## 3 Algorithm Design  
This paper overcomes the shortcomings mentioned earlier. Firstly, based on the historical demand for each beam coverage area, a model is trained using the combination of Seq2 Seq and LSTM deep learning to predict future demand. Then, based on this model, the historical demand of the desired beam coverage area is inputted, and the model calculates and outputs the final future demand. The accuracy of the prediction model depends heavily on the quality and quantity of historical demand data. Therefore, it is crucial to have sufficient and detailed historical data to train the model effectively. The use of real aviation datasets, which provide detailed traffic demand data for each geographical coordinate at each time point, allows the model to capture various demand patterns and variabilities that are essential for making accurate predictions. The data used in this study were obtained from real aviation datasets, capturing traffic demand for each geographical coordinate (latitude and longitude) at each time interval. This comprehensive and representative dataset ensures that the model is trained and evaluated on actual, operational data. The main steps include the following, as shown in Figure 3:  
1. Based on the known demand for each minute in each beam coverage area over \(N\) days, where \(N\) represents the number of days for which the data have been collected, with a total of \(B\) beams, where \(B\) denotes the total number of beam coverage areas analyzed, the demands are averaged every \(t\) minutes. Here, \(t\) is the time interval in minutes over which the data are aggregated to merge into one time slot. As a result, sequences of \(B\) beams are generated, each containing \(N\times 1440/t\) points, where 1440 represents the total number of minutes in a day. This step ensures that the data are condensed into a manageable format, capturing essential temporal patterns for each beam. 2. To train and evaluate the neural network model, datasets are created for both purposes. The training data include historical demand information from all beam coverage areas, ensuring it captures a wide range of traffic patterns and temporal variations. The testing data, which are used to assess the model's performance, are selected from the same dataset. Both datasets cover all beams to evaluate the model's ability to predict traffic demands across different regions comprehensively. It is important to maintain consistency in the data used for both training and testing to evaluate the model's performance accurately. 3. In this step, the data are preprocessed by defining the length of the input sequence \(L_{left}\) and the output sequence \(L_{right}\) for the final model. Here, \(L_{left}\) represents the number of time slots, each of duration \(t\) minutes, that are used as input to predict the future demand over the next \(L_{right}\) time slots, which are also of \(t\) minutes each. This setup allows the model to learn from historical time slots and generate predictions for future time slots, capturing both short-term and long-term patterns in the traffic data. 4. To build a robust training dataset, the number of samples \(M\) is defined by randomly selecting \(M\) sequence segments of length \(L_{left}+L_{right}\) from all data used for training the model, with some potential overlap. Here, \(M\) represents the total number of samples extracted from the dataset. This step involves segmenting the time series data into windows to create input-output pairs necessary for training the Seq2 Seq model and ensures a diverse and representative sample set for training. 5. Adjust and organize the data into a structured format that aligns with the requirements of the model for effective training. This involves structuring the data into input-output pairs, ensuring consistency and compatibility with the Seq2 Seq and LSTM frameworks. Proper formatting is crucial for effective model training and accurate predictions. 6.