聚变科学与工程资源库
检索物理代码、实验数据、代理模型、工程几何、技术文档与验证资料，并核对来源、版本和使用条件。
数据集CSV / XLSX / TXT多装置偏滤器脱靶供气数据库
Gauss Fusion / UKAEA / Max Planck IPP / KIT汇集托卡马克与仿星器脱靶研究中的燃料供气、杂质注入、装置几何和等离子体参数，用于比较脱靶运行空间与标度关系。
ConStellaration仿星器优化数据集
Proxima FusionProxima Fusion发布的仿星器等离子体边界、设计指标与VMEC平衡数据集，支持优化研究和机器学习任务。
IAEA全球聚变装置信息系统
International Atomic Energy Agency由IAEA维护并经国际聚变研究理事会评审的全球聚变装置目录，包含装置状态、构型和0D技术设计参数。
FENDL聚变评价核数据库
IAEA Nuclear Data SectionIAEA协调建设的聚变应用评价核数据库，涵盖输运、活化、衰变、剂量和验证基准等数据子库。
Gkeyll TCV Miller几何参数扫描数据集
École Polytechnique Fédérale de Lausanne / Gkeyll Team包含256组面向TCV类托卡马克工况的回旋动理学湍流模拟，覆盖拉长比、三角度与加热功率参数扫描，并记录分布函数、矩、诊断量和几何数据。
EAST托卡马克等离子体探针数据
EAST Research TeamEAST氘等离子体放电中的可移动多针探针测量数据，用于分析局部等离子体参数、静电涨落、等离子体团传播及跨磁场反常输运。
SOLPS-NN代理模型训练数据
Forschungszentrum Jülich由SOLPS-ITER参数扫描生成的训练与测试数据，覆盖边缘等离子体温度、密度、粒子与热通量、辐射及偏滤器靶板积分量，用于粒子与功率排散代理模型研究。
HSX仿星器综述数据与配套代码
Helically Symmetric eXperiment TeamHSX仿星器综述论文的配套数据和Python代码，用于复现装置历史、设计、建设与运行相关图表。
仿星器阿尔法粒子约束贝叶斯优化数据
Stellarator Optimization Research Team包含仿星器MHD平衡、优化软件输出以及论文图表生成数据与脚本，用于研究数据驱动参数空间和降维方法下的阿尔法粒子约束优化。
SERUMSPEC-PCa: analysis code, extended tables and additional figures for a tumour-type specificity audit of serum microRNA classifiers in prostate cancer (GEO GSE112264)
Research Foundation for Development and Innovation in Science and Engineering (FRDISI), Casablanca, MoroccoThis deposit contains the complete analysis code, frozen result files, extended data tables and additional figures supporting a computational reanalysis of GEO series GSE112264 (2,565 mature microRNA probes; 1,591 sera: 809 prostate cancers, 282 controls, 500 patients with ten other cancer types) on the 3D-Gene GPL21263 platform. The study evaluates tumour-type specificity as an explicit endpoint for organ-specific circulating biomarker classifiers. It comprises leakage-controlled nested 5×5 cross-validation with per-fold decision thresholds, cross-application of the frozen case–control classifier to other-cancer sera, deliberate-leakage optimism quantification, conformal coverage checks, sample-quality control including a haemolysis index, independent renormalisation, moderated differential expression with false discovery rate control, covariate-adjusted analyses using clinical metadata recovered from GSE211692, microRNA target and pathway enrichment, protein–protein interaction networks, unsupervised structure analysis and feature-selection stability. The deposit also includes a structured comparison of nine published serum and plasma microRNA diagnostic studies in prostate cancer, giving the performance each reported and whether it was evaluated against other cancer types. All numerical values are traceable to the result files included here. Random seeds are fixed, the expression matrix is rebuilt from the public GEO source files with SHA-256 checksum verification, and the software manifest declares the three execution environments used. Associated article: Cancer Genomics & Proteomics (submitted).
MaizeHorizon: a forward-motion, range-stratified per-plant maize detection dataset
University of Engineering and Technology, Vietnam National University, Hanoi 10000, VietnamMaizeHorizon is a per-plant maize detection dataset recorded from a forward-facing RGB camera on a ground robot driving along crop rows toward the horizon. This viewpoint makes detection a far-field small-object problem: plants recede until they occupy a handful of pixels and merge into soil and canopy clutter. The record accompanies the paper Far-Field Per-Plant Maize Detection: Network Input Size Does Not Lower the Floor , and is built to support its central measurement: a dissociation between the pixels the sensor resolves on a plant and the network input size. Contents 5,979 RGB frames at 1920x1080 across 28 forward-motion clips, collected in two maize fields in Dan Phuong, Hanoi, Vietnam over three days. Hand-labeled test set : 3 held-out clips, 120 frames, 4,067 plant instances and 941 ignore regions, stratified by pixels-on-target into near (2,747), mid (1,238) and far (82) tiers. An explicit ignore class covering the unresolvable green band along the horizon, so that far-field evaluation neither penalizes correct detections nor rewards misses. Clip-disjoint splits for two training arms, preventing temporal leakage between near and far views of the same plant. Optically minted labels : 3,636 far-tier pseudo-label files and 10,355 near/far pairs of the same physical plant, obtained by detecting each plant when near and tracking it backward with optical flow. Metadata : a human-verified per-plant count ledger and the test-set box-area cumulative distribution. Files MaizeHorizon-images.tar (5.7 GB) holds the frames; MaizeHorizon-annotations.tar.gz (3.0 MB) holds labels, splits and metadata and is usable on its own. Both extract into a single data/ directory. Verify with SHA256SUMS.txt ; see README.md for the full layout, label format and usage. Code Analysis and training code: github.com/manhhv87/MaizeHorizon (AGPL-3.0, inherited from Ultralytics YOLO). The data here are CC BY 4.0. Scope The data come from a single locality. Absolute recall, AP and counting figures characterize this domain rather than guaranteeing cross-domain transfer, and no claim of season or cultivar invariance is made.
A deep learning-driven traveling wave method for GPS-free and noise-resilient fault location in compensated power networks
University Ferhat Abbas of SetifThis paper introduces a novel hybrid fault location technique for highvoltage transmission lines, integrating travelling wave (TW) principles, discrete wavelet transforms (DWT), and long short-term memory (LSTM) neural networks. The proposed method enhances fault detection speed, improves location accuracy, and demonstrates resilience against highimpedance faults. The LSTM network is specifically trained to detect the arrival of the initial wavefront through single-ended measurements, while DWT effectively extracts the high-frequency components of transient signals. A simulation of a 400 kV, 120 km transmission line, modeled on real parameters from the Algerian grid, was conducted using ATP-EMTP. The methodology was implemented in MATLAB and compared with several state-of-the-art approaches, including global positioning system (GPS) synchronized TW methods, under various noise conditions with signal-tonoise ratios (SNR) as low as 5 dB. Additionally, the influence of thyristorcontrolled series compensators (TCSC) on location accuracy was explored. The results confirm the applicability of the proposed technique in modern wide-area protection schemes, especially for remote relays and nextgeneration digital fault recorders (DFRs).
Synthesis of Entropy-Locked Bio-Organic Hybrid Macro-Cables via Magnetohydrodynamic Plasma Forging and Cyclic Abyss-Quenching OPEN Design Concept
ZenodoThis technical report is an open design concept that provides a comprehensive overview of the design framework, manufacturing workflow, and physical characteristics of the Entropy-Locked Hybrid Macro-Cable . Developed for the CERN Conceptual Design Review (CDR) phase, the paper outlines a revolutionary "space-to-trench" manufacturing loop that allows engineers to combine the extreme tensile strength of diamond into the highly flexible matrix of silicon rubber. The report details how a 10-mile orbital plasma forge, multi-spectrum acoustic alignment, and extreme high-pressure ocean tempering remove all atomic irregularities. This process brings the material to its absolute lowest energy state—the Entropy Point of Perfection . Additionally, it breaks down the cable's smart defense layers, featuring a drag-reducing dimpled ceramic skin and a self-healing liquid gallium "nervous system." The document serves as a foundational blueprint for planetary-scale engineering projects, including space elevators, deep-sea power grids, and indestructible hull designs. Metadata Keywords Core Tech & Materials #EntropyLockedCable #DiamondSiliconHybrid #BioOrganicPolymers #LiquidGalliumNervousSystem #DiamondNanothreads #FlexibleCeramicShield Manufacturing Processes #OrbitalPlasmaForge #AbyssTempering #AcousticLevitationAlignment #ZeroGravityManufacturing #HighPressureQuenching #AiryBeamModulation Advanced Physics Concepts #EntropyPointOfPerfection #MagneticShearVortex #IonicZipperCovalentBonding #InductivelyCoupledPlasma #MacromolecularElasticity #HydrostaticCompression Applications & Infrastructure #SpaceElevatorTether #DeepSeaRecoveryConduit #MacroScaleInfrastructure #SelfHealingArmor #CERNMaterialScience #PlanetaryEngineering
Compiled Data for "Parsimonious Subset Selection for Generalized Linear Models with Biomedical Applications"
ZenodoThis deposit contains the compiled, model-ready datasets used in the two real-data applications presented in the Biometrics manuscript “Parsimonious Subset Selection for Generalized Linear Models with Biomedical Applications” (manuscript BIOM2026208M). The rice dataset contains the processed genome-wide association study design matrix for 1,155 rice accessions, comprising 158,210 SNP predictors, together with the binary grain-length response used in the logistic regression analysis. The underlying rice data are publicly available through NCBI GEO (GSE71553), BioProject (PRJNA291537), and dbSNP (batch ID 1062024). The Khan small round blue cell tumor dataset contains the established training and independent test partitions used in the multinomial classification analysis. The training data contain 63 observations and the test data contain 20 observations, with 2,308 gene-expression predictors and one response variable. These publicly available partitions are also distributed through the ISLR2 R package. README files describe the contents, processing, provenance, checksums, and instructions for reading the deposited files. Analysis code and additional reproduction instructions accompany the associated manuscript.
Ecological niche models for British Columbia's rare species (Red-, Blue-, and SARA-listed) -- Climate normal and future (2050s & 2080s) distributions
ZenodoDataset overview This dataset represents Maxent ecological niche models for Red-listed, Blue-listed and SARA Schedule 1 species in British Columbia. Models were calibrated for species' ranges across Turtle Island under climate normal conditions (1991-2020) and projected into future periods (2050s and 2080s) under 3 separate general circulation models. Spatial layers are available as GeoTIFF (.tif) and PNG (.png) file formats. Both probabilistic and binary (presence/absence of habitat suitability) for each species are included for current and future climate periods. This work is part of a larger collaborative project on climate-informed conservation planning in British Columbia. Methods overview Species distribution modeling We developed an updated suite of ecological niche models for rare species under current and future climate scenarios. Ecological niche models attempt to demonstrate the relationships between species and the environments where they occur. We used Maxent ( Phillips et al., 2006 ) to model habitat suitability across Turtle Island for rare species (Red- and Blue-listed species and species-at-risk listed under Schedule 1 of the federal Species at Risk Act in British Columbia. Gathering species occurrence data from the Global Biodiversity Information Facility ( GBIF.org, 2023 ), there were 833 species in total with sufficient data points to estimate reliable models. Using climate data from Climate NA ( Wang et al., 2016 ; Mahony et al., 2022 ) and the United States Geological Survey ( USGS, 2026 ), we selected the following environmental predictor variables: mean annual temperature, mean annual precipitation, continentality (mean temp. of warmest month minus mean temp. of coldest month), annual heat-to-moisture index, extreme max temperature over last 30 years, and surficial geology (rock type). Models were used to predict current (1991-2020) and future species distributions at a spatial resolution of 1 km^2. For future projections we used an emissions trajectory adopted by the most recent IPCC report named the Shared Socioeconomic Pathway (SSP) 2-4.5 (intermediate climate-change scenario; IPCC, 2023 ). Projections were made for the 2041-2070 (2050s) and 2071-2100 (2080s) time periods for the following General Circulation models (GCMs): CNRM-ESM2-1, EC-Earth3, and MPI-ESM1-2-HR. Training and testing data were spilt 70/30. Probability of suitable habitat was then converted (i.e., thresholded) to binary predictions of presence/absence of habitat suitability where model sensitivity and specificity were balanced (i.e., equal chance of errors of omission and commission). This work is part of a larger collaborative effort on climate-informed conservation planning in British Columbia. File nomenclature: List_of_species_revised.xlsx: -Species common and scientific names along with conservation rankings. data_points_for_SDMs.7z: -Data used to run Maxent. lookup_tables_all_taxa_BY_FAMILY_red_blue_SARA_BC.xlsx: -Lookup tables sorted by families. %taxon_name%.7z: -Contains .tif files for tresholded SDMs for each taxonomic group under climate normal (1991-2020, 2050s and 2080s. Separate GCMS for future periods (as described above). %taxon_name%_png.7z: -Contains .png files of SDMs proabilities for each taxonomic group under climate normal (1991-2020, 2050s and 2080s. Separate GCMS for future periods (as described above). Metadata Spatial reference: XY Coordinate System: NA_Lambert_Azimuthal_Equal_Area Linear Unit: Meter (1.000000) Angular Unit: Degree (0.0174532925199433) false_easting: 0 false_northing: 0 central_meridian: -100 latitude_of_origin: 45 Datum: D_WGS_1984 Extent: Top 4,276,000.0 m Bottom -3,341,000.0 m Left -4,352,000.0 m Right 3,336,000.0 m Disclaimer The University of Alberta (UofA) is furnishing this deliverable "as is". UofA does not provide any warranty of the contents of the deliverable whatsoever, whether express, implied, or statutory, including, but not limited to, any warranty of merchantability or fitness for a particular purpose or any warranty that the contents of the deliverable will be error-free. Funding We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship and the Ministry of Environment and Climate Change Strategy), the BC Conservation Fund, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation.
Virtual Cell (VCell)
University of Connecticut School of MedicineThe Virtual Cell (VCell) is a comprehensive platform for modeling and simulation in cell biology, developed since 1997 at the Center for Cell Analysis and Modeling (CCAM) at UConn Health.
Teaching analysis of sub-synchronous resonance of thermal power plants using virtual laboratory
Institut Teknologi Nasional YogyakartaThe development of a MATLAB/Simulink-based virtual laboratory for studying sub-synchronous resonance (SSR) offers a robust educational platform for analyzing complex power system interactions. Based on the IEEE first benchmark model, this virtual environment provides a safe, efficient, and comprehensive tool for engineering students to study the dangerous interactions between series-compensated lines and turbo-generator shaft systems. The simulation features a 920 MVA, 60 Hz turbo-generator connected to an infinite bus through a series-compensated line. Students can analyze torsional interaction, which result from energy exchange between the electrical network and the mechanical shaft. Higher degrees of series compensation increase the risk of SSR, as the electrical resonant frequency matches the complement of one of the mechanical shaft torsional modes.
Control Capacity Can Convert Failure into Effort: Endpoint-Dependent Attenuation in a Simulated Governance Control Loop
ZenodoIn a deliberately minimal governance control loop, we test whether a binary terminal endpoint can conceal the continuing burden of one explicitly compound six-parameter cognition perturbation. Holding the controller, dynamics and random seeds fixed, and varying only intervention budget across five levels, terminal failure falls monotonically from 1.000 at budget 0.20 to 0.023 at budget 1.00, pooled over 600 runs per cell across three deterministic seed blocks. A matched analysis converts 193-196 of 200 matched pairs from failure to non-failure with zero reversals in every block; each block contains 199 low-budget failures. At operational threshold 0.800 and the highest budget, 35.7% of runs occupy a recovered rather than persistently-stable class; this percentage is threshold-specific and ranges from 12.8% to 79.5% across the frozen operational sweep. The 214 recovered runs at threshold 0.800 spend a median five total steps below threshold and reach a median maximum excursion depth of 0.028. The perturbed condition also produces greater controller demand and lower minimum stability than intact in 600 of 600 matched pairs. A prospectively hash-frozen 64-subset component factorial (192,000 runs) finds no single component above half of the declared absolute-share allocation; three components account for 96.6% under that normalization. Operational and measurement-only threshold analyses preserve the relevant threshold-dependent directions throughout 0.750-0.850. A paired comparison using the frozen trajectories shows that continuous minimum-stability separation also attenuates but remains large at budget 1.00 (Cohen dz = 1.65) while the failure-risk difference is 0.023. We call this one-model pattern endpoint-dependent attenuation. A trust-pathway ablation fails algebraically under every budget and remains an implementation control, not evidence. The result is a reproducible existence demonstration, not a general perturbation class, prevalence estimate, empirical governance finding, or universal reporting law. This record contains the MB-R9 preprint, editable manuscript sources, bounded core reproducibility materials, run-level robustness data, a public revision note, license mapping, file guide, and SHA-256 manifest. An earlier CoMSES release remains private, unpublished, under review, and without a DOI; it is not this record's version of record and is not relied upon for public availability.
2024-04-08 Total Solar Eclipse ESID#878
ARISA Lab, L.L.C.These are audio recordings taken by an Eclipse Soundscapes (ES) Data Collector during the week of the April 08, 2024 Total Solar Eclipse. It was decided to include only raw, unprocessed audio data files in each site-specific Zenodo record. This decision was so that any researcher can independently verify, reproduce, and extend the analysis performed. As a result, some sites have WAV files with 0 bytes of data or timestamps outside the range of probable recording times. Procedures used by the Eclipse Soundscapes team to process audio data for its purposes are outlined in the Data Management reports located in the Eclipse Soundscapes Zenodo community. Data with 0 bytes of data were included for completeness. When possible, all site-specific files, including the audio files, are included in a single zip file for ease of download. If a single zip file upload was not possible due to upload or bandwidth limitations, audio files are included in multiple zip files based on the day of the observation. Data Site location information: Latitude: 36.8949341 Longitude: -90.3967577 Local Eclipse Type: Total Solar Eclipse Eclipse Percent (%): 100 WAV files Time & Date Settings: Set with Automated AudioMoth Time Chime (More information on TimeStamp Setting below) Data Collector Start Time Notes: N/A Included Data: Audio files in WAV format with the date and time in UTC within the file name: YYYYMMDD_HHMMSS meaning YearMonthDay_HourMinuteSecond For example, 20240411_141600.WAV means that this audio file starts on April 11, 2024 at 14:16:00 Coordinated Universal Time (UTC) CONFIG Text file: Includes AudioMoth device setting information, such as sample rate in Hertz (Hz), gain, firmware, etc. README.md: Markdown formatted file with information about the recording and recording site. file_list.csv: A machine and human file that gives the following information on each file in the record: File Name, File Type, Description, File Size in kilobytes, Name of Associated Data Dictionary with the file, calculated SHA-512 Hash of the file as a unique identifier to insure data integrity during transfer and compression. total_eclipse_data.csv: A machine and human readable file that gives the following information about the site where the audio data recording was taken: ESID#, Latitude, Longitude, Eclipse_type, CoveragePercent, Eclipse Start UTC (1st contact), Totality Start UTC (2nd contact), Totality End UTC (3rd Contact), Eclipse End UTC (4th Contact), Max Eclipse Time UTC License.txt: A human readable file that explains the terms and conditions under which the data can be used. AudioMoth_Operation_Manual.pdf: A human readable document that explains the use of an AudioMoth device. The document is current up to the time of the AudioMoth's use in the Eclipse Soundscapes project. file_list_data_dict.csv: A machine and human data dictionary file that gives information on the variables contained within the file_list.csv file. CONFIG_data_dict.csv: A machine and human data dictionary file that gives information on the variables contained within the CONFIG.TXT file. eclipse_data_data_dict.csv: A machine and human data dictionary file that gives information on the variables contained within the total_eclipse_data.csv file. WAV_data_dict.csv: A machine and human data dictionary file that gives information on the variables contained within the *.WAV files. ES_Data_Management_Pre-Eclipse_Data_Infrastructure_Stage_0.pdf: PDF document that describes Stage 0 (Pre-Eclipse Infrastructure and Data Stewardship Planning) of the Eclipse Soundscapes (ES) data lifecycle. ES_Data_Management_Receipt_Sorting_and_Metadata_Organization_Stage_1.pdf: PDF document that describes Stage 1 (Receipt, Sorting, and Metadata Organization) of the Eclipse Soundscapes (ES) data lifecycle. ES_Data_Management_Data_Processing_Stage_2.pdf: PDF document that describes Stage 2 (Data Processing) of the Eclipse Soundscapes (ES) data Volunteer Scientists. 2023 and 2024 solar eclipse soundscapes audio datalifecycle. ES_Data_Management_Data_Sharing_Stage_3.pdf: PDF document that describes Stage 3 (Public Data Sharing) of the Eclipse Soundscapes (ES) data lifecycle. Eclipse Information for this location: Eclipse Date: April 08, 2024 Eclipse Start Time (UTC) (1st Contact): 17:39:50 Totality Start Time (UTC) (2nd Contact): [N/A if partial eclipse] 18:56:22 Eclipse Maximum Time [when the most possible amount of the Sun in blocked] (UTC): 18:58:28 Totality End Time (UTC) (3rd Contact): [N/A if partial eclipse] 19:00:33 Eclipse End Time (UTC) (4th Contact): [N/A if partial eclipse] 20:15:54 Audio Data Collection During Eclipse Week ES Data Collectors used AudioMoth devices to record audio data, known as soundscapes, over a 5-day period during the eclipse week: 2 days before the eclipse, the day of the eclipse, and 2 days after. The complete raw audio data collected by the Data Collector at the location mentioned above is provided here. This data may or may not cover the entire requested timeframe due to factors such as availability, technical issues, or other unforeseen circumstances. ES ID# Information: Each AudioMoth recording device was assigned a unique Eclipse Soundscapes Identification Number (ES ID#). This identifier connects the audio data, submitted via a MicroSD card, with the latitude and longitude information provided by the data collector through an online form. The ES team used the ES ID# to link the audio data with its corresponding location information and then uploaded this raw audio data and location details to Zenodo. This process ensures the anonymity of the ES Data Collectors while allowing them to easily search for and access their audio data on Zenodo. TimeStamp Information: The ES team and the Data Collectors took care to set the date and time on the AudioMoth recording devices using an AudioMoth time chime before deployment, ensuring that the recordings would have an automatic timestamp. However, participants also manually noted the date and start time as a backup in case the time chime setup failed. The notes above indicate whether the WAV audio files for this site were timestamped manually or with the automated AudioMoth time chime. Common Timestamp Error: Some AudioMoth devices experienced a malfunction where the timestamp on audio files reverted to a date in 1970 or before, even after initially recording correctly. Despite this issue, the affected data was still included in this ES site's collected raw audio dataset. Latitude & Longitude Information: The latitude and longitude for each site was taken manually by data collectors and submitted to the ES team, either via a web form or on paper. It is shared in Decimal Degrees format. General Project Information: The Eclipse Soundscapes Project is a NASA Volunteer Science project funded by NASA Science Activation that is studying how eclipses affect life on Earth during the October 14, 2023 annular solar eclipse and the April 8, 2024 total solar eclipse. Eclipse Soundscapes revisits an eclipse study from almost 100 years ago that showed that animals and insects are affected by solar eclipses! Like this study from 100 years ago, ES asked for the public's help. ES uses modern technology to continue to study how solar eclipses affect life on Earth! Eclipse Soundscapes is an enterprise of ARISA Lab, LLC and is supported by NASA award No. 80NSSC21M0008. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Aeronautics and Space Administration. Eclipse map/figure/table/predictions courtesy of Fred Espenak, NASA/Goddard Space Flight Center, from eclipse.gsfc.nasa.gov . Eclipse Data Version Definitions {1st digit = year, 2nd digit = Eclipse type (1=Total Solar Eclipse, 9=Annular Solar Eclipse, 0=Partial Solar Eclipse), 3rd digit is unused and in place for future use} 2023.9.0 = Week of October 14, 2023 Annular Eclipse Audio Data, Path of Annularity (Annular Eclipse) 2023.0.0 = Week of October 14, 2023 Annular Eclipse Audio Data, OFF the Path of Annularity (Partial Eclipse) 2024.1.0 = Week of April 8, 2024 Total Solar Eclipse Audio Data, Path of Totality (Total Solar Eclipse) 2024.0.0 = Week of April 8, 2024 Total Solar Eclipse Audio Data , OFF the Path of Totality (Partial Solar Eclipse) An A at the end of the version number indicates that the record has multiple zip files. Each zip file is an archive of the WAV files recorded on a particular day. The formatting of the name of these zip archives is ESID_NNN_YYYY_MM_DD.zip. *Please note that this dataset's version number is listed below. Eclipse Soundscapes Data Collector Role Training and Implementation Resources Manual (2023-2024) (Archival Copy) This site-level record includes the Eclipse Soundscapes Data Collector Role Training and Implementation Resources Manual (2023-2024) . The manual documents the participant training, device setup procedures, metadata submission requirements, ES ID system, timestamp protocols, data return workflow, and public archiving processes used during the October 14, 2023 annular solar eclipse and the April 8, 2024 total solar eclipse. The manual is preserved for transparency and reproducibility and reflects the procedures under which this dataset was collected and processed. (DOI 10.5281/zenodo.18623442) Data Receipt, Processing, and Analysis Methods All programs supporting Stages 1–3 are openly available in the: Eclipse Soundscapes GitHub repository: https://github.com/ARISA-Lab-LLC/ESCSP Data Management Lifecycle The following section documents the relationship of this record to the full Eclipse Soundscapes (ES) data lifecycle, a multi-stage workflow designed to support large-scale participatory science, long-term data stewardship, open science, and scientific reuse. Each stage addressed a different operational need, beginning before eclipse deployment and continuing through validation, public archiving, and scientific analysis. Together, these stages transformed distributed volunteer-submitted audio recordings into structured, documented, publicly accessible NASA-funded research assets. Stage 0: Pre-Eclipse Infrastructure and Deployment Preparation Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Pre-Eclipse Infrastructure and Deployment Preparation (Stage 0). Zenodo. https://doi.org/10.5281/zenodo.20413370 Stage 0 focused on building the operational foundation required to support geographically distributed eclipse data collection at national scale. This stage included AudioMoth device preparation, accessibility modifications, ES ID # assignment systems, metadata collection workflows, participant training materials, deployment logistics, and planning for downstream data stewardship and archival workflows. The 2023 annular eclipse served as both a scientific investigation and a large-scale operational beta test that informed improvements for the 2024 total solar eclipse campaign. Related Citations and Resources: Severino, M., & Kline, T. (2025, November 24). Eclipse Soundscapes Apprentice Role Curriculum: Solar Eclipses and Multi-Sensory Observing (Informal Education). Zenodo. https://doi.org/10.5281/zenodo.17703003 Severino, M., & Bauer, D. J. (2026). Eclipse Soundscapes Observer Role Training and Resources Manual (2023–2024). Zenodo. https://doi.org/10.5281/zenodo.18633602< /li> Severino, M., Winter, H., & Bauer, D. J. (2026). Eclipse Soundscapes Data Collector Role Training and Implementation Manual (2023–2024). Zenodo. https://doi.org/10.5281/zenodo.18623443 Stage 1: Receipt, Sorting, and Metadata Organization Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Receipt, Sorting, and Metadata Organization (Stage 1). Zenodo. https://doi.org/10.5281/zenodo.19471425 Stage 1 transformed returned participant materials into organized, traceable site-level records. This included receiving mailed microSD cards, consolidating participant-submitted metadata, reconciling handwritten and online records, organizing physical audio media by ES ID #, and deriving eclipse timing and coverage information using NASA eclipse prediction datasets. The outputs of Stage 1 established the structured metadata relationships required for downstream validation, processing, archiving, and analysis workflows. Related Citations and Resources: Winter, H., & Goncalves, J. (2026). EPTT (Eclipse Phase Timing Tool) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/ESCSP-Eclipse-Phase-Timing-Tool /li> Espenak, F. (n.d.). Eclipse predictions by Fred Espenak, NASA's GSFC Eclipse Web Site. NASA Goddard Space Flight Center. http://eclipse.gsfc.nasa.gov/eclipse.html Stage 2: Data Processing and Validation Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Data Processing (Stage 2). Zenodo. https://doi.org/10.5281/zenodo.18683402 Stage 2 focused on centralized audio ingestion, validation, timestamp verification, metadata reconciliation, and preparation of datasets for analysis and public sharing. During this stage, returned audio recordings were processed using custom open-source tools developed by the ES team, including ES WAVES and ES AMES. The project implemented scalable infrastructure capable of processing large volumes of participant-submitted microSD cards while preserving all raw audio data without modification. Stage 2 established the validated dataset structure required for long-term preservation and scientific analysis. Related Citations and Resources: Winter, H., & Goncalves, J. (2026). ES WAVES (Eclipse Soundscapes WAV Audio Validation & Extraction Suite) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/ESCSP-ES-WAV-Audio-Validation-Extraction-Suite Winter, H., & Goncalves, J. (2026). ES AMES (Eclipse Soundscapes AudioMoth Metadata Extractor Suite) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/ESCSP-ES-AMES-AudioMoth-Metadata-Extractor-Suite Stage 3: Public Data Sharing and Open Archiving Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Public Audio Data Sharing (Stage 3). Zenodo. https://doi.org/10.5281/zenodo.18683437 Stage 3 transformed validated site-level datasets into publicly archived, DOI-assigned research records published through the Eclipse Soundscapes Zenodo Community. This stage included dataset packaging, metadata standardization, README generation, integrity verification, DOI assignment, and automated repository upload workflows using the Automated Zenodo Upload Software (AZUS). These workflows established the project's long-term open-science infrastructure and ensured that datasets remained findable, accessible, interoperable, reusable, and citable for future scientific and educational use. Related Citations and Resources: Winter, H., & Goncalves, J. (2026). AZUS (Automated Zenodo Upload Software) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/AZUS-Automated-Zenodo-Upload-Software Stage 4: Scientific Analysis and Research Use Stage 4 involves the scientific analysis and interpretation of validated eclipse soundscape datasets. Analysis workflows utilized datasets verified during earlier stages to investigate eclipse-related environmental and animal vocalization changes across hundreds of recording sites. This stage also includes broader scientific interpretation, publication development, and continued reuse of Eclipse Soundscapes datasets and infrastructure for future research, education, and open-science applications. Related Citations and Resources: Pease, B., Gilbert, N., & Severino, M. (2026). Eclipse Soundscapes Preliminary Findings – How Eclipses Affect Nature as determined by Sound (Recorded Webinar). Zenodo. https://doi.org/10.5281/zenodo.18613979 Gilbert, N. A., Pease, B. S., Severino, M., & Winter, H. III. (2026). Photic niche explains avian behavioral responses to solar eclipses. Ecology and Evolution, 16(2), e73090. https://doi.org/10.1002/ece3.73090 Analysis code repository: https://github.com/BrentPease1/eclipse-traits Companion Zenodo record archiving structured analysis scripts and derived outputs: https://doi.org/10.5281/zenodo.15790879[r] Public Archiving, Privacy, and Data Transparency The Eclipse Soundscapes Data Collector Role Training and Implementation Manual (2023-2024) includes a detailed explanation of how Eclipse Soundscapes audio data are publicly archived on Zenodo, how participant privacy is protected through the ES ID system, and how transparency and traceability are maintained. It also outlines the criteria for determining which recordings are included in the public archive, as well as the distinction between publicly shared archival data and datasets used for ES-led scientific analyses. Participants and data users can consult this section for full documentation of the project's open science and privacy practices. Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Collector Role Training and Implementation Manual (2023–2024). Zenodo. https://doi.org/10.5281/zenodo.18623443 Citations Individual Site Citation: APA Citation (7th edition) Winter, H., Severino, M., & Volunteer Scientist. (2026). 2024 solar eclipse soundscapes audio data [Audio dataset, ES ID# 878]. Zenodo.{Insert DOI} Collected by volunteer scientists as part of the Eclipse Soundscapes Project. This project is supported by NASA award No. 80NSSC21M0008. Eclipse Community Citation Winter, H., Severino, M., & Volunteer Scientists. 2023 and 2024 solar eclipse soundscapes audio data [Collection of audio datasets]. Eclipse Soundscapes Community, Zenodo. https://zenodo.org/communities/eclipsesoundscapes/ Collected by volunteer scientists as part of the Eclipse Soundscapes Project This project is supported by NASA award No. 80NSSC21M0008.
Data supporting: Nonstationary Behavior of Resistivity–Moisture Relationships in Time-Lapse ERT Monitoring of Sandy Soils
ZenodoThis dataset contains processed data supporting the figures and results presented in the manuscript: "Nonstationary Behavior of Resistivity–Moisture Relationships in Time-Lapse ERT Monitoring of Sandy Soils". The dataset includes: - Processed electrical resistivity tomography (ERT) data (e.g., Δlogρ) used to generate spatial profiles and temporal analyses - Soil moisture time series from in situ sensors - Rainfall data associated with selected recharge events - Event-based summaries and derived quantities used in the analysis (e.g., inferred water-content changes) All data are provided in a format suitable for reproducing the figures and key results of the study. Raw ERT data and inversion outputs are not included in this dataset but are available from the corresponding author upon reasonable request.
Remaining useful life estimation for predictive battery maintenance with improved recurrent singular spectrum analysis algorithm
Mahanakorn University of TechnologyAs the global electric vehicle (EV) battery market is projected to reach a valuation of over USD 100 billion by the end of 2026, the demand for sophisticated battery management systems (BMS) has become more critical than ever. Accurate remaining useful life (RUL) prediction is essential for ensuring vehicle safety, optimizing maintenance, and evaluating retired batteries for second-life applications. However, existing prognostic methods often struggle to balance computational efficiency with predictive accuracy, especially during the early stages of battery usage. This research proposes combined weighted similarity-based and recurrent singular spectrum analysis (CWS-RSSA), a hybrid forecasting framework that integrates RSSA with a similarity-based approach through a weighted logistic switching mechanism. The algorithm is designed to be computationally lightweight, making it suitable for resource-constrained BMS hardware. The proposed method was validated using NASA and a large-scale dataset from MIT-Stanford consisting of 124 lithium-ion cells. Experimental results demonstrate that CWS-RSSA is capable of early-stage prediction with a relative error of 19.8%, whereas existing methods are unable to provide predictions. In later stages, once sufficient data becomes available, the algorithm achieves near-perfect accuracy with a negligible relative error on the NASA dataset and an average relative error of only 0.14% across the 124 MIT-Stanford batteries. Furthermore, the algorithm demonstrates robust performance in handling capacity regeneration phenomena. These findings suggest that CWS-RSSA represents a scalable and practical advancement for battery health management, supporting the transition toward a sustainable circular energy economy and providing a reliable foundation for second-life battery certification.
A scalable hybrid deep learning framework for mining actionable knowledge from large-scale and uncertain Twitter data
REVA UniversityExisting deep learning approaches often exhibit limitations in contextual comprehension, high computational overhead, and restricted generalization when processing large-scale, tweet-level, and semantically ambiguous text. Moreover, deploying such computationally intensive models in real-time internet of things (IoT)-enabled monitoring systems and embedded platforms introduces additional constraints related to latency, memory footprint, and energy efficiency. To address these challenges, this work proposes a scalable hybrid deep learning framework (SHDLF). The proposed framework effectively captures semantic, syntactic, and temporal dependencies in both short and long social media texts through a novel integration of transformer-based representations and attention-driven feature fusion mechanisms. The architecture is designed with a modular and parallelizable structure to facilitate hardware-aware optimization and potential deployment on embedded and reconfigurable computing platforms, enabling efficient edge-level processing of high-velocity Twitter streams. Extensive experimental evaluations conducted on a large benchmark Twitter dataset demonstrate that SHDLF consistently outperforms state-of-the-art models, including convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and baseline bidirectional encoder representations from transformers (BERT)-based architectures, in terms of accuracy, F1-score, and robustness under noisy conditions. The results confirm that SHDLF offers a robust, scalable, and computationally efficient solution for extracting reliable sentiment insights from noisy and dynamically evolving social media data.
Design and characterization of a flexible ultra-low-power analog front-end circuit using organic thin-film transistors for wearable electrocardiogram monitoring
Al Mansour University CollegeThe shift from reactive to proactive healthcare has accelerated the development of flexible, comfortable, and energy-efficient wearable health monitoring systems. This study addresses a critical technological gap: the inherent trade-off between the mechanical flexibility of organic thin-film transistors (OTFTs) and their limited electrical performance (low charge carrier mobility) compared to rigid silicon-based technologies. To overcome this, we outline the system-level design of a fully configurable analog front-end (AFE) utilizing OTFTs for precise electrocardiogram (ECG) measurement. A theoretical transfer-function-based co-design approach is used to balance gain, noise rejection, and power efficiency via high-fidelity MATLAB/Simulink continuous-time simulations. Simulation results demonstrate that the proposed AFE achieves a 40 dB gain, a precise diagnostic bandwidth of 0.5–150 Hz, a common-mode rejection ratio (CMRR) of 65 dB, and an ultra-low power consumption of 33 µW. These metrics strictly align with standard clinical ECG requirements, outperforming current state-of-the-art all-organic architectures primarily in power efficiency. Consequently, the input signal-to-noise ratio (SNR) is significantly enhanced by 24.53 dB (from 8.01 to 32.54 dB). The novelty of this work lies in achieving silicon-like clinical functionality within an allorganic structure at minimal power. This establishes a new benchmark for flexible electronics and paves the way for invisible, skin-like devices for continuous cardiovascular monitoring.
Analysis of VHF Pulse Frequency and Relative Power Across Pre-, During-, and Post-Branching Phases of Lightning Channels
College of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou, ChinaThis dataset contains the data associated with Figures 3–6 of the manuscript entitled “Analysis of VHF Pulse Frequency and Relative Power Across Pre-, During-, and Post-Branching Phases of Lightning Channels.” The dataset provides the data used to analyze the VHF pulse frequency and relative power characteristics of lightning channels during the pre-branching, branching, and post-branching phases. The data are organized into separate folders corresponding to Figures 3, 4, 5, and 6 of the manuscript. A README file is included in each folder, providing detailed descriptions of the data files, variables, formats, and their relationship to the corresponding figure.