## 4 Data Set Generation and Processing
### Data Set Structure and Format  
The generated data set is composed of the \(C/N_{0}\) data generated by the Septentrio receiver for almost 30 min of signal playback at a rate of 1 Hz. Thus, a time series composed of 1748 \(C/N_{0}\) values was generated for each of the considered satellites (32) for each level of bit-depth (i.e., five bit-depths), three power levels (e.g., \(L_{I}=M\)), and four types of interference scenarios (i.e., three types of interference and the absence of interference). Taking into consideration that the chosen step is 4 and the window size is 16 \(C/N_{0}\) value, 433 window samples were generated on which the feature and the CNN were applied. As mentioned before, an analysis of the \(C/N_{0}\) samples from the different satellites showed that not all the satellites are suitable for the analysis because of periods of signal unavailability, which led to missing samples or samples degraded to low values of \(C/N_{0}\). The rule was to consider only satellite, where the number of generated values of \(C/N_{0}\) was not less than 90% of the 1748 samples and value of the \(C/N_{0}\) was higher than 34 dB-Hz for two-thirds of the data samples. This was an empirical approach, which can be further refined in future developments, and it is based on the consideration that there should be enough valid samples (i.e., \(C/N_{0}\) values corresponding to the signals of GNSS satellites with LOS propagation conditions of satellite fix) to support the ML classification. The threshold of 90% was inspired by similar studies where such threshold values were used on the \(C/N_{0}\) to distinguish between propagation conditions [20]. The result of this filtering step identified only eight satellites that were suitable for the subsequent phases.  
Even these eight satellites could have significant variability in the number of samples, and a resampling step was performed with the MATLAB _interp_1 function and two different  
Figure 5: \(C/N_{0}\) for the satellite considered in this study with the impact of the Gaussian noise (moderate level: 30 dB attenuation) for the 16 bit playback.  
algorithms: linear interpolation and nearest neighbor interpolation. These algorithms were chosen on the basis of the characteristics of the \(C/N_{0}\) time series with steep transients, which prompted the authors to discard other algorithms (i.e., cubic spline). The result from the two interpolation algorithms were basically equivalent and the linear interpolation was chosen for its computational speed.  
Because the number of samples for each type of interference and bit-depth could be limited to train the CNN algorithm, a simple augmentation step was implemented to increase the number of samples. Two other sets of 433 samples were generated with AWGN of 1 dB higher and lower than the measured SNR of the original \(C/N_{0}\) time series. Considering the case of interference classification, this resulted in a DL problem with 1299 samples \(\times\) 3 types of interference to have 3897 samples in total as input to the CNN. In addition, to provide an input wide enough for the CNN classifier, the \(C/N_{0}\) time series was resampled by a factor of 2 after the interpolation (i.e., the window given in input to the CNN has length 32).