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4,376 results for “magnetism”
Dataset supporting the paper "Superconducting Scanning Tunneling Microscope Tip to Reveal Sub-millielectronvolt Magnetic Energy Variations on Surfaces. J. Phys. Chem Lett. 12, 2983 (2021)"
<p>Dataset corresponding to theoretical calculations in the supporting information of the paper "Superconducting Scanning Tunneling Microscope Tip to Reveal Sub-millielectronvolt Magnetic Energy Variations on Surfaces" J. Phys. Chem Lett. 12, 2983 (2021), <a href="https://doi.org/10.1021/acs.jpclett.1c00328">https://doi.org/10.1021/acs.jpclett.1c00328</a></p> <p>List of files:</p> <p>Several folders corresponding to the figures of the supporting information. They contain:</p> <ul> <li>.siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>).</li> <li>.agr files: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).</li> </ul>
Raw Data for "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction"
<p>This upload contains the raw data used for Fig. 3-5 in "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction". Experimental conditions and details about the datasets are given in a "ReadMe.txt" file.</p>
Dataset for the published article "ITER relevant multi-emissive sheaths at normal magnetic field inclination"
<p>The data contained in the zip files constitute the main research data of the publication entitled as "<a href="https://iopscience.iop.org/article/10.1088/1741-4326/acaabd">ITER relevant multi-emissive sheaths at normal magnetic field inclination</a>" [1]. All the datasets constitute post-processed output from the 2D3V SPICE2 Particle-In-Cell (PIC) code. All the PIC simulations have been performed by M. Komm and A. Podolnik. The input is specified by the plasma density, the electron temperature and the surface temperature. The plasma parameters are relevant to partially mitigated ITER edge-localized modes (ELMs). The output concerns the incident plasma current densities, the emitted electron current densities and their standard deviation, the normal wall electrostatic field, the average electron incident energy, the average electron incident angle with respect to the wall normal and the virtual cathode depth. </p> <p>The assumptions below are followed in all simulations: (i) The Bohm pre-sheath structure is unaltered by the escaping emitted electrons, since the ions are injected at the plasma boundary with a speed distribution satisfying the Bohm criterion. (ii) Irrespective of the emission, the wall is biased with respect to the plasma boundary with a magnitude fixed by the ambipolarity of the plasma fluxes. (iii) The sheath is collisionless. (iv) The wall is perfectly planar. (v) A homogeneous quasi-neutral plasma boundary and an infinite emitting wall with a homogeneous prescribed surface temperature are considered.</p> <p>Sheaths that form between plasma-facing components (PFCs) and standard scrape-off-layer plasmas can be described by the classical model of one-dimensional magnetized multi-positive ion sheaths. There are various conditions that need to be satisfied for this model to be valid such as negligible cross-field drifts, low collisionality and weak electron emission.</p> <p>In contemporary metallic tokamaks, the weak emission condition is violated in the divertor region during intra-ELM as well as inter-ELM periods; thermionic emission being an effective electron emission mechanism from hot tungsten PFCs. As a result of the localized ELM-wetted area, the incident plasma currents can be assumed to remain nearly ambipolar and thus the non-ambipolar current should be equal to the emitted current that escapes to the Bohm pre-sheath. This escaping current density generates a strong volumetric Lorentz force that drives melt layer motion leading to macroscopic PFC erosion. At very elevated surface temperatures, the nominal thermionic current densities are so large that they become incompatible with the classical Bohm pre-sheath structure. As a consequence, space charge accumulation in the sheath leads to the formation of a virtual cathode that limits the escaping thermionic current to a constant value causing the recapture of a fraction of the thermo-electrons. Thus, there is a transition from a monotonic to a non-monotonic potential profile, with the latter known as the space-charge limited (SCL) regime of the emissive sheath. In the case of oblique magnetic field inclination angles, the SCL transition is still realized, but further complications arise due to the suppression of the nominal thermionic current by recapture during Larmor gyration. In contemporary tokamaks, this transition generally occurs at temperatures below the tungsten melting point, thus particular attention has been paid to the SCL sheaths, since they nearly exclusively surround the molten tungsten PFCs. The thermionic emissive sheath in the SCL regime has been thoroughly investigated in our previous works, where an accurate semi-empirical expression for the limited value of the escaping thermionic current as function of the plasma conditions and magnetic field inclination angle was constructed on the basis of systematic PIC simulations [2-4].</p> <p>On the other hand, during ITER intra-ELM periods, the predicted elevated electron temperatures and high plasma densities of the pre-sheath edge should have a strong impact on the emissive sheath established above hot tungsten PFCs. In particular, the high plasma electron temperatures could enable significant contributions from electron-induced electron emission (secondary electron emission and electron backscattering), the intense normal surface electrostatic fields indicate that thermionic emission is coupled with field emission (in the Schottky regime) and the strong plasma currents suggest that virtual cathodes are formed at much higher surface temperatures (so that the monotonic potential profile regime is of primary interest for melt motion). In order to explore this novel multi-emissive sheath regime, a a comprehensive tungsten electron emission model has been implemented that features accurate analytical descriptions of the yields, energy and angular distributions for the processes of field-assisted thermionic emission, secondary electron emission and electron backscattering [5]. In the present publication [1], at normal magnetic field inclinations, highly accurate analytical semi-empirical expressions are provided for the secondary electron emission current, electron backscattering current and thermionic current in the monotonic regime as well as for the total escaping current in the SCL regime. These semi-empirical expressions have been benchmarked against comprehensive PIC simulations, whose primary post-processed data are provided herein.</p> <p>[1] P. Tolias, M. Komm, S. Ratynskaia and A. Podolnik, "ITER relevant multi-emissive sheaths at normal magnetic field inclination", Nucl. Fusion 63 (2023) 026007.<br> [2] M. Komm, S. Ratynskaia, P. Tolias, J. Cavalier, R. Dejarnac, J. P. Gunn and A. Podolnik, "On thermionic emission from plasma-facing components in tokamak-relevant conditions", Plasma Phys. Control. Fusion 59 (2017) 094002.<br> [3] M. Komm, P. Tolias, S. Ratynskaia, R. Dejarnac, J. P. Gunn, K. Krieger, A. Podolnik, R. A. Pitts and R. Panek, "Simulations of thermionic suppression during tungsten transient melting experiments", Phys. Scr. T170 (2017) 014069.<br> [4] M. Komm, S. Ratynskaia, P. Tolias and A. Podolnik, "Space-charge limited thermionic sheaths in magnetized fusion plasmas", Nucl. Fusion 60 (2020) 054002.<br> [5] P. Tolias, M. Komm, S. Ratynskaia and A. Podolnik, "Origin and nature of the emissive sheath surrounding hot tungsten tokamak surfaces", Nucl. Mater. Energy 25 (2020) 100818.</p> <p> </p>
Dataset A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN)
<p>This dataset gathers synthetic T2-weighted magnetic resonance (MR) images generated using FaBiAN, a Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates fast spin echo (FSE) sequences of the developing fetal brain throughout gestation.<br> This dataset is associated with the following paper:<br> Lajous H. et al. (2022) A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Scientific Reports. https://doi.org/10.1038/s41598-022-10335-4</p> <p>This dataset provides images simulated by FaBiAN based on the specific implementation of FSE sequences by two MR vendors (Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE), Siemens Healthcare, and Single-Shot Fast Spin Echo (SS-FSE), GE Healthcare) at 1.5 T or 3 T.<br> Automated brain tissue annotations of the low-resolution series and super-resolution (SR) reconstructions are also included.</p> <p>Works using any of these data should cite the following references:<br> - Lajous, H. et al. A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Scientific Reports (2022). https://doi.org/10.1038/s41598-022-10335-4<br> - Lajous, H., Roy, C. W., Yerly, J. & Bach Cuadra, M. Medical-Image-Analysis-Laboratory/FaBiAN: FaBiAN v1.2 (1.2). Zenodo (2022). https://doi.org/10.5281/zenodo.5471094<br> - Lajous, H. et al. Dataset A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Zenodo (2022). https://doi.org/10.5281/zenodo.6477946</p> <p><br> Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2022.</p>
MHD Model of Ganymede's Magnetosphere: Predicted OCFB and magnetic footprint surface locations for Juno's flyby
<p>This dataset contains model results from a magnetohydrodynamic (MHD) model of Ganymede's magnetosphere adapted to Juno's PJ34 flyby in 2021. Here we publish coordinates for the predicted location of the open-closed-field line-boundary (OCFB) on Ganymede's surface. Additionally we provide coordinates of Juno's magnetic footprint, namely the surface locations that connect to Juno's trajectory through magnetic field lines.</p> <p>For the surface locations we use a western longitude planetographic coordinate system where 0° longitude is in direction of the y-axis and 90° in direction of the x-axis of the cartesian GPhiO system. The GPhiO system is defined by the primary direction<br> z parallel to Jupiter’s rotation axis, the secondary direction y is pointing towards Jupiter barycenter<br> and x completes the right-handed system approximately in direction of plasma flow.</p> <p><strong>Duling2022_JunoGanymede_modeled_surface_OCFB.txt</strong></p> <p>Columns:</p> <p>Longitude [°]<br> Northern OCFB latitude [°]<br> Southern OCFB latitude [°]</p> <p><strong>Duling2022_JunoGanymede_modeled_magnetic_footprint.txt</strong></p> <p>Columns:</p> <p>Spacecraft time [UTC]<br> Magnetic footprint longitude [°]<br> Magnetic footprint latitude [°]<br> Length of field line between Juno and surface [radii]<br> Length of field line between Juno and surface [km]<br> r coordinate of Juno [radii]<br> Latitude of Juno [°]<br> Longitude of Juno [°]<br> x of Juno in GPhiO [km]<br> y of Juno in GPhiO [km]<br> z of Juno in GPhiO [km]</p> <p><strong>Duling2022_JunoGanymede_surface_map.png</strong></p> <p>A plot that visualizes the data of this repository.</p>
Dataset for: "Dynamical properties of solid and hydrated collagen: Insight from nuclear magnetic resonance relaxometry"
<p>The dataset contains a full set of 1H magnetization curves (1H magnetization versus time) for solid and hydrated collagen and collagen-based artificial tissues.</p> <p>DOI of article: <a href="https://doi.org/10.1063/5.0191409" target="_blank" rel="noopener">https://doi.org/10.1063/5.0191409</a></p> <p>This research was funded by the National Science Centre, Poland, Grant No. 2021/43/B/NZ5/01602.</p>
Experimental data for "Yu-Shiba-Rusinov bands in a self-assembled kagome lattice of magnetic molecules"
<p>Here, we provide all original data used in the manuscript "Yu-Shiba-Rusinov bands in a self-assembled kagome lattice of magnetic molecules"</p> <p>We acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through projects 277101999 (CRC 183, project C03) and FR2726/10-1.</p>
Magnetic Anomaly Map of Paraná State - Final gridded data
<p>This gridded data is part of the article entitled: "THE MAGNETIC ANOMALY MAP OF PARANÁ STATE: AN<br>INTEGRATION OF AIRBORNE SURVEYS PERFORMED OVER THE YEARS", which was submitted in December 2023 to the Brazilian Journal of Geophysics. The article is still under review. </p> <p>These files include airborne magnetic data integrated at 1800m altitude, and the upwarded data to 2700m. Details of the integration and general interpretations are described in the related article.</p>
Dataset In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging
<p>This is the dataset related to the paper "In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging", E. Najdenovska*, Y. Aléman-Gómez*, G. Battistella, M. Descoteaux, P. Hagmann, S. Jacquemont, P. Maeder, J.-P. Thiran, E. Fornari and M. Bach Cuadra, Sci. Data. 5:180270 doi: 10.1038/sdata.2018.270 (2018). *Equally contributed authors.</p> <p>We provide NifTI-1 files representing a digital atlas of seven thalamic subparts per hemisphere. More precisely, the files include the spatial probabilistic atlas maps for each thalamic subpart (Thalamus_Nuclei-HCP-4DSPAMs.nii.gz) and the maximum likelihood atlas (Thalamus_Nuclei-HCP-MaxProb.nii.gz) in MNI space. The region corresponding to each labeled thalamic part respectively is given in the look-up table Thalamic_Nuclei-ColorLUT.txt. The NIFTI files can be visualised with the main available tools such as tkmedit, freeview or 3D-Slicer.</p> <p>We also provide a step by step pseudo code for creating the atlas.</p>
Rotation of electron beams in the presence of localised, longitudinal magnetic fields
<p>Electron Bessel beams have been generated by inserting an annular aperture in the illumination system of a TEM.</p> <p>These beams have passed through a localised magnetic field.</p> <p>As a result a low amount of image rotation (which is expected to be proportional to the longitudinal component of the magnetic field) is observed in the far field.</p> <p>A measure of this rotation should give access to the magneti field.</p> <p>The two datasets have been acquired in a FEI Titan<sup>3</sup> microscope, operated at 300kV.</p> <p>The file focal_series.tif contains a series of images acquired varying the magnetic field through the objective lens.</p> <p>The file line_profile.ser contains a series of images acquired by scanning the beam over a sample with several magnetised nanopillars.</p> <p>For reference, check the associated publication:<br> <em>Giulio Guzzinati, Armand Béché, Damien McGrouther and Jo Verbeeck</em>, <strong>Prospects for out-of-plane magnetic field measurements through interference of electron vortex modes in the TEM, </strong><a href="https://doi.org/10.1088/2040-8986/ab51fc">Journal of Optics 21 124002 (2019)</a></p>
Data sets for "Magnetic helicity dissipation and production in an ideal MHD code"
<pre>The tar archive Helicity_in_IdealMHDCode.tar contains an index.html file with links to a directory with "Add-ons" to the FLASH code and the flash.par file. We also list the IDL directory with secondary data and plot routines for each figure used in the paper "Magnetic helicity dissipation and production in an ideal MHD code" by Axel Brandenburg (Nordita) and Evan Scannapiecoo (Arizona State University) with the URL https://arxiv.org/abs/1910.06074.</pre>
Segmenting magnetized plasma turbulence with aweSOM
<p>This dataset contains a snapshot of a fully kinetic particle-in-cell simulation of freely evolving plasma turbulence, as described in <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ac1c76" target="_blank" rel="noopener">Nättilä & Beloborodov (2021)</a>.</p> <p>This dataset was used in the analysis of <a href="https://arxiv.org/abs/2410.01878" target="_blank" rel="noopener">Ha et al. (2024)</a> and partially to develop <a href="https://github.com/tvh0021/aweSOM"><strong>aweSOM</strong></a>.</p> <p>See the section: "Example : Intermittency detection in decaying plasma turbulence simulation" in the documentation of <strong>aweSOM</strong> for instructions on how to use these datasets.</p>
Supplement for Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland
<p>Supplement to Jackisch et al., 2021: Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland.</p> <p><a href="https://se.copernicus.org/articles/13/793/2022/se-13-793-2022.html">https://se.copernicus.org/articles/13/793/2022/se-13-793-2022.html</a></p> <p>Data set contains 3D model in dxf file, additional images, selected handheld spectra.</p> <p>Publication summary:</p> <p>We integrate UAS-based magnetic and remote sensing mineral exploration data with legacy exploration data of a Ni-Cu-PGE prospect on Disko Island, West Greenland. The basalt unit has a complex magnetization, and we use a 3D magnetic vector inversion on the UAS magnetics to estimate magnetic properties and spatial dimensions of the mineralized unit. Our 3D modelling reveals a horizontal sheet and a strong remanent magnetization component. We highlight the advantage of UAS in rugged terrain.</p> <p> </p>
Displacement measurements of the open-hardware sandbox using the AS5311 high-resolution magnetic sensor
<p>This dataset includes the experimental data from the AS5311 sensor for measuring the displacement of the Open-Hardware Geological Sandbox.</p> <p>These experiments are explained in the journal article: <a href="https://doi.org/10.1109/ACCESS.2023.3262617">Designing low-cost open-hardware electromechanical scientific equipment: A geological analogue modeling sandbox</a></p> <p>To understand this dataset, go to the Tectonic Open Hardware (TectOH) Sandbox project: <a href="https://github.com/URJCMakerGroup/TectOH">https://github.com/URJCMakerGroup/TectOH</a>. Then go to the <a href="https://github.com/URJCMakerGroup/TectOH/tree/main/optional">optional</a> folder and to the <a href="https://github.com/URJCMakerGroup/TectOH/tree/main/optional/as5311_magn_sens">magnetic sensor</a> folder.</p> <p>This data set contains two kind of files:</p> <ul> <li><strong>bin</strong>: raw binary files received from the AS5311 high resolution sensor. Although this sensor sends 12 bit data, we have truncated the most significant bits and receive only 8 bits (one byte). Therefore, each byte of these binary files is a measurement of the distance. Each distance increment corresponds to ~0.488nm (2mm/2048)</li> <li><strong>csv</strong>: csv files that can be opened with any spreadsheet app, such as Libreoffice Calc or Microsoft Excel, or even with a text editor. This file contains the processed data from the binary files. These files have been generated with the proc_magn_sensor.py Python script located in the <a href="https://github.com/URJCMakerGroup/TectOH">project repository</a>. There are some columns, which are: <ul> <li>index: measurement number</li> <li>time in milliseconds: each measurement is taken every 250 us</li> <li>median2: in micrometers, since the sensor may jitter, we have applied the median filter twice. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>median1: in micrometers, median filter only applied once. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>mean: in micrometers, mean filter. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>mean int: in micrometers, mean filter rounded to an integer value. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>orig_base: this is not in micrometers, but in the units of the sensor (~0.488nm). The only processing done is that when there is an overflow of 255 to 0, or from 0 to 255, it adds the overflow to continue the trend. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>original: this is the data received from the sensor with no processing, each value is ~0.488nm</li> <li>mean2: in micrometers, mean filter applied twice. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> </ul> </li> </ul> <p>There are two set of experiments:</p> <ul> <li><strong>Experiments with no load</strong>. These files start with <em>noload_</em><br> In these experiments the gantry is moved 1 mm alternatively to the front and then reversing direction. Moving in this alternate way a few times. There are five experiments each of them with a different speed: v= 10 mm/h; 25 mm/h; 50 mm/h; 82 mm/h and 100 mm/h. The name of the file indicates the speed: <ol> <li>noload_100mmh_1mm: FBFBF: 1mm forth, 1mm back, 1mm forth, 1mm back, 1 mm forth</li> <li>noload_25mmh_1mm: FBFFBBFB</li> <li>noload_50mmh_1mm: FBFBFB</li> <li>noload_82mmh_1mm: FBFBFB</li> <li>noload_100mmh_1mm: FBFBFB</li> </ol> </li> <li><strong>Experiments pushing a 5kg sand load</strong>. These files start with <em>load5kg_</em> <ol> <li>load5kg_25mmh_5mm: moving 5kg at 25mm/h a distance of 5mm</li> <li>load5kg_25mmh_10mm: moving 5kg at 25mm/h a distance of 10mm</li> <li>load5kg_25mmh_20mm: moving 5kg at 25mm/h a distance of 20mm</li> <li>load5kg_75mmh_20mm: moving 5kg at 75mm/h a distance of 20mm</li> <li>load5kg_75mmh_50mm: moving 5kg at 75mm/h a distance of 50mm</li> <li>load5kg_100mmh_25mm: moving 5kg at 100mm/h a distance of 20mm</li> <li>load5kg_100mmh_50mm: moving 5kg at 100mm/h a distance of 50mm</li> </ol> </li> </ul> <p> </p> <p> </p> <p> </p>
MHD Model of Ganymede's Magnetosphere: Predicted magnetic field on Juno's trajectory
<p>This dataset contains model results from a magnetohydrodynamic (MHD) model of Ganymede's magnetosphere adapted to Juno's PJ34 flyby in 2021. Here we publish predicted magnetic field components on Juno's trajectory that can be compared to MAG measurements and are displayed in Figure 3 of Duling et al. (2022).</p> <p>Each file contains data from one model. The dataset includes all models with parameter variations from Duling et al. (2022). These are summarized in Table 1 of Duling et al. (2022) and displayed in Figure 3 with the gray lines.</p> <p>If not varied, all models are run with the following parameters:</p> <p>Upstream Jovian background magnetic field B<sub>0 </sub>= (−15,24,−75) nT<br> Upstream plasma velocity v<sub>0</sub> = 140 km/s<br> Upstream plasma mass density <span class="math-tex">\(\rho\)</span><sub>0</sub> = 100 amu/cm<sup>3</sup><br> Upstream plasma thermal pressure p<sub>0</sub> = 2.8 nPa<br> Ionization frequency <span class="math-tex">\(\nu_{ion}\)</span> = 2.2e-8/s<br> Atmospheric surface mass density <span class="math-tex">\(n_{n,0}\)</span> = 8e6/cm<sup>3</sup><br> Dipole Gauss coefficient <span class="math-tex">\(g_1^0\)</span> = −716.8 nT</p> <p> </p> <p>The published data files correspond to the following models with each one parameter variation:</p> <table> <thead> <tr> <th scope="col">Parameter</th> <th scope="col">Value</th> <th scope="col">Filename Suffix</th> </tr> </thead> <tbody> <tr> <td>default model</td> <td> - </td> <td>default</td> </tr> <tr> <td>Upstream Jovian background magnetic field (measured before flyby)</td> <td>B<sub>0 </sub>= (−16,3,−70) nT</td> <td>B0before</td> </tr> <tr> <td>Upstream Jovian background magnetic field (measured after flyby)</td> <td>B<sub>0 </sub>= (−14,43,−80) nT</td> <td>B0after</td> </tr> <tr> <td>Upstream plasma velocity (min)</td> <td>v<sub>0</sub> = 120 km/s</td> <td>v-</td> </tr> <tr> <td>Upstream plasma velocity (max)</td> <td>v<sub>0</sub> = 160 km/s</td> <td>v+</td> </tr> <tr> <td>Upstream plasma mass density (min)</td> <td><span class="math-tex">\(\rho\)</span><sub>0</sub> = 10 amu/cm<sup>3</sup></td> <td>rho-</td> </tr> <tr> <td>Upstream plasma mass density (max)</td> <td><span class="math-tex">\(\rho\)</span><sub>0</sub> = 160 amu/cm<sup>3</sup></td> <td>rho+</td> </tr> <tr> <td>Upstream plasma thermal pressure (min)</td> <td>p<sub>0</sub> = 1.0 nPa</td> <td>p-</td> </tr> <tr> <td>Upstream plasma thermal pressure (max)</td> <td>p<sub>0</sub> = 5.0 nPa</td> <td>p+</td> </tr> <tr> <td>Ionization frequency (min)</td> <td> <span class="math-tex">\(\nu_{ion}\)</span> = 0.5e-8/s</td> <td>prod-</td> </tr> <tr> <td>Ionization frequency (max)</td> <td> <span class="math-tex">\(\nu_{ion}\)</span> = 10.0e-8/s</td> <td>prod+</td> </tr> <tr> <td>Atmospheric surface mass density (min)</td> <td> <span class="math-tex">\(n_{n,0}\)</span> = 1.6e6/cm<sup>3</sup></td> <td>nn-</td> </tr> <tr> <td>Atmospheric surface mass density (max)</td> <td> <span class="math-tex">\(n_{n,0}\)</span> = 40e6/cm<sup>3</sup></td> <td>nn+</td> </tr> <tr> <td>Dipole Gauss coefficient (min)</td> <td> <span class="math-tex">\(g_1^0\)</span> = −702.5 nT</td> <td>dipole-</td> </tr> <tr> <td>Dipole Gauss coefficient (max)</td> <td> <span class="math-tex">\(g_1^0\)</span> = −731.1 nT</td> <td>dipole+</td> </tr> </tbody> </table> <p>Magnetic Field components and Juno's position are in GPhiO system. GPhiO is defined by the primary direction z parallel to Jupiter’s rotation axis, the secondary direction y is pointing from Ganymede's towards Jupiter's barycenter and x completes the right-handed system approximately in direction of plasma flow.</p> <p>Columns:</p> <p>Spacecraft time [UTC]<br> Bx modeled magnetic field in GPhiO [nT]<br> By modeled magnetic field in GPhiO [nT]<br> Bz modeled magnetic field in GPhiO [nT]<br> B modeled magnetic field magnitude [nT]<br> x of Juno in GPhiO [km]<br> y of Juno in GPhiO [km]<br> z of Juno in GPhiO [km]</p>
Predicted times of bow Shock crossings at Venus from the ESA/Venus Express mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm
<p><strong>CHARACTERISTICS</strong><br> Planet: <strong>Venus</strong><br> Radius: <strong>R<sub>V</sub> = 6051.8 km</strong> (volumetric mean planetary radius)<br> Spacecraft: <strong>ESA/Venus Express</strong><br> Spacecraft coordinates system: <strong>Venus Solar Orbital (VSO)</strong> equivalent to <em>Sun-State </em>coordinate system:</p> <ul> <li>+<em>X<sub>VSO</sub></em> points towards the Sun from the planet’s centre,</li> <li>+<em>Z<sub>VSO</sub></em> towards Venus’ North pole and perpendicular to the orbital plane defined as the <em>X<sub>VSO</sub></em>–<em>Y<sub>VSO</sub></em> plane passing through the centre of Venus,</li> <li><em>Y<sub>VSO</sub></em> completes the orthogonal system.</li> </ul> <p>Time span: <strong>01/04/2006 to 25/11/2014</strong><br> Total number N of candidate bow shock crossings in the database: <strong>N = 4950</strong><br> Number of quasi-parallel bow shock crossings: <strong>N<sub>||</sub> = 844</strong><br> Number of quasi-perpendicular bow shock crossings: <strong>N<sub><span class="math-tex">\(\perp\)</span></sub> = 4106</strong></p> <p><strong>ORIGINAL DATASETS USED</strong><br> The original Venus Express/MAG data repository on which these algorithms were applied is available on ESA's Planetary Science Archive system (PSA) at: https://archives.esac.esa.int/psa/ftp/VENUS-EXPRESS/MAG/. For this study, 1-Hz magnetic field data was used.</p> <p><strong>METHOD</strong><br> To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described for the Mars case in:<br> Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2021), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, <em>Journal of Geophysical Research</em>, <strong>127</strong>, e2021JA029942. <a href="https://doi.org/10.1029/2021JA029942">https://doi.org/10.1029/2021JA029942</a></p> <p>They consist of two consecutive steps: </p> <ol> <li>Predictor geometric algorithm based on 2D or 3D existing fits for prediction of the Venus bow shock position. The original fits were taken from 2D conic fits in the plane <span class="math-tex">\(\left(X_\text{VSO}, \sqrt{Y_\text{VSO}^2+Z_\text{VSO}^2}\right)\)</span>performed on the datasets of <strong>Persson et al. (2023)</strong>, Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, <em>Zenodo</em> (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>).</li> <li>Corrector algorithm based on magnetic field measurements.</li> </ol> <p>We also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted <span class="math-tex"><em>θ</em><sub><em>B</em><em>n</em></sub></span> (ThetaBn). Assuming a locally smooth shock surface, this gives a first indication of the geometry of the shock, so that:</p> <ul> <li><span class="math-tex">45<sup>∘</sup><<em>θ</em><sub><em>B</em><em>n</em></sub><135<sup>∘</sup></span>: quasi-perpendicular shock condition</li> <li><span class="math-tex"><em>θ</em><sub><em>B</em><em>n</em></sub>≤45<sup>∘</sup> and <em>θ</em><sub><em>B</em><em>n</em></sub><span class="math-tex">\(\geq\)</span>135<sup>∘</sup></span>: quasi-parallel shock condition</li> </ul> <p>Uncertainty on these angles is estimated to be ± 5º. </p> <p>For details, see <strong>Simon Wedlund et al. (2022)</strong> above, §2.3 pp. 10-12.</p> <p><strong>VARIABLES DESCRIPTION</strong></p> <p>This database contains the following ASCII variables:</p> <ul> <li>Bow shock times in Venus Express' database (1-s resolution): <em>T</em><sub>bs</sub></li> <li>Venus Solar Orbital coordinates of the shock, in units of Venus radius <em>R</em><sub>V </sub>(<em>R</em><sub>V</sub> = 6051.8 km):<br> <em>X<sub>VSO</sub></em>,<sub> </sub><em>Y<sub>VSO</sub></em>, <em>Z<sub>VSO</sub></em> and Euclidean distance <span class="math-tex">\(R_{VSO} = \sqrt{X_{VSO}^2 + Y_{VSO}^2 + Z_{VSO}^2}\)</span> (in <em>R<sub>V</sub></em>)</li> <li>Solar Zenith angle in degrees: <em>SZA</em> = <span class="math-tex">\(\tan^{-1}{Y_{VSO}^2+Z_{VSO}^2 \over X_{VSO}^2}\)</span> (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface <span class="math-tex">\(\theta_{Bn}\)</span> (ThetaBn, in º, calculated with atan2(norm(cross(<strong>B</strong>,<strong>ñ</strong>),dot(<strong>B</strong>,<strong>ñ</strong>)), with <strong>B</strong> the magnetic field vector and <strong>ñ</strong> the vector normal to the shock surface): <ul> <li>45 < ThetaBn < 135 deg: quasi-<span class="math-tex">\(\perp\)</span> shock</li> <li>ThetaBn <span class="math-tex">\(\leq\)</span> 45 deg & ThetaBn <span class="math-tex">\(\geq\)</span> 135 deg: quasi-|| shock</li> </ul> </li> <li>Interplanetary Magnetic Field (IMF) upstream average vector in VSO coordinates, <em>B<sub>x</sub></em>, <em>B<sub>y</sub></em>, <em>B<sub>z</sub></em> (in nT).</li> <li>Flag for direction of crossing: <ul> <li>flag = 0: magnetosheath <span class="math-tex">\(\longrightarrow\)</span> solar wind (2447 events)</li> <li>flag = 1: solar wind <span class="math-tex">\(\longrightarrow\)</span> magnetosheath (2503 events)</li> </ul> </li> </ul> <p><strong>WARNING</strong></p> <ol> <li>This version of the database is currently in a preliminary stage of application and, as such, is not fully tested. Solar wind upstream magnetic field values (IMF) are given only as a first approximation for each orbit segment. See point 2 for caveats. For carefully manually picked shock crossings, the user is referred to the database of:<br> <strong>Persson et al. (2023)</strong>, Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, <em>Zenodo</em> (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>)</li> <li>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic fields. This is aimed at giving a first approximation of the shock area times in the Venus Express data. It is particularly suited to statistical studies and region identification in the Venus Express datasets. As such, this database should be used as a <em>first indicator</em> of the shock location, and <em>with</em> <em>caution</em>: it <strong>CANNOT</strong>, and <strong>WILL NOT </strong>substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.040 R<sub>V</sub> (with R<sub>V</sub> = 6051.8 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</li> </ol> <p>Current formatting optimised for MATLAB.</p> <p><strong>ACKNOWLEDGEMENTS</strong><br> C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. </p> <p><strong>LICENSE AND RIGHTS</strong><br> This database is shared under a Creative Commons CC-BY-4.0 license.</p> <p>Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF), <br> Austrian Academy of Sciences, 2022-10-05<br> Contact email: cyril.simon.wedlund@gmail.com</p>
Data from: Plasma acceleration in a magnetic arch
<p><strong>Data from: Plasma acceleration in a magnetic arch</strong></p> <p>- Authors: Mario Merino, Diego García, Eduardo Ahedo</p> <p>- Contact emails: mario.merino@uc3m.es, dieggarc@ing.uc3m.es</p> <p>- Date: 2023-06-08</p> <p>- Keywords: electric propulsion, electrodeless plasma thruster, magnetic arch, plasma expansion</p> <p>- Version: 1.0.4</p> <p>- Digital Object Identifier (DOI): 10.5281/zenodo.7919577</p> <p>- License: This dataset is made available under the [Open Data Commons Attribution License](http://opendatacommons.org/licenses/by/1.0/)</p> <p> </p> <p><strong>Abstract</strong></p> <p>This dataset contains the data found in the plots of the paper:</p> <p>Mario Merino, Diego García, Eduardo Ahedo, "Plasma acceleration in a magnetic arch"</p> <p>Published in the journal Plasma Sources Science and Technology</p> <p> </p> <p><strong>Dataset description</strong></p> <p>The data in this repository has been extracted from the fluid simulations as described in the article. (https://iopscience.iop.org/article/10.1088/1361-6595/acd476).</p> <p>For further information on the setup for the simulation please refer to the article.</p> <p> </p> <p><strong>Data files</strong></p> <p>The data files are in .csv format. They were produced in numpy using the numpy.savetxt() function and can be easily read with numpy.loadtxt() or in any other language with the apropiate reader for .csv files.</p> <p>The files are organised following the order of the figures in the article. Therefore each file contains a different sized array. In the following one can find a description of all the data contained in each of the files:</p> <p>- fig2.csv</p> <p> - Applied magnetic field 'Ba/Ba0'</p> <p>- fig3.csv</p> <p> - Thermalised potential 'He'</p> <p> - Electron out of plane velocity 'uye'</p> <p>- fig4.csv</p> <p> - Plasma density 'n'</p> <p> - Electron temperature 'Te'</p> <p> - Electric potential 'phi'</p> <p> - Ion in-plane velocity 'uitilde'</p> <p> - Ion Mach number 'Mi'</p> <p>- fig5.csv</p> <p> - In-plane electric current density 'jitilde'</p> <p>- fig6.csv</p> <p> - Radial magnetic force density 'jyBz'</p> <p> - Axial magnetic force density '-jyBx'</p> <p>- fig7.csv</p> <p> - Thrust integral, beta = 0.00 case 'F_F0_beta_0.00'</p> <p> - Thrust integral, beta = 0.02 case 'F_F0_beta_0.02'</p> <p> - Thrust integral, beta = 0.04 case 'F_F0_beta_0.04'</p> <p> - Thrust integral, beta = 0.08 case 'F_F0_beta_0.08' </p> <p>- fig8.csv</p> <p> - Normalised induced magnetic field strength 'Bp_beta0_Ba0'</p> <p>- fig9.csv</p> <p> - Total magnetic field, beta = 0.00 case 'B_beta_0.00'</p> <p> - Total magnetic field, beta = 0.02 case 'B_beta_0.02'</p> <p> - Total magnetic field, beta = 0.04 case 'B_beta_0.04'</p> <p> - Total magnetic field, beta = 0.08 case 'B_beta_0.08'</p> <p>All files contain a matrix of comma separated values with 400 rows. The number of columns depends on the specific file, for the files corresponding to two dimensional maps (all files except fig7.csv) the number of columns is a multiple of 400, where the first 400 columns correspond to the Z positions values and the following 400 the X position values. These two 400 by 400 matrices correspond to a meshgrid common in Matlab and NumPy. The following columns correspond to the values of each quantity in the positions given by the grid. For example, files containing only one field such as 'fig2.csv' have 400 rows and 1200 columns with columns 801 to 1200 corresponding to the values of the given field. As an example for files containing multiple fields let us take 'fig3.csv', this file contains 400 rows and 1600 columns where columns 801 to 1200 contain the values for 'He' and columns 1201 to 1600 contain 'uye'.</p> <p>The file 'fig7.csv' contains the data for a 1D plot with multiple lines. In this case the data is matrix with 400 rows and 5 columns where column 1 contains the z axis positions column 2 contains the values for 'F_F0_beta_0.00' column 3 contains 'F_F0_beta_0.02' and so on.</p> <p>All values are normalised as explained in the article.</p> <p> </p> <p><strong>Citation</strong></p> <p>Any works using this dataset or any part of it in any form shall cite it as follows:</p> <p>The prefered means of citation is to reference the publication as soon as it is available.</p> <p>The BibTex is also provided for the sake of convinience:</p> <p>@article{Merino_2023,</p> <p>doi = {10.1088/1361-6595/acd476},</p> <p>url = {https://dx.doi.org/10.1088/1361-6595/acd476},</p> <p>year = {2023},</p> <p>month = {jun},</p> <p>publisher = {IOP Publishing},</p> <p>volume = {32},</p> <p>number = {6},</p> <p>pages = {065005},</p> <p>author = {Mario Merino and Diego García-Lahuerta and Eduardo Ahedo},</p> <p>title = {Plasma acceleration in a magnetic arch},</p> <p>journal = {Plasma Sources Science and Technology},</p> <p>abstract = {}</p> <p>}</p> <p>Optionally the dataset can be cited by referencing the DOI: 10.5281/zenodo.7919577</p> <p><strong>Acknowledgments</strong></p> <p>This dataset was created by the [ERC-ZARATHUSTRA project](https://erc-zarathustra.uc3m.es/).</p> <p>The ERC-ZARATHUSTRA project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 950466).</p>
Data and simulation files for "Constraints on the intergalactic magnetic field using Fermi-LAT and H.E.S.S. blazar observations"
<p>In this repository, we provide data files in connection to our paper “Constraints on the intergalactic magnetic field using Fermi-LAT and H.E.S.S. blazar observations” accepted for publication in the Astrophysical Journals and soon available on Arxiv.</p> <p>In the publication, we perform a joint analysis of observations of five blazars with the Fermi Large Area Telescope (LAT) and the High Energy Stereoscopic System (H.E.S.S.) in order to search for signatures of a gamma-ray halo around these sources. The non-detection of such extended emission allows us to place lower limits on the intergalactic magnetic field (IGMF).</p> <p>In this repository, we provide our data analysis products of both H.E.S.S. and LAT data for the case when a template for the halo flux is <em>not</em> included in the data. Furthermore, we provide files that contain the log likelihood profiles as functions of the IGMF in case the halo emission <em>is</em> included. Lastly, we also provide our template files for the halo, generated with <a href="https://crpropa.github.io/CRPropa3/">CRPropa 3</a>.</p> <p>Below, we provide minimal code examples to demonstrate how to read in the specific files.</p> <p><strong>H.E.S.S. observational results</strong></p> <p>We provide the best-fit spectral parameters as well as the flux points (spectral energy distribution; SED) for the H.E.S.S. observations of the five blazars under consideration. The corresponding files are:</p> <ul> <li>hess_fit_result_*.fits which contain the best-fit parameters,</li> <li>hess_sed_file_*.fits which contain the flux points.</li> </ul> <p>In the file names above, the '*' should be replaced with a the corresponding source name, e.g. 1ES0229+200. The files can be read in using astropy:</p> <pre><code class="language-python">from astropy.table import Table src = "1ES0229+200" best_fit_pars = Table.read("hess_fit_result_1ES0229+200.fits") sed = Table.read("hess_sed_file_1ES0229+200.fits")</code></pre> <p><strong>Fermi observational results</strong></p> <p>For Fermi-LAT, we provide the SED files as well as the best-fit models for the region of interests. These files are called:</p> <ul> <li>fermi_avg_file_*.npy provides the best-fit ROI model</li> <li>fermi_sed_file_*.npy provides the SED.</li> </ul> <p>Both of these files are generated with <a href="https://fermipy.readthedocs.io/en/latest/">fermipy</a> and can be read-in the following way:</p> <pre><code class="language-python">import numpy as np # first a little helper function since the # fermipy analysis was run under python 2.7 def convert(data): if isinstance(data, bytes): return data.decode('ascii') if isinstance(data, dict): return dict(map(convert, data.items())) if isinstance(data, tuple): return map(convert, data) return data # Load the ROI fit roi_fit_file = "fermi_avg_file_1ES0229+200.npy" roi_fit = np.load(avg_file, allow_pickle=True, encoding="latin1").flat[0] # if you want to inspect the dictionaries in python 3, you need to run the convert function. # For example, to inspect the central source of the ROI # you would first get the source name src_fgl_name = roi_fit['config']['selection']['target'] # and then you can get the dictionary for the central source src_dict = convert(roi_fit['sources'])[src_fgl_name] # Load the SED sed_file = "fermi_sed_file_1ES0229+200.npy" sed = np.load(sed_file, allow_pickle=True, encoding='latin1').flat[0] # to plot the SED, you can use the SEDPlotter class from fermipy from fermipy.plotting import SEDPlotter SEDPlotter.plot_sed(sed)</code></pre> <p><strong>Likelihood profiles</strong></p> <p>The likelihood profiles as function of the IGMF strengths are provided in the files logl_profile_*_*yr.npz. Their are provided for all five sources and all tested blazar activity times of 10, 10<sup>4</sup>, and 10<sup>7</sup> years. They can be read in with the following code snippet:</p> <pre><code class="language-python">import numpy as np logl = dict(np.load("logl_profile_1ES0229+200_1.0e+07yr.npz")) b_fields = np.array([1.00000e-16, 3.16228e-16, 1.00000e-15, 3.16228e-15, 1.00000e-14, 3.16228e-14, 1.00000e-13]) for k, v in logl.items(): print(k,v)</code></pre> <p>As the print command shows, the python dictionary contains 3 entries: "fermi_only" are the likelihood values for the Fermi data as a function of magnetic field, "combined" are the likelihood values from Fermi and H.E.S.S. combined, and "ps" is the likelihood value of the Fit without halo to the H.E.S.S. data only.</p> <p><strong>Halo simulations</strong></p> <p>Lastly, we also provide the output simulations files from CRPropa. For details how the simulations were run, please consult the accompanying paper, in particular Section 3.1 and Appendix C. For each source redshift, a tar file is provided, which in itself contains 7 hdf5 files with the simulation outputs for each tested magnetic field strength. The name of the files is casc_file_z*.tar.gz. After unpacking the files, they can be read in with your favorite hdf5 library; in python you would need to install h5py. We recommend that you check out <a href="https://github.com/me-manu/simCRpropa">this github repository</a> which provides an advanced python wrapper for CRPropa and functions to read in the files. In particular, you can use <a href="https://github.com/me-manu/simCRpropa/blob/b3f39b5c77c6b97d19f7db387427d857690444d2/simCRpropa/cascmaps.py#L28">this function</a> to read in the files. It also writes a new hdf5 file with parallel transport applied. The written data is also returned together with the configuration dictionary.</p> <pre><code class="language-python">from simCRpropa.cascmaps import stack_results_lso data, config = stack_results_lso("casc_file_z0.140_B1.00e-16.hdf5", "casc_file_z0.140_B1.00e-16_theta_obs0.0.hdf5" )</code></pre> <p>You can provide arbitrary angles between the observer and the jet angles using the theta_obs keyword. Note, however, that the simulations used a jet opening angle of 3 degrees and going beyond that value will return zero halo photons.</p>
Data from Simulations of a Magnetic Shielding System to Deflect Background-Inducing Secondary Electrons away from Space-Based X-ray Detectors
<p>Data from simulations examining the effect of cosmic rays and secondary particles generated by them on background induced in an X-ray astronomy space telescope, and the effectiveness of a surrounding magnetic field at reducing this background.</p> <p>These simulations were performed for the paper “Effectiveness of a dual solenoid magnetic shield at reducing X-ray-like background in silicon-based X-ray detectors” (2023) published in the Journal of Astronomical Telescopes Instruments and Systems. The paper can also be found at https://openresearch.surrey.ac.uk/esploro/outputs/journalArticle/The-Effectiveness-of-a-Dual-Solenoid/99777566602346/filesAndLinks?forceView=true&mode=quickaccess&index=0 .</p> <p>This data was also used for the simulations and analysis described in the thesis "The Simulation, Composition and Shielding of Radiation-Induced X-ray-like Background in Space-Based X-ray Astronomy Missions" (2021), which can be found at <a href="https://doi.org/10.21954/ou.ro.00012e1f">https://doi.org/10.21954/ou.ro.00012e1f</a> .</p>
Solar and interplanetary magnetic field data analyzed in "Optimal frequency-domain analysis for spacecraft time series: Introducing the missing-data multitaper power spectrum estimator"
<p>This dataset contains simultaneous measurements of the interplanetary magnetic field magnitude <B> and the sun's radio flux at 10.7 cm <F10.7>. <B> measurements come from a series of spacecraft located at the L1 point, while <F10.7> was measured by the ongoing monitoring program by Canada's Dominion Radio Astrophysical Observatory. Bartels rotation-averaged data were downloaded from NASA's OMNIWeb, https://omniweb.gsfc.nasa.gov/html/ow_data.html. The file contains other solar wind plasma parameters that were not used in the analysis.</p>
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