{"type": "FeatureCollection", "features": [{"id": "10.1016/j.egyr.2022.06.076", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-22T16:15:42Z", "type": "Journal Article", "created": "2022-07-07", "title": "Geospatial assessment of elevated agrivoltaics on arable land in Europe to highlight the implications on design, land use and economic level", "description": "Agrivoltaic systems (a combination of agricultural crop production and photovoltaics (PV) on the same land) have an increasing interest. Realizing this upcoming technology raises still many challenges at design, policy and economic level. This study addresses a geospatial methodology to quantify the important design and policy questions across Europe. An elevated agrivoltaic system on arable land is evaluated: three crop light requirements (shade-loving, shade-tolerant and shade-intolerant) are simulated at a spatial resolution of 25 km across the European Union (EU). As a result, this study gives insight into the needed optimal ground coverage ratio (GCR) of the agrivoltaic system for a specific place. Additionally, estimations of the energy production, levelized cost of energy (LCOE) and land equivalent ratio (LER) are performed in comparison with a separated system. The results of the study show that the location-dependent solar insolation and crop shade tolerance have a major influence on the financial competitiveness and usefulness of these systems, where a proper European policy system and implementation strategy is required. Finally, a technical study shows an increase in PV power of 1290 GWp (almost \u00d7 10 of the current EU\u2019s PV capacity) if potato cultivation alone (1% of the total arable agricultural area) is converted into agrivoltaic systems.", "keywords": ["Photovoltaics", "13. Climate action", "EU energy strategy", "0202 electrical engineering", " electronic engineering", " information engineering", "0401 agriculture", " forestry", " and fisheries", "Geospatial assessment", "Electrical engineering. Electronics. Nuclear engineering", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "7. Clean energy", "Agrivoltaics", "TK1-9971"]}, "links": [{"href": "https://doi.org/10.1016/j.egyr.2022.06.076"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Energy%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.egyr.2022.06.076", "name": "item", "description": "10.1016/j.egyr.2022.06.076", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.egyr.2022.06.076"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-01T00:00:00Z"}}, {"id": "10.1016/j.egyr.2022.07.052", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-22T16:15:42Z", "type": "Journal Article", "created": "2022-07-26", "title": "Performance analysis and application of a hybrid electromagnetic-triboelectric nanogenerator for energy harvesting", "description": "In recent years, the possibility of harvesting the small-scale energies from the environment has been the subject of many scientific studies. Nanogenerators are emerging to be good candidates for converting the small-scale energies from the environment into electrical energy without need for battery. In this paper, a hybrid nanogenerator that integrates three different working mechanisms for conversion of mechanical energy into electrical energy is presented. The hybrid nanogenerator is composed of a zig-zag contact mode triboelectric nanogenerator (TENG), a sliding mode TENG and two electromagnetic generators (EMGs). Triboelectric surfaces are oppositely charged aluminium and Kapton layers for a zig-zag contact mode TENG and aluminium and PTFE layers for a sliding mode TENG. Aluminium layer is used as an electron donor, and also as an electrode. EMG unit is composed of two home-made copper coils and a neodymium magnet. All individual units are integrated into a two-piece acrylic shell. The whole device is of a compact, low-cost, and lightweight design. It has a size of 37\u00a0mm \u00d7 37\u00a0mm \u00d7 70\u00a0mm, which was optimized by modelling. Performance characterization verified the proposed hybrid nanogenerator as an efficient energy harvester. Output characteristics were tested under different loads (in a range from 10 k\u03a9 to 100 M\u03a9). The maximum output voltage and current of the hybrid nanogenerator were estimated to be about 65 V and 15.25 \u03bcA, respectively. The maximum output power was 1.13\u00a0mW at 200 \u03a9. Charging performance analysis showed that the hybrid nanogenerator significantly enhanced the voltage level and charging speed of the tested capacitors in comparison with individual units. The hybrid nanogenerator charged 1\u03bcF capacitor to 9.1 V within 60\u00a0s. Individual units could simultaneously power at least 144 light-emitting diodes (LEDs). A hybrid signal could power at least 94 LEDs connected in series and at least 50 LEDs connected in parallel. Electrical energy produced by the hybrid nanogenerator was stored in a 47 \u03bcF capacitor bank and used to efficiently power a calculator.", "keywords": ["Triboelectric nanogenerators", "Electromagnetic generators", "Hybrid nanogenerator", "Electrical engineering. Electronics. Nuclear engineering", "02 engineering and technology", "0210 nano-technology", "Mechanical energy harvesting", "01 natural sciences", "Modelling", "TK1-9971", "0104 chemical sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.egyr.2022.07.052"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Energy%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.egyr.2022.07.052", "name": "item", "description": "10.1016/j.egyr.2022.07.052", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.egyr.2022.07.052"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-01T00:00:00Z"}}, {"id": "10.1109/access.2019.2945084", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-22T16:17:41Z", "type": "Journal Article", "created": "2019-10-02", "title": "Data-Driven Structuring of the Output Space Improves the Performance of Multi-Target Regressors", "description": "The task of multi-target regression (MTR) is concerned with learning predictive models capable of predicting multiple target variables simultaneously. MTR has attracted an increasing attention within research community in recent years, yielding a variety of methods. The methods can be divided into two main groups: problem transformation and problem adaptation. The former transform a MTR problem into simpler (typically single target) problems and apply known approaches, while the latter adapt the learning methods to directly handle the multiple target variables and learn better models which simultaneously predict all of the targets. Studies have identified the latter group of methods as having competitive advantage over the former, probably due to the fact that it exploits the interrelations of the multiple targets. In the related task of multi-label classification, it has been recently shown that organizing the multiple labels into a hierarchical structure can improve predictive performance. In this paper, we investigate whether organizing the targets into a hierarchical structure can improve the performance for MTR problems. More precisely, we propose to structure the multiple target variables into a hierarchy of variables, thus translating the task of MTR into a task of hierarchical multi-target regression (HMTR). We use four data-driven methods for devising the hierarchical structure that cluster the real values of the targets or the feature importance scores with respect to the targets. The evaluation of the proposed methodology on 16 benchmark MTR datasets reveals that structuring the multiple target variables into a hierarchy improves the predictive performance of the corresponding MTR models. The results also show that data-driven methods produce hierarchies that can improve the predictive performance even more than expert constructed hierarchies. Finally, the improvement in predictive performance is more pronounced for the datasets with very large numbers (more than hundred) of targets.", "keywords": ["multi-target regression", "clustering", " feature ranking", " hierarchy", " multi-target regression", " target space", "target space", "hierarchy", "Electrical engineering. Electronics. Nuclear engineering", "Clustering", "feature ranking", "clustering", "TK1-9971"]}, "links": [{"href": "https://doi.org/10.1109/access.2019.2945084"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Access", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/access.2019.2945084", "name": "item", "description": "10.1109/access.2019.2945084", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/access.2019.2945084"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.1109/access.2023.3339884", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-22T16:17:41Z", "type": "Journal Article", "created": "2023-12-05", "title": "Classifying the Vermicompost Production Stages Using Thermal Camera Data", "description": "The procedure of processing the vermicompost production includes several stages, where the vermicompost material has different temperatures during these different stages. Thermal sensors play a key role in numerous fields, such as medical and agricultural applications. Thermal cameras can produce a thermal image or an array of values representing the array of sensory data. i.e., an array of temperatures. In this study, we proposed the first thermal imagery dataset of the vermicompost production process. The contributions of this work are two-fold using the proposed dataset. First, we framed the process of predicting the vermicompost production process as a classification problem. Second, we compared classifying the different stages of the process of vermicompost production based on two different input types, namely, thermal images and an array of temperatures. In other words, the classifier will be fed with an input (an image or an array of temperatures), and then the classifier will predict the vermicompost production stage. In this context, we utilized several machine and deep learning models as classifiers. For the utilized dataset, the study has been conducted on a set of images collected during the vermicompost production procedure which was collected every 14 days over 42 consecutive days, i.e., four classes. We proposed running a series of experiments to determine which input type yields better classification accuracy. The obtained results show that using thermal images for the sake of classifying the vermicompost production stages achieved higher accuracy, about 92%, in comparison to using the sensor array data, about 60%.", "keywords": ["machine learning", "SENet", "deep learning", "Electrical engineering. Electronics. Nuclear engineering", "sensor array", "Classification", "ResNet", "TK1-9971"]}, "links": [{"href": "https://doi.org/10.1109/access.2023.3339884"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Access", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/access.2023.3339884", "name": "item", "description": "10.1109/access.2023.3339884", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/access.2023.3339884"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.1109/access.2025.3569213", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-22T16:17:41Z", "type": "Journal Article", "created": "2025-05-12", "title": "An Efficient Encoding Spectral Information in Hyperspectral Images for Transfer Learning of Mask R-CNN for Instance Segmentation of Tomato Sepals", "description": "The most vulnerable parts of tomatoes are the tips of the sepals, which are the primary entry points for fungal spores. Their precise segmentation within hyperspectral images (HSIs) plays a pivotal role in the development of automated and non-destructive systems for assessing tomatoes’ sensitivity to fungal infections. This research addresses the critical need for encoding spectral information in hyperspectral imaging to enhance the efficiency of such automated systems. We investigate four different techniques: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Probabilistic Principal Component Analysis (PPCA), and Non-Negative Matrix Factorization (NMF), to perform transfer learning for tomato sepal instance segmentation using models previously trained on RGB images. A comparative analysis of three Mask Region-based Convolutional Neural Network (Mask R-CNN) backbone models is conducted: the Faster R-CNN, Deformable ConvNet, and Feature Pyramid Network (FPN) on spectral-encoded HSIs of the Brioso tomato variety. The Mask R-CNN with FPN, integrated with the NMF technique achieved the highest level of accuracy, yielding a Mean Average Precision (mAP) of 94.05%. Furthermore, on the second dataset, which included an additional three tomato varieties: Capricia, Provine, and Sao Paolo, the same model achieved mAP score of 86.42% across all tomato varieties with only a single false positive detection. Additionally, we incorporated a custom convolutional layer initialized it with estimated NMF coefficients, and achieved a mAP score of 87.40%. This demonstrates the potential of integrating spectral information encoding with trained deep learning-based instance segmentation models to enable robust and accurate transfer learning for automated agricultural food quality assessments.", "keywords": ["instance segmentation", "deep learning", "Electrical engineering. Electronics. Nuclear engineering", "transfer learning", "tomato", "encoding spectral information in HSI", "Hyperspectral imaging (HSI)", "TK1-9971"]}, "links": [{"href": "https://doi.org/10.1109/access.2025.3569213"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Access", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/access.2025.3569213", "name": "item", "description": "10.1109/access.2025.3569213", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/access.2025.3569213"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Electrical+engineering.+Electronics.+Nuclear+engineering&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Electrical+engineering.+Electronics.+Nuclear+engineering&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Electrical+engineering.+Electronics.+Nuclear+engineering&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Electrical+engineering.+Electronics.+Nuclear+engineering&offset=5", "hreflang": "en-US"}], "numberMatched": 5, "numberReturned": 5, "distributedFeatures": [], "timeStamp": "2026-09-22T23:58:05.603200Z"}