## 1 Introduction
With the movement of satellites at both the signal transmitting and receiving ends, the signal path sweeps through the atmosphere for a few tens of seconds to minutes, either from high to low or from low to high. This phenomenon is known as an occultation event, and measurements of it may be sensitive to ionosphere effects. The geometry of GNSS RO is displayed in Figure 1. GNSS RO operates globally, covering all geographical regions. It presents the opportunity to detect and record diverse scintillations geographically and builds on existing LEO satellites to reduce the cost of large-scale system monitoring. GNSS RO measurements currently play a significant role in the observation of ionospheric space weather [16; 17]. RO observations from the FengYun-3C (FY3C) satellite  
Figure 1: GNSS RO geometry. during the St. Patrick's Day geomagnetic storm in 2015 demonstrated a significantly altered likelihood of ionospheric scintillation events [18]. The incidence of scintillation was found to have increased in the New Zealand region during the main phase of the storm, as determined by the TEC gradient. Conversely, during the recovery phase after the geomagnetic storm, the incidence of scintillation was reduced. Previous research has primarily focused on detecting changes in satellite signal amplitude and phase caused by ionospheric scintillation. This involves computing the amplitude scintillation index (\(S_{4}\)) and the phase scintillation index (\(\sigma_{\varphi}\)) and using dedicated ionospheric scintillation monitoring receivers [19] that are programmed with predetermined thresholds for the \(S_{4}\) and \(\sigma_{\varphi}\) indices to detect ionospheric scintillation events [20]. However, the utilization of this thresholding technique necessitates complex filtering and detrending operations on the observations. It neglects the high-dimensional feature information of the GNSS signals [21]. Moreover, the conventional thresholding techniques are highly susceptible to false alarms stimulated by multipaths and cannot determine precise thresholds under varying conditions, leading to poor detection accuracy for weak scintillation events. Wavelet transformation techniques [22] have been proposed as a replacement to detrending operations. However, said techniques rely on intricate implementations. In recent times, researchers have explored different machine learning methods for detecting ionospheric scintillation. [PERSON] et al. utilized Support Vector Machines (SVMs) to detect ionospheric amplitude and phase scintillations [23; 24], which involved the incorporation of a significant amount of authentic scintillation data during the training phase. As a result, they attained a detection accuracy of 91-96%, surpassing conventional thresholding techniques. However, Support Vector Machine (SVM) classifiers require significant memory allocations for sizable datasets, which incurs high computational expenses. Furthermore, the overfitting issue persists, thereby leading to misleading outcomes. [PERSON] et al. have introduced a decision tree (DT)-based algorithm for detecting ionospheric scintillation [25], utilizing in-phase and orthogonal values from the receiver tracking loop output as sample features. The algorithm can achieve a detection accuracy of 98%, with a very low false alarm rate. However, obtaining in-phase and quadrature correlator output values is challenging and requires a specific receiver. In this study, an ionospheric scintillation detection model is developed utilizing the Sparrow Search Algorithm (SSA)-optimized XGBoost. The GNSS radio occultation technique is used to compute the power spectral density (PSD) of the GNSS signal strength by extracting the signal strength during an occultation event, which is then employed as a training feature in the detection model to achieve ionospheric scintillation detection and subsequently apply it to global regions, especially the polar and equator regions.