## 2 Data Collection  
Ionospheric scintillations can be discerned through parameters such as Total Electron Content (TEC), rate of TEC index, and amplitude scintillation [39]. Slant TEC (STEC) is ascertained by measuring the path of GNSS signals from the ground to the satellite, focusing on the intersection with the ionosphere at the Ionospheric Pierce Point (IPP). This intersection allows for the calculation of STEC at a specific altitude within the ionosphere [40]. Further, the line-of-sight STEC observations enable the derivation of the Vertical TEC (VTEC) mapping function \(M(\epsilon)\) as follows:  
\[VTEC=STEC/M(\epsilon) \tag{1}\]  
\[M(\epsilon)=[1-(\frac{R.cos\ (\epsilon)}{R+h})^{2}]^{-1/2} \tag{2}\]  
where the elevation angle \(\epsilon\), which signifies the angle between the GNSS receiver and the satellite, Earth's radius R, and the ionospheric layer's altitude h, are crucial parameters. The focus is on ionospheric measurements at an altitude of approximately 450 km. In this investigation, the S4 parameter was employed to detect ionospheric amplitude scintillation. The categorization of ionospheric irregularities was based on the carrier-to-noise density (C/N0) parameter, derived from data acquired by GNSS receivers [41]. The C/N0 was determined using S1 and S2 values from the GNSS data file. The signal-to-noise ratio (S/N0) was calculated from C/N0, per Equation (3), followed by a detrending process outlined in Equation (4). The resulting detrended S/N0 values (SI\({}_{\text{detrended}}^{\text{S/N}_{0}}\)) were computed in the following manner [42; 43]:  
\[S/N_{0}=10^{0.1(C/N_{0})} \tag{3}\]  
\[SI_{detrended}^{S/N_{0}}=\frac{(S/N_{0})_{k}}{\sum_{i=1}^{60\times f_{k}}\frac {(S/N_{0})_{k=i}}{60\times f_{k}}} \tag{4}\]where, \"fs\" denotes the interval between samples, while \"SI\" refers to the signal intensity after detrending, quantified using the signal-to-noise ratio (S/N0). The S4 parameter in question was derived using the methodology laid out in Equation (3) and Equation (4) as follows:  
\[S4=\sqrt{\frac{\langle{SI_{detrended}^{S/N_{0}}}^{2}\rangle-langles{SI_{detrended }^{S/N_{0}}}\rangle^{2}}{\langle{SI_{detrended}^{S/N_{0}}}\rangle^{2}}} \tag{5}\]  
The S4 parameter is defined as the normalized standard deviation of the Signal Intensity (SI) for each relevant epoch, calculated relative to the mean SI over the chosen interval, and is unitless. These parameters elucidate the intensity of the signal-to-noise ratio for waves transmitted via L1 and L2 frequencies from the satellite [44]. The application of S4 spans various durations, from several seconds to hours, contingent on the model's requirements. For the purposes of this research, S4 values are computed based on average data collected over five-minute periods [45]. In the context of ionospheric scintillation analysis, categorization of the scintillation's intensity and type can be effectively accomplished by establishing a threshold criterion for the S4 index values. This approach allows for a structured classification as detailed subsequently [46]:  
\[S4=\left\{\begin{array}{ccc}S4<0.2&\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\  
inputs in a sequence. Fundamentally, RNNs are engineered to perform iterative operations across a data series. Here, each output is influenced not just by the immediate input but is also a function of preceding computational outcomes. This iterative process facilitates a unique temporal linkage within the network, where an output at one instance is intricately linked to inputs at a subsequent instance. Such a design allows for the previous outputs to amalgamate with new inputs, creating a continuous flow of information across time steps. In essence, RNNs embody a loop-like structure within their architecture, providing them with the capability to seamlessly channel information through the neurons while processing sequential inputs.