Abstract
In this study, a comparison of five topologies of active AC/DC conversion chain rectifiers is performed. The purpose of this work is to investigate the implementation of these topologies in the context of an electrolyzer system. The main purpose is to help identify the eventual advantages of active rectifiers. The studies under consideration provide an appreciation of typical three-phase two-level PWM rectifiers and three-phase Vienna rectifiers with an additional stage based on buck DC-DC conversion, for electrolyzer application. The comparative analysis is based on the following metrics: the current ripple, the power factor, the total harmonic distortion, the active switch utilization ratio, and the complexity of the solution. A Simulink model corresponding to each topology was developed to obtain the performance values for the comparison. The procedure involved conducting a steady-state analysis of each topology through simulations to obtain the main waveforms and the values for each criterion. The scores for each technical solution were then calculated. The solution based on the Vienna 3-switch rectifier presented the best results overall.

Abstract
This paper presents a current estimation approach for four-phase Switched Reluctance Machines (SRMs) using only two current sensors. The power converter structure under consideration is an asymmetric half-bridge topology in which the upper switch is shared by two phases, resulting in a reduced number of measurable current paths. Two current estimation methods are developed and compared in a simulation environment. Both techniques aim to reconstruct the instantaneous phase currents to enable advanced torque and flux control strategies without the need for individual current sensors on each phase. The effectiveness of each method is validated through Matlab/Simulink simulations, and their performance is assessed under different operating scenarios.

Abstract
Underwater environments pose unique challenges to optical systems due to physical phenomena that induce severe data degradation. Current imaging sensors rarely address these effects comprehensively, resulting in the need to integrate complementary information sources. This article presents a multimodal data fusion approach to combine information from diverse sensing modalities into a single dense and accurate tridimensional representation. The proposed fusiNg tExture with apparent motion information for underwater Scene recOnstruction (NESO) encoder-decoder network leverages motion perception principles to extract relative depth cues, fusing them with textured information through an early fusion strategy. Evaluated on the FLSea-Stereo dataset, NESO outperforms state-of-the-art methods by 58.7%. Dense depth maps are achieved using multi-stage skip connections with attention mechanisms that ensure propagation of key features across network levels. This representation is further enhanced by incorporating sparse but millimeter-precise depth measurements from active imaging techniques. A regression-based algorithm maps depth displacements between these heterogeneous point clouds, using the estimated curves to refine the dense NESO prediction. This approach achieves relative errors as low as 0.41% when reconstructing submerged anode structures, accounting for metric improvements of up to 0.1124 m relative to the initial measurements. Validation at the ATLANTIS Coastal Testbed demonstrates the effectiveness of this multimodal fusion approach in obtaining robust tri-dimensional representations in real underwater conditions.

Abstract
This work presents a bio-inspired Autonomous Underwater Vehicle (AUV) concept called Raya that enables high manoeuvrability required for close-range inspection and intervention tasks, while fostering endurance for long-range operations by enabling efficient navigation. The AUV has an estimated terminal velocity of 0.82 m/s in an optimal environment, and a capacity to acquire visual data and sonar measurements in all directions. Raya was designed with the potential to incorporate an electric manipulator arm of 6 degrees of freedom (DoF) for free-floating underwater intervention. Smart and biologically inspired principles applied to morphology and a strategic thruster configuration assure that Raya is capable of manoeuvring in all 6 DoFs even when equipped with a manipulator with a 5 kg payload. Extensive experiments were conducted using simulation tools and real-life environments to validate Raya's requirements and functionalities. The stresses and displacements of the rigid bodies were analysed using finite element analysis (FEA), and an estimation of the terminal forward velocity was achieved using a dynamic model. To assess the accuracy of the perception system, a reconstruction task took place in an indoor pool, resulting in a 3D reconstruction with average length, width, and depth errors below 1. 5%. The deployment of Raya in the ATLANTIS Coastal Testbed and Porto de Leix & otilde;es allowed the validation of the propulsion system and the gathering of valuable 2D and 3D data, thus proving the suitability of the vehicle for operation and maintenance (O&M) activities of underwater structures.

Abstract
The integration of precise landing capabilities into unmanned aerial vehicles (UAVs) is crucial for enabling autonomous operations, particularly in challenging environments such as the offshore scenarios. This work proposes a heterogeneous perception system that incorporates a multimodal fiducial marker, designed to improve the accuracy and robustness of autonomous landing of UAVs in both daytime and nighttime operations. This work presents ViTAL-TAPE, a visual transformer-based model, that enhance the detection reliability of the landing target and overcomes the changes in the illumination conditions and viewpoint positions, where traditional methods fail. VITAL-TAPE is an end-to-end model that combines multimodal perceptual information, including photometric and radiometric data, to detect landing targets defined by a fiducial marker with 6 degrees-of-freedom. Extensive experiments have proved the ability of VITAL-TAPE to detect fiducial markers with an error of 0.01 m. Moreover, experiments using the RAVEN UAV, designed to endure the challenging weather conditions of offshore scenarios, demonstrated that the autonomous landing technology proposed in this work achieved an accuracy up to 0.1 m. This research also presents the first successful autonomous operation of a UAV in a commercial offshore wind farm with floating foundations installed in the Atlantic Ocean. These experiments showcased the system's accuracy, resilience and robustness, resulting in a precise landing technology that extends mission capabilities of UAVs, enabling autonomous and Beyond Visual Line of Sight offshore operations.

Abstract
Solar energy is a compelling and growing avenue for transitioning towards reliance on sustainable and environmentally friendly energy sources. However, the performance of individual photovoltaic (PV) panels is vulnerable to defects inflicted by weather exposure. The industry currently addresses these challenges by conducting manual inspections at each PV installation, a process that is highly time-consuming, financially burdensome, difficult to scale, prone to human error, and sometimes unfeasible due to topographical constraints. This study aims to develop a solution that detects these defects in real time resorting to an Autonomous Aerial Vehicle (AAV) equipped with for a thermal and a visual sensor. This paper contributes three major advancements to PV defect detection: a robust, standardized dataset built following IEC TS 62446-3 specifications with annotations from a real PV power plant, a novel multi-spectral approach that leverages early fusion of visual and thermal data captured via an AAV, and benchmark performance metrics for defect detection using state-of-the-art AI models. Our implementation uses Real-Time DEtection TRansformer (RT-DETR) and You Only Look Once (YOLO) models, achieving a mean Average Precision@50 (mAP) of 94% for RT-DETR and a mAP@50 of nearly 95% for YOLOv11. These results demonstrate the effectiveness of our approach in real-world settings, addressing a significant operational challenge in PV plant maintenance while establishing new performance benchmarks for automated defect detection in industrial solar installations.