Abstract
Given the increased interest in offshore wind energy, there is a greater need for advancements in operation and maintenance technology. As a result, robotic solutions are required to avoid human risky behavior and reduce associated operational costs. In order to accommodate the need for inspecting multiple domains, multiple robotic vehicles are utilized, which requires the deployment of control stations that can effectively monitor, facilitate communication among different vehicles, and ensure successful completion of the overall mission. A shore control centre (SCC) is a communication software infrastructure capable of monitoring, localizing and planning missions for a group of multi-domain heterogeneous robots within a local network. This paper proposes an SCC as: (i) an active monitor by continuously observing the local behaviour of each robot and the global progress of the mission and its safety; (ii) a mission planner that provides and supervises its execution while constantly checking for critical failures and intervening in the case of unexpected events. Also, The control centre is able to connect to multiple vehicles from various domains and monitor real-time data. Accordingly, validation procedures were carried out in real conditions.

Abstract
Semantic segmentation consists of classifying each pixel according to a set of classes. Conventional models spend as much effort classifying easy-to-segment pixels as they do classifying hard-to-segment pixels. This is inefficient, especially when deploying to situations with computational constraints. In this work, we propose a framework wherein the model first produces a rough segmentation of the image, and then patches of the image estimated as hard to segment are refined. The framework is evaluated in four datasets (autonomous driving and biomedical), across four state-of-the-art architectures. Our method accelerates inference time by four, with additional gains for training time, at the cost of some output quality.

Abstract
RESUMO Esta pesquisa investiga o uso de recursos lúdicos estruturados com a ferramenta Power Point no ensino bilíngue de estudantes surdos, em uma escola situada no município de Timon, no estado do Maranhão. O objetivo é analisar a contribuição de jogos digitais no processo de aquisição linguística. Metodologicamente, trata-se de uma pesquisa de campo, do tipo exploratória e de cunho qualitativo. Para coleta e análise dos dados, foram realizadas observação das aulas, aplicação de questionário semiaberto e pesquisas bibliográficas. Os resultados demonstram que os jogos promovem engajamento e motivação para a aprendizagem. Observa-se ainda que a presença de elementos culturais surdos proporciona a criação de um ambiente escolar mais empático e inclusivo.

Abstract
This paper presents several case studies that show that it is possible to use clean energy to produce electricity, we have environmental benefits and benefits for the management of the electrical transmission network. In this case wind energy are used.For this work, software was developed in Matlab for the model we developed and the results of this were compared with the results obtained by the simulator Power World.To make the decision to replace generators fossil generators with wind generators, Local Marginal Prices (LMP) were used. Some case studies were created using a model system, with the objective of evaluating the benefits of this allocation based on the LMP.The test network presented in this paper is a 9 Bus network. However, the developed software was also tested on an IEEE 30 Bus network. © 2023 IEEE.

Abstract
Nowadays, the semantic segmentation of the images of the underwater world is crucial, as these results can be used in various applications such as manipulation or one of the most important in the semantic mapping of the environment. In this way, the structure of the scene observed by the robot can be recovered, and at the same time, the robot can identify the class of objects seen and choose the next action during the mission. However, semantic segmentation using cameras in underwater environments is a non-trivial task, as it depends on the quality of the acquired images (which change over time due to various factors), the diversification of objects and structures that can be inspected during the mission, and the quality of the training performed prior to the evaluation, as poor training means an incorrect estimation of the object class or a poor delineation of the object. Therefore, in this paper, a comparative study of suitable modern semantic segmentation algorithms is conducted to determine whether they can be used in underwater scenarios. Nowadays, it is very important to equip the robot with the ability to inspect port facilities, as this scenario is of particular interest due to the large variety of objects and artificial structures, and to know and recognise most of them. For this purpose, the most suitable dataset available online was selected, which is the closest to the intended context. Therefore, several parameters and different conditions were considered to perform a complete evaluation, and some limitations and improvements are described. The SegNet model shows the best overall accuracy, reaching more than 80%, but some classes such as robots and plants degrade the quality of the performance (considering the mean accuracy and the mean IoU metric).

Abstract
Indispensable in many contexts, images are fundamental in the tasks of representation and transmission of information. In the scientific context, images can be tools for researchers seeking to see their data properly managed. Research data management guides in this direction as it determines necessary phases in the life cycle of projects. The description phase is fundamental as it is an essential means for data context, safeguarding, and reuse. The description often occurs through metadata models composed of descriptors capable of attributing context. However, there is one common aspect: the values associated with these descriptors are always textual or numeric. Through studies and work developed over the last few years, we propose a new approach to description, where images can have a preponderant role in the description of data, assuming the role of metadata. We present several pieces of evidence, point out their challenges and determine the opportunities this new perspective can have in the research. Images have specific characteristics that can be leveraged in improving data description. Historical evidence establish that images have always been used and produced in research, yet their representational ability has never been harnessed to describe data and give more context to the scientific process. ©, Joana Rodrigues and Carla Teixeira Lopes. Published with license by Taylor & Francis Group, LLC.