This paper presents a comprehensive comparison of graph neural networks, specifically Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT), for traffic classification in satellite communication channels. The performance of these GNN-based methods is benchmarked against traditional Multi-Layer Perceptron (MLP) algorithms. The results indicate that GNNs demonstrate superior accuracy and efficiency compared to MLPs, emphasizing their potential for application in satellite communication systems. Moreover, the study investigates the impact of various factors on GNN algorithm performance, providing insights into the most effective strategies for implementing GNNs in traffic classification tasks. This research offers valuable knowledge on the benefits and prospective use cases of GNNs within satellite communication systems.

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The article deals with the testing of popular domestic and foreign cloud videoconferencing systems. A methodology for testing and comparing qualitative parameters of videoconferencing applications based on international experience is proposed. Assessment and comparison of videoconferencing services is based on the calculation of Hurst data flows, formed by the investigated cloud videoconferencing systems. Various applications of foreign and domestic videoconferencing systems have been tested in different operation modes with different channel quality parameters.

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