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535 results for “DUST”
Solar radiation estimation in West Africa: impact of dust conditions during 2021 dry season
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Inhomogeneous super-spheroid dust model
<p>The database contains the single scattering properties of the inhomogeneous super-spheroid models in the ultraviolet to thermal bands.</p> <p> </p>
Circumnuclear Dust in Luminous Early-Type Galaxies -- I. Sample Properties and Stellar Luminosity Models
<p>This dataset contains the HST mosaic images created and described in the paper "Circumnuclear Dust in Luminous Early-Type Galaxies -- I. Sample Properties and Stellar Luminosity Models" by J. Davidson, B. Boizelle, J. Walsh, A. Barth, E. Rasmussen, A. Baker, D. Buote, J. Darling, L. Ho, K. Kabasares, and J. Cohn.</p> <p>Each mosaic was created by drizzling the data to a pixel scale of 0.08 and a pixel fraction of 0.75. The data are mostly in the infrared and optical and only occasionally in the ultraviolet.</p> <h2>Data Structure</h2> <p>These data are organized by target and each target has its own .zip file. Each .zip file is organized by HST camera, then by filter. For example, for the target Hydra A, the data are sorted into the following folders:</p> <ul> <li><strong>acs_sbc</strong> <ul> <li><strong>f140lp</strong></li> <li><strong>f150lp</strong></li> </ul> </li> <li><strong>acs_wfc</strong> <ul> <li><strong>f814w</strong></li> </ul> </li> <li><strong>wfc3</strong><br> <ul> <li><strong>f110w</strong></li> <li><strong>f160w</strong></li> <li><strong>f475w</strong></li> </ul> </li> <li><strong>aligned</strong></li> </ul> <p>The <strong>aligned</strong> folder for each target contains all the aligned mosaic images. These were aligned to their respective <em>wfc3/f160w</em> image using TweakReg, then further corrected using interpolation to obtain sub-pixel accuracy.</p> <h2>File Naming</h2> <p>All mosaic image files for all filters follow the same naming convention: <strong><target name + filter + pixel fraction + pixel scale>_ drz_sci.fits.</strong> For example, the Hydra A <em>f160w</em> mosaic is named <strong>hydraa_f160w_pxfr075_pxsc008_drz_sci.fits.</strong></p> <p>PSF files, both empirical and Tiny Tim (each drizzled to the same pixel scale and pixel fraction as the HST data), are included in each <strong>f110w</strong> and <strong>f160w</strong> folder and follow a similar naming convention. To these file names are added either *emp_psfdrz* for the empirical PSF or *psfdrz* for the Tiny Tim PSF. For example, the Hydra A <em>f160w</em> empirical PSF file is named <strong>hydraa_f160w_emp_psfdrz_pxfr075_pxsc008_drz_sci.fits,</strong> whereas the Tiny Tim PSF file is named <strong>hydraa_f160w_psfdrz_pxfr075_pxsc008_drz_sci.fits.</strong></p> <p>The <strong>f160w</strong> folders for all targets also contain a few extra files:</p> <ol> <li>Mask files, called <strong>f160w_mask.fits</strong> in all cases, which indicate the positions and extents of all extraneous sources of light (foreground stars, background galaxies, cosmic ray hits, etc.) in the respective <em>f160w</em> mosaics.</li> <li>A .txt file that contains the final GALFIT MGE solution and follows the naming convention <strong><target>_f160w_galfitmge_dustmask_tinytim_sol.txt.</strong> Note that the Tiny Tim PSF was used to obtain the final MGE solution in every case.</li> <li> A .fits file that contains the model based on the final GALFIT MGE solution. These follow the naming convention <strong><target>_f160w_pxfr075_pxs0080_drz_galfit_ttpsf_qbounds020.fits.</strong></li> <li><em>For NGC 3862 only</em>, we also obtained an MGE solution using the empirical PSF for comparison purposes. For this target, the .txt MGE solution that utilizes the empirical PSF is named <strong>ngc3862_f160w_galfitmge_dustmask_emp_sol.txt </strong>and the corresponding model .fits file is named <strong>ngc3862_f160w_pxfr075_pxs0080_drz_galfit_cenpsf_emppsf_qbounds020.fits.</strong></li> </ol> <p>For NGC 1387, NGC 3862, NGC 4261, and NGC 4435, a second round of observations were obtained using a subarray and shorter exposure times. The <strong>f110w</strong> and <strong>f160w</strong> folders for these galaxies therefore also have a few extra files that contain the drizzled images and empirical and Tiny Tim PSFs for these subarray exposures. These files follow the same naming conventions as previously described, with the addition of *subarr* to distinguish between the full array and subarray files. For example, the NGC 1387 drizzled subarray image is named <strong>ngc1387_f160w_subarr_pxfr075_pxsc008_drz_sci.fits, </strong>whereas the full-array, longer exposure time image is named <strong>ngc1387_f160w_pxfr075_pxsc008_drz_sci.fits. </strong>Please note that these subarray images were not used in any of the final MGEs or the analysis described in the paper, but are included here for sake of completeness.</p> <p>In the <strong>aligned </strong>folders, all aligned images follow the same naming convention: <strong><target>_<camera>_<filter>_pxfr075_pxsc008_drz_align_sci.fits.</strong></p>
"A Comprehensive Global Modelling Assessment of Nitrate Heterogeneous Formation on Desert Dust": Column loads monthly means per species.
<p>Column load monthly means of the different species traced with the MONARCH model. <br>The period is 201707 - 201712 (spin-up) and 201801 - 201812 (study period). <br><br>Files are distributed by type of mechanism (fTEQ, HYB and DBCLL) and species in the structure: <br><br><mechanism>_outputs/<methodology>/monthly_means/<species>/<species>_<YYYYMM>.nc</p>
Quantifying the large-scale electrification equilibrium effects in dust storms using field observations at Qingtu Lake Observatory
<p>The measured divergence of the electric field, PM<sub>10</sub> concentration, ambient temperature and relative humidity time series during dust storms (at Qingtu Lake observatory) are given in these datasets.</p>
Age-Dependent Pulmonary Reactivity to House Dust Mite Allergen: A Model of Adult-Onset Asthma?
<p>3mo and 9mo mice treated with HDM or PBS. Right lung lavages and fixed in 4% PFA. Table represents average IHC scores for 6 different antibodies: IFN-gamma, IL-4, IL-13, IL-17A, Eotaxin-1, CD-4</p>
Galactic dust absorption maps
<p>This dataset contains the 3D structure of interstelllar dust in the vicinity of the Sun, as well as its logarithmic spatial correlation power spectrum. It was computed from Gaia absorption data using Gaussian process regression.</p> <p>The simple_cube contains only mean and standard deviation while the full_cube contains 50 approximate posterior samples.</p> <p>The corresponding paper is available on arxiv:1901.05971</p> <p>In this version the documentation was corrected. The coordinate system does not have the half-pixel shift that is described in the previous data set versions, leading to an exactly centered box. Additionally, the description of the relation of power spectrum and dust realization was extended to include the a-priori mean of 0.001 e-folds of absorption per parsec.<br> The numerical content of the data set is identical to version 1.1</p>
Atmospheric processing of loess dust particles in a polluted urban area of China
<p>STATEMENT</p> <p> </p> <p>This file is for the upload of data for 2018JD029956; the basic data for the article are available. The research data follows the FAIR Data Principles and demonstrate compliance with international standards for data repositories.</p> <p>Please contact Prof. Yang Chen (<a href="mailto:chenyang@cigit.ac.cn">chenyang@cigit.ac.cn</a>) if you have any questions.</p>
Simulation data files of the article: "Gas accretion damped by dust back-reaction at the snowline"
<p>We make available here the simulation output files used in the paper "Gas accretion damped by dust back-reaction at the snowline" by Gárate et al. (submitted to A&A). The simulation shows the gas and dust evolution of a protoplanetry disk around the snowline, considering the dust back-reaction.</p> <p>The simulations were performed using TwoPopPy (Birnstiel et al, 2012) to solve the advection of gas and dust.</p> <p>Each file corresponds to one simulation. Each files contains the radial profile of the physical properties at different snapshots.</p> <p>The simulation data is saved in numpy format. We include the jupyter notebook as a guide of how to read and plot the simulation files.</p>
Gas permeability and mechanical properties of dust grains at hyper- and zero-gravity
<p>Two-phase flow experimental data obtained from the TEMPusVoLA facility over the course of three Zero-gravity parabolic flight campaigns (<strong>PFCs</strong>). The experiment, designated 'System 1' , contains low-pressure gas and analogue dust particles (of diverse compositions, shapes, and sizes) that experience a low-pressure gas cross-wind normal to the gravitational force vector. During steady flight, the particles pack into the lower end of the chamber and the layer of granular media creates a pressure differential, which is used to extract permeability coefficients in both the continuum and molecular-diffusion dominated regimes. We compare the permeability both with and without the effect of gravity and make comparisons with the conditions on low-gravity objects in the solar system, such as planetesimals and the dust particles that comprise them. In particular, we are able to compare and contrast the cohesiveness of the dust-regolith analogue types, which has consequences for formation theories of Solar-system bodies. </p> <p> </p> <p><strong>Data products: </strong></p> <p><strong>Pressure, acceleration</strong></p> <p>Each file corresponds to a particular parabolic flight campaign and day of flight. The 4th Swiss PFC lasted 1 day in 2020. The 75 ESA PFC lasted 3 days in 2021, and the the 78th ESA PFC lasted 3 days in 2022. Data was obtained in System 1 during all flights of all campaigns, and for a set of 12-16 parabolas. On each parabola, we studied the sample in one out of three chambers and changed the gas mass flux rate after cycling through all three samples. The below files were used to correlate changes in sample gas permeability with changes in gravitational loading. </p> <p>-- Raw pressure files output from 6-channel pressure logger measuring pressure as function of time as .csv files: DATALOG_DATE. </p> <p>-- Raw acceleration files output from accelerometer measuring gravitational load on aircraft.</p> <p><strong>Imaging data</strong></p> <p>-- Movie sequences obained with high-resolution thor-labs camera. All three chambers were imaged, despite only one chamber being subject to active gas flow at any given time. movie1 and movie 2 are referenced in the associated publication. </p> <p><strong>Multimeter data</strong></p> <p><strong>-- </strong>Output from photo-diodes measured across 9 channels using a Keithley Multimeter/Multiplexor. Acquisition speed was 27 Hz. </p>
FIGURE 11 in Plant diversity of Khuzestan and dust sources in the southwest of Iran, with a checklist of vascular plants
FIGURE 11. Selected several plants of Khuz in their habitats. a: Mandragora officinalis, a medicin species (recorded by the author for Iran); b: Heliotropium curassavicum (recorded by the author for Iran); c: Satureja khuzistanica, an endangered, endemic and medicinal species (recorded as new species by the author); d: Pluchea dioscoridis, an aromatic species (recorded by the author for Iran); e: Dionysia khuzistanica, an endangered and endemic species (recorded as new species by the author); f: Astragalus gypsocola, an endangered and endemic species; g: Echium khuzistanicum, an endangered and endemic species in its habitat in the southern part of Khuz; h: Azilia eryngioides, an endemic monotypic genus (Photos by M. Dinarvand).
FIGURE 10 in Plant diversity of Khuzestan and dust sources in the southwest of Iran, with a checklist of vascular plants
FIGURE 10. Characteristic landscapes in the Zagros (Khuz territory). a: Open forests of Quercus brantii; b: Juniperus woodlands as small patches creeping on rocks; c: Open to semi-dense shrublands of Lonicera nummulariifolia in the Zagros; d: Open to semi-dense shrublands of Amygdalus haussknechtii; e: Colchicum wendelboi, as a subalpine vegetation; f: Subalpine vegetation dominated by thorncushion plants and aromatic species (Photos by M. Dinarvand).
FIGURE 9 in Plant diversity of Khuzestan and dust sources in the southwest of Iran, with a checklist of vascular plants
FIGURE 9. Characteristic landscapes in the plain area of Khuz. a: Salty soil places with halophytes; b: Salt marsh vegetation dominated by Halocnemum strobilaceum; c: Horalazim wetland; d: Dune area with Calligonum intertextum shrubs on top of hills; e: Dust source (in Mahshahr); f: Natural biologic fixation of soil with local plants (Capparis spinosa); g: Water pit in dust source area; h: Grazing pressure (Suaeda vermiculata and Halocnemum strobilaceum) in dust sources (Photos by M. Dinarvand).
FIGURE 8 in Plant diversity of Khuzestan and dust sources in the southwest of Iran, with a checklist of vascular plants
FIGURE 8. The life-form spectrum of the flora of dust areas of Khuz. Ch: chamaephytes; He: hemicryptophytes; Ph: phanerophytes; C: cryptophytes; Th: therophytes.
FIGURE 6 in Plant diversity of Khuzestan and dust sources in the southwest of Iran, with a checklist of vascular plants
FIGURE 6. The proportion of phytogeographical groups in the flora of Khuz. IT: Irano-Turanian, SS: Sahara-Sindian, Cosm: Cosmopolitan, Endemic.
FIGURE 3. Ombrothermic graphs prepared using 22 in Plant diversity of Khuzestan and dust sources in the southwest of Iran, with a checklist of vascular plants
FIGURE 3. Ombrothermic graphs prepared using 22-year (1996–2018) data from three synoptic stations close to the dust resources area (south, east, and center of Khuz).
FIGURE 5 in Plant diversity of Khuzestan and dust sources in the southwest of Iran, with a checklist of vascular plants
FIGURE 5. The life-form spectrum of the flora of Khuz. Ch: chamaephytes, He: hemicryptophytes, Ph: phanerophytes, C: cryptophytes, and Th: therophytes.
FIGURE 2 in Plant diversity of Khuzestan and dust sources in the southwest of Iran, with a checklist of vascular plants
FIGURE 2. Map of dust resources in Sub-Basin of Khuz rivers (prepared by Study section of dust resources-RIFR).
FIGURE 7 in Plant diversity of Khuzestan and dust sources in the southwest of Iran, with a checklist of vascular plants
FIGURE 7. sd: dune species; s: species of salty soil area; sd, s: species adapted to both climate and soil of Khuz.
FIGURE 1 in Plant diversity of Khuzestan and dust sources in the southwest of Iran, with a checklist of vascular plants
FIGURE 1. (Right): Map of Iran in the Middle East, the inset shows the position of Khuzestan province; (Left): Topographic map of Khuzestan province (prepared by F. Soozangar).
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