## 3 Discussion
### Application for Positioning Accuracy
Seven GNSS configurations were considered: first the three constellation pairs (GPS + GLONASS + Galileo), then the two-and-two pairs (GPS + GLONASS, GPS + Galileo, GLONASS + Galileo), and finally the single pair (GPS, GLONASS, Galileo). Figure 7: On the left panel: a pseudo-coloured canopy height model with over-imposed trees’ heights represented with the same white-green palette. On the right panel, the number of GLONASS and GPS satellites, V1 and V2, are represented with black lines and green lines, respectively. The maximum number of satellites is represented by a dashed red line. Adapted from [121]. The results (Table 2) showed a significant improvement in accuracy when using the _Estimated Zenith Tropospheric Delay_ model compared to the _Saastamoinen_ model (the average standard deviation by applying the ZTD was approximately one-third that achieved by applying the Saastamoinen model); also, estimates using combinations of GNSS constellations (in particular GPS+Galileo with the ZTD estimate) performed more accurately than single constellations. Subsequently, many authors focused on the effects that differential NRTK corrections, with respect to CORS network geometry, can have on GNSS solutions [123] by testing different network configurations and inter-distances between CORS stations (Figure 8). A stable pillar benchmark on the top of the _Department of Engineering_ was used to perform several tests in static positioning with a dataset of over 40 h of acquisition time. Different geometric schemes were used in this study, evaluating the position that the benchmark could have with regard to the different arrangements (inside the network, outside, and near). Data processing was conducted using alternative network solutions, including Near (closest), FKP, and VRS. The results established the high reliability of the GNSS network, with centimetre accuracy in terms of planimetric coordinates and ellipsoid heights. In terms of the standard deviation of the residuals, results were similar without distinction between the method of correction used and the surveyed network design. Of course, the lower standard deviation of residuals characterised the full CORS network \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**GNSS Constellation(s)** & \multicolumn{3}{c}{**Estimated ZTD**} & \multicolumn{3}{c}{**Saastamoinen Model**} \\  & \(\mathbf{\sigma_{Fast}}\) **(m)** & \(\mathbf{\sigma_{North}}\) **(m)** & \(\mathbf{\sigma_{Up}}\) **(m)** & \(\mathbf{\sigma_{East}}\) **(m)** & \(\mathbf{\sigma_{North}}\) **(m)** & \(\mathbf{\sigma_{Up}}\) **(m)** \\ \hline GPS & 23 & 27 & 38 & 74 & 33 & 60 \\ GLONASS & 28 & 24 & 11 & 87 & 29 & 60 \\ Galileo & 30 & 29 & 16 & 101 & 51 & 67 \\ GPS + GLONASS & 25 & 10 & 10 & 69 & 17 & 55 \\ GPS + Galileo & 10 & 10 & 6 & 160 & 40 & 63 \\ GLONASS + Galileo & 33 & 26 & 23 & 77 & 28 & 59 \\ GPS + GLONASS + Galileo & 30 & 17 & 13 & 74 & 22 & 68 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: East, North, and Up standard deviation, \(\sigma\), of the ZTD, by employing two different tropospheric models (Estimated Zenith Tropospheric Delay and Saastamoinen) for a given satellite constellation (GPS, GLONASS, Galileo) and their combinations. Values are pseudo-coloured using a green-white-red palette to highlight the models’ performances (adapted from [122]). Figure 8: Screenshot of UNIPA CORS geometry. Distances are represented through a green, red, and yellow colour palette for increasing distances, representing the whole network (**a**) or by deactivating 1 PS (**b**–**e**), 2 PSs (**f**,**g**), or 3 selected PSs (**h**,**j**). The standard deviation of the residuals for each network solution is also reported (**I**). Adapted from [123]. (\(\sim\)4 mm, on average), similar values characterised the CORS without one or two permanent stations (\(\sim\)5\(-\)6 mm, on average), and the worst values obtained by deactivating three permanent stations (\(\sim\)11\(-\)12 mm, on average).