Steps of processing (this one uses Python and Matlab) and run this on a computer with at least 16GB of RAM memory
1.	Download the science and calibration raw data (up to 1.5GB each) for RCP and LCP channels from 
	(https://atmos.nmsu.edu/data_and_services/atmospheres_data/Cassini/Cassini/RSS%20PDS%20page%202019-01-23/rss/TI_10_bis.html)
1a. 	For the case of T106, we included the labels of the data you have to download in the respective directories 
	(i.e., \codes\T106\X\S86TIBI2014297_0045NNNX43\RD)
2.	Use the python code provided by the RSS team on GitHub and also included here (rsr_reader.py) to decode the raw data and then 
	save it in a csv file with three colums (spm, I, Q).
4.	Read this file in matlab with the provided RSS_BSR_READ_CVS.m, that will create a file called ancillary.mat and then will 
	segment all the raw data in smaller (30/40 MB) data packets in .mat format.
5.	Now you can run the matlab processor RSS_BSR_PROC.m giving in input the RD directory for the flyby of interest 
	(i.e., \T106\X\S86TIBI2014297_0045NNNX43\RD)
5a.	Note that Tamb temperatures of calibration necessary for the calibration procedure are reported in pdf files provided by 
	the PDS node (i.e., the file called BistaticTables70m_D43_T106_filled.pdf).
5b.	The processor will first analyze the RCP sceince data, then the LCP science data, followed by the RCP and LCP calibration data.
	And for each of these will produce several plots.
5c.	The calibration will be performed by manually selecting the range of times on which to calculate the average lavels of noise
	with and without the ambient load.
5d.	The processor will finally provide the values of relative dielectric costant in the final plot produced.