Abstract

Abstract
Fishing for deep-sea species occurs on continental slopes, ridges, and seamounts. Fishing operations using fishing gears that contact the bottom (e.g., trawls and bottom longlines) may have significant impacts on Vulnerable Marine Ecosystems (VMEs). VMEs refer to marine ecosystems with a population or community of sensitive taxa or habitats that are likely to experience substantial alteration from short-term to chronic disturbance and that are unlikely to recover during the timeframe in which the disturbance occurs. The VME concept, introduced in the United Nations General Assembly Resolution 61/105, has been worldwide applied to the management of deep-sea fisheries. However, the effective identification and management of VMEs is highly constrained by the scarcity of data on VME indicator taxa. This data deficiency is usually surpassed by the use of VME predictive modelling. Video footage is a non-destructive method commonly used for exploring and investigating areas of seabed and for characterising and identifying habitat types. Remotely Operated Vehicles (ROVs) are one of the tools for seabed mapping. ROVs range in size from small observation-class to large work-class vehicles. Their sizes determine the payload, manoeuvrability, depth rating and ultimately uses of the vehicle. For epifaunal imaging, ROVs can be used in two modes: qualitative inspections and quantitative assessments. This paper presents the development of an innovative system composed of a compact support research vessel and a hybrid autonomous underwater vehicle capable of accurate georeferenced high-resolution imaging and profiling of the seabed for a detailed survey of the seabed for biodiversity studies. The experimental results obtained by the developed system in field work in real VME survey at 600m depth are presented.

Abstract
This paper presents a 1-bit graphene-based reflective reconfigurable intelligent surface (RIS), namely a reflectarray antenna, that operates in the Ka-band (27 - 31 GHz). The reflectarray unit-cell features a simple structure with one metal layer, a Rogers RT5880 substrate and a Graphene Sandwich Structure (GSS) on top. The GSS comprises two layers of graphene separated by a diaphragm paper and a thin PVC layer to enhance its durability. The reflectarray can ensure a 1-bit phase shift resolution, by alternating the bias voltage applied to the graphene. The unit-cell simulation shows that the losses are around 3 dB over the studied band for both unit-cell states. An equivalent circuit model is presented to facilitate the analysis and design of GSS-based unit-cells. The full-wave simulation results of a 32x32 reflectarray indicate a gain of 25 dBi for a steering angle of 10 deg., displaying a 1 dB gain bandwidth of 15%, confirming the promise of the graphene-based radiating elements.

Abstract
Hydrogen production through renewable energy-powered electrolysis is pivotal for fostering a sustainable future hydrogen market. In the electricity sector, hydrogen production bears an additional demand that affects electricity price, and mathematical models are needed for the joint simulation, analysis, and planning of electricity and hydrogen sectors. This study develops a Cournot and a perfect competition model to analyze the links of the electricity and hydrogen sectors. In contrast to other solving methods approaches, the Cournot model is solved by convex reformulation techniques, substantially easing the resolution. The case studies, focusing on the Iberian Peninsula, demonstrate the region's potential for competitive hydrogen production, and the advantages of perfect competition to maximize the use of renewable energies, in contrast to Cournot's oligopoly, where the exercise of market power raises electricity prices. Sensitivity analyses highlight the importance of strategic decision-making in mitigating market inefficiencies, with valuable insights for stakeholders and policymakers.

Abstract
Carbon dioxide (CO2) holds paramount significance in nature, serving as a vital component in Earth's ecosystems. Its evaluation has become increasingly important across various sectors, spanning from environmental conservation to industrial operations. Therefore, this study investigates the viability of utilizing a pH colorimetric dye as a CO2-sensitive material. The material's effectiveness relies on chemical modifications induced in the dye structure through the action of a phase transfer agent, which establishes a stable equilibrium with the dye, thereby promoting its receptivity to CO2 molecules. As the resulting physicochemical changes primarily exhibit colorimetric alterations, an optical system was developed to assess the performance of this material upon exposure to CO2. Employing a dual-wavelength method, the system also incorporates a ratiometric relationship between the two signals to provide the most precise information. The conducted experiments generated promising results when the dye was subjected to varying concentrations of CO2, ranging from 0% to 4%, in comparison to nitrogen (N-2). The application of the ratiometric method emerged as a crucial factor in this system, enabling its potential use in environments characterized by instability. Finally, the dye-sensitive characteristics experienced enhancement through the integration of an ionic liquid within the membrane matrix.

Abstract
Nowadays, there has been a growing interest in the use of mobile robots for various applications, where the analysis of the operational environment is a crucial component to conduct our special tasks or missions. The main aim of this work was to implement artificial intelligence (AI) for object detection and distance estimation navigating the developed unmanned platform in unknown environments. Conventional approaches are based on vision systems analysis using neural networks for object detection, classification, and distance estimation. Unfortunately, in the case of precise operation, the used algorithms do not provide accurate data required by platforms operators as well as autonomy subsystems. To overcome this limitation, the authors propose a novel approach using the spatial data from laser scanners supplementing the acquisition of precise information about the detected object distance in the operational environment. In this article, we introduced the application of pretrained neural network models, typically used for vision systems, in analysing flat distributions of LiDAR point cloud surfaces. To achieve our goal, we have developed software that fuses detection algorithm (based on YOLO network) to detect objects and estimate their distances using the MiDaS depth model. Initially, the accuracy of distance estimation was evaluated through video stream testing in various scenarios. Furthermore, we have incorporated data from a laser scanner into the software, enabling precise distance measurements of the detected objects. The paper provides discussion on conducted experiments, obtained results, and implementation to improve performance of the described modular mobile platform.