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opencc-by-4.0Feb 2022View details →

Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) Dataset of a study with 5 real-world large numerical variability models.

The publications and research associated to cite is in:<a href="https://doi.org/10.1016/j.knosys.2023.110558">https://doi.org/10.1016/j.knosys.2023.110558</a>In that research we detail the Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) approach, and provide a web-tool prototype in <a href="https://hadas.caosd.lcc.uma.es/savrus">https://hadas.caosd.lcc.uma.es/savrus</a>In the study, we model 5 different real-world software product lines to then analysed them with SAVRUS:Detailed real-world variability models ordered by their search space size, of which GEC QA is incompletely measured NVM Description #Booleans #Numericals Space QA #Measurements Dune1 Multi-grid solver 11 3 2,304 Complex.. 2,304 HSMGP1 Stencil-grid solver 14 3 3,456 ..equation.. 3,456 HiPAcc1 Image processing framework 33 2 13,485 ..solving.. 13,485 Trimesh2 Triangle mesh library 13 4 239,360 ..time 239,360 GEC Generic edge computing 552 2 ~5.3*108 Energy Consumption 132500 The dataset zip file contains:<ul><li>5 numerical variability models in Clafer format (.txt) for each software product line.</li><li>5 CSV files with the respective quality attribute measurements</li><li>An .xlsx file containing SAVRUS scalability results divided in different tabs.</li></ul>References:[1] N. Siegmund, A. Grebhahn, S. Apel, C. Kastner, Performance-influence models for highly configurable systems, in: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, Association for Computing Machinery, New York, NY, USA, 2015, p.284–294. doi:10.1145/2786805.2786845.[2] M. Bauer, A comparison of six constraint solvers for variability analysis, Tech. rep., University of Passau (2019).

opencc-by-4.0Apr 2024View details →

Accelerometer and Force/Torque Sensor Measurements for Parameter and State Estimation of an Unknown Robot End Effector

<h1>Introduction</h1> This dataset was created as part of a study on the development of an estimator for the contact wrench (force and torque) of an unknown robot end effector. A conference paper from this study has been submitted and accepted for the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR 2024) [1].  A force/torque sensor (FTS) was attached to the robot wrist, and the unknown end effector was attached to the FTS. An inertial measurement unit (IMU) was in turn attached to the end effector. The FTS measurement can be decomposed into the (1) sensor bias, (2) contact wrench, and the effects from (3) gravity, (4) inertia, (5) vibrations,  and (6) noise. Estimation of the contact wrench requires that the remaining effects are compensated for. The FTS and IMU sensor biases, as well as mass and mass center of the unknown end effector, were estimated as described by Vougioukas [2]. His method requires FTS and IMU samples from 24 specific orientations of the sensors. See his paper for a description of this calibration method. The hardware used to generate this dataset were: <ul> <li>KUKA LBR Med 14 serial robot (KUKA AG, Germany)</li> <li>ATI Gamma FTS (ATI Industrial Automation, Inc., USA)</li> <li>ATI Netbox (ATI Industrial Automation, Inc., USA)</li> <li>MPU6886 IMU (M5Stack, China) </li> <li>Arduino Mega 2580 with a W5500 Ethernet Shield </li> </ul> <h1>Method</h1> The robot was used to move the end effector, FTS, and IMU such that a trajectory could be replicated with high precision and accuracy. The trajectory was a simple rotation about the FTS y-axis. This trajectory and the resulting measurements were performed three times. The sensor signals were sampled during each iteration when: <ol> <li>The robot moved freely without any kind of disturbance (basline).</li> <li>The robot moved freely with gentle taps to the robot body, using a rubber hammer (vibrations).</li> <li>The robot moved with gentle taps to the body using the hammer, and with a manual force exerted on the end effector (vibrations and contact).</li> </ol> The IMU signal was obtained by the Arduino Mega using I2C, and the signal was sent from the Arduino to the external PC using the ethernet shield. This setup resulted in a phase of the IMU signal by 8416 μs. This was compensated for in the offline analysis of the study on the contact wrench estimator [1]. The sensor samplig rates were different for each sensor; they were approximately 100 Hz for the robot controller (FTS orientation measurements), 700 Hz for the FTS, and 254 Hz for the IMU. The frequency for each signal can be obtained through the timestamps in the dataset. <h1>Dataset</h1> Each CSV file has a row which serves as the header, which labels the columns of each file. The following nomenclature of the column labels were used: t  - Timestep in microseconds. Epoch time.  fx, fy, fz - The force components as measured by the FTS. tx, ty, tz - The torque components as measured by the FTS. ax, ay, az  - The acceleration components measured by the IMU. gx,gy,gz - The direction of the gravitational vector in the FTS frame. r11, r12, r13, r21, r22, r23, r31, r32, r33 - The components of the rotation matrix that represents the FTS orientation in the world frame. (R_wf) The measurements from the FTS and IMU signals from the 24 orientations (as required for the calibration method described by Vougioukas [2]), are stored in 0-calibration_fts-accel.csv. Additionally, the files 0-steady-state_wrench.csv and 0-steady-state_accel.csv contains the continuous sensor signal from the FTS and IMU, respectively, while they were at rest; these two files can be used to calculate the sensor signal variances. After calibration, each sensor signal was recorded independently and stored in a separate file from the other sensors. The raw (biased) values were stored. Each test iteration produced three files: <ol> <li>The end effector/FTS/IMU orientation in [test_iteration]_orientation.csv</li> <li>The unbiased wrench as measured by the FTS in [test iteration]_wrench.csv</li> <li>The unbiased acceleration as measured by the IMU in [test_iteration]_accel.csv</li> </ol> The test iteration prefix for these files are: 1-baseline, 2-vibrations, and 3-vibrations-contact, as described in the previous section "Method". To obtain the relative time between samples across the test iteration files ([]_orientation, []_wrench, and []_accel.csv), load each dataset and determine which has the earliest timestamped sample on the first row. Then, subtract this initial timestamp value from all timestamps across the files for the respective test iteration. Note that the IMU frame does not align with the FTS frame (_accel.csv vs _wrench.csv), the following table describes the rotation matrix R_fa which can be used to transform the acceleration measurements from the IMU frame {a} to the FTS frame {f}.  R_fa =  <table> <tbody> <tr> <td>0</td> <td>-1</td> <td>0</td> </tr> <tr> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <td>-1</td> <td>0</td> <td>0</td> </tr> </tbody> </table> <h1>References</h1> [1] A. Skrede, "A Linear Discrete Kalman Filter to Estimate the Contact Wrench of an Unknown Robot End Effector", Accepted for the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR), Ålesund, Norway, June 2024  [2] S. Vougioukas, “Bias Estimation and Gravity Compensation For Force-Torque Sensors,” in Recent Advances in Simulation, Computational Methods and Soft Computing. WSEAS Press, 2001, pp. 82–85.

opencc-by-4.0May 2024View details →

Data from "Into the unknown: The role of post-fire soil erosion in the carbon cycle"

Wildfires directly emit 2.1 Pg carbon (C) to the atmosphere annually. The net effect of wildfires on the C cycle, however, involves many interacting source and sink processes beyond these emissions from combustion. Among those, the role of post-fire enhanced soil organic carbon (SOC) erosion as a C sink mechanism remains essentially unquantified. Wildfires can greatly enhance soil erosion due to the loss of protective vegetation cover and changes to soil structure and wettability. Post-fire SOC erosion acts as a C sink when off-site burial and stabilization of C eroded after a fire, together with the on-site recovery of SOC content, exceed the C losses during its post-fire transport. Here we synthesize published data on post-fire SOC erosion and evaluate its overall potential to act as longer-term C sink. To explore its quantitative importance, we also model its magnitude at continental scale using the 2017 wildfire season in Europe. Our estimations show that the C sink ability of SOC water erosion during the first post-fire year could account for around 13% of the C emissions produced by wildland fires. This indicates that post-fire SOC erosion is a quantitatively important process in the overall C balance of fires, and highlights the need for more field data to further validate this initial assessment. Here we provide the post-fire SOC erosion dataset ("Post-fire SOC erosion rates" file) used for calculating the SOC ratio of eroded sediments implemented in the RUSLE modelling; as well as the list of data sources ("List of data sources" file).

opencc-by-4.0Sep 2021View details →

Photos of an old actigraph (model unknown) from Ambulatory Monitoring, Inc. (AMI)

Photos of an old actigraph (model unknown) from <a href="http://www.ambulatory-monitoring.com/">Ambulatory Monitoring, Inc</a> (AMI). If you have any information regarding this device, please let me know (danvartan@gmail.com).

opencc-by-4.0May 2023View details →

Data to support the publication "Unknown risk: assessing refugee camp flood risk in Ethiopia"

This dataset supports the publication "Unknown risk: assessing refugee camp flood risk in Ethiopia". This dataset contains the delineated boundaries for 24 refugee camps in Ethiopia. Also included are refugee camp building footprint data (where available). All datasets are in shapefile format.

FIG. 22. — A, B in The unknown bathyal of the Canaries: new species and new records of deep-sea Mollusca

FIG. 22. — A, B, Gymnobela abyssorum (Locard, 1897), shell from DW120 (10.5 mm); new to the Canaries; C, D, Kurtziella serga (Dall, 1881), shell from DW130 (7.7 mm); new to the Canaries; E, Kurtziella serga, shell from DW129 (8.8 mm); F, G, Famelica monotropis (Dautzenberg & H. Fischer, 1896), shell from DW130 (5.7 mm); new to Spanish waters; H-I, Neopleurotomoides callembryon (Dautzenberg & H. Fischer, 1896), shell from DW130 (2.3 mm); new to Spanish waters; J, Neopleurotomoides callembryon, scanning electron micrograph of the protoconch of another shell from DW130; K-L, Pleurotomella demosia (Dautzenberg & H. Fischer, 1896), shell from DW126 (8.7 mm); new to the Canaries; M-N, Pleurotomella eurybrocha (Dautzenberg & H. Fischer, 1896), shell from DW130 (3.8 mm); new to the Canaries. Scale bar: J, 500 µm, all measurements refer to shell height.

FIG. 25. — A-C in The unknown bathyal of the Canaries: new species and new records of deep-sea Mollusca

FIG. 25. — A-C, Spirolaxis lamellifer (Rehder, 1935), shell from DW120 (diameter 3.7 mm); new to the Canaries; D-F, Orbitestella pruinosa n. sp., holotype from DW130 (diameter 0.8 mm); G, Graphis gracilis (Monterosato, 1874), shell from DW130 (height 2.1 mm); H, I, Odostomia madeirensis Peñas, Rolán & Swinnen, 2014, shell from DW133 (height 2.2 mm); new to Spanish waters; J, K, Liostomia canaliculata n. sp., holotype from DW130 (height 1 mm); L, M, Ringicula pirulina Locard, 1897, shell from DW130 (6.4 mm); N, Colpodaspis pusilla M. Sars, 1870, shell from DW126 (1.4 mm); new to the Canaries.

FIG. 11. — A-C in The unknown bathyal of the Canaries: new species and new records of deep-sea Mollusca

FIG. 11. — A-C, Discaclis canariensis Moolenbeek & Warén, 1987, shell from DW133 (diameter 1.2 mm); D-J, Discaclis lamellata n. sp.: D-F, holotype (sh.) from DW130 (diameter 1.0 mm); G, paratype (sh.), same locality (diameter 0.95 mm); H, protoconch of another paratype; I, protoconch of another paratype in apical view; J, microsculpture on the shoulder, same shell as I. Scale bars: H, I, 100 µm; J, 20 µm.

FIG. 2. — A, B in The unknown bathyal of the Canaries: new species and new records of deep-sea Mollusca

FIG. 2. — A, B, Bathysciadium costulatum (Locard, 1898), shell from DW130 (2.3 mm length); new to Spanish waters; C, D, Copulabyssia corrugata (Jeffreys, 1883), shell from DW130 (1.6 mm length); new to the Canaries; E, F, Profundisepta profundi (Jeffreys, 1877), shell from DW130 (3.6 mm length); new to Spanish waters; G, H, Fissurisepta granulosa Jeffreys, 1883, shell from DW130 (2.3 mm length); new to the Canaries; I-J, Satondella danieli Segers, Swinnen & Abreu, 2009, shell from DW130 (1.8 mm maximum diameter); new to Spanish waters.

Fig. 6 in Description of new Ceratitis species (Diptera: Tephritidae) from Africa, or how morphological and DNA data are complementary in discovering unknown species and matching sexes

Fig. 6. Ceratitis serrata De Meyer, 1996. a. Head, frontal view. b. Head and thorax, anterodorsal view. c. Thorax, lateral view. d. Thorax, dorsal view. e. Abdomen, dorsal view. Scale bars: A = 0.5 mm; B–D = 1 mm.

Fig. 3 in Description of new Ceratitis species (Diptera: Tephritidae) from Africa, or how morphological and DNA data are complementary in discovering unknown species and matching sexes

Fig. 3. Ceratitis taitaensis De Meyer & Copeland sp. nov. a. Head and thorax, anterodorsal view. b. Thorax, lateral view. c. Thorax, dorsal view. d. Abdomen, dorsal view. e. Male wing. f. Female wing. g. Female aculeus. h. Aculeus tip. Scale bars: A–F = 1 mm; G–H = 0.1mm.

Fig. 4 in Description of new Ceratitis species (Diptera: Tephritidae) from Africa, or how morphological and DNA data are complementary in discovering unknown species and matching sexes

Fig. 4. Ceratitis sawahilensis De Meyer & Virgilio sp. nov. a. Head and thorax, anterodorsal view. b. Thorax, lateral view. c. Thorax, dorsal view. d. Abdomen, dorsal view. e. Wing. f. Habitus image (credit: S.A. Marshall). g. Female aculeus. h. Aculeus tip. Scale bars: A–F = 1 mm; G–H = 0.1 mm.

Fig. 2 in Description of new Ceratitis species (Diptera: Tephritidae) from Africa, or how morphological and DNA data are complementary in discovering unknown species and matching sexes

Fig. 2. Ceratitis pallidula De Meyer, Mwatawala & Virgilio sp. nov. a. Head and thorax, anterodorsal view. b. Head and thorax, lateral view. c. Thorax, dorsal view. d. Abdomen, dorsal view. e. Wing. Scale bars = 1 mm.

Fig. 1. — a–f in Description of new Ceratitis species (Diptera: Tephritidae) from Africa, or how morphological and DNA data are complementary in discovering unknown species and matching sexes

Fig. 1. — a–f. Ceratitis quilicii De Meyer, Mwatawala & Virgilio sp. nov. Ƌ. a. Head and thorax, anterodorsal view. b. Head and thorax, lateral view. c. Thorax, dorsal view. d. Midleg, anterior view. e. Wing. f. Midtibia, anterior view. — g. C. rosa Karsch, 1887 s.str. midtibia, anterior view. Scale bars = 1 mm.

FIG. 12 in Revision of the Traumatomutilla gemella species-group (Hymenoptera, Mutillidae) with the description of its hitherto unknown males

FIG. 12. — Traumatomutilla peismatara Bartholomay & Cambra n. sp., female, holotype, Peru, Loreto, Pucallpa, BMNH: A, dorsal habitus; B, lateral habitus. Scale bars: 2 mm. Photos: P. R. Bartholomay.

FIG. 11 in Revision of the Traumatomutilla gemella species-group (Hymenoptera, Mutillidae) with the description of its hitherto unknown males

FIG. 11. — Traumatomutilla gemella (André, 1906), male, Brazil, Sao Paulo, EMUS: A, lateral habitus; B-F; genitalia: B, dorsal view (halved); C, ventral view (halved); D, lateral/inner view (penis valve removed); E, cuspis, lateral/inner view (removed not to scale); F, Penis valve, lateral/outer view (removed, not to scale). Scale bar: 2 mm. Photos: K. A. Williams.

FIG. 7 in Revision of the Traumatomutilla gemella species-group (Hymenoptera, Mutillidae) with the description of its hitherto unknown males

FIG. 7. — Traumatomutilla diopthalma (Klug, 1821), female, Brazil, Bahia, ZMB: A, dorsal habitus; B, lateral habitus; C, type labels. Scale bars: 2 mm. Photos: P. R. Bartholomay.

FIG. 4 in Revision of the Traumatomutilla gemella species-group (Hymenoptera, Mutillidae) with the description of its hitherto unknown males

FIG. 4. — Traumatomutilla rastra Casal, 1969 n. syn., female, holotype, Brazil, Santa Catarina, Nova Teutônia, AMNH: A, dorsal habitus; B, type labels; C, lateral habitus. Scale bars: 2 mm. Photos: Steve Thurston.