diff --git a/.gitignore b/.gitignore
index fd83840..4895238 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,5 +4,5 @@ Results/
 CSV_Files/
 Configurations/
 run/
-src/script_ICC.jl
+src/script_ICC_A.jl
 src/script.jl
\ No newline at end of file
diff --git a/src/script.jl b/src/script.jl
deleted file mode 100644
index 17b5642..0000000
--- a/src/script.jl
+++ /dev/null
@@ -1,92 +0,0 @@
-
-using RiFyFi
-using Infiltrator
-using RFImpairmentsModels 
-
-include("Augmentation/src/Augmentation.jl")
-using .Augmentation
-
-include("RiFyFi_VDG/src/RiFyFi_VDG.jl")
-using .RiFyFi_VDG
-
-include("RiFyFi_IdF/src/RiFyFi_IdF.jl")
-using .RiFyFi_IdF
-
-include("Results/src/Results.jl")
-using .Results
-
-# Parameters DataBase Synth
-name= "Emma_Beta_PM"
-nameModel= name
-nbRadioTx=6
-NbSignals=1000
-Chunksize = 256
-features="IQsamples"
-E="E3"
-S="S1"
-C="C2"#_20dB"
-RFF="all_impairments" 
-Normalisation=true
-pourcentTrain = 0.9
-configuration ="scenario"
-seed_model =  234567
-seed_data = 123456
-if E == "E1" || E == "E2"
-    seed_modelTest = seed_model 
-else 
-    seed_modelTest = 15987654321 * 100000000
-end 
-if S == "S1" || S == "S2"
-    seed_dataTest = seed_data 
-else 
-    seed_dataTest = 9999246912 * 100000000
-end 
-
-# Augmentation
-#nb_Augment=1
-Table_nb_Augment=[1]
-augmentationType="sans"
-Channel="etu"
-Channel_Test="eva"
-
-
-# Network Parameters
-Networkname="AlexNet"
-NbClass =nbRadioTx
-#Chunksize 
-#NbSignals
-Table_Seed_Network= [11]#,44,55]
-
-# Train Args
-η = 1e-5   # learning rate 
-dr = 0.25      # dropout 
-batchsize = 64
-epochs = 2000
-use_cuda=true  
-
-savepathbson=""
-#for i =1: 1 :size(Table_nb_Augment,1)
-    nb_Augment = Table_nb_Augment[1]
-    Augmentation_Value = Augmentation.Data_Augmented_construct(augmentationType=augmentationType,nb_Augment=nb_Augment,Channel=Channel,Channel_Test=Channel_Test)
-    Param_Data = RiFyFi_VDG.Data_Synth(name,nameModel,nbRadioTx, NbSignals, Chunksize,features,S,E,C,RFF,Normalisation,pourcentTrain,configuration,seed_data,seed_model,seed_dataTest,seed_modelTest,Augmentation_Value)
-   
-     setSynthetiquecsv(Param_Data)
-    #=
-    for k = 1 :1: size(Table_Seed_Network,1)
-    Seed_Network= Table_Seed_Network[k]
-    Train_args = RiFyFi_IdF.Args(η = η ,dr=dr, epochs= epochs,batchsize=batchsize)
-    Param_Network = RiFyFi_IdF.Network_struct(;Networkname,NbClass,Chunksize,NbSignals,Seed_Network,Train_args) 
-    savepathbson = RiFyFi.main(Param_Data,Param_Network)   #filename is the .pkl file    
-    end 
-    
-    nameTable = [name]
-    Seed_Network= Table_Seed_Network[1]
-    Train_args = RiFyFi_IdF.Args(η = η ,dr=dr, epochs= epochs,batchsize=batchsize)
-    Param_Network = RiFyFi_IdF.Network_struct(;Networkname,NbClass,Chunksize,NbSignals,Seed_Network,Train_args) 
-    Results.main(Param_Data,Param_Network,"F1_score",Table_Seed_Network,savepathbson)
-    Results.main(Param_Data,Param_Network,"Confusion_Matrix",Table_Seed_Network,savepathbson)
-
-end 
-
-
-=#
diff --git a/src/script_ICC.jl b/src/script_ICC.jl
deleted file mode 100644
index 09094eb..0000000
--- a/src/script_ICC.jl
+++ /dev/null
@@ -1,328 +0,0 @@
-
-using RiFyFi
-using Infiltrator
-using RFImpairmentsModels 
-
-include("Augmentation/src/Augmentation.jl")
-using .Augmentation
-
-include("RiFyFi_VDG/src/RiFyFi_VDG.jl")
-using .RiFyFi_VDG
-
-include("RiFyFi_IdF/src/RiFyFi_IdF.jl")
-using .RiFyFi_IdF
-
-include("Results/src/Results.jl")
-using .Results
-
-#=
-# Parameters DataBase Synth
-name= "control_1_fixe"
-nameModel= name
-nbRadioTx=6
-NbSignals= 1000
-Chunksize = 256
-features="IQsamples"
-E="E3"
-S="S1"
-C="C2"#_20dB"
-RFF="all_impairments" 
-Normalisation=true
-pourcentTrain = 0.9
-configuration ="scenario"
-seed_model =  234567
-seed_data = 123456
-if E == "E1" || E == "E2"
-    seed_modelTest = seed_model 
-else 
-    seed_modelTest = 15987654321 * 100000000
-end 
-if S == "S1" || S == "S2"
-    seed_dataTest = seed_data 
-else 
-    seed_dataTest = 9999246912 * 100000000
-end 
-
-# Augmentation
-#nb_Augment=1
-Table_nb_Augment=[1]
-augmentationType="sans"
-Channel="etu"
-Channel_Test="etu"
-
-
-# Network Parameters
-Networkname="AlexNet"
-NbClass =nbRadioTx
-#Chunksize 
-#NbSignals
-Table_Seed_Network= [12,13,14]#,44,55]
-
-# Train Args
-η = 1e-5   # learning rate 
-dr = 0.25      # dropout 
-batchsize = 64
-epochs = 2000
-use_cuda=true  
-
-savepathbson=""
-for i =1: 1 :size(Table_nb_Augment,1)
-    nb_Augment = Table_nb_Augment[i]
-    Augmentation_Value = Augmentation.Data_Augmented_construct(augmentationType=augmentationType,nb_Augment=nb_Augment,Channel=Channel,Channel_Test=Channel_Test)
-    Param_Data = RiFyFi_VDG.Data_Synth(name,nameModel,nbRadioTx, NbSignals, Chunksize,features,S,E,C,RFF,Normalisation,pourcentTrain,configuration,seed_data,seed_model,seed_dataTest,seed_modelTest,Augmentation_Value)
-   
-     setSynthetiquecsv(Param_Data)
-    
-    for k = 1 :1: size(Table_Seed_Network,1)
-    Seed_Network= Table_Seed_Network[k]
-    Train_args = RiFyFi_IdF.Args(η = η ,dr=dr, epochs= epochs,batchsize=batchsize)
-    Param_Network = RiFyFi_IdF.Network_struct(;Networkname,NbClass,Chunksize,NbSignals,Seed_Network,Train_args) 
-    savepathbson = RiFyFi.main(Param_Data,Param_Network)   #filename is the .pkl file    
-    end 
-    
-    nameTable = [name]
-    Seed_Network= Table_Seed_Network[1]
-    Train_args = RiFyFi_IdF.Args(η = η ,dr=dr, epochs= epochs,batchsize=batchsize)
-    Param_Network = RiFyFi_IdF.Network_struct(;Networkname,NbClass,Chunksize,NbSignals,Seed_Network,Train_args) 
-    Results.main(Param_Data,Param_Network,"F1_score",Table_Seed_Network,savepathbson)
-   # Results.main(Param_Data,Param_Network,"Confusion_Matrix",Table_Seed_Network,savepathbson)
-
-end 
-
-
-
-
-name= "control_2_fixe"
-nameModel= name
-
-for i =1: 1 :size(Table_nb_Augment,1)
-    nb_Augment = Table_nb_Augment[i]
-    Augmentation_Value = Augmentation.Data_Augmented_construct(augmentationType=augmentationType,nb_Augment=nb_Augment,Channel=Channel,Channel_Test=Channel_Test)
-    Param_Data = RiFyFi_VDG.Data_Synth(name,nameModel,nbRadioTx, NbSignals, Chunksize,features,S,E,C,RFF,Normalisation,pourcentTrain,configuration,seed_data,seed_model,seed_dataTest,seed_modelTest,Augmentation_Value)
-   
-     setSynthetiquecsv(Param_Data)
-    
-    for k = 1 :1: size(Table_Seed_Network,1)
-    Seed_Network= Table_Seed_Network[k]
-    Train_args = RiFyFi_IdF.Args(η = η ,dr=dr, epochs= epochs,batchsize=batchsize)
-    Param_Network = RiFyFi_IdF.Network_struct(;Networkname,NbClass,Chunksize,NbSignals,Seed_Network,Train_args) 
-    savepathbson = RiFyFi.main(Param_Data,Param_Network)   #filename is the .pkl file    
-    end 
-    
-    nameTable = [name]
-    Seed_Network= Table_Seed_Network[1]
-    Train_args = RiFyFi_IdF.Args(η = η ,dr=dr, epochs= epochs,batchsize=batchsize)
-    Param_Network = RiFyFi_IdF.Network_struct(;Networkname,NbClass,Chunksize,NbSignals,Seed_Network,Train_args) 
-    Results.main(Param_Data,Param_Network,"F1_score",Table_Seed_Network,savepathbson)
-   # Results.main(Param_Data,Param_Network,"Confusion_Matrix",Table_Seed_Network,savepathbson)
-
-end 
-
-
-name= "control_3_fixe"
-nameModel= name
-
-for i =1: 1 :size(Table_nb_Augment,1)
-    nb_Augment = Table_nb_Augment[i]
-    Augmentation_Value = Augmentation.Data_Augmented_construct(augmentationType=augmentationType,nb_Augment=nb_Augment,Channel=Channel,Channel_Test=Channel_Test)
-    Param_Data = RiFyFi_VDG.Data_Synth(name,nameModel,nbRadioTx, NbSignals, Chunksize,features,S,E,C,RFF,Normalisation,pourcentTrain,configuration,seed_data,seed_model,seed_dataTest,seed_modelTest,Augmentation_Value)
-   
-     setSynthetiquecsv(Param_Data)
-    
-    for k = 1 :1: size(Table_Seed_Network,1)
-    Seed_Network= Table_Seed_Network[k]
-    Train_args = RiFyFi_IdF.Args(η = η ,dr=dr, epochs= epochs,batchsize=batchsize)
-    Param_Network = RiFyFi_IdF.Network_struct(;Networkname,NbClass,Chunksize,NbSignals,Seed_Network,Train_args) 
-    savepathbson = RiFyFi.main(Param_Data,Param_Network)   #filename is the .pkl file    
-    end 
-    
-    nameTable = [name]
-    Seed_Network= Table_Seed_Network[1]
-    Train_args = RiFyFi_IdF.Args(η = η ,dr=dr, epochs= epochs,batchsize=batchsize)
-    Param_Network = RiFyFi_IdF.Network_struct(;Networkname,NbClass,Chunksize,NbSignals,Seed_Network,Train_args) 
-    Results.main(Param_Data,Param_Network,"F1_score",Table_Seed_Network,savepathbson)
-   # Results.main(Param_Data,Param_Network,"Confusion_Matrix",Table_Seed_Network,savepathbson)
-
-end 
-
-
-name= "control_5_fixe"
-nameModel= name
-
-for i =1: 1 :size(Table_nb_Augment,1)
-    nb_Augment = Table_nb_Augment[i]
-    Augmentation_Value = Augmentation.Data_Augmented_construct(augmentationType=augmentationType,nb_Augment=nb_Augment,Channel=Channel,Channel_Test=Channel_Test)
-    Param_Data = RiFyFi_VDG.Data_Synth(name,nameModel,nbRadioTx, NbSignals, Chunksize,features,S,E,C,RFF,Normalisation,pourcentTrain,configuration,seed_data,seed_model,seed_dataTest,seed_modelTest,Augmentation_Value)
-   
-     setSynthetiquecsv(Param_Data)
-    
-    for k = 1 :1: size(Table_Seed_Network,1)
-    Seed_Network= Table_Seed_Network[k]
-    Train_args = RiFyFi_IdF.Args(η = η ,dr=dr, epochs= epochs,batchsize=batchsize)
-    Param_Network = RiFyFi_IdF.Network_struct(;Networkname,NbClass,Chunksize,NbSignals,Seed_Network,Train_args) 
-    savepathbson = RiFyFi.main(Param_Data,Param_Network)   #filename is the .pkl file    
-    end 
-    
-    nameTable = [name]
-    Seed_Network= Table_Seed_Network[1]
-    Train_args = RiFyFi_IdF.Args(η = η ,dr=dr, epochs= epochs,batchsize=batchsize)
-    Param_Network = RiFyFi_IdF.Network_struct(;Networkname,NbClass,Chunksize,NbSignals,Seed_Network,Train_args) 
-    Results.main(Param_Data,Param_Network,"F1_score",Table_Seed_Network,savepathbson)
-   # Results.main(Param_Data,Param_Network,"Confusion_Matrix",Table_Seed_Network,savepathbson)
-
-end 
-
-=#
-
-
-###############################
-
-
-# Parameters DataBase Synth
-name= "control_3_fixe"
-nameModel= name
-nbRadioTx=6
-NbSignals= 1000
-Chunksize = 256
-features="IQsamples"
-E="E3"
-S="S1"
-C="C2"#_20dB"
-RFF="all_impairments" 
-Normalisation=true
-pourcentTrain = 0.9
-configuration ="scenario"
-seed_model =  234567
-seed_data = 123456
-if E == "E1" || E == "E2"
-    seed_modelTest = seed_model 
-else 
-    seed_modelTest = 15987654321 * 100000000
-end 
-if S == "S1" || S == "S2"
-    seed_dataTest = seed_data 
-else 
-    seed_dataTest = 9999246912 * 100000000
-end 
-
-# Augmentation
-#nb_Augment=1
-Table_nb_Augment=[2,3,4,6,8,10]
-augmentationType="augment"
-Channel="etu"
-Channel_Test="eva"
-
-
-# Network Parameters
-Networkname="AlexNet"
-NbClass =nbRadioTx
-#Chunksize 
-#NbSignals
-Table_Seed_Network= [12,13,14]#,44,55]
-
-# Train Args
-η = 1e-5   # learning rate 
-dr = 0.25      # dropout 
-batchsize = 64
-epochs = 2000
-use_cuda=true  
-
-savepathbson=""
-
-for i =1: 1 :size(Table_nb_Augment,1)
-    nb_Augment = Table_nb_Augment[i]
-    Augmentation_Value = Augmentation.Data_Augmented_construct(augmentationType=augmentationType,nb_Augment=nb_Augment,Channel=Channel,Channel_Test=Channel_Test)
-    Param_Data = RiFyFi_VDG.Data_Synth(name,nameModel,nbRadioTx, NbSignals, Chunksize,features,S,E,C,RFF,Normalisation,pourcentTrain,configuration,seed_data,seed_model,seed_dataTest,seed_modelTest,Augmentation_Value)
-   
-     setSynthetiquecsv(Param_Data)
-    
-    for k = 1 :1: size(Table_Seed_Network,1)
-    Seed_Network= Table_Seed_Network[k]
-    Train_args = RiFyFi_IdF.Args(η = η ,dr=dr, epochs= epochs,batchsize=batchsize)
-    Param_Network = RiFyFi_IdF.Network_struct(;Networkname,NbClass,Chunksize,NbSignals,Seed_Network,Train_args) 
-    RiFyFi.main(Param_Data,Param_Network)   #filename is the .pkl file    
-    end 
-    
-    nameTable = [name]
-    Seed_Network= Table_Seed_Network[1]
-    Train_args = RiFyFi_IdF.Args(η = η ,dr=dr, epochs= epochs,batchsize=batchsize)
-    Param_Network = RiFyFi_IdF.Network_struct(;Networkname,NbClass,Chunksize,NbSignals,Seed_Network,Train_args) 
-    Results.main(Param_Data,Param_Network,"F1_score",Table_Seed_Network,savepathbson)
-   # Results.main(Param_Data,Param_Network,"Confusion_Matrix",Table_Seed_Network,savepathbson)
-
-end 
-
-
-
-name= "control_5_fixe"
-nameModel= name
-nbRadioTx=6
-NbSignals= 1000
-Chunksize = 256
-features="IQsamples"
-E="E3"
-S="S1"
-C="C2"#_20dB"
-RFF="all_impairments" 
-Normalisation=true
-pourcentTrain = 0.9
-configuration ="scenario"
-seed_model =  234567
-seed_data = 123456
-if E == "E1" || E == "E2"
-    seed_modelTest = seed_model 
-else 
-    seed_modelTest = 15987654321 * 100000000
-end 
-if S == "S1" || S == "S2"
-    seed_dataTest = seed_data 
-else 
-    seed_dataTest = 9999246912 * 100000000
-end 
-
-# Augmentation
-#nb_Augment=1
-Table_nb_Augment=[2,3,4,6,8,10]
-augmentationType="augment"
-Channel="etu"
-Channel_Test="eva"
-
-
-# Network Parameters
-Networkname="AlexNet"
-NbClass =nbRadioTx
-#Chunksize 
-#NbSignals
-Table_Seed_Network= [12,13,14]#,44,55]
-
-# Train Args
-η = 1e-5   # learning rate 
-dr = 0.25      # dropout 
-batchsize = 64
-epochs = 2000
-use_cuda=true  
-
-savepathbson=""
-
-for i =1: 1 :size(Table_nb_Augment,1)
-    nb_Augment = Table_nb_Augment[i]
-    Augmentation_Value = Augmentation.Data_Augmented_construct(augmentationType=augmentationType,nb_Augment=nb_Augment,Channel=Channel,Channel_Test=Channel_Test)
-    Param_Data = RiFyFi_VDG.Data_Synth(name,nameModel,nbRadioTx, NbSignals, Chunksize,features,S,E,C,RFF,Normalisation,pourcentTrain,configuration,seed_data,seed_model,seed_dataTest,seed_modelTest,Augmentation_Value)
-   
-     setSynthetiquecsv(Param_Data)
-    
-    for k = 1 :1: size(Table_Seed_Network,1)
-    Seed_Network= Table_Seed_Network[k]
-    Train_args = RiFyFi_IdF.Args(η = η ,dr=dr, epochs= epochs,batchsize=batchsize)
-    Param_Network = RiFyFi_IdF.Network_struct(;Networkname,NbClass,Chunksize,NbSignals,Seed_Network,Train_args) 
-    savepathbson = RiFyFi.main(Param_Data,Param_Network)   #filename is the .pkl file    
-    end 
-    
-    nameTable = [name]
-    Seed_Network= Table_Seed_Network[1]
-    Train_args = RiFyFi_IdF.Args(η = η ,dr=dr, epochs= epochs,batchsize=batchsize)
-    Param_Network = RiFyFi_IdF.Network_struct(;Networkname,NbClass,Chunksize,NbSignals,Seed_Network,Train_args) 
-    Results.main(Param_Data,Param_Network,"F1_score",Table_Seed_Network,savepathbson)
-   # Results.main(Param_Data,Param_Network,"Confusion_Matrix",Table_Seed_Network,savepathbson)
-
-end