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.

The heart rate and its variability, known as Heart Rate Variability (HRV), are indispensable measurements for cardiorespiratory monitoring, recognition and quantification of emotions, detection of abnormalities, and heart disease control. In general, the acquisition systems for heart rate and HRV require a contact area for sensor's installation and positioning, creating restrictions and/or obstructions on user's movements. This paper proposes a noninvasive and noncontact technique for heart rate and HRV acquisition using a single camera. This method consists in the automatic detection of the user's face and utilization of an Independent Component Analysis (ICA) algorithm to separate the necessary signals to determine the heart rate and the HRV. The experiments have shown more than 80% of similarity between the results of the proposed heuristic in comparison to the results of the photoplethysmography sensor (PPG). General TermsBiofeedback, Image Processing.