Abstract
The European Project Semester (EPS), offered by the Instituto Superior de Engenharia do Porto (ISEP), is a capstone programme designed for undergraduate students in engineering, product design, and business. EPS@ISEP fosters project-based learning, promotes multicultural and interdisciplinary teamwork, and ethics- and sustainability-driven design. This study applies Natural Language Processing techniques, specifically text mining, to analyse project papers produced by EPS@ISEP teams. The proposed method aims to identify evidence of ethical concerns within EPS@ISEP projects. An innovative keyword mapping approach is introduced that first defines and refines a list of ethics-related keywords through prompt engineering. This enriched list of keywords is then used to systematically map the content of project papers. The findings indicate that the EPS@ISEP robotics project papers analysed demonstrate awareness of ethical considerations and actively incorporate them into design processes. The method presented is adaptable to various application areas, such as monitoring compliance with responsible innovation or sustainability policies.

Abstract
The wine production, included in the primary sector is a great cultural and economic deal, both nationally and internationally matters. However, it is highly dependent on natural resources, and traditionally involves high energy and water consumption. Given the global climate change scenario and the need for efficient resource management, it is necessary to implement a sustainable plan for the wine sector to realize sustainable practices. Data from the International Organization of Vine and Wine (OIV), states that global wine production exceeded 260 million hectoliters, in 2022. These has resulted in significant water and energy consumption, with around 500-1200 m(3) of water used per hectare for irrigation and 1.2 gigajoules per hectoliter of wine produced, concluding that more than 80% of total water consumption is associated with irrigation, while more than 90% of energy consumption, is associated with winery processes. In this context, the scarcity of water or the need to achieve carbon neutrality by 2050 makes it essential to adopt energy and water efficiency measures that allow for the sustainable management of resources without endangering the sector's viability. With this in mind, a case study applied to a Portuguese wine industry is presented, including data analysis from water and energy consumption. Also, efficiency metrics will be analyzed, proposing management and decision-support tools based on monitoring and sensor-based techniques. In fact, one example of these efficiency measures deals with the adoption of systems that provide real-time data on consumption patterns and resource availability in order to improve sustainability of the global process production.

Abstract
Swarm robotics enables groups of autonomous agents to coordinate and perform tasks beyond the capabilities of individual robots. This state-of-the-art review focuses on the defining features, principles, and algorithms of swarm robotic systems, with an emphasis on recent advances. It examined classical and modern bio-inspired coordination strategies, decentralized control algorithms and hybrid approaches, highlighting their strengths, limitations and applicability to real-world deployments. Key challenges, such as scalability, robustness, adaptability and the gap between simulation and hardware implementation are analyzed.

Abstract
The automotive sector is undergoing continuous technological evolution driven by the demand for sustainable and safe vehicles. Among the main factors influencing safety, driver behaviour has been identified as a critical contributor to road crashes. This systematic review explores recent innovations in detecting risky driver behaviours, addressing six research questions: the most relevant datasets used for algorithm development and evaluation; system architectures and methodologies for anomaly detection; the most studied driver behaviours and related environmental, human, and mechanical factors; advances in machine learning, deep learning, and statistical methods; performance metrics and validation approaches; and the role of embedded technologies and sensors in practical applications. The review included 93 peer-reviewed articles published between 2020 and 2024, sourced from ACM, IEEE, ScienceDirect, and Scopus. Exclusion criteria were duplicates, non-open access, retracted works, and studies unrelated to outlier detection or driver behaviour. The Parsifal tool was used to support systematic data processing. Results highlight the most frequently used datasets, proposed models, and their performance in detecting driver behaviours, as well as the influence of contextual factors such as traffic rules, road conditions, and sensor limitations. Despite advances, real-world integration remains challenging, requiring further research and development. This review aims to guide researchers in understanding the current state of anomaly detection in driving contexts and to emphasize the need for broader collaboration to create effective, deployable solutions that enhance road safety worldwide.

Abstract
This review examines the vehicle communication systems, its evaluation measures, security concern and impact of contemporary technology. By making electronic searches through different databases, 20 articles were identified to include in the study. Findings have demonstrated that more sophisticated protocols are being implemented, e.g., FlexRay and Dedicated Short-Range Communication (DSRC), though older protocols, e.g., Controller Area Network (CAN) and Local Interconnect Network (LIN), remain widespread. Additionally, the use of Ethernet-based systems in automotive communications is increasing. However, many of these protocols have substantial vulnerabilities, which pose significant security challenges. The findings suggest adopting enhanced communication and security measures supported by Artificial Intelligence (AI) and Machine Learning (ML) for future vehicles. Overall, this work systematically evaluates in-vehicle communication protocols and proposes methods for addressing contemporary security challenges in the automotive industry.

Abstract
Accurate segmentation of grapevines in imagery acquired from unmanned aerial vehicles (UAVs) is important for precision viticulture, as it supports vineyard management by monitoring grapevine health, growth, and environmental stress. However, the structural diversity of vineyards, including differences in training systems, row curvatures, and foliage density, presents challenges for grapevine segmentation methods. This study evaluates the performance of deep learning (DL) models-Feature Pyramid Network (FPN), Pyramid Scene Parsing Network (PSPNet) and U-Net-each combined with different backbones for grapevine segmentation in UAV-based RGB orthophoto mosaics. Data were collected under a range of vineyard conditions and scenarios from Portugal's Douro and Vinhos Verdes regions, providing a representative dataset across multiple vineyard configurations. The DL models were trained, tested, and evaluated using orthorectified RGB imagery, and their segmentation accuracy was compared to thresholding techniques. The results show that DL models, particularly U-Net, achieved accurate grapevine segmentation and reduced over-segmentation and false detections that are common in thresholding methods. FPN models with Inception-v4 and Xception backbones performed well in vineyards with inter-row vegetation, while PSPNet models showed segmentation limitations. Overall, DL-based segmentation models demonstrated advantages over thresholding approaches, demonstrating their suitability for UAV-based grapevine segmentation in diverse and challenging vineyard environments. These results support the scalability of DL-based segmentation for vineyard monitoring applications and indicate that improved segmentation accuracy can contribute to decision support in precision viticulture.