## 4 Results
### PPP Time Transfer in Static Model with HAS Service
Furthermore, Figure 6 supplements this analysis by displaying the receiver clock offsets for six other stations, also determined under static mode conditions using single-constellation Galileo and Galileo/GPS integrated PPP calculations. Upon comprehensive analysis, a few key insights emerge. First and foremost, there is a remarkable consistency between the receiver clock offsets determined by Galileo-only and Galileo/GPS integrated PPP, which can be largely attributed to the prioritization of receiver clock offsets from Galileo observation equations in the integrated Galileo/GPS PPP process. Secondly, it is noted that the Galileo-only PPP calculations experience several instances of re-convergence, likely due to the scarcity of available satellites. Conversely, the receiver clock offsets from the Galileo/GPS combined PPP exhibit a more consistent and stable time series, devoid of any re-convergence events. A closer examination of Figure 7, which details the satellite count utilized in the PPP calculations for GPS-only, Galileo-only, and Galileo/GPS combined PPP, further substantiates this, indicating that at certain intervals, the Galileo-only approach might encounter situations where the number of satellites drops below the threshold of four. Lastly, a comparison between Figures 2 and 5 reveals that the receiver clock offsets from Galileo and Galileo/GPS PPP calculations are not only free from abrupt jumps but also remarkably stable. This stability can be primarily attributed to the fact that the Galileo satellite clock offsets in the HAS service are aligned with the  
Figure 4: GPS PPP time transfer solutions in the static model. Figure 8 presents the outcomes of the time transfer assessments conducted in static mode, utilizing Galileo-only and the integrated Galileo/GPS PPP approaches. A comparison with Figure 4 reveals that the time transfer results from either Galileo-only or the Galileo/GPS combined PPP are in alignment with the general trends observed in the GPS-only PPP time transfer. This concurrence is logically sound, as the essence of PPP time transfer is to serve as an intermediary, with the ultimate objective of ascertaining the temporal disparity between two distinct locations, irrespective of the specific satellite constellation involved. Furthermore, mirroring the earlier observations on receiver clock offset behavior, the time transfer results for Galileo PPP exhibit multiple instances of re-convergence, an issue that is prevalent in scenarios where the satellite coverage or geometry is suboptimal. In contrast, the Galileo/GPS combined PPP time transfer demonstrates a consistent and stable performance, effectively circumventing the re-convergence phenomenon. This affirms that the integration of multiple GNSS systems is highly beneficial, if not essential, for achieving precise and reliable time synchronization across the globe, particularly when leveraging the Galileo HAS service. The fusion of Galileo with GPS enhances the satellite availability and geometric distribution, thereby providing a more robust framework for high-precision timing applications. In order to conduct a thorough numerical analysis of the performance of GPS, Galileo, and the integrated Galileo/GPS PPP time transfer in static mode, we have utilized the GPS PPP time transfer results derived from IGS final products as a benchmark. We have computed the standard deviation (STD) of the discrepancies between the time transfer outcomes for GPS, Galileo, and the combined Galileo/GPS PPP when compared to their counterparts based on IGS final products, with the results summarized in Table 3. Furthermore, Figure 9 illustrates the modified Allan variance deviation (MDEV) values for the time transfer performance of GPS, Galileo, and Galileo/GPS PPP across six distinct time links. This comprehensive analysis provides a detailed assessment of the temporal stability and precision of the various PPP time transfer methodologies under static conditions. Upon a meticulous review of Table 3 and Figure 9, we can distill several key insights.