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This paper addresses the topic of emergency landing spot detection for Unmanned Aerial Vehicles (UAVs). During operation, the vehicle is susceptible to faults and must be able to predict the land spot able to ensure that the UAV will be able to land without damages and injuries to humans and structures. A method was developed, based on geometric features extracted from Light Detection And Ranging (LIDAR) data. A simulation environment was developed in order to validate the effectiveness and the robustness of the proposed method.

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This paper presents the results of the experimental tests performed to validate the functionality of a variable pitch system (VPS), designed for pitch attitude control of the novel underwater robotic vehicle explorer UX-1. The VPS is composed of a mass suspended from a central rod mounted across the hull. This mass is rotated around the transverse axis of the vehicle in order to perform a change in the inclination angle for navigation in vertical mine shafts. In this work, the equations of motion are first derived with a quaternion attitude representation, and are then extended to include the dynamics of the VPS. The performance of the VPS is demonstrated in real underwater experimental tests that validate the pitch angle control independently, and coupled with the heave motion control system.

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Contribution: An analysis of the extent to which sustainability is present in the syllabi, project briefs, report templates, and student final reports of the three Iberian European project semester (EPS) providers, over a five-year period. Background: EPS is a one-semester capstone project framework that adopts project-based learning and multicultural, multidisciplinary teamwork. Educating engineers for sustainable development requires fostering critical and ethical thinking and a desire for equity, solidarity and preservation of natural resources, and cultural and genetic diversity. Existing engineering capstone design programs emphasize solving real world problems, hands-on training, and soft skills, but few focus on sustainability aspects of engineering design. The three Iberian EPS providers adopt project-based learning and teamwork methodologies, promoting the development of transversal skills and addressing sustainability in a multicultural and multidisciplinary background. Intended Outcomes: To show that the three Iberian EPS providers follow these recommendations and contribute to raising students' awareness of sustainable development. Application Design: The proposed sustainability learning assessment method collects evidence from syllabi, project briefs, report templates, and final reports to extract faculty and student perspectives. The sustainability-related terms collected were processed into word cloud format, allowing a simple and intuitive interpretation of students' understanding of sustainability, and in co-occurrence network format, to understand if sustainability has a pervasive or confined presence within the reports. Findings: Iberian EPS faculty and students are aware of the social, economic, and environmental impact of their projects, in terms of quality of life, social responsibility, the use of resources, and environmentally friendly technology.

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Tourism crowdsourcing platforms accumulate and use large volumes of feedback data on tourism-related services to provide personalized recommendations with high impact on future tourist behavior. Typically, these recommendation engines build individual tourist profiles and suggest hotels, restaurants, attractions or routes based on the shared ratings, reviews, photos, videos or likes. Due to the dynamic nature of this scenario, where the crowd produces a continuous stream of events, we have been exploring stream-based recommendation methods, using stochastic gradient descent (SGD), to incrementally update the prediction models and post-filters to reduce the search space and improve the recommendation accuracy. In this context, we offer an update and comment on our previous article (Veloso et al., 2019a) by providing a recent literature review and identifying the challenges laying ahead concerning the online recommendation of tourism resources supported by crowdsourced data.