Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections? - Data Collected and Presented for the Experiments

This dataset contains images collected (and processed) for the experiments of Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections?" journal article, a work that defines a new path planning problem for UAVs - the Fast Inspection of Scattered Regions (FISR) - and introduces a novel method that deals with this problem - the multi-UAV Disjoint Areas Inspection (mUDAI) method. For the validation of the introduced methodology, two sets of real-world experiments were executed, one small-scale in Galatsi, Athens, were two mUDAI missions were depolyed, with two different optimization objectives for the data collection procedure (Mazimized Coverage Objective - MCO, and Balanced Coverage Objective - BCO), and one large scale in ZEP-Kissos, Thessaloniki, where a Coverage Path Planning (CPP) mission, and 2 mUDAI missions, one with a single and one with two UAVs, using both the MCO criterion for the data collection, were deployed. Regarding the CPP mission, both the collected images, and the processed results (to generate 2D, 3D, elevation, and plant health maps) are included. In this <a title="mUDAI - ChoosePath platform guide" href="https://sites.google.com/view/mudai-platform/" target="_blank" rel="noopener">page</a> you can find a guide for the on-line platform hosting demo instances of the algorithms used for the deployment of all experiments. In case you use this data, please cite the article: (Article under review - more information to be included soon)

opencc-by-4.0Sep 2024View details →

Advancing Robotic Swarms with Blockchain Technology: A Dynamic Two-Factor Authentication Consensus Framework

<h1>Data Description and File Structure:</h1> This data repository contains the raw data collected across all the experiments describe from the paper entitled “Advancing Robotic Swarms with Blockchain Technology: A Dynamic Two-Factor Authentication Consensus Framework”. These are available as CSV files under the appropriate directories. Three main folders are found in this repository: <ul> <li><code>1FA-single-factor-auth/</code> <ul> <li>Contains raw data from experiments using the Single-Factor Authentication (1FA) framework, where only on-chain consensus validation (OCV) is applied without the off-chain peer verification (OPV) phase.</li> </ul> </li> <li><code>2FBC_two-factor-blockchain/</code> <ul> <li>Includes data from experiments employing the Two-Factor Blockchain Consensus (2FBC) framework, which integrates both off-chain peer verification (OPV) and on-chain consensus validation (OCV) phases for enhanced security. This also contains the baseline results.</li> </ul> </li> <li><code>BB_blockchain-base/</code> <ul> <li>Stores the experimental data from the Blockchain Base (BB) framework, where a basic blockchain model was used without the multi-factor authentication features of 1FA or 2FBC. Most data points here are obtained from the work of Strobel et al. (2023) in their work, doi/10.1126/scirobotics.abm4636</li> </ul> </li> </ul> Under each directory, we have the following folders: <ul> <li><code>exp_1/</code> <ul> <li>Contains data from scalability experiments, where swarm size was increased within a fixed 3.6 m² arena to evaluate the framework’s performance as the number of robots grows.</li> </ul> </li> <li><code>exp_2/</code> <ul> <li>Includes data from accuracy tests that varied the percentage of white tiles in the environment to assess the framework's ability to reach accurate consensus under different conditions.</li> </ul> </li> <li><code>exp_3a/</code> <ul> <li>Stores data from robustness experiments focused on testing the swarm's resilience to different numbers of Byzantine robots within the network.</li> </ul> </li> <li><code>exp_3b/</code> <ul> <li>Contains data from experiments evaluating the robustness of the swarm when subjected to various Byzantine attack types, testing the framework’s ability to handle adversarial behaviors.</li> </ul> </li> <li><code>exp_4/</code> <ul> <li>Holds data from the resource efficiency experiments, which measured the computational resource usage (CPU, RAM, and blockchain size) during a prolonged 10-hour swarm operation.</li> </ul> </li> </ul> Each experiment configuration is carried out in 20 repetitions. <h3>Experiment 1 (exp_1):</h3> <ul> <li><code>8rob-2byz/</code> Data for scalability experiments with 8 robots, 2 of which are Byzantine.</li> <li><code>16rob-4byz/</code> Data for scalability experiments with 16 robots, 4 of which are Byzantine.</li> <li><code>24rob-6byz/</code> Data for scalability experiments with 24 robots, 6 of which are Byzantine.</li> <li><code>48rob-12byz/</code> Data for scalability experiments with 48 robots, 12 of which are Byzantine.</li> </ul> <h3>Experiment 2 (exp_2):</h3> <ul> <li><code>24rob-5floor-6byz/</code> Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 5% white floor tiles.</li> <li><code>24rob-25floor-6byz/</code> Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 25% white floor tiles.</li> <li><code>24rob-45floor-6byz/</code> Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 45% white floor tiles.</li> <li><code>24rob-75floor-6byz/</code> Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 75% white floor tiles.</li> </ul> <h3>Experiment 3a (exp_3a):</h3> <ul> <li><code>24rob-0byz/</code> Data for robustness experiments with 24 robots and no Byzantine robots.</li> <li><code>24rob-3byz/</code> Data for robustness experiments with 24 robots and 3 Byzantine robots.</li> <li><code>24rob-6byz/</code> Data for robustness experiments with 24 robots and 6 Byzantine robots.</li> <li><code>24rob-9byz/</code> Data for robustness experiments with 24 robots and 9 Byzantine robots.</li> </ul> <h3>Experiment 3b (exp_3b):</h3> <ul> <li><code>24rob-6byz-1style/</code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 1 or 0% white tile estimate</li> <li><code>24rob-6byz-2style/</code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 2 or 100% white tile estimate</li> <li><code>24rob-6byz-3style/</code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 3 or attack from a Bernoulli distribution</li> <li><code>24rob-6byz-4style/</code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 4 or attack from a Uniform distribution</li> <li><code>24rob-6byz-5style/</code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 5 or flooding</li> <li><code>24rob-6byz-6style/</code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 6 or eavesdropping</li> </ul> <h3>Experiment 4 (exp_4):</h3> <ul> <li><code>8rob-2byz/</code> Data for resource efficiency experiments with 8 robots, 2 of which are Byzantine.</li> <li><code>16rob-4byz/</code> Data for resource efficiency experiments with 16 robots, 4 of which are Byzantine.</li> <li><code>24rob-6byz/</code> Data for resource efficiency experiments with 24 robots, 6 of which are Byzantine.</li> <li><code>48rob-12byz/</code> Data for resource efficiency experiments with 48 robots, 12 of which are Byzantine.</li> <li><code>72rob-18byz/</code> Data for resource efficiency experiments with 72 robots, 18 of which are Byzantine.</li> <li><code>96rob-24byz/</code> Data for resource efficiency experiments with 96 robots, 24 of which are Byzantine.</li> <li><code>120rob-30byz/</code> Data for resource efficiency experiments with 120 robots, 30 of which are Byzantine.</li> </ul> <h3>Relevant Files:</h3> <ul> <li><code>block.csv</code> Contains information about each blockchain block generated during the experiment, including block number, size, timestamp, and the number of transactions. The TELAPSED column indicates the time elapsed since the previous block was generated.</li> <li><code>estimate.csv</code> Stores the estimates collected by each robot during the simulation. Each entry includes the time of the estimate and the estimated percentage of white tiles in the arena.</li> <li><code>sc.csv</code> Contains information on smart contract interactions, including the mean estimate across robots, vote counts, and whether consensus was achieved (C?).</li> <li><code>extra.csv</code> Records additional performance metrics during the experiments, including CPU and RAM usage, as well as the size of the blockchain data folder.</li> </ul> <h3>Relevant Data Fields:</h3> <ul> <li><code>ID</code> The identifier assigned to each robot participating in the experiment. It remains constant across all entries for a particular robot.</li> <li><code>TIME</code> The timestamp (in seconds) at which the data was recorded. This is relative to the start of the simulation.</li> <li><code>TELAPSED</code> Indicates the time elapsed between blocks or events, recorded in seconds.</li> <li><code>TIMESTAMP </code>Represents the Unix timestamp when a blockchain block was generated, denoting the actual system time.</li> <li><code>BLOCK </code>The blockchain block number created by the system during the simulation. This value increments as new blocks are added.</li> <li><code>SIZE</code> The size of each block in bytes, indicating the data storage requirement of each blockchain entry.</li> <li><code>ESTIMATE</code> The estimate provided by the robot, representing the percentage of white tiles detected in the arena.</li> <li><code>MEAN</code> The mean estimate across the swarm, as calculated on-chain via the smart contract.</li> <li><code>VOTECOUNT</code> Total number of estimates submitted to the smart contract for consensus validation.</li> <li><code>VOTEOKCOUNT</code> The number of valid votes that passed the validation process (e.g., not flagged as outliers).</li> <li><code>C?</code>  A Boolean value indicating whether consensus has been achieved for a given block of estimates.</li> <li><code>CPU</code> Percentage of CPU utilization, showing the computational load on the robot during the simulation.</li> <li><code>RAM</code> The amount of RAM used by each robot during the experiment, measured in percent or bytes.</li> <li><code>KB</code> The size of the blockchain data folder, measured in kilobytes (KB). This indicates how much data was stored by the blockchain system during the experiment.</li> </ul>

opencc-by-4.0Aug 2021View details →

Satellite swarm versus night sky beauty, by Torsten Hansen, Germany

Third place in the 2021 IAU OAE Astrophotography Contest, category Light pollution. This image of Venus and the Pleiades also shows the tracks of the Starlink satellites. These satellites which are located at an altitude of approximately 550 kilometres, are part of an ever-growing constellation of satellites aimed to provide worldwide internet access. The reflective surfaces of the satellites, coupled with the fact that they are orbiting around the Earth, means that astronomical observations which require very long exposures capture “tracks” of the satellites in their images. Astronomical images used for scientific research are not usable because the measurements and data will contain these “tracks”. Because the number of satellites is expected to grow, it is likely that in the near future there will be no place on Earth where these satellites will not be visible crossing the sky. This is a new type of light pollution that seems to be an upcoming problem we will have to deal with, as these satellites might prevent optimal observation of the sky. Credit: Torsten Hansen/IAU OAE

Fig. 7 in Evolution of Caste in Neotropical Swarm-Founding Wasps (Hymenoptera: Vespidae; Epiponini)

Fig. 7. Caste syndromes defined as in fig. 6, optimized on cladogram produced by using caste syndrome as a character in combination with the matrix of Wenzel and Carpenter.

Fig. 3 in Evolution of Caste in Neotropical Swarm-Founding Wasps (Hymenoptera: Vespidae; Epiponini)

Fig. 3. Caste discrimination found in some epiponines based on size and shape. Individuals identified by dissection to be ''queens'' are represented as squares, while those identified as ''workers'' are solid dots. Separate regression lines are drawn for illustration only; see text for discussion.

Fig. 6 in Evolution of Caste in Neotropical Swarm-Founding Wasps (Hymenoptera: Vespidae; Epiponini)

Fig. 6. Caste syndromes defined by data of table 1 and ovarian dissection, optimized on cladogram of Wenzel and Carpenter. Where genera are polymorphic for caste syndrome, terminals are coded according to parsimonious optimization.

Fig. 5 in Evolution of Caste in Neotropical Swarm-Founding Wasps (Hymenoptera: Vespidae; Epiponini)

Fig. 5. Plots of Wilks' lambda values versus queens correctly classified using discriminant function analysis for some epiponines. See text for discussion.

Fig. 4 in Evolution of Caste in Neotropical Swarm-Founding Wasps (Hymenoptera: Vespidae; Epiponini)

Fig. 4. Comparison of the level of caste distinction between epiponines (1–59, white circles indicate workers with ovarian development, black diamonds indicate workers without ovarian development) and a Vespinae (60, black square) based on wing length queen–worker ratio versus length of tergite I (except 14, 16, 17, 33–35, 37: hamuli number and 56: width of tergite I). 1. Agelaia areata, 2. A. fulvofasciata, 3. A. lobipleura, 4. A. multipicta, 5. A. pallipes, 6. A. vicina, 7. A. yepocapa, 8. Angiopolybia pallens, 9. Apoica flavissima, 10. A. gelida, 11. A. pallens, 12. Asteloeca ujhelyii, 13. Brachygastra augusti, 14. B. bilineolata, 15. B. lecheguana, 16. B. moebiana, 17. B. scutellaris, 18. Chartergellus communis, 19. Charterginus fulvus, 20. Chartergus chartarius, 21. C. globiventris, 22. C. metanotalis, 23. Clypearia sulcata, 24. Epipona tatua, 25. E. guerini, 26. Leipomeles dorsata, 27. Metapolybia aztecoides, 28. M. docilis, 29. Nectarinella championi, 30. Parachartergus colobopterus, 31. Pa. fraternus, 32. P. smithii, 33. Polybia bicytarella, 34. Po. bistriata, 35. Po. catillifex, 36. Po. dimidiata, 37. Po. emaciata, 38. Po. erythrothorax, 39. Po. jurinei, 40. P. liliacea, 41. P. occidentalis, 42. P. parvulina, 43. P. platycepha sylvestris, 44. P. quadricincta, 45. Po. rejecta, 46. Po. ruficeps, 47. Po. scutellaris, 48. Po. singularis, 49. Po. spinifex, 50. Po. striata, 51. Protonectarina sylveirae, 52. Protopolybia exigua, 53. Pr. minutissima, 54. Pr. pumilla, 55. Pr. sedula, 56. Pseudopolybia difficilis, 57. Ps. vespiceps, 58. Synoeca cyanea, 59. S. surinama, 60. Vespula squamosa. For references for the species, see table 1.

Fig. 2 in Evolution of Caste in Neotropical Swarm-Founding Wasps (Hymenoptera: Vespidae; Epiponini)

Fig. 2. Slight caste discrimination found in some epiponines based mostly on shape (when applicable). Individuals identified by dissection to be ''queens'' are represented as squares, while those identified as ''workers'' are solid dots. Separate regression lines are drawn for illustration only; see text for discussion.

Fig. 1 in Evolution of Caste in Neotropical Swarm-Founding Wasps (Hymenoptera: Vespidae; Epiponini)

Fig. 1. Representative measures for morphometric analyses of this paper: head width (HW), minimum interorbital distance (IDm), gena width (GW), width of mesoscutum (MSW), alitrunk length (AL), length of gastral tergite I (T1L), basal height of T1 (T1BH), basal widths of tergite II (T2BW), and partial length of the forewing (WL).

Fig. 8 in Evolution of Caste in Neotropical Swarm-Founding Wasps (Hymenoptera: Vespidae; Epiponini)

Fig. 8. Wheeler's (1991) model of the evolution of caste, stepwise in graphical terms, representing morphometric variation measured within a colony. See text for explanation.

opencc-by-4.0Oct 2021View details →

A niching particle swarm optimization strategy combined with cluster analysis for the multimodal inversion of surface waves

The data include two study cases used for multimodal surface wave inversion. For case 1, the data present a combination of active and passive surface wave methods. For case 3, we use Rayleigh waves to detect a low-velocity soft interlayer underneath the road. Detailed description can be found in the data description document.

opencc-by-4.0Feb 2023View details →

Effect of Vertical Shear in the Zonal Wind on Equatorial Electrojet Sidebands: An Observational Perspective Using Swarm and ICON Data

This data set consists of outputs from the EEJ model by Richmond (1973). The data provided is used in the manuscript titled 'Effect of Vertical Shear in the Zonal Wind on Equatorial Electrojet Sidebands: An Observational Perspective Using Swarm and ICON Data' by J. Sreelakshmi et al. Abstract of the manuscript: The wind dynamo in the ionosphere leads to differential motion of ions and electrons, which in turn sets up electric fields and currents. Observations show that daytime lower thermospheric horizontal winds have large vertical gradients. Numerical modelling conducted approximately 50 years ago demonstrated that the zonal wind shears in the ~130-180km altitude range can generate off-equatorial relative minima (dips) in the daytime height-integrated eastward current density, appearing as westward sidebands north and south of the equatorial electrojet (EEJ). This study observationally confirms this connection for the first time by combining Ionospheric CONnection explorer zonal wind profiles and Swarm latitudinal zonal currents. We demonstrate observationally that the magnitude of the EEJ sideband current is proportional to the strength of westward turning winds with altitude in the Pedersen conductivity dominated region. Additional numerical experiments explain the importance of wind shear in different altitude regions in generating the sideband current. This study contributes to the better understanding of the neutral wind effect on local current generation.

opencc-by-4.0Dec 2022View details →

Moment tensors of 2017 seismic swarm in Reykjanes Peninsula, Iceland

Data for the paper: Pavla Hrubcová and Václav Vavryčuk, 2023. Tectonic stress changes related to plate spreading prior to the 2021 Fagradalsfjall eruption in SW Iceland. Tectonophysics, https://doi.org/10.1016/j.tecto.2023.229761

opencc-by-4.0Jun 2023View details →

Multiscale Spatial Patterns in Giant Dike Swarms Identified through Objective Feature Extraction Datasets

S1 - Linked dike clusters for the Columbia River Flood Basalt group including the four identified subswarms: Chief Joseph, Monument, Ice Harbor, and Steens as compiled in Morriss et al., 2020. This dataset uses the a UTM Zone 11N projection (EPSG:26911). S2 - Linked dike clusters for the Deccan Traps including the four identified subswarms: Saurashtra, Narmada-Tapi, Central and Coastal. Due to their overlap Central and Coastal Swarms have been combined in this dataset into the Central Swarm. This dataset uses the a WGS 84 projection (EPSG:3857).  S3 - Dike segment data for Spanish Peaks and Dike Mountain located in the Rio Grande Rift of Colorado. This dataset was digitized using QGIS based on the map by Johnson (1961). This dataset uses the a UTM Zone13N projection (EPSG:32613). The file includes the start, end points, and midpoints of the dikes; segment length; calculated $\rho$ and $\theta$ for the Hough Transform; the origin used for the Hough Transform which is different for each subswarm (xc,yc); dike rock type if known; and a unique identification calculated based on the start and endpoints. This dataset has been preprocessed to remove curving dikes and is the data set used to produce later products (Data set S4).  S4 - Linked dike clusters for the Spanish Peaks and Dike Mountain. This dataset was produced using the Agglomerative Clustering algorithms using the parameters set in Table 1. This dataset uses the a UTM Zone 13N projection (EPSG:32613).      These datasets were produced using the Agglomerative Clustering algorithms using the parameters set in Table 1. The datasets are in the format of a CSV file but can be read into GIS programs using Well Known Text (WKT) linestring. TThe file includes the start and end points of the average line in the cluster and it's mid points, cluster length and width (Xstart, Xend, Xmid, Ymid, in meters and UTM coordinates, Dike Cluster Width  or R\_Width, Dike Cluster Length or R\_Length all in meters); calculated average $\rho$ and $\theta$ for the Hough Transform $\rho$ units measured in meters, $\theta$ units measured in degrees, unless otherwise stated); the origin used for the Hough Transform which is different for each subswarm ($xc$,$yc$, meters in UTM coordinates); average slope and intercept (AvgSlope, AvgIntercept meters); range and standard deviation for $\rho$ and $\theta$ for all objects in the cluster ($\rho$ units measured in meters, $\theta$ units measured in degrees); cluster size (Size); sum of segment lengths in a cluster (SegmentLSum, meters); whether the cluster crosses between negative and positive values (ClusterCrossesZero, boolean); overlap as calculated in the main text where the length of overlap is normalized by the sum of segment lengths in a cluster; maximum number of overlapping segments (nOverlapingSegments); twist angle which is the difference in angle betweeen the average cluster line and the average line formed by cluster midpoints (EnEchelonAngleDiff, degrees); the p-value for the midpoint line fit of the segments where $p<0.05$ is considered to be a significant fit (EEPValue); the maximum, median, and minimum segment nearest neighbors distances in the cluster which is calculated using the cartesian midpoints of each segment and normalized by the Cluster Length (MaxSegNNDist, MedianSegNNDist, MinSegNNDist); characterization of each cluster as filtered or not, filtered clusters are of size greater than $3$ and have a MaxSegNNDist of less than $0.5$ (TrustFilter, boolean); the date edited (Date\_Changed), and the clustering parameters used for each cluster (Rho\_Threshold in meters, Theta\_Threshold in degrees) and a unique identification calculated based on the start and endpoints (ClusterHash).

Creating speech zones with self-distributing acoustic swarms (Augmented Dataset Part 1 of 2)

Datasets used in the paper: "Creating speech zones with self-distributing acoustic swarms" This deposit contains the first part of the augmented dataset containing simulated and real world collected data. The datasets contains 18000 training mixtures of 3-5 speakers, of which 6000 are simulated using PyRoomAcoustics, 6000 are created from synchronized real world recordings in an anechoic chamber, and 6000 are created from synchronized recordings in ordinary reverberant rooms. It also includes a validation set of 500 mixtures from reverberant rooms, and a testing set of 1000 mixtures from reverberant rooms. The source sounds are various utterances from the VCTK dataset. For real world data, the utterances are played over a Rokono Bass+ Mini Speaker. The recordings are captured from an array of 7 microphones, as they are recorded by our robotic swarm as it is distributed across the table. The recorded audio in the real world has been subjected to audio compression and decompression using the Opus Codec to enable multiple simultaneous streams. You must download both the first and the second part of this dataset in order to use it properly. To uncompress the two datasets, download both and execute: ```cat *.tar.gz.* | tar xvfz -``` Please see the Readme for more information. Please see related identifiers for other datasets.

Creating speech zones with self-distributing acoustic swarms (Augmented Dataset Part 2 of 2)

Datasets used in the paper: "Creating speech zones with self-distributing acoustic swarms" This deposit contains the second part of the augmented dataset containing simulated and real world collected data. The datasets contains 18000 training mixtures of 3-5 speakers, of which 6000 are simulated using PyRoomAcoustics, 6000 are created from synchronized real world recordings in an anechoic chamber, and 6000 are created from synchronized recordings in ordinary reverberant rooms. It also includes a validation set of 500 mixtures from reverberant rooms, and a testing set of 1000 mixtures from reverberant rooms. The source sounds are various utterances from the VCTK dataset. For real world data, the utterances are played over a Rokono Bass+ Mini Speaker. The recordings are captured from an array of 7 microphones, as they are recorded by our robotic swarm as it is distributed across the table. The recorded audio in the real world has been subjected to audio compression and decompression using the Opus Codec to enable multiple simultaneous streams. You must download both the first and the second part of this dataset in order to use it properly. To uncompress the two datasets, download both and execute: ```cat *.tar.gz.* | tar xvfz -``` Please see the Readme for more information. Please see related identifiers for other datasets.

Creating speech zones with self-distributing acoustic swarms (Simulated + Clutter)

Datasets used in the paper: "Creating speech zones with self-distributing acoustic swarms" This deposit contains 2 distinct datasets:  <ol> <li>A dataset of speech mixtures containing 2-5 speakers simulated using PyRoomAcoustics. The dataset consists of 8000 training mixtures, 500 validation mixtures and 1000 testing mixtures.</li> <li>A dataset of speech mixtures containing 3-5 speakers created from synchronized recordings in reverberant rooms with objects cluttering the table. The dataset consists of 500 testing mixtures.</li> </ol> The source sounds are various utterances from the VCTK dataset. For real world data, the utterances are played over a Rokono Bass+ Mini Speaker. The recordings are captured from an array of 7 microphones, as they are recorded by our robotic swarm as it is distributed across the table. The recorded audio in the real world has been subjected to audio compression and decompression using the Opus Codec to enable multiple simultaneous streams. Please see the Readme for more infromation. Please see related identifiers for other datasets.

Three-dimensional CAD model of the robotic system used for acquiring samples from bacterial swarms

This CAD model shows the robotic sampling system that was used in the scientific article "Simultaneous spatiotemporal transcriptomics and microscopy of Bacillus subtilis swarm development reveal cooperation across generations" by the following authors: Hannah Jeckel*, Kazuki Nosho*, Konstantin Neuhaus, Alasdair D. Hastewell, Dominic J. Skinner, Dibya Saha, Niklas Netter, Nicole Paczia, Jörn Dunkel, Knut Drescher. The symbol "*" indicates an equal contribution.  The CAD model consists of 81 individual files in IPT or IAM format, which need to be loaded together into a AutoDesk Inventor to be viewed. We used AutoDesk Inventor 2021 to create and view this CAD model.