## 2 Materials and Methods  
In this work, we utilized data from a single-station Scintillation Network and Decision Aid (SCINDA) receiver installed at Helwan (29.86\({}^{\circ}\) N, 31.32\({}^{\circ}\) E) in Egypt from the years 2010-2017 that forms part of the maximum and minimum phases of solar cycle 24. The inclusion of solar cycle 24 is significant, as solar activity directly influences ionospheric conditions, with heightened solar activity during the cycle's maximum phase increasing the occurrence and intensity of scintillations [18], while reduced activity during the minimum phase results in fewer scintillations [19]. This receiver was installed as part of the International Heliophysical Year program, which supported the African region with the installation of GPS receivers to enhance the continuous operating reference stations within the region for positioning, navigation, and timing (PNT) applications. This high sampling rate receiver also tracks GPS signals at the L1 (1575.42 MHz) and L2 (1227.60 MHz) bands, which are used for Total Electron Content Estimation [20]. The raw data logged from this receiver include the receiver position (.psn), range records (.rng), signal-to-noise ratio observables, message (.msg), scintillation (.scn) and pseudorange, and phase (.obs) which contain raw S/No ratio files. We are interested in computing the amplitude scintillation from the signal-to-noise ratio due to its sensitivity to ionospheric disturbances, which are particularly influenced by Pc5 pulsations. Amplitude scintillation, unlike phase scintillation, directly reflects fluctuations in signal power caused by plasma density irregularities in the ionosphere, making it a more reliable indicator of the large-scale plasma instabilities often associated with Pc5 pulsations. This makes amplitude scintillation particularly relevant for studying the impact of geomagnetic and ionospheric disturbances. Hence, we employed Equation (1) to compute the S4 index. When we take the standard deviation of the normalized ratio of signal intensity fluctuations to the mean signal intensity, we obtain the unitless S4 index [21; 22]. \[S4=\sqrt{\frac{<l^{2}>-<l>^{2}}{<l>^{2}}} \tag{1}\]  
where \(I\) is the signal intensity and \(<>\) is the mean parameter applied over 60 s cadence. The 60 s cadence is appropriate for this computation as it provides a balanced temporal resolution that captures the relevant ionospheric fluctuations associated with scintillations without introducing excessive noise or averaging out short-term variations [18], thereby allowing for an accurate representation of amplitude scintillation over Pc5 pulsation timescales. To aid these computations, the GPS-TEC application software version 3.03 [23] was employed. The software inputs the raw data files and outputs a text (.cmn) file containing corrected ionospheric data parameters such as the slant and vertical TEC, S4 index, azimuth, and elevation at the various ionospheric piercing points (IPP). We then applied an elevation mask of 30\({}^{\circ}\) to mitigate the effects of multipath, backscattering, and tropospheric delays, which are more pronounced at lower elevation angles. This cutoff is commonly used in ionospheric studies to ensure higher signal quality and reduce noise, particularly in regions like the equatorial anomaly where signal distortion can be significant. While different studies may adopt varying elevation masks depending on geographical location and specific research objectives, 30\({}^{\circ}\) provides an optimal balance between data accuracy and the number of usable observations in this context. We intend to quantify ULF-associated geomagnetic Pc5 pulsations that could potentially trigger scintillations. For this reason, short (long) fluctuations in the amplitude scintillations that are within the frequency (period) domain of Pc5 are characterized using time-frequency analysis. Additionally, we obtained high cadence (1-min resolution) magnetic field measurements from the Bear Island (BJN) magnetometer station.