## 4 Real-Time Multi-GNSS Dataset Processing  
To investigate the ZED-F9P's accuracy, dual-frequency quad-constellation GNSS datasets were collected over a reference station in Aswan city, Egypt (Figure 1) spanning three consecutive days (i.e., Day of year (DOY) = 225, 226, and 227 in 2023). The setup of the ZED-F9P module over the examined station is illustrated in Figure 2. The observation time window is three hours with a 30 s interval. To provide a reference solution, GNSS measurements were collected using the geodetic-grade Trimble R4s receiver over the same examined point, also with an observation interval of 30 s; thereafter, the collected observations were processed using the GNSS PPP technique by employing the final precise GNSS orbit and clock products. Figure 1: Examined station spatial location. Net_Diff GNSS version 1.13 software [36] has been utilized to process the ZED-F9P datasets in real-time PPP mode. Various instantaneous PPP solutions have been adopted, namely, GPS PPP, GLONASS PPP, Galileo PPP, and BeiDou PPP. For our proposed RT-PPP models, the hydrostatic error has been determined using the Saastamoinen tropospheric model; moreover, dry and wet Vienna mapping functions have been applied [37]. A Kalman filter has been adopted for parameter estimation. To model the observations stochastically, the elevation-dependent plus the signal-to-noise ratio (SNR) model has been employed. For static data processing, a 30 s time interval has been used. Furthermore, to mitigate the GNSS orbit and clock errors, the broadcasted navigation message from the ZED-F9P module has been used; additionally, the satellite orbit and clock corrections available from multiple data streams, namely, BKG, CNES, IGS, GFZ, and WHU, have been employed. In our paper, the SSRA has been utilized, including corrections to GNSS orbits and clocks in the SSR format every 5 s using BKG version 2.13.1 software [38]. Table 1 outlines parameters for each adopted precise point positioning solution. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Receiver Type** & \multicolumn{4}{c}{**ZED-F9P**} & \multicolumn{2}{c}{**Trimble R4s**} \\ \hline PPP solution & GPS & GLONASS & Galileo & BeiDou & GPS \\ \hline Frequency & L\({}_{1}\)/L\({}_{2}\) & G\({}_{1}\)/G\({}_{2}\) & E\({}_{1}\)/E\({}_{\text{Sb}}\) & B\({}_{1}\)/B\({}_{2}\) & L\({}_{1}\)/L\({}_{2}\) \\ \hline Data streams & BKG & BKG & BKG & IGS & Final NRCan \\ \hline  & CNE & CNE & CNE & GFZ & \\ \hline  & IGS & IGS & IGS & WHU & \\ \hline  & GFZ & GFZ & GFZ & \\ \hline  & WHU & WHU & WHU & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the adopted PPP solutions. Figure 2: Setup of ZED-F9P over the examined stations. Another objective of our paper was to examine the obtained real-time PWV's accuracy through the ZED-F9P module. It should be mentioned that to provide reference PWV values, our examined station has neither IGS Zenith tropospheric delay (ZTD) product file nor radiosonde measurements; hence, to achieve this, GNSS measurements have been simultaneously acquired from the two receivers (Figure 3). As presented in Figure 3, the reference station was occupied by the low-cost module, while another point, about 1-m distant, was occupied by the geodetic-grade receiver. For the ZED-F9P datasets, the acquired datasets were processed in the real-time PPP domain; on the other hand, the Trimble R4s datasets have been processed in post-processed PPP mode to supply reference PWV values. For the PWV calculation, the surface temperature (\(T_{s}\)) has been estimated through the GPT2w model [39] because our selected station was not equipped with metrological sensors. To investigate the number of tracked satellites and the position dilution of precision (PDOP) over our reference station, their values have been computed over the three days and are depicted in Figure 4. It can be noticed that there are more GPS tracked satellites than other systems, while the GLONASS system has the lowest number of tracked satellites.