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
This work addresses the historical development of techniques and methodologies oriented to the measurement of the internal diameter of transparent tubes since the original contributions of Anderson and Barr published in 1923 in the first issue of Measurement Science and Technology. The progresses on this field are summarized and highlighted the emergence and significance of the measurement approaches supported by the optical fiber.

Abstract
This paper describes the development of a complete controller for the FANUC S-420FD 6-axis industrial robot. The original controller of the robot presented failures that made it impossible to operate and that negatively impacted the academic and research activities. To solve this problem, it was proposed the development of a new open-technology controller and also the design of an intuitive and functional graphical interface, allowing the programming, control and monitoring of the robot parameters. The developed interface offers advanced features such as trajectory programming, custom parameter configuration, and real-time visualization of the robot's state. This work highlights the importance of efficient and affordable solutions for the maintenance of industrial robots in university environments, encouraging scientific and technological advancement in these areas of study.

Abstract
Lung cancer is a significant global health concern, and accurate classification of lung nodules plays a crucial role in its early detection and treatment. This paper evaluates and compares the performance of Vision Transformer (ViT) and Convolutional Neural Network (CNN) models for lung nodule classification using the Pylung tool proposed in this work. The study aims to address the lack of research on ViT in lung nodule classification and proposes ViT as an alternative to CNN. The Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset is utilized for training and evaluation. The Pylung tool is employed for dataset preprocessing and comparison of models. Three models, ViT, VGG16, and ResNet50, are analyzed, and their hyperparameters are optimized using Optuna. The results show that ViT achieves the highest accuracy (99.06%) in nodule classification compared to VGG16 (98.71%) and ResNet50 (98.46%). The study contributes by introducing ViT as a model for lung nodule classification, presenting the Pylung tool for model comparison, and suggesting further investigations to improve the accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Squalius alburnoides (Steindachner, 1866) is an endemic threatened species from the Iberian Peninsula. Here, we report the first observations of intraspecific cleaning behavior in isolated summer pools in the Guadiana River Basin (Portugal). We found that focal S. alburnoides solicited cleaning by adopting an immobile tail-stand position known as posing, which immediately signaled a response to a few conspecifics that approached and inspect them. Our study expands the list of cleanerfish species in freshwaters, giving emphasis to the importance of mutual positive behavior within an endangered species, particularly when facing seasonal disturbance.

Abstract
Imbalanced data are present in various business sectors and must be handled with the proper resampling methods and classification algorithms. To handle imbalanced data, there are numerous resampling and learning method combinations; nonetheless, their effective use necessitates specialised knowledge. In this paper, several approaches, ranging from more accessible to more advanced in the domain of data resampling techniques, will be considered to handle imbalanced data. The application developed delivers recommendations of the most suitable combinations of techniques for a specific dataset by extracting and comparing dataset meta-feature values recorded in a knowledge base. It facilitates effortless classification and automates part of the machine learning pipeline with comparable or better results than state-of-the-art solutions and with a much smaller execution time.