Abstract
The increasing adoption of robotic solutions for inspection tasks in challenging environments is becoming increasingly prevalent, particularly in the offshore wind energy industry. This trend is driven by the critical need to safeguard the integrity and operational efficiency of offshore infrastructure. Consequently, the design of inspection vehicles must comply with rigorous requirements established by the offshore Operation and Maintenance (O&M) industry. This work presents the design of an autonomous surface vehicle (ASV), named Nautilus, specifically tailored to withstand the demanding conditions of offshore O&M scenarios. The design encompasses both hardware and software architectures, ensuring Nautilus's robustness and adaptability to the harsh maritime environment. It presents a compact hull capable of operating in moderate sea states (wave height up to 2.5 m), with a modular hardware and software architecture that is easily adapted to the mission requirements. It has a perception payload and communication system for edge and real-time computing, communicates with a Shore Control Center and allows beyond visual line-of-sight operations. The Nautilus software architecture aims to provide the necessary flexibility for different mission requirements to offer a unified software architecture for O&M operations. Nautilus's capabilities were validated through the professional testing process of the ATLANTIS Test Center, involving operations in both near-real and real-world environments. This validation process culminated in Nautilus's reaching a Technology Readiness Level 8 and became the first ASV to execute autonomous tasks at a floating offshore wind farm located in the Atlantic.

Abstract
Quality inspection is a critical step in ensuring the quality and efficiency of textile production processes. With the increasing complexity and scale of modern textile manufacturing systems, the need for accurate and efficient quality inspection and defect detection techniques has become paramount. This paper compares supervised and unsupervised Machine Learning techniques for defect detection in the context of industrial textile production, in terms of their respective advantages and disadvantages, and their implementation and computational costs. We explore the use of an autoencoder for the detection of defects in textiles. The goal of this preliminary work is to find out if unsupervised methods can successfully train models with good performance without the need for defect labelled data. (c) 2023 The Authors. Published by Elsevier B.V.

Abstract

Abstract
Effective traffic management is crucial in addressing the growing complexities of urban mobility, and variable message signs (VMSs) play a vital role in delivering real-time information to road users. Despite their widespread application, there is limited comprehensive understanding of how VMS influence user behavior and optimize traffic flow. This systematic literature review aims to address this gap by examining the effectiveness of VMS in shaping user interactions and enhancing traffic management systems. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, a thorough analysis of relevant studies was conducted to identify key factors influencing VMS impact, including message content and characteristics, complementary sources of information, user demographics, VMS location, and users' reliance on these signs. Additionally, the review explores the implications of displaying non-critical information on VMS and introduces virtual dynamic message signs (VDMSs) as an innovative approach for delivering public traveler information. The study identifies several research gaps, such as the integration of VMS with vehicle-to-everything (V2X) technologies, navigation systems, the need for validation in real-world scenarios, and understanding behavioral responses to non-critical information on VMS. This review highlights the importance of optimizing VMS for improved user engagement and traffic management, providing valuable insights and directions for future research in this evolving field.

Abstract
Proactive participation of uncertain renewable generation in the day-ahead (DA) wholesale market effectively reduces the system marginal price and carbon emissions, whilst significantly increasing the volumes of real-time balancing mechanism prices to ensure system security and stability. To solve the conflicting interests over the two timescales, this article: 1) proposes a novel hierarchical optimization model to align with the actual operation paradigms of the hierarchical market, whereby the capacity allocation matrix is adopted to coordinate the DA and balancing markets; 2) mathematically formulates and quantitatively analyses the long-term driving factors of balancing actions, enabling system operators (SOs) to design efficient and well-functioning market structures to meet economic and environmental targets; 3) empowers renewable generating units and flexible loads to participate in the balancing market (BM) as 'active' actors and enforces the non-discriminatory provision of balancing services. The performance of the proposed model is validated on a modified IEEE 39-bus power system and a reduced GB network. Results reveal that with effective resource allocation in different timescales of the hierarchical market, the drop speed of balancing costs soars while the intermittent generation climbs. The proposed methodology enables SOs to make the most of all resources available in the market and balance the system flexibly and economically. It thus safeguards the climate mitigation pathways against the risks of substantially higher balancing costs.

Abstract
Robot Learning is one of the most important areas in Robotics and its relevance has only been increasing. The Robot Operating System (ROS) has been one of the most used architectures in Robotics but learning it is not a simple task. Additionally, ROS 1 is reaching its end-of-life and a lot of users are yet to make the transition to ROS 2. Reinforcement Learning (RL) and Robotics are rarely taught together, creating greater demand for tools to teach all these components. This paper aims to develop a learning kit that can be used to teach Robot Learning to students with different levels of expertise in Robotics. This kit works with the Flatland simulator using open-source free software, namely the OpenAI Gym and Stable-Baselines3 packages, and contains tutorials that introduce the user to the simulation environment as well as how to use RL to train the robot to perform different tasks. User tests were conducted to better understand how the kit performs, showing very positive feedback, with most participants agreeing that the kit provided a productive learning experience.