Abstract
All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller skate variant of team hockey, it is of great interest to automatically track player movements, positions, and sticks, and also to make other judgments, such as being able to locate the ball. In this work, we present a real-time pipeline consisting of an object detection model specifically designed for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and fast movements, our deep learning object detection model effectively identifies and tracks important visual elements in real time, such as: ball, players, sticks, referees, crowd, goalkeeper, and goal. Using a curated dataset consisting of a collection of rink hockey videos containing 2525 annotated frames, we trained and evaluated the algorithm's performance and compared it to state-of-the-art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80% and, according to our results, good performance in terms of accuracy and speed, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected an important event type in rink hockey games, namely, the occurrence of penalties.

Abstract
This paper introduces a new variable structure controller designed for depth control of an autonomous underwater sensor platform equipped with a variable buoyancy module. To that end, the prototype linear model is presented, and a finite element-based method is used to estimate one of its parameters, the hull deformation due to pressure. To manage potential internal disturbances like hull deformation or external disturbances like weight changes, a disturbance observer is developed. An analysis of the observer steady-state estimation error in relation to input disturbances and system parameter uncertainties is developed. The locations of the observer poles according to its parameters are also identified. The variable structure controller is developed, keeping energy savings in mind. The proposed controller engages when system dynamics are unfavorable, causing the vehicle to deviate from the desired reference, and disengages when dynamics are favorable, guiding the vehicle toward the target reference. A detailed analysis determines the necessary switching control actions to ensure the system reaches the desired reference. Finally, simulations are run to compare the proposed controller's performance with that of PID-based controllers recently developed in the literature, assessing dynamic response and energy consumption under various operating conditions. Both the VBM- and propeller-actuated vehicles were evaluated. The results demonstrate that the proposed controller achieves an average energy consumption reduction of 22% compared to the next most efficient PID-based controller for the VBM-actuated vehicle, though with some impact on control performance.

Abstract

Abstract
This article presents a systematic review of Multiple Instance Learning (MIL) applied to image classification, specifically highlighting its applications in medical imaging. Motivated by the need for a comprehensive and up-to-date analysis due to the scarcity of recent reviews, this study uses defined selection criteria to systematically assess the quality and synthesize data from relevant studies. Focusing on MIL, a subfield of machine learning that deals with learning from sets of instances or bags, this review is crucial for medical diagnosis, where accurate lesion detection is a challenge. The review details the methodologies, advances and practical implementations of MIL, emphasizing the attention-grabbing and transformative mechanisms that improve the analysis of medical images. Challenges such as the need for extensive annotated datasets and significant computational resources are discussed. In addition, the review covers three main topics: the characterization of MIL algorithms in various imaging domains, a detailed evaluation of performance metrics, and a critical analysis of data structures and computational resources. Despite these challenges, MIL offers a promising direction for research with significant implications for medical diagnostics, highlighting the importance of continued exploration and improvement in this area.

Abstract
Motivated by the imperative of sustainable practices, the wine industry is increasingly adopting renewable energy technologies to address environmental concerns and ensure its long-term viability amidst rising fossil fuel costs and greenhouse gas emissions. Hybrid renewable energy systems (HRES) have emerged as a solution to improve energy efficiency and mitigate the variability of renewable resources, allowing for better system load factors, greater stability of power supply, and optimized use of infrastructure. Therefore, this study aims to design a HRES that integrates solar and wind energy to sustainably fed an irrigation system in a favorable technical-economic context. This research presents a Mixed Integer Linear Programming (MILP) model that optimizes the profit generated by a grid-connected HRES over 20 years and obtains the optimal system sizing. The study focuses on the farm Quinta do Vallado, Portugal, and examines two distinct Cases. Over 20 years, the implementation of the hybrid system has resulted in savings of approximately 61%.

Abstract
The increasing proliferation of cyber-attacks threatening the security of computer networks has driven the development of more effective methods for identifying malicious network flows. The inclusion of statistical laws, such as Benford's Law, and distance functions, applied to the first digits of network flow metadata, such as IP addresses or packet sizes, facilitates the detection of abnormal patterns in the digits. These techniques also allow for quantifying discrepancies between expected and suspicious flows, significantly enhancing the accuracy and speed of threat detection. This paper introduces a novel method for identifying and analyzing anomalies within computer networks. It integrates Benford's Law into the analysis process and incorporates a range of distance functions, namely the Mean Absolute Deviation (MAD), the Kolmogorov-Smirnov test (KS), and the Kullback-Leibler divergence (KL), which serve as dispersion measures for quantifying the extent of anomalies detected in network flows. Benford's Law is recognized for its effectiveness in identifying anomalous patterns, especially in detecting irregularities in the first digit of the data. In addition, Bayes' Theorem was implemented in conjunction with the distance functions to enhance the detection of malicious traffic flows. Bayes' Theorem provides a probabilistic perspective on whether a traffic flow is malicious or benign. This approach is characterized by its flexibility in incorporating new evidence, allowing the model to adapt to emerging malicious behavior patterns as they arise. Meanwhile, the distance functions offer a quantitative assessment, measuring specific differences between traffic flows, such as frequency, packet size, time between packets, and other relevant metadata. Integrating these techniques has increased the model's sensitivity in detecting malicious flows, reducing the number of false positives and negatives, and enhancing the resolution and effectiveness of traffic analysis. Furthermore, these techniques expedite decisions regarding the nature of traffic flows based on a solid statistical foundation and provide a better understanding of the characteristics that define these flows, contributing to the comprehension of attack vectors and aiding in preventing future intrusions. The effectiveness and applicability of this joint method have been demonstrated through experiments with the CICIDS2017 public dataset, which was explicitly designed to simulate real scenarios and provide valuable information to security professionals when analyzing computer networks. The proposed methodology opens up new perspectives in investigating and detecting anomalies and intrusions in computer networks, which are often attributed to cyber-attacks. This development culminates in creating a promising model that stands out for its effectiveness and speed, accurately identifying possible intrusions with an F1 of nearly 80%, a recall of 99.42%, and an accuracy of 65.84%.