## 4 Conclusions  
In this study, we developed a model (the Sparrow-Search-Algorithm-optimized XGBoost model) to detect ionospheric scintillation and subsequently applied it to global regions, applying it to the polar and equator regions in particular. This involved analyzing GNSS RO measurements obtained from the MetOp-A satellite between 2015 and 2018. Our findings are consistent with similar studies employing data from ground-based ionospheric scintillation monitoring stations [23].  
The power spectral density of the GNSS signal is used as input in the ionospheric scintillation detection model, unlike other conventional methods like computation of amplitude and phase scintillation indices [41], and the power spectral density does not require a complicated detrending procedure. Rather, it only necessitates a fast Fourier transform (FFT) module [42], which can be obtained more easily. This benefit facilitates the integration of the ionospheric scintillation detection method using GNSS signal power spectral density as a detection characteristic into LEO satellites, which holds immense importance in building space-based ionospheric scintillation monitoring networks [43].  
The algorithm proposed in this study underwent evaluation to ascertain its test set performance. The classification achieved an overall accuracy of 97.1542%, with just a 12.9% miss detection rate for scintillation occasions. This performance was higher than that of the SVM and XGBoost models. It is shown that the model proposed in this study is of great value for the detection of ionospheric scintillation in the polar and equator regions.  
This study involved solely detecting ionospheric scintillation in GNSS RO measurements from the MetOp-A satellite. In future research, RO measurements from various LEO satellite constellations in diverse geographic locations will be collected to expand on the characteristics of scintillation.  
Conceptualization, [PERSON], [PERSON] and W.Z.; methodology, [PERSON] and R.J.; analysis, [PERSON]; validation, [PERSON]; discussion, [PERSON], [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] All authors took part in the discussion of the work described in this paper. All authors have read and agreed to the published version of the manuscript.  
The National Natural Science Foundation of China (No. 52371354).  
Not applicable.  
Code and data can be provided by the authors upon request.  
The authors wish to extend their gratitude to the research teams specializing in ionospheric scintillation detection, and the authors would like to thank the University Corporation for Atmospheric Research/COSMIC Data Analysis and Archive Center (UCAR/CDAAC) for sharing the MetOp-A RO data, which were used in this work.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline SVM & 97.1\% & 98.1\% & 76.5\% & 77.1\% \\ XGBoost & 96.9\% & 90.7\% & 79\% & 84.4\% \\ SSA-XGBoost & 97.8\% & 91.5\% & 87.1\% & 89.2\% \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of the performances of the different algorithms on the test set.