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98 results for “UAS”
UAS spherical photography for the vertical characterisation of canopy structural traits
<p>Data and scripts for New Phytologist manuscript:</p> <p><strong>UAS spherical photography for the vertical characterisation of canopy structural traits </strong><br> Vicent Agustí Ribas Costa, Maxime Durand, T Matthew Robson, Albert Porcar-Castell, Ilkka Korpela, Jon Atherton</p> <p><strong>The current version is 2.0.0; </strong>please do <strong><em>not</em></strong> use the data/code from the peer review version 1<strong>. </strong> In the Early View version 2.0.0 we have<strong> </strong>fixed minor bugs in data (incorrectly flipped images), and updated our code. We also reduced the data archive size to approx 5 GB. </p> <p>Note that individual (i.e. pre-stitched) UAS photos from vertical profiles are not included due to the size (>50 GB). If you want all the individual files then contact Jon. Data was compiled by Vicent, but feel free to contact Jon in case of any issues.</p>
White River Narrows - Locus 04 All Panels UAS
White River Narrows, Nevada - Locus 04 All Panels UAS . This model was created from UAS images taken during a pre-graffiti removal project in August of 2019 by Dr. Loubser of Stratum Unlimited, Inc for the Bureau of Land Management. Graffiti has been removed since this model was done. Source: Objaverse 1.0 / Sketchfab
UA - Gaussian Depth Disc (GDD dataset)
<div> <div>Dear reader,</div> <br> <div>Welcome! You must be an avid profilometry person to be interested in downloading our dataset.</div> <div>Before you start tinkering with the dataset package please do install the requirements.txt libraries for a more easy step into operating this system.</div> <div>We hope to have made the hierarchy of the package as clear as possible! Also note that this system was written in VScode.</div> <div>The Matlab script that creates the surface has been included for if you yourself would like to print one out and do in depth research!</div> <div>Find your way to the examples folder, there you can find "entire_dataset". This folder contains a script to divide the original h5 file (480x640) containing all data</div> <div>into whatever sub-options you'd like (had to be made int16 as to not exceed to 50GB limit of Zenodo, you are more than welcome to reach out for the 32int format!). We included a downsampled format of 120x160 (int32) as this makes the file size more managable for pc's with not enough RAM. An example divided dataset has already been given namely the 80/20 division of respectively training and validation data in the "example_dataset" folder, of the downsampled dataset.</div> <div>In the folder models you will find the two models mentioned in the publication related to this dataset. These two were published with the dataset since they</div> <div>had either the highest performance on the training and validation dataset (DenseNet) or on the random physical object test (CNN).</div> <div>A training script is included (training_script.py) to show you how these models were created, so if you wish to add new models to the networks.py file in the classes folder, you can!</div> <div>The validation jupyter notebook contains two visualisation tools to quickly and neatly show the performance of your model on the recorded dataset.</div> <div>Lastly to test on the recorded objects you can run the "test_physical_data.py" and "test_physical_data_sine_deivation.py" scripts.</div> <br> <div>We hope this helps you in your research and we hope it further improves any and all research within the single shot profilometry field! 😊</div> <br> <div>Kind regards,</div> <br> <div>Rhys Evans,</div> <div>InViLab,</div> <div>University of Antwerp, Belgium</div> </div>
Flights of a Multirotor UAS with Structural Faults: Failures on Composite Propeller(s)
<p>Data acquired from several flights of a custom-fabricated Hexacopter UAS with composite structure (carbon fiber arms and central hub) and composite (carbon fiber) propellers is presented here. The Hexacopter was assembled from a commercially available kit (Tarot 690) and was flown in manual and autonomous mode; take-offs and landings were under manual control and the bulk of the flight tests were conducted with the Hexacopter in a “Position Hold” mode. All flights were flown within the UAS flight cage at Parks College of Engineering, Aviation and Technology at Saint Louis University, for approximately 5 minutes each. Several failure conditions (different types of manually induced) on the composite (carbon fiber) propellers were tested, including failures on up to two propellers. The data set described in this article contains flight data from the onboard flight controller (Pixhawk) as well as three 3-axis accelerometers mounted on the arms of the Hexacopter UAS. The data is included as supplemental material.</p>
26 DESEMBER 2022 NASKAH UJIAN AKHIR SEMESTER (UAS) AKUNTANSI KEUANGAN LANJUTAN 1_UNIVERSITAS GRESIK JAM 18.00-20.00
<p><strong>Capaian Pembelajaran Mata Kuliah (CPMK)</strong></p> <p>Mengevaluasi hasil pemahaman dari pertemuan ke 8 sampai pertemuan 14</p> <p><strong>Indikator Penilaian</strong></p> <p><strong>1. Tes Lisan</strong></p> <p><strong>2. Tes Tulis</strong></p> <p><strong>Note: Close Book and No HP</strong></p>
Increasing Bridge Durability and Service Life with LIDAR Enhanced Unmanned Aerial Systems (UAS)
<p>Bridge construction inspections require quantitative measurements and location information. The conventional approach is visual inspection, which in general, is rather time-consuming, expensive due to traffic closure, subjective, and needs special access. Therefore an automated rebar layout detection algorithm was developed to quickly extract quantitative rebar layout information from the LiDAR data. This systematic method can automatically cluster the bridge elements from a 3D point cloud by using LiDAR-equipped UAS data collection and unsupervised machine learning techniques. A new automated inspection system using a LIDAR-equipped UAS can eventually if developed and tested be more reliable as well as less expensive. In the future, if it can be automatized, it can be implemented to simplify the complexity of inspections. The authors developed a platform to mount the camera, sonar laser, and DAQ on the UAS and remotely controlled the data collection operation. Additionally, an algorithm was developed which can automatically obtain the geometric information of the rebar. The proposed automated RGBD-equipped UAS system was developed, fabricated, and tested in the Balloon Fiesta Park on a simulated bridge deck at different heights and with different UAS motions to obtain the best distance, speed, and motion for real construction field. The authors also conducted an outdoor experiment in a construction field at White Rock to validate the capability of the proposed system on the real site with vertical rebar and the challenges of the real construction site. The result confirmed that the LIDAR-equipped UAS system has the potential to help the inspection process in terms of time, accuracy, safety, and generating a permanent record of the inspection. Bridge construction information collected by LiDAR-equipped UAS technology can eventually provide bridge managers with transparent condition assessment and one-step decision-making support through quantitative measurement combined with 3D visualization to facilitate repair planning that can greatly facilitate maintenance.</p>
A Study to Assess the Effect of Intensive Uric Acid (UA) Lowering Therapy With RDEA3170, Febuxostat, Dapagliflozin on Urinary Excretion of UA
ClinicalTrials.gov study NCT03316131. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Early detection of encroaching woody Juniperus virginiana and its classification in multi-species forest using UAS imagery and semantic segmentation algorithms
Open the record for dataset details and reuse information.
Quantifying microhabitat selection of snowshoe hares using forest metrics from UAS-based LiDAR
Open the record for dataset details and reuse information.
Asynchronous event-based clustering and tracking for intrusion monitoring in UAS
<p>This dataset describes a collection of rosbag files for event-based intruder monitoring using UAS. A DAVIS346 camera was mounted over a DJI Flamewheel F550 Drone, and an onboard computer recorded the sensor information from the event camera. Each dataset includes events, frames, and IMU measurements. The monitoring scenes were recorded outdoors at the School of Engineering of the University of Seville. In each dataset, an intruder moves and hides from the field of view of the camera simulating a scape-intrusion situation. A total of four monitoring setting were recorded:</p> <p><strong>Daylight monitoring:</strong> A daylight scene for intruder monitoring. An intruder runs and hides behind the objects of the scene to evade the camera field of view.<br> <br> <strong>Night light monitoring:</strong> A monitoring scene during the night without the presence of any artificial light. The low light condition increases the difficulty of monitoring task due to the increment of noisy events.<br> <br> <strong>Multi-target:</strong> An experiment with a suspect and a chaser drone moving in the monitoring area. The drone follows the suspect by simulating a pursuit operation.<br> <br> <strong>Monitoring under illumination changes:</strong> A night scene where the lighting conditions changes by the movement of artificial lights in the scene.</p>
UAS Trajectory Model Dynamics at different flight heights: An In-depth Analysis of PPK Georeferencing Results for an Urban Area
<p>In-depth analysis of the PPK georeferencing results when using three different Continuously Operating Reference Station (CORS) stations and one local base station.</p>
UAS dataset for Crop Water Stress Index computation and Triangle Method applications
<p>This dataset is related with a field campaigns carried out on a tangerine field located near Palermo (Sicily), within the Harmonious COST action CA16219 - Harmonization of UAS techniques for agricultural and natural ecosystems monitoring-, activities framework. The general aim was to acquire UAS (Unmanned Aerial Systems) remotely sensed imagery to detect the vegetation stress status. A multispectral camera (RIKOLA) and a thermal one (FLIR) were used. The images are already georeferenced with 3.7 cm pixel size. The dataset could be used to compute the Crop Water Stress Index and to compute the moisture status of the field by means of a combined use of the optical and thermal images. The dataset includes meteo data registered by a meteo station nearby the site.</p>
UAS dataset for soil moisture mapping
<p>This dataset is obtained from a UAS survey on 13-14 June 2019 on the field site MFC2 located in Monteforte Cilento (SA, Italy), within the Harmonious COST action CA16219 - Harmonization of UAS techniques for agricultural and natural ecosystems monitoring-, activities framework. The general aim was soil moisture mapping using the thermal inertia method and vegetation-temperature triangle model method. A multispectral camera (tetracam) and a thermal one (FLIR tau2) were used. The NDVI was calculated from the red and near-infrared reflectance from the multispectral imagery, and the land surface temperature was generated from the thermal imagery</p>
HARMONIOUS Sele River UAS Sample Data
<p>This dataset has been produced during a field campaign along the Sele River, Italy during 22-23 March 2022 within the framework of the <a href="http://www.costharmonious.eu">HARMONIOUS COST Action</a></p> <p>The area interest has a size of ~0.27 hectares. </p> <p>The dataset consists of two main data types:</p> <ol> <li>Thermal Infrared mosaic</li> <li>Multispectral mosaic</li> </ol> <p>The data acquisition was performed using a DJI Matrice M210 UAS and two main sensor systems:</p> <ol> <li>Zenmuse XT2 radiometric</li> <li>MicaSense RedEdge-MX Dual system</li> </ol> <p>The raw data were processed using Pix4D Mapper. Georeferencing was provided by using Ground Control Points (UTM33N)</p> <p><em>Data owner:<br> Dr. László Bertalan,<br> University of Debrecen, Hungary<br> <a href="https://sites.google.com/view/laszlobertalangeo">Website</a></em></p>
UAS 3D Araios
Made by Nicholas Ignasius Susanto NIM 2201744134 Source: Objaverse 1.0 / Sketchfab
Pre-Islamic Silver Coin, Mleiha, Sharjah, UA
Pre-Islamic Silver Coin, Mleiha, Sharjah, UAE. in the collection of the Sharjah Archaeology Authority. . Catalog number unk. Processed in Reality Capture from 343 images. GDH ID Silvercoin 2 Source: Objaverse 1.0 / Sketchfab
CHARMM36-UA POPS simulations (versions 1 and 2) 298 K 1.0 nm LJ switching
<p>CHARMM36-UA POPS simulations (298 K, starting structure from the CHARMM-GUI) performed with a 1.0 nm point at which to switch off the van der Waals interactions. Two different simulations generated with different starting velocities are provided (the files are named v1 and v2 for these different simulations). The trajectories contain only the data from 400-500 ns of the simulations (as per the analysis provided on the nmrlipids blog) and additionally they have been processed with trjconv -skip 10 to keep the upload small.</p>
CHARMM36-UA DOPS simulations (versions 1 and 2) 303 K 1.0 nm LJ switching
<p>CHARMM36-UA DOPS simulations (303 K, starting structure from the CHARMM-GUI with appropriate hydrogen atoms removed) performed with a 1.0 nm point at which to switch off the van der Waals interactions. Two different simulations generated with different starting velocities are provided (the files are named v1 and v2 for these different simulations). The trajectories contain only the data from 400-500 ns of the simulations (as per the analysis provided on the nmrlipids blog) and additionally they have been processed with trjconv -skip 10 to keep the upload small.</p>
OPLS-UA POPE Simulations (versions 1 and 2) 303 K with vdW on H atoms
<p>Two OPLS-UA POPE bilayer simulations performed using GROMACS 4.5.7 for 200 ns with different starting velocities. Simulations were performed with a 1.0 nm cut-off with PME for the Coulombic and a 1.0 nm cut-off for the van der Waals interactions interactions. These simulations were performed at 303 K with a 128 lipid bilayer. The full trajectories are provided bar the initial 100 ns. The starting structure was made through the conversion of an equilibrated OPLS-UA POPC membrane. The PE parameters were constructed by modifying the OPLS-UA POPC of Ulmschneider and Ulmschnider with the standard OPLS lysine parameters and the addition of LJ parameters on the hydrogen atoms in the head group (designed to increase the area per lipid).</p>
OPLS-UA POPE Simulations (versions 1 and 2) 303 K
<p>Two OPLS-UA POPE bilayer simulations performed using GROMACS 4.5.7 for 200 ns with different starting velocities. Simulations were performed with a 1.0 nm cut-off with PME for the Coulombic and a 1.0 nm cut-off for the van der Waals interactions interactions. These simulations were performed at 303 K with a 128 lipid bilayer. The full trajectories are provided bar the initial 100 ns. The starting structure was made through the conversion of an equilibrated OPLS-UA POPC membrane. The PE parameters were constructed by modifying the OPLS-UA POPC of Ulmschneider and Ulmschnider with the standard OPLS lysine parameters.</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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Annotated Behaviour and Observability Dataset (ABODe)
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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.