Abstract
Humans are good at understanding subjective or vague statements which, however, are hard to express in classical logic. Fuzzy logic is an evolution of classical logic that can cope with vague terms by handling degrees of truth and not just the crisp values true and false. Logic is the formal basis of computing, enabling the formal design of systems supported by tools such as model checkers and theorem provers.This paper shows how a model checker such as Alloy can evolve to handle both classical and fuzzy logic, enabling the specification of high-level quantitative relational models in the fuzzy domain. In particular, the paper showcases how QAlloy-F (a conservative, general-purpose quantitative extension to standard Alloy) can be used to tackle fuzzy problems, namely in the context of validating the design of fuzzy controllers. The evaluation of QAlloy-F against examples taken from various classes of fuzzy case studies shows the approach to be feasible.

Abstract
This paper explores the potential use of acoustic imaging and the use of a multi-frequency multibeam-echosounder (MBES) for monitoring marine litter in the water column. The main goal is to perform a test and validation setup using a simulation and actual experimental setup to determine if the MBES data can detect marine litter in a water column image (WCI) and if using multi-frequency MBES data will allow to better distinguish and characterize marine litter debris in detection applications. Results using simulated HoloOcean Environment and actual marine litter data revealed the successful detection of objects commonly found in ocean litter hotspots at various ranges and frequencies, enablingthe pursue of novel means of automatic detection and classification in MBES WCI data while using multi-frequency capabilities.

Abstract
Team Automata is a formalism for interacting component-based systems proposed in 1997, whereby multiple sending and receiving actions from concurrent automata can synchronise. During the past 25+ years, team automata have been studied and applied in many different contexts, involving 25+ researchers and resulting in 25+ publications. In this paper, we first revisit the specific notion of synchronisation and composition of team automata, relating it to other relevant coordination models, such as Reo, BIP, Contract Automata, Choreography Automata, and Multi-Party Session Types. We then identify several aspects that have recently been investigated for team automata and related models. These include communication properties (which are the properties of interest?), realisability (how to decompose a global model into local components?) and tool support (what has been automatised or implemented?). Our presentation of these aspects provides a snapshot of the most recent trends in research on team automata, and delineates a roadmap for future research, both for team automata and for related formalisms.

Abstract
Ground Penetrating Radar (GPR) is a geophysical imaging technique used for the characterization of a sub surface’s electromagnetic properties, allowing for the detection of buried objects. The characterization of an object’s parameters, such as position, depth and radius, is possible by identifying the distinct hyperbolic signature of objects in GPR B-scans. This paper proposes an automated system to detect and characterize the presence of buried objects through the analysis of GPR data, using GPR and computer vision data pro cessing techniques and YOLO segmentation models. A multi-channel encoding strategy was explored when training the models. This consisted of training the models with images where complementing data processing techniques were stored in each image RGB channel, with the aim of maximizing the information. The hy perbola segmentation masks predicted by the trained neural network were related to the mathematical model of the GPR hyperbola, using constrained least squares. The results show that YOLO models trained with multi-channel encoding provide more accurate models. Parameter estimation proved accurate for the object’s position and depth, however, radius estimation proved inaccurate for objects with relatively small radii. © 2024 by SCITEPRESS– Science and Technology Publications, Lda.

Abstract
With the expansion of offshore infrastructure, the necessity for efficient Operation and Maintenance (O&M) procedures intensifies. This article introduces DADDI, a multimodal dataset obtained from a real offshore floating structure, aimed at facilitating comprehensive inspections and 3D model creation. Leveraging Unmanned Aerial Vehicles (UAVs) equipped with advanced sensors, DADDI provides synchronized data, including visual images, thermal images, point clouds, GNSS, IMU, and odometry data. The dataset, gathered during a campaign at the ATLANTIS Coastal Testbed, offers over 2500 samples of each data type, along with intrinsic and extrinsic sensor calibrations. DADDI serves as a vital resource for the development and evaluation of algorithms, models, and technologies tailored to the inspection, monitoring, and maintenance of complex maritime structures. © 2024 IEEE.

Abstract
Background: With the emergence of eHealth and mHealth, the use of mental health apps has increased significantly as an accessible and convenient approach as an adjunct to promoting well-being and mental health. There are several apps available that can assist with mental health monitoring and management, each with specific features to meet different needs. The intersection of mental health and cyber technology presents a number of critical legal and ethical issues. As mental health monitoring apps and devices become more integrated into clinical practice, cybersecurity takes on paramount importance. Objective: To address the ethical and legal aspects of health cybersecurity related to applications in mental health monitoring and management. Methods: We carried out a thematic synthesis of the best scientific evidence. Results: These tools have the potential to significantly improve access to and quality of care for users with mental health conditions, but they also raise substantial concerns about privacy and informed consent.  Cybersecurity in mental health is not only a matter of technology, but also of human rights. The protection of sensitive mental health information is critical, and legal and ethical measures to safeguard this information must be implemented in a robust and transparent manner. Conclusion: the use of information technologies and mobile devices is now part of the clinical reality and its future perspectives. It is important to mention that while these apps can be helpful for self-care and mental well-being management, they are not a substitute for the advice and support of a qualified mental health professional (psychologist or psychiatrist). As we move into the digital age, it is imperative that mental health monitoring and management apps are developed and used responsibly, ensuring the safety, dignity, and well-being of users.