Abstract
Robotic competitions stand as platforms to propel the forefront of robotics research while nurturing STEM education, serving as hubs of both applied research and scientific innovation. In Portugal, the Portuguese Robotics Open (FNR) is an event with several robotic competitions, including the Robot@Factory 4.0 competition. This competition presents an example of deploying autonomous robots on a factory shop floor. Although the literature has works proposing frameworks for the original version of the Robot@Factory competition, none of them proposes a system framework for the Robot@Factory 4.0 version that presents the hardware, firmware, and software to complete the competition and achieve autonomous navigation. This paper proposes a complete robotic framework for the Robot@Factory 4.0 competition that is modular and open-access, enabling future participants to use and improve it in future editions. This work is the culmination of all the knowledge acquired by winning the 2022 and 2023 editions of the competition.

Abstract
This paper analyses the process of generalisation of an innovative government-led public practice in the healthcare sector. The scaling and embedding involved in this generalisation process are assumed to be dependent on the multiple implementation processes (consecutive or simultaneous) that lead to a routine use of the innovation in different adopters. This paper, therefore, proposes the use of a configurational theory approach to conceptualise each implementation of the innovation during the generalisation process and shed light on the generalisation's scaling and embedding efforts. It suggests a set of recommendations and practices for generalisation managers, most notably: i) they should regard generalisations as organic processes where their main role is to create space for experimentation, learning and negotiation, and ii) they should adopt different modes of governance to identify adequate mechanisms and strategies and guide their actions. This configurational perspective allows them to monitor and manage the evolution of implementations, informs the valuable learning processes that take place in a generalisation and has been found to be a useful tool to support the crucial collaboration among the actors involved in a generalisation.

Abstract
Vulnerability detection and repair is a demanding and expensive part of the software development process. As such, there has been an effort to develop new and better ways to automatically detect and repair vulnerabilities. DifFuzz is a state-of-the-art tool for automatic detection of timing side-channel vulnerabilities, a type of vulnerability that is particularly difficult to detect and correct. Despite recent progress made with tools such as DifFuzz, work on tools capable of automatically repairing timing side-channel vulnerabilities is scarce. In this paper, we propose DifFuzzAR, a tool for automatic repair of timing side-channel vulnerabilities in Java code. The tool works in conjunction with DifFuzz and it is able to repair 56% of the vulnerabilities identified in DifFuzz's dataset. The results show that the tool can automatically correct timing side-channel vulnerabilities, being more effective with those that are control-flow based. In addition, the results of a user study show that users generally trust the refactorings produced by DifFuzzAR and that they see value in such a tool, in particular for more critical code.

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.