Abstract
Lack of information and perceptual ambiguity are key problems in sonar-based mapping applications. We propose a technique for mapping of underwater environments, building on the finite, positive, sonar beamwidth. Our approach models the free-space covered by each emitted acoustic pulse, employing volumetric techniques to create grid-based submaps of the unoccupied water volumes through images collected from imaging sonars. A representation of the occupied space is obtained by exploration of the free-space frontier. Special attention is given to acoustic image preparation and segmentation. Experimental results are provided based on real data collected from a dam shaft scenario.

Abstract
There is a widespread social awareness for the need of adequate accessibility (e.g. missing ramps at crosswalks, obstacles and potholes at sidewalks) in the planning of safe and inclusive city spaces for all citizens. Therefore, municipal authorities responsible for planning urban spaces could benefit from the use of tools for automating the identification of areas in need of accessibility improving interventions. This paper builds on the assumption that it is possible to use Machine Learning (ML) pipelines for automating the detection of accessibility constraints in public spaces, particularly on sidewalks. Those pipelines rely mostly on Deep Learning algorithms to automate the detection of common accessibility issues. Current literature approaches rely on the use of traditional classifiers focused on images' datasets containing single-labelled accessibility classes. We propose an alternative approach using object-detection models that provide a more generic and human-like mode, as it will look into wider city pictures to spot multiple accessibility problems at once. Hence, we evaluate and compare the results of a more generic YOLO model against previous results obtained by more traditional ResNet classification models. The ResNet models used in Project Sidewalk were trained and tested on per-city basis datasets of images crowd-labeled with accessibility attributes. By combining the use of the Project Sidewalk and Google Street View (GSV) service APIs, we re-assembled a world-cities-mix dataset used to train, validate and test the YOLO object-detection model, which exhibited precision and recall values above 84%. Our team of architects and civil engineers also collected a labeled image dataset from two central areas of Porto city, which was used to jointly train and test the YOLO model. The results show that training (even with a small dataset of Porto) the cities-mix-trained YOLO model, provides comparable precision values against the ones obtained by ResNet per-city classifiers. Furthermore, the YOLO approach offers a more human-like generic and efficient pipeline, thus justifying its future exploitation on automating cataloging accessibility mappings in cities.

Abstract
Integrated local energy communities (ILECs) introduction involves a set of challenges for the existing energy infrastructure. As a result of the development and research performed in projects on this topic, several guidelines and recommendations are formulated. This chapter recaps major problems of the implementation of ILECs identified in the reviewed literature and provides recommendations to overcome them by covering five dimensions. In the technical dimension, the implementation of strategies to avoid the grid reinforcement as well as coordination between system operators become crucial for the development of ILEC-related technologies. In terms of regulations, tax exemptions, additional financial funding, and simplification of paperwork for projects should be introduced backed by a clear EU strategy. In the environmental dimension, ILECs boost the transition toward decentralized renewable generation contributing to the gradual replacement of fossil-fuel generation plants and this benefit can be maximized by performing deeper environmental assessments. Additionally, there is a need of cost-effective financial tools for planning and management as well as the development of suitable economic incentives. Lastly, the implementation of strategies to increase the social acceptance of the ILEC paradigm through the organization of engagement activities between citizens, stakeholders, and other actors arises as the key action. © 2025 WILEY-VCH GmbH. Published 2025 by WILEY-VCH GmbH. All rights reserved.

Abstract
This paper introduces GERF, a Gamified Educational Virtual Escape Room Framework designed to enhance micro-learning and adaptive learning experiences in educational settings. The framework incorporates a user taxonomy based on the user type hexad, addressing the preferences and motivations of different learners profiles. GERF focuses on two key facets: interoperability and analytics. To ensure seamless integration of Escape Room (ER) platforms with Learning Management Systems (LMS), the Learning Tools Interoperability (LTI) specification is used. This enables smooth and efficient communication between ERs and LMS platforms. Additionally, GERF uses the xAPI specification to capture and transmit experiential data in the form of xAPI statements, which are then sent to a Learning Record Store (LRS). By leveraging these learning analytics, educators gain valuable insights into students’ interactions within the ER, facilitating the adaptation of learning content based on individual learning needs. Ultimately, GERF empowers educators to create personalized learning experiences within the ER environment, fostering student engagement and learning outcomes. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Indoor air quality (IAQ) problems in school environments are very common and have significant impacts on students' performance, development and health. Indoor air conditions depend on the adopted ventilation practices, which in Mediterranean countries are essentially based on natural ventilation controlled through manual window opening. Citizen science projects directed to school communities are effective strategies to promote awareness and knowledge acquirement on IAQ and adequate ventilation management. Our multidisciplinary research team has developed a framework-SchoolAIR-based on low-cost sensors and a scalable IoT system architecture to support the improvement of IAQ in schools. The SchoolAIR framework is based on do-it-yourself sensors that continuously monitor air temperature, relative humidity, concentrations of carbon dioxide and particulate matter in school environments. The framework was tested in the classrooms of University Fernando Pessoa, and its deployment and proof of concept took place in a high school in the north of Portugal. The results obtained reveal that CO2 concentrations frequently exceed reference values during classes, and that higher concentrations of particulate matter in the outdoor air affect IAQ. These results highlight the importance of real-time monitoring of IAQ and outdoor air pollution levels to support decision-making in ventilation management and assure adequate IAQ. The proposed approach encourages the transfer of scientific knowledge from universities to society in a dynamic and active process of social responsibility based on a citizen science approach, promoting scientific literacy of the younger generation and enhancing healthier, resilient and sustainable indoor environments.

Abstract
Total arthroplasty is one of the most common knee surgeries and, due to the ageing of the population, the number of procedures performed each year is expected to increase. With almost a quarter of patients dissatisfied, systems for computer assistance in orthopaedic surgery have been on the rise, appearing to have better outcomes than conventional techniques by reproducing a planned alignment with a similar learning curve. The search for inexpensive solutions to improve these prototypes is extremely relevant since most systems in the market involve expensive robots. The development of a simulation for an extended reality system, specifically spatial augmented reality, with a projector and a depth camera to project the desired total knee arthroplasty bone cuts onto a simulated knee joint has been proposed. It was created with Gazebo and communicates with the Robotic Operating System (ROS) framework so that it can easily be transposed to the real world. An evaluation of the simulator was performed regarding the projection's accuracy. The performance of the simulator was fitting for surgery, with the highest mean position error between the desired bone cut and the simulated bone cut of 1.11 +/- 0.86 mm (minimum = 0.00 mm, maximum = 2.60 mm) for the tibia cut. These values could be further improved with the implementation of a feature-matching algorithm and a dynamic projection.