## 2 Data Collection
### Loss Function
The calculation method is detailed below:  
\[L_{log-\text{cosh }(y,g)}=\frac{1}{N}{\sum\limits_{i=1}^{N}}\;w_{i}\cdot\log \left(\text{cosh }\left(y_{i}-\hat{y}_{i}\right)\right) \tag{21}\]  
It is known that for small values of y, the function Log(cosh(y)) is approximately equal to \(\frac{y^{2}}{2}\), and for large values of y it is approximately equal to \(|y|-\log\left(2\right)\). Consequently, this loss function remains unaffected by outliers and, furthermore, it possesses a second derivative. The presence of the second derivative is an advantage that was lacking in the L\({}_{8}\). Therefore, the use of the L\({}_{\text{log-cosh}}\) not only provides us with the advantages of the L\({}_{8}\) but also enables the application of methods that require second derivatives for optimization [57]. Owing to the pronounced imbalances in magnitudes and substantial data gaps observed in S4 values, common loss functions such as L\({}_{8}\) and L\({}_{\text{log-cosh}}\) may prove insufficient for predicting moderate or strong levels of ionospheric scintillation. It is firmly established that each of the aforementioned loss functions possesses inherent merits and drawbacks. Consequently, we endeavor to foster innovation by amalgamating these two loss functions into a unique composite that integrates features from both. Drawing inspiration from [PERSON] et al. [35], we will employ a custom loss function tailored to our specific needs for predicting ionospheric scintillation data with high noise levels. The formalized equation for this custom loss function is articulated as follows:  
\[L_{\text{C}}=\lambda_{1}\cdot L_{\delta}+\lambda_{2}\cdot L_{log-cosh}+\lambda_{3} \cdot DP \tag{22}\]  
\[DP=\max\left(|y_{i}-\hat{y}_{i}|\right) \tag{23}\]  
This innovative custom loss function is crafted by combining two distinct loss functions, L\({}_{8}\) and L\({}_{\text{log-cosh}}\), and introducing a Dynamic Penalty (DP) component, uniquely suited to our needs for ionospheric scintillation prediction. The dynamic penalty term signifies the maximum absolute difference between the actual and predicted values for each S4 data point. The scale factors, denoted as \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\), govern the weighting of L\({}_{8}\), L\({}_{\text{log-cosh}}\), and the dynamic penalty, respectively. Through empirical investigations, we ascertained that setting \(\lambda_{1}=1\), \(\lambda_{2}=1\), and \(\lambda_{3}=0.1\) yields optimal predictive performance on both training and validation datasets. In our modeling approach, the calculation of loss function values was conducted using the L\({}_{\text{C}}\) and L\({}_{8}\) methods. To ensure these loss function values correspond effectively with observational data, we incorporated w\({}_{i}\) as a weighting function in our equations. The application of w\({}_{i}\) values has been similarly utilized in other studies to enhance prediction performance [35]. These w\({}_{i}\) values, particularly applied to S4 threshold, were derived through a heuristic method obtained as:  
\[w_{i}=\left\{ \begin{array}{ll}0.25&S4<0.2\\ 0.50&0.2\leq S4<0.4\\ 0.75&0.4\leq S4<0.6\\ 1.00&0.6\leq S4\end{array} \right. \tag{24}\]  
In our study, the methodology for determining threshold values for the variable w\({}_{i}\) was primarily inspired by the approach outlined in previous research [35]. Subsequent to introducing modifications to this existing framework, we engaged in a series of empirical tests aimed at identifying optimal threshold levels. This rigorous testing led to the adoption of elevated threshold parameters for the weight values within our model's loss function. The significance of this approach lies in its ability to weight the loss function effectively, enabling the model to dynamically adjust to observational data. This is particularly crucial in scenarios characterized by abrupt increases in observational values, thereby enhancing the model's predictive accuracy.