## 1 Introduction  
The next-generation broadband communication satellites will achieve throughput rates of several gigabits per second or terabits per second (Tbps), with the number of beams increasing from dozens to hundreds. With the proliferation of mobile terminals such as airborne, maritime, and high-speed rail terminals, communication satellites face challenges such as an uneven distribution of user geographic locations and tidal effects and varying business hour demands. Traditional communication satellites often encounter situations where beams cannot meet demands, leading to resource wastage. The application of flexible payloads underscores the importance of predicting related traffic demands. In a flexible communication satellite resource management system, resource prediction serves as input for resource management. By forecasting future traffic demands based on past demand patterns, resource utilization efficiency can be improved, aiming to achieve effective resource management [1]. In study [2], the authors discuss that the demand for each beam in a satellite communication system is primarily influenced by factors like population density in service areas. They propose a traffic demand prediction model that relies on specific population parameters to estimate these demands. The model defines key factors such as the throughput per user, population density, penetration rate, and concurrency rate. These parameters are used to calculate the expected traffic demand within a given area. The authors propose that the total traffic demand for each beam is determined by the product of the beam area and the average traffic demand per unit area. They define a function to compute the average traffic demand based on throughput per user, population density, and other relevant parameters. While this model offers a framework for estimating traffic demands, it has notable limitations. It requires extensive statistical data on population characteristics specific to different regions, making it highly dependent on large-scale empirical measurements. This reliance on detailed regional data limits its generalizability to areas with differing demographic and geographic characteristics. Furthermore, the model focuses on short-term capacity predictions, which restricts its utility for proactive and long-term resource management. Similarly, in their research on the satellite traffic demand, scholars [PERSON]. [PERSON] et al. derived the total traffic demand for each unit based on the global population distribution. In addition to the aforementioned shortcomings, neither approach takes into account the mobile terminals in aviation and maritime sectors [3]. In study [4], a deep learning Convolutional Neural Network (CNN)-based architecture is proposed to predict significant rain-fade in satellite communication using satellite and radar image data, as well as link power measurements. For better understanding, a CNN is a type of neural network specifically designed for deep learning. It learns directly from data using convolutional layers that help detect patterns and relevant features in the input data, such as images. By processing data through multiple layers of convolutions, a CNN can identify complex patterns and extract significant features that are crucial for tasks like image recognition, object detection, and, in this case, predicting rain-fade for the next hour using satellite images from the past 30 min. To capture the spatiotemporal dependencies between satellite and radar images, the authors selected a three-dimensional Convolutional Neural Network (3D CNN) as the core component of their baseline architecture. To validate the effectiveness of the 3D CNN in this context, they employed accuracy and the F1-score as key metrics for model evaluation. Accuracy measures the overall proportion of correctly classified instances, while the F1-score balances precision and recall, providing a more nuanced assessment of the model's performance, particularly in imbalanced datasets.