# Estimating BDS-3 Satellite Differential Code Biases with the Single-Frequency Uncombined PPP Model
## 4 Experimental Results and Analysis  
### Satellite DCBs Estimated by SF Data from Single Station  
During the period of March 2023 (DOY 60-90), satellite DCBs were estimated individually at each station using the SF uncombined PPP model, employing BDS-3 BI observations gathered from 25 stations. The absolute offset of DCB for each satellite was calculated on a daily basis, serving as an assessment of the accuracy and performance of the model employed in this study. To evaluate the overall accuracy and reliability of the estimated DCBs, the daily RMS of the absolute offsets was computed at each station. The RMS value provides a quantitative measure of the accuracy and stability of the estimated DCBs on a daily basis. Due to space limitations, we only present the results from six selected days in March 2023. The selected days, namely DOY 62, 67, 72, 77, 82, and 87, were chosen to represent different temporal points within the month. Figure 4 displays the station distribution and the corresponding RMS values of satellite DCB estimates for the selected days. The magnitude of the RMS values is portrayed through a color scheme, with intensifying \begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Strategies** \\ \hline Filtering method & Least square filter \\ Processing mode & Day-by-day processing \\ Observations & BDS-3 B1 code and phase observations \\ Cut-off mask angle & 15\({}^{\circ}\) \\ Precise products (orbits and clocks) & Wuhan University (WUM) \\ Topospheric zenith wet delay & Random walk estimation \\ Hydrostatic delay & Empirical model [29] \\ Topospheric projecting function & GMF/NMF \\ Slant ionospheric delay & Estimated as the white noise process \\ Priori station coordinates & IGS weekly station coordinates product \\ Antenna corrections & PCO/PCV parameters provided by jgs14.atx \\ Earth rotation parameters & IGS weekly product \\ Tidal corrections & Solid Earth tide, ocean tide, and pole tide \\ Phase wind-up effect & Empirical model \\ C1 observations & IGS P1-C1 DCB corrections \\ Ambiguity parameter & Float ambiguity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data and Processing Strategies. shades of red indicating higher RMS values. This visualization provides an overview of the distribution and magnitude of the RMS values across different latitudes. Notably, the results shown in Figure 4 exhibit minimal variation across different days. The visual depiction clearly demonstrates a noticeable pattern: the RMS values of the satellite DCB estimates are higher for low-latitude (30\({}^{\circ}\) N-30\({}^{\circ}\) S) stations, indicating lower precision in these estimates. Conversely, the RMS values of the satellite DCB estimates from mid- and high-latitude IGS stations are lower, indicating higher precision. This discrepancy can be ascribed to the heightened activity and dynamic behavior of the ionosphere in low-latitude regions. When comparing with the mid- and high-latitude ionosphere, the low-latitude ionosphere presents greater spatial gradients and more intensive temporal variations, posing challenges in accurately characterizing the ionospheric variations within this region [28]. Consequently, such ionospheric complexities adversely affect the performance of ionosphere modeling, leading to a degradation in the accuracy of estimated satellite DCBs in this particular region. The variation in RMS values of the satellite DCB estimates across different latitudes underscores the impact of ionospheric variability on the precision and dependability of these estimations. To provide a more comprehensive assessment of the accuracy of the estimated satellite DCBs, the RMS values for all stations over 30 consecutive days are presented in the form of a line graph in Figure 5. This representation employs various line colors to differentiate between individual days throughout the duration of the experiment. The line graph highlights the variations in the RMS values of the satellite DCB estimates across different stations.