Abstract
Agriculture and energy use are increasingly linked, especially as farms' energy needs grow. Renewable Energy Communities (RECs) help farmers, particularly in remote areas, access affordable surplus energy from other producers, while sellers gain extra revenue. This study focuses on the creation of RECs as a sustainable and economically viable solution for small and medium-sized agribusinesses to address their energy challenges. We explore the complementarities and potential benefits of RECs from the experience learned in the Tools4AgriEnergy project, using RECreation digital platform for the management of RECs. A case study is used, based on the Alqueva region in Portugal with six members that develop different agri-food sector activities. Using tariffs compliant with Portuguese regulations, results indicate that the development of self-consumption activities can achieve significant energy cost savings annually.

Abstract
With the recent surge in the electric vehicle market, there is a pressing demand for solutions and platforms to enhance vehicle lifecycle management. This is particularly pertinent for motorcycles, which are widely used in urban environments (e.g., for food delivery services) and require frequent maintenance. The present study proposes the research and development of a platform, along with mobile and web applications, focusing on optimizing the lifecycle of electric motorcycles. Central to this project is the implementation of Product Lifecycle Management (PLM) to simplify the planning of technical maintenance and the recording and access to technical events and information in the most transparent and non-intrusive way for all involved parties. This project aims to establish innovative and effective communication between owners, manufacturers, and service partners, ensuring the longevity and reliability of motorcycles.

Abstract
Underwater environments pose unique challenges to optical systems due to physical phenomena that induce severe data degradation. Current imaging sensors rarely address these effects comprehensively, resulting in the need to integrate complementary information sources. This article presents a multimodal data fusion approach to combine information from diverse sensing modalities into a single dense and accurate tridimensional representation. The proposed fusiNg tExture with apparent motion information for underwater Scene recOnstruction (NESO) encoder-decoder network leverages motion perception principles to extract relative depth cues, fusing them with textured information through an early fusion strategy. Evaluated on the FLSea-Stereo dataset, NESO outperforms state-of-the-art methods by 58.7%. Dense depth maps are achieved using multi-stage skip connections with attention mechanisms that ensure propagation of key features across network levels. This representation is further enhanced by incorporating sparse but millimeter-precise depth measurements from active imaging techniques. A regression-based algorithm maps depth displacements between these heterogeneous point clouds, using the estimated curves to refine the dense NESO prediction. This approach achieves relative errors as low as 0.41% when reconstructing submerged anode structures, accounting for metric improvements of up to 0.1124 m relative to the initial measurements. Validation at the ATLANTIS Coastal Testbed demonstrates the effectiveness of this multimodal fusion approach in obtaining robust tri-dimensional representations in real underwater conditions.

Abstract

Abstract
Energy community developers are relevant actors for the deployment of energy communities as they can overcome initial investment costs and better navigate complex licensing processes. Their strategy depends on the chosen business model, typically aimed at maximizing their profit while providing tangible benefits to the potential members of the energy communities to encourage their engagement. This works describes strategies for an energy management system adapted to energy community developers whose business model consists in installing, owning and managing energy assets (such as photovoltaic panels and batteries) in its own facilities and in the facilities of those energy community members able and willing to provide them, to sell the locally produced energy for self-consumption in the energy community.

Abstract
Manual text annotation is a complex and time-consuming task. However, recent advancements demonstrate that such a task can be accelerated with automated pre-annotation. In this paper, we present a methodology to improve the efficiency of manual text annotation by leveraging LLMs for text pre-annotation. For this purpose, we train a BERT model for a token classification task and integrate it into the INCEpTION annotation tool to generate span-level suggestions for human annotators. To assess the usefulness of our approach, we conducted an experiment where an experienced linguist annotated plain text both with and without our model’s pre-annotations. Our results show that the model-assisted approach reduces annotation time by nearly 23%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.