## 2 Materials and Methods
The BJN station is located along the geomagnetic latitude and longitude of 71.09\({}^{\circ}\) and 108.07\({}^{\circ}\), respectively. The Bear Island (BJN) magnetometer station was specifically chosen for its geomagnetic location, which lies within a high-latitude region known for its sensitivity to geomagnetic disturbances, such as Pc5 pulsations. This makes BJN particularly relevant for studying the magnetospheric dynamics that drive ionospheric scintillations. Its geomagnetic latitude allows for the monitoring of Pc5 wave activity, which can propagate equatorward and influence lower-latitude regions like Helwan, Egypt. The close longitudinal alignment of BJN with the Helwan SCINDA station facilitates coordinated observations of Pc5-triggered scintillations, enabling a more accurate correlation between magnetospheric pulsations and ionospheric disturbances in the equatorial anomaly. Pc5 waves were obtained from the Bz component of the magnetic field by applying a Butterworth filter with a sampling rate of 16 mHz and a cutoff frequency range of 1.7-6.7 mHz. The Butterworth filter was selected due to its maximally flat frequency response in the passband, which ensures minimal signal distortion, particularly within the critical 1.7-6.7 mHz range for Pc5 wave detection. This flat response is crucial for maintaining the integrity of the Pc5 wave signals, as it allows for smooth and accurate preservation of the waveforms while effectively attenuating high-frequency noise and incoherent signals from the surrounding space environment. Additionally, the gradual transition from the passband to the stopband offered by the Butterworth filter further supports its suitability, ensuring that essential frequency components of the Pc5 waves are retained with minimal interference, thereby enhancing the accuracy of geomagnetic field data analysis. Furthermore, we obtained the high cadence (1-min resolution) solar wind parameters from the OMNI Web database. The parameters retrieved are IMF, \(B_{z\_GSE}\), \(B_{y\_GSE}\), solar wind speed, solar wind pressure, \(E_{y}\), and F10.7. In addition, we included the year, day of the year (DOY), and the hour on which the scintillation events took place to capture diurnal scintillation variations. During the preprocessing of the data, we noticed that approximately 10% of the entire data was missing. The missing data were evenly distributed throughout the years under consideration. Handling missing data is a challenging problem in machine learning model construction. Several techniques have been reported to treat missing data gaps during preprocessing. In this work, we adopted the data gap removal approach used in previous works e.g., in Ref. [14], since the retained data are large enough for our model construction. The procedure was necessary to enhance continuity in the dataset for effective machine learning. At the end of the preprocessing, 92,413 datasets were obtained and used for model construction. We utilized the SKlearn train-test-split function to partition our dataset based on 70% training and 15% each for validation and testing, respectively, to ensure consistency. Following this approach, the training set numbered 64,689, and the validation and testing sets numbered 13,862, respectively. To explicitly prevent data leakage, we employed temporal blocking to ensure that sequential relationships within the data were preserved. This approach involved dividing the dataset chronologically, ensuring that events from the training set would not overlap with those in the validation or testing sets. By avoiding random shuffling, we maintained the temporal integrity of the dataset, which is critical when dealing with time-series data like ionospheric scintillations, where temporal dependencies between events could lead to leakage if not properly accounted for. This method ensures the model's ability to generalize to unseen data without overfitting.