Abstract
Unmanned Aerial Vehicles (UAVs) are versatile platforms for carrying communications nodes such as Wi-Fi Access Points and cellular Base Stations. Flying Networks (FNs) offer on-demand wireless connectivity where terrestrial networks are impractical or unsustainable. However, managing communications resources in FNs presents challenges, particularly in optimizing UAV placement to maximize Quality of Service (QoS) for Ground Users (GUs) while minimizing energy consumption, given the UAVs' limited battery life. Existing multi-UAV placement solutions primarily focus on maximizing coverage areas, assuming static UAV positions and uniform GU distribution, overlooking energy efficiency and heterogeneous QoS requirements. We propose the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which defines and optimizes UAV trajectories to reduce energy consumption while ensuring QoS based on Signal-to-Noise Ratio (SNR) in the links with GUs. Additionally, we introduce the Multi-UAV Energy Consumption (MUAVE) simulator to evaluate energy consumption. Using both MUAVE and ns-3 simulators, we evaluate SUPPLY in typical and random networking scenarios, focusing on energy consumption and network performance. Results show that SUPPLY reduces energy consumption by up to 25% with minimal impact on throughput and delay.

Abstract
Artificial Intelligence (AI) is now ubiquitous in daily life, significantly impacting society by supporting decision-making. However, in many application areas, understanding the rationale behind AI decisions is crucial, highlighting the need for explainable AI (XAI). AI algorithms often lack transparency, making it hard to understand their inner workings. This work presents an overview of XAI solutions for decision support in mobility context. It addresses the complexity of explaining decision support models by offering explanations in various formats tailored to different user profiles. By integrating language models, XAI models may generate texts with varying technical detail levels, aiding ethical AI deployment and bridging the gap between complex models and human interpretability. This work explores the need for flexible explanation formats, supporting varied user profiles with graphical, textual, and tabular explanations. By integrating natural language processing models personalized explanations that are accurate, understandable, and accessible to a diverse audience can be generated. This study ultimately aims to support the task of making XAI robust and user-friendly, boosting its widespread use and application. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Subgroup discovery (SD) aims at finding significant subgroups of a given population of individuals characterized by statistically unusual properties of interest. SD on event logs provides insight into particular behaviors of processes, which may be a valuable complement to the traditional process analysis techniques, especially for low -structured processes. This paper proposes a scalable and efficient method to search significant SD rules on frequent sequences of events, exploiting their multidimensional nature. With this method, it is intended to identify significant subsequences of events where the distribution of values of some target aspect is significantly different than the same distribution for the entire event log. A publicly available real -life event log of a Dutch hospital is used as a running example to demonstrate the applicability of our method. The proposed approach was applied on a real -life case study based on the public transport of a medium size European city (Porto, Portugal), for which the event data consists of 133 million smartcard travel validations from buses, trams and trains. The results include a characterization of mobility flows over multiple aspects, as well as the identification of unexpected behaviors in the flow of commuters (public transport). The generated knowledge provided a useful insight into the behavior of travelers, which can be applied at operational, tactical and strategic business levels, enhancing the current view of the transport services to transport authorities and operators.

Abstract
Similar to software systems, programming languages evolve substantially over time. Indeed, the community has more recently seen the release of new versions of mainstream languages in shorter and shorter time frames. For instance, the C++ working group has begun to release a new version of the language every 3 years, which now has a greater number of modern C++ features and improvements in modern standards (C++11, C++14, C++17, and C++ 20). Nonetheless, there is little empirical evidence on how developers are transitioning to use modern C++ constructs in legacy systems, and not understanding the trends and reasons for adopting these new modern C++ features might hinder software developers in conducting rejuvenation efforts. In this paper, we conduct an in-depth study to understand the development practices of KDE contributors to evolve their projects toward the use of modern C++ features. Our results show a trend in the widespread adoption of some modern C++ features (lambda expressions, auto-typed variables, and range-based for) in KDE community projects. We also found that developers in the KDE community are making large efforts to modernize their programs using automated tools, and we present some modernization scenarios and the benefits of adopting modern C++ features of the C++ programming language. Our results might help C++ software developers, in general, to evolve C++ legacy systems and tools builders to implement more effective tools that could help in rejuvenation efforts.

Abstract
Previous studies show that digital transition brings several benefits and challenges for companies. Among those challenges, particularly for Small and Medium-sized Enterprises (SMEs), the main one is increased capacitation, from technical roles to management. Considering this, the main objective of this study is to identify the training needs and the ecosystem support in the face of the digital transition for Portuguese manufacturing SMEs.Semi-structured interviews were conducted with industry experts and company professionals in the automotive and textile sectors. It was concluded that all workers, from technical roles to middle and top management, need more digital capabilities and would benefit from training programmes. The most desired areas for training are data science, virtualisation skills, quality assurance, technical training, and soft skills. The preferred format is physical (or hybrid at most) during working hours and with theoretical training before on-the-job learning. Both industrial companies and experts believe in the value of involving external entities in the training of employees, with the three most referred entities being technology and interface centres, universities, and business associations.

Abstract
This chapter presents key insights for the planning and operation of distribution power grids integrating high shares of renewable generation and charging capacity for electric vehicles (EVs). Case studies are presented to illustrate the impact of expected trends for vehicle electrification in the operation and future expansion of distribution power grids. The potential of innovative approaches is also exploited. The smart-transformer concept based on solid-state-transformer architectures as well as hybrid AC/DC distribution grids is qualitatively evaluated as a suitable solution for the massive integration of EV charging.