## 3 Results  
Figure 2 shows Pc5 pulsations observed on a geomagnetically quiet day, specifically on 22 March 2016, at the BJN station and the associated S4 measurement from the SCINDA receiver. We selected 22 March 2016, for analysis because it represents a geomagnetically quiet day, which is critical for isolating the effects of Pc5 pulsations on ionospheric scintillations without the interference of other geomagnetic disturbances. On quiet days, the background geomagnetic conditions are relatively stable, allowing for clearer identification of the influence of low-frequency Pc5 waves on the ionosphere. The data from this specific day provide a clean baseline for studying the relationship between Pc5 pulsations observed at the BJN station and the associated S4 index from the SCINDA receiver, reflecting broader trends in the modulation of ionospheric scintillations by magnetospheric waves under calm space weather conditions. Figure 2c,d shows the wave characteristics in the time-frequency domain retrieved using the wavelet analysis technique. The wavelet-based analysis revealed key characteristics of Pc5 pulsations and S4 scintillation indices in the time-frequency domain, offering insights into their temporal and spectral dynamics. Specifically, the analysis identified coherent oscillations within the Pc5 frequency range (1.7-6.7 mHz), which were temporally aligned with increases in the S4 index, indicating a direct correlation between Pc5 wave activity and enhanced ionospheric scintillations. The wavelet analysis also allowed for the detection of intermittent bursts of wave energy and localized anomalies, which were found to coincide with periods of heightened scintillation activity. These observations suggest that Pc5 pulsations play a significant role in modulating ionospheric plasma irregularities, contributing to scintillation occurrences in the equatorial anomaly. Figure 3 provides a statistical analysis of the correlation between the Kp index, Pc5 pulsations, and the S4 scintillation index. The results indicate a strong relationship between higher Kp index values and the increased occurrence of Pc5 events, particularly during periods of heightened geomagnetic activity in 2012. Conversely, 2016 experienced a notable decline in both Kp index values and Pc5-driven S4 scintillation events. Nearly half of the Pc5 events recorded at the BJN station were associated with S4 scintillations, predominantly during elevated Kp periods in 2012, with fewer occurrences during the geomagnetically quieter conditions in 2016. These findings highlight the influence of geomagnetic activity on Pc5-driven scintillations. The figure also highlights that the strongest correlations between the Kp index and S4 occur during intervals of moderate to high geomagnetic activity, further reinforcing the role of Pc5 pulsations in modulating ionospheric scintillations under  
varying space weather conditions. The Kp index value is depicted as a line plot, while the blue and orange bar graphs represent the Pc5 and S4 events, respectively. Figure 4a,b displays the performance metrics of the RF and XGBoost models, respectively. The RF model demonstrates strength in handling high-dimensional data and reducing overfitting due to its ensemble-based approach and inherent variance reduction through bagging. However, it may suffer from slower prediction times, especially with large datasets, and can be less effective in capturing complex patterns compared  
Figure 3: Statistical correlation of Pc5 events and S4 and their correlation with the Kp index. The Kp index is represented by the line plot and the blue and orange graphs represent the Pc5 and S4 events respectively. Figure 2: Pc5 pulsations extracted from the BJN station together with the S4 from the SCINDA station. From top to bottom: (**a**) Pc5 pulsations extracted from BJN station; (**b**) S4 from the SCINDA station; (**c**) wavelet-based analysis of the Pc5 pulsation; (**d**) wavelet-based analysis of the S4. to XGBoost.