Abstract
In 2010, the European Commission set out the development of an economy based on knowledge and innovation as one of the priorities of its Europe 2020 strategy for smart, sustainable, and inclusive growth. This culminated in the 'Youth on the Move' flagship initiative, aimed at enhancing the performance and international attractiveness of Europe's higher education institutions and raising the Union's overall education and training levels. Therefore, it is relevant to assess the performance of the 'Youth on the Move' initiative via the creation of composite indicators (CIs) and, ultimately, monitor the progress made by European countries in creating a positive environment supporting learner mobility. For this reason, we make use of the CI-building 'Benefit-of-the-Doubt' approach, in its robust and conditional setting to account for outliers and the human development of those nations, to exploit the European Commission's Mobility Scoreboard framework between 2015/2016 and 2022/2023. Furthermore, we incorporate the value judgements of experts in the sector to construct utility scales and compute weight restrictions through multi-criteria decision analysis. This enables the conversion of ordinal scales into interval ones based on knowledgeable information about reality in higher education. In the end, the results point to a slight performance improvement, but highlight the need to improve the 'Recognition of learning outcomes', 'Foreign language preparation', and 'Information and guidance'.

Abstract
The rapid proliferation of Internet of Things (IoT) systems, encompassing a wide range of devices and sensors with limited battery life, has highlighted the critical need for energy-efficient solutions to extend the operational lifespan of these battery-powered devices. One effective strategy for reducing energy consumption is minimizing the number and size of retransmitted packets in case of communication errors. Among the potential solutions, Incremental Redundancy Hybrid Automatic Repeat reQuest (IR-HARQ) communication schemes have emerged as particularly compelling options by adopting the best aspects of error control, namely, automatic repetition and variable redundancy. This work addresses the challenge by developing a simulator capable of executing and analysing several (H)ARQ schemes using different channel models, such as the Additive White Gaussian Noise (AWGN) and Gilbert-Elliott (GE) models. The primary objective is to compare their performance across multiple metrics, enabling a thorough evaluation of their capabilities. The results indicate that IR-HARQ outperforms alternative methods, especially in the presence of burst errors. Furthermore, its potential for further adaptation and enhancement opens up new ways for optimizing energy consumption and extending the lifespan of battery-powered IoT devices.

Abstract
The methodology presented in this work allows for the creation of a real-time adjustment of Kalman Filter process noise for lithium battery state-of-charge estimation. This work innovates by creating a methodology for adjusting the process (Q) and measurement (R) Kalman Filter noise matrices in real-time. The filter algorithm with this adaptative mechanism achieved an average accuracy of 99.56% in real tests by comparing the estimated battery voltage and measured battery voltage. A cell-balancing strategy was also implemented, capable of guaranteeing the safety and efficiency of the battery pack in all conducted tests. This work presents all the methods, equations, and simulations necessary for the development of a battery management system and applies the system in a practical, real environment. The battery management system hardware and firmware were developed, evaluated, and validated on a battery pack with eight LiFePO4 cells, achieving excellent performance on all conducted tests.

Abstract
Pathologies in concrete structures can be visually evidenced on the concrete surface, such as by fissures or cracks, fragmentation of part of the concrete, concrete efflorescence, corrosion stains on the concrete surface, or exposed steel bars, the latter two occurring in reinforced concrete. Therefore, these pathologies can be analyzed via the images of concrete structures. This article proposes a methodology for visually inspecting concrete structures using deep neural networks. This method makes it possible to speed up the detection task and increase its effectiveness by saving time in preparing the identifications to be analyzed and eliminating or reducing errors, such as those resulting from human errors caused by the execution of tedious, repetitive analysis tasks. The methodology was tested to analyze its accuracy. The neural network architecture used for detection was YOLO, versions 4 and 8, which was tested to analyze the gain with migration to a more recent version. The dataset for classification was Ozgnel, which was trained with YOLO version 8, and the detection dataset was CODEBRIM. The use of a dedicated classification dataset allows for a better-trained network for this function and results in the elimination of false positives in the detection stage. The classification achieved 99.65% accuracy.

Abstract
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Abstract
Digital images from orbital platforms are the main source of information for mapping and decision-making. Their use has become increasingly popular over the years and has expanded into various areas. Feature extraction in digital images has been widely researched in Image Analysis, Photogrammetry, and Computer Vision. Works related to feature extraction for the generation and updating of GISs are generally divided into anthropic features such as buildings and/or highways and natural features such as vegetation areas or bodies of water. One attractive methodology for feature extraction, especially for rivers and bodies of water, is based on active contours, formulated based on the evolution of curves, which can have parametric models (Snakes) or geometric models (Level set). In this context, this work intends to identify and compare some characteristics of parametric and geometric active contour methods and apply them to orbital images from the OLI and PAN sensors of the LANDSAT 8 and CBERS 4 satellites for feature extraction, correlating these characteristics with the parameters required in the mathematical models of active contours. The present work makes use of Digital Image Processing (DIP) methods, with the first processing stage known as pre-processing, consisting of interconnected tasks that can be used to extract some information about the objects present in the scene. Subsequently, in the processing stage, the features of interest are extracted with the help of the Fiji and Icy software using Level Set and Snake, respectively. Regardless of the method used, the results presented in this work show an extraction time compatible with application needs, as they are developed semi-automatically.