Abstract
The integration of Distributed Energy Resources (DERs) into low-voltage (LV) distribution grids poses significant challenges for grid management, particularly regarding the need for accurate information on the connection phases of installations to ensure proper load balancing and to enhance hosting capacity. This paper presents a novel voltage-based phase mapping approach using the Mahalanobis-Wasserstein (MW) distance - a metric that exploits voltage time series data to accurately assign users to their corresponding phases without requiring additional hardware or prior knowledge of the grid's topology. The proposed method demonstrates strong resilience to missing data, a frequent issue in real-world deployments, and incorporates a confidence score to quantify the reliability of the phase assignments.

Abstract
This paper leverages image processing techniques, including edge detection and feature extraction, to identify and perform pose tracking of cylindrical-like structures within underwater scenes. Examples of these cylindrical-shaped objects are pipes typically used in offshore oil and gas, pillars of bridge structures, or mooring line cables when their sag angle is near zero, making them approximately flat and thus can be approximated as rectilinear. In addition to the pipe's contour identification, this algorithm provides relative distance and bearing to the vision sensor to enable a convergence framework between the structure and any vehicle equipped with this sensor. Furthermore, the algorithm proposed was tested in a pollsimulated scene with a digital twin of the actual vision sensor, onboard an in-house developed ROV prototype. Additionally, the effects of common underwater challenges, such as lighting variability, shadows, turbidity, and visual noise from the pool's geometric structure, were all analyzed to describe the algorithm's performance and robustness fully. Performance was evaluated for distances, bearing angles, FOV, turbidity, camera resolutions, and algorithm processing complexity. © 2025 Marine Technology Society.

Abstract
Renewable energy resources are crucial for addressing global economic and environmental challenges. Energy communities, which unite consumers to pursue shared energy goals, present a promising solution for reducing energy costs and enhancing sustainability. This study analyzes the optimal sizing and operation of energy community resources, formulating the problem as mixed-integer linear programming (MILP) models. Two tools are employed: one for daily operation, calculating energy setpoints for community assets such as battery energy storage systems (BESS) and electric vehicles (EVs), and another for sizing photovoltaic (PV) panels and BESS capacities to minimize costs while optimizing local energy trades. Due to the high computational demands of MILP, three optimization methods are compared: deterministic, hybrid particle swarm optimization (PSO), and evolutionary PSO (EPSO). The hybrid PSO method handles binary and continuous variables efficiently, while EPSO introduces diversity to improve solution quality in complex scenarios. These metaheuristic approaches address the trade-off between solution accuracy and computational effort, providing reliable tools for decision-makers in energy communities.

Abstract
The development of safety-critical systems is heavily governed by domain-specific standards. In the aerospace industry, the DO-178C-Software Considerations in Airborne Systems and Equipment Certification-serves as the primary certification standard used by agencies such as the FAA and EASA to review and approve software-based systems. Although DO-178C aims to ensure system safety while providing evidence for certification, it does not prescribe a specific software development process, allowing flexibility for traditional Waterfall, Agile, or hybrid methods with appropriate adaptations for the aerospace context. This study proposes Scrum4DO178C, an Agile process based on Scrum, to meet the demanding requirements of aerospace software, including safety, robustness, reliability, and integrity. Scrum4DO178C introduces novel process enhancements specifically tailored to meet these criticality needs, while aligning with the standard. Unlike previous proposals that lack detail, this research presents a comprehensive, validated process applied in a real-world industry project at the highest criticality level (Level A - Catastrophic), offering insights beyond theoretical scenarios. The findings demonstrated that the Scrum4DO178C process improves project performance, allows frequent and manageable requirement changes, reduces Verification & Validation (V&V) effort, and increases efficiency while maintaining full compliance with DO-178C. The study also identifies areas for further improvement and suggests exploring the process in additional case studies, both within the aerospace industry and other domains with similarly stringent safety-critical requirements. Finally, it confirms that appropriate automation, namely for documentation production, is a central element to further improve the process.

Abstract
Deep Inferior Epigastric Perforator (DIEP) flap breast reconstruction relies on the precise identification of perforator vessels supplying blood to transferred tissue. Traditional manual mapping from preoperative imaging is timeconsuming and subjective. To address this, AVA, a semi-automated perforator detection algorithm, was developed to analyze angiography images. AVA follows a three-step process: automated anatomical segmentation, manual annotation of perforators, and segmentation of perforator courses. This approach enhances accuracy, reduces subjectivity, and accelerates the mapping process while generating quantitative reports for surgical planning. To streamline integration into clinical workflows, AVA has been embedded into PACScenter, a medical imaging platform, leveraging DICOM encapsulation for seamless data exchange within a Vendor Neutral Archive (VNA). This integration allows surgeons to interactively annotate perforators, adjust parameters iteratively, and visualize detailed anatomical structures. AVA-PACScenter integration eliminates workflow disruptions by providing real-time perforator analysis within the surgical environment, ultimately improving preoperative planning and intraoperative guidance. Currently undergoing clinical feasibility testing, this integration aims to enhance DIEP flap reconstruction efficiency by reducing manual inputs, improving mapping precision, and facilitating long-term report storage within Dicoogle. By automating perforator analysis, AVA represents a significant advancement toward data-driven, patient-centered surgical planning.

Abstract
The topology of low-voltage (LV) distribution grids is often partially known or inaccurately documented by grid operators, including line and cable characteristics, hindering the effective integration and management of Distributed Energy Resources (DERs). This paper presents a data-driven method to reconstruct LV grid topologies using only voltage measurements from customers' smart meters. The approach relies on an adapted genetic algorithm (GA) that iteratively explores candidate configurations, guided by a score function that evaluates both the physical plausibility of estimated line impedances and their consistency with noisy voltage data, which is progressively corrected throughout the process, i.e., the method also filters out errors affecting the initial measurements. The method requires no prior information on grid connectivity and demonstrates robustness to measurement noise, making it well suited for real-world deployment.