This paper presents a traffic demand prediction method based on deep learning algorithms, aiming to address the dynamic traffic demands in satellite communication and enhance resource management efficiency. Integrating Seq2Seq and LSTM networks, the method improves prediction accuracy and applicability, especially for mobile terminals such as aviation and maritime ones. Unlike traditional approaches, it does not require extensive statistical data and can be generalized to real-world systems, providing stable long-term traffic demand predictions. This study utilizes real-world flight data mapped to corresponding satellite beams, allowing the precise prediction of beam-specific traffic demands. The results show that aggregating historical demand data for beams with similar trends achieves an average predictive Mean Squared Error (MSE) of 0.0007 and a maximum MSE fluctuation of 0.014, significantly outperforming predictions based on average values in terms of stability and accuracy. This novel solution for resource management in satellite communication ensures efficient and accurate long-term traffic demand predictions.

Graphene has much higher mechanical strength than other materials, but can be significantly influenced by the vacancy defect or Stone-Wales defect in the lattices. In this paper, we use molecular dynamics simulation to study the effects of vacancy and Stone-Wales defects in fracture behavior of the defective graphene. The results show that the fracture strength of graphene is more sensitive to the vacancy defects than that of Stone-Wales defect. When the defect is located in the center of graphene, the fracture strength of defect graphene is less than that of defect-free graphene. When the defect is far away from the center of graphene, the fracture strength of defect graphene will be enhanced, even greater than that of defect-free graphene. This study may help to understand the properties of graphene with defect, but also show the potential for graphene-based materials in engineering.

With the expansion of the very-high-throughput satellite (VHTS) system, the uneven distribution of traffic demands in time and space has become increasingly significant and cannot be ignored. It is a significant challenge to efficiently and dynamically allocate scarce on-board resources to ensure capacity and demand matching. The advancement of flexible payload technology provides the possibility to overcome this challenge. However, computational complexity is increasing due to the unsynchronized resource adjustment and the time-varying demands of the VHTS system. Therefore, we propose a double-timescale bandwidth and power allocation (DT-BPA) scheme to effectively manage the available resources in the flexible payload architecture. We use a multi-agent deep reinforcement learning (MADRL) algorithm aiming to meet the time-varying traffic demands of each beam and improve resource utilization. The simulation results demonstrate that the proposed DT-BPA algorithm enhanced the matching degree of capacity and demand as well as reduced the system’s power consumption. Additionally, it can be trained offline and implemented online, providing a more cost-effective solution for the VHTS system.

Recently, the demand for high data traffic transmission is experiencing unstoppable growth, and multi-beam high-throughput satellite (HTS) systems have proven to be effective solutions. However, how to manage and eliminate the co-channel interference caused by frequency reuse in multi-beam HTS systems is still a challenging issue. Motivated by this background, this paper begins with discussing the interference mechanism in the uplink and downlink of HTS systems and then uses the quantification method of carrier-to-interference ratio (CIR), whereby the influencing factors of the CIR are accordingly analyzed. In addition, the common CIR selection strategies are summarized and compared with each other in terms of their advantages and disadvantages. Based on the proposed CIR quantification and statistical selection methods, two frequency plan cases are simulated for the scenario of 19 beams with 4-color reuse. The results show that the proposed quantification and selection methods are rather practical and offer a feasible approach for frequency plan adjustment based on CIR optimization. Finally, a simulation analysis of the CIR under different power spectral density (PSD) values is conducted, and the results show that the PSD has a substantial impact on the CIR.