Abstract
Blending green hydrogen into gas networks is subject to strict quality regulation. As Europe diversifies its Liquefied Natural Gas supply, existing literature often ignores the extreme chemical variability of these sources by assuming a static composition. This study proposes a nonlinear steady-state optimization model, strictly adhering to European standards, to maximize hydrogen injection across eight real-world LNG profiles. Results reveal severe sensitivity to the carrier gas: lean gas (Trinidad and Tobago, USA) restricts maximum hydrogen integration to 8.89% and 11.40% by volume due to insufficient heavy hydrocarbons. Conversely, carbon-rich gas (Nigeria) allows up to 20.10%, although inert gases degrade this capacity. Ultimately, this 20.10% maximum blend yields only a 6.77% emission reduction. The imported chemical profile dictates absolute limits, proving that static universal hydrogen quotas are thermodynamically unachievable year-round without continuous dynamic quality tracking.

Abstract
The rapid advancement of wireless technologies, particularly Wi-Fi, has spurred significant research into indoor human activity detection across various domains (e.g., healthcare, security, and industry). This work explores the non-invasive and cost-effective Wi-Fi paradigm and the application of deep learning for human activity recognition using Wi-Fi signals. Focusing on the challenges in machine interpretability, motivated by the increase in data availability and computational power, this paper uses explainable artificial intelligence to understand the inner workings of transformer-based deep neural networks designed to estimate human pose (i.e., human skeleton key points) from Wi-Fi channel state information. Using different strategies to assess the most relevant sub-carriers (i.e., rollout attention and masking attention) for the model predictions, we evaluate the performance of the model when it uses a given number of sub-carriers as input, selected randomly or by ascending (high-attention) or descending (low-attention) order. We concluded that the models trained with fewer (but relevant) sub-carriers are competitive with the baseline (trained with all sub-carriers) but better in terms of computational efficiency (i.e., processing more data per second).

Abstract
This study systematically characterizes Do It Yourself (DIY) and open-source wheeled robotic platforms used in higher education and academic competitions. It also analyzes Robot Operating System (ROS)-based designs with respect to real-time performance and multi-sensor integration, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. A total of 20 high-quality studies were identified across five major digital libraries (Dimensions, Web of Science, SpringerLink, ScienceDirect, and IEEE Xplore), which were searched on 12 January 2026. Eligibility was restricted to peer-reviewed English-language studies published between 2005 and 2026 that explicitly implement ROS-based wheeled platforms in higher education contexts. Results were synthesized through qualitative analysis using a structured data extraction form implemented in the Parsifal systematic review platform. Methodological quality and risk of bias were assessed using a structured appraisal checklist. The results show a dominant trend toward distributed dual-processor architectures, which separate low-level real-time control from high-level processing. Most platforms target an accessible price range of 50 & euro; to 500 & euro; for open-source and DIY platforms. ROS has emerged as the standard middleware, enabling multi-sensor integration and supporting digital twin workflows. There is also a clear shift toward open-source hardware and Three-Dimensional (3D)-printed modular designs, which reduce production costs. However, challenges remain, including software obsolescence and the lack of maintenance plans. The findings highlight the need for interoperable reference architectures and automated deployment workflows to ensure long-term sustainability. Evidence is limited by heterogeneity, inconsistent reporting, and small sample sizes, which introduce risks of bias and imprecision. This review was formally registered with protocols.io.

Abstract
Hydrotherapy is widely used in rehabilitation because it reduces mechanical loading while preserving neuromuscular and cardiovascular stimulation. However, the biomechanical characterization of deep-water running remains limited, particularly when using accessible wearable systems for cycle-based movement analysis. This study aimed to evaluate the concurrent validity and agreement of a low-cost accelerometry device for cycle-based analysis of deep-water running, using a commercial accelerometry system as the reference measurement system. Twenty-one healthy participants performed a 25 m deep-water running task with simultaneous data acquisition from mechanically coupled sensors to ensure alignment. A total of 75 synchronized cycles were processed using a standardized pipeline that included filtering, synchronization, cycle detection, and parameter extraction. Statistical analysis was conducted using the Wilcoxon signed-rank test, intraclass correlation coefficient, Spearman's correlation, Bland-Altman analysis, and error metrics. The results showed good agreement for temporal and volumetric variables, including cycle duration (ICC = 0.84), cumulative acceleration (ICC = 0.82), and area under the curve (ICC = 0.68). However, lower agreement and systematic bias were observed for intensity-related variables, particularly RMS and peak acceleration, despite more than 92% of cycles falling within the 95% limits of agreement (LoA). These findings suggest that the proposed device provides acceptable agreement for temporal and volumetric variables during deep-water running and may represent a low-cost alternative for movement monitoring in aquatic environments. However, intensity-related variables should be interpreted with caution due to the systematic differences observed between systems.

Abstract
Deep reinforcement learning (DRL) is a promising solution for mobile-robot navigation, yet its performance often degrades in harsh terrain where wheel-ground interactions vary rapidly. Uneven and deformable surfaces can change the effective wheel radius and introduce persistent left-right actuation asymmetries, which distort odometry and action execution and ultimately reduce goal-reaching success. We propose a two-stage, predictor-augmented DRL framework that improves robustness to these non-idealities without relying on external supervision or privileged sensors. In Stage 1, the robot collects randomized trajectories in an ideally flat environment while sampling episode-wise left/right wheel-radius factors, and trains a lightweight self-supervised predictor from onboard signals (odometry, IMU, and wheel joint states) to estimate the mismatch factors. In Stage 2, the trained predictor is frozen and its outputs are appended to the observation of a Soft Actor Critic (SAC) navigation policy, enabling the policy to condition its decisions on estimated actuation drift during navigation in harsh terrain. We evaluate the proposed approach in ROS 2/Gazebo across multiple harsh-terrain scenarios with wheel-radius perturbations. Our method outperforms all baselines in success, slip-related metrics, and cumulative roll-pitch inclination, while producing modestly longer paths as a deliberate trade-off to mitigate wheel-radius mismatch. We further validate sim-to-real transfer on a real robot under unmodeled effects (sensor noise, calibration drift, and actuator saturation). The full source code and a demonstration video of both simulation and real-world experiments are publicly available on GitHub.

Abstract
Daylight Saving Time (DST) has long been justified as a measure to reduce electricity consumption by better aligning human activity with daylight. However, its effectiveness under current energy-use patterns remains uncertain. This study provides a comprehensive assessment of the impact of DST on electricity-related needs for lighting and climatization across multiple European countries.The analysis is based on a unified methodological framework that evaluates the number of hours requiring artificial lighting and thermal conditioning, using empirical solar irradiance and temperature data. By focusing on the extensive margin, the approach enables consistent comparisons between DST and non-DST scenarios under alternative legal time configurations.The results show that DST reduces lighting needs, but these reductions are modest when realistic irradiance data are taken into account. In contrast, DST increases climatization requirements, primarily due to higher heating demand. As these effects operate in opposite directions, the overall impact of DST on electricity-related needs is limited. Additionally, the analysis indicates that adopting a uniform legal time across the European Union may be suboptimal from an energy perspective, because the alignment between civil time and environmental conditions varies with longitude.Overall, the findings indicate that the energy implications of DST are relatively small and context-dependent, and that legal time settings should account for regional differences.