dkZCQgIKCAiQkJKBr164IDw9HdHS03CkSkYGxYCGq4NSpU7h586ak7caNG1wx24BUKhUiIiIwfPhw7N+/H3369IGtrS369OmD/fv3Y/jw4Vi8eDEXPyQyMixYZKRQKHQaRw/nlVdewZo1a7SuELxmzRq88sorMmVmXOLj45GamorXXnutSodzExMTLF26FCkpKYiPj5cpQyKSAwsWGY0cOVKncdRwZWVlkkX2tFm3bh3KysoMlJHxSk9PBwD4+flpPa5p18QRkXFgwSKjY8eO6TSOGu7DDz8UO3Q6OzsjKioK6enpiIqKEoc0q9VqfPjhh3KmaRTc3NwAAImJiVqPa9o1cURkHFiwyKhy59qHjaOG++mnnwAALVq0QFJSEs6fP4+pU6fi/PnzSEpKQosWLSRxpD+aiePefvvtKqOC1Go1IiMj4e3tzYnjiIwMCxYZsQ9L43Hr1i0AgI2NDezt7bFx40YcOXIEGzduhL29vbi2kCaO9EczcVxMTAxCQ0Mlo4RCQ0MRExPDieOIjBALFhl17txZp3HUcJp1aW7dugVzc3MsWbIEV69exZIlS2Bubo7bt29L4ki/wsLCsGfPHly8eFEyJ05iYiL27NnDIeZERkjnBYuXlxcUCkWV27x587TGb9mypUqspaWlrtNqlHJzc3UaRw3Xp08fcdvBwQHe3t6wsrKCt7c3HBwctMaRfoWFheHq1auIjY3Fjh07EBsbi6SkJBYrREZK54sf/vbbb5L5ERITE/HUU09h7Nix1d7H3t4eV65cEfeN5RJIXUc5cDSE/lWc/j07OxuzZ8+uNY70T6lUcv0mIgKgh4Kl8iyh77zzDnx8fDBgwIBq76NQKODq6qrrVIjqrK6FCAsWIiJ56LUPS1lZGbZt24bnn3++xrMmhYWFaNeuHTw8PDBq1Cj88ccfNT5uaWkp8vPzJbemiKs1Nx4dOnTQaRwREemWXj8J9+/fj9zcXEybNq3amE6dOuGzzz7DgQMHsG3bNqjVagQGBlaZHr2iyMhIODg4iDcPDw89ZK9/oaGhkn0XFxfMnz8fLi4uNcaR7i1fvlzcVigUGDRoECZNmoRBgwZJiu2KcUREZDgKofI85DoUEhICc3NzfPPNN3W+z/379/HII49gwoQJeOutt7TGlJaWorS0VNzPz8+Hh4dHnZanbkxsbW1RVFRUa5yNjQ3nYtGz4cOH49tvvwXwoN+Et7c3TExMoFarkZKSIvbLGjZsGGJiYuRMlYio2cjPz4eDg0OdPr913odF4/r16/jhhx/qvaqqmZkZevTogatXr1YbY2FhAQsLi4dNUXYlJSU6jaOGS0hIAACYmpqivLy8yt+fpl0TR0REhqW3S0Kff/45nJ2dMWzYsHrdT6VS4eLFi0Yx7bZmMjJdxVHDafoJlZeXaz2uaWd/IiIieejl3VetVuPzzz/H1KlTYWoqPYkzZcoULF26VNx/8803ceTIEVy7dg1nz57FpEmTcP36dbzwwgv6SK1Rqfwz2tvbY8aMGVVOixnD70JuISEhOo0jIiLd0kvB8sMPPyAtLQ3PP/98lWNpaWmSeUXu3r2LmTNn4pFHHsEzzzyD/Px8nDx5Eo8++qg+UmtUPv30U8l+fn4+Pv300yqjnirHke5lZmbqNI6IiHRLr51uDaU+nXYak/pMkNcMnqZGzczMrNrLQRWZmpri/v37BsiIiKj5q8/nNy/IE6H6visNjSMiIt1iwSKjFi1a6DSOGq7y2S4XFxc89thjVebEMZZlI4iIGhsWLDLq1KmTZN/CwgL9+vWrMmS7chzpXuXO4YMHD0ZUVBQGDx5cYxwRERkG+7DIiH1YGg8TE5M6/Y4VCgXUarUBMiLgwfIemzZtQnJyMnx8fDB37lyYm5vLnRYR6UijmDiOiOhhvPLKK1i/fr2k39DLL7+Ml156CatXr5YxMyKSAy8JEQHo2LGjTuPo4bzyyitYs2YNHB0dERUVhfT0dERFRcHR0RFr1qzBK6+8IneKRGRgvCQko169euHMmTPivqurK8LDw7Fnzx5kZGSI7T179sTp06flSNFopKWloV27drXGXb9+HZ6engbIyHiVlZXBxsYGjo6OuHnzpqTfUHl5Odq2bYucnBwUFRXx8hBRE8dhzU1ExWIFADIyMrBhwwZJsaItjnRvzpw5Oo2jhtu0aRPKy8uxatUqKBQKxMXFYefOnYiLi4NCocCbb76J8vJybNq0Se5UiciA2IeFCMBPP/2k0zhquOTkZAAPOjj7+voiNTVVPObl5YV//etfkjgiMg48w0KEuo/Y4jws+ufj4wPgwRpa/v7+SEhIQEFBARISEuDv74+ZM2dK4ojIOLBgkZGfn1+VNl9f3zrFkW65u7uL261atZJ09GzVqpXWONKP2bNnAwDMzc2xZ88e9OnTB7a2tujTpw/27Nkj9lvRxBGRceAlIRllZWVVabt69Wqd4ki3HBwcxO2///4bmzdvxtGjR5GcnIy///5baxzpxy+//ALgQedbDw8PTJo0CT4+PkhOTsa2bdtQVlYmxgUHB8uYqfFRqVSIj49Heno63NzcEBQUBKVSKXdaZCRYsMioroUICxb9q/w7PnPmjNbOznwu9E+zmvuwYcPw7bff4r333pMc17RXXPWd9C86OhoRERFV+hStW7cOYWFh8iVGRoMFCxEANzc3yRtxTXGkX5rf8aFDhzBs2DD4+vqipKQEVlZWuHr1Kg4dOiSJI/2Ljo5GeHg4hg8fjp07d8LPzw+JiYl4++23xakYWLSQvrEPCxGAESNG6DSOGi4wMBCmpqZwdnbGrl274OXlBXNzc3h5eWHXrl1wdnaGqakpAgMD5U7VKKhUKkRERGD48OHYv3+/pE/R/v37MXz4cCxevBgqlUruVKmZ4xkWGU2aNAnbtm2rUxzpV10XNeTih/p38uRJlJeXIzMzE7a2tpI1nhYtWiTunzx5kn1YDCA+Ph6pqanYuXMnTEyk33FNTEywdOlSBAYGIj4+ns8H6RXPsMho586dOo2jhrt27ZpO46jhKvZNqTyMvOI++7AYhub3XN1oRU07nw/SNxYsMqrrKVSeatW/W7duAQCUSmWVD0kTExNxJIQmjvTH0dERAGBpaan1uKZdE0f6pekrlJiYqPW4pp19ikjfWLAQAbhz5w6AB5d82rRpIznm7u4uXgrSxJH+XLx4EQBw7949ODk5SebEcXJywr179yRxpF9BQUHw8vLC22+/DbVaLTmmVqsRGRkJb29vBAUFyZQhGQsWLDKqfD340UcfxcGDB/Hoo4/WGEe6p/kdl5aWIisrC6+++ir++usvvPrqq8jKykJpaakkjvSn4pT7vXr1QpcuXWBjY4MuXbqgV69eWuNIf5RKJdatW4eYmBiEhoZKZh4ODQ1FTEwM1q5dy/lYSO/Yg1BGLVq0kExKdunSJYwcOVJrHOnXiBEjcOLECSgUCpSVleHdd9/Fu+++Kx5XKBQQBIGjhAxA0xdiwIAB+OOPPySjgTTf5DWTl5FhhIWFYc+ePYiIiKjyfHBIMxkKCxYZ2dvbSwqWmuJIv3r06AEAkhEpFWnaNXGkP66urgCAs2fPomXLlpJjKpUK58+fl8SRYYSFhWHUqFGc6ZZkw4JFRkVFRTqNo4bLycnRaRw1XKdOnQAABQUFKC0txauvvooZM2bg008/xfr168Wp+TVxZDhKpZJDl0k2vCAvI29vb53GUcNV7kz4sHHUcJpFDU1MTKBSqfDuu++iY8eOePfdd6FWq8V+RFz8kMi48AyLjCpe6rG1tYWFhYU4BXlpaSkKCwurxJF+zJo1q85xzz33nJ6zMW6axQ/VajWcnZ0xYMAA2NjYoKioCMePHxfXc+Lih4bHxQ9JTixYZKR5YwaAwsJCsUApLi6uNo70o/Lv/GHjqOE0nWkXLlyIjRs3Yvfu3eIxU1NTLFy4EO+//z473RpYdHQ0Fi1ahOvXr4tt7dq1w3vvvcdOt2QQvCQko/v37wOofrp3TbsmjsgYaCYge/bZZ1FUVIT169dj/vz5WL9+PYqKijB+/HhJHOlfdHQ0xowZg7S0NEl7WloaxowZg+joaJkyI2OiEKobFtGE5Ofnw8HBAXl5eU3q8kmPHj3EEQ816d69O86dO6f/hIyYlZWVOCEZ8KD/hJeXF1JTUyX9ViwtLVFSUiJHikZDpVLB19cX/v7+2L9/v2TuG7VajdDQUCQmJiIpKYmXIwxApVLB0dEReXl5cHZ2xr///W8MHz4cMTEx+Ne//oWsrCw4ODggJyeHzwfVW30+v3mGRUZvvfWWZL9z585YsmQJOnfuXGMc6Z5mYjgNtVqNa9euVelkWzmOdI8TlTUuP/74I/Ly8tCyZUvcunULL7zwAlxdXfHCCy/g1q1baNmyJfLy8vDjjz/KnSo1cyxYZFT5DffPP//EO++8gz///LPGONK9up5obAYnJJsEzURlFy9eRGBgIOzt7REYGIjExEROVGZgW7duBQC8+eabVS5fm5qaYsWKFZI4In1hp1sZ7dixo85xQ4cO1XM2RI0LJyprHAoKCgBUP72Cl5eXJI5IX3iGRUZ1fYHzjYCI5KJZ1PBf//qX1sUPly1bJokj0hedFywrVqyAQqGQ3Cr3yahs9+7d6Ny5MywtLeHv749Dhw7pOq1Gqa5Ti3MKcv2r7W+0vnH08KKjo+Hr64uBAwdi4sSJGDhwIHx9fTkixcDmz58PExMTXLhwAaNGjZL0KRo1ahR+//13mJiYYP78+XKnSs2cXs6wdOnSBenp6eLt559/rjb25MmTmDBhAmbMmIFz584hNDRUHAXQ3FlYWIjbDg4OGDduHKZPn45x48bBwcFBaxzpR+V+Qw8bRw8nOjoa4eHh8Pf3l3xA+vv7Izw8nEWLAZmbmyMiIgIAcOjQIUmfou+++w4AEBERAXNzcznTJCOg82HNK1aswP79++s0XBcAxo8fj6KiIsTExIhtffr0Qffu3bF58+Y6PUZTHdbs5OSEO3fu1BrXunVrZGdnGyAj46VQKOocy463+lVxWPPOnTvx6quvIikpCR06dMC7776LCRMmcFizDF555RW89957UKlUYptSqcSiRYuwevVqGTOjpkz2Yc1JSUlwd3dH+/bt8dxzz1WZbKiihIQEDB48WNIWEhKChIQEfaTWqHDxQ6Kq4uPjkZqaitzcXNja2mLjxo04cuQINm7cCFtbW+Tm5iIlJQXx8fFyp2pUVq9ejeLiYslEfsXFxSxWyGB0PkooICAAW7ZsQadOnZCeno6VK1ciKCgIiYmJsLOzqxKfkZEBFxcXSZuLiwsyMjKq/T9KS0sl82Hk5+fr7gcwoJYtW9ZpErKWLVsaIBvjplAo6nTmpD5nYqhhNFPuV1eQaNo5Nb/hmZub48UXX5Q7DTJSOj/DMnToUIwdOxZdu3ZFSEgIDh06hNzcXOzatUtn/0dkZCQcHBzEm4eHh84e25CefPJJcbvyB2HF/YpxpB+ch6XxqFigt27dGlFRUUhPT0dUVBRat26tNY6Imj+9D2tu0aIFOnbsiKtXr2o97urqiszMTElbZmZmjSNjli5diry8PPF248YNneZsKBWn26/8QVhxn9PykzHZt28fgAfLI6SkpKCwsBD//ve/UVhYiJSUFHGqfk0cERkHvU8cV1hYiOTkZEyePFnr8b59++LYsWOS04xHjx5F3759q31MCwuLZjFyJi8vT6dxRM3B8ePHATyY48Pe3l5SvC9atEjc18QRkXHQ+RmWxYsX4/jx40hNTcXJkycxevRoKJVKTJgwAQAwZcoULF26VIxfuHAhDh8+jHXr1uHPP//EihUrcPr0aaMY09+pUydxu/Joh4pTYFeMI/2oawHcHArlxs7MzKzaYxUvldYUR0TNj84Llps3b2LChAno1KkTxo0bB0dHR5w6dQpOTk4AHixHXrGzXGBgIHbs2IFPPvkE3bp1w549e7B//374+fnpOrVGRzO3AQC0atUKixYtwoYNG7Bo0SLJ9fmKcaQfL7/8sk7jqOGeffZZcbtt27aSY23atNEaR0TNn84vCX311Vc1Ho+Li6vSNnbsWIwdO1bXqTR6FSdays7OxnvvvVdrHOlHXYfRG8Nwe7lV/Huv3D+t4j5fF0TGhWsJySgrK0uncdRwde243VQ7eDclqampOo0jouaBBYuM3NzcdBpHDVf50sPDxlHDlZeX6zSOiJoHvY8SouoFBASI20OHDsXw4cNhZWWFkpISxMTEiOt0VIwj/QgKCsKPP/4o7vfq1Qs+Pj5ITk7G6dOnJXGkX1zFnIi04RkWGW3atEncViqV6NGjB8LDw9GjRw/JqKGKcaQfBw4ckOyfPn0aX3/9taRY0RZHulfxUo9CocDkyZNx7tw5TJ48WTJKiJeEDE+lUiEuLg47d+5EXFycZF0hIn1jwSIjzSrWS5cuxfnz5yWroF64cAFLliyRxJH+VDexYUPjqOHu3bsnbisUCmzduhU9evTA1q1bJQVLxTjSv+joaPj4+GDgwIGYOHEiBg4cCB8fH66cTQbDgkVGtra2AICoqCjcvHlTcuzGjRuIioqSxJH+cB6WxqPiOmGV51qpuF8xjvQrOjoaY8aMqbKQbVpaGsaMGcOihQyCBYuMNLP/3rlzR+vxnJwcSRzpT/fu3SX7vXr1wvjx49GrV68a40j3Ks5BVLkoqbjPtYQMQ6VSYfr06QAAZ2dnydpOzs7OAIDp06fz8hDpHQsWGVX+MHRzc8MXX3xRZVRQ5TjSvcuXL0v2q+vDUjmOdG/EiBE6jaOHc+zYMeTn56NVq1a4efMmXnjhBbi6uuKFF17AzZs30bJlS+Tn5+PYsWNyp0rNHAsWGYWEhIjbCoUC6enpmDp1KtLT0yXX6ivGkX5kZ2frNI4arq6zXBvDbNiNwdatWwEAK1eulCwZAjxYQmTFihWSOCJ9YcEio7NnzwIAnJyc4OHhITnm6ekJR0dHSRzpT+U34oeNo4arbbbs+sbRwyksLAQAeHt7az3u5eUliSPSFxYsjUDbtm1x7do1xMbGYseOHYiNjUVycjInKTOgJ554Qqdx1HB1/eDjB6Rh9O/fHwDw2muvQa1WS46p1WosW7ZMEkekLyxYZKRZyO3cuXMoLS1FcHAwJkyYgODgYJSWluLChQuSONKfunbgZEdP/QsMDNRpHD2cBQsWwMTEBL///jtGjhyJhIQEFBQUICEhASNHjsTvv/8OExMTLFiwQO5UqZljwSKjM2fOiNs2NjYICQlBfHw8QkJCYGNjozWO9IPf6hsP9mFpXMzNzcUV4w8dOiSZL0ozG3dERAQXoyS9Y8EiIycnJzg4OIj7R44cwRNPPIEjR46IbQ4ODnBycpIjPaNy+/ZtncZRw1VcIkEXcfTwVq9ejVGjRkEQBEm7Wq3GqFGjsHr1apkyI2OiECr/BTZB+fn5cHBwQF5eHuzt7eVOp95atGiBvLy8Ku0ODg7Izc01fEJGSKlUSq7POzg4wMLCAqWlpZLnxsTEhPNN6Fnbtm1x69YtmJubo7y8XPK8KJVKKJVKlJWVoU2bNlUmXCT9iI6ORnh4OJ555hn4+vqipKQEVlZWuHr1Kg4dOoQ9e/YgLCxM7jSpCarP5zcLlkYiOzsbjz/+OLKzs+Hk5IRff/2VZ1YMqOIw8to0g5dMo+bg4ID8/Hw4OzvDwsICN27cEI95eHjg3r17yM7Ohr29vdZCn3RLpVLB19cX/v7+2L9/P0xM/ndiXq1WIzQ0FImJiUhKSpKsgUZUF/X5/OYYzUbCyckJKSkpcqdBJDsXFxfk5+cjKyuryrGKxYuLi4sh0zJa8fHxSE1Nxc6dO1FeXo5NmzYhOTkZPj4+mDt3LpYuXYrAwEDEx8cjODhY7nSpGWPBQkSNytq1azFq1Kg6xZH+paenA3gw701QUBDKy8vFYy+//DLmzZsniSPSFxYsRADat2+Pa9eu1SmO9KuulxV4+cEwNEuFvP/++3BxccHkyZPF18vWrVvx/vvvS+KI9IUFCxEezOJZl4Klutk+SXc0H4B1iRs2bJies6GAgAAAD2Z5trCwkJzZ8vT0hKmpKcrLy8U4In3hsGYiAD///LNO46jh7t69q9M4ejgff/wxAKC8vLzKqKybN2+Kl4g0cUT6woKFCMD9+/d1GkcN98gjj4jbSqUSEyZMwPr16zFhwgTJZaCKcaQ/SUlJ4raFhYXkWMX9inFE+sBLQkQALC0tUVxcLO6bmZnB0dEROTk5kiLF0tJSjvSMSlxcnLj99NNPY8GCBfDz80NAQAByc3PF2VUrxpH+aObBcXV1hYWFBa5fvy4ec3Z2xr1795CZmVllnSEiXeMZFiJUneb9/v37yMjIqHJGhdPB61/F0Sbfffed1qngK8eR/rRo0QIAkJGRAT8/P8laQn5+fsjMzJTEEekLCxYiAOfPn9dpHDWc5rJP5csPGpo1azhKyDAqThR3+vRp/P7778jPz8fvv/+O06dPa40j0gdeEiICJHNL6CKOGm78+PH48ssvUVpaqvV4WVmZGEf616pVKwAPJrfMzs7G7NmzxWNKpVJs18QR6QtLYiJAsgxCTR0LuVyC/m3cuFGncfRwXF1dATxYPqRyPxWVSoXs7GxJHJG+sGAhgvRSj+YbvLZ9XhLSv7p2pmWnW8No06aNTuOIGoqLHxL9PxsbG8lIocqsra1RVFRkwIyMU8+ePXH27Nla4x577DGcOXPGABkZt5KSElhbWwOAuIK5hqWlJe7duwcAKC4uhpWVlSw5UtNVn89vnmEh+n9bt259qOOkG7dv39ZpHD2cTZs2iduDBw/Ghg0b8Omnn2LDhg0YNGiQ1jgifWDBQoQH1+IjIiLg4OCg9biDgwMWL14MlUpl4MyMj42Njbjt5OSEqKgopKenIyoqStKHqGIc6Y9mduelS5fijz/+wPz58zFjxgzMnz8fly5dwpIlSyRxRPrCgoUIQHx8PFJTU5GXlweFQoHJkyfjwoULmDx5MhQKBfLy8pCSkoL4+Hi5U232KvaF6NGjBxITE7F8+XIkJiaiR48eWuNIf2xtbQEAWVlZqNyDQK1WIysrSxJHpC8c1kwEICUlBQCgUChQXFwszmj75Zdf4pNPPoG1tTUEQUBKSgqCg4NlzLT5y8nJEbePHDmCI0eO1BpH+jN58mRs27YNn376KYYNG4avvvoKfn5+SExMxKpVq/DZZ5+JcUT6pPMzLJGRkejduzfs7Ozg7OyM0NBQXLlypcb7bNmyBQqFQnLjFOhkSJ9++ikAICQkpMrfnqWlJZ566ilJHOlPXWdM5cyqhhEcHAyFQgEA+PXXX7F79258+eWX2L17N3799VcADwp9FvKkbzo/w3L8+HHMmzcPvXv3Rnl5OV577TU8/fTTuHTpUo3XnO3t7SWFjeYFQmQIeXl5AB6MdFCr1ZJZO9VqNUpKSiRxpD/Dhg3DiRMn6hRH+nfy5EnxUlB2djbee++9KjGCIODkyZMsWkivdF6wHD58WLK/ZcsWODs748yZM3jiiSeqvZ9CoeDEQyQbX19fJCYm4qeffkJoaCiWLl0qnvaOjIwU+674+vrKnGnzV9cvK/xSYxh1XbOJazuRvum9063mG2lt0zYXFhaiXbt28PDwwKhRo/DHH3/oOzUikWbIskKhwIULFyQL7v3+++/ihyOHNutfTEyMTuPo4Tg7O4vbTk5OWLx4MTZt2oTFixdLRm1VjCPSB712ulWr1XjxxRfRr1+/Gle57dSpEz777DN07doVeXl5WLt2LQIDA/HHH3+gbdu2VeJLS0slkxfl5+frJX8yHra2tujduzd+++033LhxA4MHD8bAgQMRGxuLY8eOQRAE9O7dmyMhDKCuc1k2gzkvmwTN+lmmpqa4efOmuPgkAPz73/+GjY0NysvLuc4W6Z1eC5Z58+YhMTGx1vH5ffv2Rd++fcX9wMBAPPLII/j444/x1ltvVYmPjIzEypUrdZ4vGbdff/0Vjz/+OH777Tf88MMP+OGHH8RjvXv3FjsYkn516dIFJ0+eBPDgG/3AgQNhY2ODoqIixMbGimvXdOnSRc40jcb27dsBPChcwsLC4OPjg3v37sHS0hLJycliobJ9+3aEhITImSo1c3orWObPn4+YmBj89NNPWs+S1MTMzAw9evTA1atXtR5funQpFi1aJO7n5+fDw8PjofIlAh4ULYWFhZg8eTKSk5Ph4+ODrVu38syKAVUe/TNo0CAMHz4cMTExiI2NrTaO9KOgoAAAEBAQgG+//bbK8YCAAPzyyy9iHJG+6LxgEQQBCxYswL59+xAXFwdvb+96P4ZKpcLFixfxzDPPaD1uYWFRZUVdIl2xtbXFvn375E7DaFVcYDI7OxuzZ8+uNY70JygoCPv378cvv/wChUIhuRSnUCjwyy+/iHFE+qTzTrfz5s3Dtm3bsGPHDtjZ2SEjIwMZGRnisFAAmDJlCpYuXSruv/nmmzhy5AiuXbuGs2fPYtKkSbh+/TpeeOEFXadHRI2cZqE9FxcXKJVKyTGlUgkXFxdJHOnXrFmzxO3K/YYq7leMI9IHnZ9h+eijjwCgynj8zz//HNOmTQMApKWlSea5uHv3LmbOnImMjAy0bNkSPXv2xMmTJ/Hoo4/qOj0iauSCgoJw4MABZGZm4plnnoGvr6/YZ+Lq1as4dOiQGEf69/HHH9c5LiIiQs/ZkDFTCM2gq319lqcmosatrKwMVlZWUKvVsLS0xL1798RjVlZWKCkpgYmJCUpKSiQjVkg/Ro4ciW+++abWuBEjRuDgwYMGyIiak/p8fnPxQyJqVMzNzcVv6mVlZZJjmukMIiIiWKwYSG1Lq9Q3jqihWLAQUaOzevVqjBo1Cmq1WtKuVqsxatQorF69WqbMjE/lJVXc3Nzw5Zdfws3NrcY4Il1jwUJEjU50dDQOHDig9diBAwcQHR1t4IyMV8VZyh0dHbFixQoMGjQIK1asgKOjo9Y4In1gHxYialRUKhVatWqF/Px8uLi4YNWqVeI8LK+//joyMzNhb2+Pv//+u8ooItI9GxsbFBcXA4DWYc2afWtraxQVFcmSIzVd9fn8ZsFCVIlKpUJ8fDzS09Ph5uaGoKAgfjAa0JEjRxASEoJWrVohMzMTpqb/G8xYXl4OZ2dn3L17F99//z2efvppGTM1DkqlssqlOW1MTEygUqkMkBEBzed9ip1uiRooOjoavr6+GDhwICZOnIiBAwfC19eXlyAMSLPA5MqVKyXFCvBgPZsVK1ZI4ki/6volkF8WDcdY36dYsBD9v+joaISHh8Pf3x8JCQkoKChAQkIC/P39ER4e3uzfDBqLwsJCAIC3tzdUKhXi4uKwc+dOxMXFQaVSwcvLSxJH+nXhwgVxe/DgwRgzZgyefPJJjBkzBoMHD9YaR/pjzO9TvCREhAenV319feHv74/9+/dLJjZUq9UIDQ1FYmIikpKSmuRp16Zk3bp1WLx4Mby8vCAIAq5fvy4ea9euHQDg+vXrWLt2LScqM4C4uDgMHDiw1rjY2NgqE4aSbjXH9yleEiKqp/j4eKSmpuK1116TvAkAD67NL126FCkpKYiPj5cpQ+OxYMECKBQKpKamoqSkBJ988glu376NTz75BCUlJbh+/ToUCgUWLFggd6pGIT09HQCqnfdG066JI/0x9vcpFixE+N+brZ+fH7Kzs+Ht7Q1bW1t4e3sjOzsbfn5+kjjSH6VSCTs7OwDAnTt3MGvWLLi7u2PWrFnIyckBANjZ2TWZb5BNnWa+lYCAAK3HNe2V52Uh3av4PqVNc3+fYsFChP+92bq5ucHZ2RmpqakoKipCamoqnJ2d4e7uLokj/YmPj0d+fj6ee+45KBQKyTGFQoGJEyciPz+/2X6LbGyCgoJgZWWF+Ph4mJubY8mSJbh69SqWLFkCc3NzxMfHw8rKims7GYDm/ScxMVHrcU17c32fYsFChAdvygqFQuzI2aVLF8TExKBLly4AHnTwVCgUfFM2AM23w82bN6O4uBjr16/H/PnzsX79ehQVFWHz5s2SONKvsrIylJSUAHjQ6XbkyJFwdnbGyJEjxU63JSUlVZZRIN0LCgqCl5cX3n77ba2zQEdGRsLb27vZvk+xYCEC8Pfff4sTYD399NOIiorCE088gaioKHGuD0EQ8Pfff8uZplGo+C3S3NwcL774Ij788EO8+OKLMDc3b/bfIhubl19+GQAwevRoXLp0CYGBgbC3t0dgYCAuX76M0NBQSRzpj1KpxLp16xATE4PQ0FDJKKHQ0FDExMRg7dq1zfZyKUcJNRLNZRKgpsrb2xupqanw8PCAUqlEamqq5Nj9+/dx8+ZNeHl5ISUlRb5EjUBzHAnRlIWEhODIkSO4evUqvLy8qrxPJScno1OnTnj66afx/fffy52uUYiOjkZERESV96m1a9ciLCxMvsQagKOEmpjo6Gh4e3tLJgHy9vZu1uPpG5vs7GwAwKRJk6qcalWpVJgwYYIkjvSn4rfIIUOGoFWrVrC0tESrVq0wZMiQZv8tsrHp0KEDAOC///0vlEolgoODMWHCBAQHB0OpVOLTTz+VxJH+hYWF4erVq4iNjcWOHTsQGxuLpKSkJles1BfPsMgsOjoaY8aMqfb43r17m/0fYWOgOcMCAFZWVuI1+8r7PMNiOBYWFlr7RZibm6O0tFSGjIxTSUkJrK2tYW5ujoKCAsnw5rKyMtjZ2aGsrAzFxcWwsrKSMVNqiniGpYlQqVQYO3ZsjTFjx47l+hwGkJCQIG73799fcm24f//+WuNIf2xsbMRixdXVFQsWLICrqyuABx+SNjY2cqZnVKysrDBq1CiUlZXB1tYWbdu2hZubG9q2bQtbW1uUlZVh1KhRLFZI71iwyCgmJkZy+WHIkCFISEjAkCFDxDa1Wo2YmBg50jMqf/zxh7h99OhRPP/885J/tcWRfmRkZIirA9+9exfp6en44IMPkJ6ejrt37wIAiouLkZGRIWeaRmX//v2wtrbG/fv3cevWLWRkZODWrVu4f/8+rK2tsX//frlTJCPAgkVGc+bMEbdv376NgoICjBs3DgUFBbh9+7bWONKPuLg4ABC/uV++fBlhYWG4fPkyAMDa2loSR/rTvXt3AA8u07Vo0UJyrEWLFuL0/Jo40j9fX1+xiPTw8EDv3r3h4eEB4EHx6OvrK2d6ZCRYsMgoMzMTwIMVaN3d3XHixAncuHEDJ06cgLu7u7hSrSaO9O/gwYPIysqCl5cXbGxs4OXlhaysLBw4cEDu1IxGbm4uAGD16tVaj7/99tuSONKvvLw8JCcnAwCKioqQlpaGX3/9FWlpaSgqKgIAJCcnIy8vT840yQiwYJGRZhbP8vJyrcc17ZVn+yTd0yzatnz5cjg6OiIlJQWFhYVISUmBo6MjVqxYIYkj/dGcVXnllVe0Hn/ttdckcaRfw4YNA/DgkrWpqSn+85//YMGCBfjPf/4DU1NTcZ4iTRyRvpjKnYAx69q1K86dOyfud+nSBe+88w6WLFki6SvRtWtXOdIzKsHBwXB2dsbPP/+MkSNHYujQoeLooO+++w4nTpyAs7MzCxYDOH/+PNzc3JCSkoLc3FxJYZKbmyuu3nz+/Hl5EjQyaWlpAIBWrVrB2tpaMghg8eLF4sABTRwZhlHO3SU0A3l5eQIAIS8vT+5U6qV79+4CgFpv3bt3lztVo7B3794an4e9e/fKnaLRsLa2Fn/vlpaWQtu2bQVLS0uxzdraWu4UjUa/fv3q9D7Vr18/uVM1Gnv37hW8vLwkv38vL68m+R5Vn89vXhKS0cWLF3UaR9RcFBUViZdC7927h5s3b+LevXsAHlwi1fSdIP3bt2+fuG1qaipZ/FDTz65yHOlPdHQ0wsPD4e/vL5l+wd/fH+Hh4c16wlEWLER4cHo1PDy8xpjw8HDOiWMgjz/+uLi2k4WFBczMzGBhYQHgwZpOjz/+uJzpGZXPPvtM3C4vL8fZs2dx+/ZtnD17VtL/rmIc6YdKpUJERASGDx+OzZs3IzQ0FK1bt0ZoaCg2b96M4cOHY/Hixc32fYoz3cqoU6dO+OuvvwAAgwYNQnZ2NnJycuDo6AgnJyccO3YMANCxY0dcuXJFzlSbvT179kgm8Xv88cfxzDPP4NChQ/j111/F9t27d9da2NDDKSwshJ2dHRQKBYqLi2FpaSkeu3fvHqytrSEIAgoKCmBraytjpsbB398fiYmJsLe3R35+fpXjmnY/Pz+eDdazuLg4DBw4EBYWFlpne9a0x8bGNpn+dpzptomIjIwUt48dOwZzc3M8//zzMDc3F4uVynGkH7NmzRK3HR0d8euvv2LFihX49ddf4ejoqDWO9GPy5MkAHqzrdPPmTZibm0OhUMDc3Bw3b97ExIkTJXGkX5oZhzWX5CrTtGtbRoF0Kz09HQDEYsXb2xu7d++Gt7e3pF0T19ywYJFRxRFCAHD69Gm89dZbOH36dI1xpHuaGVQBICcnR3Ks4n7FONIPzZwfW7duRYcOHXD//n0AwP3799GhQwds375dEkf6pfmmrilIunTpgpiYGHTp0kXS3lS+0TdlZmZm4vbly5dRXFyMSZMmobi4WJzksnJcc8JhzUQATExMqqzSXF0c6ZePj4/k0oK1tTVWrlyJ5cuXi7OtauJI//71r3/hk08+AfBgaHN6ejrGjRsnrqD9999/i3HUcMXFxfjzzz9rjJk9e7a4/cgjj4jbmZmZkv3Zs2ejffv21T5O586dxdm7mxL2YZHRkSNHEBISUmvc999/L07ORPpRcbXmmnC1Zv07f/48evToAQBISUmBl5eXeCw1NVU8/X3u3DlOz28AfG00XFJSEgoKCuoUe/nyZUyaNEnPGT2wbds2SYFTGzs7O3To0EEvudTn85tnWGRU14X0/vjjDxYselbXSa84OZb+VRwBpClOqotjvwn9y87O1mmcsbhw4QKG9O8BN9u6z1Tew9UwZ3DXLZ5Sr/j0QgE/nb2it6KlrliwyGjRokV1jnvppZf0nI1xq8vloPrEUcNp+qzoKo4eTosWLeo07w2XSpD67bffMLunOVYEW8idykNbEVd1RJIcWLAQUaNiZmZWp2KkuXYsbGzqupYZ1zyTCg0NxfeqfJzzaCUZml+dlJQUvP766wbIDFi1alWNZy8rmxLWDu1lPrsC6LFg2bhxI9asWYOMjAx069YNH374YY2TPe3evRvLli1DamoqOnTogHfffRfPPPOMvtJrtFq2bMmRKGTUXnrppWpXaq4cR1J37tzB93u/hLWq6nwp2pSVlVUZFVeZsyodTpUuVVhaWuDePem3boUqHZuWzUZ1HB0dYW5uXqe8Wnt3QdDQsbUHNmKtW7fGc7PrdhYdANoVF+O/PWru09izZ886P96ZM2eqPcZOtxV8/fXXmDJlCjZv3oyAgAD85z//we7du3HlyhU4OztXiT958iSeeOIJREZGYvjw4dixYwfeffddnD17Fn5+frX+f021023FbyRubm7Yu3cv/Pz8kJiYiDFjxkjG0jeDvtGNWn2+HfK5eDi1jYbQ9qbctm1b3Lx5s0p7TW/KQNN9Y26o//73v7i5bX6TvwyxIq4Uz24+j86dO8udSqNS8X3q8uXLCA4OFhcIjYuLk3SkbSrvU/X5/NZLwRIQEIDevXtjw4YNAB5c9/fw8MCCBQuwZMmSKvHjx49HUVERYmJixLY+ffqge/fu2Lx5c63/nyEKlvp+cwFq//by3//+t86P9cILL1R7rD7fXICm/+2lMT8XgPF9k6zv83Hjxg1s2bJFv0n9v2nTpsHDw6NOsXwu9MvYngt9sLS0lMxw265dO7z99tt47bXXxFXMgQcz3lY30V9jI2vBUlZWBmtra+zZswehoaFi+9SpU5Gbm4sDBw5UuY+npycWLVqEF198UWxbvnw59u/fjwsXLlSJLy0tlTxp+fn58PDw0GvB0ly+uQBN/9sLn4vGpbk8H83huaivusz9UfmMV7t27RAaGor9+/dLPiSB5nkZojFJS0tDu3btao27fv06PD09DZDRw5N1WPOdO3egUqng4uIiaXdxcan2hZGRkaE1PiMjQ2t8ZGQkVq5cqZuE60jTgWpfPb7VG+rbS32+uQDAoFe7NOk35cb8XAD1ez6a+nMB1P/5qM/ZrieffBKenp7iae+0tDT8+OOP4nFdnu1qDs9FfVlbW+Oxxx6r132uX7+O999/X+ux+j4W1Y+npydMTU0li05WZmpq2mSKlfpqkqOEli5dKhkSrDnDok/17UAFPPj20n/svGqP79ixA+vWrav1cSIiIsT1U7Qxtm8u+ngudNWZDeDz8bDmrfpE3D634wcADwoPbUXO3Lc+1tn/S9rt2LGjxvefinGkf/fv34eZmZnWosXU1LRZD/fXecHSunVrKJVKZGZmStozMzPh6uqq9T6urq71irewsBCXmm/Mavv28thjj9WpYFm7dq0u0zJKtT0Xo0ePxr59+2p9nNGjR/NbpJ6dPHkSgYGBkjZtxcrJkycNlZJRmzBhQp0KlgkTJhggGwIeFC1paWno0qULiouLYW1tjT/++KPZnlnR0Pm0eubm5ujZs6dktWG1Wo1jx46hb9++Wu/Tt29fSTwAHD16tNr45qS2LkRNpad3UxcdHa3TOGq4ur7ujeH9obHg+1Tj4+npiYKCAqhUKhQUFDT7YgXQ02rNixYtQlRUFL744gtcvnwZc+bMQVFREaZPnw4AmDJlCpYuXSrGL1y4EIcPH8a6devw559/YsWKFTh9+jTmz5+vj/QaHUEQxBFVGhs2bOCbgIHxTbnx4HPR+AiCUOWyz44dO/hckOEIevLhhx8Knp6egrm5ufD4448Lp06dEo8NGDBAmDp1qiR+165dQseOHQVzc3OhS5cuwrffflvn/ysvL08AIOTl5ekqfTJio0ePFgCIt9GjR8udktE6efKk5Lk4efKk3CkRkQ7V5/ObqzUTERGRLOrz+W2YpSGJiIiIHgILFiIiImr0WLAQERFRo8eChYiIiBo9FixERETU6LFgISIiokaPBQsRERE1eixYiIiIqNFjwUJERESNns5Xa5aDZrLe/Px8mTMhIiKiutJ8btdl0v1mUbAUFBQAADw8PGTOhIiIiOqroKAADg4ONcY0i7WE1Go1bt++DTs7OygUCrnTabD8/Hx4eHjgxo0bXBNJZnwuGg8+F40Ln4/Gozk8F4IgoKCgAO7u7jAxqbmXSrM4w2JiYoK2bdvKnYbO2NvbN9k/vuaGz0XjweeiceHz0Xg09eeitjMrGux0S0RERI0eCxYiIiJq9FiwNCIWFhZYvnw5LCws5E7F6PG5aDz4XDQufD4aD2N7LppFp1siIiJq3niGhYiIiBo9FixERETU6LFgISIiokaPBQsRERE1eixYGpGNGzfCy8sLlpaWCAgIwK+//ip3Ss1eZGQkevfuDTs7Ozg7OyM0NBRXrlyRxAQHB0OhUEhu//jHP2TKuPlasWJFld9z586dxeP37t3DvHnz4OjoCFtbW4wZMwaZmZkyZtx8eXl5VXkuFAoF5s2bB4CvCUMrKCjAiy++iHbt2sHKygqBgYH47bffxOOCIOCNN96Am5sbrKysMHjwYCQlJcmYsX6wYGkkvv76ayxatAjLly/H2bNn0a1bN4SEhCArK0vu1Jq148ePY968eTh16hSOHj2K+/fv4+mnn0ZRUZEkbubMmUhPTxdvq1evlinj5q1Lly6S3/PPP/8sHnvppZfwzTffYPfu3Th+/Dhu376NsLAwGbNtvn777TfJ83D06FEAwNixY8UYviYM54UXXsDRo0exdetWXLx4EU8//TQGDx6MW7duAQBWr16NDz74AJs3b8Yvv/wCGxsbhISE4N69ezJnrmMCNQqPP/64MG/ePHFfpVIJ7u7uQmRkpIxZGZ+srCwBgHD8+HGxbcCAAcLChQvlS8pILF++XOjWrZvWY7m5uYKZmZmwe/duse3y5csCACEhIcFAGRqvhQsXCj4+PoJarRYEga8JQyouLhaUSqUQExMjaX/ssceEf/3rX4JarRZcXV2FNWvWiMdyc3MFCwsLYefOnYZOV694hqURKCsrw5kzZzB48GCxzcTEBIMHD0ZCQoKMmRmfvLw8AECrVq0k7du3b0fr1q3h5+eHpUuXori4WI70mr2kpCS4u7ujffv2eO6555CWlgYAOHPmDO7fvy95jXTu3Bmenp58jehZWVkZtm3bhueff16yuCxfE4ZRXl4OlUoFS0tLSbuVlRV+/vlnpKSkICMjQ/LacHBwQEBAQLN7bTSLxQ+bujt37kClUsHFxUXS7uLigj///FOmrIyPWq3Giy++iH79+sHPz09snzhxItq1awd3d3f8/vvvePXVV3HlyhVER0fLmG3zExAQgC1btqBTp05IT0/HypUrERQUhMTERGRkZMDc3BwtWrSQ3MfFxQUZGRnyJGwk9u/fj9zcXEybNk1s42vCcOzs7NC3b1+89dZbeOSRR+Di4oKdO3ciISEBvr6+4t+/ts+P5vbaYMFC9P/mzZuHxMRESb8JAJg1a5a47e/vDzc3NwwaNAjJycnw8fExdJrN1tChQ8Xtrl27IiAgAO3atcOuXbtgZWUlY2bG7dNPP8XQoUPh7u4utvE1YVhbt27F888/jzZt2kCpVOKxxx7DhAkTcObMGblTMyheEmoEWrduDaVSWWXEQ2ZmJlxdXWXKyrjMnz8fMTExiI2NRdu2bWuMDQgIAABcvXrVEKkZrRYtWqBjx464evUqXF1dUVZWhtzcXEkMXyP6df36dfzwww944YUXaozja0K/fHx8cPz4cRQWFuLGjRv49ddfcf/+fbRv3178+zeGzw8WLI2Aubk5evbsiWPHjoltarUax44dQ9++fWXMrPkTBAHz58/Hvn378OOPP8Lb27vW+5w/fx4A4ObmpufsjFthYSGSk5Ph5uaGnj17wszMTPIauXLlCtLS0vga0aPPP/8czs7OGDZsWI1xfE0Yho2NDdzc3HD37l18//33GDVqFLy9veHq6ip5beTn5+OXX35pfq8NuXv90gNfffWVYGFhIWzZskW4dOmSMGvWLKFFixZCRkaG3Kk1a3PmzBEcHByEuLg4IT09XbwVFxcLgiAIV69eFd58803h9OnTQkpKinDgwAGhffv2whNPPCFz5s1PRESEEBcXJ6SkpAgnTpwQBg8eLLRu3VrIysoSBEEQ/vGPfwienp7Cjz/+KJw+fVro27ev0LdvX5mzbr5UKpXg6ekpvPrqq5J2viYM7/Dhw8J3330nXLt2TThy5IjQrVs3ISAgQCgrKxMEQRDeeecdoUWLFsKBAweE33//XRg1apTg7e0tlJSUyJy5brFgaUQ+/PBDwdPTUzA3Nxcef/xx4dSpU3Kn1OwB0Hr7/PPPBUEQhLS0NOGJJ54QWrVqJVhYWAi+vr7Cyy+/LOTl5cmbeDM0fvx4wc3NTTA3NxfatGkjjB8/Xrh69ap4vKSkRJg7d67QsmVLwdraWhg9erSQnp4uY8bN2/fffy8AEK5cuSJp52vC8L7++muhffv2grm5ueDq6irMmzdPyM3NFY+r1Wph2bJlgouLi2BhYSEMGjSoyvPWHCgEQRBkPMFDREREVCv2YSEiIqJGjwULERERNXosWIiIiKjRY8FCREREjR4LFiIiImr0WLAQERFRo8eChYiIiBo9FixE9NCmTZuG0NBQcT84OBgvvvhine4bFxcHhUJRZZ2g5mDLli1VVpgmooZhwULUhGzfvh0eHh5o2bIlFi1aJDmWmpqKjh07Ij8/v8bHSE1NhUKhgFKpxK1btyTH0tPTYWpqCoVCgdTU1AbnGR0djbfeeqtOsYGBgUhPT4eDg0OD/z8iav5YsBA1EXfu3MELL7yAtWvX4siRI9i2bRtiYmLE43PnzsU777wDe3v7Oj1emzZt8OWXX0ravvjiC7Rp0+ahc23VqhXs7OzqFGtubg5XV1coFIqH/n+JqPliwULURFy7dg0ODg4YP348evfujYEDB+Ly5csAgJ07d8LMzAxhYWF1frypU6fi888/l7R9/vnnmDp1qqRNpVJhxowZ8Pb2hpWVFTp16oT333+/xseufEmotLQUr776Kjw8PGBhYQFfX198+umnALRfEtq7dy+6dOkCCwsLeHl5Yd26dZLHVygU2L9/v6StRYsW2LJlCwCgrKwM8+fPh5ubGywtLdGuXTtERkZWm6/mktbatWvh5uYGR0dHzJs3D/fv3xdj7t69iylTpqBly5awtrbG0KFDkZSUJHmcLVu2wNPTE9bW1hg9ejRycnKq/F8HDhzAY489BktLS7Rv3x4rV65EeXk5gAerh69YsQKenp6wsLCAu7s7/vnPf1abN5ExYcFC1ER06NABxcXFOHfuHP7++2/89ttv6Nq1K+7evYtly5Zhw4YN9Xq8kSNH4u7du/j5558BAD///DPu3r2LESNGSOLUajXatm2L3bt349KlS3jjjTfw2muvYdeuXXX+v6ZMmYKdO3figw8+wOXLl/Hxxx/D1tZWa+yZM2cwbtw4PPvss7h48SJWrFiBZcuWicVIXXzwwQc4ePAgdu3ahStXrmD79u3w8vKq8T6xsbFITk5GbGwsvvjiC2zZskXyf06bNg2nT5/GwYMHkZCQAEEQ8Mwzz4hFzS+//IIZM2Zg/vz5OH/+PAYOHIhVq1ZJ/o/4+HhMmTIFCxcuxKVLl/Dxxx9jy5Yt+Pe//w3gQaG2fv16fPzxx0hKSsL+/fvh7+9f55+bqFmTd+1FIqqP6Ohowc/PT/Dx8RGWL18uCIIgPP/888L69euF48ePC927dxe6dOki7N69u9rHSElJEQAI586dE1588UVh+vTpgiAIwvTp04WXXnpJOHfunABASElJqfYx5s2bJ4wZM0bcnzp1qjBq1Chxf8CAAcLChQsFQRCEK1euCACEo0ePan2s2NhYAYBw9+5dQRAEYeLEicJTTz0liXn55ZeFRx99VNwHIOzbt08S4+DgIK6yvWDBAuHJJ58U1Gp1tT9DRVOnThXatWsnlJeXi21jx44Vxo8fLwiCIPz1118CAOHEiRPi8Tt37ghWVlbCrl27BEEQhAkTJgjPPPOM5HHHjx8vODg4iPuDBg0S3n77bUnM1q1bBTc3N0EQBGHdunVCx44dhbKysjrlTWRMeIaFqAkZPXo0Ll68iKtXr2LFihU4fvw4fv/9d8yaNQvPPvss/vOf/2Dv3r2YMWMGsrKyan28559/Hrt370ZGRgZ2796N559/Xmvcxo0b0bNnTzg5OcHW1haffPIJ0tLS6pTz+fPnoVQqMWDAgDrFX758Gf369ZO09evXD0lJSVCpVHV6jGnTpuH8+fPo1KkT/vnPf+LIkSO13qdLly5QKpXivpubm/g7vHz5MkxNTREQECAed3R0RKdOncTLcpcvX5YcB4C+fftK9i9cuIA333wTtra24m3mzJlIT09HcXExxo4di5KSErRv3x4zZ87Evn37xMtFRMaOBQtRE1VaWoq5c+fi448/xtWrV1FeXo4BAwagU6dO6NixI3755ZdaH8Pf3x+dO3fGhAkT8Mgjj8DPz69KzFdffYXFixdjxowZOHLkCM6fP4/p06ejrKysTnlaWVnV+2erjUKhgCAIkraK/U0ee+wxpKSk4K233kJJSQnGjRuH8PDwGh/TzMysyv+hVqt1lzSAwsJCrFy5EufPnxdvFy9eRFJSEiwtLeHh4YErV65g06ZNsLKywty5c/HEE09IfjYiY8WChaiJWrVqFYYMGYLHHnsMKpVK8k38/v37dT4b8fzzzyMuLq7asysnTpxAYGAg5s6dix49esDX1xfJycl1ztPf3x9qtRrHjx+vU/wjjzyCEydOVMmhY8eO4hkQJycnpKeni8eTkpJQXFwsuY+9vT3Gjx+PqKgofP3119i7dy/+/vvvOuddOafy8nJJEZiTk4MrV67g0UcfFWMqF4mnTp2S7D/22GO4cuUKfH19q9xMTB68HVtZWWHEiBH44IMPEBcXh4SEBFy8eLFBeRM1J6ZyJ0BE9Xfp0iV8/fXXOHfuHACgc+fOMDExwaeffgpXV1f8+eef6N27d50ea+bMmRg7dmy1E5x16NABX375Jb7//nt4e3tj69at+O233+Dt7V2nx/fy8sLUqVPx/PPP44MPPkC3bt1w/fp1ZGVlYdy4cVXiIyIi0Lt3b7z11lsYP348EhISsGHDBmzatEmMefLJJ7Fhwwb07dsXKpUKr776quQMyXvvvQc3Nzf06NEDJiYm2L17N1xdXRs8iVuHDh0watQozJw5Ex9//DHs7OywZMkStGnTBqNGjQIA/POf/0S/fv2wdu1ajBo1Ct9//z0OHz4seZw33ngDw4cPh6enJ8LDw2FiYoILFy4gMTERq1atwpYtW6BSqRAQEABra2ts27YNVlZWaNeuXYPyJmpOeIaFqIkRBAGzZs3Ce++9BxsbGwAPvpVv2bIFb775JmbMmIENGzbUeT4VU1NTtG7dGqam2r+/zJ49G2FhYRg/fjwCAgKQk5ODuXPn1ivnjz76COHh4Zg7dy46d+6MmTNnoqioSGvsY489hl27duGrr76Cn58f3njjDbz55puYNm2aGLNu3Tp4eHggKCgIEydOxOLFi2FtbS0et7Ozw+rVq9GrVy/07t0bqampOHTokHgWoyE+//xz9OzZE8OHD0ffvn0hCAIOHTokFkp9+vRBVFQU3n//fXTr1g1HjhzB66+/LnmMkJAQxMTE4MiRI+jduzf69OmD9evXiwVJixYtEBUVhX79+qFr16744Ycf8M0338DR0bHBeRM1Fwqh8oVgIiIiokaGZ1iIiIio0WPBQkRERI0eCxYiIiJq9FiwEBERUaPHgoWIiIgaPRYsRERE1OixYCEiIqJGjwULERERNXosWIiIiKjRY8FCREREjR4LFiIiImr0WLAQERFRo/d/hohm1fR8MVgAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig3, ax3 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in msg_df:\n",
+    "\n",
+    "    vsdf = msg_df[key].loc[(msg_df[key]['nodeType'] == 'regular')]\n",
+    "    data.append(vsdf['bytesOut']/1024/1024)\n",
+    "\n",
+    "ax3.boxplot(data)\n",
+    "\n",
+    "ax3.set_xticklabels([0,25,50,75,90])\n",
+    "ax3.set_title(\"MBs sent per slot regular node\")\n",
+    "ax3.set_xlabel(\"% Malicious nodes\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f94b650c",
+   "metadata": {},
+   "source": [
+    "This is the data sent by regular nodes in MB. This amount of data is smaller compared to validators, since nodes validators tend to prioritize nodes withe more samples when fetching. Also, regular nodes have less capacity, and therefore samples sent by them may arrive later on, limiting their participation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cf282f0",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Malicious builder (withhold attack)\n",
+    "\n",
+    "* Builder withholds some specific samples from the matrix, without sending them to validators.\n",
+    "\n",
+    "\n",
+    "## Simulation parameters\n",
+    "\n",
+    "* Number of nodes: 5000\n",
+    "* Number of validators: 50% nodes are validators\n",
+    "* Redundancy parameter: 2\n",
+    "* Latency between nodes: 5ms - 125ms\n",
+    "* Number of samples per block : 512x512\n",
+    "* Sample size: 512 bytes\n",
+    "* Blocks: 1 blocks simulation.\n",
+    "* Number of random samples fetch: 75. \n",
+    "* 75% samples withheld by builder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ddd499e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "ops_path = {'DAS': '../logsDasEvil0.5000/operation.csv', \n",
+    "            'Evil': '../logsDasBuilderEvil5000/operation.csv'\n",
+    "           }\n",
+    "msgs_path = {'DAS': '../logsDasEvil0.5000/messages.csv', \n",
+    "            'Evil': '../logsDasBuilderEvil5000/messages.csv'\n",
+    "          }\n",
+    "\n",
+    "\n",
+    "builder_address = '83814183170291850251680823880522715558189094423550585243365458794131648333116'\n",
+    "\n",
+    "op_df={}\n",
+    "msg_df={}\n",
+    "for key in ops_path:\n",
+    "    op_df[key] = pd.read_csv(ops_path[key],index_col=False,low_memory=False)\n",
+    "for key in msgs_path:\n",
+    "    msg_df[key] = pd.read_csv(msgs_path[key],index_col=False,low_memory=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e1f09c13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Withhold')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "fig3, ax3 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in op_df:\n",
+    "\n",
+    "    vsdf = op_df[key].loc[(op_df[key]['type'] == 'ValidatorSamplingOperation')]\n",
+    "    data.append(vsdf['completion_time']/1000)\n",
+    "\n",
+    "ax3.boxplot(data)\n",
+    "\n",
+    "\n",
+    "ax3.set_ylim([0,12])\n",
+    "ax3.set_xticklabels([0,75])\n",
+    "ax3.set_title(\"Validator row/column fetching time (seconds)\")\n",
+    "ax3.set_xlabel(\"% Withhold\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "84859565",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Withhold')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig3, ax3 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in op_df:\n",
+    "\n",
+    "    vsdf = op_df[key].loc[(op_df[key]['type'] == 'RandomSamplingOperation')]\n",
+    "    #vsdf = op_df[key].loc[(op_df[key]['validator'] == 'no')]\n",
+    "    data.append(vsdf['completion_time']/1000)\n",
+    "\n",
+    "ax3.boxplot(data)\n",
+    "\n",
+    "#ax3.legend()\n",
+    "#ax18.set_xlim([0,2])\n",
+    "ax3.set_ylim([0,15])\n",
+    "ax3.set_xticklabels([0,75])\n",
+    "ax3.set_title(\"Random sampling time regular nodes (seconds)\")\n",
+    "ax3.set_xlabel(\"% Withhold\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e8193010",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Withhold')"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig3, ax3 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in msg_df:\n",
+    "\n",
+    "    vsdf = msg_df[key].loc[(msg_df[key]['nodeType'] == 'builder')]\n",
+    "    data.append(vsdf['bytesOut'].values[0]/1024/1024/1024)\n",
+    "\n",
+    "ax3.bar([1,2],data)\n",
+    "\n",
+    "ax3.set_xticks([1,2])\n",
+    "ax3.set_xticklabels([0,75])\n",
+    "ax3.set_title(\"GBs sent by the builder per sampling process\")\n",
+    "ax3.set_xlabel(\"% Withhold\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "36b2f17e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Withhold')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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AgKkIHwAAwFSEDwAAYCrCBwAAMFWNm+EUNw6bzaYNGzbo6NGjatq0qXr06CGr1erqsgAA1YwrH3CJlJQUtWrVSjExMRo6dKhiYmLUqlUrpaSkuLo0AEA1I3zAdCkpKRowYICioqK0adMmFRYWatOmTYqKitKAAQMIIADg5iyGYRiuLqK8goICBQYGKj8/nxfLuSGbzaZWrVopKipKy5Ytk4fHf/NvaWmp4uLitGvXLmVmZnILBgBqkar8/ubKB0y1YcMGZWdn6+WXX3YIHpLk4eGhxMREZWVlacOGDS6qEABQ3QgfMNXRo0clSW3btq10e1l7WT8AgPshfMBUTZs2lSTt2rWr0u1l7WX9AADuh/ABU/Xo0UNhYWF6/fXXVVpa6rCttLRUSUlJCg8PV48ePVxUIQCguhE+YCqr1ao//vGPWrFiheLi4hyedomLi9OKFSs0Y8YMBpsCgBtjkjGYLj4+XkuXLtXvfvc7de/e3d4eHh6upUuXKj4+3oXVAQCqG4/awmWY4RQA3EdVfn9z5QMuY7VaFR0d7eoyAAAmY8wHAAAwFeEDAACYitsucBnGfADAjYkrH3AJ3moLADcuwgdMx1ttAeDGxqO2MBVvtQUA98RbbVFj8VZbAADhA6birbYAAMIHTFX+rbY2m01paWlatGiR0tLSZLPZeKstANwAGPMBU5WN+WjYsKGOHz+ugwcP2reFhoaqUaNGOnnyJGM+AKCWYcwHaiyr1aqBAwcqPT1dOTk5DttycnKUnp6uAQMGEDwAwI1VOXysX79eDz74oIKDg2WxWLRs2bJL9v3tb38ri8Wi5OTk6ygR7sRms2nu3LmSJE9PT4dtZevz5s2TzWYzuzQAgEmqHD7Onj2rdu3a6b333rtsv08//VSbN29WcHDwNRcH95OWlqbjx4/rrrvuUn5+vlJTU7Vw4UKlpqYqPz9fd911l/Ly8pSWlubqUgEA1aTK06vHxsYqNjb2sn0OHz6sp59+WmvWrFGfPn2uuTi4n7JQMXXqVN10000V3mo7efJk3XfffUpLS9O9995rfoEAgGrn9DEfpaWlevTRR/XCCy/otttuc/bhAQBALef08PHmm2+qTp06euaZZ66qf0lJiQoKChwWuK+yKx2TJ09WaWmpw7bS0lJNmTLFoR8AwP04NXxkZGToz3/+s+bOnSuLxXJV+yQlJSkwMNC+hISEOLMk1DDR0dFq3LixvvrqK/Xr18/h3S79+vXT119/rcaNGxM+AMCNXdc8HxaLRZ9++qni4uIkScnJyUpISHCYNttms8nDw0MhISHKzs6ucIySkhKVlJTY1wsKChQSEsI8H26s7MVyXl5eKi4utrf7+PiouLhYS5cuVXx8vAsrBABUlcvm+Xj00Ue1c+dO7dixw74EBwfrhRde0Jo1ayrdx9PTUwEBAQ4L3Ft8fLyWLl2qJk2aOLQ3adKE4AEAN4AqP+1y5swZHThwwL6elZWlHTt2qH79+mrRooUaNGjg0P+mm25SUFCQbrnlluuvFm4jPj5e/fr104YNG3T06FE1bdpUPXr0YHIxALgBVDl8pKenKyYmxr6ekJAgSRo+fLh98ijgalitVsZ2AMANqMrhIzo6WlUZJlLZOA8AAHDj4t0uAADAVIQPAABgKsIHAAAwVZXHfADOYrPZeNoFAG5AXPmAS6SkpKhVq1aKiYnR0KFDFRMTo1atWiklJcXVpQEAqhnhA6Yrm+E0KirKYXr1qKgoDRgwgAACAG7uuqZXrw5VmZ4VtY/NZlOrVq0UFRWlZcuWOUzFX1paqri4OO3atUuZmZncggGAWsRl06sDV7JhwwZlZ2fr5ZdflmEYSktL06JFi5SWlibDMJSYmKisrCxt2LDB1aUCAKoJA05hqqNHj0qSvv/+ez388MMOk9CFhYVp+vTpDv0AAO6HKx8wVdOmTSX98hLCysZ8PProow79AADuhzEfMNX58+fl6+urBg0a6Mcff1SdOv+9+HbhwgU1b95cJ0+e1NmzZ1W3bl0XVgoAqArGfKDG2rhxoy5cuKBjx44pPj7e4cpHfHy8jh07pgsXLmjjxo2uLhUAUE0IHzBV2ViOBQsW6Ntvv1X37t0VEBCg7t27a+fOnVqwYIFDPwCA+yF8wFRlYzlWrVqlw4cPO2z78ccftXLlSod+AAD3w5gPmMpms6l+/foqKCiQh4eHSktL7dvK1gMCAvTTTz8xzwcA1CKM+UCNZbPZVFhYKElq2LChPvjgAx05ckQffPCBGjZsKEkqLCyUzWZzZZkAgGpE+ICpZs6cKcMwFBoaKm9vb40ZM0bBwcEaM2aMfHx8FBoaKsMwNHPmTFeXCgCoJoQPmKps5tL33ntP+/fv15/+9CeNHz9ef/rTn7Rv3z698847Dv0AAO6HGU5hKn9/f0nSsmXLNG7cOB08eNC+LTk5Wffcc49DPwCA+2HAKUy1du1a/frXv5YkWSwWlf/xK7/+r3/9S/fdd59LagQAVB0DTlFj9ezZ02H9vvvu0+uvv14haFzcDwDgPrjtAlOlpaXZvzYMQ2vXrtXatWsr7Vd2hQQA4F648gFTzZ8/3/61t7e3w7by6+X7AQDcC+EDpiqb46Nz587Kz89XamqqFi5cqNTUVOXn56tz584O/QAA7ofwAVMFBwdLkk6dOlXp9p9++smhHwDA/TDmA6bq1q2bZs2apQMHDigwMFDFxcX2bd7e3vb1bt26uapEAEA148oHTBUSEmL/unzwuHi9fD8AgHshfMBU3bt3V506dVS3bt1Kt9etW1d16tRR9+7dTa4MAGAWbrvAVBs3btSFCxckSY0aNdKtt95q37Z7924dP37c3i86OtoVJQIAqhnhA6Y6fPiwJCk8PFyHDh3Sl19+ad9mtVoVHh6urKwsez8AgPshfMBUZVc2srKy1LdvX8XGxtoHmq5atUorVqxw6AegZioqKtLevXsv26e4uFjZ2dkKCwurMK9PZSIiIuTj4+OsElGDET5gqgYNGkiSGjdurE8//VR16vz3R3DMmDFq1qyZ8vLy7P0A1Ex79+5Vx44dnXrMjIwMdejQwanHRM1E+ICpTp48KemXKxvx8fFKTExU27ZttWvXLiUlJdmveJT1A1AzRUREKCMj47J99uzZo2HDhmnBggWKjIy8qmPixkD4gKkaNWokSWrfvr2+++47h6dawsPD1b59e23fvt3eD0DN5OPjc9VXKSIjI7miAQeED5iqWbNmkqQdO3aoT58+ev755+1jPlavXq3PP//coR8AwP0QPmCqHj16KCwsTA0bNtSuXbvsA0ylX658dOzYUSdPnlSPHj1cWCUAoDpVeZKx9evX68EHH1RwcLAsFouWLVtm3/bzzz9r4sSJioqKkq+vr4KDg/XYY4/pyJEjzqwZtZjVatUf//hHZWRkqG3btpo5c6Y+/PBDzZw5U7fddpsyMjI0Y8YMWa1WV5cKAKgmVb7ycfbsWbVr106PP/644uPjHbYVFRVp27ZtmjRpktq1a6dTp07p2Wef1UMPPaT09HSnFY3aLT4+XkuXLlVCQoLDlY+wsDAtXbq0ws8VAMC9VDl8xMbGKjY2ttJtgYGBWrt2rUPbzJkz1aVLFx06dEgtWrS4tipxQzAMw9UlAABMUO3vdsnPz5fFYtHNN99c6faSkhIVFBQ4LHBvKSkpGjBggPLy8hza8/LyNGDAAKWkpLioMgCAGao1fJw7d04TJ07Uww8/rICAgEr7JCUlKTAw0L7wNlP3ZrPZ9OSTT8owDN17773atGmTCgsLtWnTJt17770yDENPPvmkbDabq0sFAFSTagsfP//8swYNGiTDMDRr1qxL9ktMTFR+fr59ycnJqa6SUAOkpaUpLy9Pd911l5YvX6477rhDfn5+uuOOO7R8+XLdeeedysvLU1pamqtLBQBUk2oJH2XB4+DBg1q7du0lr3pIkqenpwICAhwWuK+yUDF16lR5eDj++Hl4eGjKlCkO/QAA7sfp83yUBY/MzEylpqbyjg4AAOCgylc+zpw5ox07dmjHjh2Sfnk76Y4dO3To0CH9/PPPGjBggNLT0/XRRx/JZrMpNzdXubm5On/+vLNrRy0UHR0tSZo8ebJKS0sdtpWWlmrq1KkO/QAA7qfKVz7S09MVExNjX09ISJAkDR8+XFOmTNFnn30m6Zd3d5SXmprKLxQoOjpajRo10ldffaWHHnpIsbGx9unVV61apa+++kqNGzfmZwUA3FiVw0d0dPRl52NgrgZcjtVq1ezZs9W/f3+tXLnS/i4XSbJYLJKkWbNmMcMpALixap/nA6iMxWKRp6enQ5uXl5c9gAAA3BfhA6ay2Wz63e9+p759++rYsWOKi4tTVFSU4uLilJubq759++r5559nng8AcGO81Ram2rBhg7KzsxUSEqLAwEB7+3fffafAwED16NFDWVlZ2rBhA+M+AMBNceUDpjp69KikX0JIZcray/oBANwPVz5gqvLv+KlTp44GDRqkTp06KT09XR9//LEuXLhQoR8AwL0QPmCq8i+Na9y4sRYuXKiFCxdKkoKDg3XkyBF7v0u9PRkAULtx2wWmWr16tf3rsqBR2Xr5fgAA90L4AAAApiJ8wFS33367U/sBAGofwgdM5eXl5dR+AIDah/ABU3399ddO7QcAqH0IHzBVQUGBU/sBAGofHrWFqcq/u6V3794qKirSyZMn1aBBA/n4+GjNmjUV+gEA3AvhA6Zq0aKF9u7dK0n2oHGpfgAA98RtF5hq5MiRTu0HAKh9CB8w1dNPP+3UfgCA2ofwAVNt3LjRqf0AALUP4QOmSktLc2o/AEDtQ/iAqcreWuvr66vQ0FCHbaGhofL19XXoBwBwPzztAlOdPn1aknT27FlFR0frxRdflLe3t4qLi7Vy5Up9/vnnDv0AAO6H8AGX+eKLL+xhQ5I8PT1dWA0AwCzcdoGprFar/euff/7ZYVv5Wy3l+wEA3AvhA6bq3LmzpF/CRUhIiMO2kJAQe+go6wcAcD/cdoGpTp06JUmy2Ww6dOiQw7aDBw/KMAyHfgAA98OVD5iqUaNG9q/LgkZl6+X7AQDcC+EDpgoKCrJ/ffHL48qvl+8HAHAvhA+YqrS01Kn9AAC1D+EDpio/c+nlbrswwykAuC/CB0x18OBBp/YDANQ+hA+Y6mqnTWd6dQBwX4QPmOrEiRNO7QcAqH0IHzBVYWGhU/sBAGofwgdM9cMPP9i/9vLyctjm7e1daT8AgHshfMBUF7/PpbzyT7tcrh8AoHYjfMBUjRs3tn9dUlLisK38evl+AAD3UuXwsX79ej344IMKDg6WxWLRsmXLHLYbhqFXXnlFTZs2lbe3t3r16qXMzExn1Yta7g9/+IP968vN81G+HwDAvVT5xXJnz55Vu3bt9Pjjjys+Pr7C9rfeekvvvPOO5s2bp/DwcE2aNEm9e/fW7t27K9zjh3srKirS3r17HdqCg4Pl4eFx2RlMrVargoODtW3btgrbIiIi5OPj4/RaAQDmqXL4iI2NVWxsbKXbDMNQcnKyfv/736tfv36SpH/84x9q0qSJli1bpiFDhlxftahV9u7dq44dO1Z5P5vNpi5dulS6LSMjQx06dLje0gAALlTl8HE5WVlZys3NVa9evextgYGB6tq1qzZt2lRp+CgpKXG4119QUODMkuBCERERysjIqHTbv//9b7311ls6fvy4va1x48Z64YUXdM8991z2mACA2s2p4SM3N1eS1KRJE4f2Jk2a2LddLCkpSVOnTnVmGaghfHx8LnmVokOHDnruuef04YcfauzYsXr//fc1atQoWa1Wk6sEAJjN5U+7JCYmKj8/377k5OS4uiSYxGq1qlOnTpKkTp06ETwA4Abh1PARFBQkSTp27JhD+7Fjx+zbLubp6amAgACHBQAAuC+nho/w8HAFBQVp3bp19raCggJ988036tatmzNPBQAAaqkqj/k4c+aMDhw4YF/PysrSjh07VL9+fbVo0UITJkzQ9OnT1bp1a/ujtsHBwYqLi3Nm3QAAoJaqcvhIT09XTEyMfT0hIUGSNHz4cM2dO1cvvviizp49qzFjxuj06dO66667tHr1aub4AAAAkq4hfERHR1eYmbI8i8WiadOmadq0addVGAAAcE8uf9oFAADcWAgfAADAVIQPAABgKsIHAAAwFeEDAACYivABAABMRfgAAACmInwAAABTET4AAICpCB8AAMBUhA8AAGAqwgcAADAV4QMAAJiK8AEAAExF+AAAAKYifAAAAFMRPgAAgKkIHwAAwFSEDwAAYCrCBwAAMBXhAwAAmIrwAQAATEX4AAAApiJ8AAAAUxE+AACAqQgfAADAVIQPAABgKsIHAAAwFeEDAACYivABAABMRfgAAACmInwAAABTET4AAICpCB8AAMBUhA8AAGAqp4cPm82mSZMmKTw8XN7e3mrZsqVeffVVGYbh7FMBAIBaqI6zD/jmm29q1qxZmjdvnm677Talp6dr5MiRCgwM1DPPPOPs0wEAgFrG6eFj48aN6tevn/r06SNJCgsL06JFi7RlyxZnnwoAANRCTr/t0r17d61bt0779++XJH377bf66quvFBsb6+xTAQCAWsjpVz5eeuklFRQUKCIiQlarVTabTa+99poeeeSRSvuXlJSopKTEvl5QUODskgAAQA3i9CsfH3/8sT766CMtXLhQ27Zt07x58zRjxgzNmzev0v5JSUkKDAy0LyEhIc4uCQAA1CBODx8vvPCCXnrpJQ0ZMkRRUVF69NFH9dxzzykpKanS/omJicrPz7cvOTk5zi4JAADUIE6/7VJUVCQPD8dMY7VaVVpaWml/T09PeXp6OrsMAABQQzk9fDz44IN67bXX1KJFC912223avn273n77bT3++OPOPhUAAKiFnB4+3n33XU2aNElPPfWU8vLyFBwcrLFjx+qVV15x9qkAAEAt5PTw4e/vr+TkZCUnJzv70AAAwA3wbhcAAGAqwgcAADAV4QMAAJiK8AEAAExF+AAAAKYifAAAAFMRPgAAgKkIHwAAwFSEDwAAYCrCBwAAMBXhAwAAmIrwAQAATEX4AAAApiJ8AAAAUxE+AACAqQgfAADAVIQPAABgqjquLgAAUPNkZmaqsLDwuo6xZ88eh/9eL39/f7Vu3dopx4JrET4AAA4yMzPVpk0bpx1v2LBhTjvW/v37CSBugPABAHBQdsVjwYIFioyMvObjFBcXKzs7W2FhYfL29r6umvbs2aNhw4Zd99UY1AyEDwBApSIjI9WhQ4frOsadd97ppGrgTggfuGbcEwYAXAvCB64J94QBANeK8IFrwj1hAMC1InzgunBPGABQVUwyBgAATEX4AAAApiJ8AAAAUxE+AACAqQgfAADAVIQPAABgKsIHAAAwFeEDAACYivABAABMRfgAAACmInwAAABTVUv4OHz4sIYNG6YGDRrI29tbUVFRSk9Pr45TAQCAWsbpL5Y7deqU7rzzTsXExGjVqlVq1KiRMjMzVa9ePWefCgAA1EJODx9vvvmmQkJCNGfOHHtbeHi4s08DAABqKaffdvnss8/UqVMnDRw4UI0bN9avfvUr/fWvf71k/5KSEhUUFDgsAADAfTk9fPzwww+aNWuWWrdurTVr1ujJJ5/UM888o3nz5lXaPykpSYGBgfYlJCTE2SUBAIAaxOnho7S0VB06dNDrr7+uX/3qVxozZoxGjx6t2bNnV9o/MTFR+fn59iUnJ8fZJQEAgBrE6eGjadOmuvXWWx3aIiMjdejQoUr7e3p6KiAgwGEBAADuy+nh484779S+ffsc2vbv36/Q0FBnnwoAANRCTn/a5bnnnlP37t31+uuva9CgQdqyZYs++OADffDBB84+FVwsyM8i79P7pSM1Y64679P7FeRncXUZAIArcHr46Ny5sz799FMlJiZq2rRpCg8PV3Jysh555BFnnwouNrZjXUWuHyutd3Ulv4jULzUBAGo2p4cPSerbt6/69u1bHYdGDfJ+xnkNfmWuIiMiXF2KJGnP3r16/49D9ZCrCwEAXFa1hA/cGHLPGCq+uY0U3N7VpUiSinNLlXvGcHUZAIArqBk36wEAwA2D8AEAAExF+AAAAKYifAAAAFMRPgAAgKkIHwAAwFSEDwAAYCrCBwAAMBXhAwAAmIrwAQAATEX4AAAApiJ8AAAAUxE+AACAqQgfAADAVIQPAABgKsIHAAAwFeEDAACYivABAABMRfgAAACmInwAAABTET4AAICpCB8AAMBUhA8AAGAqwgcAADAV4QMAAJiK8AEAAExF+AAAAKaq4+oCUDsVFRVJkrZt23ZdxykuLlZ2drbCwsLk7e19Xcfas2fPde0PADAH4QPXZO/evZKk0aNHu7iSivz9/V1dAgDgMggfuCZxcXGSpIiICPn4+Fzzcfbs2aNhw4ZpwYIFioyMvO66/P391bp16+s+DgCg+hA+cE0aNmyoJ554wmnHi4yMVIcOHZx2PABAzcWAUwAAYCrCBwAAMBXhAwAAmKraw8cbb7whi8WiCRMmVPepAABALVCt4WPr1q16//33dfvtt1fnaQAAQC1SbeHjzJkzeuSRR/TXv/5V9erVq67TAACAWqbaHrUdN26c+vTpo169emn69OmX7FdSUqKSkhL7ekFBQXWVBAC4SkF+Fnmf3i8dqRlDA71P71eQn8XVZcBJqiV8/POf/9S2bdu0devWK/ZNSkrS1KlTq6MMAMA1GtuxriLXj5XWu7qSX0Tql5rgHpwePnJycvTss89q7dq18vLyumL/xMREJSQk2NcLCgoUEhLi7LIAAFXwfsZ5DX5lriIjIlxdiiRpz969ev+PQ/WQqwuBUzg9fGRkZCgvL89htkqbzab169dr5syZKikpkdVqtW/z9PSUp6ens8sAAFyH3DOGim9uIwW3d3UpkqTi3FLlnjFcXQacxOnh495779V3333n0DZy5EhFRERo4sSJDsEDAADceJwePvz9/dW2bVuHNl9fXzVo0KBCOwAAuPHUjGHMAADghmHKW23T0tLMOA0AAKgFuPIBAABMRfgAAACmInwAAABTET4AAICpCB8AAMBUhA8AAGAqwgcAADAV4QMAAJiK8AEAAExF+AAAAKYifAAAAFMRPgAAgKkIHwAAwFSEDwAAYCrCBwAAMBXhAwAAmIrwAQAATEX4AAAApiJ8AAAAUxE+AACAqQgfAADAVIQPAABgKsIHAAAwFeEDAACYivABAABMRfgAAACmInwAAABTET4AAICpCB8AAMBUhA8AAGAqwgcAADAV4QMAAJiK8AEAAExF+AAAAKYifAAAAFM5PXwkJSWpc+fO8vf3V+PGjRUXF6d9+/Y5+zQAAKCWcnr4+PLLLzVu3Dht3rxZa9eu1c8//6xf//rXOnv2rLNPBQAAaqE6zj7g6tWrHdbnzp2rxo0bKyMjQ3fffbezTwcAAGqZah/zkZ+fL0mqX79+dZ8KAADUAk6/8lFeaWmpJkyYoDvvvFNt27attE9JSYlKSkrs6wUFBdVZEgAAcLFqvfIxbtw47dq1S//85z8v2ScpKUmBgYH2JSQkpDpLAgAALlZt4WP8+PFasWKFUlNT1bx580v2S0xMVH5+vn3JycmprpIAAEAN4PTbLoZh6Omnn9ann36qtLQ0hYeHX7a/p6enPD09nV0GAACooZwePsaNG6eFCxdq+fLl8vf3V25uriQpMDBQ3t7ezj4dAACoZZx+22XWrFnKz89XdHS0mjZtal8WL17s7FMBAIBaqFpuuwAAAFwK73YBAACmqtZ5PgAAtU9RUZEkadu2bdd1nOLiYmVnZyssLOy6x/zt2bPnuvZHzUL4AAA42Lt3ryRp9OjRLq6kIn9/f1eXACcgfAAAHMTFxUmSIiIi5OPjc83H2bNnj4YNG6YFCxYoMjLyuuvy9/dX69atr/s4cD3CBwDAQcOGDfXEE0847XiRkZHq0KGD046H2o8BpwAAwFSEDwAAYCrCBwAAMBXhAwAAmIrwAQAATEX4AAAApiJ8wGVsNpvS09MlSenp6bLZbC6uCABgBsIHXCIlJUV+fn4aO3asJGns2LHy8/NTSkqKiysDAFQ3wgdMl5KSov79++vcuXMO7efOnVP//v0JIADg5ggfMJXNZlP//v0v26d///7cggEAN8b06qg2RUVF9hdUlUlOTr6qfUeOHKkJEyZUaL/ed00AAFyP8IFqs3fvXnXs2PGa9p0/f77mz59foT0jI4N3RABALUf4QLWJiIhQRkaGQ1tVwsjF+5YdEwBQuxE+UG18fHyu6yoFVzgAwD0x4BQAAJiK8AEAAExF+AAAAKYifAAAAFMRPgAAgKkIHwAAwFSED5jKYrE4tR8AoPYhfMBUfn5+Tu0HAKh9mGQMpvL19VVhYeFV9QNQc1X27qaL7dmzx+G/V8K7m24chA8AQJVV5d1Nw4YNu6p+vLvpxkH4gKmCg4OVm5t7Vf0A1FyVvbvpYsXFxcrOzlZYWJi8vb2v6pi4MVgMwzBcXUR5BQUFCgwMVH5+vgICAlxdDpxs5cqV6tOnzxX7ff7553rggQdMqAgA4AxV+f3NgFOYqnfv3qpbt+5l+9StW1e9e/c2qSIAgNkIHzCV1WrVokWLLttn0aJFslqtJlUEADAb4QOmi4+P1yeffKIWLVo4tIeGhuqTTz5RfHy8iyoDAJiBMR9wGZvNpg0bNujo0aNq2rSpevTowRUPAKilqvL7m6dd4DJWq1XR0dGuLgMAYLJqu+3y3nvvKSwsTF5eXuratau2bNlSXacCAAC1SLWEj8WLFyshIUGTJ0/Wtm3b1K5dO/Xu3Vt5eXnVcToAAFCLVEv4ePvttzV69GiNHDlSt956q2bPni0fHx/9/e9/r47TAQCAWsTp4eP8+fPKyMhQr169/nsSDw/16tVLmzZtqtC/pKREBQUFDgsAAHBfTg8fJ06ckM1mU5MmTRzamzRpUum02klJSQoMDLQvISEhzi4JAADUIC6f5yMxMVH5+fn2JScnx9UlAQCAauT0R20bNmwoq9WqY8eOObQfO3ZMQUFBFfp7enrK09PT2WUAAIAayulXPurWrauOHTtq3bp19rbS0lKtW7dO3bp1c/bpAABALVMtk4wlJCRo+PDh6tSpk7p06aLk5GSdPXtWI0eOrI7TAQCAWqRawsfgwYN1/PhxvfLKK8rNzVX79u21evXqCoNQK1M22ztPvQAAUHuU/d6+mre21Lh3u/z444888QIAQC2Vk5Oj5s2bX7ZPjQsfpaWlOnLkiPz9/WWxWFxdDqpZQUGBQkJClJOTw4sEATfD3+8bi2EYKiwsVHBwsDw8Lj+ktMa9WM7Dw+OKiQnuJyAggP85AW6Kv983jsDAwKvq5/J5PgAAwI2F8AEAAExF+IBLeXp6avLkyUw0B7gh/n7jUmrcgFMAAODeuPIBAABMRfgAAACmInwAAABTET4AAICpCB9wqffee09hYWHy8vJS165dtWXLFleXBKCKwsLCZLFYKizjxo2TJEVHR1fY9tvf/tbFVcOVCB9wmcWLFyshIUGTJ0/Wtm3b1K5dO/Xu3Vt5eXmuLg1AFWzdulVHjx61L2vXrpUkDRw40N5n9OjRDn3eeustV5WLGoBHbeEyXbt2VefOnTVz5kxJv7zXJyQkRE8//bReeuklF1cH4FpNmDBBK1asUGZmpiwWi6Kjo9W+fXslJye7ujTUEFz5gEucP39eGRkZ6tWrl73Nw8NDvXr10qZNm1xYGYDrcf78eS1YsECPP/64w8tBP/roIzVs2FBt27ZVYmKiioqKXFglXK3GvVgON4YTJ07IZrOpSZMmDu1NmjTR3r17XVQVgOu1bNkynT59WiNGjLC3DR06VKGhoQoODtbOnTs1ceJE7du3TykpKa4rFC5F+AAAOM2HH36o2NhYBQcH29vGjBlj/zoqKkpNmzbVvffeq++//14tW7Z0RZlwMW67wCUaNmwoq9WqY8eOObQfO3ZMQUFBLqoKwPU4ePCgvvjiCz3xxBOX7de1a1dJ0oEDB8woCzUQ4QMuUbduXXXs2FHr1q2zt5WWlmrdunXq1q2bCysDcK3mzJmjxo0bq0+fPpftt2PHDklS06ZNTagKNRG3XeAyCQkJGj58uDp16qQuXbooOTlZZ8+e1ciRI11dGoAqKi0t1Zw5czR8+HDVqfPfXy3ff/+9Fi5cqAceeEANGjTQzp079dxzz+nuu+/W7bff7sKK4UqED7jM4MGDdfz4cb3yyivKzc1V+/bttXr16gqDUAHUfF988YUOHTqkxx9/3KG9bt26+uKLL+z/uAgJCVH//v31+9//3kWVoiZgng8AAGAqxnwAAABTET4AAICpCB8AAMBUhA8AAGAqwgcAADAV4QMAAJiK8AEAAExF+ADgdNHR0ZowYcJl+8ydO1c333zzZfuMGDFCcXFx11XL1ZxnypQpat++/XWdB8DVI3wAbuqjjz5SSEiI6tWrp4SEBIdt2dnZatOmjQoKCi57jKZNm+qNN95waHvppZdksViUlpbm0B4dHa1HH31UkpSSkqJXX33Vvi0sLEzJycnX/mEAuBXCB+CGTpw4oSeeeEIzZszQv/71Ly1YsEArVqywb3/qqaf0xhtvKCAg4LLHiY6OrhAyUlNTFRIS4tB+7tw5bd68Wffcc48kqX79+vL393fa5wHgXggfgBv64YcfFBgYqMGDB6tz586KiYnRnj17JEmLFi3STTfdpPj4+CseJyYmRl9//bUuXLggSSosLNT27ds1ceJEh/CxadMmlZSUKCYmRpLjbZfo6GgdPHhQzz33nCwWiywWi8M51qxZo8jISPn5+en+++/X0aNHK9QxY8YMNW3aVA0aNNC4ceP0888/27edOnVKjz32mOrVqycfHx/FxsYqMzPzsp/rjTfeUJMmTeTv769Ro0bp3LlzV/xeAHAewgfghlq3bq2ioiJt375dP/30k7Zu3arbb79dp06d0qRJkzRz5syrOk5MTIzOnDmjrVu3SpI2bNigNm3aqH///vrmm2/sv7RTU1MVFhamsLCwCsdISUlR8+bNNW3aNB09etQhXBQVFWnGjBmaP3++1q9fr0OHDun555932D81NVXff/+9UlNTNW/ePM2dO1dz5861bx8xYoTS09P12WefadOmTTIMQw888IBDQCnv448/1pQpU/T6668rPT1dTZs21V/+8per+n4AcA7CB+CG6tWrp3nz5umxxx5Tly5d9Nhjj6l37956/vnnNX78eGVlZelXv/qV2rZtq6VLl17yOK1bt1azZs3sVznS0tLUs2dPBQUFqUWLFtq0aZO9veyqx8Xq168vq9Uqf39/BQUFKSgoyL7t559/1uzZs9WpUyd16NBB48eP17p16yp8lpkzZyoiIkJ9+/ZVnz597H0yMzP12Wef6W9/+5t69Oihdu3a6aOPPtLhw4e1bNmySutJTk7WqFGjNGrUKN1yyy2aPn26br311qv91gJwAsIH4KZ+85vf6LvvvtOBAwc0ZcoUffnll9q5c6fGjBmjIUOGKDk5WZ988olGjRqlvLy8Sx6n/LiPtLQ0RUdHS5J69uyptLQ0FRcX65tvvrlk+LgcHx8ftWzZ0r7etGnTCrXcdtttslqtlfbZs2eP6tSpo65du9q3N2jQQLfccov9NtPF9uzZ49Bfkrp161bl2gFcO8IHcAMoKSnRU089pffff18HDhzQhQsX1LNnT91yyy1q06aNvvnmm0vuWzbu4+TJk9q+fbt69uwp6ZfwkZqaqo0bN+r8+fP2waZVcdNNNzmsWywWGYZxxT6lpaVVPheAmoPwAdwApk+frvvvv18dOnSQzWazDyCVfrn1YbPZLrlvTEyMzp49q7ffflutW7dW48aNJUl33323tmzZolWrVtlvz1xK3bp1L3uOaxUZGakLFy44hKeTJ09q3759l7yVEhkZWSFsbd682em1Abg0wgfg5nbv3q3Fixdr2rRpkqSIiAh5eHjoww8/1Oeff669e/eqc+fOl9z/f/7nf9SiRQu9++679qsekhQSEqLg4GB98MEHV7zlEhYWpvXr1+vw4cM6ceKEcz6YfhmT0q9fP40ePVpfffWVvv32Ww0bNkzNmjVTv379Kt3n2Wef1d///nfNmTNH+/fv1+TJk/Wf//zHaTUBuDLCB+DGDMPQmDFj9Pbbb8vX11eS5O3trblz52ratGkaNWqUZs6cedmrFtIvVz8KCwvt4z3K9OzZU4WFhVcMH9OmTVN2drZatmypRo0aXddnuticOXPUsWNH9e3bV926dZNhGFq5cmWF2zVlBg8erEmTJunFF19Ux44ddfDgQT355JNOrQnA5VmMi2+wAgAAVCOufAAAAFMRPgAAgKkIHwAAwFSEDwAAYCrCBwAAMBXhAwAAmIrwAQAATEX4AAAApiJ8AAAAUxE+AACAqQgfAADAVIQPAABgqv8HIO4OSQUFUNsAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig3, ax3 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in msg_df:\n",
+    "\n",
+    "    vsdf = msg_df[key].loc[(msg_df[key]['nodeType'] == 'validator')]\n",
+    "    data.append(vsdf['bytesOut']/1024/1024)\n",
+    "\n",
+    "ax3.boxplot(data)\n",
+    "\n",
+    "ax3.set_xticklabels([0,75])\n",
+    "ax3.set_title(\"MBs sent per validator\")\n",
+    "ax3.set_xlabel(\"% Withhold\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "874a8373",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Withhold')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig3, ax3 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in msg_df:\n",
+    "\n",
+    "    vsdf = msg_df[key].loc[(msg_df[key]['nodeType'] == 'regular')]\n",
+    "    data.append(vsdf['bytesOut']/1024/1024)\n",
+    "\n",
+    "ax3.boxplot(data)\n",
+    "\n",
+    "\n",
+    "ax3.set_xticklabels([0,75])\n",
+    "ax3.set_title(\"MBs sent per slot regular node\")\n",
+    "ax3.set_xlabel(\"% Withhold\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simulator/python/analysis_withhold.ipynb b/simulator/python/analysis_withhold.ipynb
new file mode 100644
index 00000000..594575ad
--- /dev/null
+++ b/simulator/python/analysis_withhold.ipynb
@@ -0,0 +1,352 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f2905ced",
+   "metadata": {},
+   "source": [
+    "# DAS evaluation builder withholding attack\n",
+    "\n",
+    "## DAS protocol\n",
+    "\n",
+    "* The network is formed of a single builder that is generating the block with all samples and `N` nodes distributed between validators and regular nodes.\n",
+    "* Builder knows all the validators\n",
+    "* All nodes have a unique 256 bits identifier, following the Kademlia procotol used in DEVP2P.\n",
+    "* We assume DAS protocol works on top of Kademlia DHT. We reuse identifiers and we can use the DHT to discover peers. \n",
+    "However it is not necessary to use DEVP2P or Kademlia as long as we have nodes with random uniformly distributed identifiers and a way to discover nodes and resolve nodes identifiers to connection parameters.\n",
+    "* Builder divides the hash space into 512*512 sample-specific regions, according to the block matrix.\n",
+    "* Builder transforms the 2D block matrix into a two 1D line of samples (1 line row-wise, and 1 line column-wise, i.e, 1 line row-wise means that after all samples in the first row we continue by the first sample of the second row. 1 line column-wise we mean we first start with all the samples of the first column and then we continue with the samples of the second column)\n",
+    "* We assign each sample with a 256 identifier in the hash space with the double 1D aligment. Every sample is defined by a double id in the hash space, one following row-wise and another column-wise order. This way when fetching rows we can find colocated samples, but also when fetching columns.\n",
+    "* Builder chooses the redundancy factor `redundancy` - the higher `redundancy` the higher overhead but the more resistant the scheme becomes to malicious validators.\n",
+    "* The region of samples is defined as all the validators such that `dict(c, v) <= ((2^256 -1) * redundancy * 2) / N_validators `\n",
+    "* The validator pushes each sample to all the validators within the sample's region (Note that it's done in batches - i.e., the builder connects to each validator and gives them all the samples they should hold).\n",
+    "\n",
+    "* When a new block is released, nodes got the samples from builder, according to the redundancy parameter and their identifier. \n",
+    "After getting the assigned samples, they start the sampling process.\n",
+    "* There are two types of sampling processes: validator sampling and random sampling.\n",
+    "* Validator sampling consists in getting 2 rows and 2 columns of the block matrix.\n",
+    "* Random sampling consists in gettting 75 random samples from any row or column.\n",
+    "* One of the rows and one of the columns to download is choosen by selecting the row or column that the node is already in possession of the highest number of samples from the builder.\n",
+    "* The second row and column to download is selected randomly.\n",
+    "* Validators perform validator sampling and random sampling. \n",
+    "* Non-validator (regular) nodes they just perform random sampling.\n",
+    "* The validator sampling and random sampling follows the same process with the only difference of the selection of the samples to obtain. \n",
+    "We call sampling nodes, the nodes requesting samples, and serving nodes the nodes replying with samples:\n",
+    "    - Sampling nodes select a set of replying nodes from a list of known nodes that are within the redundancy range following the id space for the samples requesting (how all nodes of the network are discovered is not included in this initial report, but will be in the following reports. Validators are supposed to know all other validators by default).\n",
+    "    - Sampling nodes send samples requests to the replying nodes selected. Samples requests include the wanted samples (all samples pending to be received are included in the requests).\n",
+    "    - Replying nodes reply with the samples requested they have, and also with known nodes to the same distance, from their Kademlia routing table. This way nodes can easily discover kew nodes in the network.\n",
+    "    - Only Alpha requests can be sent at the same time. After sending initially Alpha requests, a response must be received after sending the next request. Alpha is a configurable parameter.\n",
+    "    - For validator sampling the process is stopped after getting half of the row or column. We consider is not necessary to obtain the rest of the samples, since can be obtained reconstructing.\n",
+    "    - Random sampling process stops when all samples are obtained.\n",
+    "\n",
+    "## Malicious builder\n",
+    "\n",
+    "* Builder withholds some specific samples from the matrix, without sending them to validators.\n",
+    "\n",
+    "\n",
+    "## Simulation parameters\n",
+    "\n",
+    "* Number of nodes: 15000\n",
+    "* Number of attackers: Range from 0% to 90%\n",
+    "* Number of validators: 50% nodes are validators\n",
+    "* Redundancy parameter: 2\n",
+    "* Latency between nodes: 5ms - 125ms\n",
+    "* Number of samples per block : 512x512\n",
+    "* Sample size: 512 bytes\n",
+    "* Blocks: 1 blocks simulation.\n",
+    "* Number of random samples fetch: 75. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edd080e7",
+   "metadata": {},
+   "source": [
+    "The following cell is just loading traces files into a dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "2983fa0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "ops_path = {'DAS': '../logsDasEvil0.5000/operation.csv', \n",
+    "            'Evil-0.25': '../logsDasBuilderEvil5000/operation.csv'\n",
+    "           }\n",
+    "msgs_path = {'DAS': '../logsDasEvil0.5000/messages.csv', \n",
+    "            'Evil-0.25': '../logsDasBuilderEvil5000/messages.csv'\n",
+    "          }\n",
+    "\n",
+    "\n",
+    "builder_address = '83814183170291850251680823880522715558189094423550585243365458794131648333116'\n",
+    "\n",
+    "op_df={}\n",
+    "msg_df={}\n",
+    "for key in ops_path:\n",
+    "    op_df[key] = pd.read_csv(ops_path[key],index_col=False,low_memory=False)\n",
+    "for key in msgs_path:\n",
+    "    msg_df[key] = pd.read_csv(msgs_path[key],index_col=False,low_memory=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6cc3d58",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "0c418d95",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Withhold')"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "fig3, ax3 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in op_df:\n",
+    "\n",
+    "    vsdf = op_df[key].loc[(op_df[key]['type'] == 'ValidatorSamplingOperation')]\n",
+    "    data.append(vsdf['completion_time']/1000)\n",
+    "\n",
+    "ax3.boxplot(data)\n",
+    "\n",
+    "\n",
+    "ax3.set_ylim([0,12])\n",
+    "ax3.set_xticklabels([0,75])\n",
+    "ax3.set_title(\"Validator row/column fetching time (seconds)\")\n",
+    "ax3.set_xlabel(\"% Withhold\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "95bea67e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Withhold')"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig3, ax3 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in op_df:\n",
+    "\n",
+    "    vsdf = op_df[key].loc[(op_df[key]['type'] == 'RandomSamplingOperation')]\n",
+    "    #vsdf = op_df[key].loc[(op_df[key]['validator'] == 'no')]\n",
+    "    data.append(vsdf['completion_time']/1000)\n",
+    "\n",
+    "ax3.boxplot(data)\n",
+    "\n",
+    "#ax3.legend()\n",
+    "#ax18.set_xlim([0,2])\n",
+    "ax3.set_ylim([0,15])\n",
+    "ax3.set_xticklabels([0,75])\n",
+    "ax3.set_title(\"Random sampling time regular nodes (seconds)\")\n",
+    "ax3.set_xlabel(\"% Withhold\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "64a9d6f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Withhold')"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig3, ax3 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in msg_df:\n",
+    "\n",
+    "    vsdf = msg_df[key].loc[(msg_df[key]['nodeType'] == 'builder')]\n",
+    "    data.append(vsdf['bytesOut'].values[0]/1024/1024/1024)\n",
+    "\n",
+    "ax3.bar([1,2],data)\n",
+    "\n",
+    "ax3.set_xticks([1,2])\n",
+    "ax3.set_xticklabels([0,75])\n",
+    "ax3.set_title(\"GBs sent by the builder per sampling process\")\n",
+    "ax3.set_xlabel(\"% Withhold\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "0726c136",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Withhold')"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig3, ax3 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in msg_df:\n",
+    "\n",
+    "    vsdf = msg_df[key].loc[(msg_df[key]['nodeType'] == 'validator')]\n",
+    "    data.append(vsdf['bytesOut']/1024/1024)\n",
+    "\n",
+    "ax3.boxplot(data)\n",
+    "\n",
+    "ax3.set_xticklabels([0,75])\n",
+    "ax3.set_title(\"MBs sent per validator\")\n",
+    "ax3.set_xlabel(\"% Withhold\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "40b59007",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '% Withhold')"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig3, ax3 = plt.subplots()\n",
+    "\n",
+    "data = []\n",
+    "for key in msg_df:\n",
+    "\n",
+    "    vsdf = msg_df[key].loc[(msg_df[key]['nodeType'] == 'regular')]\n",
+    "    data.append(vsdf['bytesOut']/1024/1024)\n",
+    "\n",
+    "ax3.boxplot(data)\n",
+    "\n",
+    "\n",
+    "ax3.set_xticklabels([0,75])\n",
+    "ax3.set_title(\"MBs sent per slot regular node\")\n",
+    "ax3.set_xlabel(\"% Withhold\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simulator/run.sh b/simulator/run.sh
index ee1bc43b..f3b9f152 100755
--- a/simulator/run.sh
+++ b/simulator/run.sh
@@ -4,4 +4,4 @@ if [ "$#" -ne 1 ]; then
 fi
 mkdir -p logs
 rm -f logs/*.csv
-java -Xmx200000m -cp lib/djep-1.0.0.jar:lib/jep-2.3.0.jar:target/service-discovery-1.0-SNAPSHOT.jar:lib/gs-core-2.0.jar:lib/pherd-1.0.jar:lib/mbox2-1.0.jar:lib/gs-ui-swing-2.0.jar -ea peersim.Simulator $1
+java -Xmx300000m -cp lib/djep-1.0.0.jar:lib/jep-2.3.0.jar:target/service-discovery-1.0-SNAPSHOT.jar:lib/gs-core-2.0.jar:lib/pherd-1.0.jar:lib/mbox2-1.0.jar:lib/gs-ui-swing-2.0.jar -ea peersim.Simulator $1
diff --git a/simulator/src/main/java/peersim/kademlia/KademliaObserver.java b/simulator/src/main/java/peersim/kademlia/KademliaObserver.java
index 9ac6b326..78ed900f 100755
--- a/simulator/src/main/java/peersim/kademlia/KademliaObserver.java
+++ b/simulator/src/main/java/peersim/kademlia/KademliaObserver.java
@@ -3,6 +3,7 @@
 import java.io.File;
 import java.io.FileWriter;
 import java.io.IOException;
+import java.math.BigInteger;
 import java.util.HashMap;
 import java.util.HashSet;
 import java.util.Map;
@@ -11,6 +12,9 @@
 import peersim.core.CommonState;
 import peersim.core.Control;
 import peersim.core.Network;
+import peersim.core.Node;
+import peersim.kademlia.das.Neighbour;
+import peersim.kademlia.das.SearchTable;
 import peersim.kademlia.operations.Operation;
 import peersim.util.IncrementalStats;
 
@@ -28,6 +32,8 @@ public class KademliaObserver implements Control {
 
   private static final String PAR_FOLDER = "logfolder";
 
+  private static final String PAR_PROTOCOL = "protocol";
+
   /** keep statistics of the number of hops of every message delivered. */
   public static IncrementalStats hopStore = new IncrementalStats();
 
@@ -54,12 +60,21 @@ public class KademliaObserver implements Control {
   private static HashMap<Long, Map<String, Object>> operations =
       new HashMap<Long, Map<String, Object>>();
 
+  /** Log of operations in the Kademlia network */
+  private static HashMap<Long, Map<String, Object>> peerDiscoveries =
+      new HashMap<Long, Map<String, Object>>();
+
+  private static HashMap<BigInteger, Integer> msgsIn = new HashMap<>();
+  private static HashMap<BigInteger, Integer> msgsOut = new HashMap<>();
+  private static HashMap<BigInteger, Integer> bytesIn = new HashMap<>();
+  private static HashMap<BigInteger, Integer> bytesOut = new HashMap<>();
   /** Name of the folder where experiment logs are written */
   private static String logFolderName;
 
   /** The time granularity of reporting metrics */
   private static int observerStep;
 
+  private static int kademliaid;
   /**
    * Constructor to initialize the observer.
    *
@@ -69,8 +84,8 @@ public KademliaObserver(String prefix) {
     observerStep = Configuration.getInt(prefix + "." + PAR_STEP);
 
     logFolderName = Configuration.getString(prefix + "." + PAR_FOLDER, "./logs");
-
-    System.out.println("Logfolder: " + logFolderName);
+    kademliaid = Configuration.getPid(prefix + "." + PAR_PROTOCOL);
+    // System.out.println("Logfolder: " + logFolderName);
   }
 
   private static void writeLogs(Map<Long, Map<String, Object>> map, String filename) {
@@ -117,13 +132,18 @@ public static void writeOut() {
     if (!directory.exists()) {
       directory.mkdir();
     }
+
+    HashMap<Long, Map<String, Object>> msgsInOut = writeMessages();
     // Write messages log to file if not empty
-    if (!messages.isEmpty()) {
-      writeLogs(messages, logFolderName + "/" + "messages.csv");
+    if (!msgsInOut.isEmpty()) {
+      writeLogs(msgsInOut, logFolderName + "/" + "messages.csv");
     }
     if (!operations.isEmpty()) {
       writeLogs(operations, logFolderName + "/" + "operation.csv");
     }
+    if (!peerDiscoveries.isEmpty()) {
+      writeLogs(peerDiscoveries, logFolderName + "/" + "peerDiscoveries.csv");
+    }
   }
 
   /**
@@ -171,14 +191,56 @@ public boolean execute() {
    * @param m The message to report
    * @param sent a boolean indicating whether the message was sent or received.
    */
-  public static void reportMsg(Message m, boolean sent) {
+  public static void reportMsg(Message m, boolean sent, BigInteger id) {
     // Messages without a source are control messages sent by the traffic control,
     // so we don't want to log them.
-    if (m.src == null) return;
+
+    // BigInteger id2 = new
+    // BigInteger("83814183170291850251680823880522715558189094423550585243365458794131648333116");
+    // if(id.compareTo(id2)==0){
+    /*if (sent) {
+      if (m.getType() == Message.MSG_GET_SAMPLE)
+        System.out.println(id + " sending sample request " + m.getSize());
+      else System.out.println(id + " sending sample response " + m.getSize());
+    } else {
+      if (m.getType() == Message.MSG_GET_SAMPLE)
+        System.out.println(id + " receiving sample request " + m.getSize());
+      else System.out.println(id + " receiving sample response " + m.getSize());
+    }*/
+    // }
+    if (m.src == null) {
+      return;
+    }
+    // System.out.println("Reporting msg " + m);
 
     // Add the message to the message log, but first check if it hasn't already been added
-    assert (!messages.keySet().contains(m.id));
-    messages.put(m.id, m.toMap(sent));
+    // assert (!messages.keySet().contains(m.id));
+    // messages.put(m.id, m.toMap(sent));
+    if (sent) {
+      if (msgsOut.get(id) == null) {
+        msgsOut.put(id, 1);
+        bytesOut.put(id, m.getSize());
+      } else {
+        int msgs = msgsOut.get(id);
+        int bytes = bytesOut.get(id);
+        bytes += m.getSize();
+        msgs++;
+        msgsOut.put(id, msgs);
+        bytesOut.put(id, bytes);
+      }
+    } else {
+      if (msgsIn.get(id) == null) {
+        msgsIn.put(id, 1);
+        bytesIn.put(id, m.getSize());
+      } else {
+        int msgs = msgsIn.get(id);
+        int bytes = bytesIn.get(id);
+        msgs++;
+        bytes += m.getSize();
+        msgsIn.put(id, msgs);
+        bytesIn.put(id, bytes);
+      }
+    }
   }
 
   /**
@@ -193,4 +255,72 @@ public static void reportOperation(Operation op) {
     op.setStopTime(CommonState.getTime() - op.getTimestamp());
     operations.put(op.getId(), op.toMap());
   }
+
+  public static void reportPeerDiscovery(Message m, SearchTable st) {
+
+    if (m.src == null) return;
+    // Add the message to the message log, but first check if it hasn't already been added
+    assert (!peerDiscoveries.keySet().contains(m.id));
+    Map<String, Object> result = new HashMap<String, Object>();
+    Neighbour[] neighs = (Neighbour[]) m.value;
+
+    int notKnown = 0;
+    for (Neighbour n : neighs) {
+      if (!st.isNeighbourKnown(n)) notKnown++;
+    }
+    result.put("time", CommonState.getTime());
+    result.put("message_id", m.id);
+    result.put("dst_id", m.dst.getId());
+    result.put("src_id", m.src.getId());
+    result.put("total_peers", st.getAllNeighboursCount());
+    result.put("total_peers_alive", st.getAllAliveNeighboursCount());
+    result.put("peers_in_message", neighs.length);
+    result.put("peers_not_known", notKnown);
+    result.put("malicious_peers", st.getMaliciousNeighboursCount());
+    result.put("validators_discovered", st.getValidatorsNeighboursCount());
+    peerDiscoveries.put(m.id, result);
+  }
+
+  private static HashMap<Long, Map<String, Object>> writeMessages() {
+    HashMap<Long, Map<String, Object>> msgs = new HashMap<>();
+
+    Long msgId = (long) 0;
+    for (BigInteger id : msgsIn.keySet()) {
+      // System.out.println("Writing messages log " + id);
+      Map<String, Object> result = new HashMap<String, Object>();
+      result.put("id", id);
+      Node n = Util.nodeIdtoNode(id, kademliaid);
+      boolean builder = n.getDASProtocol().isBuilder();
+      boolean validator = n.getDASProtocol().isValidator();
+      result.put("msgsIn", msgsIn.get(id));
+      result.put("msgsOut", msgsOut.get(id));
+      result.put("bytesIn", bytesIn.get(id));
+      result.put("bytesOut", bytesOut.get(id));
+      if (builder) result.put("nodeType", "builder");
+      else if (validator) result.put("nodeType", "validator");
+      else result.put("nodeType", "regular");
+      msgs.put(msgId, result);
+      msgId++;
+    }
+    for (BigInteger id : msgsOut.keySet()) {
+      if (msgsIn.keySet().contains(id)) {
+        continue;
+      }
+      Map<String, Object> result = new HashMap<String, Object>();
+      result.put("id", id);
+      Node n = Util.nodeIdtoNode(id, kademliaid);
+      boolean builder = n.getDASProtocol().isBuilder();
+      boolean validator = n.getDASProtocol().isValidator();
+      result.put("msgsIn", msgsIn.get(id));
+      result.put("msgsOut", msgsOut.get(id));
+      result.put("bytesIn", bytesIn.get(id));
+      result.put("bytesOut", bytesOut.get(id));
+      if (builder) result.put("nodeType", "builder");
+      else if (validator) result.put("nodeType", "validator");
+      else result.put("nodeType", "regular");
+      msgs.put(msgId, result);
+      msgId++;
+    }
+    return msgs;
+  }
 }
diff --git a/simulator/src/main/java/peersim/kademlia/KademliaProtocol.java b/simulator/src/main/java/peersim/kademlia/KademliaProtocol.java
index 6fe4c3d9..7ee61169 100755
--- a/simulator/src/main/java/peersim/kademlia/KademliaProtocol.java
+++ b/simulator/src/main/java/peersim/kademlia/KademliaProtocol.java
@@ -557,7 +557,7 @@ public void processEvent(Node myNode, int myPid, Object event) {
     // If the event is a message, report the message to the Kademlia observer.
     if (event instanceof Message) {
       m = (Message) event;
-      KademliaObserver.reportMsg(m, false);
+      // KademliaObserver.reportMsg(m, false);
     }
 
     // Handle the event based on its type.
diff --git a/simulator/src/main/java/peersim/kademlia/Message.java b/simulator/src/main/java/peersim/kademlia/Message.java
index f1faa2ee..a835e6c4 100755
--- a/simulator/src/main/java/peersim/kademlia/Message.java
+++ b/simulator/src/main/java/peersim/kademlia/Message.java
@@ -1,7 +1,10 @@
 package peersim.kademlia;
 
+import java.math.BigInteger;
 import java.util.HashMap;
 import java.util.Map;
+import peersim.kademlia.das.Sample;
+import peersim.kademlia.das.SeedingSampleBody;
 
 /**
  * Message class provide all functionalities to magage the various messages, principally LOOKUP
@@ -76,6 +79,8 @@ public class Message extends SimpleEvent {
 
   public static final int MSG_SEED_SAMPLE = 17;
 
+  public static final int MSG_CANCEL_SAMPLE = 18;
+
   /**
    * Message Type: INIT_FIND_REGION_BASED (command to a node to start looking for node within a
    * region)
@@ -108,6 +113,8 @@ public class Message extends SimpleEvent {
   /** Available to count the number of hops the message did. */
   protected int nrHops = 0;
 
+  protected int size = 0;
+
   // ______________________________________________________________________________________________
   /**
    * Creates an empty message by using default values (message type = MSG_LOOKUP and <code>
@@ -137,6 +144,26 @@ public Message(int messageType, Object body) {
     super(messageType);
     this.id = (ID_GENERATOR++);
     this.body = body;
+
+    if (body instanceof BigInteger[]) {
+      BigInteger[] reqs = (BigInteger[]) body;
+      size += 32 * reqs.length; // req size
+      size += 64; // src / dst ids
+      size += 4; // msg type
+    } else if (body instanceof Sample[]) {
+      Sample[] samples = (Sample[]) body;
+      size += 512 * samples.length; // samples
+      size += 15 * 32; // neighbours
+      size += 64; // src dst id
+      size += 4; // message type
+    } else if (body instanceof SeedingSampleBody) {
+      SeedingSampleBody ssb = (SeedingSampleBody) body;
+      Sample[] samples = (Sample[]) ssb.getsamplesList();
+      size += 512 * samples.length; // samples
+      size += 15 * 32; // neighbours
+      size += 64; // src dst id
+      size += 4; // message type
+    }
   }
 
   // ______________________________________________________________________________________________
@@ -225,6 +252,10 @@ public static final Message makeSeedSample(Object body) {
     return new Message(MSG_SEED_SAMPLE, body);
   }
 
+  public static final Message makeCancelSample(Object body) {
+    return new Message(MSG_CANCEL_SAMPLE, body);
+  }
+
   // ______________________________________________________________________________________________
   /**
    * Encapsulates the creation of a find value request
@@ -242,6 +273,9 @@ public String toString() {
     return s + "[Type=" + typeToString() + "] BODY=(...)";
   }
 
+  public int getSize() {
+    return size;
+  }
   // ______________________________________________________________________________________________
   public Message copy() {
     Message dolly = new Message();
@@ -289,6 +323,8 @@ public String typeToString() {
         return "MSG_GET_ANY_SAMPLE_RESPONSE";
       case MSG_SEED_SAMPLE:
         return "MSG_SEED_SAMPLE";
+      case MSG_CANCEL_SAMPLE:
+        return "MSG_CANCEL_SAMPLE";
       default:
         return "UNKNOW:" + type;
     }
diff --git a/simulator/src/main/java/peersim/kademlia/StateBuilder.java b/simulator/src/main/java/peersim/kademlia/StateBuilder.java
index 8d764ae2..1160665a 100755
--- a/simulator/src/main/java/peersim/kademlia/StateBuilder.java
+++ b/simulator/src/main/java/peersim/kademlia/StateBuilder.java
@@ -1,6 +1,5 @@
 package peersim.kademlia;
 
-import java.math.BigInteger;
 import java.util.Comparator;
 import peersim.config.Configuration;
 import peersim.core.CommonState;
@@ -98,14 +97,15 @@ public int compare(Node o1, Node o2) {
         });
 
     int sz = Network.size();
-    // For every node, add 100 random nodes to its k-bucket - not sure why this was 50 previously...
+    // For every node, add 10 boostrap nodes
+
     for (int i = 0; i < sz; i++) {
       Node iNode = Network.get(i);
       KademliaProtocol iKad = (KademliaProtocol) (iNode.getProtocol(kademliaid));
-
-      for (int k = 0; k < 100; k++) {
+      for (int k = 0; k < 25; k++) {
         KademliaProtocol jKad =
             (KademliaProtocol) (Network.get(CommonState.r.nextInt(sz)).getProtocol(kademliaid));
+        // (KademliaProtocol) (Network.get(k).getProtocol(kademliaid));
         if (jKad.getKademliaNode().isServer()) {
           iKad.getRoutingTable().addNeighbour(jKad.getKademliaNode().getId());
         }
@@ -113,7 +113,7 @@ public int compare(Node o1, Node o2) {
     }
 
     // Add 50 nearby nodes to each node's k-bucket
-    for (int i = 0; i < sz; i++) {
+    /*for (int i = 0; i < sz; i++) {
       Node iNode = Network.get(i);
       KademliaProtocol iKad = (KademliaProtocol) (iNode.getProtocol(kademliaid));
       BigInteger iNodeId = iKad.getKademliaNode().getId();
@@ -133,7 +133,7 @@ public int compare(Node o1, Node o2) {
           }
         }
       }
-    }
+    }*/
     return false;
   } // end execute()
 }
diff --git a/simulator/src/main/java/peersim/kademlia/TrafficGenerator.java b/simulator/src/main/java/peersim/kademlia/TrafficGenerator.java
index d6897e63..b36ac700 100755
--- a/simulator/src/main/java/peersim/kademlia/TrafficGenerator.java
+++ b/simulator/src/main/java/peersim/kademlia/TrafficGenerator.java
@@ -46,6 +46,20 @@ private Message generateFindNodeMessage() {
     }
     BigInteger dst = ((KademliaProtocol) (n.getProtocol(pid))).getKademliaNode().getId();
 
+    System.out.println("Sending find node " + dst);
+    Message m = Message.makeInitFindNode(dst);
+    m.timestamp = CommonState.getTime();
+
+    return m;
+  }
+
+  /**
+   * Generates a random find node message, by selecting randomly the destination.
+   *
+   * @return Message
+   */
+  private Message generateFindNodeMessage(BigInteger dst) {
+    // Get an existing active destination node
     Message m = Message.makeInitFindNode(dst);
     m.timestamp = CommonState.getTime();
 
@@ -83,18 +97,34 @@ private Message generateRegionBasedFindNodeMessage() {
    */
   public boolean execute() {
 
-    Node start;
-    do {
-      start = Network.get(CommonState.r.nextInt(Network.size()));
-    } while ((start == null) || (!start.isUp()));
-
-    // send message
-    EDSimulator.add(0, generateFindNodeMessage(), start, pid);
-    // EDSimulator.add(0, generateRegionBasedFindNodeMessage(), start, pid);
-
+    if (first) {
+      for (int i = 0; i < Network.size(); i++) {
+        Node start = Network.get(i);
+        if (start.isUp()) {
+          for (int j = 0; j < 3; j++) {
+            // send message
+            EDSimulator.add(0, generateFindNodeMessage(), start, pid);
+          }
+          EDSimulator.add(
+              0,
+              generateFindNodeMessage(start.getKademliaProtocol().getKademliaNode().getId()),
+              start,
+              pid);
+        }
+      }
+      first = false;
+    } else {
+      Node start;
+      do {
+        start = Network.get(CommonState.r.nextInt(Network.size()));
+      } while ((start == null) || (!start.isUp()));
+
+      // send message
+      EDSimulator.add(0, generateFindNodeMessage(), start, pid);
+      // EDSimulator.add(0, generateRegionBasedFindNodeMessage(), start, pid);
+    }
     return false;
   }
-
   // ______________________________________________________________________________________________
 
 } // End of class
diff --git a/simulator/src/main/java/peersim/kademlia/Util.java b/simulator/src/main/java/peersim/kademlia/Util.java
index 569e78fe..bf30c84b 100755
--- a/simulator/src/main/java/peersim/kademlia/Util.java
+++ b/simulator/src/main/java/peersim/kademlia/Util.java
@@ -1,7 +1,13 @@
 package peersim.kademlia;
 
+import com.google.common.base.Functions;
 import java.math.BigInteger;
+import java.util.Collection;
+import java.util.Comparator;
+import java.util.Map.Entry;
+import java.util.Objects;
 import java.util.Set;
+import java.util.stream.Collectors;
 import peersim.core.CommonState;
 import peersim.core.Network;
 import peersim.core.Node;
@@ -200,4 +206,15 @@ public static Node nodeIdtoNode(BigInteger searchNodeId, int kademliaid) {
     // If no node with the desired ID was found, return null
     return null;
   }
+
+  public static <V> V mostCommon(final Collection<V> items) {
+    return items.stream()
+        .filter(Objects::nonNull)
+        .collect(Collectors.groupingBy(Functions.identity(), Collectors.counting()))
+        .entrySet()
+        .stream()
+        .max(Comparator.comparing(Entry::getValue))
+        .map(Entry::getKey)
+        .orElse(null);
+  }
 }
diff --git a/simulator/src/main/java/peersim/kademlia/das/Block.java b/simulator/src/main/java/peersim/kademlia/das/Block.java
index f07af034..4399b804 100644
--- a/simulator/src/main/java/peersim/kademlia/das/Block.java
+++ b/simulator/src/main/java/peersim/kademlia/das/Block.java
@@ -2,12 +2,10 @@
 
 import java.math.BigInteger;
 import java.util.ArrayList;
-import java.util.Collection;
 import java.util.HashMap;
 import java.util.Iterator;
 import java.util.List;
 import java.util.Stack;
-import java.util.TreeSet;
 import peersim.core.CommonState;
 import peersim.core.Network;
 import peersim.kademlia.KademliaCommonConfig;
@@ -39,12 +37,14 @@ public class Block implements Iterator<Sample>, Cloneable {
   private int numSamples;
 
   // private TreeSet<BigInteger> samples;
-  private TreeSet<BigInteger> samplesByRow;
-  private TreeSet<BigInteger> samplesByColumn;
+  // private TreeSet<BigInteger> samplesByRow;
+  // private TreeSet<BigInteger> samplesByColumn;
   private HashMap<BigInteger, Parcel> parcelMap;
   private HashMap<Integer, List<Parcel>> parcelByRow;
   private HashMap<Integer, List<Parcel>> parcelByColumn;
 
+  private HashMap<BigInteger, Sample> sampleMap;
+
   // Constructor with block id
   public Block(long id) {
 
@@ -52,23 +52,24 @@ public Block(long id) {
     this.numSamples = this.SIZE * this.SIZE;
     _init();
 
-    samplesByRow = new TreeSet<>();
-    samplesByColumn = new TreeSet<>();
-    // sampleMap = new HashMap<>();
+    // samplesByRow = new TreeSet<>();
+    // samplesByColumn = new TreeSet<>();
+    sampleMap = new HashMap<>();
 
     this.blockId = id;
     blockSamples = new Sample[SIZE][SIZE];
     row = column = 0;
-
+    sampleMap = new HashMap<>();
     parcelMap = new HashMap<>();
     parcelByRow = new HashMap<>();
     parcelByColumn = new HashMap<>();
     for (int i = 1; i <= blockSamples.length; i++) {
       for (int j = 1; j <= blockSamples[0].length; j++) {
         blockSamples[i][j] = new Sample(blockId, i + 1, j + 1, this);
-        samplesByRow.add(blockSamples[i][j].getIdByRow());
-        samplesByColumn.add(blockSamples[i][j].getIdByColumn());
-        // sampleMap.put(blockSamples[i][j].getIdByColumn(), blockSamples[i][j].getIdByRow());
+        // samplesByRow.add(blockSamples[i][j].getIdByRow());
+        // samplesByColumn.add(blockSamples[i][j].getIdByColumn());
+        sampleMap.put(blockSamples[i][j].getIdByRow(), blockSamples[i][j]);
+        sampleMap.put(blockSamples[i][j].getIdByColumn(), blockSamples[i][j]);
       }
     }
   }
@@ -79,14 +80,14 @@ public Block(int size, long id) {
     SIZE = size;
     this.numSamples = this.SIZE * this.SIZE;
     _init();
-    samplesByRow = new TreeSet<>();
-    samplesByColumn = new TreeSet<>();
-    // sampleMap = new HashMap<>();
+    // samplesByRow = new TreeSet<>();
+    // samplesByColumn = new TreeSet<>();
+    sampleMap = new HashMap<>();
 
     this.blockId = id;
     blockSamples = new Sample[SIZE][SIZE];
     row = column = 0;
-
+    sampleMap = new HashMap<>();
     parcelMap = new HashMap<>();
     parcelByRow = new HashMap<>();
     parcelByColumn = new HashMap<>();
@@ -94,9 +95,10 @@ public Block(int size, long id) {
     for (int i = 0; i < blockSamples.length; i++) {
       for (int j = 0; j < blockSamples[0].length; j++) {
         blockSamples[i][j] = new Sample(blockId, i + 1, j + 1, this);
-        samplesByRow.add(blockSamples[i][j].getIdByRow());
-        samplesByColumn.add(blockSamples[i][j].getIdByColumn());
-        // sampleMap.put(blockSamples[i][j].getIdByColumn(), blockSamples[i][j].getIdByRow());
+        // samplesByRow.add(blockSamples[i][j].getIdByRow());
+        // samplesByColumn.add(blockSamples[i][j].getIdByColumn());
+        sampleMap.put(blockSamples[i][j].getIdByRow(), blockSamples[i][j]);
+        sampleMap.put(blockSamples[i][j].getIdByColumn(), blockSamples[i][j]);
       }
     }
   }
@@ -110,15 +112,17 @@ public Block(Sample[][] blockSamples, int size, long id) {
     row = column = 0;
     this.blockId = id;
     // samples = new TreeSet<>();
-    samplesByRow = new TreeSet<>();
-    samplesByColumn = new TreeSet<>();
+    // samplesByRow = new TreeSet<>();
+    // samplesByColumn = new TreeSet<>();
+    sampleMap = new HashMap<>();
 
     for (int i = 0; i < blockSamples.length; i++) {
       for (int j = 0; j < blockSamples[0].length; j++) {
         blockSamples[i][j] = new Sample(blockId, i + 1, j + 1, this);
-        samplesByRow.add(blockSamples[i][j].getIdByRow());
-        samplesByColumn.add(blockSamples[i][j].getIdByColumn());
-        // sampleMap.put(blockSamples[i][j].getIdByColumn(), blockSamples[i][j].getIdByRow());
+        // samplesByRow.add(blockSamples[i][j].getIdByRow());
+        //  samplesByColumn.add(blockSamples[i][j].getIdByColumn());
+        sampleMap.put(blockSamples[i][j].getIdByRow(), blockSamples[i][j]);
+        sampleMap.put(blockSamples[i][j].getIdByColumn(), blockSamples[i][j]);
       }
     }
   }
@@ -264,6 +268,39 @@ public BigInteger[] getNRandomSamplesIds(int n) {
     return samples;
   }
 
+  /*public int findClosestRow(BigInteger nodeid, BigInteger radius) {
+    BigInteger bottom = nodeid.subtract(radius);
+    if (radius.compareTo(nodeid) == 1) bottom = BigInteger.ZERO;
+
+    BigInteger top = nodeid.add(radius);
+    if (top.compareTo(Block.MAX_KEY) == 1) top = Block.MAX_KEY;
+
+    Collection<BigInteger> subSet = samplesByRow.subSet(bottom, true, top, true);
+    List<Integer> rows = new ArrayList<>();
+    for (BigInteger id : subSet) {
+      rows.add(sampleMap.get(id).getRow());
+    }
+    // System.out.println(rows.size() + " " + KademliaCommonConfigDas.validatorsSize + " " +
+    // radius);
+    return Util.mostCommon(rows);
+  }
+
+  public int findClosestColumn(BigInteger nodeid, BigInteger radius) {
+
+    BigInteger bottom = nodeid.subtract(radius);
+    if (radius.compareTo(nodeid) == 1) bottom = BigInteger.ZERO;
+
+    BigInteger top = nodeid.add(radius);
+    if (top.compareTo(Block.MAX_KEY) == 1) top = Block.MAX_KEY;
+
+    Collection<BigInteger> subSet = samplesByColumn.subSet(bottom, true, top, true);
+    List<Integer> column = new ArrayList<>();
+    for (BigInteger id : subSet) {
+      column.add(sampleMap.get(id).getColumn());
+    }
+    return Util.mostCommon(column);
+  }*/
+
   /* Returns  n random selected samples */
   public Sample[] getNRandomSamples(int n) {
 
@@ -325,16 +362,16 @@ public int getNumSamples() {
   }
 
   /* Returns the total number of samples in the block */
-  public BigInteger[] getSamplesByRadius(BigInteger peerId, BigInteger radius) {
+  /*public BigInteger[] getSamplesByRadius(BigInteger peerId, BigInteger radius) {
     BigInteger top = peerId.add(radius);
     BigInteger bottom = peerId.subtract(radius);
 
     Collection<BigInteger> subSet = samplesByRow.subSet(bottom, true, top, true);
     return (BigInteger[]) subSet.toArray(new BigInteger[0]);
-  }
+  }*/
 
-  /* Returns the ids of the samples within the radius to the peerId specified*/
-  public BigInteger[] getSamplesByRadiusByRow(BigInteger peerId, BigInteger radius) {
+  // Returns the ids of the samples within the radius to the peerId specified
+  /*public BigInteger[] getSamplesByRadiusByRow(BigInteger peerId, BigInteger radius) {
     BigInteger top = peerId.add(radius);
     BigInteger bottom = peerId.subtract(radius);
 
@@ -342,20 +379,15 @@ public BigInteger[] getSamplesByRadiusByRow(BigInteger peerId, BigInteger radius
     return (BigInteger[]) subSet.toArray(new BigInteger[0]);
   }
 
-  /* Returns the ids of the samples within the radius to the peerId specified, using sample column id*/
+  // Returns the ids of the samples within the radius to the peerId specified, using sample column id
   public BigInteger[] getSamplesByRadiusByColumn(BigInteger peerId, BigInteger radius) {
     BigInteger top = peerId.add(radius);
     BigInteger bottom = peerId.subtract(radius);
 
     Collection<BigInteger> subSet = samplesByColumn.subSet(bottom, true, top, true);
 
-    /*List<BigInteger> result = new ArrayList<>();
-    for (BigInteger sampleId : subSet) {
-      result.add(sampleMap.get(sampleId));
-    }
-    return (BigInteger[]) result.toArray(new BigInteger[0]);*/
     return (BigInteger[]) subSet.toArray(new BigInteger[0]);
-  }
+  }*/
 
   /* Returns the ids of the all the samples in a specific row*/
   public BigInteger[] getSamplesIdsByRow(int row) {
@@ -375,6 +407,10 @@ public BigInteger[] getSamplesIdsByColumn(int column) {
     return samples;
   }
 
+  public Sample getSample(BigInteger id) {
+    return sampleMap.get(id);
+  }
+
   private void _init() {
 
     // execute once
diff --git a/simulator/src/main/java/peersim/kademlia/das/CustomDistributionDas.java b/simulator/src/main/java/peersim/kademlia/das/CustomDistributionDas.java
index 54f233da..c285572a 100644
--- a/simulator/src/main/java/peersim/kademlia/das/CustomDistributionDas.java
+++ b/simulator/src/main/java/peersim/kademlia/das/CustomDistributionDas.java
@@ -25,8 +25,8 @@ public class CustomDistributionDas implements peersim.core.Control {
   private static final String PAR_PROT_DAS_BUILDER = "protocoldasbuilder";
   private static final String PAR_PROT_DAS_VALIDATOR = "protocoldasvalidator";
   private static final String PAR_PROT_DAS_NON_VALIDATOR = "protocoldasnonvalidator";
-  private static final String PAR_PROT_EVIL_KAD = "protocolEvilkad";
   private static final String PAR_PROT_EVIL_DAS = "protocolEvildas";
+  private static final String PAR_PROT_EVIL_VAL_DAS = "protocolEvilValDas";
   private static final String PAR_EVIL_RATIO_VAL = "evilNodeRatioValidator";
   private static final String PAR_EVIL_RATIO_NONVAL = "evilNodeRatioNonValidator";
 
@@ -35,12 +35,13 @@ public class CustomDistributionDas implements peersim.core.Control {
   /** Protocol identifiers for Kademlia, DAS, etc. * */
   private int protocolKadID;
 
-  private int protocolEvilKadID;
   private int protocolDasBuilderID;
   private int protocolDasValidatorID;
   private int protocolDasNonValidatorID;
 
   private int protocolEvilDasID;
+  private int protocolEvilValDasID;
+
   /** Ratio of evil nodes to total number of nodes * */
   private double evilRatioValidator;
 
@@ -52,28 +53,24 @@ public class CustomDistributionDas implements peersim.core.Control {
 
   public CustomDistributionDas(String prefix) {
     protocolKadID = Configuration.getPid(prefix + "." + PAR_PROT_KAD);
-    protocolEvilKadID = Configuration.getPid(prefix + "." + PAR_PROT_EVIL_KAD, protocolKadID);
     protocolDasBuilderID = Configuration.getPid(prefix + "." + PAR_PROT_DAS_BUILDER);
     protocolDasValidatorID = Configuration.getPid(prefix + "." + PAR_PROT_DAS_VALIDATOR);
     protocolDasNonValidatorID = Configuration.getPid(prefix + "." + PAR_PROT_DAS_NON_VALIDATOR);
     protocolEvilDasID = Configuration.getPid(prefix + "." + PAR_PROT_EVIL_DAS, 0);
+    protocolEvilValDasID = Configuration.getPid(prefix + "." + PAR_PROT_EVIL_VAL_DAS, 0);
+
     evilRatioValidator = Configuration.getDouble(prefix + "." + PAR_EVIL_RATIO_VAL, 0.0);
     evilRatioNonValidator = Configuration.getDouble(prefix + "." + PAR_EVIL_RATIO_NONVAL, 0.0);
     urg = new UniformRandomGenerator(KademliaCommonConfig.BITS, CommonState.r);
     validatorRate = Configuration.getDouble(prefix + "." + PAR_VALIDATOR_RATE, 1.0);
   }
 
-  /**
-   * Scan over the nodes in the network and assign a randomly generated NodeId in the space
-   * 0..2^BITS, where BITS is a parameter from the kademlia protocol (usually 160)
-   *
-   * @return boolean always false
-   */
   public boolean execute() {
 
     int numValidators = (int) (Network.size() * validatorRate);
 
-    System.out.println("Validators " + numValidators);
+    System.out.println(
+        "Validators " + numValidators + " " + evilRatioValidator + " " + evilRatioNonValidator);
 
     int numEvilValidatorNodes = (int) (numValidators * evilRatioValidator);
     int numEvilNonValidatorNodes = (int) ((Network.size() - numValidators) * evilRatioNonValidator);
@@ -81,6 +78,11 @@ public boolean execute() {
         "Number of malicious nodes: " + numEvilValidatorNodes + " " + numEvilNonValidatorNodes);
     List<BigInteger> validatorsIds = new ArrayList<>();
     List<BigInteger> nonValidatorsIds = new ArrayList<>();
+    List<Node> evilNodes = new ArrayList<>();
+    List<Node> validators = new ArrayList<>();
+    List<BigInteger> evilIds = new ArrayList<>();
+    numValidators = numValidators - numEvilValidatorNodes;
+    SearchTable searchTable = new SearchTable();
 
     for (int i = 0; i < Network.size(); ++i) {
       Node generalNode = Network.get(i);
@@ -92,145 +94,38 @@ public boolean execute() {
       KademliaProtocol kadProt = null;
       DASProtocol dasProt = null;
 
-      /** Generate honest and evil nodes * */
-      /*if (i == 0) {
-        kadProt = ((KademliaProtocol) (Network.get(i).getProtocol(protocolKadID)));
-        dasProt = ((DASProtocol) (Network.get(i).getProtocol(protocolDasBuilderID)));
-        System.out.println("Init builder " + i);
-        kadProt.setProtocolID(protocolKadID);
-        dasProt.setDASProtocolID(protocolDasBuilderID);
-        dasProt.setKademliaProtocol(kadProt);
-        kadProt.setEventsCallback(dasProt);
-        kadProt.setNode(node);
-
-        builderAddress = dasProt.getKademliaProtocol().getKademliaNode().getId();
-
-        generalNode.setProtocol(protocolDasValidatorID, null);
-        generalNode.setProtocol(protocolDasNonValidatorID, null);
-        generalNode.setProtocol(protocolDasBuilderID, dasProt);
-        generalNode.setProtocol(protocolEvilDasID, null);
-      } else if ((i > 0) && (i < (numEvilValidatorNodes + 1))) {
-        System.out.println("Init evil validator " + i);
-
-        kadProt = ((KademliaProtocol) (Network.get(i).getProtocol(protocolEvilKadID)));
-        dasProt = ((EvilDASProtocol) (Network.get(i).getProtocol(protocolEvilDasID)));
-        kadProt.setProtocolID(protocolEvilKadID);
-        dasProt.setDASProtocolID(protocolEvilDasID);
-        dasProt.setKademliaProtocol(kadProt);
-        kadProt.setEventsCallback(dasProt);
-        kadProt.setNode(node);
-        node.setEvil(true);
-        dasProt.setBuilderAddress(builderAddress);
-
-        generalNode.setProtocol(protocolDasValidatorID, null);
-        generalNode.setProtocol(protocolDasNonValidatorID, null);
-        generalNode.setProtocol(protocolDasBuilderID, null);
-        generalNode.setProtocol(protocolEvilDasID, dasProt);
-
-        validatorsIds.add(dasProt.getKademliaId());
-        validators.add(dasProt);
-      } else if (i >= (numEvilValidatorNodes + 1)
-          && i < (numEvilValidatorNodes + numEvilNonValidatorNodes + 1)) {
-        System.out.println("Init evil non validator " + i);
-        kadProt = ((KademliaProtocol) (Network.get(i).getProtocol(protocolEvilKadID)));
-        dasProt = ((EvilDASProtocol) (Network.get(i).getProtocol(protocolEvilDasID)));
-        kadProt.setProtocolID(protocolEvilKadID);
-        dasProt.setDASProtocolID(protocolEvilDasID);
-        dasProt.setKademliaProtocol(kadProt);
-        kadProt.setEventsCallback(dasProt);
-        kadProt.setNode(node);
-        node.setEvil(true);
-        dasProt.setBuilderAddress(builderAddress);
-
-        generalNode.setProtocol(protocolDasValidatorID, null);
-        generalNode.setProtocol(protocolDasNonValidatorID, null);
-        generalNode.setProtocol(protocolDasBuilderID, null);
-        generalNode.setProtocol(protocolEvilDasID, dasProt);
-
-      } else if (i >= (numEvilValidatorNodes + numEvilNonValidatorNodes + 1)
-          && i
-              < (numEvilValidatorNodes
-                  + numEvilNonValidatorNodes
-                  + (numValidators - numEvilValidatorNodes)
-                  + 1)) {
-        assert (!node.isEvil());
-        System.out.println("Init validator " + i);
-
-        kadProt = ((KademliaProtocol) (Network.get(i).getProtocol(protocolKadID)));
-        dasProt = ((DASProtocol) (Network.get(i).getProtocol(protocolDasValidatorID)));
-        kadProt.setProtocolID(protocolKadID);
-        dasProt.setDASProtocolID(protocolDasValidatorID);
-        dasProt.setBuilderAddress(builderAddress);
-        dasProt.setKademliaProtocol(kadProt);
-        kadProt.setEventsCallback(dasProt);
-        kadProt.setNode(node);
-
-        validatorsIds.add(kadProt.getKademliaNode().getId());
-        validators.add(dasProt);
-
-        generalNode.setProtocol(protocolDasValidatorID, dasProt);
-        generalNode.setProtocol(protocolDasNonValidatorID, null);
-        generalNode.setProtocol(protocolDasBuilderID, null);
-        generalNode.setProtocol(protocolEvilDasID, null);
-
-      } else {
-        assert (!node.isEvil());
-        System.out.println("Init non-validator " + i);
-        kadProt = ((KademliaProtocol) (Network.get(i).getProtocol(protocolKadID)));
-        dasProt = ((DASProtocol) (Network.get(i).getProtocol(protocolDasNonValidatorID)));
-        kadProt.setProtocolID(protocolKadID);
-        dasProt.setDASProtocolID(protocolDasNonValidatorID);
-        dasProt.setBuilderAddress(builderAddress);
-        dasProt.setKademliaProtocol(kadProt);
-        kadProt.setEventsCallback(dasProt);
-        kadProt.setNode(node);
-
-        generalNode.setProtocol(protocolDasValidatorID, null);
-        generalNode.setProtocol(protocolDasNonValidatorID, dasProt);
-        generalNode.setProtocol(protocolDasBuilderID, null);
-        generalNode.setProtocol(protocolEvilDasID, null);
-      }*/
-
       kadProt = ((KademliaProtocol) (Network.get(i).getProtocol(protocolKadID)));
       kadProt.setProtocolID(protocolKadID);
       kadProt.setNode(node);
 
       if (i == 0) {
         dasProt = ((DASProtocol) (Network.get(i).getProtocol(protocolDasBuilderID)));
-
+        builderAddress = node.getId();
+        validators.add(generalNode);
       } else if ((i > 0) && (i < (numEvilValidatorNodes + 1))) {
+        dasProt = ((DASProtocol) (Network.get(i).getProtocol(protocolEvilValDasID)));
+        validatorsIds.add(kadProt.getKademliaNode().getId());
+        evilNodes.add(generalNode);
+        evilIds.add(id);
+      } else if ((i > numEvilValidatorNodes)
+          && (i < (numEvilValidatorNodes + numEvilNonValidatorNodes + 1))) {
         dasProt = ((DASProtocol) (Network.get(i).getProtocol(protocolEvilDasID)));
-      } else if (i >= (numEvilValidatorNodes + numEvilNonValidatorNodes + 1)
-          && i
-              < (numEvilValidatorNodes
-                  + numEvilNonValidatorNodes
-                  + (numValidators - numEvilValidatorNodes)
-                  + 1)) {
+        nonValidatorsIds.add(kadProt.getKademliaNode().getId());
+        evilNodes.add(generalNode);
+        evilIds.add(id);
+      } else if (i > (numEvilValidatorNodes + numEvilNonValidatorNodes)
+          && i < (numEvilValidatorNodes + numEvilNonValidatorNodes + (numValidators) + 1)) {
         dasProt = ((DASProtocol) (Network.get(i).getProtocol(protocolDasValidatorID)));
         validatorsIds.add(kadProt.getKademliaNode().getId());
-
       } else {
         dasProt = ((DASProtocol) (Network.get(i).getProtocol(protocolDasNonValidatorID)));
         nonValidatorsIds.add(kadProt.getKademliaNode().getId());
-        node.setServer(false);
       }
 
       dasProt.setKademliaProtocol(kadProt);
       kadProt.setEventsCallback(dasProt);
 
-      if (i == 0) builderAddress = dasProt.getKademliaProtocol().getKademliaNode().getId();
-      dasProt.setBuilderAddress(builderAddress);
-
-      /*System.out.println(
-      "Dasprot id "
-          + protocolDasBuilderID
-          + " "
-          + protocolDasValidatorID
-          + " "
-          + protocolDasNonValidatorID
-          + " "
-          + protocolKadID);*/
-
+      if (dasProt instanceof DASProtocolBuilder) System.out.println("DASProtocol Builder " + i);
       generalNode.setProtocol(protocolKadID, kadProt);
       generalNode.setKademliaProtocol(kadProt);
       generalNode.setDASProtocol(dasProt);
@@ -238,19 +133,26 @@ public boolean execute() {
 
       generalNode.setProtocol(protocolDasBuilderID, dasProt);
       generalNode.setProtocol(protocolEvilDasID, null);
+      generalNode.setProtocol(protocolEvilValDasID, null);
       generalNode.setProtocol(protocolDasValidatorID, null);
       generalNode.setProtocol(protocolDasNonValidatorID, null);
-    }
 
-    // System.out.println("Validators " + validatorsIds.size());
-    // for (DASProtocol validator : validators) {
-    for (int i = 0; i < Network.size(); i++) {
-      Node generalNode = Network.get(i);
-      if (i == 0) generalNode.getDASProtocol().setNonValidators(nonValidatorsIds);
-      generalNode.getDASProtocol().addKnownValidator(validatorsIds.toArray(new BigInteger[0]));
+      generalNode.getDASProtocol().setSearchTable(searchTable);
+      generalNode.getDASProtocol().setBuilderAddress(builderAddress);
     }
+
+    System.out.println("Validators " + validatorsIds.size());
+    System.out.println("Non-Validators " + nonValidatorsIds.size());
+
+    searchTable.setBuilderAddress(builderAddress);
+    searchTable.addNodes(nonValidatorsIds.toArray(new BigInteger[0]));
+    searchTable.addValidatorNodes(validatorsIds.toArray(new BigInteger[0]));
+    searchTable.setEvil(evilNodes);
+    searchTable.setEvilIds(evilIds);
+
     KademliaCommonConfigDas.networkSize = Network.size();
     KademliaCommonConfigDas.validatorsSize = numValidators;
+
     return false;
   }
 }
diff --git a/simulator/src/main/java/peersim/kademlia/das/DASDHTProtocol.java b/simulator/src/main/java/peersim/kademlia/das/DASDHTProtocol.java
index a2d535ed..e632fab5 100644
--- a/simulator/src/main/java/peersim/kademlia/das/DASDHTProtocol.java
+++ b/simulator/src/main/java/peersim/kademlia/das/DASDHTProtocol.java
@@ -31,7 +31,8 @@ public Object clone() {
   protected void handleInitNewBlock(Message m, int myPid) {
     time = CommonState.getTime();
     currentBlock = (Block) m.body;
-    kv.erase();
+    // kv.erase();
+    kv.clear();
     // samplesRequested = 0;
     row = new int[KademliaCommonConfigDas.BLOCK_DIM_SIZE + 1];
     column = new int[KademliaCommonConfigDas.BLOCK_DIM_SIZE + 1];
diff --git a/simulator/src/main/java/peersim/kademlia/das/DASDHTProtocolNonValidator.java b/simulator/src/main/java/peersim/kademlia/das/DASDHTProtocolNonValidator.java
index 28df26a0..b5c659b3 100644
--- a/simulator/src/main/java/peersim/kademlia/das/DASDHTProtocolNonValidator.java
+++ b/simulator/src/main/java/peersim/kademlia/das/DASDHTProtocolNonValidator.java
@@ -119,9 +119,9 @@ protected void startRandomSampling() {
             currentBlock,
             searchTable,
             this.isValidator,
-            validatorsList.length,
+            KademliaCommonConfigDas.validatorsSize,
             this);
-    op.elaborateResponse(this.kadProtocol.kv.getAll().toArray(new Sample[0]));
+    // op.elaborateResponse(this.kadProtocol.kv.getAll().toArray(new Sample[0]));
     samplingOp.put(op.getId(), op);
     logger.warning("Sampling operation started random");
     doSampling(op);
diff --git a/simulator/src/main/java/peersim/kademlia/das/DASDHTProtocolValidator.java b/simulator/src/main/java/peersim/kademlia/das/DASDHTProtocolValidator.java
index dd772f2d..a8ceeff4 100644
--- a/simulator/src/main/java/peersim/kademlia/das/DASDHTProtocolValidator.java
+++ b/simulator/src/main/java/peersim/kademlia/das/DASDHTProtocolValidator.java
@@ -23,7 +23,7 @@ public DASDHTProtocolValidator(String prefix) {
 
   @Override
   protected void handleSeedSample(Message m, int myPid) {
-    logger.warning("seed sample receveived");
+    logger.warning("seed sample received");
     if (m.body == null) return;
   }
 
@@ -73,10 +73,11 @@ private void createValidatorSamplingOperation(int row, int column, long timestam
             row,
             column,
             this.isValidator,
-            validatorsList.length,
+            KademliaCommonConfigDas.validatorsSize,
+            null,
             this);
 
-    op.elaborateResponse(this.kadProtocol.kv.getAll().toArray(new Sample[0]));
+    // op.elaborateResponse(this.kadProtocol.kv.getAll().toArray(new Sample[0]));
     samplingOp.put(op.getId(), op);
     logger.warning("Sampling operation started validator " + op.getId() + " " + row + " " + column);
 
@@ -158,7 +159,7 @@ protected void startRandomSampling() {
             currentBlock,
             searchTable,
             this.isValidator,
-            validatorsList.length,
+            KademliaCommonConfigDas.validatorsSize,
             this);
     op.elaborateResponse(this.kadProtocol.kv.getAll().toArray(new Sample[0]));
     samplingOp.put(op.getId(), op);
diff --git a/simulator/src/main/java/peersim/kademlia/das/DASProtocol.java b/simulator/src/main/java/peersim/kademlia/das/DASProtocol.java
index 3edea34c..7dcc1f5a 100644
--- a/simulator/src/main/java/peersim/kademlia/das/DASProtocol.java
+++ b/simulator/src/main/java/peersim/kademlia/das/DASProtocol.java
@@ -10,12 +10,11 @@
 import java.math.BigInteger;
 import java.util.ArrayList;
 import java.util.Arrays;
-import java.util.Collections;
+import java.util.HashMap;
 import java.util.HashSet;
 import java.util.LinkedHashMap;
 import java.util.List;
 import java.util.TreeMap;
-import java.util.TreeSet;
 import java.util.logging.Logger;
 import peersim.config.Configuration;
 import peersim.core.CommonState;
@@ -26,7 +25,6 @@
 import peersim.kademlia.KademliaEvents;
 import peersim.kademlia.KademliaObserver;
 import peersim.kademlia.KademliaProtocol;
-import peersim.kademlia.KeyValueStore;
 import peersim.kademlia.Message;
 import peersim.kademlia.SimpleEvent;
 import peersim.kademlia.Timeout;
@@ -45,6 +43,10 @@ public abstract class DASProtocol implements Cloneable, EDProtocol, KademliaEven
   protected static final String PAR_KADEMLIA = "kademlia";
   protected static final String PAR_ALPHA = "alpha";
   protected static final String PAR_PARCEL = "parcelSize";
+  protected static final String PAR_DISC = "reportDiscovery";
+  protected static final String PAR_MSG = "reportMsg";
+
+  private boolean reportDiscovery, msgReport;
   private static String prefix = null;
   private UnreliableTransport transport;
   /** Store the time until which this node's uplink is busy sending data */
@@ -65,7 +67,8 @@ public abstract class DASProtocol implements Cloneable, EDProtocol, KademliaEven
 
   protected boolean isValidator;
 
-  protected KeyValueStore kv;
+  // protected KeyValueStore kv;
+  protected HashSet<BigInteger> kv;
 
   protected Block currentBlock;
 
@@ -79,20 +82,20 @@ public abstract class DASProtocol implements Cloneable, EDProtocol, KademliaEven
 
   protected int[] row, column;
 
-  // protected int samplesRequested;
-
-  protected BigInteger[] validatorsList;
-
-  protected HashSet<BigInteger> queried;
-
   protected int dasID;
 
   /** trace message sent for timeout purpose */
-  protected TreeMap<Long, Long> sentMsg;
+  protected TreeMap<Long, Message> sentMsg;
 
   protected long time;
 
-  protected TreeSet<BigInteger> nonValidatorsIndexed; // , samplesIndexed;
+  protected boolean isEvil;
+
+  protected boolean init;
+
+  protected HashMap<BigInteger, List<Message>> missingSamples;
+
+  protected List<Long> opsToRemove;
 
   /**
    * Replicate this object by returning an identical copy.<br>
@@ -111,6 +114,7 @@ public abstract class DASProtocol implements Cloneable, EDProtocol, KademliaEven
   public DASProtocol(String prefix) {
 
     DASProtocol.prefix = prefix;
+    isEvil = false;
     _init();
     tid = Configuration.getPid(prefix + "." + PAR_TRANSPORT);
     // samplesRequested = 0;
@@ -121,18 +125,26 @@ public DASProtocol(String prefix) {
 
     KademliaCommonConfigDas.PARCEL_SIZE =
         Configuration.getInt(prefix + "." + PAR_PARCEL, KademliaCommonConfigDas.PARCEL_SIZE);
-    kv = new KeyValueStore();
+
+    reportDiscovery = Configuration.getBoolean(prefix + "." + PAR_DISC, false);
+    msgReport = Configuration.getBoolean(prefix + "." + PAR_MSG, false);
+
+    // System.out.println("Msgreport " + msgReport + " " + prefix);
+    // kv = new KeyValueStore();
+    kv = new HashSet<>();
     samplingOp = new LinkedHashMap<Long, SamplingOperation>();
     kadOps = new LinkedHashMap<Operation, SamplingOperation>();
     samplingStarted = false;
 
-    queried = new HashSet<BigInteger>();
     uploadInterfaceBusyUntil = 0;
 
-    sentMsg = new TreeMap<Long, Long>();
+    sentMsg = new TreeMap<Long, Message>();
 
-    searchTable = new SearchTable();
-    nonValidatorsIndexed = new TreeSet<>();
+    opsToRemove = new ArrayList<Long>();
+    // searchTable = new SearchTable();
+    isBuilder = false;
+    missingSamples = new HashMap<>();
+    init = false;
   }
 
   /**
@@ -146,6 +158,9 @@ private void _init() {
     _ALREADY_INSTALLED = true;
   }
 
+  public void setSearchTable(SearchTable searchTable) {
+    this.searchTable = searchTable;
+  }
   /**
    * manage the peersim receiving of the events
    *
@@ -159,8 +174,18 @@ public void processEvent(Node myNode, int myPid, Object event) {
     SimpleEvent s = (SimpleEvent) event;
     if (s instanceof Message) {
       m = (Message) event;
+      logger.info("Reporting " + msgReport + " " + m.getType() + " " + m.getSize());
+
       // m.dst = this.kadProtocol.getKademliaNode();
-      KademliaObserver.reportMsg(m, false);
+      if (msgReport
+          && (m.getType() == Message.MSG_GET_SAMPLE
+              || m.getType() == Message.MSG_GET_SAMPLE_RESPONSE
+              || m.getType() == Message.MSG_SEED_SAMPLE))
+        KademliaObserver.reportMsg(m, false, this.getKademliaId());
+      /*if (m.src != null) {
+        Node n = Util.nodeIdtoNode(m.src.getId(), kademliaId);
+        searchTable.addNeighbour(new Neighbour(m.src.getId(), n, n.getDASProtocol().isEvil()));
+      }*/
     }
 
     switch (((SimpleEvent) event).getType()) {
@@ -180,9 +205,13 @@ public void processEvent(Node myNode, int myPid, Object event) {
         m = (Message) event;
         handleSeedSample(m, myPid);
         break;
+      case Message.MSG_CANCEL_SAMPLE:
+        m = (Message) event;
+        handleCancelSampling(m, myPid);
+        break;
       case Message.MSG_GET_SAMPLE_RESPONSE:
         m = (Message) event;
-        // logger.warning("Send message removed " + m.ackId);
+        logger.info("Send message removed " + m.ackId);
         sentMsg.remove(m.ackId);
         handleGetSampleResponse(m, myPid);
         break;
@@ -191,18 +220,23 @@ public void processEvent(Node myNode, int myPid, Object event) {
         Timeout t = (Timeout) event;
         if (sentMsg.containsKey(t.msgID)) { // the response msg isn't arrived
           // remove form sentMsg
-          logger.warning("Timeouuuuut! " + t.msgID);
+          logger.warning("Timeouuuuut! " + t.msgID + " " + t.node);
+          SamplingOperation sop = samplingOp.get(t.opID);
           sentMsg.remove(t.msgID);
           // this.searchTable.removeNode(t.node);
-          SamplingOperation sop = samplingOp.get(t.opID);
           if (sop != null) {
             if (!sop.completed()) {
-              logger.warning("Samping operation found");
-
+              sop.elaborateResponse(null, t.node);
+              logger.warning("Sampling operation found " + sop.getId() + " " + sop.getPending());
               doSampling(sop);
             }
           }
+          // searchTable.removeNode(t.node);
         }
+        /*if (sop != null) {
+          logger.warning("Doing sampling timeout");
+          doSampling(sop);
+        }*/
         break;
     }
   }
@@ -235,6 +269,10 @@ public boolean isValidator() {
     return this.isValidator;
   }
 
+  public boolean isEvil() {
+    return this.isEvil;
+  }
+
   public void setBuilderAddress(BigInteger address) {
     this.builderAddress = address;
     searchTable.setBuilderAddress(address);
@@ -251,23 +289,35 @@ public BigInteger getBuilderAddress() {
    * @param myPid the sender Pid
    */
   protected void handleInitNewBlock(Message m, int myPid) {
+    init = true;
     time = CommonState.getTime();
     currentBlock = (Block) m.body;
-    kv.erase();
+    // kv.erase();
+    kv.clear();
+    missingSamples.clear();
     // samplesRequested = 0;
     row = new int[currentBlock.getSize()];
     column = new int[currentBlock.getSize()];
-
+    sentMsg.clear();
     // clearing any pending operation from previous block
     for (SamplingOperation sop : samplingOp.values()) {
       KademliaObserver.reportOperation(sop);
       if (sop instanceof ValidatorSamplingOperation)
-        logger.warning("Sampling operation finished validator " + sop.getId());
-      else logger.warning("Sampling operation finished random " + sop.getId());
+        logger.warning("Sampling operation finished validator failed" + sop.getId());
+      else {
+        logger.warning("Sampling operation finished random failed" + sop.getId());
+        for (BigInteger id : sop.getSamples()) {
+          logger.warning("Missing sample when failed " + id);
+        }
+        for (peersim.kademlia.das.operations.Node n : sop.getNodes()) {
+
+          logger.warning("Missing sample failed pending node " + n.getId());
+        }
+      }
     }
     samplingOp.clear();
     kadOps.clear();
-    queried.clear();
+    opsToRemove.clear();
   }
 
   protected void handleSeedSample(Message m, int myPid) {
@@ -285,127 +335,302 @@ protected void handleSeedSample(Message m, int myPid) {
 
   protected void handleGetSample(Message m, int myPid) {
     // kv is for storing the sample you have
-    logger.info("KV size " + kv.occupancy() + " from:" + m.src.getId() + " " + m.id);
+    logger.info("KV size " + kv.size() + " from:" + m.src.getId() + " " + m.id);
     // sample IDs that are requested in the message
     List<BigInteger> samples = Arrays.asList((BigInteger[]) m.body);
-    // samples to return
-    List<Sample> s = new ArrayList<>();
-    // if (!isValidator())
-    //  logger.warning("Non-validator received " + samples.size() + " from " + m.src.getId());
 
-    List<BigInteger> nodes = new ArrayList<>();
+    // boolean sampleFound = false;
+
+    List<Sample> samplesToSend = new ArrayList<>();
+
     for (BigInteger id : samples) {
-      Sample sample = (Sample) kv.get(id);
-      if (sample != null) {
-        s.add(sample);
+      logger.info("Requesting sample " + id + " from " + m.src.getId());
+      // Sample sample = (Sample) kv.get(id);
+      // if (sample != null) {
+      if (kv.contains(id)) {
+        // s.add(sample);
+
+        /*Message response = new Message(Message.MSG_GET_SAMPLE_RESPONSE, new Sample[] {sample});
+        response.operationId = m.operationId;
+        response.dst = m.src;
+        response.src = this.kadProtocol.getKademliaNode();
+        response.ackId = m.id; // set ACK number
+        response.value = searchTable.getNeighbours();
+
+        sendMessage(response, m.src.getId(), myPid);
+        sampleFound = true;*/
+        // samplesToSend.add(sample);
+        samplesToSend.add(currentBlock.getSample(id));
+        if (isEvil && samplesToSend.size() > 0) break;
+      } else {
+        if (missingSamples.get(id) != null) missingSamples.get(id).add(m);
+        else {
+          List<Message> requests = new ArrayList<>();
+          requests.add(m);
+          missingSamples.put(id, requests);
+        }
+        // logger.warning("Sample request missing");
       }
-      // node close to the sample ID
-      nodes.addAll(
-          Arrays.asList(
-              this.getKademliaProtocol()
-                  .getRoutingTable()
-                  .getNeighbours(Util.logDistance(id, this.getKademliaId()))));
     }
-    // return a random subset of samples (if more than MAX_SAMPLES_RETURNED)
-    Collections.shuffle(s);
-    Sample[] returnedSamples;
-    if (s.size() > KademliaCommonConfigDas.MAX_SAMPLES_RETURNED)
-      returnedSamples =
-          new HashSet<Sample>(s.subList(0, KademliaCommonConfigDas.MAX_SAMPLES_RETURNED))
-              .toArray(new Sample[0]);
-    else returnedSamples = s.toArray(new Sample[0]);
-
-    Collections.shuffle(nodes);
-    BigInteger[] returnedNodes;
-    if (nodes.size() > KademliaCommonConfigDas.MAX_NODES_RETURNED)
-      returnedNodes =
-          new HashSet<BigInteger>(nodes.subList(0, KademliaCommonConfigDas.MAX_NODES_RETURNED))
-              .toArray(new BigInteger[0]);
-    else returnedNodes = nodes.toArray(new BigInteger[0]);
-
-    logger.info("Get sample request responding with " + s.size() + " samples");
-
-    Message response = new Message(Message.MSG_GET_SAMPLE_RESPONSE, returnedSamples);
-    response.operationId = m.operationId;
-    response.dst = m.src;
-    response.src = this.kadProtocol.getKademliaNode();
-    response.ackId = m.id; // set ACK number
-    response.value = returnedNodes;
-    sendMessage(response, m.src.getId(), myPid);
-  }
-
-  private void reconstruct(Sample s) {
+    if (samplesToSend.isEmpty()) {
+      /*Message response = new Message(Message.MSG_GET_SAMPLE_RESPONSE, new Sample[] {});
+      response.operationId = m.operationId;
+      response.dst = m.src;
+      response.src = this.kadProtocol.getKademliaNode();
+      response.ackId = m.id; // set ACK number
+      response.value = searchTable.getNeighbours(KademliaCommonConfigDas.MAX_NODES_RETURNED);
+
+      sendMessage(response, m.src.getId(), myPid);*/
+    } else {
+      Message response =
+          new Message(Message.MSG_GET_SAMPLE_RESPONSE, samplesToSend.toArray(new Sample[0]));
+      response.operationId = m.operationId;
+      response.dst = m.src;
+      response.src = this.kadProtocol.getKademliaNode();
+      response.ackId = m.id; // set ACK number
+      /*response.value =
+          searchTable.getNeighbours(
+              KademliaCommonConfigDas.MAX_NODES_RETURNED * samplesToSend.size());
+      if (isEvil)
+        response.value =
+            searchTable.getEvilNeighbours(
+                KademliaCommonConfigDas.MAX_NODES_RETURNED * samplesToSend.size());*/
+      sendMessage(response, m.src.getId(), myPid);
+    }
+  }
+
+  protected void reconstruct(Sample s) {
     column[s.getColumn() - 1]++;
     row[s.getRow() - 1]++;
     if (column[s.getColumn() - 1] >= column.length / 2
         && column[s.getColumn() - 1] != column.length) {
       Sample[] samples = currentBlock.getSamplesByColumn(s.getColumn());
       for (Sample sam : samples) {
-        kv.add((BigInteger) sam.getIdByRow(), sam);
-        kv.add((BigInteger) sam.getIdByColumn(), sam);
+        kv.add(sam.getIdByRow());
+        // kv.add((BigInteger) sam.getIdByRow(), sam);
+        // kv.add((BigInteger) sam.getIdByColumn(), sam);
       }
       column[s.getColumn() - 1] = currentBlock.getSize();
     }
     if (row[s.getRow() - 1] >= row.length / 2 && row[s.getRow() - 1] != row.length) {
       Sample[] samples = currentBlock.getSamplesByRow(s.getRow());
       for (Sample sam : samples) {
-        kv.add((BigInteger) sam.getIdByRow(), sam);
-        kv.add((BigInteger) sam.getIdByColumn(), sam);
+        kv.add(sam.getIdByRow());
+        // kv.add((BigInteger) sam.getIdByRow(), sam);
+        // kv.add((BigInteger) sam.getIdByColumn(), sam);
       }
       row[s.getRow() - 1] = currentBlock.getSize();
     }
   }
 
+  protected void handleCancelSampling(Message m, int myPid) {
+    logger.warning("Canceling pending samples requests from " + m.src.getId());
+    opsToRemove.add(m.operationId);
+  }
+
   protected void handleGetSampleResponse(Message m, int myPid) {
 
     if (m.body == null) return;
 
     Sample[] samples = (Sample[]) m.body;
-    searchTable.addNodes((BigInteger[]) m.value);
+    // searchTable.addNodes((BigInteger[]) m.value);
+
+    if (reportDiscovery && !isEvil()) KademliaObserver.reportPeerDiscovery(m, searchTable);
+    /*for (Neighbour neigh : (Neighbour[]) m.value) {
+      if (neigh.getId().compareTo(builderAddress) != 0) searchTable.addNeighbour(neigh);
+    }*/
+
     for (Sample s : samples) {
+      logger.info(
+          "Sample received "
+              + s.getId()
+              + " "
+              + s.getIdByColumn()
+              + " from "
+              + m.src.getId()
+              + " "
+              + m.id);
 
-      kv.add((BigInteger) s.getIdByRow(), s);
-      kv.add((BigInteger) s.getIdByColumn(), s);
+      kv.add((BigInteger) s.getIdByRow());
+      // kv.add((BigInteger) s.getIdByRow(), s);
+      // kv.add((BigInteger) s.getIdByColumn(), s);
       // count # of samples for each row and column and reconstruct if more than half received
       reconstruct(s);
     }
+    if (samplingOp.get(m.operationId) != null)
+      logger.warning(
+          "Samples received  "
+              + samples.length
+              + " from "
+              + m.src.getId()
+              + " "
+              + samplingOp.get(m.operationId).getPending()
+              + " "
+              + kv.size()
+              + " "
+              + samplingOp.get(m.operationId).getId());
+    // + kv.occupancy());
+    else
+      logger.warning(
+          "Samples received  " + samples.length + " from " + m.src.getId() + " " + kv.size());
+    //    "Samples received  " + samples.length + " from " + m.src.getId() + " " + kv.occupancy());
+    HashMap<Message, List<Sample>> toSend = findMissingSamples(samples);
+
+    for (Message msg : toSend.keySet()) {
+      if (msg.src.getId().compareTo(m.src.getId()) == 0) continue;
+      Sample[] samplesToSend = toSend.get(msg).toArray(new Sample[0]);
+      if (isEvil) {
+        samplesToSend = new Sample[] {samplesToSend[0]};
+      }
+      Message response = new Message(Message.MSG_GET_SAMPLE_RESPONSE, samplesToSend);
+      response.operationId = msg.operationId;
+      response.dst = msg.src;
+      response.src = this.kadProtocol.getKademliaNode();
+      response.ackId = msg.id; // set ACK number
+      /*response.value =
+          searchTable.getNeighbours(
+              KademliaCommonConfigDas.MAX_NODES_RETURNED * toSend.get(msg).size());
+      if (isEvil)
+        response.value = searchTable.getEvilNeighbours(KademliaCommonConfigDas.MAX_NODES_RETURNED);*/
+      for (Sample s : samplesToSend)
+        logger.info("Sending sample cached " + s.getId() + " to " + msg.src.getId() + " " + msg.id);
+      sendMessage(response, msg.src.getId(), myPid);
+    }
+    toSend.clear();
 
-    SamplingOperation op = (SamplingOperation) samplingOp.get(m.operationId);
+    // SamplingOperation op = (SamplingOperation) samplingOp.get(m.operationId);
     // We continue an existing operation
-    if (op != null) {
+
+    /*logger.info(
+    "Nodes discovered "
+        + ((Neighbour[]) m.value).length
+        + " "
+        + searchTable.getAllNeighboursCount()
+        + " "
+        + searchTable.getValidatorsNeighboursCount());*/
+    // + " "
+    // + op);
+    List<Long> toRemove = new ArrayList<>();
+    for (SamplingOperation op : samplingOp.values()) {
+      // if (op != null) {
       // keeping track of received samples
       op.elaborateResponse(samples, m.src.getId());
+      // op.elaborateResponse(kv.getAll().toArray(new Sample[0]));
+      // logger.warning(
+      //    "operation " + op.getId() + " " + op.samplesCount() + " " + op.getSamples().length);
+      // }
+      // SamplingOperation op = (SamplingOperation) samplingOp.get(m.operationId);
+      // if (op != null) {
       logger.warning(
           "Continue operation "
               + op.getId()
               + " "
-              + op.getHops()
+              + op.getAgressiveness()
               + " "
               + searchTable.nodesIndexed().size()
               + " "
-              + ((SamplingOperation) op).samplesCount());
-
-      if (!op.completed() && op.getHops() < KademliaCommonConfigDas.MAX_HOPS) {
-        if (op instanceof ValidatorSamplingOperation
-                && (CommonState.getTime() - op.getTimestamp())
-                    > KademliaCommonConfigDas.VALIDATOR_DEADLINE
-            || op instanceof RandomSamplingOperation
-                && (CommonState.getTime() - op.getTimestamp())
-                    > KademliaCommonConfigDas.RANDOM_SAMPLING_DEADLINE) {
-          samplingOp.remove(m.operationId);
-          logger.warning("Sampling operation finished");
-          KademliaObserver.reportOperation(op);
-        }
+              + ((SamplingOperation) op).samplesCount()
+              + " "
+              + ((SamplingOperation) op).getPending()
+              + " "
+              + op.completed());
+
+      if (!op.completed()
+      /*&& op.getHops() < KademliaCommonConfigDas.MAX_HOPS
+      && (op instanceof ValidatorSamplingOperation
+              && (CommonState.getTime() - op.getTimestamp())
+                  <= KademliaCommonConfigDas.VALIDATOR_DEADLINE
+          || op instanceof RandomSamplingOperation
+              && (CommonState.getTime() - op.getTimestamp())
+                  <= KademliaCommonConfigDas.RANDOM_SAMPLING_DEADLINE)*/ ) {
         doSampling(op);
-      } else {
-        logger.warning("Operation completed");
-        samplingOp.remove(m.operationId);
+      } // else {
+      /*if (!op.completed()
+          && (op instanceof ValidatorSamplingOperation
+                  && (CommonState.getTime() - op.getTimestamp())
+                      > KademliaCommonConfigDas.VALIDATOR_DEADLINE
+              || op instanceof RandomSamplingOperation
+                  && (CommonState.getTime() - op.getTimestamp())
+                      > KademliaCommonConfigDas.RANDOM_SAMPLING_DEADLINE)) {
+        toRemove.add(op.getId());
+        KademliaObserver.reportOperation(op);
         if (op instanceof ValidatorSamplingOperation)
-          logger.warning("Sampling operation finished validator completed " + op.getId());
-        else logger.warning("Sampling operation finished random completed " + op.getId());
+          logger.warning("Sampling operation finished validator failed " + op.getId());
+        else logger.warning("Sampling operation finished random failed " + op.getId());
+      }*/
+
+      if (op.completed()) {
+        // logger.warning("Operation completed");
+        // samplingOp.remove(m.operationId);
+        /*List<Message> sentMsgsOp = new ArrayList<>();
+        for (Message msg : sentMsg.values()) {
+          if (msg.operationId == op.getId()) sentMsgsOp.add(msg);
+        }
+        for (Message msg : sentMsgsOp) {
+          Message response = new Message(Message.MSG_CANCEL_SAMPLE);
+          response.operationId = op.getId();
+          response.dst = msg.dst;
+          response.src = this.kadProtocol.getKademliaNode();*/
+        // response.ackId = msg.id; // set ACK number*/
+        /*response.value =
+            searchTable.getNeighbours(
+                KademliaCommonConfigDas.MAX_NODES_RETURNED * toSend.get(msg).size());
+        if (isEvil)
+          response.value = searchTable.getEvilNeighbours(KademliaCommonConfigDas.MAX_NODES_RETURNED);*/
+        /*sendMessage(response, response.dst.getId(), myPid);
+          sentMsg.remove(msg.id);
+        }*/
+        toRemove.add(op.getId());
+        if (op instanceof ValidatorSamplingOperation)
+          logger.warning(
+              "Sampling operation finished validator completed "
+                  + op.getId()
+                  + " "
+                  + op.getMessages().size());
+        else
+          logger.warning(
+              "Sampling operation finished random completed "
+                  + op.getId()
+                  + " "
+                  + op.getMessages().size());
         KademliaObserver.reportOperation(op);
       }
     }
+    for (long id : toRemove) {
+      // logger.warning("Sampling operation finished validator completed " + op.getId());
+      samplingOp.remove(id);
+    }
+  }
+
+  private HashMap<Message, List<Sample>> findMissingSamples(Sample[] samples) {
+    HashMap<Message, List<Sample>> toSend = new HashMap<>();
+    List<BigInteger> toRemove = new ArrayList<>();
+    for (BigInteger id : missingSamples.keySet()) {
+      if (kv.contains(id)) {
+        Sample s = currentBlock.getSample(id);
+        // if (kv.get(id) != null) {
+        //  Sample s = (Sample) kv.get(id);
+        for (Message msg : missingSamples.get(id)) {
+          if (!opsToRemove.contains(msg.operationId)) {
+            if (toSend.get(msg) != null) {
+              toSend.get(msg).add(s);
+            } else {
+              List<Sample> sToSend = new ArrayList<>();
+              sToSend.add(s);
+              toSend.put(msg, sToSend);
+            }
+          } else {
+            opsToRemove.remove(msg.operationId);
+          }
+        }
+        toRemove.add(s.getId());
+      }
+    }
+    for (BigInteger id : toRemove) {
+      missingSamples.remove(id);
+    }
+
+    return toSend;
   }
 
   /**
@@ -425,6 +650,11 @@ protected void sendMessage(Message m, BigInteger destId, int myPid) {
     Node src = this.kadProtocol.getNode();
     Node dest = Util.nodeIdtoNode(destId, kademliaId);
     transport = (UnreliableTransport) (Network.prototype).getProtocol(tid);
+    if (msgReport
+        && (m.getType() == Message.MSG_GET_SAMPLE
+            || m.getType() == Message.MSG_GET_SAMPLE_RESPONSE
+            || m.getType() == Message.MSG_SEED_SAMPLE))
+      KademliaObserver.reportMsg(m, true, this.getKademliaId());
 
     if (m.getType() != Message.MSG_GET_SAMPLE_RESPONSE && m.getType() != Message.MSG_SEED_SAMPLE) {
       transport.send(src, dest, m, myPid);
@@ -432,12 +662,19 @@ protected void sendMessage(Message m, BigInteger destId, int myPid) {
       // Send message taking into account the transmission delay and the availability of upload
       // interface
       // Timeout t = new Timeout(destId, m.id, m.operationId);
-      Sample[] samples = (Sample[]) m.body;
-      BigInteger[] nghbrs = (BigInteger[]) m.value;
+      Sample[] samples;
+      if (m.getType() == Message.MSG_SEED_SAMPLE) {
+        SeedingSampleBody body = (SeedingSampleBody) m.body;
+        samples = (Sample[]) body.getsamplesList();
+      } else {
+        samples = (Sample[]) m.body;
+      }
+      // Sample[] samples = (Sample[]) m.body;
+      // Neighbour[] nghbrs = (Neighbour[]) m.value;
       double samplesSize = 0.0;
       if (samples != null) samplesSize = samples.length * KademliaCommonConfigDas.SAMPLE_SIZE;
       double nghbrsSize = 0.0;
-      if (nghbrs != null) nghbrsSize = nghbrs.length * KademliaCommonConfigDas.NODE_RECORD_SIZE;
+      // if (nghbrs != null) nghbrsSize = nghbrs.length * KademliaCommonConfigDas.NODE_RECORD_SIZE;
       double msgSize = samplesSize + nghbrsSize;
       long propagationLatency = transport.getLatency(src, dest);
       // Add the transmission time of the message (upload)
@@ -465,7 +702,7 @@ protected void sendMessage(Message m, BigInteger destId, int myPid) {
       }
       logger.info("Transmission " + latency + " " + transDelay);
       // add to sent msg
-      this.sentMsg.put(m.id, m.timestamp);
+      // this.sentMsg.put(m.id, m.timestamp);
       EDSimulator.add(latency, m, dest, myPid);
     }
 
@@ -473,11 +710,15 @@ protected void sendMessage(Message m, BigInteger destId, int myPid) {
     if (m.getType() == Message.MSG_GET_SAMPLE) { // is a request
       Timeout t = new Timeout(destId, m.id, m.operationId);
       long latency = transport.getLatency(src, dest);
-      logger.warning("Send message added " + m.id + " " + latency);
+      logger.info("Send message added " + m.id + " " + latency + " " + destId);
 
       // add to sent msg
-      this.sentMsg.put(m.id, m.timestamp);
-      EDSimulator.add(4 * latency, t, src, myPid); // set delay = 2*RTT
+      this.sentMsg.put(m.id, m);
+      /// BigInteger[] samples = (Sample[]) m.body;
+
+      long timeout = latency * 2 * 4; // 4 RTT
+      if (timeout < 250) timeout = 250;
+      EDSimulator.add(timeout, t, src, myPid); // set delay = 2*RTT
     }
   }
 
@@ -520,17 +761,22 @@ protected int columnWithHighestNumSamples() {
     return max;
   }
 
-  public void addKnownValidator(BigInteger[] ids) {
+  /*public void addKnownValidator(BigInteger[] ids) {
     logger.info("Adding validator list " + ids.length);
-    validatorsList = ids;
-    if (validatorsList != null) searchTable.addValidatorNodes(validatorsList);
+    // validatorsList = ids;
+    // if (validatorsList != null && isBuilder()) searchTable.addValidatorNodes(validatorsList);
+    // if (isBuilder())
+    searchTable.addValidatorNodes(ids);
   }
 
   public void setNonValidators(List<BigInteger> nonValidators) {
-    // searchTable.addNodes(nonValidators.toArray(new BigInteger[0]));
-    for (BigInteger id : nonValidators) {
-      nonValidatorsIndexed.add(id);
-    }
+
+    // if (isBuilder())
+    searchTable.addNodes(nonValidators.toArray(new BigInteger[0]));
+  }*/
+
+  public SearchTable getSearchTable() {
+    return searchTable;
   }
   /**
    * Starts the random sampling operation
@@ -549,30 +795,42 @@ protected void startRandomSampling() {
             currentBlock,
             searchTable,
             this.isValidator,
-            validatorsList.length,
+            KademliaCommonConfigDas.validatorsSize,
             this);
-    op.elaborateResponse(kv.getAll().toArray(new Sample[0]));
+    // op.elaborateResponse(kv.getAll().toArray(new Sample[0]));
     samplingOp.put(op.getId(), op);
-    logger.warning("Sampling operation started random");
-    /*while (!doSampling(op)) {
-      op.increaseRadius(2);
-    }*/
+    logger.warning("Sampling operation started random " + op.getId());
+
+    /*List<BigInteger> samples = Arrays.asList(op.getSamples());
+    Collections.sort(samples);
+    for (BigInteger id : samples) logger.warning("Sampling operation sample " + id);*/
+
     doSampling(op);
   }
 
   protected boolean doSampling(SamplingOperation sop) {
 
+    logger.warning("Doingsampling " + sop.getId() + " " + sop.getPending());
     if (sop.completed()) {
       samplingOp.remove(sop.getId());
       KademliaObserver.reportOperation(sop);
       // logger.warning("Sampling operation finished " + sop.getId());
       if (sop instanceof ValidatorSamplingOperation)
-        logger.warning("Sampling operation finished validator dosampling " + sop.getId());
-      else logger.warning("Sampling operation finished random dosampling " + sop.getId());
+        logger.warning(
+            "Sampling operation completed validator dosampling "
+                + sop.getId()
+                + " "
+                + sop.getMessages().size());
+      else
+        logger.warning(
+            "Sampling operation completed random dosampling "
+                + sop.getId()
+                + " "
+                + sop.getMessages().size());
       return true;
     } else {
       boolean success = false;
-      if (sop.getAvailableRequests() == 0) {
+      if (sop.getPending() == 0) {
         logger.warning("Doing sampling again " + sop.getId());
         BigInteger[] nextNodes = sop.doSampling();
         for (BigInteger nextNode : nextNodes) {
@@ -583,33 +841,29 @@ protected boolean doSampling(SamplingOperation sop) {
                   + " "
                   + reqSamples.length
                   + " "
-                  + sop.getAvailableRequests()
+                  + sop.getPending()
                   + " "
                   + sop.getId());
 
           Message msg = generateGetSampleMessage(reqSamples);
           msg.operationId = sop.getId();
           msg.src = this.kadProtocol.getKademliaNode();
+          // if (missing) msg.value = reqSamples;
           success = true;
           msg.dst = Util.nodeIdtoNode(nextNode, kademliaId).getKademliaProtocol().getKademliaNode();
-          /*if (nextNode.compareTo(builderAddress) == 0) {
-            logger.warning("Error sending to builder or 0 samples assigned");
-            continue;
-          }*/
+
           sop.addMessage(msg.id);
           // logger.warning("Send message " + dasID + " " + this);
           sendMessage(msg, nextNode, dasID);
           sop.getMessages();
         }
-        if (!success) {
-
-          if (sop instanceof ValidatorSamplingOperation)
-            logger.warning("Sampling operation finished validator failed " + sop.getId());
-          else logger.warning("Sampling operation finished random failed " + sop.getId());
+        /*if (!success && !sop.getStarted()) {
+          Timeout t = new Timeout(this.getKademliaId(), sop.getId(), sop.getId());
+          // add to sent msg
+          this.sentMsg.put(sop.getId(), CommonState.getTime());
 
-          samplingOp.remove(sop.getId());
-          KademliaObserver.reportOperation(sop);
-        }
+          EDSimulator.add(100, t, this.getKademliaProtocol().getNode(), dasID); // set delay = 2*RTT
+        }*/
       }
       return success;
     }
@@ -634,29 +888,11 @@ public void operationComplete(Operation op) {
       FindOperation fop = (FindOperation) op;
       List<BigInteger> list = fop.getNeighboursList();
       list.remove(builderAddress);
-      searchTable.addNodes(list.toArray(new BigInteger[0]));
-      logger.warning(
-          "Search table operation complete"
-              // + searchTable.samplesIndexed().size()
-              // + " "
-              + searchTable.nodesIndexed().size()
-              + " "
-              //       + kadOps.get(op).nrHops
-              //       + " "
-              + list.size());
-
-      if (kadOps.get(op) == null) return;
-
-      if (!kadOps.get(op).completed()) {
-        doSampling(kadOps.get(op));
-      }
-
-      logger.warning(
-          "Sampling operation found "
-              + kadOps.size()
-              + " "
-              + kadOps.get(op).getAvailableRequests());
-      kadOps.remove(op);
+      // searchTable.addNodes(list.toArray(new BigInteger[0]));
+      /*for (BigInteger id : list) {
+        Node n = Util.nodeIdtoNode(id, kademliaId);
+        searchTable.addNeighbour(new Neighbour(id, n, n.getDASProtocol().isEvil()));
+      }*/
     }
   }
 
@@ -673,50 +909,18 @@ public void nodesFound(Operation op, BigInteger[] neighbours) {
       logger.warning("No neighbours found");
       return;
     }
-    searchTable.addNodes(list.toArray(new BigInteger[0]));
-    logger.info(
-        "Search table nodes found "
-            // + searchTable.samplesIndexed().size()
-            // + " "
-            + searchTable.nodesIndexed().size()
-            + " "
-            + neighbours.length);
-
-    if (kadOps.get(op) != null) {
-      if (!kadOps.get(op).completed()) {
-        logger.info("Sampling operation found");
-        doSampling(kadOps.get(op));
-      }
-    }
+    // searchTable.addNodes(list.toArray(new BigInteger[0]));
+    /*for (BigInteger id : list) {
+      Node n = Util.nodeIdtoNode(id, kademliaId);
+      searchTable.addNeighbour(new Neighbour(id, n, n.getDASProtocol().isEvil()));
+    }*/
   }
 
   @Override
   public void missing(BigInteger sample, Operation op) {
 
-    logger.warning("Missing nodes for sample " + sample + " " + kadOps.size());
-
-    List<BigInteger> ids =
-        searchTable.getValidatorNodesbySample(
-            sample,
-            currentBlock.computeRegionRadius(
-                KademliaCommonConfigDas.NUM_SAMPLE_COPIES_PER_PEER, validatorsList.length));
-    for (BigInteger id : ids) {
-      logger.warning("Found id " + id + " for sample " + sample);
-    }
-  }
-
-  // ______________________________________________________________________________________________
-  /**
-   * generates a GET message for a specific sample.
-   *
-   * @return Message
-   */
-  protected Message generateSeedSampleMessage(Sample[] s) {
-
-    Message m = new Message(Message.MSG_SEED_SAMPLE, s);
-    m.timestamp = CommonState.getTime();
-
-    return m;
+    logger.info("Missing nodes for sample " + sample + " " + kadOps.size());
+    // missing = true;
   }
 
   // ______________________________________________________________________________________________
@@ -725,11 +929,15 @@ protected Message generateSeedSampleMessage(Sample[] s) {
    *
    * @return Message
    */
-  protected Message generateNewSampleMessage(BigInteger s) {
+  protected Message generateNewSampleMessage(BigInteger[] s) {
 
     Message m = Message.makeInitGetSample(s);
     m.timestamp = CommonState.getTime();
 
     return m;
   }
+
+  public void refreshSearchTable() {
+    // searchTable.refresh();
+  }
 }
diff --git a/simulator/src/main/java/peersim/kademlia/das/DASProtocolBuilder.java b/simulator/src/main/java/peersim/kademlia/das/DASProtocolBuilder.java
index a278b464..f9153b75 100644
--- a/simulator/src/main/java/peersim/kademlia/das/DASProtocolBuilder.java
+++ b/simulator/src/main/java/peersim/kademlia/das/DASProtocolBuilder.java
@@ -2,10 +2,11 @@
 
 import java.math.BigInteger;
 import java.util.ArrayList;
-import java.util.Collection;
+import java.util.HashSet;
 import java.util.List;
+import java.util.Set;
+import peersim.core.CommonState;
 import peersim.core.Node;
-import peersim.edsim.EDSimulator;
 import peersim.kademlia.Message;
 import peersim.kademlia.Util;
 
@@ -21,114 +22,220 @@ public DASProtocolBuilder(String prefix) {
   }
 
   @Override
-  protected void handleGetSample(Message m, int myPid) {
-    /** Ignore sample request * */
-    logger.warning("Builder handle get sample - return nothing " + this);
-  }
-
-  @Override
-  protected void handleSeedSample(Message m, int myPid) {
-    System.err.println("Builder should not receive seed sample");
+  protected void handleInitGetSample(Message m, int myPid) {
+    logger.warning("Init block  builder node - getting samples " + this);
+    System.err.println("Wrong eventInit block  builder node - getting samples ");
     System.exit(-1);
   }
 
   @Override
   protected void handleInitNewBlock(Message m, int myPid) {
     super.handleInitNewBlock(m, myPid);
-    logger.warning(
-        "Builder new block:"
-            + currentBlock.getBlockId()
-            + " "
-            + validatorsList.length
-            + " "
-            + nonValidatorsIndexed.size());
-
-    int samplesWithinRegion = 0; // samples that are within at least one node's region
-    int samplesValidators = 0;
-    int samplesNonValidators = 0;
-
-    while (currentBlock.hasNext()) {
-      Sample s = currentBlock.next();
-      boolean inRegion = false;
-
-      BigInteger radiusNonValidator =
-          currentBlock.computeRegionRadius(KademliaCommonConfigDas.NUM_SAMPLE_COPIES_PER_PEER);
-
-      List<BigInteger> idsValidatorsRows = SearchTable.getNodesBySample(s.getIdByRow());
-      List<BigInteger> idsValidatorsColumns =
-          // searchTable.getValidatorNodesbySample(s.getIdByColumn(), radiusValidator);
-          SearchTable.getNodesBySample(s.getIdByColumn());
-
-      List<BigInteger> idsNonValidatorsRows =
-          getNonValidatorNodesbySample(s.getIdByRow(), radiusNonValidator);
-      List<BigInteger> idsNonValidatorsColumns =
-          getNonValidatorNodesbySample(s.getIdByColumn(), radiusNonValidator);
+    logger.warning("Builder new block:" + currentBlock.getBlockId());
 
+    rowSeeding();
+    columnSeeding();
+  }
+
+  // Generating specific messages to be sent
+  protected Message generateSeedSampleMessage(
+      Sample[] s, List<BigInteger> validators, boolean isRow) {
+    SeedingSampleBody body = new SeedingSampleBody(s, validators, isRow);
+    Message m = new Message(Message.MSG_SEED_SAMPLE, body);
+    m.timestamp = CommonState.getTime();
+
+    return m;
+  }
+
+  private void rowSeeding() {
+    // ===============
+    // Row Seeding
+    // ===============
+    int actualRow = 1;
+    while (currentBlock.getSize() >= actualRow) {
+
+      Sample[] sampleRow = currentBlock.getSamplesByRow(actualRow); // get all sample of the row
+      BigInteger radiusValidator =
+          currentBlock.computeRegionRadius(1, searchTable.getValidatorsIndexed().size());
+
+      // Get the id of all validators we need to send the message
       List<BigInteger> idsValidators = new ArrayList<>();
-      idsValidators.addAll(idsValidatorsRows);
-      idsValidators.addAll(idsValidatorsColumns);
+      for (Sample sample : sampleRow) {
+        List<BigInteger> ids =
+            searchTable.getValidatorNodesbySample(sample.getIdByRow(), radiusValidator);
+        if (ids != null && ids.size() > 0) idsValidators.addAll(ids);
+      }
+
+      if (idsValidators.size() == 0) {
+        actualRow++;
+        continue;
+      }
+      // remove duplicate
+      Set<BigInteger> set = new HashSet<>(idsValidators);
+      idsValidators = new ArrayList<>(set);
+
+      int numberValidatorRow = idsValidators.size(); // Get the number of validators
+      logger.warning(
+          "Block "
+              + currentBlock.getBlockId()
+              + " Number of Validator for row"
+              + actualRow
+              + " is: "
+              + numberValidatorRow
+              + " radius "
+              + radiusValidator
+              + " validators "
+              + searchTable.getValidatorsIndexed().size()
+              + " row "
+              + sampleRow.length);
 
-      List<BigInteger> idsNonValidators = new ArrayList<>();
-      idsNonValidators.addAll(idsNonValidatorsRows);
-      idsNonValidators.addAll(idsNonValidatorsColumns);
+      // Get size of Parcels to send
+      int sizeParcels = 0;
+      sizeParcels = (currentBlock.getSize() / numberValidatorRow);
+      int redundancyFactor = 1;
 
-      /*  + " "
-      + +idsNonValidators.size());*/
+      int indexSampleList = 0;
 
       for (BigInteger id : idsValidators) {
 
-        logger.warning("Sending sample " + s.getIdByRow() + " " + s.getIdByColumn() + " to " + id);
+        // --------------------------
+        // Create Row Parcels to send
+        // --------------------------
+
+        Sample[] validatorParcel = new Sample[sizeParcels * redundancyFactor];
+        int k = 0;
+        while (k != sizeParcels * redundancyFactor) {
+          Sample s = sampleRow[indexSampleList % sampleRow.length];
+          validatorParcel[k] = s;
+          indexSampleList++;
+          k++;
+        }
+
+        // ----------------
+        // Send Row Parcels
+        // ----------------
+
+        logger.warning(
+            "Sending row "
+                + actualRow
+                + " "
+                + "parcel to validator "
+                + id
+                + " samples "
+                + validatorParcel.length);
         Node n = Util.nodeIdtoNode(id, kademliaId);
         DASProtocol dasProt = ((DASProtocol) (n.getDASProtocol()));
         if (dasProt.isBuilder()) continue;
         if (n.isUp()) {
-          Sample[] samples = {s};
-          Message msg = generateSeedSampleMessage(samples);
+          Sample[] samples = validatorParcel;
+          Message msg = generateSeedSampleMessage(samples, idsValidators, true);
           msg.operationId = -1;
           msg.src = this.getKademliaProtocol().getKademliaNode();
           msg.dst = n.getKademliaProtocol().getKademliaNode();
           sendMessage(msg, id, dasProt.getDASProtocolID());
-          samplesValidators++;
-          if (inRegion == false) {
-            samplesWithinRegion++;
-            inRegion = true;
-          }
+          // samplesValidators++;
         }
       }
 
-      for (BigInteger id : idsNonValidators) {
+      // samplesWithinRegion += sampleRow.length;
+      actualRow++;
+    }
+  }
+
+  private void columnSeeding() {
+    // ===============
+    // Column Seeding
+    // ===============
+    int actualColumn = 1;
+    while (currentBlock.getSize() >= actualColumn) {
+
+      Sample[] sampleColumn =
+          currentBlock.getSamplesByColumn(actualColumn); // get all sample of the column
+      BigInteger radiusValidator =
+          currentBlock.computeRegionRadius(1, searchTable.getValidatorsIndexed().size());
+
+      // Get the id of all validators we need to send the message
+      List<BigInteger> idsValidators = new ArrayList<>();
+      for (Sample sample : sampleColumn) {
+        List<BigInteger> ids =
+            searchTable.getValidatorNodesbySample(sample.getIdByColumn(), radiusValidator);
+        if (ids != null && ids.size() > 0) idsValidators.addAll(ids);
+      }
+
+      if (idsValidators.size() == 0) {
+        actualColumn++;
+        continue;
+      }
+      // remove duplicate
+      Set<BigInteger> set = new HashSet<>(idsValidators);
+      idsValidators = new ArrayList<>(set);
+
+      int numberValidatorColumn = idsValidators.size(); // Get the number of validators
+      logger.warning(
+          "Block "
+              + currentBlock.getBlockId()
+              + " Number of Validator for column"
+              + actualColumn
+              + " is: "
+              + numberValidatorColumn
+              + " radius "
+              + radiusValidator
+              + " validators "
+              + searchTable.getValidatorsIndexed().size()
+              + " column "
+              + sampleColumn.length);
+
+      // Get size of Parcels to send
+      int sizeParcels = 0;
+      sizeParcels = (currentBlock.getSize() / numberValidatorColumn);
+      int redundancyFactor = 1;
+
+      int indexSampleList = 0;
+
+      for (BigInteger id : idsValidators) {
+
+        // --------------------------
+        // Create column Parcels to send
+        // --------------------------
+
+        Sample[] validatorParcel = new Sample[sizeParcels * redundancyFactor];
+        int k = 0;
+        while (k != sizeParcels * redundancyFactor) {
+          Sample s = sampleColumn[indexSampleList % sampleColumn.length];
+          validatorParcel[k] = s;
+          indexSampleList++;
+          k++;
+        }
+
+        // ----------------
+        // Send column Parcels
+        // ----------------
+
+        logger.warning(
+            "Sending column "
+                + actualColumn
+                + " "
+                + "parcel to validator "
+                + id
+                + " samples "
+                + validatorParcel.length);
         Node n = Util.nodeIdtoNode(id, kademliaId);
         DASProtocol dasProt = ((DASProtocol) (n.getDASProtocol()));
         if (dasProt.isBuilder()) continue;
         if (n.isUp()) {
-          if (inRegion == false) {
-            samplesWithinRegion++;
-            inRegion = true;
-          }
-          samplesNonValidators++;
-
-          if (!dasProt.isValidator()) {
-            EDSimulator.add(2, generateNewSampleMessage(s.getId()), n, dasProt.getDASProtocolID());
-          }
+          Sample[] samples = validatorParcel;
+          Message msg = generateSeedSampleMessage(samples, idsValidators, false);
+          msg.operationId = -1;
+          msg.src = this.getKademliaProtocol().getKademliaNode();
+          msg.dst = n.getKademliaProtocol().getKademliaNode();
+          sendMessage(msg, id, dasProt.getDASProtocolID());
+          // samplesValidators++;
         }
       }
-    }
 
-    logger.warning(
-        samplesWithinRegion
-            + " samples out of "
-            + currentBlock.getNumSamples()
-            + " samples are within a node's region"
-            + " "
-            + samplesValidators
-            + " "
-            + samplesNonValidators);
-  }
-
-  @Override
-  protected void handleInitGetSample(Message m, int myPid) {
-    logger.warning("Error. Init block builder node - getting samples. do nothing " + this);
-    // super.handleInitGetSample(m, myPid);
+      // samplesWithinRegion += sampleRow.length;
+      actualColumn++;
+    }
   }
 
   @Override
@@ -136,11 +243,6 @@ protected void handleGetSampleResponse(Message m, int myPid) {
     logger.warning("Received sample builder node: do nothing");
   }
 
-  public void setNonValidators(List<BigInteger> nonValidators) {
-    for (BigInteger id : nonValidators) {
-      nonValidatorsIndexed.add(id);
-    }
-  }
   /**
    * Replicate this object by returning an identical copy.<br>
    * It is called by the initializer and do not fill any particular field.
@@ -151,18 +253,4 @@ public Object clone() {
     DASProtocolBuilder dolly = new DASProtocolBuilder(DASProtocolBuilder.prefix);
     return dolly;
   }
-
-  private List<BigInteger> getNonValidatorNodesbySample(BigInteger sampleId, BigInteger radius) {
-
-    BigInteger bottom = sampleId.subtract(radius);
-    if (radius.compareTo(sampleId) == 1) bottom = BigInteger.ZERO;
-
-    BigInteger top = sampleId.add(radius);
-
-    Collection<BigInteger> subSet = nonValidatorsIndexed.subSet(bottom, true, top, true);
-    return new ArrayList<BigInteger>(subSet);
-
-    // return sampleMap.get(sampleId);
-
-  }
 }
diff --git a/simulator/src/main/java/peersim/kademlia/das/DASProtocolEvilNonValidator.java b/simulator/src/main/java/peersim/kademlia/das/DASProtocolEvilNonValidator.java
new file mode 100644
index 00000000..8a735e3f
--- /dev/null
+++ b/simulator/src/main/java/peersim/kademlia/das/DASProtocolEvilNonValidator.java
@@ -0,0 +1,47 @@
+package peersim.kademlia.das;
+
+import peersim.kademlia.Message;
+
+public class DASProtocolEvilNonValidator extends DASProtocolNonValidator {
+
+  protected static String prefix = null;
+
+  public DASProtocolEvilNonValidator(String prefix) {
+    super(prefix);
+    DASProtocolEvilNonValidator.prefix = prefix;
+    isEvil = true;
+    isValidator = false;
+    isBuilder = false;
+  }
+
+  @Override
+  protected void handleInitGetSample(Message m, int myPid) {
+    logger.warning("Init block evil node validator - getting samples " + this);
+    // super.handleInitGetSample(m, myPid);
+  }
+
+  /*protected void handleGetSample(Message m, int myPid) {
+    // kv is for storing the sample you have
+    logger.info("KV size " + kv.occupancy() + " from:" + m.src.getId() + " " + m.id);
+    // sample IDs that are requested in the message
+
+    Message response = new Message(Message.MSG_GET_SAMPLE_RESPONSE, new Sample[] {});
+    response.operationId = m.operationId;
+    response.dst = m.src;
+    response.src = this.kadProtocol.getKademliaNode();
+    response.ackId = m.id; // set ACK number
+    response.value = searchTable.getEvilNeighbours(KademliaCommonConfigDas.MAX_NODES_RETURNED);
+    sendMessage(response, m.src.getId(), myPid);
+  }*/
+  /**
+   * Replicate this object by returning an identical copy.<br>
+   * It is called by the initializer and do not fill any particular field.
+   *
+   * @return Object
+   */
+  public Object clone() {
+    DASProtocolEvilNonValidator dolly =
+        new DASProtocolEvilNonValidator(DASProtocolEvilNonValidator.prefix);
+    return dolly;
+  }
+}
diff --git a/simulator/src/main/java/peersim/kademlia/das/DASProtocolEvilValidator.java b/simulator/src/main/java/peersim/kademlia/das/DASProtocolEvilValidator.java
new file mode 100644
index 00000000..8115be81
--- /dev/null
+++ b/simulator/src/main/java/peersim/kademlia/das/DASProtocolEvilValidator.java
@@ -0,0 +1,53 @@
+package peersim.kademlia.das;
+
+import java.util.List;
+import peersim.core.Node;
+import peersim.kademlia.Message;
+
+public class DASProtocolEvilValidator extends DASProtocolValidator {
+
+  protected static String prefix = null;
+
+  public DASProtocolEvilValidator(String prefix) {
+    super(prefix);
+    DASProtocolEvilValidator.prefix = prefix;
+    isEvil = true;
+    isValidator = true;
+    isBuilder = false;
+  }
+
+  @Override
+  protected void handleInitGetSample(Message m, int myPid) {
+    logger.warning("Init block evil node validator - getting samples " + this);
+    // super.handleInitGetSample(m, myPid);
+  }
+
+  /*protected void handleGetSample(Message m, int myPid) {
+    // kv is for storing the sample you have
+    logger.info("KV size " + kv.occupancy() + " from:" + m.src.getId() + " " + m.id);
+    // sample IDs that are requested in the message
+
+    Message response = new Message(Message.MSG_GET_SAMPLE_RESPONSE, new Sample[] {});
+    response.operationId = m.operationId;
+    response.dst = m.src;
+    response.src = this.kadProtocol.getKademliaNode();
+    response.ackId = m.id; // set ACK number
+    response.value = searchTable.getEvilNeighbours(KademliaCommonConfigDas.MAX_NODES_RETURNED);
+    sendMessage(response, m.src.getId(), myPid);
+  }*/
+
+  public void setEvil(List<Node> evilIds) {
+    searchTable.setEvil(evilIds);
+  }
+
+  /**
+   * Replicate this object by returning an identical copy.<br>
+   * It is called by the initializer and do not fill any particular field.
+   *
+   * @return Object
+   */
+  public Object clone() {
+    DASProtocolEvilValidator dolly = new DASProtocolEvilValidator(DASProtocolEvilValidator.prefix);
+    return dolly;
+  }
+}
diff --git a/simulator/src/main/java/peersim/kademlia/das/DASProtocolNonValidator.java b/simulator/src/main/java/peersim/kademlia/das/DASProtocolNonValidator.java
index 614e1336..d9a72fa7 100644
--- a/simulator/src/main/java/peersim/kademlia/das/DASProtocolNonValidator.java
+++ b/simulator/src/main/java/peersim/kademlia/das/DASProtocolNonValidator.java
@@ -1,7 +1,8 @@
 package peersim.kademlia.das;
 
 import java.math.BigInteger;
-import java.util.HashSet;
+import java.util.ArrayList;
+import java.util.List;
 import peersim.core.Node;
 import peersim.kademlia.Message;
 import peersim.kademlia.Util;
@@ -9,53 +10,43 @@
 public class DASProtocolNonValidator extends DASProtocol {
 
   protected static String prefix = null;
-
-  protected HashSet<BigInteger> reqSamples;
+  protected List<BigInteger> validatorsContacted;
 
   public DASProtocolNonValidator(String prefix) {
     super(prefix);
     DASProtocolNonValidator.prefix = prefix;
     isValidator = false;
     isBuilder = false;
-    reqSamples = new HashSet<>();
-  }
-
-  @Override
-  protected void handleSeedSample(Message m, int myPid) {
-    logger.warning("Non-validator should not receive seed sample");
-    System.exit(-1);
-    /*if (m.body == null) return;
-
-    Sample[] samples = (Sample[]) m.body;
-    for (Sample s : samples) {
-      logger.warning("Received sample:" + kv.occupancy() + " " + s.getRow() + " " + s.getColumn());
-
-      kv.add((BigInteger) s.getIdByRow(), s);
-      kv.add((BigInteger) s.getIdByColumn(), s);
-      // count # of samples for each row and column
-      column[s.getColumn() - 1]++;
-      row[s.getRow() - 1]++;
-    }*/
+    validatorsContacted = new ArrayList<>();
   }
 
   @Override
   protected void handleInitGetSample(Message m, int myPid) {
-    logger.warning("Init block non-validator node - getting samples " + this);
-    // super.handleInitGetSample(m, myPid);
-    BigInteger[] samples = {(BigInteger) m.body};
+    if (!init) return;
+    if (currentBlock == null) System.err.println("Error block not init yet");
+    BigInteger[] samples = (BigInteger[]) m.body;
     BigInteger radius =
         currentBlock.computeRegionRadius(
-            KademliaCommonConfigDas.NUM_SAMPLE_COPIES_PER_PEER, validatorsList.length);
+            KademliaCommonConfigDas.NUM_SAMPLE_COPIES_PER_PEER,
+            KademliaCommonConfigDas.validatorsSize);
     for (BigInteger sample : samples) {
-      if (!reqSamples.contains(sample)) {
-        for (BigInteger id : searchTable.getValidatorNodesbySample(sample, radius)) {
+      logger.warning("Init block non-validator node - getting samples " + sample);
+      List<BigInteger> nodes = searchTable.getValidatorNodesbySample(sample, radius);
+      if (nodes.size() == 0) {
+        // logger.warning("Sample not found");
+        nodes = new ArrayList<>();
+        nodes.add(builderAddress);
+      }
+      for (BigInteger id : nodes) {
+        if (!validatorsContacted.contains(id)) {
           Message msg = generateGetSampleMessage(samples);
           msg.operationId = -1;
           msg.src = this.kadProtocol.getKademliaNode();
           Node n = Util.nodeIdtoNode(id, kademliaId);
           msg.dst = n.getKademliaProtocol().getKademliaNode();
           sendMessage(msg, id, myPid);
-          reqSamples.add(sample);
+          validatorsContacted.add(id);
+          // break;
         }
       }
     }
@@ -64,11 +55,17 @@ protected void handleInitGetSample(Message m, int myPid) {
   @Override
   protected void handleInitNewBlock(Message m, int myPid) {
     logger.warning("Init block non-validator node - start sampling " + this);
+    // super.handleInitNewBlock(m, myPid);
+    validatorsContacted.clear();
     super.handleInitNewBlock(m, myPid);
-    logger.warning("Starting random sampling");
-    startRandomSampling();
+    if (!isEvil) {
+      startRandomSampling();
+    }
   }
 
+  public void setEvil(List<Node> evilIds) {
+    searchTable.setEvil(evilIds);
+  }
   /**
    * Replicate this object by returning an identical copy.<br>
    * It is called by the initializer and do not fill any particular field.
diff --git a/simulator/src/main/java/peersim/kademlia/das/DASProtocolValidator.java b/simulator/src/main/java/peersim/kademlia/das/DASProtocolValidator.java
index 1d1c16d2..7a5d4b84 100644
--- a/simulator/src/main/java/peersim/kademlia/das/DASProtocolValidator.java
+++ b/simulator/src/main/java/peersim/kademlia/das/DASProtocolValidator.java
@@ -1,61 +1,86 @@
 package peersim.kademlia.das;
 
 import java.math.BigInteger;
-import peersim.core.CommonState;
+import java.util.ArrayList;
+import java.util.List;
+import peersim.core.Node;
 import peersim.kademlia.Message;
+import peersim.kademlia.Util;
 import peersim.kademlia.das.operations.ValidatorSamplingOperation;
 
 public class DASProtocolValidator extends DASProtocol {
 
   protected static String prefix = null;
+  protected boolean started;
 
   public DASProtocolValidator(String prefix) {
     super(prefix);
     DASProtocolValidator.prefix = prefix;
     isValidator = true;
     isBuilder = false;
+    started = false;
+  }
+
+  @Override
+  protected void handleInitGetSample(Message m, int myPid) {
+    if (!init) return;
+    logger.warning("Init block validator node - getting samples " + this);
+    if (currentBlock == null) System.err.println("Error block not init yet");
+    BigInteger[] samples = (BigInteger[]) m.body;
+
+    Message msg = generateGetSampleMessage(samples);
+    msg.operationId = -1;
+    msg.src = this.kadProtocol.getKademliaNode();
+    Node n = Util.nodeIdtoNode(builderAddress, kademliaId);
+    msg.dst = n.getKademliaProtocol().getKademliaNode();
+    sendMessage(msg, builderAddress, myPid);
   }
 
   @Override
   protected void handleSeedSample(Message m, int myPid) {
-    logger.warning("seed sample receveived");
-    if (m.body == null) return;
 
-    Sample[] samples = (Sample[]) m.body;
+    SeedingSampleBody body = (SeedingSampleBody) m.body;
+    Sample[] samples = (Sample[]) body.getsamplesList();
+    logger.warning("Seed received " + samples.length + " samples.");
     for (Sample s : samples) {
       logger.warning(
-          "Received sample:"
-              + kv.occupancy()
-              + " "
-              + s.getRow()
+          "Sample received "
+              + s.getId()
               + " "
-              + s.getColumn()
+              + s.getIdByColumn()
+              + " from "
+              + m.src.getId()
               + " "
-              + s.getIdByRow()
-              + " "
-              + s.getIdByColumn());
+              + m.id);
 
-      kv.add((BigInteger) s.getIdByRow(), s);
-      kv.add((BigInteger) s.getIdByColumn(), s);
-      // count # of samples for each row and column
-      column[s.getColumn() - 1]++;
-      row[s.getRow() - 1]++;
+      kv.add((BigInteger) s.getIdByRow());
+      // kv.add((BigInteger) s.getIdByRow(), s);
+      // kv.add((BigInteger) s.getIdByColumn(), s);
+      // count # of samples for each row and column and reconstruct if more than half received
+      reconstruct(s);
     }
-  }
-
-  @Override
-  protected void handleInitGetSample(Message m, int myPid) {
-    logger.warning("Error. Init block validator node - getting samples. do nothing " + this);
-    // super.handleInitGetSample(m, myPid);
+    List<BigInteger> validatorList = body.getValidators();
+    boolean isRow = body.getIsRow();
+    if (isRow) {
+      createValidatorSamplingOperation(samples[0].getRow(), 0, time, validatorList);
+    } else {
+      createValidatorSamplingOperation(0, samples[0].getColumn(), time, validatorList);
+    }
+    /*if (!started) {
+      started = true;
+      startRowsandColumnsSampling();
+    }*/
   }
 
   @Override
   protected void handleInitNewBlock(Message m, int myPid) {
     super.handleInitNewBlock(m, myPid);
-    logger.warning("Starting validator (rows and columns) sampling");
-    startRowsandColumnsSampling();
-    logger.warning("Starting random sampling");
-    startRandomSampling();
+    started = false;
+    if (!isEvil) startRandomSampling();
+    /*if (!isEvil) {
+      startRowsandColumnsSampling();
+      startRandomSampling();
+    }*/
   }
 
   /**
@@ -77,17 +102,46 @@ protected void startRowsandColumnsSampling() {
 
     // start 2 row 2 column Validator operation (1 row/column with the highest number of samples
     // already downloaded and another random)
+    /*createValidatorSamplingOperation(
+        currentBlock.findClosestRow(
+            this.getKademliaId(),
+            currentBlock.computeRegionRadius(
+                KademliaCommonConfigDas.NUM_SAMPLE_COPIES_PER_PEER,
+                KademliaCommonConfigDas.validatorsSize)),
+        0,
+        time);
     createValidatorSamplingOperation(
-        CommonState.r.nextInt(KademliaCommonConfigDas.BLOCK_DIM_SIZE) + 1, 0, time);
-    createValidatorSamplingOperation(
-        0, CommonState.r.nextInt(KademliaCommonConfigDas.BLOCK_DIM_SIZE) + 1, time);
-    createValidatorSamplingOperation(
-        CommonState.r.nextInt(KademliaCommonConfigDas.BLOCK_DIM_SIZE) + 1, 0, time);
-    createValidatorSamplingOperation(
-        0, CommonState.r.nextInt(KademliaCommonConfigDas.BLOCK_DIM_SIZE) + 1, time);
+        0,
+        currentBlock.findClosestColumn(
+            this.getKademliaId(),
+            currentBlock.computeRegionRadius(
+                KademliaCommonConfigDas.NUM_SAMPLE_COPIES_PER_PEER,
+                KademliaCommonConfigDas.validatorsSize)),
+        time);*/
+    // createValidatorSamplingOperation(
+    //     CommonState.r.nextInt(KademliaCommonConfigDas.BLOCK_DIM_SIZE) + 1, 0, time);
+    // createValidatorSamplingOperation(
+    //     0, CommonState.r.nextInt(KademliaCommonConfigDas.BLOCK_DIM_SIZE) + 1, time);
+    // createValidatorSamplingOperation(
+    //     CommonState.r.nextInt(KademliaCommonConfigDas.BLOCK_DIM_SIZE) + 1, 0, time);
+    //  createValidatorSamplingOperation(
+    //      0, CommonState.r.nextInt(KademliaCommonConfigDas.BLOCK_DIM_SIZE) + 1, time);
+
+    /*int row = searchTable.getValidatorRow(this.getKademliaId());
+    if (row == 0) {
+      row = CommonState.r.nextInt(currentBlock.getSize()) + 1;
+    }
+    int column = searchTable.getValidatorColumn(this.getKademliaId());
+    if (column == 0) {
+      column = CommonState.r.nextInt(currentBlock.getSize()) + 1;
+    }
+
+    createValidatorSamplingOperation(row, 0, time);*/
+    // createValidatorSamplingOperation(0, column, time);
   }
 
-  private void createValidatorSamplingOperation(int row, int column, long timestamp) {
+  private void createValidatorSamplingOperation(
+      int row, int column, long timestamp, List<BigInteger> validatorList) {
     ValidatorSamplingOperation op =
         new ValidatorSamplingOperation(
             this.getKademliaId(),
@@ -97,12 +151,30 @@ private void createValidatorSamplingOperation(int row, int column, long timestam
             row,
             column,
             this.isValidator,
-            validatorsList.length,
+            KademliaCommonConfigDas.validatorsSize,
+            validatorList,
             this);
     samplingOp.put(op.getId(), op);
     logger.warning("Sampling operation started validator " + op.getId());
 
-    op.elaborateResponse(kv.getAll().toArray(new Sample[0]));
+    List<Sample> samplesFound = new ArrayList<>();
+    if (row > 0) {
+      Sample[] samples = currentBlock.getSamplesByRow(row);
+      for (Sample s : samples) {
+        if (kv.contains(s.getIdByRow())) {
+          samplesFound.add(s);
+        }
+      }
+    } else {
+      Sample[] samples = currentBlock.getSamplesByColumn(column);
+      for (Sample s : samples) {
+        if (kv.contains(s.getIdByColumn())) {
+          samplesFound.add(s);
+        }
+      }
+    }
+    op.elaborateResponse(samplesFound.toArray(new Sample[0]));
+    // op.elaborateResponse(kv.getAll().toArray(new Sample[0]));
     doSampling(op);
   }
 
diff --git a/simulator/src/main/java/peersim/kademlia/das/EvilDASProtocol.java b/simulator/src/main/java/peersim/kademlia/das/EvilDASProtocol.java
deleted file mode 100644
index 978292bb..00000000
--- a/simulator/src/main/java/peersim/kademlia/das/EvilDASProtocol.java
+++ /dev/null
@@ -1,54 +0,0 @@
-package peersim.kademlia.das;
-
-import peersim.kademlia.Message;
-
-public class EvilDASProtocol extends DASProtocol {
-
-  protected static String prefix = null;
-
-  public EvilDASProtocol(String prefix) {
-    super(prefix);
-    EvilDASProtocol.prefix = prefix;
-  }
-
-  @Override
-  protected void handleSeedSample(Message m, int myPid) {
-    logger.warning("seed sample receveived - do nothing " + this);
-  }
-
-  @Override
-  protected void handleGetSample(Message m, int myPid) {
-    /** Ignore sample request * */
-    logger.warning("Handle get sample - return nothing " + this);
-  }
-
-  @Override
-  protected void handleInitNewBlock(Message m, int myPid) {
-    logger.warning("Init block evil node - do nothing");
-  }
-
-  @Override
-  protected void handleInitGetSample(Message m, int myPid) {
-    logger.warning("Init block evil node - getting samples " + this);
-    // super.handleInitGetSample(m, myPid);
-  }
-
-  @Override
-  protected void handleGetSampleResponse(Message m, int myPid) {
-    logger.warning("Received sample evil node: do nothing");
-  }
-
-  /*public void processEvent(Node myNode, int myPid, Object event) {
-    logger.warning("Process event " + myPid + " " + this);
-  }*/
-  /**
-   * Replicate this object by returning an identical copy.<br>
-   * It is called by the initializer and do not fill any particular field.
-   *
-   * @return Object
-   */
-  public Object clone() {
-    EvilDASProtocol dolly = new EvilDASProtocol(EvilDASProtocol.prefix);
-    return dolly;
-  }
-}
diff --git a/simulator/src/main/java/peersim/kademlia/das/KademliaCommonConfigDas.java b/simulator/src/main/java/peersim/kademlia/das/KademliaCommonConfigDas.java
index 92db762f..b6f6a643 100644
--- a/simulator/src/main/java/peersim/kademlia/das/KademliaCommonConfigDas.java
+++ b/simulator/src/main/java/peersim/kademlia/das/KademliaCommonConfigDas.java
@@ -1,5 +1,7 @@
 package peersim.kademlia.das;
 
+import peersim.kademlia.KademliaCommonConfig;
+
 /**
  * Fixed Parameters for the DAS protocol.
  *
@@ -25,6 +27,8 @@ public class KademliaCommonConfigDas {
   /** Number of samples retrieved for the random sampling */
   public static int N_SAMPLES = 75;
 
+  public static int MAX_SAMPLING_FAILED = 0;
+
   public static int PARCEL_SIZE = 128;
   /**
    * Size of a node record (a single neighbor information returned alongside samples in
@@ -33,21 +37,21 @@ public class KademliaCommonConfigDas {
   public static double NODE_RECORD_SIZE = 0.0024;
 
   /** Size of a sample in Mbits - each cell contains 512 B of data + 48 B KZG commitment */
-  public static double SAMPLE_SIZE = 0.00448;
+  public static double SAMPLE_SIZE = 0.004096;
 
   /** Number of samples returned by a single node */
   public static int MAX_SAMPLES_RETURNED = 1000;
 
-  public static int MAX_NODES_RETURNED = 15;
+  public static int MAX_NODES_RETURNED = KademliaCommonConfig.K;
 
   /** Number of max hops during a sampling operation */
   public static int MAX_HOPS = 5000;
 
   /** Default upload bandwith of a validator in Mbits/sec */
-  public static int VALIDATOR_UPLOAD_RATE = 10000;
+  public static int VALIDATOR_UPLOAD_RATE = 1000;
 
   /** Default upload bandwith of a non-validator in Mbits/sec */
-  public static int NON_VALIDATOR_UPLOAD_RATE = 10000;
+  public static int NON_VALIDATOR_UPLOAD_RATE = 100;
 
   public static int BUILDER_UPLOAD_RATE = 10000;
 
@@ -59,4 +63,6 @@ public class KademliaCommonConfigDas {
 
   public static int validatorsSize = 0;
   public static int networkSize = 0;
+
+  public static long TTL = 30000;
 }
diff --git a/simulator/src/main/java/peersim/kademlia/das/Neighbour.java b/simulator/src/main/java/peersim/kademlia/das/Neighbour.java
new file mode 100644
index 00000000..c531215e
--- /dev/null
+++ b/simulator/src/main/java/peersim/kademlia/das/Neighbour.java
@@ -0,0 +1,66 @@
+package peersim.kademlia.das;
+
+import java.math.BigInteger;
+import peersim.core.CommonState;
+import peersim.core.Node;
+
+public class Neighbour implements Comparable<Neighbour> {
+
+  protected BigInteger id;
+  protected long last_seen;
+  protected Node n;
+  protected boolean isEvil;
+
+  Neighbour(BigInteger id, Node n, boolean isEvil) {
+    this.id = id;
+    this.last_seen = CommonState.getTime();
+    this.n = n;
+    this.isEvil = isEvil;
+  }
+
+  public BigInteger getId() {
+    return id;
+  }
+
+  public long getLastSeen() {
+    return last_seen;
+  }
+
+  public void updateLastSeen(long time) {
+    last_seen = time;
+  }
+
+  public boolean expired() {
+    if (CommonState.getTime() - last_seen >= KademliaCommonConfigDas.TTL) return true;
+    else return false;
+  }
+
+  public Node getNode() {
+    return n;
+  }
+
+  public boolean isEvil() {
+    return isEvil;
+  }
+
+  @Override
+  public int compareTo(Neighbour arg0) {
+    if (this.last_seen < arg0.last_seen) return 1;
+    else if (this.last_seen == arg0.last_seen) {
+      return 0;
+    } else {
+      return -1;
+    }
+  }
+
+  @Override
+  public boolean equals(Object object) {
+    boolean sameSame = false;
+
+    if (object != null && object instanceof Neighbour) {
+      sameSame = this.id.compareTo(((Neighbour) object).id) == 0;
+    }
+
+    return sameSame;
+  }
+}
diff --git a/simulator/src/main/java/peersim/kademlia/das/RefreshSearchTable.java b/simulator/src/main/java/peersim/kademlia/das/RefreshSearchTable.java
new file mode 100644
index 00000000..c2ca0923
--- /dev/null
+++ b/simulator/src/main/java/peersim/kademlia/das/RefreshSearchTable.java
@@ -0,0 +1,27 @@
+package peersim.kademlia.das;
+
+import peersim.core.Control;
+import peersim.core.Network;
+import peersim.core.Node;
+
+public class RefreshSearchTable implements Control {
+  private String prefix;
+
+  // ______________________________________________________________________________________________
+  public RefreshSearchTable(String prefix) {
+    this.prefix = prefix;
+  }
+
+  // ______________________________________________________________________________________________
+  public boolean execute() {
+    for (int i = 0; i < Network.size(); i++) {
+      Node iNode = Network.get(i);
+      if (iNode.getFailState() == Node.OK) {
+        DASProtocol das = iNode.getDASProtocol();
+        das.refreshSearchTable();
+      }
+    }
+
+    return false;
+  }
+}
diff --git a/simulator/src/main/java/peersim/kademlia/das/Sample.java b/simulator/src/main/java/peersim/kademlia/das/Sample.java
index a4ff4baf..432adc06 100644
--- a/simulator/src/main/java/peersim/kademlia/das/Sample.java
+++ b/simulator/src/main/java/peersim/kademlia/das/Sample.java
@@ -9,45 +9,38 @@ public class Sample {
 
   /** Row and column numbers of a sample within a block */
   private int row, column;
-  /** The unique ID of the block that this sample belongs to */
-  private long blockId;
-
   /** The key of a sample in the DHT keyspace using rows number as reference */
   private BigInteger idByRow;
   /** The key of a sample in the DHT keyspace using column number as reference */
   private BigInteger idByColumn;
-  /** Block that this sample is part of */
-  private Block block;
 
   /** Initialise a sample instance and map it to the keyspace */
   public Sample(long blockId, int row, int column, Block b) {
 
     this.idByColumn = this.idByRow = null;
-    this.block = b;
     this.row = row;
     this.column = column;
-    this.blockId = blockId;
-    computeID();
+    computeID(blockId, b);
   }
 
   /**
    * Sample numbering to map each sample to an integer in the range 1 to SIZE*SIZE Samples are
    * ordered by row
    */
-  public int sampleNumberByRow() {
-    return (this.row - 1) * this.block.getSize() + (this.column - 1);
+  public int sampleNumberByRow(Block b) {
+    return (this.row - 1) * b.getSize() + (this.column - 1);
   }
 
   /**
    * Sample numbering to map each sample to an integer in the range 1 to SIZE*SIZE Samples are
    * ordered by column
    */
-  public int sampleNumberByColumn() {
-    return (this.column - 1) * this.block.getSize() + (this.row - 1);
+  public int sampleNumberByColumn(Block b) {
+    return (this.column - 1) * b.getSize() + (this.row - 1);
   }
 
   /** Map this sample to the DHT keyspace */
-  public void computeID() {
+  public void computeID(long blockId, Block b) {
     if (KademliaCommonConfigDas.MAPPING_FN == KademliaCommonConfigDas.SAMPLE_MAPPING_RANDOM) {
       try {
         String idName =
@@ -62,22 +55,14 @@ public void computeID() {
       }
     } else if (KademliaCommonConfigDas.MAPPING_FN
         == KademliaCommonConfigDas.SAMPLE_MAPPING_REGION_BASED) {
-      /*System.out.println(
-      "ComputeId "
-          + this.row
-          + " "
-          + this.column
-          + " "
-          + this.sampleNumberByRow()
-          + " "
-          + this.sampleNumberByColumn());*/
+
       this.idByRow =
           Block.INTER_SAMPLE_GAP
-              .multiply(BigInteger.valueOf(this.sampleNumberByRow()))
+              .multiply(BigInteger.valueOf(this.sampleNumberByRow(b)))
               .add(BigInteger.valueOf(blockId));
       this.idByColumn =
           Block.INTER_SAMPLE_GAP
-              .multiply(BigInteger.valueOf(this.sampleNumberByColumn()))
+              .multiply(BigInteger.valueOf(this.sampleNumberByColumn(b)))
               .add(BigInteger.valueOf(blockId))
               .add(BigInteger.valueOf(1));
 
@@ -125,11 +110,6 @@ public int getColumn() {
     return this.column;
   }
 
-  /** Block id which the sample is part of */
-  public long getBlockId() {
-    return blockId;
-  }
-
   /** Computed identifier of the sample, depending of the mapping mode */
   public BigInteger getId() {
     return idByRow;
diff --git a/simulator/src/main/java/peersim/kademlia/das/SearchTable.java b/simulator/src/main/java/peersim/kademlia/das/SearchTable.java
index 7c1c4493..bd022d99 100644
--- a/simulator/src/main/java/peersim/kademlia/das/SearchTable.java
+++ b/simulator/src/main/java/peersim/kademlia/das/SearchTable.java
@@ -3,91 +3,70 @@
 import java.math.BigInteger;
 import java.util.ArrayList;
 import java.util.Collection;
+import java.util.Collections;
 import java.util.HashMap;
 import java.util.HashSet;
 import java.util.List;
-import java.util.Random;
 import java.util.Set;
 import java.util.TreeSet;
-// import peersim.kademlia.KademliaCommonConfig;
+import peersim.core.Node;
 
 public class SearchTable {
 
-  // private HashMap<BigInteger, List<BigInteger>> sampleMap;
-
-  // private Block currentBlock;
+  private HashMap<BigInteger, Neighbour> neighbours;
 
   private TreeSet<BigInteger> nodesIndexed; // , samplesIndexed;
 
-  private static TreeSet<BigInteger> validatorsIndexed = new TreeSet<>(); // , samplesIndexed;
+  protected TreeSet<BigInteger> validatorsIndexed; // , samplesIndexed;
 
   private TreeSet<BigInteger> nonValidatorsIndexed; // , samplesIndexed;
 
   private HashSet<BigInteger> blackList; // , samplesIndexed;
 
-  private static HashMap<BigInteger, List<BigInteger>> sampleMap = new HashMap<>();
-  private BigInteger builderAddress;
+  protected BigInteger builderAddress;
+
+  private List<Node> evilNodes;
+  private List<BigInteger> evilIds;
 
-  public SearchTable(/*Block currentblock , BigInteger id*/ ) {
+  public SearchTable() {
 
-    // this.currentBlock = currentblock;
-    // this.sampleMap = new HashMap<>();
     this.nodesIndexed = new TreeSet<>();
     this.nonValidatorsIndexed = new TreeSet<>();
-
+    this.validatorsIndexed = new TreeSet<>();
     this.blackList = new HashSet<>();
-
-    // routingTable = new RoutingTable(KademliaCommonConfig.K, KademliaCommonConfig.BITS, 0);
-    // routingTable.setNodeId(id);
+    this.neighbours = new HashMap<>();
   }
 
-  /*public void setBlock(Block currentBlock) {
-    this.currentBlock = currentBlock;
-  }*/
-
-  /*public BigInteger[] getSamples(BigInteger peerId) {
-
-    List<BigInteger> result = new ArrayList<>();
-    Collections.addAll(
-        result,
-        currentBlock.getSamplesByRadiusByColumn(
-            peerId,
-            currentBlock.computeRegionRadius(KademliaCommonConfigDas.NUM_SAMPLE_COPIES_PER_PEER)));
-    Collections.addAll(
-        result,
-        currentBlock.getSamplesByRadiusByRow(
-            peerId,
-            currentBlock.computeRegionRadius(KademliaCommonConfigDas.NUM_SAMPLE_COPIES_PER_PEER)));
-
-    return result.toArray(new BigInteger[0]);
-  }*/
-
-  public void addNodes(BigInteger[] nodes) {
-
-    for (BigInteger id : nodes) {
-      if (!blackList.contains(id)
-          && !validatorsIndexed.contains(id)
-          && !builderAddress.equals(id)) {
-        nonValidatorsIndexed.add(id);
+  public void addNeighbour(Neighbour neigh) {
+    if (neigh.getId().compareTo(builderAddress) != 0) {
+      if (neighbours.get(neigh.getId()) == null) {
+        neighbours.put(neigh.getId(), neigh);
+        nodesIndexed.add(neigh.getId());
+      } else {
+        if (neighbours.get(neigh.getId()).getLastSeen() < neigh.getLastSeen())
+          neighbours.get(neigh.getId()).updateLastSeen(neigh.getLastSeen());
       }
     }
   }
 
-  /*public void addNonValidatorNodes(BigInteger[] nodes) {
+  public void addNodes(BigInteger[] nodes) {
 
     for (BigInteger id : nodes) {
-      if (!blackList.contains(id) && !validatorsIndexed.contains(id)) {
-        nodesIndexed.add(id);
+      if (id.compareTo(builderAddress) != 0) {
+        if (!blackList.contains(id)
+            && !validatorsIndexed.contains(id)
+            && !builderAddress.equals(id)) {
+          nonValidatorsIndexed.add(id);
+        }
       }
     }
-  }*/
+  }
 
   public void addValidatorNodes(BigInteger[] nodes) {
     for (BigInteger id : nodes) {
-      if (!blackList.contains(id)) {
+      if (!blackList.contains(id) && id.compareTo(builderAddress) != 0) {
         validatorsIndexed.add(id);
       }
-      // routingTable.addNeighbour(id);
     }
   }
 
@@ -96,24 +75,20 @@ public void setBuilderAddress(BigInteger builderAddress) {
   }
 
   public void removeNode(BigInteger node) {
-    // this.blackList.add(node);
     this.nodesIndexed.remove(node);
-    // this.validatorsIndexed.remove(node);
     this.nonValidatorsIndexed.remove(node);
+    this.neighbours.remove(node);
+    validatorsIndexed.remove(node);
   }
 
   public TreeSet<BigInteger> nodesIndexed() {
-    return nonValidatorsIndexed;
+    return nodesIndexed;
   }
 
   public TreeSet<BigInteger> getValidatorsIndexed() {
     return validatorsIndexed;
   }
 
-  /*public HashSet<BigInteger> samplesIndexed() {
-    return samplesIndexed;
-  }*/
-
   public List<BigInteger> getNodesbySample(BigInteger sampleId, BigInteger radius) {
 
     BigInteger bottom = sampleId.subtract(radius);
@@ -124,9 +99,6 @@ public List<BigInteger> getNodesbySample(BigInteger sampleId, BigInteger radius)
 
     Collection<BigInteger> subSet = nodesIndexed.subSet(bottom, true, top, true);
     return new ArrayList<BigInteger>(subSet);
-
-    // return sampleMap.get(sampleId);
-
   }
 
   public List<BigInteger> getValidatorNodesbySample(BigInteger sampleId, BigInteger radius) {
@@ -138,9 +110,6 @@ public List<BigInteger> getValidatorNodesbySample(BigInteger sampleId, BigIntege
     if (top.compareTo(Block.MAX_KEY) == 1) top = Block.MAX_KEY;
     Collection<BigInteger> subSet = validatorsIndexed.subSet(bottom, true, top, true);
     return new ArrayList<BigInteger>(subSet);
-
-    // return sampleMap.get(sampleId);
-
   }
 
   public List<BigInteger> getNonValidatorNodesbySample(BigInteger sampleId, BigInteger radius) {
@@ -153,9 +122,6 @@ public List<BigInteger> getNonValidatorNodesbySample(BigInteger sampleId, BigInt
 
     Collection<BigInteger> subSet = nonValidatorsIndexed.subSet(bottom, true, top, true);
     return new ArrayList<BigInteger>(subSet);
-
-    // return sampleMap.get(sampleId);
-
   }
 
   public List<BigInteger> getNodesbySample(Set<BigInteger> samples, BigInteger radius) {
@@ -163,124 +129,126 @@ public List<BigInteger> getNodesbySample(Set<BigInteger> samples, BigInteger rad
     List<BigInteger> result = new ArrayList<>();
 
     for (BigInteger sample : samples) {
-      // if (sampleMap.get(sample) != null) result.addAll(sampleMap.get(sample));
       result.addAll(getNodesbySample(sample, radius));
     }
     return result;
   }
 
-  public static void createSampleMap(Block currentBlock) {
-
-    int validatorParcelSize =
-        (currentBlock.getSize()
-                * currentBlock.getSize()
-                * KademliaCommonConfigDas.NUM_SAMPLE_COPIES_PER_PEER)
-            / KademliaCommonConfigDas.validatorsSize;
-
-    if (validatorParcelSize == 0) validatorParcelSize = 1;
-    Random r = new Random();
-    List<BigInteger> list = new ArrayList<BigInteger>(validatorsIndexed);
-
-    List<BigInteger> sampleList = new ArrayList<>();
-
-    // Assigning samples to node by row
-    for (int h = 0; h < KademliaCommonConfigDas.NUM_SAMPLE_COPIES_PER_PEER; h++) {
-      for (int i = 0; i < currentBlock.getSize(); i++) {
-        for (int j = 0; j < currentBlock.getSize(); j++) {
-          sampleList.add(currentBlock.getSample(i, j).getIdByRow());
-          if (sampleList.size() == validatorParcelSize) {
-            BigInteger node = list.get(r.nextInt(list.size()));
-            // samples.put(node, sampleList);
-            for (BigInteger sample : sampleList) {
-              if (sampleMap.containsKey(sample)) {
-                /*System.out.println(
-                "assign samples to node " + node + " " + sampleMap.get(sample).size());*/
-                sampleMap.get(sample).add(node);
-              } else {
-                List<BigInteger> nList = new ArrayList<>();
-                nList.add(node);
-                sampleMap.put(sample, nList);
-              }
-            }
-            sampleList.clear();
-          }
-        }
-      }
+  public List<BigInteger> getAllNeighbours() {
+
+    List<BigInteger> result = new ArrayList<>(neighbours.keySet());
+    return result;
+  }
+
+  public Neighbour[] getNeighbours(int n) {
+
+    List<Neighbour> result = new ArrayList<>();
+    List<Neighbour> neighs = new ArrayList<>();
+    for (Neighbour neigh : neighbours.values()) {
+      neighs.add(neigh);
     }
-    if (sampleList.size() != 0) {
-      BigInteger node = list.get(r.nextInt(list.size()));
-      // samples.put(node, sampleList);
-      for (BigInteger sample : sampleList) {
-        if (sampleMap.containsKey(sample)) sampleMap.get(sample).add(node);
-        else {
-          List<BigInteger> nList = new ArrayList<>();
-          nList.add(node);
-          sampleMap.put(sample, nList);
-        }
-      }
-      sampleList.clear();
+    Collections.shuffle(neighs);
+
+    for (Neighbour neigh : neighs) {
+      if (result.size() < n) result.add(neigh);
+      else break;
     }
+    return result.toArray(new Neighbour[0]);
+  }
 
-    // Assigning samples to node by column
-    for (int h = 0; h < KademliaCommonConfigDas.NUM_SAMPLE_COPIES_PER_PEER; h++) {
-      for (int i = 0; i < currentBlock.getSize(); i++) {
-        for (int j = 0; j < currentBlock.getSize(); j++) {
-          sampleList.add(currentBlock.getSample(j, i).getIdByColumn());
-          if (sampleList.size() == validatorParcelSize) {
-            BigInteger node = list.get(r.nextInt(list.size()));
-            // samples.put(node, sampleList);
-            for (BigInteger sample : sampleList) {
-              // System.out.println("assign samples to node " + node + " " + nodeMap.size());
-              if (sampleMap.containsKey(sample)) {
-                /*System.out.println(
-                "assign samples to node " + node + " " + sampleMap.get(sample).size());*/
-                sampleMap.get(sample).add(node);
-              } else {
-                List<BigInteger> nList = new ArrayList<>();
-                nList.add(node);
-                sampleMap.put(sample, nList);
-              }
-            }
-            sampleList.clear();
-          }
-        }
+  public void setEvil(List<Node> nodes) {
+    this.evilNodes = nodes;
+  }
+
+  public boolean isEvil(BigInteger id) {
+    if (evilIds.contains(id)) return true;
+    else return false;
+  }
+
+  public void setEvilIds(List<BigInteger> ids) {
+    this.evilIds = ids;
+  }
+
+  public Neighbour[] getEvilNeighbours(int n) {
+
+    List<Neighbour> result = new ArrayList<>();
+    if (evilNodes != null) {
+      Collections.shuffle(evilNodes);
+      for (Node neigh : evilNodes) {
+        if (result.size() < n)
+          result.add(new Neighbour(neigh.getDASProtocol().getKademliaId(), neigh, true));
+        else break;
       }
     }
-    if (sampleList.size() != 0) {
-      BigInteger node = list.get(r.nextInt(list.size()));
-      // samples.put(node, sampleList);
-      for (BigInteger sample : sampleList) {
-        if (sampleMap.containsKey(sample)) sampleMap.get(sample).add(node);
-        else {
-          List<BigInteger> nList = new ArrayList<>();
-          nList.add(node);
-          sampleMap.put(sample, nList);
-        }
-      }
-      sampleList.clear();
+    return result.toArray(new Neighbour[0]);
+  }
+
+  public Neighbour[] getNeighbours(BigInteger id, BigInteger radius) {
+
+    List<BigInteger> nodes = getNodesbySample(id, radius);
+    List<Neighbour> neighs = new ArrayList<>();
+    List<Neighbour> result = new ArrayList<>();
+    for (BigInteger n : nodes) {
+      neighs.add(neighbours.get(n));
     }
+    Collections.shuffle(neighs);
 
-    System.out.println(
-        "samples "
-            + validatorParcelSize
-            + " "
-            + sampleMap.size()
-            + " "
-            + sampleList.size()
-            + " "
-            + currentBlock.getSize());
-
-    /*for (List<BigInteger> l : sampleMap.values()) {
-      System.out.println("samples " + l.size());
-    }*/
+    for (Neighbour neigh : neighs) {
+      if (result.size() < KademliaCommonConfigDas.MAX_NODES_RETURNED) result.add(neigh);
+      else break;
+    }
+    return result.toArray(new Neighbour[0]);
   }
 
-  public static List<BigInteger> getNodesBySample(BigInteger s) {
-    return sampleMap.get(s);
+  public int getAllNeighboursCount() {
+    return neighbours.size();
   }
 
-  /*public BigInteger[] findKClosestValidators(BigInteger sampleId) {
+  public int getValidatorsNeighboursCount() {
+    int count = 0;
+    for (Neighbour neigh : neighbours.values()) {
+      if (neigh.getNode().getDASProtocol().isValidator()) count++;
+    }
+    return count;
+  }
 
-    return routingTable.getNeighbours(sampleId, sampleId);
-  }*/
+  public int getNonValidatorsNeighboursCount() {
+    int count = 0;
+    for (Neighbour neigh : neighbours.values()) {
+      if (!neigh.getNode().getDASProtocol().isValidator()) count++;
+    }
+    return count;
+  }
+
+  public int getAllAliveNeighboursCount() {
+    int count = 0;
+    for (Neighbour neigh : neighbours.values()) {
+      if (neigh.getNode().isUp()) count++;
+    }
+    return count;
+  }
+
+  public int getMaliciousNeighboursCount() {
+    int count = 0;
+    for (Neighbour neigh : neighbours.values()) {
+      if (neigh.isEvil()) count++;
+    }
+    return count;
+  }
+
+  public boolean isNeighbourKnown(Neighbour neighbour) {
+    return neighbours.containsKey(neighbour.getId());
+  }
+
+  public void refresh() {
+
+    List<Neighbour> toRemove = new ArrayList<>();
+    for (Neighbour neigh : neighbours.values()) {
+      if (neigh.expired()) {
+        toRemove.add(neigh);
+        nodesIndexed.remove(neigh.getId());
+      }
+    }
+    for (Neighbour n : toRemove) neighbours.remove(n.getId());
+  }
 }
diff --git a/simulator/src/main/java/peersim/kademlia/das/SearchTableV2.java b/simulator/src/main/java/peersim/kademlia/das/SearchTableV2.java
new file mode 100644
index 00000000..afea2128
--- /dev/null
+++ b/simulator/src/main/java/peersim/kademlia/das/SearchTableV2.java
@@ -0,0 +1,192 @@
+package peersim.kademlia.das;
+
+import java.math.BigInteger;
+import java.util.*;
+import peersim.core.CommonState;
+import peersim.core.Network;
+
+public class SearchTableV2 extends SearchTable {
+
+  private HashMap<BigInteger, List<BigInteger>> validatorsSamples;
+  private HashMap<BigInteger, Integer> validatorsRow;
+  private HashMap<BigInteger, Integer> validatorsColumn;
+  private HashMap<Integer, List<BigInteger>> rowsValidator;
+  private HashMap<Integer, List<BigInteger>> columnsValidator;
+
+  public SearchTableV2() {
+    validatorsSamples = new HashMap<>();
+    validatorsRow = new HashMap<>();
+    validatorsColumn = new HashMap<>();
+    rowsValidator = new HashMap<>();
+    columnsValidator = new HashMap<>();
+  }
+
+  public void assignSamplesRandom(Block b, int r) {
+    for (int i = 0; i < Network.size(); i++) {
+      BigInteger id = Network.get(i).getDASProtocol().getKademliaId();
+      validatorsRow.put(id, 0);
+      validatorsColumn.put(id, 0);
+    }
+    ArrayList<BigInteger> list = new ArrayList<>(validatorsIndexed);
+    while (b.hasNext()) {
+      Sample s = b.next();
+      validatorsSamples.put(s.getIdByRow(), new ArrayList<>());
+      validatorsSamples.put(s.getIdByColumn(), new ArrayList<>());
+      BigInteger randomNode = list.get(CommonState.r.nextInt(validatorsIndexed.size()));
+      validatorsRow.put(randomNode, s.getRow());
+      validatorsRow.put(randomNode, s.getColumn());
+      validatorsSamples.get(s.getIdByRow()).add(randomNode);
+      validatorsSamples.get(s.getIdByColumn()).add(randomNode);
+    }
+  }
+
+  public void assignSamplesRadius(Block b, int r) {
+    BigInteger radiusValidator = b.computeRegionRadius(r, this.getValidatorsIndexed().size());
+    while (b.hasNext()) {
+      BigInteger radiusUsed = radiusValidator;
+      boolean inRegion = false;
+      Sample s = b.next();
+      while (!inRegion) {
+
+        List<BigInteger> idsValidators = this.getValidatorNodesbySample(s.getIdByRow(), radiusUsed);
+        if (idsValidators.size() > 0) {
+          inRegion = true;
+          validatorsSamples.put(s.getIdByRow(), idsValidators);
+          rowsValidator.put(s.getRow(), idsValidators);
+          for (BigInteger id : idsValidators) {
+            validatorsRow.put(id, s.getRow());
+          }
+        }
+        idsValidators = this.getValidatorNodesbySample(s.getIdByColumn(), radiusUsed);
+        if (idsValidators.size() > 0) {
+          inRegion = true;
+          validatorsSamples.put(s.getIdByColumn(), idsValidators);
+          columnsValidator.put(s.getRow(), idsValidators);
+          for (BigInteger id : idsValidators) {
+            validatorsColumn.put(id, s.getColumn());
+          }
+        }
+        if (!inRegion) radiusUsed = radiusUsed.multiply(BigInteger.valueOf(2));
+      }
+    }
+  }
+
+  public void assignByRowColumn(Block b) {
+
+    int i = 0;
+    for (int row = 0; row < b.getSize(); row++) {
+      BigInteger id = Network.get(i).getDASProtocol().getKademliaId();
+      if (id.compareTo(builderAddress) == 0) {
+        id = Network.get(i + 1).getDASProtocol().getKademliaId();
+      }
+      validatorsRow.put(id, row);
+
+      for (int column = 0; column < b.getSize(); column++) {
+        BigInteger sampleRow = b.getSample(row, column).getIdByRow();
+        if (validatorsSamples.get(sampleRow) == null) {
+          validatorsSamples.put(sampleRow, new ArrayList<>());
+        }
+        validatorsSamples.get(sampleRow).add(id);
+      }
+
+      i++;
+      if (i >= Network.size()) i = 0;
+    }
+
+    for (int column = 0; column < b.getSize(); column++) {
+      BigInteger id = Network.get(i).getDASProtocol().getKademliaId();
+      if (id.compareTo(builderAddress) == 0) {
+        id = Network.get(i + 1).getDASProtocol().getKademliaId();
+      }
+      validatorsColumn.put(id, column);
+
+      for (int row = 0; row < b.getSize(); row++) {
+        BigInteger sampleColumn = b.getSample(row, column).getIdByColumn();
+        if (validatorsSamples.get(sampleColumn) == null) {
+          validatorsSamples.put(sampleColumn, new ArrayList<>());
+        }
+        validatorsSamples.get(sampleColumn).add(id);
+      }
+
+      i++;
+      if (i >= Network.size()) i = 0;
+    }
+  }
+
+  public List<BigInteger> getValidatorNodesbySample(BigInteger sampleId, BigInteger radius) {
+    return validatorsSamples.get(sampleId);
+  }
+
+  public List<BigInteger> getNonValidatorNodesbySample(BigInteger sampleId, BigInteger radius) {
+    return new ArrayList<>();
+  }
+  /*int nodesPerRow =
+      Network.size() / (b.getSize() * KademliaCommonConfigDas.NUM_SAMPLE_COPIES_PER_PEER);
+
+  int row = 1;
+  int counter = 0;
+  for (int i = 0; i < Network.size(); i++) {
+    counter++;
+    BigInteger id = Network.get(i).getDASProtocol().getKademliaId();
+    if (rowsValidator.get(row) != null) {
+      rowsValidator.get(row).add(id);
+    } else {
+      List<BigInteger> list = new ArrayList<>();
+      list.add(id);
+      rowsValidator.put(row, list);
+    }
+    validatorsRow.put(id, row);
+    if (counter == nodesPerRow) {
+      row++;
+      counter = 0;
+    }
+    if (row > b.getSize()) break;
+  }
+
+  int column = 1;
+  counter = 0;
+  for (int i = 0; i < Network.size(); i++) {
+    counter++;
+    BigInteger id = Network.get(i).getDASProtocol().getKademliaId();
+    if (columnsValidator.get(column) != null) {
+      columnsValidator.get(column).add(id);
+    } else {
+      List<BigInteger> list = new ArrayList<>();
+      list.add(id);
+      columnsValidator.put(column, list);
+    }
+    validatorsColumn.put(id, column);
+
+    if (counter == nodesPerRow) {
+      column++;
+      counter = 0;
+    }
+    if (column > b.getSize()) break;
+  }
+
+  for (int i : rowsValidator.keySet()) {
+    System.out.println("Row " + i + " nodes " + rowsValidator.get(i).size());
+  }
+
+  for (int i : columnsValidator.keySet()) {
+    System.out.println("Column " + i + " nodes " + columnsValidator.get(i).size());
+  }
+
+  for (int r=1;r<=b.getSize();r++) {
+    Sample s = b.getSample(row, column)
+    List<BigInteger> vals = rowsValidator.get(s.getRow());
+    validatorsSamples.put(s.getId(),vals.get(CommonState.r.nextInt(vals.size())) )
+  }*/
+
+  public List<BigInteger> getNodesBySample(BigInteger sampleId) {
+    return validatorsSamples.get(sampleId);
+  }
+
+  public int getValidatorRow(BigInteger id) {
+    return validatorsRow.get(id);
+  }
+
+  public int getValidatorColumn(BigInteger id) {
+    return validatorsColumn.get(id);
+  }
+}
diff --git a/simulator/src/main/java/peersim/kademlia/das/SeedingSampleBody.java b/simulator/src/main/java/peersim/kademlia/das/SeedingSampleBody.java
new file mode 100644
index 00000000..862f2cce
--- /dev/null
+++ b/simulator/src/main/java/peersim/kademlia/das/SeedingSampleBody.java
@@ -0,0 +1,29 @@
+package peersim.kademlia.das;
+
+import java.math.BigInteger;
+import java.util.List;
+
+public class SeedingSampleBody {
+  public List<BigInteger>
+      validatorList; // Assuming sample is an array of integers, you can change the type as needed
+  public Sample[] samplesList;
+  public boolean isRow; // true if it's a row
+
+  public SeedingSampleBody(Sample[] samples, List<BigInteger> validators, boolean isRow) {
+    this.samplesList = samples;
+    this.validatorList = validators;
+    this.isRow = isRow;
+  }
+
+  public List<BigInteger> getValidators() {
+    return this.validatorList;
+  }
+
+  public Sample[] getsamplesList() {
+    return this.samplesList;
+  }
+
+  public boolean getIsRow() {
+    return this.isRow;
+  }
+}
diff --git a/simulator/src/main/java/peersim/kademlia/das/TrafficGeneratorSample.java b/simulator/src/main/java/peersim/kademlia/das/TrafficGeneratorSample.java
index 179839e0..6633418b 100755
--- a/simulator/src/main/java/peersim/kademlia/das/TrafficGeneratorSample.java
+++ b/simulator/src/main/java/peersim/kademlia/das/TrafficGeneratorSample.java
@@ -1,15 +1,12 @@
 package peersim.kademlia.das;
 
-import java.math.BigInteger;
 import peersim.config.Configuration;
 import peersim.core.CommonState;
 import peersim.core.Control;
 import peersim.core.Network;
 import peersim.core.Node;
 import peersim.edsim.EDSimulator;
-import peersim.kademlia.KademliaCommonConfig;
 import peersim.kademlia.Message;
-import peersim.kademlia.UniformRandomGenerator;
 
 /**
  * This control generates samples every 5 min that are stored in a single node (builder) and starts
@@ -36,12 +33,13 @@ public class TrafficGeneratorSample implements Control {
 
   final String PAR_BLK_DIM_SIZE = "block_dim_size";
 
+  final String PAR_NUM_SAMPLES = "num_samples";
+
   int mapfn;
 
   Block b;
   private long ID_GENERATOR = 0;
-
-  private boolean first = true, second = false;
+  long lastTime = 0;
 
   // ______________________________________________________________________________________________
   public TrafficGeneratorSample(String prefix) {
@@ -53,6 +51,8 @@ public TrafficGeneratorSample(String prefix) {
     KademliaCommonConfigDas.BLOCK_DIM_SIZE =
         Configuration.getInt(
             prefix + "." + PAR_BLK_DIM_SIZE, KademliaCommonConfigDas.BLOCK_DIM_SIZE);
+    KademliaCommonConfigDas.N_SAMPLES =
+        Configuration.getInt(prefix + "." + PAR_NUM_SAMPLES, KademliaCommonConfigDas.N_SAMPLES);
   }
 
   // ______________________________________________________________________________________________
@@ -69,38 +69,6 @@ private Message generateNewBlockMessage(Block b) {
     return m;
   }
 
-  // ______________________________________________________________________________________________
-  /**
-   * generates a random find node message, by selecting randomly the destination.
-   *
-   * @return Message
-   */
-  private Message generateFindNodeMessage() {
-
-    UniformRandomGenerator urg =
-        new UniformRandomGenerator(KademliaCommonConfig.BITS, CommonState.r);
-    BigInteger id = urg.generate();
-
-    Message m = Message.makeInitFindNode(id);
-    m.timestamp = CommonState.getTime();
-
-    return m;
-  }
-
-  // ______________________________________________________________________________________________
-  /**
-   * generates a random find node message, by selecting randomly the destination.
-   *
-   * @return Message
-   */
-  private Message generateFindNodeMessage(BigInteger id) {
-
-    Message m = Message.makeInitFindNode(id);
-    m.timestamp = CommonState.getTime();
-
-    return m;
-  }
-
   // ______________________________________________________________________________________________
   /**
    * every call of this control generates and send a random find node message
@@ -110,40 +78,14 @@ private Message generateFindNodeMessage(BigInteger id) {
   public boolean execute() {
     Block b = new Block(KademliaCommonConfigDas.BLOCK_DIM_SIZE, ID_GENERATOR);
 
-    if (first) {
-      for (int i = 0; i < Network.size(); i++) {
-        Node start = Network.get(i);
-        if (start.isUp()) {
-          for (int j = 0; j < 3; j++) {
-            // send message
-            EDSimulator.add(
-                CommonState.r.nextInt(300000), generateFindNodeMessage(), start, kadpid);
-          }
-          EDSimulator.add(
-              0,
-              generateFindNodeMessage(start.getKademliaProtocol().getKademliaNode().getId()),
-              start,
-              kadpid);
-        }
-      }
-      first = false;
-      second = true;
-    } else /*if (second)*/ {
-
-      SearchTable.createSampleMap(b);
-      for (int i = 0; i < Network.size(); i++) {
-        Node n = Network.get(i);
-        // b.initIterator();
-        // we add 1 ms delay to be sure the builder starts before validators.
-        if (n.getDASProtocol().isBuilder())
-          EDSimulator.add(0, generateNewBlockMessage(b), n, n.getDASProtocol().getDASProtocolID());
-        else
-          EDSimulator.add(1, generateNewBlockMessage(b), n, n.getDASProtocol().getDASProtocolID());
+    for (int i = 0; i < Network.size(); i++) {
+      Node n = Network.get(i);
+      if (n.isUp()) {
+        EDSimulator.add(0, generateNewBlockMessage(b), n, n.getDASProtocol().getDASProtocolID());
       }
-      ID_GENERATOR++;
-      second = false;
     }
-
+    ID_GENERATOR++;
+    lastTime = CommonState.getTime();
     return false;
   }
 
diff --git a/simulator/src/main/java/peersim/kademlia/das/TurbulenceDas.java b/simulator/src/main/java/peersim/kademlia/das/TurbulenceDas.java
index a7f6dd02..1cb1c788 100644
--- a/simulator/src/main/java/peersim/kademlia/das/TurbulenceDas.java
+++ b/simulator/src/main/java/peersim/kademlia/das/TurbulenceDas.java
@@ -34,9 +34,11 @@
  */
 public class TurbulenceDas implements Control {
 
-  private static final String PAR_PROT = "protocol";
-  private static final String PAR_PROT_DAS = "protocoldas";
-  private static final String PAR_PROT_EVIL_DAS = "protocolevildas";
+  private static final String PAR_PROT = "protocolkad";
+  private static final String PAR_PROT_DAS_BUILDER = "protocoldasbuilder";
+  private static final String PAR_PROT_DAS_VAL = "protocoldasvalidator";
+  private static final String PAR_PROT_DAS_NONVAL = "protocoldasnonvalidator";
+  private static final String PAR_PROT_EVIL_DAS = "protocolEvildas";
   private static final String PAR_TRANSPORT = "transport";
   private static final String PAR_INIT = "init";
 
@@ -64,7 +66,9 @@ public class TurbulenceDas implements Control {
 
   private String prefix;
   private int kademliaid;
-  private int dasprotid;
+  private int dasprotbuildid;
+  private int dasprotvalid;
+  private int dasprotnonvalid;
   private int dasevilprotid;
   private int transportid;
   private int maxsize;
@@ -72,12 +76,15 @@ public class TurbulenceDas implements Control {
   private double p_idle;
   private double p_add;
   private double p_rem;
-
+  private UniformRandomGenerator urg;
   // ______________________________________________________________________________________________
   public TurbulenceDas(String prefix) {
     this.prefix = prefix;
     kademliaid = Configuration.getPid(this.prefix + "." + PAR_PROT);
-    dasprotid = Configuration.getPid(this.prefix + "." + PAR_PROT_DAS);
+    dasprotbuildid = Configuration.getPid(this.prefix + "." + PAR_PROT_DAS_BUILDER);
+    dasprotvalid = Configuration.getPid(this.prefix + "." + PAR_PROT_DAS_VAL);
+    dasprotnonvalid = Configuration.getPid(this.prefix + "." + PAR_PROT_DAS_NONVAL);
+
     dasevilprotid = Configuration.getPid(this.prefix + "." + PAR_PROT_EVIL_DAS);
 
     transportid = Configuration.getPid(this.prefix + "." + PAR_TRANSPORT);
@@ -88,7 +95,7 @@ public TurbulenceDas(String prefix) {
     Object[] tmp = Configuration.getInstanceArray(prefix + "." + PAR_INIT);
     inits = new NodeInitializer[tmp.length];
     for (int i = 0; i < tmp.length; ++i) inits[i] = (NodeInitializer) tmp[i];
-
+    urg = new UniformRandomGenerator(KademliaCommonConfig.BITS, CommonState.r);
     // Load probability from configuration file
     p_idle = Configuration.getDouble(this.prefix + "." + PAR_IDLE, 0); // idle default 0
     p_add = Configuration.getDouble(this.prefix + "." + PAR_ADD, 0.5); // add default 0.5
@@ -127,39 +134,72 @@ public boolean add() {
     for (int j = 0; j < inits.length; ++j) inits[j].initialize(newNode);
     Network.add(newNode);
 
-    int count = 0;
-    for (int i = 0; i < Network.size(); ++i) if (Network.get(i).isUp()) count++;
-
-    System.out.println("Adding non-validator node " + count);
-
     // Get kademlia protocol of new node
     KademliaProtocol newKad = (KademliaProtocol) (newNode.getProtocol(kademliaid));
-    newNode.setKademliaProtocol(newKad);
-    newKad.setProtocolID(kademliaid);
     // newNode.setProtocol(kademliaid, newKad);
     // Set node ID
-    UniformRandomGenerator urg =
-        new UniformRandomGenerator(KademliaCommonConfig.BITS, CommonState.r);
+
     KademliaNode node = new KademliaNode(urg.generate(), "127.0.0.1", 0);
-    ((KademliaProtocol) (newNode.getProtocol(kademliaid))).setNode(node);
 
-    DASProtocol dasProt = ((DASProtocol) (newNode.getProtocol(dasprotid)));
+    DASProtocol dasProt = ((DASProtocol) (newNode.getProtocol(dasprotnonvalid)));
 
-    newNode.setProtocol(dasprotid, dasProt);
     newKad.setNode(node);
+    newKad.setProtocolID(kademliaid);
+    newKad.setEventsCallback(dasProt);
 
     dasProt.setKademliaProtocol(newKad);
-    dasProt.setDASProtocolID(dasprotid);
-    newKad.setEventsCallback(dasProt);
+    dasProt.setDASProtocolID(dasprotbuildid);
+
+    newNode.setKademliaProtocol(newKad);
+    newNode.setProtocol(dasprotbuildid, dasProt);
+
     newNode.setDASProtocol(dasProt);
-    newNode.setProtocol(dasprotid, dasProt);
-    newNode.setProtocol(dasevilprotid, dasProt);
-
-    DASProtocol builder = ((DASProtocol) (Network.get(0).getProtocol(dasprotid)));
-    // dasProt.setBuilder(false);
-    BigInteger builderAddress = builder.getKademliaProtocol().getKademliaNode().getId();
-    dasProt.setBuilderAddress(builderAddress);
-    // dasProt.setValidator(false);
+    newNode.setProtocol(dasprotvalid, null);
+    newNode.setProtocol(dasevilprotid, null);
+    newNode.setProtocol(dasprotnonvalid, null);
+
+    int count = 0;
+    BigInteger builderAddress = BigInteger.valueOf(0);
+    for (int i = 0; i < Network.size(); ++i) {
+      if (Network.get(i).isUp()) count++;
+      if (Network.get(i).getDASProtocol().isBuilder()) {
+        builderAddress = Network.get(i).getDASProtocol().getKademliaId();
+        dasProt.setBuilderAddress(builderAddress);
+      }
+    }
+
+    /*Node builder = Util.nodeIdtoNode(builderAddress, dasprotbuildid);
+    DASProtocol builderDAS = builder.getDASProtocol();
+    builderDAS.getSearchTable().addValidatorNodes(new BigInteger[] {dasProt.getKademliaId()});*/
+    int k = 0;
+    while (k < 100) {
+      Node n = Network.get(CommonState.r.nextInt(Network.size()));
+      if (n.isUp()) {
+        KademliaProtocol jKad = (KademliaProtocol) n.getProtocol(kademliaid);
+        if (jKad.getKademliaNode().isServer()) {
+          newKad.getRoutingTable().addNeighbour(jKad.getKademliaNode().getId());
+          dasProt.searchTable.addNeighbour(
+              new Neighbour(jKad.getKademliaNode().getId(), n, n.getDASProtocol().isEvil()));
+        }
+      }
+      k++;
+    }
+    /*for (int j = 0; j < 3; j++) {
+      // send message
+      EDSimulator.add(0, Util.generateFindNodeMessage(), newNode, kademliaid);
+    }
+    EDSimulator.add(
+        0, Util.generateFindNodeMessage(newKad.getKademliaNode().getId()), newNode, kademliaid);*/
+
+    System.out.println(
+        CommonState.getTime()
+            + " Adding non-validator node "
+            + count
+            + " "
+            + newKad.getKademliaNode().getId()
+            + " "
+            + dasProt.getBuilderAddress());
+
     return false;
   }
 
@@ -171,7 +211,7 @@ public boolean rem() {
     int i = Network.size();
     do {
       remove = Network.get(CommonState.r.nextInt(Network.size()));
-      dasProt = ((DASProtocol) (remove).getProtocol(dasprotid));
+      dasProt = ((DASProtocol) (remove).getProtocol(dasprotbuildid));
       // } while ((remove == null) || dasProt.isBuilder() || dasProt.isValidator() ||
       // (!remove.isUp()));
       i--;
@@ -179,7 +219,7 @@ public boolean rem() {
         && i > 0);
     // Remove node (set its state to DOWN)
     remove.setFailState(Node.DOWN);
-    System.out.println("Removing validator " + dasProt.getKademliaId());
+    System.out.println("Removing non-validator " + dasProt.getKademliaId());
 
     return false;
   }
diff --git a/simulator/src/main/java/peersim/kademlia/das/TurbulenceDasValidator.java b/simulator/src/main/java/peersim/kademlia/das/TurbulenceDasValidator.java
index 2d6f0c80..331fb536 100644
--- a/simulator/src/main/java/peersim/kademlia/das/TurbulenceDasValidator.java
+++ b/simulator/src/main/java/peersim/kademlia/das/TurbulenceDasValidator.java
@@ -1,8 +1,6 @@
 package peersim.kademlia.das;
 
 import java.math.BigInteger;
-import java.util.ArrayList;
-import java.util.List;
 import peersim.config.Configuration;
 import peersim.core.CommonState;
 import peersim.core.Control;
@@ -13,6 +11,7 @@
 import peersim.kademlia.KademliaNode;
 import peersim.kademlia.KademliaProtocol;
 import peersim.kademlia.UniformRandomGenerator;
+import peersim.kademlia.Util;
 
 /**
  * Turbulcen class is only for test/statistical purpose. This Control execute a node add or remove
@@ -36,9 +35,11 @@
  */
 public class TurbulenceDasValidator implements Control {
 
-  private static final String PAR_PROT = "protocol";
-  private static final String PAR_PROT_DAS = "protocoldas";
-  private static final String PAR_PROT_EVIL_DAS = "protocolevildas";
+  private static final String PAR_PROT = "protocolkad";
+  private static final String PAR_PROT_DAS_BUILDER = "protocoldasbuilder";
+  private static final String PAR_PROT_DAS_VAL = "protocoldasvalidator";
+  private static final String PAR_PROT_DAS_NONVAL = "protocoldasnonvalidator";
+  private static final String PAR_PROT_EVIL_DAS = "protocolEvildas";
   private static final String PAR_TRANSPORT = "transport";
   private static final String PAR_INIT = "init";
 
@@ -66,7 +67,9 @@ public class TurbulenceDasValidator implements Control {
 
   private String prefix;
   private int kademliaid;
-  private int dasprotid;
+  private int dasprotbuildid;
+  private int dasprotvalid;
+  private int dasprotnonvalid;
   private int dasevilprotid;
   private int transportid;
   private int maxsize;
@@ -74,14 +77,17 @@ public class TurbulenceDasValidator implements Control {
   private double p_idle;
   private double p_add;
   private double p_rem;
+  private UniformRandomGenerator urg;
 
   // ______________________________________________________________________________________________
   public TurbulenceDasValidator(String prefix) {
     this.prefix = prefix;
     kademliaid = Configuration.getPid(this.prefix + "." + PAR_PROT);
-    dasprotid = Configuration.getPid(this.prefix + "." + PAR_PROT_DAS);
-    dasevilprotid = Configuration.getPid(this.prefix + "." + PAR_PROT_EVIL_DAS);
+    dasprotbuildid = Configuration.getPid(this.prefix + "." + PAR_PROT_DAS_BUILDER);
+    dasprotvalid = Configuration.getPid(this.prefix + "." + PAR_PROT_DAS_VAL);
+    dasprotnonvalid = Configuration.getPid(this.prefix + "." + PAR_PROT_DAS_NONVAL);
 
+    dasevilprotid = Configuration.getPid(this.prefix + "." + PAR_PROT_EVIL_DAS);
     transportid = Configuration.getPid(this.prefix + "." + PAR_TRANSPORT);
 
     minsize = Configuration.getInt(this.prefix + "." + PAR_MINSIZE, 1);
@@ -90,6 +96,7 @@ public TurbulenceDasValidator(String prefix) {
     Object[] tmp = Configuration.getInstanceArray(prefix + "." + PAR_INIT);
     inits = new NodeInitializer[tmp.length];
     for (int i = 0; i < tmp.length; ++i) inits[i] = (NodeInitializer) tmp[i];
+    urg = new UniformRandomGenerator(KademliaCommonConfig.BITS, CommonState.r);
 
     // Load probability from configuration file
     p_idle = Configuration.getDouble(this.prefix + "." + PAR_IDLE, 0); // idle default 0
@@ -124,51 +131,75 @@ public TurbulenceDasValidator(String prefix) {
   // ______________________________________________________________________________________________
   public boolean add() {
 
-    int count = 0;
-    List<BigInteger> validatorList = new ArrayList<>();
-    for (int i = 0; i < Network.size(); ++i) {
-      if (Network.get(i).isUp()) {
-        DASProtocol dasprot = Network.get(i).getDASProtocol();
-        if (dasprot.isValidator()) validatorList.add(dasprot.getKademliaId());
-        count++;
-      }
-    }
     // Add Node
     Node newNode = (Node) Network.prototype.clone();
     for (int j = 0; j < inits.length; ++j) inits[j].initialize(newNode);
     Network.add(newNode);
 
-    System.out.println("Adding validator " + count);
-
     // Get kademlia protocol of new node
     KademliaProtocol newKad = (KademliaProtocol) (newNode.getProtocol(kademliaid));
-    newNode.setKademliaProtocol(newKad);
-    newKad.setProtocolID(kademliaid);
     // newNode.setProtocol(kademliaid, newKad);
     // Set node ID
-    UniformRandomGenerator urg =
-        new UniformRandomGenerator(KademliaCommonConfig.BITS, CommonState.r);
+
     KademliaNode node = new KademliaNode(urg.generate(), "127.0.0.1", 0);
-    ((KademliaProtocol) (newNode.getProtocol(kademliaid))).setNode(node);
 
-    DASProtocol dasProt = ((DASProtocol) (newNode.getProtocol(dasprotid)));
+    DASProtocol dasProt = ((DASProtocol) (newNode.getProtocol(dasprotvalid)));
 
-    newNode.setProtocol(dasprotid, dasProt);
     newKad.setNode(node);
+    newKad.setProtocolID(kademliaid);
+    newKad.setEventsCallback(dasProt);
 
     dasProt.setKademliaProtocol(newKad);
-    dasProt.setDASProtocolID(dasprotid);
-    newKad.setEventsCallback(dasProt);
+    dasProt.setDASProtocolID(dasprotbuildid);
+
+    newNode.setKademliaProtocol(newKad);
+    newNode.setProtocol(dasprotbuildid, dasProt);
+
     newNode.setDASProtocol(dasProt);
-    newNode.setProtocol(dasprotid, dasProt);
-    newNode.setProtocol(dasevilprotid, dasProt);
-
-    DASProtocol builder = ((DASProtocol) (Network.get(0).getProtocol(dasprotid)));
-    // dasProt.setBuilder(false);
-    BigInteger builderAddress = builder.getKademliaProtocol().getKademliaNode().getId();
-    dasProt.setBuilderAddress(builderAddress);
-    // dasProt.setValidator(true);
-    dasProt.addKnownValidator(validatorList.toArray(new BigInteger[0]));
+    newNode.setProtocol(dasprotvalid, null);
+    newNode.setProtocol(dasevilprotid, null);
+    newNode.setProtocol(dasprotnonvalid, null);
+
+    int count = 0;
+    BigInteger builderAddress = BigInteger.valueOf(0);
+    for (int i = 0; i < Network.size(); ++i) {
+      if (Network.get(i).isUp()) count++;
+      if (Network.get(i).getDASProtocol().isBuilder()) {
+        builderAddress = Network.get(i).getDASProtocol().getKademliaId();
+        dasProt.setBuilderAddress(builderAddress);
+      }
+    }
+
+    Node builder = Util.nodeIdtoNode(builderAddress, kademliaid);
+    DASProtocol builderDAS = builder.getDASProtocol();
+    builderDAS.getSearchTable().addValidatorNodes(new BigInteger[] {dasProt.getKademliaId()});
+    int k = 0;
+    while (k < 100) {
+      Node n = Network.get(CommonState.r.nextInt(Network.size()));
+      if (n.isUp()) {
+        KademliaProtocol jKad = (KademliaProtocol) n.getProtocol(kademliaid);
+        if (jKad.getKademliaNode().isServer()) {
+          newKad.getRoutingTable().addNeighbour(jKad.getKademliaNode().getId());
+          dasProt.searchTable.addNeighbour(
+              new Neighbour(jKad.getKademliaNode().getId(), n, n.getDASProtocol().isEvil()));
+        }
+      }
+      k++;
+    }
+    /*for (int j = 0; j < 3; j++) {
+      // send message
+      EDSimulator.add(0, Util.generateFindNodeMessage(), newNode, kademliaid);
+    }
+    EDSimulator.add(
+        0, Util.generateFindNodeMessage(newKad.getKademliaNode().getId()), newNode, kademliaid);*/
+    System.out.println(
+        CommonState.getTime()
+            + " Adding validator node "
+            + count
+            + " "
+            + newKad.getKademliaNode().getId()
+            + " "
+            + dasProt.getBuilderAddress());
     return false;
   }
 
@@ -180,7 +211,7 @@ public boolean rem() {
     int i = Network.size();
     do {
       remove = Network.get(CommonState.r.nextInt(Network.size()));
-      dasProt = ((DASProtocol) (remove).getProtocol(dasprotid));
+      dasProt = ((DASProtocol) (remove).getProtocol(dasprotbuildid));
       // } while ((remove == null) || dasProt.isBuilder() || dasProt.isValidator() ||
       // (!remove.isUp()));
       i--;
diff --git a/simulator/src/main/java/peersim/kademlia/das/operations/FetchingSample.java b/simulator/src/main/java/peersim/kademlia/das/operations/FetchingSample.java
index a22e10ce..5e23f30a 100644
--- a/simulator/src/main/java/peersim/kademlia/das/operations/FetchingSample.java
+++ b/simulator/src/main/java/peersim/kademlia/das/operations/FetchingSample.java
@@ -25,6 +25,10 @@ public BigInteger getId() {
     return s.getId();
   }
 
+  public Sample getSample() {
+    return s;
+  }
+
   public BigInteger getIdByColumn() {
     return s.getIdByColumn();
   }
diff --git a/simulator/src/main/java/peersim/kademlia/das/operations/Node.java b/simulator/src/main/java/peersim/kademlia/das/operations/Node.java
index fb4432f8..ada7da64 100644
--- a/simulator/src/main/java/peersim/kademlia/das/operations/Node.java
+++ b/simulator/src/main/java/peersim/kademlia/das/operations/Node.java
@@ -30,6 +30,10 @@ public List<FetchingSample> getSamples() {
     return samples;
   }
 
+  public void removeFetchingSample(FetchingSample s) {
+    samples.remove(s);
+  }
+
   public void setBeingAsked(boolean value) {
     this.beingAsked = value;
   }
@@ -42,7 +46,6 @@ public int getScore() {
     int score = 0;
 
     for (FetchingSample s : samples) {
-
       if (!s.isDownloaded() && s.beingFetchedFrom.size() < aggressiveness) {
         score += 1;
       }
@@ -56,8 +59,10 @@ public void setAgressiveness(int agr) {
 
   @Override
   public int compareTo(Node n) {
-    if (this.getScore() < n.getScore()) return 1;
-    else if (this.getScore() > n.getScore()) return -1;
+    // if (this.getScore() < n.getScore()) return 1;
+    // else if (this.getScore() > n.getScore()) return -1;
+    if (this.samples.size() > n.samples.size()) return -1;
+    else if (this.samples.size() < n.samples.size()) return 1;
     else return 0;
   }
 }
diff --git a/simulator/src/main/java/peersim/kademlia/das/operations/RandomSamplingOperation.java b/simulator/src/main/java/peersim/kademlia/das/operations/RandomSamplingOperation.java
index 9672e7fe..58c61e2d 100755
--- a/simulator/src/main/java/peersim/kademlia/das/operations/RandomSamplingOperation.java
+++ b/simulator/src/main/java/peersim/kademlia/das/operations/RandomSamplingOperation.java
@@ -2,14 +2,17 @@
 
 import java.math.BigInteger;
 import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
 import java.util.HashMap;
+import java.util.LinkedHashSet;
 import java.util.List;
 import java.util.Map;
+import peersim.core.CommonState;
 import peersim.kademlia.das.Block;
 import peersim.kademlia.das.KademliaCommonConfigDas;
 import peersim.kademlia.das.MissingNode;
 import peersim.kademlia.das.Sample;
-// import peersim.kademlia.das.SearchTable;
 import peersim.kademlia.das.SearchTable;
 
 /**
@@ -47,16 +50,20 @@ public RandomSamplingOperation(
       samples.put(rs.getIdByRow(), s);
       // samples.put(rs.getIdByColumn(), s);
     }
-    createNodes();
+    // createNodes();
   }
 
   public boolean completed() {
 
     boolean completed = true;
+    int failed = 0;
     for (FetchingSample s : samples.values()) {
       if (!s.isDownloaded()) {
-        completed = false;
-        break;
+        failed++;
+        if (failed > KademliaCommonConfigDas.MAX_SAMPLING_FAILED) {
+          completed = false;
+          break;
+        }
       }
     }
     return completed;
@@ -65,11 +72,13 @@ public boolean completed() {
   public void elaborateResponse(Sample[] sam) {
 
     for (Sample s : sam) {
-      if (samples.containsKey(s.getId()) || samples.containsKey(s.getIdByColumn())) {
+      if (samples.containsKey(s.getId()) /*|| samples.containsKey(s.getIdByColumn())*/) {
         FetchingSample fs = samples.get(s.getId());
-        if (!fs.isDownloaded()) {
-          samplesCount++;
-          fs.setDownloaded();
+        if (fs != null) {
+          if (!fs.isDownloaded()) {
+            samplesCount++;
+            fs.setDownloaded();
+          }
         }
       }
     }
@@ -77,83 +86,172 @@ public void elaborateResponse(Sample[] sam) {
   }
 
   public void elaborateResponse(Sample[] sam, BigInteger node) {
-    this.available_requests--;
+    // this.available_requests--;
     // if (this.available_requests == 0) nodes.clear();
-
+    pendingNodes.remove(node);
     Node n = nodes.get(node);
     if (n != null) {
       for (FetchingSample s : n.getSamples()) {
         s.removeFetchingNode(n);
+        // s.setDownloaded();
       }
     }
 
-    for (Sample s : sam) {
+    if (sam != null) {
+      for (Sample s : sam) {
+        if (samples.containsKey(s.getId()) /*|| samples.containsKey(s.getIdByColumn())*/) {
+          FetchingSample fs = samples.get(s.getId());
+          // FetchingSample fs2 = samples.get(s.getIdByColumn());
+          if (fs != null) {
+            if (!fs.isDownloaded()) {
+              samplesCount++;
+              fs.setDownloaded();
+              fs.removeFetchingNode(nodes.get(node));
+            }
+          } /*else if(fs2!=null){
+               if(!fs2.isDownloaded()){
+            samplesCount++;
+            fs2.setDownloaded();
+            fs2.removeFetchingNode(nodes.get(node));
+               }
 
-      if (samples.containsKey(s.getId()) || samples.containsKey(s.getIdByColumn())) {
-        FetchingSample fs = samples.get(s.getId());
-        if (!fs.isDownloaded()) {
-          samplesCount++;
-          fs.setDownloaded();
-          fs.removeFetchingNode(nodes.get(node));
+             }*/
         }
       }
     }
-
     nodes.remove(node);
-    askNodes.add(node);
+    // askedNodes.add(node);
+  }
+
+  public BigInteger[] getSamples() {
+    List<BigInteger> result = new ArrayList<>();
+
+    for (FetchingSample sample : samples.values()) {
+      if (!sample.isDownloaded()) result.add(sample.getId());
+    }
+
+    return result.toArray(new BigInteger[0]);
   }
 
   protected void createNodes() {
+
+    List<BigInteger> toRemove = new ArrayList<>();
     for (BigInteger sample : samples.keySet()) {
       if (!samples.get(sample).isDownloaded()) {
 
-        List<BigInteger> validatorsBySampleRow =
-            SearchTable.getNodesBySample(samples.get(sample).getId());
-        List<BigInteger> validatorsBySampleColumn =
-            SearchTable.getNodesBySample(samples.get(sample).getIdByColumn());
-
-        List<BigInteger> validatorsBySample = new ArrayList<>();
+        List<BigInteger> nodesBySample = new ArrayList<>();
 
-        validatorsBySample.addAll(validatorsBySampleRow);
-        validatorsBySample.addAll(validatorsBySampleColumn);
+        BigInteger radiusUsed = radiusValidator;
 
-        List<BigInteger> nonValidatorsBySample = new ArrayList<>();
-        nonValidatorsBySample.addAll(
+        boolean notInRegion = false;
+        while (!notInRegion) {
+          nodesBySample.addAll(
+              searchTable.getValidatorNodesbySample(samples.get(sample).getId(), radiusUsed));
+          nodesBySample.addAll(
+              searchTable.getValidatorNodesbySample(
+                  samples.get(sample).getIdByColumn(), radiusUsed));
+          if (!nodesBySample.isEmpty()) notInRegion = true;
+          if (!notInRegion) radiusUsed = radiusUsed.multiply(BigInteger.valueOf(2));
+        }
+        nodesBySample.addAll(
             searchTable.getNonValidatorNodesbySample(
                 samples.get(sample).getId(), radiusNonValidator));
-        nonValidatorsBySample.addAll(
+        nodesBySample.addAll(
             searchTable.getNonValidatorNodesbySample(
                 samples.get(sample).getIdByColumn(), radiusNonValidator));
 
         boolean found = false;
+        nodesBySample.removeAll(askedNodes);
 
-        if (validatorsBySampleRow != null && validatorsBySampleRow.size() > 0) {
-          for (BigInteger id : validatorsBySampleRow) {
-            if (!nodes.containsKey(id)) {
-              nodes.put(id, new Node(id));
-              nodes.get(id).addSample(samples.get(sample));
-            } else {
-              nodes.get(id).addSample(samples.get(sample));
+        if (nodesBySample != null && nodesBySample.size() > 0) {
+          for (BigInteger id : nodesBySample) {
+            if (!searchTable.isEvil(id)) {
+              if (!nodes.containsKey(id)) {
+                nodes.put(id, new Node(id));
+                nodes.get(id).addSample(samples.get(sample));
+              } else {
+                nodes.get(id).addSample(samples.get(sample));
+              }
+              found = true;
             }
           }
-          found = true;
         }
 
-        if (nonValidatorsBySample != null && nonValidatorsBySample.size() > 0) {
-          for (BigInteger id : nonValidatorsBySample) {
-            if (!nodes.containsKey(id)) {
-              nodes.put(id, new Node(id));
-              nodes.get(id).addSample(samples.get(sample));
-            } else {
-              nodes.get(id).addSample(samples.get(sample));
-            }
-          }
-          found = true;
+        if (!found && callback != null) {
+          callback.missing(sample, this);
+          toRemove.add(sample);
         }
+      }
+    }
+    if (toRemove.size() > 0) {
+      System.out.println(CommonState.getTime() + "Removing sample " + toRemove.size());
+      for (BigInteger sample : toRemove) {
+        samples.remove(sample);
+      }
+      Sample[] randomSamples = currentBlock.getNRandomSamples(toRemove.size());
+      for (Sample rs : randomSamples) {
+        FetchingSample s = new FetchingSample(rs);
+        samples.put(rs.getIdByRow(), s);
+        // samples.put(rs.getIdByColumn(), s);
+      }
+    }
+  }
 
-        if (!found && callback != null) callback.missing(sample, this);
+  protected void addExtraNodes() {
+    aggressiveness_step = KademliaCommonConfigDas.aggressiveness_step * 2;
+    if (CommonState.getTime() - this.getTimestamp() > 1000)
+      aggressiveness_step = KademliaCommonConfigDas.aggressiveness_step * 4;
+    if (CommonState.getTime() - this.getTimestamp() > 2000)
+      aggressiveness_step = KademliaCommonConfigDas.aggressiveness_step * 8;
+    List<BigInteger> missingSamples = Arrays.asList(getSamples());
+    Collections.shuffle(missingSamples);
+    for (BigInteger sample : missingSamples) {
+      int count = 0;
+      List<BigInteger> nodesBySample = new ArrayList<>();
+      BigInteger radiusUsed = radiusValidator;
+      Sample[] sRow = currentBlock.getSamplesByRow(samples.get(sample).getSample().getRow());
+      List<BigInteger> nodesToAdd = new ArrayList<>();
+      for (Sample s : sRow) {
+        // nodesToAdd.addAll(searchTable.getNodesbySample(s.getId(), radiusUsed));
+        nodesToAdd.addAll(searchTable.getValidatorNodesbySample(s.getId(), radiusUsed));
+      }
+      Sample[] sColumn =
+          currentBlock.getSamplesByColumn(samples.get(sample).getSample().getColumn());
+      for (Sample s : sColumn) {
+        // nodesToAdd.addAll(searchTable.getNodesbySample(s.getIdByColumn(), radiusUsed));
+        nodesToAdd.addAll(searchTable.getValidatorNodesbySample(s.getId(), radiusUsed));
+      }
+      nodesBySample.addAll(new ArrayList<>(new LinkedHashSet<>(nodesToAdd)));
+
+      nodesBySample.removeAll(askedNodes);
+      if (nodesBySample != null && nodesBySample.size() > 0) {
+        for (BigInteger id : nodesBySample) {
+          if (!nodes.containsKey(id)) {
+            nodes.put(id, new Node(id));
+            nodes.get(id).addSample(samples.get(sample));
+          } else {
+            nodes.get(id).addSample(samples.get(sample));
+          }
+          count++;
+          if (count == aggressiveness) return;
+        }
       }
     }
+
+    /*if (nodes.size() == 0 && getSamples().length > 0) {
+      List<Sample> randomSamples = new ArrayList<>();
+      while (randomSamples.size() < getSamples().length * 10) {
+        Sample[] sample = currentBlock.getNRandomSamples(1);
+        if (!Arrays.asList(getSamples()).contains(sample[0].getId())) randomSamples.add(sample[0]);
+      }
+      for (BigInteger id : getSamples()) samples.remove(id);
+      askedNodes.clear();
+      for (Sample rs : randomSamples) {
+        FetchingSample s = new FetchingSample(rs);
+        samples.put(rs.getIdByRow(), s);
+      }
+      createNodes();
+    }*/
   }
 
   public Map<String, Object> toMap() {
@@ -164,7 +262,7 @@ public Map<String, Object> toMap() {
     result.put("src", this.srcNode);
     result.put("type", "RandomSamplingOperation");
     result.put("messages", getMessagesString());
-    result.put("num_messages", getMessages().size());
+    result.put("nodes_contacted", getMessages().size());
     result.put("start", this.timestamp);
     result.put("completion_time", this.stopTime);
     result.put("hops", this.nrHops);
diff --git a/simulator/src/main/java/peersim/kademlia/das/operations/SamplingOperation.java b/simulator/src/main/java/peersim/kademlia/das/operations/SamplingOperation.java
index 354047bc..ea474641 100644
--- a/simulator/src/main/java/peersim/kademlia/das/operations/SamplingOperation.java
+++ b/simulator/src/main/java/peersim/kademlia/das/operations/SamplingOperation.java
@@ -2,9 +2,12 @@
 
 import java.math.BigInteger;
 import java.util.ArrayList;
+import java.util.Collection;
+import java.util.Collections;
 import java.util.HashMap;
 import java.util.LinkedHashMap;
 import java.util.List;
+import peersim.core.CommonState;
 import peersim.kademlia.das.Block;
 import peersim.kademlia.das.KademliaCommonConfigDas;
 import peersim.kademlia.das.MissingNode;
@@ -29,7 +32,12 @@ public abstract class SamplingOperation extends FindOperation {
   protected LinkedHashMap<BigInteger, Node> nodes;
   protected HashMap<BigInteger, FetchingSample> samples;
 
-  protected List<BigInteger> askNodes;
+  protected List<BigInteger> askedNodes;
+  protected List<BigInteger> pendingNodes;
+
+  protected boolean started;
+
+  protected int aggressiveness_step;
 
   public SamplingOperation(
       BigInteger srcNode,
@@ -49,10 +57,13 @@ public SamplingOperation(
         currentBlock.computeRegionRadius(KademliaCommonConfigDas.NUM_SAMPLE_COPIES_PER_PEER);
     samples = new HashMap<>();
     nodes = new LinkedHashMap<>();
-    this.available_requests = 0;
+    // this.available_requests = 0;
     aggressiveness = 0;
-    askNodes = new ArrayList<>();
+    askedNodes = new ArrayList<>();
+    pendingNodes = new ArrayList<>();
     timesIncreased = 0;
+    started = false;
+    aggressiveness_step = KademliaCommonConfigDas.aggressiveness_step;
   }
 
   public SamplingOperation(
@@ -70,30 +81,25 @@ public SamplingOperation(
     this.isValidator = isValidator;
     this.callback = callback;
     currentBlock = block;
-    this.available_requests = 0;
+    // this.available_requests = 0;
     aggressiveness = 0;
     radiusValidator =
         currentBlock.computeRegionRadius(
             KademliaCommonConfigDas.NUM_SAMPLE_COPIES_PER_PEER, numValidators);
     radiusNonValidator =
         currentBlock.computeRegionRadius(KademliaCommonConfigDas.NUM_SAMPLE_COPIES_PER_PEER);
-    askNodes = new ArrayList<>();
+    askedNodes = new ArrayList<>();
     timesIncreased = 0;
+    pendingNodes = new ArrayList<>();
+    started = false;
+    aggressiveness_step = KademliaCommonConfigDas.aggressiveness_step;
     // queried = new HashSet<>();
     // TODO Auto-generated constructor stub
   }
 
   public abstract boolean completed();
 
-  public BigInteger[] getSamples() {
-    List<BigInteger> result = new ArrayList<>();
-
-    for (FetchingSample sample : samples.values()) {
-      if (!sample.isDownloaded()) result.add(sample.getId());
-    }
-
-    return result.toArray(new BigInteger[0]);
-  }
+  public abstract BigInteger[] getSamples();
 
   public BigInteger getRadiusValidator() {
     return radiusValidator;
@@ -103,59 +109,98 @@ public BigInteger getRadiusNonValidator() {
     return radiusNonValidator;
   }
 
-  protected void createNodes() {
-    for (BigInteger sample : samples.keySet()) {
-      if (!samples.get(sample).isDownloaded()) {
-
-        List<BigInteger> validatorsBySample = SearchTable.getNodesBySample(sample);
-        List<BigInteger> nonValidatorsBySample =
-            searchTable.getNonValidatorNodesbySample(sample, radiusNonValidator);
-
-        boolean found = false;
-        if (validatorsBySample != null && validatorsBySample.size() > 0) {
-          for (BigInteger id : validatorsBySample) {
-            if (!nodes.containsKey(id)) {
-              nodes.put(id, new Node(id));
-              nodes.get(id).addSample(samples.get(sample));
-            } else {
-              nodes.get(id).addSample(samples.get(sample));
-            }
-          }
-          found = true;
-        }
-        if (nonValidatorsBySample != null && nonValidatorsBySample.size() > 0) {
-          for (BigInteger id : nonValidatorsBySample) {
-            if (!nodes.containsKey(id)) {
-              nodes.put(id, new Node(id));
-              nodes.get(id).addSample(samples.get(sample));
-            } else {
-              nodes.get(id).addSample(samples.get(sample));
-            }
-          }
-          found = true;
-        }
+  protected abstract void createNodes();
 
-        if (!found && callback != null) callback.missing(sample, this);
-      }
-    }
-  }
+  protected abstract void addExtraNodes();
 
   public BigInteger[] doSampling() {
 
-    aggressiveness += KademliaCommonConfigDas.aggressiveness_step;
+    if (CommonState.getTime() < 200) aggressiveness = aggressiveness_step;
+    else aggressiveness += aggressiveness_step;
+    // System.out.println("[" + srcNode + "]  nodes " + nodes.size());
+    List<BigInteger> toRemove = new ArrayList<>();
+    for (BigInteger n : nodes.keySet()) {
+      List<FetchingSample> sToRemove = new ArrayList<>();
+      for (FetchingSample s : nodes.get(n).getSamples()) {
+        /*System.out.println(
+        "["
+            + srcNode
+            + "]  nodes "
+            + n
+            + " "
+            + nodes.get(n).isBeingAsked()
+            + " "
+            + nodes.get(n).getScore()
+            + " "
+            + aggressiveness
+            + " "
+            + s.isDownloaded()
+            + " "
+            + +s.beingFetchedFrom.size());*/
+        if (s.isDownloaded()) sToRemove.add(s);
+      }
+      for (FetchingSample s : sToRemove) {
+        nodes.get(n).removeFetchingSample(s);
+      }
+      if (nodes.get(n).getSamples().size() == 0) toRemove.add(n);
+    }
+    for (BigInteger id : toRemove) nodes.remove(id);
+
+    if (nodes.isEmpty()) {
+      createNodes();
+      System.out.println(
+          "["
+              + CommonState.getTime()
+              + "]["
+              + srcNode
+              + "] Adding nodes "
+              + this.getId()
+              + " "
+              + nodes.size()
+              + " "
+              + aggressiveness
+              + " "
+              + askedNodes.size());
+    }
+    if (nodes.isEmpty()) {
+      // if (this instanceof ValidatorSamplingOperation) addExtraNodes();
+      addExtraNodes();
+      System.out.println(
+          "["
+              + CommonState.getTime()
+              + "]["
+              + srcNode
+              + "] Adding extra nodes "
+              + this.getId()
+              + " "
+              + nodes.size()
+              + " "
+              + aggressiveness
+              + " "
+              + askedNodes.size());
+    }
     for (Node n : nodes.values()) n.setAgressiveness(aggressiveness);
-
+    List<Node> sortedNodes = new ArrayList<>(nodes.values());
+    Collections.sort(sortedNodes);
     List<BigInteger> result = new ArrayList<>();
-    for (Node n : nodes.values()) {
+    // for (Node n : nodes.values()) {
+    for (Node n : sortedNodes) {
       /*System.out.println(
-          this.srcNode + "] Querying node " + n.getId() + " " + +n.getScore() + " " + this.getId());
-      for (FetchingSample fs : n.getSamples())
-        System.out.println(
-            this.srcNode + "] Sample " + fs.beingFetchedFrom.size() + " " + fs.isDownloaded());*/
+      "["
+          + CommonState.getTime()
+          + "]["
+          + srcNode
+          + "] Sampling nodes "
+          + this.getId()
+          + " "
+          + n.getScore()
+          + " "
+          + n.getSamples().size());*/
 
       if (!n.isBeingAsked() && n.getScore() > 0) { // break;
         n.setBeingAsked(true);
-        this.available_requests++;
+        // this.available_requests++;
+        pendingNodes.add(n.getId());
         for (FetchingSample s : n.getSamples()) {
           s.addFetchingNode(n);
         }
@@ -163,9 +208,21 @@ public BigInteger[] doSampling() {
       }
     }
 
+    /*if (result.size() == 0 && this instanceof RandomSamplingOperation && aggressiveness >= 5) {
+      result = searchTable.getAllNeighbours();
+      result.remove(askedNodes);
+      pendingNodes.addAll(result);
+    }*/
+    askedNodes.addAll(result);
+
+    if (result.size() > 0) started = true;
     return result.toArray(new BigInteger[0]);
   }
 
+  public Collection<Node> getNodes() {
+    return nodes.values();
+  }
+
   public abstract void elaborateResponse(Sample[] sam, BigInteger node);
 
   public abstract void elaborateResponse(Sample[] sam);
@@ -173,4 +230,16 @@ public BigInteger[] doSampling() {
   public int samplesCount() {
     return samplesCount;
   }
+
+  public int getPending() {
+    return pendingNodes.size();
+  }
+
+  public boolean getStarted() {
+    return started;
+  }
+
+  public int getAgressiveness() {
+    return aggressiveness;
+  }
 }
diff --git a/simulator/src/main/java/peersim/kademlia/das/operations/ValidatorSamplingOperation.java b/simulator/src/main/java/peersim/kademlia/das/operations/ValidatorSamplingOperation.java
index b69eb083..76027863 100644
--- a/simulator/src/main/java/peersim/kademlia/das/operations/ValidatorSamplingOperation.java
+++ b/simulator/src/main/java/peersim/kademlia/das/operations/ValidatorSamplingOperation.java
@@ -1,7 +1,12 @@
 package peersim.kademlia.das.operations;
 
 import java.math.BigInteger;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
 import java.util.HashMap;
+import java.util.LinkedHashSet;
+import java.util.List;
 import java.util.Map;
 import peersim.kademlia.das.Block;
 import peersim.kademlia.das.MissingNode;
@@ -18,6 +23,8 @@ public class ValidatorSamplingOperation extends SamplingOperation {
 
   // private RoutingTable rou;
   protected int row, column;
+  protected HashMap<BigInteger, Integer> extras;
+  protected List<BigInteger> validatorList;
   /**
    * default constructor
    *
@@ -34,6 +41,7 @@ public ValidatorSamplingOperation(
       int column,
       boolean isValidator,
       int numValidators,
+      List<BigInteger> validatorList,
       MissingNode callback) {
     super(srcNode, null, timestamp, block, isValidator, numValidators, callback);
 
@@ -49,25 +57,28 @@ public ValidatorSamplingOperation(
       }
     } else if (column > 0) {
       for (Sample sample : block.getSamplesByColumn(column)) {
-        samples.put(sample.getIdByColumn(), new FetchingSample(sample));
+        samples.put(sample.getId(), new FetchingSample(sample));
       }
     }
     this.searchTable = searchTable;
+    this.extras = new HashMap<>();
+    this.validatorList = validatorList;
     createNodes();
   }
 
   public void elaborateResponse(Sample[] sam) {
 
     for (Sample s : sam) {
-      if (row > 0) {
-        if (samples.containsKey(s.getId())) {
-          FetchingSample fs = samples.get(s.getId());
-          if (!fs.isDownloaded()) {
-            fs.setDownloaded();
-            samplesCount++;
-          }
+      // if (row > 0) {
+      if (samples.containsKey(s.getId())) {
+        FetchingSample fs = samples.get(s.getId());
+        if (!fs.isDownloaded()) {
+          fs.setDownloaded();
+          samplesCount++;
         }
-      } else {
+      }
+      this.extras.remove(s.getId());
+      /*} else {
         if (samples.containsKey(s.getIdByColumn())) {
           FetchingSample fs = samples.get(s.getIdByColumn());
           if (!fs.isDownloaded()) {
@@ -75,25 +86,37 @@ public void elaborateResponse(Sample[] sam) {
             samplesCount++;
           }
         }
-      }
+      }*/
     }
-    // System.out.println("Row " + samplesCount + " " + samples.size());
+    /*System.out.println(
+    "["
+        + CommonState.getTime()
+        + "]["
+        + srcNode
+        + "] Completed operation "
+        + this.getId()
+        + " "
+        + samplesCount
+        + " "
+        + samples.size());*/
     if (samplesCount >= samples.size() / 2) completed = true;
   }
 
   public void elaborateResponse(Sample[] sam, BigInteger n) {
 
-    this.available_requests--;
+    // this.available_requests--;
     // if (this.available_requests == 0) nodes.clear();
 
+    pendingNodes.remove(n);
     Node node = nodes.get(n);
     if (node != null) {
       for (FetchingSample s : node.getSamples()) {
         s.removeFetchingNode(node);
       }
     }
-    for (Sample s : sam) {
-      if (row > 0) {
+    if (sam != null) {
+      for (Sample s : sam) {
+        // if (row > 0) {
         if (samples.containsKey(s.getId())) {
           FetchingSample fs = samples.get(s.getId());
           if (!fs.isDownloaded()) {
@@ -101,20 +124,34 @@ public void elaborateResponse(Sample[] sam, BigInteger n) {
             samplesCount++;
           }
         }
-      } else {
-        if (samples.containsKey(s.getIdByColumn())) {
-          FetchingSample fs = samples.get(s.getIdByColumn());
-          if (!fs.isDownloaded()) {
-            fs.setDownloaded();
-            samplesCount++;
+        this.extras.remove(s.getId());
+
+        /*} else {
+          if (samples.containsKey(s.getIdByColumn())) {
+            FetchingSample fs = samples.get(s.getIdByColumn());
+            if (!fs.isDownloaded()) {
+              fs.setDownloaded();
+              samplesCount++;
+            }
           }
-        }
+        }*/
       }
     }
+    /*System.out.println(
+    "["
+        + CommonState.getTime()
+        + "]["
+        + srcNode
+        + "] Completed operation "
+        + this.getId()
+        + " "
+        + samplesCount
+        + " "
+        + samples.size());*/
     // System.out.println("Row " + samplesCount + " " + samples.size());
     if (samplesCount >= samples.size() / 2) completed = true;
-    askNodes.add(n);
 
+    // askedNodes.add(n);
     nodes.remove(n);
   }
 
@@ -131,6 +168,165 @@ public int getColumn() {
     return column;
   }
 
+  @Override
+  protected void createNodes() {
+    for (BigInteger sample : samples.keySet()) {
+      if (!samples.get(sample).isDownloaded()) {
+
+        List<BigInteger> nodesBySample = new ArrayList<>();
+        /*if (row > 0) {
+          // BigInteger radiusUsed = radiusValidator;
+          // while (nodesBySample.isEmpty() && radiusUsed.compareTo(Block.MAX_KEY) == -1) {
+          // nodesBySample.addAll(
+          //    searchTable.getNodesbySample(samples.get(sample).getId(), radiusUsed));
+          nodesBySample.addAll(
+              searchTable.getValidatorNodesbySample(samples.get(sample).getId(), radiusValidator));
+          nodesBySample.addAll(
+              searchTable.getNonValidatorNodesbySample(
+                  samples.get(sample).getId(), radiusNonValidator));
+
+          //   radiusUsed = radiusUsed.multiply(BigInteger.valueOf(2));
+          // }
+        } else {
+          // BigInteger radiusUsed = radiusValidator;
+          // while (nodesBySample.isEmpty() && radiusUsed.compareTo(Block.MAX_KEY) == -1) {
+          // nodesBySample.addAll(
+          //    searchTable.getNodesbySample(samples.get(sample).getIdByColumn(), radiusUsed));
+          nodesBySample.addAll(
+              searchTable.getValidatorNodesbySample(
+                  samples.get(sample).getIdByColumn(), radiusValidator));
+          nodesBySample.addAll(
+              searchTable.getNonValidatorNodesbySample(
+                  samples.get(sample).getIdByColumn(), radiusNonValidator));
+
+          // radiusUsed = radiusUsed.multiply(BigInteger.valueOf(2));
+          // }
+        }*/
+        nodesBySample.addAll(validatorList);
+        boolean found = false;
+        nodesBySample.removeAll(askedNodes);
+        if (nodesBySample != null && nodesBySample.size() > 0) {
+          for (BigInteger id : nodesBySample) {
+            if (!nodes.containsKey(id)) {
+              nodes.put(id, new Node(id));
+              nodes.get(id).addSample(samples.get(sample));
+            } else {
+              nodes.get(id).addSample(samples.get(sample));
+            }
+          }
+          found = true;
+        }
+
+        if (!found && callback != null) callback.missing(sample, this);
+      }
+    }
+
+    /*for (BigInteger id : nodes.keySet()) {
+      for (FetchingSample s : nodes.get(id).getSamples())
+        System.out.println(
+            "["
+                + CommonState.getTime()
+                + "]["
+                + srcNode
+                + "] Nodes created "
+                + this.getId()
+                + " "
+                + id
+                + " "
+                + s.getId());
+    }*/
+  }
+
+  public BigInteger[] getSamples() {
+    List<BigInteger> result = new ArrayList<>();
+
+    for (FetchingSample sample : samples.values()) {
+      if (!sample.isDownloaded()) {
+        // if (column > 0) result.add(sample.getIdByColumn());
+        // else
+        result.add(sample.getId());
+      }
+    }
+
+    return result.toArray(new BigInteger[0]);
+  }
+
+  protected void addExtraNodes() {
+    // if (CommonState.getTime() - this.getTimestamp() > 1000)
+    // aggressiveness_step = KademliaCommonConfigDas.aggressiveness_step * 2;
+    /*if (CommonState.getTime() - this.getTimestamp() > 1000)
+      aggressiveness_step = KademliaCommonConfigDas.aggressiveness_step * 4;
+    if (CommonState.getTime() - this.getTimestamp() > 2000)*/
+    // aggressiveness_step = KademliaCommonConfigDas.aggressiveness_step * 4;
+    int globalcount = 0;
+    List<BigInteger> missingSamples = Arrays.asList(getSamples());
+    Collections.shuffle(missingSamples);
+    // int totalcount = 0;
+    for (BigInteger sample : missingSamples) {
+      List<BigInteger> nodesBySample = new ArrayList<>();
+      // BigInteger radiusUsed = radiusValidator;
+      if (column > 0) {
+        // Sample[] sRow = currentBlock.getSamplesByRow(samples.get(sample).getSample().getRow());
+        List<BigInteger> nodes = new ArrayList<>();
+        // for (Sample s : sRow) {
+        // nodes.addAll(searchTable.getNodesbySample(s.getId(), radiusUsed));
+        BigInteger radiusUsed = radiusValidator;
+        boolean nodeInRegion = false;
+        while (!nodeInRegion) {
+          nodes.addAll(
+              searchTable.getValidatorNodesbySample(samples.get(sample).getId(), radiusUsed));
+          if (!nodes.isEmpty()) nodeInRegion = true;
+          else radiusUsed = radiusUsed.multiply(BigInteger.valueOf(2));
+        }
+        nodes.addAll(
+            searchTable.getNonValidatorNodesbySample(
+                samples.get(sample).getId(), radiusNonValidator));
+        // }
+
+        nodesBySample.addAll(new ArrayList<>(new LinkedHashSet<>(nodes)));
+      } else {
+        // Sample[] sColumn =
+        // currentBlock.getSamplesByColumn(samples.get(sample).getSample().getColumn());
+        List<BigInteger> nodes = new ArrayList<>();
+        // for (Sample s : sColumn) {
+        // nodes.addAll(searchTable.getNodesbySample(s.getIdByColumn(), radiusUsed));
+        BigInteger radiusUsed = radiusValidator;
+        boolean nodeInRegion = false;
+        while (!nodeInRegion) {
+          nodes.addAll(
+              searchTable.getValidatorNodesbySample(
+                  samples.get(sample).getIdByColumn(), radiusUsed));
+          if (!nodes.isEmpty()) nodeInRegion = true;
+          else radiusUsed = radiusUsed.multiply(BigInteger.valueOf(2));
+        }
+        nodes.addAll(
+            searchTable.getNonValidatorNodesbySample(
+                samples.get(sample).getIdByColumn(), radiusNonValidator));
+        // }
+        nodesBySample.addAll(new ArrayList<>(new LinkedHashSet<>(nodes)));
+      }
+
+      nodesBySample.removeAll(askedNodes);
+      Collections.shuffle(nodesBySample);
+      int count = 0;
+      if (nodesBySample != null && nodesBySample.size() > 0) {
+        for (BigInteger id : nodesBySample) {
+          if (!nodes.containsKey(id)) {
+            nodes.put(id, new Node(id));
+            nodes.get(id).addSample(samples.get(sample));
+          } else {
+            nodes.get(id).addSample(samples.get(sample));
+          }
+          count++;
+          globalcount++;
+          if (count >= aggressiveness) break;
+          // if (count >= 256) return;
+        }
+      }
+      if (globalcount > 256) break;
+    }
+  }
+
   public Map<String, Object> toMap() {
     // System.out.println("Mapping");
     Map<String, Object> result = new HashMap<String, Object>();
@@ -139,7 +335,7 @@ public Map<String, Object> toMap() {
     result.put("src", this.srcNode);
     result.put("type", "ValidatorSamplingOperation");
     result.put("messages", getMessagesString());
-    result.put("num_messages", getMessages().size());
+    result.put("nodes_contacted", getMessages().size());
     result.put("start", this.timestamp);
     result.put("completion_time", this.stopTime);
     result.put("hops", this.nrHops);
diff --git a/simulator/src/main/java/peersim/kademlia/das/operations/ValidatorSamplingOperationDHT.java b/simulator/src/main/java/peersim/kademlia/das/operations/ValidatorSamplingOperationDHT.java
index 89ad0882..ef4f14f6 100644
--- a/simulator/src/main/java/peersim/kademlia/das/operations/ValidatorSamplingOperationDHT.java
+++ b/simulator/src/main/java/peersim/kademlia/das/operations/ValidatorSamplingOperationDHT.java
@@ -23,9 +23,19 @@ public ValidatorSamplingOperationDHT(
       int column,
       boolean isValidator,
       int numValidators,
+      List<BigInteger> validatorList,
       MissingNode callback) {
     super(
-        srcNode, timestamp, block, searchTable, row, column, isValidator, numValidators, callback);
+        srcNode,
+        timestamp,
+        block,
+        searchTable,
+        row,
+        column,
+        isValidator,
+        numValidators,
+        validatorList,
+        callback);
 
     // System.out.println("Row " + row + " column " + column);
     assert (row == 0 || column == 0) : "Either row or column should be set";
diff --git a/simulator/src/main/java/peersim/transport/PairwiseFixedLatencyTransport.java b/simulator/src/main/java/peersim/transport/PairwiseFixedLatencyTransport.java
index 554884ea..adc8cedb 100644
--- a/simulator/src/main/java/peersim/transport/PairwiseFixedLatencyTransport.java
+++ b/simulator/src/main/java/peersim/transport/PairwiseFixedLatencyTransport.java
@@ -4,7 +4,7 @@
 import peersim.core.*;
 import peersim.edsim.*;
 
-public final class PairwiseFixedLatencyTransport extends UniformRandomTransport {
+public class PairwiseFixedLatencyTransport extends UniformRandomTransport {
 
   /** Size of the network. */
   private int size;