Pub Date : 2025-11-22DOI: 10.1016/j.softx.2025.102450
Gerardo A. Pérez-Pérez , Moisés F. Valdez-Ávila , Mauricio G. Orozco-del-Castillo , Carlos Bermejo-Sabbagh , Juan A. Recio-García
CBR-FoX is an open-source Python software tool that provides explainable artificial intelligence capabilities for time series forecasting across multiple scientific domains. The tool implements a novel Case-Based Reasoning approach that generates explanatory case bases through sliding-window techniques, enabling researchers to understand and validate model predictions by retrieving historically similar situations. CBR-FoX integrates a flexible adapter mechanism that allows the use of established time series similarity metrics from external libraries such as sktime, alongside its own novel Combined Correlation Index, with signal processing capabilities using a locally weighted scatterplot smoothing filter to enhance explanation quality. The software is available under the European Union Public License 1.2 at https://doi.org/10.5281/zenodo.17381828, tested on Python 3+ environments, and evaluation results demonstrate that the Combined Correlation Index achieves optimal performance with 2–3 explanation cases compared to established metrics. This tool fills an important gap in the academic software landscape by offering researchers a robust solution that enhances the scientific rigor and reproducibility of forecasting studies across disciplines.
{"title":"CBR-FoX: A case-based reasoning software tool for auditing time series predictions","authors":"Gerardo A. Pérez-Pérez , Moisés F. Valdez-Ávila , Mauricio G. Orozco-del-Castillo , Carlos Bermejo-Sabbagh , Juan A. Recio-García","doi":"10.1016/j.softx.2025.102450","DOIUrl":"10.1016/j.softx.2025.102450","url":null,"abstract":"<div><div>CBR-FoX is an open-source Python software tool that provides explainable artificial intelligence capabilities for time series forecasting across multiple scientific domains. The tool implements a novel Case-Based Reasoning approach that generates explanatory case bases through sliding-window techniques, enabling researchers to understand and validate model predictions by retrieving historically similar situations. CBR-FoX integrates a flexible adapter mechanism that allows the use of established time series similarity metrics from external libraries such as sktime, alongside its own novel Combined Correlation Index, with signal processing capabilities using a locally weighted scatterplot smoothing filter to enhance explanation quality. The software is available under the European Union Public License 1.2 at <span><span>https://doi.org/10.5281/zenodo.17381828</span><svg><path></path></svg></span>, tested on Python 3+ environments, and evaluation results demonstrate that the Combined Correlation Index achieves optimal performance with 2–3 explanation cases compared to established metrics. This tool fills an important gap in the academic software landscape by offering researchers a robust solution that enhances the scientific rigor and reproducibility of forecasting studies across disciplines.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102450"},"PeriodicalIF":2.4,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-20DOI: 10.1016/j.softx.2025.102445
Jingjing Yan , Xiaoyan Shao , Lingling Li , Xuezhuan Zhao , Xiaoyu Hao
With the widespread application of semantic segmentation technology in fields such as remote sensing and industrial inspection, the evaluation of model performance and visualization of training processes have become key issues. This paper develops an integrated evaluation software based on PyQt5 and TensorBoard, which supports the calculation of eight metrics including Precision, Recall, F1, Accuracy, mPA, mIoU, Dice, ROC, and PR and provides functions such as multi-algorithm comparison and batch processing. Through TensorBoard, the software enables the visualization of model architectures, feature maps, heatmaps, and loss maps, intuitively displaying the differences between segmentation results and ground truth labels to assist in parameter optimization. With its modular design, the software combines both evaluation and visualization capabilities, providing efficient tool support for the development and deployment of segmentation models.
{"title":"SegEv: semantic segmentation performance verification and evaluation software","authors":"Jingjing Yan , Xiaoyan Shao , Lingling Li , Xuezhuan Zhao , Xiaoyu Hao","doi":"10.1016/j.softx.2025.102445","DOIUrl":"10.1016/j.softx.2025.102445","url":null,"abstract":"<div><div>With the widespread application of semantic segmentation technology in fields such as remote sensing and industrial inspection, the evaluation of model performance and visualization of training processes have become key issues. This paper develops an integrated evaluation software based on PyQt5 and TensorBoard, which supports the calculation of eight metrics including Precision, Recall, F1, Accuracy, mPA, mIoU, Dice, ROC, and PR and provides functions such as multi-algorithm comparison and batch processing. Through TensorBoard, the software enables the visualization of model architectures, feature maps, heatmaps, and loss maps, intuitively displaying the differences between segmentation results and ground truth labels to assist in parameter optimization. With its modular design, the software combines both evaluation and visualization capabilities, providing efficient tool support for the development and deployment of segmentation models.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102445"},"PeriodicalIF":2.4,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-19DOI: 10.1016/j.softx.2025.102447
Thomas Hiller , Stephan Costabel
For several decades laboratory NMR relaxometry measurements have been used within the geoscientific community to characterize porous materials. Generally, the target parameters of such measurements are porosity and pore size. NUCLEUS is a set of Matlab tools, that allow forward and inverse modelling of such NMR relaxometry data ( and relaxation). The main front-ends to these tools are two graphical user interfaces, NUCLEUSmod and NUCLEUSinv for forward and inverse modelling, respectively. NUCLEUS enables a simple way to create synthetic NMR relaxometry data based on a pore size distribution which has a specific cross-sectional shape (geometry) and a pressure-dependent (de)-saturation state. Additionally, a variety of real laboratory NMR relaxometry measurements can be imported and processed based on different user-selectable inversion and regularization options.
{"title":"NUCLEUS – A Matlab-based graphical user interface for forward and inverse modelling of NMR relaxometry data","authors":"Thomas Hiller , Stephan Costabel","doi":"10.1016/j.softx.2025.102447","DOIUrl":"10.1016/j.softx.2025.102447","url":null,"abstract":"<div><div>For several decades laboratory NMR relaxometry measurements have been used within the geoscientific community to characterize porous materials. Generally, the target parameters of such measurements are porosity and pore size. NUCLEUS is a set of Matlab tools, that allow forward and inverse modelling of such NMR relaxometry data (<span><math><msub><mi>T</mi><mn>1</mn></msub></math></span> and <span><math><msub><mi>T</mi><mn>2</mn></msub></math></span> relaxation). The main front-ends to these tools are two graphical user interfaces, NUCLEUSmod and NUCLEUSinv for forward and inverse modelling, respectively. NUCLEUS enables a simple way to create synthetic NMR relaxometry data based on a pore size distribution which has a specific cross-sectional shape (geometry) and a pressure-dependent (de)-saturation state. Additionally, a variety of real laboratory NMR relaxometry measurements can be imported and processed based on different user-selectable inversion and regularization options.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102447"},"PeriodicalIF":2.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces EMDLAB (Electrical Machines Design Laboratory), a finite element package developed in MATLAB for the electromagnetic analysis of electrical machines. The package is designed for researchers and engineers who require an open-source and flexible environment for modeling and simulation. EMDLAB includes modules for geometry creation, meshing, material modeling, boundary condition definition, solving, and post-processing. The framework is modular and scriptable, enabling automation and customization. The accuracy and performance of EMDLAB are demonstrated through benchmark examples involving switched reluctance, induction, and permanent magnet machines. Simulation results are compared with those obtained using commercial software, showing good agreement in predicted total energy and co-energy, flux linkage, torque, and flux density distributions. The package provides a transparent and reproducible tool for academic research and education. Its open-source nature encourages collaboration and facilitates easy extension of the code, making it particularly valuable for method development and academic use.
{"title":"EMDLAB: An open-source finite element package for electromagnetic analysis of electrical machines","authors":"Ali Jamali-Fard , Mojtaba Mirsalim , Tohid Sharifi , Nasrin Majlesi","doi":"10.1016/j.softx.2025.102446","DOIUrl":"10.1016/j.softx.2025.102446","url":null,"abstract":"<div><div>This paper introduces EMDLAB (Electrical Machines Design Laboratory), a finite element package developed in MATLAB for the electromagnetic analysis of electrical machines. The package is designed for researchers and engineers who require an open-source and flexible environment for modeling and simulation. EMDLAB includes modules for geometry creation, meshing, material modeling, boundary condition definition, solving, and post-processing. The framework is modular and scriptable, enabling automation and customization. The accuracy and performance of EMDLAB are demonstrated through benchmark examples involving switched reluctance, induction, and permanent magnet machines. Simulation results are compared with those obtained using commercial software, showing good agreement in predicted total energy and co-energy, flux linkage, torque, and flux density distributions. The package provides a transparent and reproducible tool for academic research and education. Its open-source nature encourages collaboration and facilitates easy extension of the code, making it particularly valuable for method development and academic use.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102446"},"PeriodicalIF":2.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-19DOI: 10.1016/j.softx.2025.102448
Zahid Khan , Zsolt T. Kosztyán
In many practical applications, we consider indeterminate and ambiguous information in data. Traditional statistical methods address data formed by precise knowledge, whereas neutrosophic statistical (NS) approaches analyze data that result from incomplete, vague, imprecise and partial information. Although several extensions of classical methods to neutrosophic data exist, they have not been implemented in an open-source computational R tool. To address this gap, we introduce the R package neutrostat, the first comprehensive package for neutrosophic statistics. The R package neutrostat provides functions for descriptive summaries of neutrosophic measures as well as tools for data visualization and reproducibility of essential statistical characteristics. By making NS methods computationally accessible, neutrostat facilitates the analysis of imprecise data and supports the reproducibility of many essential statistical characteristics of neutrosophic probabilistic models.
{"title":"Neutrostat: An R package for neutrosophic statistical models","authors":"Zahid Khan , Zsolt T. Kosztyán","doi":"10.1016/j.softx.2025.102448","DOIUrl":"10.1016/j.softx.2025.102448","url":null,"abstract":"<div><div>In many practical applications, we consider indeterminate and ambiguous information in data. Traditional statistical methods address data formed by precise knowledge, whereas neutrosophic statistical (NS) approaches analyze data that result from incomplete, vague, imprecise and partial information. Although several extensions of classical methods to neutrosophic data exist, they have not been implemented in an open-source computational R tool. To address this gap, we introduce the R package <span>neutrostat</span>, the first comprehensive package for neutrosophic statistics. The R package <span>neutrostat</span> provides functions for descriptive summaries of neutrosophic measures as well as tools for data visualization and reproducibility of essential statistical characteristics. By making NS methods computationally accessible, <span>neutrostat</span> facilitates the analysis of imprecise data and supports the reproducibility of many essential statistical characteristics of neutrosophic probabilistic models.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102448"},"PeriodicalIF":2.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1016/j.softx.2025.102429
Pablo Raul Yanyachi , Asencio Huaita-Bedregal , Brayan Espinoza-Garcia
The design and validation of Attitude Determination and Control Systems (ADCS) for microsatellites require high-fidelity simulation environments, particularly for components like magnetorquers and magnetometers. HelmholtzSim is an open-source Python package developed for the simulation and optimization of Helmholtz cages, enabling accurate hardware-in-the-loop (HIL) validation of ADCS components. The software leverages the Biot–Savart law for parallelized magnetic field calculations, integrates genetic algorithms for coil design optimization, and supports mission simulation using Two-Line Element (TLE) data. Additionally, it provides integrated 2D and 3D visualization tools. This paper details the architecture, functionalities, and applications of HelmholtzSim, presenting it as a cost-effective and accessible alternative to commercial electromagnetic simulation software.
{"title":"HelmholtzSim: Software for the design and simulation of a Helmholtz testbed for hardware “in-the-loop” microsatellite simulations","authors":"Pablo Raul Yanyachi , Asencio Huaita-Bedregal , Brayan Espinoza-Garcia","doi":"10.1016/j.softx.2025.102429","DOIUrl":"10.1016/j.softx.2025.102429","url":null,"abstract":"<div><div>The design and validation of Attitude Determination and Control Systems (ADCS) for microsatellites require high-fidelity simulation environments, particularly for components like magnetorquers and magnetometers. HelmholtzSim is an open-source Python package developed for the simulation and optimization of Helmholtz cages, enabling accurate hardware-in-the-loop (HIL) validation of ADCS components. The software leverages the Biot–Savart law for parallelized magnetic field calculations, integrates genetic algorithms for coil design optimization, and supports mission simulation using Two-Line Element (TLE) data. Additionally, it provides integrated 2D and 3D visualization tools. This paper details the architecture, functionalities, and applications of HelmholtzSim, presenting it as a cost-effective and accessible alternative to commercial electromagnetic simulation software.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102429"},"PeriodicalIF":2.4,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Research software often lacks standardized frameworks to assess its quality, sustainability, and impact. This paper introduces the Research Software Evaluation and Maturity Monitor (RSEMM), a web-based dashboard that integrates software engineering metrics, FAIRness evaluation, AI/ML classification, code generation detection, and citation tracking. By providing automated maturity scoring and actionable recommendations, RSEMM supports developers, maintainers, and researchers in improving the quality of open research software. RSEMM has been applied to 1,519 open-source research projects, with all evaluation results and metadata openly available on Mendeley Data ( https://doi.org/10.17632/t2dygzcsyt.2). The dashboard is accessible online at https://ai4rse.nl/RSEMM/.
研究软件通常缺乏标准框架来评估其质量、可持续性和影响。本文介绍了研究软件评估和成熟度监视器（RSEMM），这是一个基于web的仪表板，集成了软件工程指标，公平性评估，AI/ML分类，代码生成检测和引用跟踪。通过提供自动化的成熟度评分和可操作的建议，RSEMM支持开发人员、维护人员和研究人员提高开放研究软件的质量。RSEMM已应用于1519个开源研究项目，所有评估结果和元数据都在Mendeley Data （https://doi.org/10.17632/t2dygzcsyt.2）上公开提供。该仪表板可在https://ai4rse.nl/RSEMM/上在线访问。
{"title":"RSEMM: A dashboard for evaluating research software maturity","authors":"Kwabena Ebo Bennin, Bedir Tekinerdogan, Siamak Farshidi","doi":"10.1016/j.softx.2025.102437","DOIUrl":"10.1016/j.softx.2025.102437","url":null,"abstract":"<div><div>Research software often lacks standardized frameworks to assess its quality, sustainability, and impact. This paper introduces the <em>Research Software Evaluation and Maturity Monitor (RSEMM)</em>, a web-based dashboard that integrates software engineering metrics, FAIRness evaluation, AI/ML classification, code generation detection, and citation tracking. By providing automated maturity scoring and actionable recommendations, RSEMM supports developers, maintainers, and researchers in improving the quality of open research software. RSEMM has been applied to 1,519 open-source research projects, with all evaluation results and metadata openly available on Mendeley Data ( <span><span>https://doi.org/10.17632/t2dygzcsyt.2</span><svg><path></path></svg></span>). The dashboard is accessible online at <span><span>https://ai4rse.nl/RSEMM/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102437"},"PeriodicalIF":2.4,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-10DOI: 10.1016/j.softx.2025.102442
Ye-Song Oh , Jong-Hyun Kim
This paper presents HandWind-Sim, a web-based open-source framework that tracks hand gestures via a standard webcam and MediaPipe, converts them into intuitive wind vectors, and applies these vectors to a position-based dynamics (PBD) cloth simulation. With a simple hand movement, the system realistically simulates fluttering cloth as if blown by actual wind in real time. HandWind-Sim enables complex physics-based interactions without the need for specialized hardware, offering high accessibility to researchers and developers in fields such as HCI, virtual prototyping, and interactive art.
{"title":"HandWind-Sim: Real-time-web-based cloth simulation with gesture-driven wind interaction","authors":"Ye-Song Oh , Jong-Hyun Kim","doi":"10.1016/j.softx.2025.102442","DOIUrl":"10.1016/j.softx.2025.102442","url":null,"abstract":"<div><div>This paper presents <em>HandWind-Sim</em>, a web-based open-source framework that tracks hand gestures via a standard webcam and MediaPipe, converts them into intuitive wind vectors, and applies these vectors to a position-based dynamics (PBD) cloth simulation. With a simple hand movement, the system realistically simulates fluttering cloth as if blown by actual wind in real time. <em>HandWind-Sim</em> enables complex physics-based interactions without the need for specialized hardware, offering high accessibility to researchers and developers in fields such as HCI, virtual prototyping, and interactive art.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102442"},"PeriodicalIF":2.4,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hydrodynamics And Radiation Diffusion (HARD) is an open-source application for high-performance simulations of compressible hydrodynamics with radiation-diffusion coupling. Built on the FleCSI (Bergen et al., 2021 [1]) (Flexible Computational Science Infrastructure) framework, HARD expresses its computational units as tasks whose execution can be orchestrated by multiple back-end runtimes, including Legion (Bauer et al., 2012 [2]), MPI (Forum, 1994 [3]), and HPX (Kaiser et al., 2020 [4]). Node-level parallelism is handled through Kokkos (Edwards et al., 2014 [5]), providing a single-source, portable code base that runs efficiently on laptops, small homogeneous clusters, and the largest heterogeneous supercomputers currently available. To ensure scientific reliability, HARD includes a regression test suite that automatically reproduces canonical verification problems such as the Sod and LeBlanc shock tubes, and the Sedov blast wave, comparing numerical solutions against known analytical results. The project is distributed under an OSI-approved license, hosted on GitHub, and accompanied by reproducible build scripts and continuous integration workflows. This combination of performance portability, verification infrastructure, and community-focused development makes HARD a sustainable platform for advancing radiation hydrodynamics research across multiple domains.
{"title":"HARD: A performance portable radiation hydrodynamics code based on FleCSI framework","authors":"Julien Loiseau , Hyun Lim , Andrés Yagüe López , Mammadbaghir Baghirzade , Shihab Shahriar Khan , Yoonsoo Kim , Sudarshan Neopane , Alexander Strack , Farhana Taiyebah , Ben Bergen","doi":"10.1016/j.softx.2025.102441","DOIUrl":"10.1016/j.softx.2025.102441","url":null,"abstract":"<div><div><strong>Hydrodynamics And Radiation Diffusion</strong> (<span>HARD</span>) is an open-source application for high-performance simulations of compressible hydrodynamics with radiation-diffusion coupling. Built on the <span>FleCSI</span> (Bergen et al., 2021 <span><span>[1]</span></span>) (Flexible Computational Science Infrastructure) framework, <span>HARD</span> expresses its computational units as tasks whose execution can be orchestrated by multiple back-end runtimes, including Legion (Bauer et al., 2012 <span><span>[2]</span></span>), MPI (Forum, 1994 <span><span>[3]</span></span>), and HPX (Kaiser et al., 2020 <span><span>[4]</span></span>). Node-level parallelism is handled through Kokkos (Edwards et al., 2014 <span><span>[5]</span></span>), providing a single-source, portable code base that runs efficiently on laptops, small homogeneous clusters, and the largest heterogeneous supercomputers currently available. To ensure scientific reliability, <span>HARD</span> includes a regression test suite that automatically reproduces canonical verification problems such as the Sod and LeBlanc shock tubes, and the Sedov blast wave, comparing numerical solutions against known analytical results. The project is distributed under an OSI-approved license, hosted on GitHub, and accompanied by reproducible build scripts and continuous integration workflows. This combination of performance portability, verification infrastructure, and community-focused development makes <span>HARD</span> a sustainable platform for advancing radiation hydrodynamics research across multiple domains.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102441"},"PeriodicalIF":2.4,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-10DOI: 10.1016/j.softx.2025.102443
Andoni Salcedo-Navarro, Guillem Montalban-Faet, Jaume Segura-Garcia, Miguel Garcia-Pineda
New technologies are transforming precision agriculture by enabling real-time monitoring, data-driven decision-making, and resource optimization. We present a web-based visor system that integrates 3D point cloud visualization of multi-vegetative indices with live sensor streams to form a digital replica of agricultural fields. The Potree-based viewer overlays geolocated point clouds onto an OpenStreetMap layer. A Node.js/Express REST API ingests heterogeneous sensor data (XML, JSON, CSV) into MongoDB, with Redis caching for low-latency retrieval. A Three.js first-person module enables immersive field walkthroughs, while a lazy-load mechanism lets users toggle vegetative indices on demand. Historical data are rendered via Chart.js. Deployed on Kubernetes, the system scales dynamically and remains resilient. Future work includes advanced data normalization, WebSockets-based push updates, and AR overlays. This open-source platform demonstrates how monitoring systems can drive sustainable, high-yield agriculture.
{"title":"A scalable web system for multi-index 3D point cloud visualization and real-time sensor monitoring in precision agriculture","authors":"Andoni Salcedo-Navarro, Guillem Montalban-Faet, Jaume Segura-Garcia, Miguel Garcia-Pineda","doi":"10.1016/j.softx.2025.102443","DOIUrl":"10.1016/j.softx.2025.102443","url":null,"abstract":"<div><div>New technologies are transforming precision agriculture by enabling real-time monitoring, data-driven decision-making, and resource optimization. We present a web-based visor system that integrates 3D point cloud visualization of multi-vegetative indices with live sensor streams to form a digital replica of agricultural fields. The Potree-based viewer overlays geolocated point clouds onto an OpenStreetMap layer. A Node.js/Express REST API ingests heterogeneous sensor data (XML, JSON, CSV) into MongoDB, with Redis caching for low-latency retrieval. A Three.js first-person module enables immersive field walkthroughs, while a lazy-load mechanism lets users toggle vegetative indices on demand. Historical data are rendered via Chart.js. Deployed on Kubernetes, the system scales dynamically and remains resilient. Future work includes advanced data normalization, WebSockets-based push updates, and AR overlays. This open-source platform demonstrates how monitoring systems can drive sustainable, high-yield agriculture.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102443"},"PeriodicalIF":2.4,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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