diff --git a/pipelines/steps/prepare_ebas_submission.py b/pipelines/steps/prepare_ebas_submission.py
index 0d57ad2..a0201a7 100644
--- a/pipelines/steps/prepare_ebas_submission.py
+++ b/pipelines/steps/prepare_ebas_submission.py
@@ -12,11 +12,16 @@ except NameError:
 
 projectPath = os.path.normpath(os.path.join(thisFilePath, "..", "..",'..'))  # Move up to project root
 
+if projectPath not in sys.path:
+    sys.path.insert(0,projectPath)
+
 import argparse
 import pandas as pd
 import json, yaml
-import pandas as pd
+import numpy as np
 from utils import get_metadata
+from utils import metadata_dict_to_dataframe
+from pipelines.steps.utils import load_project_yaml_files
 
 def join_tables(csv_files: list):
     """
@@ -58,44 +63,39 @@ def join_tables(csv_files: list):
     return acum_df
 
     
-def load_acsm_to_ebas_dict():
 
-    # Implicit input
-    dict_file = os.path.normpath(os.path.join(projectPath,"pipelines/dictionaries/acsm_to_ebas.yaml"))
 
-    output_dict = {}
-    try:
-        with open(dict_file, 'r') as stream:
-            output_dict = yaml.load(stream, Loader=yaml.FullLoader)
-    except Exception as e:
-        
-        print(f'Error loading {dict_file}: {e}')
-        return {}
-    
-    return output_dict
-    
 
 if __name__ == "__main__":
     
     path1 = 'data/collection_JFJ_2024_LeilaS_2025-02-17_2025-02-17/ACSM_TOFWARE_processed/2024/ACSM_JFJ_2024_timeseries_calibrated.csv'
-    path2 = 'data/collection_JFJ_2024_LeilaS_2025-02-17_2025-02-17/ACSM_TOFWARE_processed/2024/ACSM_JFJ_2024_timeseries_calibration_factors.csv'
-    path3 = 'data/collection_JFJ_2024_LeilaS_2025-02-17_2025-02-17/ACSM_TOFWARE_flags/2024/ACSM_JFJ_2024_timeseries_flags.csv'
+    path2 = 'data/collection_JFJ_2024_LeilaS_2025-02-17_2025-02-17/ACSM_TOFWARE_processed/2024/ACSM_JFJ_2024_timeseries_calibrated_err.csv'
+    path3 = 'data/collection_JFJ_2024_LeilaS_2025-02-17_2025-02-17/ACSM_TOFWARE_processed/2024/ACSM_JFJ_2024_timeseries_calibration_factors.csv'
+    path4 = 'data/collection_JFJ_2024_LeilaS_2025-02-17_2025-02-17/ACSM_TOFWARE_flags/2024/ACSM_JFJ_2024_timeseries_flags.csv'
 
-    acum_df = join_tables([path1,path2])
+    acum_df = join_tables([path1,path2,path3])
 
-    acsm_to_ebas = load_acsm_to_ebas_dict()    
+    acsm_to_ebas = load_project_yaml_files(projectPath, "acsm_to_ebas.yaml")
 
+    # Select variables that are both in the acsm to ebas dict and acum_df
+    reduced_set_of_vars = [key for key in acum_df.columns if key in acsm_to_ebas['renaming_map'].keys()]
+
+    acum_df = acum_df.loc[:,reduced_set_of_vars].rename(columns=acsm_to_ebas['renaming_map'])
     #print("Before renaming:", acum_df.columns)
     #print("Renaming map keys:", acsm_to_ebas['renaming_map'].keys())
 
-    acum_df = acum_df.rename(columns=acsm_to_ebas['renaming_map'])
+    
+    
 
-    reduced_set_of_vars = [key for key in acum_df.columns if 'factor' not in key]
     #print(reduced_set_of_vars)
     
-    flags_acum_df = join_tables([path3])
+    flags_acum_df = join_tables([path4])
     flags_acum_df = flags_acum_df.rename(columns=acsm_to_ebas['renaming_map'])
 
+
+
+
+
     # Ensure time columns are datetime    
     acum_df['ACSM_time'] = pd.to_datetime(acum_df['ACSM_time'])
     flags_acum_df['ACSM_time'] = pd.to_datetime(acum_df['ACSM_time'])
@@ -127,32 +127,48 @@ if __name__ == "__main__":
 
     valid_rows = ~(nat_acum | nat_flags)  # Compute valid rows in one step
 
+    # Define file paths
+    #path_to_detection_limits = os.path.normpath(os.path.join(projectPath, 'pipelines/params/limits_of_detection.yaml'))
+    #path_to_station_params = os.path.normpath(os.path.join(projectPath, 'pipelines/params/station_params.yaml'))
 
-    acum_df.loc[valid_rows.to_numpy(),reduced_set_of_vars].to_csv('data/JFJ_ACSM-017_2024.txt',sep='\t',index=None, date_format="%Y/%m/%d %H:%M:%S")
+    # Load YAML files
+    #detection_limits = load_yaml(path_to_detection_limits)
+    detection_limits = load_project_yaml_files(projectPath, "limits_of_detection.yaml")
+    station_params = load_project_yaml_files(projectPath, "station_params.yaml") # load_yaml(path_to_station_params)
+
+    # Extract dictionaries from required keys
+    lod_dict = detection_limits.get('LOD', {}).get('variables',{})  # Extract "LOD" dictionary
+    jfj_dict = station_params.get('stations', {}).get('JFJ', {})  # Extract "JFJ" dictionary
+
+    # Convert dictionaries to DataFrames using the existing function
+    lod_df = metadata_dict_to_dataframe(lod_dict, shape = (len(acum_df),len(lod_dict)))
+    jfj_df = metadata_dict_to_dataframe(jfj_dict, shape = (len(acum_df),len(jfj_dict)))
+
+    # Ensure indexes are properly aligned for merging
+    acum_df = acum_df.reset_index()  # Convert index to a column for merging
+
+    # Merge with LOD DataFrame
+    acum_df = acum_df.merge(lod_df, left_index=True, right_index=True, how='left')
+
+    # Merge with JFJ DataFrame
+    acum_df = acum_df.merge(jfj_df, left_index=True, right_index=True, how='left')
+
+
+    acum_df = acum_df.rename(columns=acsm_to_ebas['renaming_map'])
+
+    
+    #reduced_set_of_vars = [key for key in reduced_set_of_vars if '' not in key]
+    acum_df.loc[valid_rows.to_numpy(),:].to_csv('data/JFJ_ACSM-017_2024.txt',sep='\t',index=None, date_format="%Y/%m/%d %H:%M:%S")
     flags_acum_df.loc[valid_rows.to_numpy(),:].to_csv('data/JFJ_ACSM-017_FLAGS_2024.txt',sep='\t',index=None, date_format="%Y/%m/%d %H:%M:%S")
 
 
 
-    
-    #acum_df['ACSM_time'] = pd.to_datetime(acum_df['ACSM_time'])
-    #flags_acum_df['ACSM_time'] = pd.to_datetime(flags_acum_df['ACSM_time'])
+    from third_party.acsmProcessingSoftware.src import rawto012
 
-    # Set datetime as index
-    #acum_df.set_index('ACSM_time', inplace=True)
-    #flags_acum_df.set_index('ACSM_time', inplace=True)
+    app = rawto012.Application()
+    infile = 'data/JFJ_ACSM-017_2024.txt'
+    acq_err_log = 'data/JFJ_ACSM-017_FLAGS_2024.txt'
+    outdir = 'data/'
+    app.process(infile, acq_err_log, outdir=outdir)
 
-    #nat_acum = acum_df['ACSM_time'].isna()
-    #nat_flags = flags_acum_df['ACSM_time'].isna()
 
-    #valid_rows = ~(nat_acum | nat_flags)  # Compute valid rows in one step
-
-    #acum_df_filtered = acum_df.loc[valid_rows.to_numpy(),:]
-    #flags_acum_df_filtered = flags_acum_df[valid_rows.to_numpy(),:]
-
-    # Step 4: Apply the valid mask to both dataframes
-    #acum_df_filtered = acum_df[valid_rows]
-    #flags_acum_df_filtered = flags_acum_df[valid_rows]
-
-    # Display results
-    #print(acum_df_filtered)
-    #print(flags_acum_df_filtered)
\ No newline at end of file