Abstract
Exploiting stronger winds at offshore farms leads to a cyclical need for maintenance due to the harsh maritime conditions. While autonomous vehicles are the prone solution for O&M procedures, sub-sea phenomena induce severe data degradation that hinders the vessel's 3D perception. This article demonstrates a hybrid underwater imaging system that is capable of retrieving tri-dimensional information: dense and textured Photogrammetric Stereo (PS) point clouds and multiple accurate sets of points through Light Stripe Ranging (LSR), that are combined into a single dense and accurate representation. Two novel fusion algorithms are introduced in this manuscript. A Joint Masked Regression (JMR) methodology propagates sparse LSR information towards the PS point cloud, exploiting homogeneous regions around each beam projection. Regression curves then correlate depth readings from both inputs to correct the stereo-based information. On the other hand, the learning-based solution (RHEA) follows an early-fusion approach where features are conjointly learned from a coupled representation of both 3D inputs. A synthetic-to-real training scheme is employed to bypass domain-adaptation stages, enabling direct deployment in underwater contexts. Evaluation is conducted through extensive trials in simulation, controlled underwater environments, and within a real application at the ATLANTIS Coastal Testbed. Both methods estimate improved output point clouds, with RHEA achieving an average RMSE of 0.0097 m -a 52.45% improvement when compared to the PS input. Performance with real underwater information proves that RHEA is robust in dealing with degraded input information; JMR is more affected by missing information, excelling when the LSR data provides a complete representation of the scenario, and struggling otherwise.

Abstract
Digitalization has begun as a transformative force within the energy sector, reforming traditional practices and paving the way for enhanced operational efficiency and sustainability. Enabled by key technologies such as smart meters, digitalization embodies a paradigm shift in energy management. Nonetheless, it is crucial to recognize that these enabling technologies are only the catalysts and not the end goal. This paper presents a comprehensive overview of digital services and products in the energy sector, with a specific focus on emerging technologies like AI and Connected Data Spaces. The objective of this review paper is to assess the maturity and adoption levels of these digital solutions, seeking to draw insights into the factors influencing their varying levels of success. This maturity and adoption assessment was carried out by applying a Fuzzy logic approach which allowed us to compensate for the lack of detailed information in current literature. By analyzing the reasons behind high maturity-low adoption and vice-versa, this study seeks to cast light on the dynamics shaping the digital transformation of the energy sector.

Abstract
Energy allocation rules are one of the core aspects of collective self-consumption (CSC) regulations. It allows final consumers to share their surplus generation with other CSC members, while keeping their full rights as consumers, i.e., maintaining a supply contract with the retailers of their choice. Some European Union member states regulations use allocation coefficients so that local allocations are integrated with wholesale settlement and directly affect the retailers' billing. Several AC methods have been proposed so far, each one adapted to distribution system operators' settlement procedures with specific rules that can impact the benefits that each CSC member obtain. This paper analyses, assesses and compares two relevant AC methods, namely pre-delivery fixed AC and post-delivery dynamic AC, by developing a settlement formulation for a community with members with flexible assets and different opportunity costs. AC policy recommendations based on findings are provided.

Abstract
Ethanol plays a crucial role in modern industrial processes and consumer products. Despite its presence in human activity, short and long-term exposure to gaseous ethanol poses risks to health conditions and material damage, making the control of its concentration in the atmosphere of high importance. Ethanol optical sensors based on electromagnetic surface waves (ESWs) are presented, with sensitivity to ethanol vapours being achieved by the inclusion of ethanol-adsorptive zinc oxide (ZnO) layers. The changes in optical properties modulate the resonant conditions of ESWs, enabling the tracking of ethanol concentration in the atmosphere. A comprehensive comparative study of sensor performance is carried out between surface plasmon resonance (SPR) and Bloch surface wave (BSW) based sensors. Sensor efficiency is simulated by transfer matrix method towards optimized figures of merit (FoM). Preliminary results validate ethanol sensitivity of BSW based sensor, showcasing a possible alternative to electromagnetic and plasmonic sensors. © The Authors.

Abstract
The potential of digital games, when transformed into Serious Games (SGs), Games for Learning (GLs), or game-based learning (GBL), is truly inspiring. These forms of games hold immense potential as effective learning tools as they have a unique ability to provide challenges that align with learning objectives and adapt to the learner's level. This adaptability empowers educators to create a flexible and customizable learning experience, crucial in acquiring knowledge, experience, and professional skills. However, the lack of a standardised design methodology for challenges that promote skill acquisition often hampers the effectiveness of games-based training. The four-step Triadic Certification Method directly responds to this challenge, although implementing it may require significant resources and expertise and adapting it to different training contexts may be challenging. This method, built on a triadic of components: competencies, mechanics, and training levels, offers a new approach for game designers to create games with embedded in-game assessment towards the certification of competencies. The model combines the competencies defined for each training plan with the challenges designed for the game on a matrix that aligns needs and levels, ensuring a comprehensive and practical learning experience. The practicality of the model is evident in its ability to balance the various components of a certification process. To validate this method, a case study was developed in the context of learning how to drive, supported by a game coupled with a realistic driving simulator. The real time collection of game and training data and its processing, based on predefined settings, learning metrics (performance) and game elements (mechanics and parameterisations), defined by both experts and game designers, makes it possible to visualise the progression of learning and to give visual and auditory feedback to the student on their behaviour. The results demonstrate that it is possible use the data generated by the player and his/her interaction with the game to certify the competencies acquired.

Abstract
The concept of Industry 4.0 and the introduction of the Internet of Things (IoT) on industrial applications, known as Industrial Internet of Things (IIoT), have been changing the scenario of industrial automation. This new paradigm is expected to optimize industrial processes, increase productivity, lower costs and improve operations integration. For that, structured Machine-to-Machine (M2M) communication is key to ensure agility, interoperability and reliability, with several solutions currently available in the literature and in industry. This paper reviews the state of the art on industrial communication protocols and architectures, providing a classification and comparison of these different solutions based on their most relevant features in the context of Industry 4.0. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.