Abstract
Underwater scenarios pose additional challenges to perception systems, as the collected imagery from sensors often suffers from limitations that hinder its practical usability. One crucial domain that relies on accurate underwater visibility assessment is underwater pipeline inspection. Manual assessment is impractical and time-consuming, emphasizing the need for automated algorithms. In this study, we focus on developing learning-based approaches to evaluate visibility in underwater environments. We explore various neural network architectures and evaluate them on data collected within real subsea scenarios. Notably, the ResNet18 model outperforms others, achieving a testing accuracy of 93.5% in visibility evaluation. In terms of inference time, the fastest model is MobileNetV3 Small, estimating a prediction within 42.45 ms. These findings represent significant progress in enabling unmanned marine operations and contribute to the advancement of autonomous underwater surveillance systems.

Abstract
This chapter presents the use of Robotics in the Inspection and Maintenance of Offshore Wind as another highly challenging environment where autonomous robotics systems and digital transformations are proving high value. © 2024 Andry Maykol Pinto | Aníbal Matos | João V. Amorim Marques | Daniel Filipe Campos | Maria Inês Pereira | Rafael Claro | Eeva Mikola | João Formiga | Mohammed El Mobachi | Jaap-Jan Stoker | Jonathan Prevosto | Shashank Govindaraj | David Ribas | Pere Ridao | Luca Aceto.

Abstract
The human brain, with its vast network of billions of neurons and trillions of synapses (connections) between diverse cell types, remains one of the greatest mysteries in science and medicine. Despite extensive research, an understanding of the underlying mechanisms that drive normal behaviors and response to disease states is still limited. Advancement in the Neuroscience field and development of therapeutics for related pathologies requires innovative technologies that can provide a dynamic and systematic understanding of the interactions between neurons and neural circuits. In this work, we provide an up-to-date overview of the evolution of neurophotonic approaches in the last 10 years through a multi-source, literature analysis. From an initial corpus of 243 papers retrieved from Scopus, PubMed and WoS databases, we have followed the PRISMA approach to select 56 papers in the area. Following a full-text evaluation of these 56 scientific articles, six main areas of applied research were identified and discussed: (1) Advanced optogenetics, (2) Multimodal neural interfaces, (3) Innovative therapeutics, (4) Imaging devices and probes, (5) Remote operations, and (6) Microfluidic platforms. For each area, the main technologies selected are discussed according to the photonic principles applied, the neuroscience application evaluated and the more indicative results of efficiency and scientific potential. This detailed analysis is followed by an outlook of the main challenges tackled over the last 10 years in the Neurophotonics field, as well as the main technological advances regarding specificity, light delivery, multimodality, imaging, materials and system designs. We conclude with a discussion of considerable challenges for future innovation and translation in Neurophotonics, from light delivery within the brain to physical constraints and data management strategies.

Abstract
The delivery of curated data from astronomical instruments has become a reality in many observatories. The European Southern Observatory (ESO) delivers science-ready data products for various instruments, ranging from imagers to integral field spectrographs. In the case of infrared long-baseline interferometry, scientists generally make their curated data available through the Optical Interferometry Database (OiDB) once it is published. We report on a project to create a curated data stream for the GRAVITY instrument at the Very Large Telescope Interferometer. We aim to transform the publicly available raw data in the ESO science archive into science-ready curated data.

Abstract
This paper presents a comprehensive assessment of the large-scale deployment of hydropower on the provision of frequency regulation services, when equipped with the extended flexibility solutions being developed and/or tested within the scope of the XFLEX HYDRO project. The current analysis is performed on the Iberian Peninsula (IP) power grid considering its interconnection to the Continental Europe (CE) system, since this power system zone is expected to have the most severe frequency transient behaviour in future scenarios with increased shares of variable renewable energies. For this purpose, prospective scenarios with increased shares of time variable renewable generation were identified and analysed. To assess the impacts of the hydropower flexibility solutions on frequency dynamics after a major active power loss, extensive time domain simulations were performed of the power system, including reliable reduced order dynamic models for the hydropower flexibility solutions under evaluation. This research assesses the effects of synchronous and synthetic inertia, and of the Frequency Containment Reserve (FCR) and Fast Frequency Response (FFR) services as specified in European grid codes. The main findings highlight the potential of hydropower inertia and of adopting a variable speed technology for enhancing frequency stability, while contribute to better understand the role of hydropower plants in future power systems.

Abstract
Prolog systems rely on an atom table for symbol management, which is usually implemented as a dynamically resizeable hash table. This is ideal for single threaded execution, but can become a bottleneck in a multi-threaded scenario. In this work, we replace the original atom table implementation in the YAP Prolog system with a lock-free hash-based data structure, named Lock-free Hash Tries (LFHT), in order to provide efficient and scalable symbol management. Being lock-free, the new implementation also provides better guarantees, namely, immunity to priority inversion, to deadlocks and to livelocks. Performance results show that the new lock-free LFHT implementation has better results in single threaded execution and much better scalability than the original lock based dynamically resizing hash table.