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395 results for “Aircraft”
Aircraft Traffic Density Database: /Data
<p>This dataset is the associated input for the software, <a href="https://github.com/mit-ll/traffic-density-database">traffic-density-database</a>. It is intended to process observed aircraft traffic information into unmitigated collision rates given user specified geographic regions or aircraft tracks. The user may also specify the time of day, month of year, and day of week to be evaluated. Unmitigated collision rates are useful for aircraft safety assessments, and may be combined with the efficacy of mitigations—e.g., air traffic control, detect and avoid, see and avoid—in order to estimate the total operation safety.</p> <p>The input data included with this release has the following attributes. See the README in the Data directory for more detail on the contents of each data file.</p> <ul> <li>The data span 8727 observed hours, from 4 November 2018 to 3 November 2019. Note that there are periods where there are no radar data available, so the data span does not correspond directly to the full date range extent.</li> <li>There are over 31.65 million flight hours observed (includes both discrete-code and 1200-code aircraft).</li> <li>The vertical spatial (height) discretization corresponds to standard altitude feature thresholds in the national airspace: <ul> <li>100 feet Above Ground Level (AGL): Cutoff to exclude on-ground traffic</li> <li>500 feet AGL: nominal lower manned aircraft operating altitude</li> <li>1200 feet AGL: typical lower altitude for Class E airspace</li> <li>3000 feet AGL: approximate upper bound for Class D airspace</li> <li>5000 feet AGL: approximate upper bound for Class C airspace</li> <li>10,000 feet Mean Sea Level (MSL): typical Class B upper bound, and lower threshold for transponder requirements</li> <li>18,000 feet MSL: lower bound for Class A Airspace</li> <li>29,000 feet MSL: Reduced Vertical Separation Minimum (RVSM) lower altitude bound</li> <li>41,100 feet MLS: RVSM upper altitude bound</li> <li>60,100 feet MSL: upper bound for Class A Airspace</li> </ul> </li> <li>The temporal discretization is 3 hours.</li> <li>The latitude range is 22.83°N to 50.00°N.</li> <li>The longitude range is 127.00°W to 64.83°W. (Note: MATLAB indicates W longitude using negative numbers, e.g., 127.00°W is -127.00.)</li> <li>Two tables with different horizontal spatial discretization are provided: one for all altitudes, where the discretization is 1/6 of a degree in latitude and longitude (corresponding to approximately 10 NM); and one for altitudes below 5000 feet, where the discretization is 1/24 of a degree (approximately 2.5 NM). The table containing all altitudes is the default.</li> </ul>
A Generic Model for Benchmark Aerodynamic Analysis of Fifth-Generation High-Performance Aircraft
<p>Openly available supplementary data to accompany paper https://doi.org/10.3390/aerospace10090746. Data set includes geometry, Pointwise (2022.1.2) and Fluent (2022R1) grid files and corrected experimental data for lift, drag and pitching moment at a freestream velocity of 20 m/s and standard sea level conditions for the SSAM-Gen5 model. When using this data, please cite:</p> <p>Giannelis, N.F.; Bykerk, T.; Vio, G.A. A Generic Model for Benchmark Aerodynamic Analysis of Fifth-Generation High-Performance Aircraft. Aerospace 2023, 10, 746.</p>
Data from: Cumulative energetic costs of military aircraft, recreational and natural disturbance in roosting shorebirds
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Tracking wildlife energy dynamics with unoccupied aircraft systems and 3-dimensional photogrammetry
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Data from: Biogeographical patterns in the seasonality of bird collisions with aircraft
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Data for: Single-blind determination of methane detection limits and quantification accuracy using aircraft-based LiDAR
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Data from: Spatiotemporal variation in disturbance impacts derived from simultaneous tracking of aircraft and shorebirds
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Heterogeneity and chemical reactivity of the remote Troposphere defined by aircraft measurements
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Supporting information for the paper: Constraints on recoil leader properties estimated from X-ray emission in aircraft triggered discharges
<p>Supporting information for the paper: Constraints on recoil leader properties estimated from X-ray emission in aircraft triggered discharges consisting of 18 data files, sorted by figure the data appears in. See 0_READ_ME for information about the individual files and variables.</p>
Mixing ratios and FRFs of ODSs observed in air samples from research aircraft campaigns in 2016 and 2017
<p>This data set consists of mixing ratios and fractional release factors of ozone-depleting substances measured in air samples collected during research aircraft campaigns in 2016 and 2017 which were both sampling air masses related to the upper part of the Asian Summer Monsoon and above. These activities were part of the EU Stratoclim-603557 project and a corresponding manuscript has been submitted to the journal JGR Atmospheres. This version also includes the supplement of that manuscript.</p>
Data from: Quantification of avian hazards to military aircraft and implications for wildlife management
Collisions between birds and military aircraft are common and can have catastrophic effects. Knowledge of relative wildlife hazards to aircraft (the likelihood of aircraft damage when a species is struck) is needed before estimating wildlife strike risk (combined frequency and severity component) at military airfields. Despite annual reviews of wildlife strike trends with civil aviation since the 1990s, little is known about wildlife strike trends for military aircraft. We hypothesized that species relative hazard scores would correlate positively with aircraft type and avian body mass. Only strike records identified to species that occurred within the U.S. (n = 36,979) and involved United States Navy or United States Air Force aircraft were used to calculate relative hazard scores. The most hazardous species to military aircraft was the snow goose (Anser caerulescens), followed by the common loon (Gavia immer), and a tie between Canada goose (Branta canadensis) and black vulture (Coragyps atratus). We found an association between avian body mass and relative hazard score (r2 = 0.76) for all military airframes. In general, relative hazard scores per species were higher for military than civil airframes. An important consideration is that hazard scores can vary depending on aircraft type. We found that avian body mass affected the probability of damage differentially per airframe. In the development of an airfield wildlife management plan, and absent estimates of species strike risk, airport wildlife biologists should prioritize management of species with high relative hazard scores.
Data from: Take-off engine particle emission indices for in-service aircraft at Los Angeles International Airport
We present ground-based, advected aircraft engine emissions from flights taking off at Los Angeles International Airport. 275 discrete engine take-off plumes were observed on 18 and 25 May 2014 at a distance of 400 m downwind of the runway. CO2 measurements are used to convert the aerosol data into plume-average emissions indices that are suitable for modelling aircraft emissions. Total and non-volatile particle number EIs are of order 1016–1017 kg−1 and 1014–1016 kg−1, respectively. Black-carbon-equivalent particle mass EIs vary between 175–941 mg kg−1 (except for the GE GEnx engines at 46 mg kg−1). Aircraft tail numbers recorded for each take-off event are used to incorporate aircraft- and engine-specific parameters into the data set. Data acquisition and processing follow standard methods for quality assurance. A unique aspect of the data set is the mapping of aerosol concentration time series to integrated plume EIs, aircraft and engine specifications, and manufacturer-reported engine emissions certifications. The integrated data enable future studies seeking to understand and model aircraft emissions and their impact on air quality.
Data from: Aeroecology meets aviation safety: early warning systems in Europe and the Middle East prevent collisions between birds and aircraft
The aerosphere is utilized by billions of birds, moving for different reasons and from short to great distances spanning tens of thousands of kilometres. The aerosphere, however, is also utilized by aviation which leads to increasing conflicts in and around airfields as well as en-route. Collisions between birds and aircraft cost billions of euros annually and, in some cases, result in the loss of human lives. Simultaneously, aviation has diverse negative impacts on wildlife. During avian migration, due to the sheer numbers of birds in the air, the risk of bird strikes becomes particularly acute for low-flying aircraft, especially during military training flights. Over the last few decades, air forces across Europe and the Middle East have been developing solutions that integrate ecological research and aviation policy to reduce mutual negative interactions between birds and aircraft. In this paper we (1) provide a brief overview of the systems currently used in military aviation to monitor bird migration movements in the aerosphere, (2) provide a brief overview of the impact of bird strikes on military low-level operations, and (3) estimate the effectiveness of migration monitoring systems in bird strike avoidance. We compare systems from the Netherlands, Belgium, Germany, Poland and Israel, which are all areas that Palearctic migrants cross twice a year in huge numbers. We show that the en-route bird strikes have decreased considerably in countries where avoidance systems have been implemented, and that consequently bird strikes are on average 45% less frequent in countries with implemented avoidance systems in place. We conclude by showing the roles of operational weather radar networks, forecast models and international and interdisciplinary collaboration to create safer skies for aviation and birds.
Data from: Aircraft sound exposure leads to song frequency decline and elevated aggression in wild chiffchaffs
Abstract: 1. The ubiquitous anthropogenic low-frequency noise impedes communication by masking animal signals. To overcome this communication barrier, animals may increase the frequency, amplitude and delivery rate of their acoustic signals, making them more easily heard. However, a direct impact of intermittent, high-level aircraft noise on birds' behaviour living close to a runway has not been studies in detail. 2. We recorded common chiffchaffs Phylloscopus collybita songs near two airports and nearby control areas, and we measured sound levels in their territories at Manchester airport. The song recordings were made in between aircraft movements, when ambient sound levels were similar between airport and control populations. We also conducted playback experiments at the airport and a control population to test the salience of airport, and control population specific songs. 3. In contrast to the general pattern of increased song frequency in noisy areas, we show that common chiffchaffs at airports show a negative relationship between noise exposure level and song frequency. 4. Experimental data show that chiffchaffs living near airports also respond more aggressively to song playback. 5. Since the decrease in song frequency results in increased overlap with aircraft noise, these findings cannot be explained as an adaptation to improve communication. The increased levels of aggression suggests that chiffchaffs, like humans, might be affected behaviourally by extreme noise pollution. These findings should influence environmental impact assessments for airport expansions globally.
H2 and H2 deuterium content data collected around the tropopause by the CARIBIC aircraft
<p>This zip file contains the final corrected data that were used for the paper: Batenburg, A. M., Schuck, T. J., Baker, A. K., Zahn, A., Brenninkmeijer, C. A. M., and Röckmann, T.: The stable isotopic composition of molecular hydrogen in the tropopause region probed by the CARIBIC aircraft, Atmos. Chem. Phys., 12, 4633-4646, doi:10.5194/acp-12-4633-2012, 2012<br> Please cite the original ACP article when using these data.<br> The paper also contains more information about how these data were collected and calibrated.</p> <p>H2 and deltaD(H2) are calibrated using one, two, or three laboratory reference air cylinders, depending on measurement period.<br> The H2 mixing ratio of the reference cylinders was determined by UHEI-IUP or MPI-BGC.<br> The deltaD(H2) of the reference cylinders is linked to the VMOW scale by measurements of air mixtures containing H2 standards of known isotopic composition.<br> H2 scale: MPI2009, Jordan and Steinberg, AMT, 2011<br> deltaD(H2) units: permil deviation from VSMOW, Gonfiantini et al., IAEA-TECDOC-825, IAEA<br> An empirical correction was applied to the H2 mixing ratios based on data collected within the EUROHYDROS project.<br> H2 mixing ratio errors can be calculated as 2.5% of the mixing ratio divided by the square root of the number of GC-IRMS repeat measurements.<br> deltaD(H2) errors can be calculated as 4.5 permil divided by the square root of the number of GC-IRMS repeat measurements.<br> More information on the collection of these samples and their measurement can be found in Batenburg et al., ACP, 2012, doi:10.5194/acp-12-4633-2012.<br> More detailed information on the GC-IRMS measurement procedure can be found in Batenburg et al., ACP, 2011, doi:10.5194/acp-11-6985-2011.<br> The 0 or 1 pollution/outlier flags were assigned by an iterative procedure that is described Batenburg et al., 2012.</p> <p>The zip file contains the data in .xls and .csv format, as well as in the NASA AMES text format that is used within the CARIBIC project.<br> We thank Armin Rauthe-Schöch for generating the AMES file.</p> <p>The corresponding author of the paper can be reached through annekebatenburg you-know-what-symbol gmail.com for questions about the hydrogen measurements.<br> For the results of other measurements that were done on these samples (like the GHG measurements), it's better to contact someone involved with the CARIBIC/IAGOS database. Armin Rauthe-Schöch (armin.rauthe-schoech you-know-what-symbol mpic.de) can probably help you out.</p>
Southern Ocean Air-Sea Carbon Fluxes from Aircraft Observations: Modeling Datasets
The Southern Ocean plays an important role in determining atmospheric CO2, yet estimates of air-sea CO2 flux for the region diverge widely. We constrain Southern Ocean air-sea CO2 exchange by relating fluxes to horizontal and vertical CO2 gradients in atmospheric transport models and then apply atmospheric observations of these gradients to estimate fluxes. Aircraft-based measurements of the vertical atmospheric CO2 gradient provide robust flux constraints. We find an annual-mean flux of –0.55±0.23 Pg C yr–1 (net uptake) south of 45°S during 2009–2018. This is consistent with the mean of atmospheric-inversion estimates and surface-ocean pCO2-based products, but our data indicate stronger annual-mean uptake than suggested by recent interpretations of profiling-float observations.
Queensland Cloud Seeding Research Program (QCSRP) Aircraft Measurements
Research aircraft collected in situ microphysical and aerosol data during the QCSRP over two summer seasons in Queensland, Australia between 2007 and 2009, as described in Tessendorf et al. (2012, BAMS). The files are netcdf and/or .txt formats, and are roughly 3 GB altogether.
MATLAB Codes for: A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults
<p><strong>Description:</strong></p> <p>This repository contains the MATLAB codes used in our paper [1] on fault diagnosis of inter-shaft aircraft bearings, published by MDPI Machines. The codes encompass all the necessary materials to reproduce the findings outlined in the paper. </p> <p><strong>Dataset Access:</strong></p> <p>The dataset utilized in this study is available under request from the authors of reference [8] in our paper. To obtain the dataset, please follow the instructions provided by the respective authors.</p> <p><strong>Data Format:</strong></p> <p>The dataset is saved in '*.npy' 3D variable format. To reproduce this study, it is necessary to transform these variables to '.mat' format since the codes are implemented in MATLAB. You can find the codes for transferring the 3D '*.npy' files to '*.mat' files here [<a href="../records/10184606">here</a>]</p> <p>We appreciate your interest in our work.</p> <p>[1] Berghout, Tarek, Toufik Bentrcia, Wei Hong Lim, and Mohamed Benbouzid. 2023. "A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults" <em>Machines</em> 11, no. 12: 1089. https://doi.org/10.3390/machines11121089</p>
Aircraft strut door.
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DT-MAS architecture for smart maintenance of aircraft fuel distribution systems
<p><strong>Project Goal:</strong> Develop a digital twin architecture using a multi-agent system and AI for smart maintenance of aircraft distribution systems.</p> <p><strong>Objective:</strong> Build a reliable model that accurately represents the real system in an offline environment.</p> <p><strong>Methodology : </strong></p> <ul> <li>Developed a simulation based on an aircraft distribution system model, mimicking real system behavior.</li> <li>Created a dataset with four runs, five scenarios per run, and five operating points per scenario.</li> <li>Simulated healthy and faulty conditions using MATLAB-injected faults across various categories.</li> <li>Focused on four system components: hydraulic pump, tanks, engines, and pumps.</li> <li>Generated data for 11 healthy and faulty scenarios, including six fault types: <ul> <li>Noise on instruments</li> <li>Abnormal instrument readings</li> <li>Minor service problems</li> <li>External leakage</li> <li>Parameter deviation</li> <li>Structural deficiency</li> </ul> </li> <li>Features used for analysis: <ul> <li>Pump flow for pumps</li> <li>Pump motor speed for hydraulic pumps</li> <li>Driver power</li> <li>Tank volume and temperature</li> </ul> </li> </ul> <p><strong>Expected Results:</strong></p> <ul> <li>Compare performance of different asset health estimation models.</li> <li>Develop new predictive maintenance strategies.</li> <li>Predict and emulate complex aircraft behavior through multi-agent systems and AI.</li> </ul>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.