## 5 Results and Discussion
### Classification of Interference  
Figure 10 and related sub-figures show the comparison of the accuracy and F1-score using the CNN and the shallow ML algorithms using the feature-based approach. These results are obtained using the \(C/N_{0}\) data obtained from all eight GNSS satellites obtained after the filtering step described in Section 4.5. In particular, Figure 10a, Figure 10b, Figure 11a, and Figure 11b present, respectively, the accuracy, F1 score, precision, and recall for different levels of bit-depth and interference level (x-axis). The figures show that the four metrics are generally coherent among themselves. The CNN algorithm generally demonstrates a superior performance to the shallow ML algorithms with the exception of the ERT, because the ERT equals CNN for some cases or it is even better with \(B_{\mathrm{D}}=16\) and \(L_{l}=M\). Considering the ERT has a significantly lower computing complexity than the CNN, ERT could be the preferred choice for computing efficient deployments of this approach. ERT mostly outperforms RaF and DT, which justifies the proposal of ERT in this study. It can be noted that for values of  
Figure 8: Comparison of the detection with the recall metric using the feature-based approach. (**a**) low interference level, (**b**) medium interference level, and (**c**) high interference level. Figure 9: Comparison of the detection with the accuracy metric using the CNN-based approach. (**a**) low interference level, (**b**) medium interference level, and (**c**) high interference level. \(B_{D}=1\) and \(B_{D}=2\), the classification performance drops significantly for the RaF and DT algorithms, especially at a low level of the interference power \(L_{I}=L\). This is not surprising because the classifiers have difficulty in exploiting the discriminating content due to the limited available information (the reduction in the \(B_{D}\)) and the minor impact of interference signals with low levels of power because the variations of the \(C/N_{0}\) are less prominent than for a high level of power. Even in the challenging conditions of low \(B_{D}\) and \(L_{I}\), the performance of the CNN classifier is remarkable as it has almost 100% accuracy apart from the case of \(B_{D}=2\) and \(L_{I}=M\), where it achieves 97% accuracy. It is also important to evaluate the performance of the proposed approach for different numbers of satellites \(N_{S}\). Figure 12 and related sub-figures show the accuracy and F1 score while Figure 13 and related sub-figures present the precision and recall for different bit-depths and levels of interference using different numbers of satellites. These figures were obtained by averaging the results for repeated execution of the CNN algorithm for the potential combinations of satellites, respectively, in sets of one (8 executions), two (28 executions), four (70 executions), six (28 executions), and eight (1 executions). It can be seen that a larger number of satellites increases the classification accuracy across the different levels of \(B_{D}\) and interference level. This is to be expected because a larger number of satellites can provide more (and more discriminating) information to the CNN classifier to identify the specific type of interference. On the other hand, for medium and high levels of interference, six satellites or even four satellites are enough to obtain an accuracy/precision and recall equal or similar to the one obtained with all eight satellites. Figure 10: Comparison of the classification performance for the different approaches (feature- and CNN-based): (a) accuracy, (b) F1 score. Figure 14 gives a more detailed view of the classification accuracy with \(L_{I}=M\) for different values of the \(B_{D}\) and for one satellite and all eight satellites. As shown in the previous figures, both the decrease in \(B_{D}\) and \(N_{S}\) lead to a decrease in classification accuracy. Figure 12: Comparison of the classification performance for different numbers of satellites (\(N_{S}\)) with CNN: (**a**) accuracy, (**b**) F1 score.