Abstract
The development of increasingly autonomous underwater vehicles has long been a research focus in underwater robotics. Recent advances in deep learning have shown promising results, offering the potential for fully autonomous behavior in underwater vehicles. However, its implementation requires improvements to the current vehicles. This paper proposes an onboard data processing framework for Deep Learning implementation. The proposed framework aims to increase the autonomy of the vehicles by allowing them to interact with their environment in real time, enabling real-time detection, control, and navigation.

Abstract
Despite the trend towards ubiquitous wireless connectivity, there are scenarios where the communications infrastructure is damaged and wireless coverage is insufficient or does not exist, such as in natural disasters and temporary crowded events. Flying networks, composed of Unmanned Aerial Vehicles (UAV), have emerged as a flexible and cost-effective solution to provide on-demand wireless connectivity in these scenarios. UAVs have the capability to operate virtually everywhere, and the growing payload capacity makes them suitable platforms to carry wireless communications hardware. The state of the art in the field of flying networks is mainly focused on the optimal positioning of the flying nodes, while the wireless link parameters are configured with default values. On the other hand, current link adaptation algorithms are mainly targeting fixed or low mobility scenarios. We propose a novel rate adaptation approach for flying networks, named Trajectory Aware Rate Adaptation (TARA), which leverages the knowledge of flying nodes’ movement to predict future channel conditions and perform rate adaptation accordingly. Simulation results of 100 different trajectories show that our solution increases throughput by up to 53% and achieves an average improvement of 14%, when compared with conventional rate adaptation algorithms such as Minstrel-HT.

Abstract
Citizens are more and more encouraged to participate in public policy decision processes and, therefore, critical questions regarding our lives are asked every day. Informed citizens need access to data, and knowledge in order to explore, understand, and reason about information of a multivariate nature; it is not obvious how to access such data, or how to work with them. Educators face the challenge of adopting new approaches, and grasping new opportunities in order to support the development of students into informed citizens as adults. Educators often do not have time to locate information sources; moreover, it is a challenge to exploit the possibilities of open data wisely. This chapter points to data sets we have found valuable in teaching Civic Statistics; data must be authentic, and reflect the complexities of data used to inform decision making about social issues (whose features are explained in Chap. 2). Topics include refugees, malnutrition, and climate change. We provide enough details so teachers can locate and employ these data sets, or similar ones, as part of regular instruction. Information is made accessible using the innovative tool CivicStatMap, developed to provide access to teaching materials, along with data and analysis tools, including tools to support data visualisation.

Abstract
The knee is one of the most stressed joints of the human body, being susceptible to ligament injuries and degenerative diseases. Due to the rising incidence of knee pathologies, the number of knee X-rays acquired is also increasing. Such X-rays are obtained for the diagnosis of knee injuries, the evaluation of the knee before and after surgery, and the monitoring of the knee joint's stability. These types of diagnosis and monitoring of the knee usually involve radiography under physical stress. This widely used medical tool provides a more objective analysis of the measurement of the knee laxity than a physical examination does, involving knee stress tests, such as valgus, varus, and Lachman. Despite being an improvement to physical examination regarding the physician's bias, stress radiography is still performed manually in a lot of healthcare facilities. To avoid exposing the physician to radiation and to decrease the number of X-ray images rejected due to inadequate positioning of the patient or the presence of artefacts, positioning systems for stress radiography of the knee have been developed. This review analyses knee positioning systems for X-ray environment, concluding that they have improved the objectivity and reproducibility during stress radiographs, but have failed to either be radiolucent or versatile with a simple ergonomic set-up.

Abstract
This study reviewed the literature to understand how does research produce impact?. Despite all discussion threads in the literature, it was clear that management of the Front-end of Innovation (FEI) of non-profit research centers has received little attention. As a result, this study was a first step toward proposing a model that describes the Front-End of R&D at non-profit Research Centers. This paper is an Integrative Literature Review that follows the PRISMA statement methodology. Building on FEI2O's High-Level Ontology, this paper's contribution elicited relevant constructs from the literature and took the first steps to extend it to the Front-End of R&D. New research-related concepts such as the RESEARCH CONCEPT, the INFLUENCING FACTORS, and the RESEARCH PROJECT were introduced. The RESEARCH PROJECT produces RESULTS that may have an IMPACT ON SOCIETY. All these concepts are interconnected through a feedback loop that feeds the FEI2O: STRATEGIC PURPOSE of the non-profit research center, contributing to the advancement of its research strategy, objectives, and goals. The current work contributed to understanding a domain of knowledge that is of interest to researchers, research managers, and those interested in bringing new scientific research results to society.

Abstract