Abstract
Offshore wind farms face harsh maritime conditions, prompting the use of sacrificial anodes to prevent rapid structural degradation. Regular maintenance and replacement of these elements are vital to ensure ongoing corrosion protection, maintain structural integrity, and optimize efficiency. This article details the design and validation of the MARESye hybrid underwater imaging system, capable of retrieving heterogeneous tri-dimensional information with millimetric precision for the close-range inspection of submerged critical structures. The optical prowess of the system is first validated during low turbidity trials where the volumetric properties of a decommissioned anode are reconstructed with absolute errors down to 0.0008 m, and its spatial dimensions are depicted with sub-millimeter precision accounting for relative errors as low as 0.31%. MARESye is later equipped as payload in a commercial ROV during areal environment inspection mission at the ATLANTIS Coastal Test Center. This experiment sees the sensor provide live reconstructions of a sacrificial anode, revealing a biofouling layer of approximately 0.0130 m thickness. The assessment of the high-fidelity 2D/3D information obtained from the MARESye sensor demonstrates its potential to enhance the situational awareness of underwater vehicles, fostering reliable O&M procedures.

Abstract
Exploring a rich administrative matched employer -employee longitudinal dataset over the 2002-2020 period and a task -based approach, this study investigates to what extent the recent paradigm shift in the electricity sector has affected the structure of employment and wages in the Portuguese case. Our results show that the liberalization in the sector led to the entry of new players and firms' downsizing of the workforce, most notably in occupations involving routine cognitive tasks and non -routine manual tasks. In two decades, the employment share of occupations involving non -routine cognitive tasks (abstract or interactive) doubled, from 29.7% in 2002 to 58.1% in 2020. Regarding wage premiums, the results reveal a clear positive trend in real hourly wages for all types of occupations in the sector. However, we observe a lower wage growth acceleration for workers employed in routine (cognitive or manual) occupations, when compared with similar workers employed in non -routine occupations (cognitive or manual). Our findings are partly consistent with the skill -biased and routine -biased technological change hypotheses in the sense that we observe, respectively, a skill up -grading translated into an increase in employment share in non -routine cognitive occupations and a substantial decline in employment share in routine cognitive occupations.

Abstract
An algorithm for blind source separation (BSS) of the second heart sound (S2) into aortic and pulmonary components is proposed. It recovers aortic (A2) and pulmonary (P2) waveforms, as well as their relative delays, by solving an alternating optimization problem on the set of S2 sounds, without the use of auxiliary ECG or respiration phase measurement data. This unsupervised and data-driven approach assumes that the A2 and P2 components maintain the same waveform across heartbeats and that the relative delay between onset of the components varies according to respiration phase. The proposed approach is applied to synthetic heart sounds and to real-world heart sounds from 43 patients. It improves over two state-of-the-art BSS approaches by 10% normalized root mean-squared error in the reconstruction of aortic and pulmonary components using synthetic heart sounds, demonstrates robustness to noise, and recovery of splitting delays. The detection of pulmonary hypertension (PH) in a Brazilian population is demonstrated by training a classifier on three scalar features from the recovered A2 and P2 waveforms, and this yields an auROC of 0.76.

Abstract
This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.

Abstract
E-commerce has become an essential aspect of modern life, providing consumers globally with convenience and accessibility. However, the high volume of short and noisy product descriptions in text streams of massive e-commerce platforms translates into an increased number of clusters, presenting challenges for standard model-based stream clustering algorithms. Standard LDA-based methods often lead to clusters dominated by single elements, effectively failing to manage datasets with varied cluster sizes. Our proposed Community-Based Topic Modeling with Contextual Outlier Handling (CB-TMCOH) algorithm introduces an approach to outlier detection in text data using transformer models for similarity calculations and graph-based clustering. This method efficiently separates outliers and improves clustering in large text datasets, demonstrating its utility not only in e-commerce applications but also proving effective for news and tweets datasets.

Abstract
This paper investigates the financial benefits stemming from the potential installation of battery energy storage systems behind the meter of a hybrid offshore farm including wind turbines and floating photovoltaic panels. The optimal investment and operation decisions concerning the energy storage system in the hybrid site are assessed by means of a mixed integer linear programming optimization model. The operation is also subject to technical constraints such as limitations on the connection capacity and ramping constraints imposed by the grid operator at the point of common coupling. Three design configurations for the battery system are analysed: I) offshore with the hybrid farm, II) onshore where the grid connection point is, III) both offshore and onshore. The results indicate the financial value of installing battery storage units, and other benefits deriving from this investment, as the reduction of curtailment.