Fraunhofer Institute for Wind Energy Systems
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Prototyp zur Integration von 5G in TSN. Datenset
No full textThis dataset presents a validation of an implementation of a 5G system with Time-Sensitive Networking (TSN). A prototype setup integrating 5G in a TSN network has been completed to evaluate the 5G-TSN performance for industrial applications. The setup is explained in details in the paper "Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics".Dieser Datensatz enthält die Validierung einer Implementierung eines 5G-Systems als Wireless-Bridge in eine Time-Sensitive Networking (TSN) Infrastruktur. Diser Prototyp einer Integration von 5G in ein TSN-Netzwerk wurde erstellt, um die Leistung von 5G-TSN für industrielle Anwendungen zu bewerten. Der Aufbau wird im Paper »Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics« ausführlich erläutert.This work was performed in the framework of the “5G-Comet” project funded by the State of NRW through the Ministry of Economic Affairs, Innovation, Digitalization and Energy of the State of North Rhine-Westphalia with the funding code 005-2008-0093.The description of the measurement setup can be found in the paper "Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics " in section two.Die Beschreibung des Messaufbaus ist im Paper »Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics« im zweiten Abschnitt zu finden
Online Supplementary Appendix - Decision Criteria for Selecting Data Infrastructure Design Options in the Private Sector
No full textThis online supplementary appendix to the paper "Decision Criteria for Selecting Data Infrastructure Design Options in the Private Sector" presents additional material from our Design Science Research project that was omitted from the original publication. Appendix A provides a complete list of publications included in the literature review process. Appendix B presents a practical methodology for applying the catalog of criteria to identify and evaluate design options for private-sector data infrastructures for data sharing. Finally, Appendix C offers a multi-criteria decision-making tool for selecting the most suitable design option for private-sector data infrastructures for data sharing
Dataset for 'Reducing Sensor Configuration for Data-Driven Shoulder Load Estimation for Exoskeleton Control'
No full textThis paper investigates the role of various sensors in estimating shoulder loads during manual manipulation tasks in the context of exoskeleton control. The sensors examined include textile-integrated electromyography (EMG) sensors forthe trapezius, deltoids, biceps, and forearm muscles; inertial measurement units (IMUs) on key body segments such as the pelvis, shoulder, upper arm, and forearm; and pressure-sensing insoles. The objective is to reduce the sensor configuration for predicting the internal torque exerted on the shoulder in the sagittal plane. To achieve this, a study involving nine subjects manipulating dumbbells in the sagittal plane was
conducted. The protocol encompasses four static positions as well as isolated elbow and shoulder flexions. Further, four distinct machine learning model architectures were trained,
systematically omitting one sensor at a time. The significance of each sensor was evaluated by assessing the impact of its omission on the predictive correlation using cross-validated
R2 scores. Consequently, a top-five sensor configuration was identified and compared against configurations based solely on domain knowledge and the full sensor array. The configuration
proposed in this study achieved a correlation of R2 = 0.83 in predicting shoulder loads, slightly surpassing the performance of the full sensor setup (R2 = 0.82) and outperforming the
domain knowledge-based (DKB) setup (R2 = 0.63). All metrics are determined in a leave-one-subject-out cross-validation (loso-cv) training strategy.Physical strain in the construction trade is enormous, leading to frequent absences from work and occupational disability. A lack of young talent exacerbates the shortage of skilled workers in the construction industry. The aim of the “HEXOBAU” project is therefore to develop a user-friendly, lightweight, hydraulic exoskeleton that supports craftsmen in lifting, overhead work, and other strenuous tasks. The project is funded by the Invest BW funding program of the state of Baden-Württemberg.The Dataset consists of raw and processed .csv-files and is additionally pickled in .pkl-files for use in python
RF Communication Signal Dataset for Wireless Protocol Recognition based on Deep Embeddings (Part II)
No full textThis dataset supplements the dataset "RF Communication Signal Dataset for Wireless Protocol Recognition based on Deep Embeddings (Part I)" (https://fordatis.fraunhofer.de/handle/fordatis/460) and is part 3/3 of the evaluation data. The file "iqengine.zip" contains
(1) signal bursts extracted from "bluetooth.sigmf-data" (https://iqengine.org/view/api/us-east-1/iqengine-gnuradio/bluetooth; Author: Jacob Gilbert; License: CC BY-SA 4.0 - https://creativecommons.org/licenses/by-sa/4.0/),
(2) signal bursts extracted from "dect6.sigmf-data" (https://iqengine.org/view/api/us-east-1/iqengine-gnuradio/dect6; Author: Jacob Gilbert; License: CC BY-SA 4.0 - https://creativecommons.org/licenses/by-sa/4.0/), and
(3) signal bursts extracted from "rfd900p.sigmf-data" (https://iqengine.org/view/api/us-east-1/iqengine-gnuradio/rfd900p; Author: Unknown; License: Unknown).Dieser Datensatz ergänzt den Datensatz „RF Communication Signal Dataset for Wireless Protocol Recognition based on Deep Embeddings (Part I)“ (https://fordatis.fraunhofer.de/handle/fordatis/460) und ist Teil 3/3 der Evaluierungsdaten. Die Datei „iqengine.zip“ enthält
(1) Signalbursts, extrahiert aus „bluetooth.sigmf-data“ (https://iqengine.org/view/api/us-east-1/iqengine-gnuradio/bluetooth; Autor: Jacob Gilbert; Lizenz: CC BY-SA 4.0 - https://creativecommons.org/licenses/by-sa/4.0/),
(2) Signalbursts, die aus „dect6.sigmf-data“ extrahiert wurden (https://iqengine.org/view/api/us-east-1/iqengine-gnuradio/dect6; Autor: Jacob Gilbert; Lizenz: CC BY-SA 4.0 - https://creativecommons.org/licenses/by-sa/4.0/), und
(3) Signalbursts extrahiert aus „rfd900p.sigmf-data“ (https://iqengine.org/view/api/us-east-1/iqengine-gnuradio/rfd900p; Autor: Unbekannt; Lizenz: Unbekannt).SigMF (https://sigmf.org) is used as format for storing signal and metadata.SigMF (https://sigmf.org) wird als Format für die Speicherung von Signalen und Metadaten verwendet
Bringing Light into the Darkness: Leveraging Hidden Markov Models for Blackbox Fuzzing
No full textComplementary data for the paper "Bringing Light into the Darkness: Leveraging Hidden Markov Models for Blackbox Fuzzing" published at the 6th ACM/IEEE International Conference on Automation of Software Test (AST 2025). A Jupyter notebook for visualizing the data can be found here: https://github.com/anneborcherding/palpebratu
Instrumented Impact Test Data for CFPA6 Crash Box Systems
No full textThis dataset contains time-resolved measurements from crash tests performed on three crash box systems (specifically numbered 5, 6, and 7). The data includes various physical quantities recorded during the impact events. Each quantity is available for two measurement points and includes both raw and filtered signals.
The impactor was instrumented using two KYOWA damped accelerometers. The acceleration data were used to calculate force, displacement, and the energy absorbed during impact. A CFC180 filter was applied to the acceleration signals for data smoothing and noise reduction.
The columns in the dataset are as follows:
Impactor Acceleration 1 – Raw acceleration data from the first impact sensor.
Filtered Impactor Acceleration 1 – Filtered acceleration data (CFC180) from the first sensor.
Impactor Acceleration 2 – Raw acceleration data from the second impact sensor.
Filtered Impactor Acceleration 2 – Filtered acceleration data (CFC180) from the second sensor.
Average Impactor Acceleration – Mean of the filtered acceleration data from both sensors.
Velocity – Integrated velocity profile of the impactor.
Displacement – Integrated displacement profile of the impactor.
Force from Impactor Acceleration 1 – Force calculated using the first acceleration signal and impactor mass.
Force from Impactor Acceleration 2 – Force calculated using the second acceleration signal and impactor mass.
Average Force – Mean of the forces derived from both sensors.
Energy – Energy absorbed by the structure, obtained by integrating the force over displacement.
Three types of crashbox samples were tested (see also Fig. 8):
Crash box 6: Fully riveted crash box system
Crash box 7: Crash box system without rivets
Crash box 5: Fully riveted crash box system with an aluminium plate (176 × 78 × 3 mm) welded on the top ringThe research leading to these results has received funding from the Horizon EU Programme under grant agreement No. 101069600 and UKRI grant agreements No. 10047227 and No.10047305 (SALIENT Project)
Discrete element simulation datasets of particle flow in representative unit cells
No full textThis publication contains two datasets of particle flow within representative unit cells, generated using the simulation software SimPARTIX (https://www.simpartix.com/). The datasets are used for evaluating a graph-based interaction-aware particle trajectory prediction model, as detailed in the paper available at https://arxiv.org/pdf/2503.00215. The first dataset, "raw_data_simple_periodic_BC.tgz", includes a unit cell calculation with simple periodic boundary conditions and a sinusoidal velocity profile. The second dataset, "raw_data_Lees-Edwards_BC.tgz", includes a unit cell calculation utilizing Lees-Edwards boundary conditions.The compressed files contain HDF5 files that can be viewed with appropriate HDF viewers or extracted using suitable Python routines and libraries
Analysis of the decomposition of an anhydride-cured epoxy resin by subcritical hydrolysis
No full textThe decomposition of an anhydride-cured epoxy resin by subcritical hydrolysis was studied under variation of reaction temperature, decomposition duration and water volume
Start and end poses for energy modeling of a collaborative robot
No full textThe data set contains the coordinates of start and end points of a point-to-point trajectory of a collaborative robot
Parameter Harmonization of Jiles-Atherton model Variants
No full textThis dataset provides the numerical results and optimization routines used to harmonize different Jiles–Atherton (JA) hysteresis model variants. The goal of the study was to determine whether parameter sets of one JA formulation can be transformed such that another formulation reproduces the same B(H) behaviour.This dataset contains the numerical routines and optimization results used to establish parameter mappings between different Jiles–Atherton (JA) model variants. Synthetic B(H) curves were first generated for a selected reference model over multiple magnetic-field amplitudes. These curves served as target data for a Differential Evolution (DE) optimization scheme implemented in MATLAB. For each target curve, the parameters of the alternative JA models were optimized such that their simulated B(H) trajectory matched the reference model