M coefficient is parameter f r can f r determined accurately.
M coefficient is parameter f r can f r determined accurately. Because the PK 11195 Autophagy periodic term coefficient is obobtained by STFT, the improved clock predictionmodel is known as the time-frequency tained by STFT, the improved clock prediction model is referred to as the time-frequency analysis model (TFAM). evaluation model (TFAM).3. Results 3. Final results In this section, according to the clock products with the WHU and also the GFZ, the functionality In this section, according to the clock solutions from the WHU and the GFZ, the perforof the TFAM is analyzed. First, the Betamethasone disodium Autophagy fitting efficiency in the TFAM is evaluated and mance from the TFAM is analyzed. First, the fitting efficiency in the TFAM is evaluated compared with that of your SAM. Then, the prediction outcomes with the TFAM plus the SAM are and compared with that of your SAM. Then, the prediction results of the TFAM as well as the discussed. Ultimately, the prediction benefits of the satellites with marked periodic variations SAM are discussed. Finally, the prediction results with the satellites with marked periodic are investigated to show the performance of TFAM totally. variations are investigated to show the efficiency of TFAM fully. 3.1. Accuracy Evaluation of Clock Offset Fitting 3.1. Accuracy Evaluation of Clock Offset Fitting In an effort to evaluate the fitting accuracy on the enhanced model, 30-day (DOY 200 As a way to evaluate the clock accuracy of are analyzed model, 30-day (DOY 200 to to 229, 2020) WHU and GFZ fittingoffset fitting the improved statistically. The root mean 229, 2020) WHU and made use of as the evaluation are analyzed statistically. The root mean square (RMS) error is GFZ clock offset fitting criterion on the fitting accuracy. Figure eight square (RMS) error is utilised as the evaluation criterion fitting residuals just after Figure 8 gives offers the RMS in the QPM, the SAM, as well as the TFAMof the fitting accuracy. removing the the RMS of your QPM, the anomalies, respectively. SAM, as well as the TFAM fitting residuals soon after removing the anomalies, respectively.Figure eight. Imply RMS in the QPM, the SAM, along with the TFAM fitting residuals. Figure 8. Mean RMS with the QPM, the SAM, and the TFAM fitting residuals.Figure 8 shows the imply fitting RMS from the WHU and GFZ clock offsets, respectively. It may be seen that both the SAM plus the TFAM have far better fitting accuracy than the QPM. For some satellites, the TFAM performs greater than the SAM, and, for other individuals, the fittingRemote Sens. 2021, 13, x FOR PEER REVIEWRemote Sens. 2021, 13,13 of13 ofFigure eight shows the imply fitting RMS from the WHU and GFZ clock offsets, respectively. It may be seen that each the SAM plus the TFAM have better fitting accuracy than the QPM. For some is essentially precisely the same.performs a time-varying SAM, periodic others, the fitting accuracy satellites, the TFAM Adding improved than the main and, for term towards the clock accuracy ismodel indeed improves the fitting accuracy, and forperiodic termwith marked prediction basically the same. Adding a time-varying key the satellites to the clock prediction model indeed improves further improve the and foraccuracy. Thewith marked periodic variations, the TFAM can the fitting accuracy, fitting the satellites BDS-3 MEO periodic variations, the TFAM can additional strengthen the fitting accuracy. The BDS-3 MEO satellites have improved fitting accuracy than BDS-2 MEO satellites, which implies that the satellites have improved clocks have improved overall performance in modeling. which implies due to the fact BDS-3 MEO satellite fitting accuracy than BDS-2 MEO satellites, This can be m.