## 5 Results and Discussion
### Discussion on the Limitations of the Proposed Approach  
The main limitation of the approach presented in this paper is related to the data set and the filtering process where the data from the GNSS satellites is collected. Even if real-world signals were collected through a GNSS antenna located in a JRC premises, the position of the antenna was static and in a limited time frame of 30 min. On the basis of these data, an empirical filtering step was implemented to select a number of satellites, X, using a pre-defined threshold with the number of generated values of \(C/N_{0}\) not less than 90% of the 1748 samples and values of the \(C/N_{0}\) higher than 34 dB-Hz for two-thirds of the data samples. This filtering step indicated the X=8 satellites used in the analysis. In a dynamic context, where there could be significant variations in the propagation environment for the GNSS signal (e.g., a vehicle driving in a urban environment), the filtering step would benefit of a dynamic threshold setting where the number of satellites is adjusted according to the quality of \(C/N_{0}\) generated by the GNSS receiver. It is also noted from the results presented in Section 5.2 that even with a number of satellites equal to four, the CNN and ERT classifiers manage to obtain a very high classification accuracy (i.e., 99% and higher). Then, the proposed approach may be robust even in a dynamic environment because even a limited set of GNSS satellites would be enough. Potential approaches to implement dynamic threshold, which have to be timely and computing-efficient, could be based on a optimization step linked to the threshold values (e.g., range of the percentage or \(C/N_{0}\) threshold) where one of the classification metrics (e.g., accuracy) could be used as a cost function. Due to the need of timely execution, a shallow ML algorithm like the DT used in this paper could be selected for its computing efficiency, even if DT demonstrated a low performance in comparison to the CNN in Section 5.2. The creation of a dynamic data set with the related collection and generation of interference signals would be much more complex than the the current data set used in this paper, and this task is deferred to future developments as described in Section 6.  
Another limitation of the proposed study is the use of only the \(C/N_{0}\) data from the GNSS receiver. Even if they are related, other measures like Horizontal Dilution Of Precision (HDOP), Vertical Dilution Of Precision (VDOP), clock bias, the carrier phase, pseudorange metrics, and even the satellite elevation features could be used taking inspiration from the studies focused on GNSS LOS/NLOS classification [21; 22]. This is also reserved to future developments.  
Finally, even if the CNN classifier has demonstrated an excellent detection and classification performance, more recent and sophisticated neural networks models could be evaluated and adopted, like Transformers used for GNSS NLOS identification in [23].  
Figure 16: Confusion matrices with different number of satellites \(N_{S}\) (range from 4 to 8) and bit-depth \(B_{D}\) with low level of interference: (**a**) \(N_{S}=4\), \(B_{D}=2\), (**b**) \(N_{S}=4\), \(B_{D}=16\), (**c**) \(N_{S}=6\), \(B_{D}=2\), (**d**) \(N_{S}=6\), \(B_{D}=16\), (**e**) \(N_{S}=8\), \(B_{D}=2\), (**f**) \(N_{S}=8\), \(B_{D}=16\).  
On the other hand, especially in a dynamic environment, there is an important trade-off between computing efficiency and accuracy and it has to be seen if DL can be applicable or other neural network algorithms (e.g., feed-forward neural networks) could be more suitable.