diff --git a/Model/acoustic_data_loader_UBSediFlow.py b/Model/acoustic_data_loader_UBSediFlow.py
index 5bbb5ac..9084dbf 100644
--- a/Model/acoustic_data_loader_UBSediFlow.py
+++ b/Model/acoustic_data_loader_UBSediFlow.py
@@ -206,8 +206,8 @@ class AcousticDataLoaderUBSediFlow:
                 self._nb_profiles[k] += 1
 
 
-            # Finally, form the 2D arrays for each frequency and store them in 3D NumPy arrays:
-
+            # Finally, store the 2D arrays for each frequency in 3D NumPy arrays:
+            
             self._BS_raw_data[k,:,:] = bs_list[k]
             self._time[k,:] = time_list[k]
             self._r[k,:] = r_list[k]
@@ -215,10 +215,48 @@ class AcousticDataLoaderUBSediFlow:
         
 
     def reshape_BS_raw_data(self):
-        BS_raw_cross_section = np.reshape(self._BS_raw_data,
-                                          (self._r.shape[1]*self._time.shape[1], len(self._freq)),
-                                          order="F")
+        
+        '''
+        Form and return a table from the raw BS (backscatter) data loaded into AcouSed. That table
+        is the one to be displayed in the "Table of values" section of the "Acoustic data" tab.
+
+        The table is a L*K NumPy array, where 'K' stands for the number of frequencies in the
+        loaded dataset, and L = R*T with 'R' the number of vertical coordinates and 'T' the number
+        of time stamps.
+
+        Finally, the BS array is flattened (i.e., converted to a 1D array) for each frequency so that
+        the index spanning the vertical coordinates runs the fastest i.e.
+
+        Line 0  : BS data at (t, r) = (t_0, r_0)
+        Line 1  : BS data at (t, r) = (t_0, r_1)
+        Line 2  : BS data at (t, r) = (t_0, r_2)
+        ...
+        Line R  : BS data at (t, r) = (t_0, r_R)
+        Line R+1: BS data at (t, r) = (t_1, r_0)
+        Line R+2: BS data at (t, r) = (t_1, r_1)
+        ...
+
+        '''
+
+
+        # Create and initialise the returned 2D array. Note:
+        # R = self._r.shape[1] = self._BS_raw_data.shape[1]
+        # T = self._time.shape[1] = self._BS_raw_data.shape[2]
+        # K = len(self._freq)
+        
+        BS_raw_cross_section = np.zeros( ( self._r.shape[1] * self._time.shape[1], len(self._freq) ) )
+
+        
+        # Fill the k-th column of 'BS_raw_cross_section' with the BS data recorded with the k-th frequency:
+        
+        for k in range( len(self._freq) ):
+
+            BS_raw_cross_section[:, k] = np.reshape( self._BS_raw_data[k,:,:],
+                                                     self._BS_raw_data.shape[1] * self._BS_raw_data.shape[2],
+                                                     order = "F" )
+            
         return BS_raw_cross_section
+    
 
     def reshape_r(self):
         r = np.zeros((self._r.shape[1]*self._time.shape[1], len(self._freq)))