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Virtualization topologies of IEDs with the IEC61850 protocol and their application in edge devices
No full textPublisher Copyright: © 2025 The Institution of Engineering & Technology.The application domain of this work lies within smart grids, with a specific focus on primary substations. These critical infrastructures serve as the backbone of power distribution networks, where efficient management of data exchange and computational resources is essential for reliable and scalable operation. Our work explores the reliability and scalability of containerized IEC 61850 logical nodes and the use of LSTM models for data network load forecasting. The deployment demonstrated satisfactory performance at SV rates of 4000 and 4800 packets per second, confirming the feasibility of hosting multiple containerized nodes without compromising reliability. Additionally, LSTM models were applied to forecast data network loads, focusing on peak demand prediction. The results highlight the potential of integrating predictive analytics with containerized logical nodes to optimize computational resources and enhance network performance in IEC 61850 environments.Peer reviewe
Engineered Protein-Based Ionic Conductors for Sustainable Energy Storage Applications
No full textPublisher Copyright: © 2025 The Author(s). Advanced Materials published by Wiley-VCH GmbH.Protein-based biomaterials offer sustainable and biocompatible alternatives to traditional ionic conductors, essential for advancing green energy storage and bioelectronic applications. In this work, a robust, intrinsically self-assembling repeat protein scaffold to enhance ionic conductivity through the selective incorporation of glutamic acids is engineered. These mutations increase the number of available protonation sites and promote the formation of well-defined charge pathways. The self-assembly properties of the system enable the propagation of molecular-level modifications to the macroscopic scale, yielding self-standing protein films with significantly improved ionic conductivity. Specifically, engineered protein-based films exhibit an order of magnitude higher conductivity than their unmodified counterparts, with a further ten-fold enhancement through controlled addition of salt ions. Mechanistic analysis shows that the conductivity enhancement originates from the intertwined contributions of proton transport, hydration, and ion diffusion, all promoted by engineered charged residues. Finally, films of the best-performing variant are integrated, as both separator and electrolyte, into a supercapacitor device with competitive energy storage performance. These findings highlight the potential of rational protein design to create biocompatible, sustainable, and efficient ionic conductors with the stability and processability required to be successfully integrated into the next generation of energy storage and bioelectronic devices.Peer reviewe
Absorption Heat Transformer and Vapor Compression Heat Pump as Alternative Options for Waste Heat Upgrade in the Industry
Full text linkPublisher Copyright: © 2025 by the authors.Increasing the temperature of waste heat is crucial to enable its recovery. Vapor compression heat pumps and absorption heat transformers are the two heat upgrade technologies most commonly used for this purpose. Heat pumps have the advantage of entirely recovering the waste heat and the disadvantage of requiring electricity as input. Heat transformers need a negligible amount of electricity but reject at part of the waste heat input at low temperature. Due to these differences, the choice between the two options depends on the application. In this work, the environmental and economic performance of heat pumps and heat transformers are compared in some relevant applications. Indications about the most suitable technology are provided based on the availability of the waste heat, of the CO2 content of the electricity and of the electricity–gas price ratio. Heat pumps perform better when the waste heat availability is limited compared to the upgraded heat requirements and has a better environmental profile when the electricity has low carbon content. Heat transformer results are often economically convenient, especially when the availability of waste heat is large.Peer reviewe
Application of resistance-capacitance (RC) models to predict soil surface temperature: A case study in the Netherlands
No full textPublisher Copyright: © 2025 University of Split, FESB.Extreme temperatures in urban environments exacerbate thermal discomfort and intensify the Urban Heat Island (UHI) effect, particularly during peak warm periods. Pavements, which constitute a significant portion of urban surfaces, contribute significantly to heat retention, whereas soil and vegetated areas aid in cooling through lower heat storage and higher moisture retention. Accurate forecasting of soil and pavement surface temperatures is critical for developing effective UHI mitigation strategies. This paper explores the application of Resistance-Capacitance (RC) models, a type of grey-box model, for soil surface temperature prediction. Unlike purely physics-based and data-driven models, RC models integrate physical principles with data-driven insights, balancing accuracy and interpretability. The proposed methodology is validated using real-world data from a dike in the Netherlands, where an optimal RC model is identified through an iterative process based on the Akaike Information Criterion (AIC). Results demonstrate that a two-node RC model provides a reliable balance between complexity and predictive accuracy, achieving an R2 of 0.862 and a mean absolute error (MAE) of 0.675°C. These findings highlight the feasibility of applying RC models for soil temperature prediction while maintaining physical interpretability. Future research could extend this methodology to various soil types and urban surfaces, including pavements, to further enhance predictive capabilities and inform climate-responsive urban design.Peer reviewe
Catalytic Role of Nickel in Hydrogen Storage and Release Using Dibenzyltoluene as a Liquid Organic Hydrogen Carrier
No full textPublisher Copyright: © 2025 by the authors.Liquid Organic Hydrogen Carriers (LOHCs) represent a promising technology for the safe storage and transport of hydrogen. Its technical development largely depends on the catalysts used in the hydrogenation and dehydrogenation processes. Typically, noble metal-based monometallic catalysts are employed, although they present limitations in terms of cost and availability. This study uses the DBT system to explore the potential of nickel (Ni) as a catalytic alternative. In dehydrogenation, its role as an additive in low-loaded Pt-based catalysts (0.25 wt%) was evaluated, showing a significant increase in activity, with dehydrogenation levels exceeding 95%, compared to 82% obtained with monometallic Pt catalysts. This improvement is attributed to the formation of Pt-Ni alloys. On the other hand, although the bimetallic systems were not effective in hydrogenation, a commercial Ni/Al2O3-SiO2 catalyst was tested, achieving hydrogenation degrees of 80% in just 40 min, after pressure and catalyst loading optimization. These results position Ni as a key component in LOHC catalysis, either as an effective additive in Pt-based systems or as an active metal itself, due to its excellent performance and low cost. This paves the way for economically viable and efficient catalytic solutions for hydrogen storage applications, bridging the gap between performance and practicality.Peer reviewe
A Singular Theory of Sensorimotor Coordination: On Targeted Motions in Space
No full textPublisher Copyright: Copyright © 2024 the authors.Gravity has long been purported to serve a unique role in sensorimotor coordination, but the specific mechanisms underlying gravity-based visuomotor realignment remain elusive. In this study, astronauts (nine males, two females) performed targeted hand movements with eyes open or closed, both on the ground and in weightlessness. Measurements revealed systematic drift in hand-path orientation seen only when eyes were closed and only in very specific conditions with respect to gravity. In weightlessness, drift in path orientation was observed in two postures (seated, supine) for two different movement axes (longitudinal, sagittal); on Earth, such drift was only observed during longitudinal (horizontal) movements performed in the supine posture. In addition to providing clear evidence that gravitational cues play a fundamental role in sensorimotor coordination, these unique observations lead us to propose an “inverted pendulum” hypothesis to explain the saliency of the gravity vector for eye–hand coordination—and why eye–hand coordination is altered during body tilt or in weightlessness.Peer reviewe
Method for early analysis of the umbilical cable in a floating offshore wind turbine
No full textPublisher Copyright: © The Author(s), under exclusive licence to Sociedade Brasileira de Engenharia Naval 2024.The expansion of floating offshore wind energy brings the industry closer to achieving commercial viability. However, the challenging marine environment—characterized by strong winds, waves, and currents—combined with the growing size of wind turbines and the dynamic behavior of floaters, raises concerns about power production efficiency and system durability due to increased loads. A critical component within floating wind platforms is the umbilical cable, responsible for transmitting generated energy. Any failure in this cable would result in the shutdown of, at least, the associated wind turbine, emphasizing the need for precise and early design. This work introduces a novel method based on catenary models, leveraging known boundary conditions to reduce computational effort and improve design accuracy. By comparing the proposed approach with an industry-standard method, the research aims to provide insights into the umbilical cable design.Peer reviewe
Crop-conditional semantic segmentation for efficient agricultural disease assessment
Full text linkPublisher Copyright: © 2025In this study, we introduced an innovative crop-conditional semantic segmentation architecture that seamlessly incorporates contextual metadata (crop information). This is achieved by merging the contextual information at a late layer stage, allowing the method to be integrated with any semantic segmentation architecture, including novel ones. To evaluate the effectiveness of this approach, we curated a challenging dataset of over 100,000 images captured in real-field conditions using mobile phones. This dataset includes various disease stages across 21 diseases and seven crops (wheat, barley, corn, rice, rape-seed, vinegrape, and cucumber), with the added complexity of multiple diseases coexisting in a single image. We demonstrate that incorporating contextual multi-crop information significantly enhances the performance of semantic segmentation models for plant disease detection. By leveraging crop-specific metadata, our approach achieves higher accuracy and better generalization across diverse crops (F1 = 0.68, r = 0.75) compared to traditional methods (F1 = 0.24, r = 0.68). Additionally, the adoption of a semi-supervised approach based on pseudo-labeling of single diseased plants, offers significant advantages for plant disease segmentation and quantification (F1 = 0.73, r = 0.95). This method enhances the model's performance by leveraging both labeled and unlabeled data, reducing the dependency on extensive manual annotations, which are often time-consuming and costly. The deployment of this algorithm holds the potential to revolutionize the digitization of crop protection product testing, ensuring heightened repeatability while minimizing human subjectivity. By addressing the challenges of semantic segmentation and disease quantification, we contribute to more effective and precise phenotyping, ultimately supporting better crop management and protection strategies.Peer reviewe
On the analysis of adapting deep learning methods to hyperspectral imaging. Use case for WEEE recycling and dataset
Full text linkPublisher Copyright: © 2024 The AuthorsHyperspectral imaging, a rapidly evolving field, has witnessed the ascendancy of deep learning techniques, supplanting classical feature extraction and classification methods in various applications. However, many researchers employ arbitrary architectures for hyperspectral image processing, often without rigorous analysis of the interplay between spectral and spatial information. This oversight neglects the implications of combining these two modalities on model performance, consumption, and inference time. This paper evaluates the impact of including different spatial (visual texture) and spectral (captured spectral information) features on deep learning architectures for hyperspectral image segmentation. To this end, it presents different architectural configurations with varying levels of spectral and spatial information and are evaluated in terms of identification performance, energy consumption, and inference time. Additionally, the transferability of knowledge from large pre-trained image foundation models, originally designed for RGB images, to the hyperspectral domain is explored. Results show that incorporating spatial information alongside spectral data leads to improved segmentation results. However, not all spectral wavelengths are necessary to obtain the optimal performance/energy consumption ratio, which is required for faster and more carbon-neutral models. Training foundation models from the RGB domain leads to lower performance and higher energy consumption models with longer inference times. It is also essential to further develop novel architectures that integrate spectral and spatial information and adapt RGB foundation models to the hyperspectral domain. Furthermore, this paper contributes to the field by cleaning and publicly releasing the Tecnalia WEEE Hyperspectral dataset. This dataset contains different non-ferrous fractions of Waste Electrical and Electronic Equipment (WEEE), including Copper, Brass, Aluminum, Stainless Steel, and White Copper, spanning the range of 400 to 1000 nm.Peer reviewe
Using offline data to speed up Reinforcement Learning in procedurally generated environments
Full text linkPublisher Copyright: © 2024 The AuthorsOne of the key challenges of Reinforcement Learning (RL) is the ability of an agent to generalize its learned policy to unseen settings. Moreover, training an RL agent requires large numbers of interactions with the environment. Motivated by the success of Imitation Learning (IL), we conduct a study to investigate whether an agent can leverage offline data in the form of trajectories to improve the sample-efficiency in procedurally generated environments. We consider two settings of using IL from offline data for RL: (1) pre-training a policy before online RL training and (2) concurrently training a policy with online RL and IL from offline data. We analyze the impact of the quality (optimality of trajectories), quantity and diversity of available offline trajectories on the effectiveness of both approaches. Across four well-known sparse reward tasks in the MiniGrid environment, we find that using IL for both pre-training and concurrently during online RL training, consistently improves sample-efficiency, and in some tasks achieves higher returns compared to using either IL or RL alone. Furthermore, we show that training a policy from as few as two trajectories can make the difference between learning an optimal policy at the end of online training and not learning at all. Evaluation in two tasks of the Procgen environment further highlights that the diversity of the training data is more important than its quality. Our findings motivate the widespread adoption of IL for pre-training and concurrent IL in procedurally generated environments whenever offline trajectories are available or can be generated.Peer reviewe