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101 results for “digital twin”
Network Digital Twin-Generated Dataset for Machine Learning-based Detection of Benign and Malicious Heavy Hitter Flows
<h3>Overview</h3> <p>This record provides a dataset created as part of the study presented in the following publication and is made <strong>publicly available for research purposes</strong>. The associated article provides a comprehensive description of the dataset, its structure, and the methodology used in its creation. If you use this dataset, please <strong>cite the following article </strong>published in the journal <strong>IEEE Communications Magazine</strong>:</p> <blockquote> <p><strong>A. Karamchandani, J. Nunez, L. de-la-Cal, Y. Moreno, A. Mozo, and A. Pastor, “On the Applicability of Network Digital Twins in Generating Synthetic Data for Heavy Hitter Discrimination,” IEEE Communications Magazine, pp. 2–8, 2025, DOI: 10.1109/MCOM.003.2400648.</strong></p> </blockquote> <p>More specifically, the record contains several synthetic datasets generated to differentiate between benign and malicious heavy hitter flows within a realistic virtualized network environment. Heavy Hitter flows, which include high-volume data transfers, can significantly impact network performance, leading to congestion and degraded quality of service. Distinguishing legitimate heavy hitter activity from malicious Distributed Denial-of-Service traffic is critical for network management and security, yet existing datasets lack the granularity needed for training machine learning models to effectively make this distinction.</p> <p>To address this, a Network Digital Twin (NDT) approach was utilized to emulate realistic network conditions and traffic patterns, enabling automated generation of labeled data for both benign and malicious HH flows alongside regular traffic.</p> <h3>Feature Set:</h3> <p>The feature set includes the following flow statistics commonly used in the literature on network traffic classification:</p> <ul> <li>The protocol used for the connection, identifying whether it is TCP, UDP, ICMP, or OSPF.</li> <li>The time (relative to the connection start) of the most recent packet sent from source to destination at the time of each snapshot.</li> <li>The time (relative to the connection start) of the most recent packet sent from destination to source at the time of each snapshot.</li> <li>The cumulative count of data packets sent from source to destination at the time of each snapshot.</li> <li>The cumulative count of data packets sent from destination to source at the time of each snapshot.</li> <li>The cumulative bytes sent from source to destination at the time of each snapshot.</li> <li>The cumulative bytes sent from destination to source at the time of each snapshot.</li> <li>The time difference between the first packet sent from source to destination and the first packet sent from destination to source.</li> </ul> <h3>Dataset Variations:</h3> <p>To accommodate diverse research needs and scenarios, the dataset is provided in the following variations:</p> <ol> <li> <p><strong><code>All at Once</code></strong>:</p> <ol> <li>Contains a synthetic dataset where all traffic types, including benign, normal, and malicious DDoS heavy hitter (HH) flows, are combined into a single dataset.</li> <li>This version represents a holistic view of the traffic environment, simulating real-world scenarios where all traffic occurs simultaneously.</li> </ol> </li> <li> <p><strong><code>Balanced Traffic Generation</code></strong>:</p> <ol> <li>Represents a balanced traffic dataset with an equal proportion of benign, normal, and malicious DDoS traffic.</li> <li>Designed for scenarios where a balanced dataset is needed for fair training and evaluation of machine learning models.</li> </ol> </li> <li> <p><strong><code>DDoS at Intervals</code></strong>:</p> <ol> <li>Contains traffic data where malicious DDoS HH traffic occurs at specific time intervals, mimicking real-world attack patterns.</li> <li>Useful for studying the impact and detection of intermittent malicious activities.</li> </ol> </li> <li> <p><strong><code>Only Benign HH Traffic</code></strong>:</p> <ol> <li>Includes only benign HH traffic flows.</li> <li>Suitable for training and evaluating models to identify and differentiate benign heavy hitter traffic patterns.</li> </ol> </li> <li> <p><strong><code>Only DDoS Traffic</code></strong>:</p> <ol> <li>Contains only malicious DDoS HH traffic.</li> <li>Helps in isolating and analyzing attack characteristics for targeted threat detection.</li> </ol> </li> <li> <p><strong><code>Only Normal Traffic</code></strong>:</p> <ol> <li>Comprises only regular, non-HH traffic flows.</li> <li>Useful for understanding baseline network behavior in the absence of heavy hitters.</li> </ol> </li> <li> <p><strong><code>Unbalanced Traffic Generation</code></strong>:</p> <ol> <li>Features an unbalanced dataset with varying proportions of benign, normal, and malicious traffic.</li> <li>Simulates real-world scenarios where certain types of traffic dominate, providing insights into model performance in unbalanced conditions.</li> </ol> </li> </ol> <p>For each variation, the output of the different packet aggregators is provided separated in its respective folder.</p> <p>Each variation was generated using the NDT approach to demonstrate its flexibility and ensure the reproducibility of our study's experiments, while also contributing to future research on network traffic patterns and the detection and classification of heavy hitter traffic flows. The dataset is designed to support research in network security, machine learning model development, and applications of digital twin technology.</p>
Bibliographic Data from the Digital Twin Anomaly Detection Decision-Making for Bridge Management Systematic Review
<p>This database contains all the bibliographic information about the 8673 records found after applying the Search Strategy used for the Digital Twin Anomaly Detection Decision-Making for Bridge Management Systematic Review. Such strategy consisted on using seven initial keywords and similar terms of interest (namely: bridge and bridges, etc.): </p> <ul> <li>Bridge.</li> <li>Digital twin.</li> <li>Bridge information modelling.</li> <li>Finite elements.</li> <li>Bridge health monitoring.</li> <li>Anomaly detection algorithm.</li> <li>Cultural heritage.</li> </ul> <p>Six initial queries were done combining the first keyword with the rest of them:</p> <ul> <li>bridge* AND "digital twin*"</li> <li>bridge* AND (BrIM OR "bridge information model*")</li> <li>bridge* AND (FEM OR FEA OR "finite element method*" OR "finite element analy*")</li> <li>bridge* AND ("bridge health monitoring" OR "structural health monitoring")</li> <li>bridge* AND (ADA OR "anomaly detection algorithm*")</li> <li>bridge* AND ("cultural heritage" OR "monument* bridge*" OR "old bridge*" OR "ancient bridge*" OR "historic* bridge*")</li> </ul> <p>As a first screening step, the combination of these 6 initial searches was done to obtain relevant works containing at least three of the main keywords of interest:</p> <ul> <li>#1 AND #2</li> <li>#1 AND #3</li> <li>#1 AND #4</li> <li>#1 AND #5</li> <li>#1 AND #6</li> <li>#2 AND #3</li> <li>#2 AND #4</li> <li>#2 AND #5</li> <li>#2 AND #6</li> <li>#3 AND #4</li> <li>#3 AND #5</li> <li>#3 AND #6</li> <li>#4 AND #5</li> <li>#4 AND #6</li> <li>#5 AND #6</li> </ul> <p>All records found in Scopus where downloaded both in .ris and .csv format and are included in this database. The search was conducted on 10/12/2022.</p> <p>Note: Searches 10, 14, 17 and 21 did not return any records.</p>
Example of Force Digital Calibration Certificate used in ComTraForce 18SIB08 project to demonstrate Digital Twin concept
<p>Force Digital Calibration Certificate (DCC) was developed in the frameworks of 18SIB08 ComTraForce project. It was used to demonstrate the way of data connection between the physical object (force transducer) and virtual object (Finite Element model) within the developed Digital Twin concept. The developed at PTB v3.1.2 xsd schema was used to convert analog calibration certificate to machine readable XML format. The DCC covers static and continuous calibration processes. Note that the current Force DCC is not a Good Practice example. Please follow further developments of force DCC Good Practice example at https://gitlab.com/ptb/dcc.</p>
D^2EPC BIM-based Digital Twin data model example and real-time building measurements
<p>An example building digital twin data model, developed within the H2020 project D^2EPC, corresponding to the first out of six Case Studies (CERTH nZEB Smart House DIH). The following files are provided:</p><p>i) The BIM-based data model of the building parameters (.json file)</p><p>ii) Building real-time collected measurements within the project (in separate .json files):</p><ul><li>Living room: CO2, temperature, humidity, luminance, presence, PM2.5, TVOCs, loudness, smoke</li><li>Office: temperature, humidity, luminance, presence</li><li>Entire ground floor: HVAC system electrical energy consumption</li><li>Entire first floor: HVAC system electrical energy consumption</li><li>Entire building: electrical energy consumption (lighting & appliances)</li><li>Building PV installation: electrical energy production</li></ul><p> </p>
Underlying data - Digital Twin for Rainbow Trout (Oncorhynchus mykiss) land-based aquaculture
<p>Datasets for replicating Figures 5, 6, 7 and 8 of the article "Digital twins for land-based aquaculture: a case study for rainbow trout (<em>Oncorhynchus mykiss</em>)", by Adriano C. Lima, Edouard Royer, Matteo Bolzonella, and Roberto Pastres.</p>
Cardiac_Digital_Twin_Data
<p>Repository creation in progress.</p> <p>meta_data can be directly used to run the codes in https://github.com/juliacamps/Cardiac-Digital-Twin to generate and visualise digital twins and reproduce the results from "Harnessing 12-lead ECG and MRI data to personalise repolarisation profiles in cardiac digital twin models for enhanced virtual drug testing" (https://doi.org/10.1016/j.media.2024.103361).</p> <p>The supplement of the publication mentioned earlier contains additional information on the code and data structure.</p> <p>The monodomain simulations were performed using the configuration and mesh files in monodomain_monoalg3D_configuration_meshes.tar and using the version of monoAlg3D that can be found at <a title="https://github.com/bergolho/monoalg3d_c/tree/t-wave-personalisation-2024" href="https://github.com/bergolho/MonoAlg3D_C/tree/t-wave-personalisation-2024" target="_blank" rel="noreferrer noopener">https://github.com/bergolho/MonoAlg3D_C/tree/t-wave-personalisation-2024</a> </p> <p>The specific custom functions that were implemented in the t-wave-personalisation-2024 branch of the monoAlg3D code to enable the simulations can be found in monodomain_monoalg3D_custom_functions.tar. </p>
Carbon emission and lifecycle costs supporting digital twins for managing railway maintenance and resilience
<p>The development of railway construction increases the system complexity, which results in difficulty in management with traditional methods. Building Information Modelling (BIM) as an interoperable concept is benefits via whole life-cycle assessment (LCA) of the project, and it has been widely adopted in architecture, construction, and engineering (ACE) fields. This dataset of lifecycle cost and carbon footprint supports the digital twins for managing railway maintenance and resilience.</p>
Data for "The Heritage Digital Twin: a bicycle made for two."
<p>The file contains the data used in the case studies of the paper "The Heritage Digital Twin: a bicycle made for two. The integration of digital methodologies into cultural heritage research" published on ORE.</p>
Numerical simulations and experimental measurements of the ULB semi-industrial furnace for the development of a Digital Twin
<p>This dataset contains the numerical and experimental data used to build the Digital Twin in Aversano et al. (https://doi.org/10.1016/j.proci.2020.06.045) and the adaptive Digital Twin in Procacci et al. (https://doi.org/10.1016/j.proci.2022.07.029).</p> <p>The directory "Numerical_data" includes 45 text files containing the data coming from the CFD simulations of the ULB furnace. <br> In each file, for each computational cell the features reported are: <br> - the cell's position in x, y, z coordinates and in meters.<br> - the cell's temperature in K. <br> - the cell's species mass fraction of NO (mf-pollut-pollutant-0), CO, OH, H2, H2O, CO2, O2, CH4.<br> The details of the setup of the numerical simulations are reported in Aversano et al.</p> <p>The numerical simulations have been computed for different values of the equivalence ratio (phi), blend of H2-CH4 (H2) and <br> inlet diameter (D).<br> The simulations for different inlet diameter where computed using different meshes, with slightly different numbers of cells.<br> In the file 'cases_parameters.csv', the value of the parameters is reported for of each simulation. There is a <br> discrepancy between the naming of the simulations in Aversano et al. and the one used in naming the files, so both are reported.</p> <p>The experimental measurements used to validate the numerical simulations can be found in the directory "Experimental_data". Each<br> file contains the value of the measured temperature along with the position in x and z in meters (y being 0). The temperature is<br> in K. The experimental uncertainty is estimated at 10 K.</p> <p>The file 'grid.vtu' contains the computational grid used to solve the CFD simulations. It can be opened using VTK-based software <br> such as Paraview or Pyvista.</p> <p>Changelog:</p> <p>- In version V1, some simulations were corrupted during data export.<br> - Added the grid file in V3</p>
Supplementary material for: Calibrating coordinate system alignment in a scanning transmission electron microscope using a digital twin.
<h1>Calibrating coordinate system alignment in a scanning transmission electron microscope using a digital twin.</h1> <h2>Supplementary material</h2> <p>This deposition contains supplementary material for a paper on coordinate system calibration in 4D STEM. A preprint of the paper is available at <a href="https://arxiv.org/abs/2403.08538">https://arxiv.org/abs/2403.08538</a>.</p> <h2>Contents</h2> <div> <div><code>20221025_154811.zip</code>: Overfocused 4D STEM test dataset</div> <div> </div> <div><code>overfocus.sif</code>: Apptainer image with complete software stack. <code>apptainer run --writable overfocus.sif</code> to execute. It starts a Jupyterlab instance with two notebooks, one to genreate test data and the other to perform the interactive adjustment. This documents the software version that was used for the figures in the paper.</div> <div> </div> <div><code>requirements.txt</code>: Python package versions of dependencies in <code>overfocus.sif</code>. </div> <div> </div> <div><code>COM - Jupyter Notebook - Google Chrome 2023-01-25 12-40-07_processed.mp4</code>: Screen capture video with explanation of the first live calibration with an early prototype.</div> <div> </div> <div><code>video description.docx</code>: Explanation of the plots and adjustment process in the screen capture video.</div> <div> </div> <div><code>Microscope-Calibration.tar.gz</code>: Repository archive of the software and examples for calibration in the version used in the paper.</div> <div> </div> <div><code>TemGym.tar.gz</code>: Repository archive of TemGym Basic in the version used in the paper.</div> </div>
Digital Twins - from industrial management to healthcare practice
<p>A guest seminar offered by SCImPULSE Foundation CTO Taghi Aliyev for the University of Parma (IT) master "ARTE" https://www.masterarte-unipr.it/</p>
Data from: Digital twin mathematical models suggest individualized hemorrhagic shock resuscitation strategies
<p><strong>Background:</strong> Optimizing resuscitation to reduce inflammation and organ dysfunction following human trauma-associated hemorrhagic shock is a major clinical hurdle. This is limited by the short duration of pre-clinical studies and the sparsity of early data in the clinical setting.</p> <p><strong>Methods:</strong> We sought to bridge this gap by linking preclinical data in the porcine model with clinical data from patients from the Prospective, Observational, Multicenter, Major Trauma Transfusion (PROMMTT) study via a three-compartment ordinary differential equation model of inflammation and coagulation.</p> <p><strong>Results:</strong> The model accurately predicts physiologic, inflammatory, and laboratory measures in both the porcine model and patients, as well as the outcome and time of death in the PROMMTT cohort. Model simulation suggests that resuscitation with plasma and red blood cells outperformed resuscitation with crystalloid or plasma alone, and that earlier plasma resuscitation reduced injury severity and increased survival time.</p> <p><strong>Conclusions:</strong> This workflow may serve as a translational bridge from pre-clinical to clinical studies in trauma-associated hemorrhagic shock and other complex disease settings.</p>
Dataset for digital twins for managing bridge climate change adaptation
<p><span>This is the dataset for embedding in the novel digital twin driven by</span><span> BIM technology to manage the climate change adaptation measures for the bridges. A 6D BIM model has been established and embeded with change adaptation measures, timeline schedule, climate change adaptation cost estimation, and carbon emission estimation.</span></p>
Control Accuracy Measurement of a Mixed Reality Application for Digital Twin based Crane Operation
<p>The dataset corresponds the measurement that was implemented on the control accuracy of a mixed reality application for a digital twin based crane. </p> <p>The dataset contains two CSV files, one "Target" ", which is the selected target positions, and one "Measurement results", which is the actual crane position after moving to the target position. </p>
An extension of Thing Descriptions from the Web of Things for Digital Twins (Sustainable Places 2022)
<p>Repository with examples for An extension of Thing Descriptions from the Web of Things for Digital Twins (Sustainable Places 2022) article.</p>
Digital Twins: A Systematic Literature Review Based on Data Analysis and Topic Modeling
<p>The digital twin has recently become a popular topic in research related to manufacturing, such as Industry 4.0, the industrial internet of things, and cyber-physical systems. In addition, digital twins are the focus of several research areas: construction, urban management, digital transformation of the economy, medicine, virtual reality, software testing, and others. The concept is not yet fully defined, its scope seems unlimited, and the topic is relatively new; all this can present a barrier to research. The main goal of this paper is to develop a proper methodology for visualizing the digital-twin science landscape using modern bibliometric tools, text-mining and topic-modelling, based on machine learning models—Latent Dirichlet Allocation (LDA) and BERTopic (Bidirectional Encoder Representations from Transformers). The scope of the study includes 8693 publications on the topic selected from the Scopus database, published between January 1993 and September 2022. Keyword co-occurrence analysis and topic-modelling indicate that studies on digital twins are still in the early stage of development. At the same time, the core of the topic is growing, and some topic clusters are emerging. More than 100 topics can be identified; the most popular and fastest-growing topic is ‘digital twins of industrial robots, production lines and objects.’ Further efforts are needed to verify the proposed methodology, which can be achieved by analyzing other research fields.</p>
Digital twin of infant microbiome (qnet models)
<p>The complexity of the gut ecosystem, with thousands of cross-talking microbial colonizers, together with sparsely observed abundance profiles, has limited progress. We have developed a computational framework to learn an approximate “digital twin” of the maturing infant microbiome, that once learned, can reliably forecast detailed ecosystem trajectories unfolding over weeks from few initial observations. This generative model (Q-net), inferred at the level of taxonomic classes of microbes automatically from standard 16S rRNA profiles, is used to uncover actionable patterns driving developmental fate in early life. Here we publish the key qnet models to accompany our upcoming publication.</p>
Data from: Digital twin mathematical models suggest individualized hemorrhagic shock resuscitation strategies
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Digital Twin of 3D Printer in Real Time Mode
<p>Digital Twin operating in real-time mode. (Please note that the window on the top right of the video is from Repetier (3D Printing Application) and it was added as a visual guide on what the 3D printer was printing. It was not part of the study.</p>
Digital Twin or Digital Model: An Analysis of Definitions along the Product Lifecycle - Research data
<p>This research data contains the statements of the authors Grieves, Stark and Tao with regard to selected characteristics of Digital Twins. According to these statements different case studies along the product life cycle are classified as Digital Twin or Digital Model.</p> <p>Version 2 added a change in characteristic 2.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.