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13,017 results for “Positionning”
WW1 machine gun position, Helsinki, Finland
First World War trench destroyed machine gun position in base XIV:1 in [Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) land front defense line in Simon Clemetssons park, Latokartano, Helsinki, Finland. The base was built in 1915 - 1918. The whole base contains: * 150 meters of thrench * 3 machine gun positions * 1 observation positions * 1 large shelter room More info: * [Location in Google Maps](https://goo.gl/maps/PGGJhi5Svi65esHg7) * [Wikipedia: Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) * [John Lagerstedt , Markku Saari: Krepost Sveaborg](http://www.novision.fi/viapori/eavaus.htm) * [Finnish Heritage Agency site info](https://www.kyppi.fi/to.aspx?id=112.1000013603) (only in finnish) Source: Objaverse 1.0 / Sketchfab
WW1 Search light position, Helsinki, Finland
First World War trench search light position in base XVIII:10 in [Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) land front defense line in Kasemattikallio park, Maununneva, Helsinki, Finland. The base was built in 1915 - 1918. The whole base contains: * 150 meters of thrench * 2 observation positions * 2 search light positions * At least 5 shelter rooms * 120 m² cave More info: * [Location in Google Maps](https://goo.gl/maps/2u5Yg7efC29X3urW9) * [Wikipedia: Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) * [John Lagerstedt , Markku Saari: Krepost Sveaborg](http://www.novision.fi/viapori/eavaus.htm) * [Finnish Heritage Agency site info](https://www.kyppi.fi/to.aspx?id=112.1000013737) (only in finnish) Source: Objaverse 1.0 / Sketchfab
WW1 observation position, Helsinki, Finland
First World War observation position and incomplete shelter room in base XXIII:7 in [Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) land front defense line in Pohjois-Haaga, Helsinki, Finland. The base was built in 1915 - 1918. The whole base contains: * 2 observations position * 3 machine gun positions * 3 shelter rooms More info: * [Location in Google Maps](https://goo.gl/maps/9uPCuipbaF3kJcrL7) * [Wikipedia: Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) * [John Lagerstedt , Markku Saari: Krepost Sveaborg](http://www.novision.fi/viapori/eavaus.htm) * [Finnish Heritage Agency site info](https://www.kyppi.fi/to.aspx?id=112.1000013840) (only in finnish) Source: Objaverse 1.0 / Sketchfab
Textiles: Polenka (2), "A Profitable Position"
The 3D model presents a digital reconstruction of historical textile materials for a theatrical costume for Polina Kukushkina in the play "A Profitable Position" (1856) by A.N. Ostrovsky. The authors used 2D scanning to capture the look of the surfaces, post processed the images in PixPlant, generated texture maps in Photoshop and put those on the 3D models in SubstancePainter. The authors of the 3D model are Aleksei Moskvin and Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design). The authors thank prof. Victor Kuzmichev (Ivanovo State Polytechnic University) for providing data required for digitization of textiles. The actual costume that can be seen in the photo was made by faculty members and students of Ivanovo State Polytechnic University. This work was supported by the Russian Historical Society and the History of the Fatherland Foundation under project titled "NashOstrovsky". DOI: 10.13140/RG.2.2.26667.11043 Please contact us if you require seamless textures. Source: Objaverse 1.0 / Sketchfab
"Bulls in defensive position", 5500 years ago
The engraving from the Bull grotto of Kamyana Mohyla in South-Eastern Ukraine. In pictures four bulls staying close to one-another in so-called "defensive position". Due to photogrammetric study and numerous references from Central Asia, this engraving is dated back to Eneolithic — Early Bronze Age of the region (approximately 5500 years ago). Some of the bulls have initially been covered with red ocher or superimposed with a number of cupmarks. The cave is currently unuavailable due to the safety reasons. Source: Objaverse 1.0 / Sketchfab
WW1 machine gun position, Helsinki, Finland
First World War trench incompleted machine gun position in [Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) land front defense line in base XXIII:2 Maununneva, Helsinki, Finland. The base was built in 1915 - 1918. More info: * [Location in Google Maps](https://goo.gl/maps/NxNSgxn8R8bDRuSu9) * [Wikipedia: Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) * [John Lagerstedt , Markku Saari: Krepost Sveaborg](http://www.novision.fi/viapori/eavaus.htm) * [Finnish Heritage Agency site info](https://www.kyppi.fi/to.aspx?id=112.1000013825) (only in finnish) Source: Objaverse 1.0 / Sketchfab
Palmetto position storing Lucene index of Dutch Wikipedia
<p>Dutch language resource for calculating topic coherence with Palmetto [1, 2]. The dataset is a position storing Lucene index of the Dutch Wikipedia [3]. It was created in the context of the Netherlands eScience Center Dilipad project [4]. The pdf file contains the results of a case study that shows best topic coherence measure for topics consisting of Dutch nouns is NPMI.</p> <p>More details can be found in the README.</p> <p>[1] M. Roeder, A. Both, and A. Hinneburg. Exploring the space of topic coherence measures. In <em>Proceedings of the Eighth ACM International Conference on Web Search and Data Mining</em>, pages 399–408, 2015.</p> <p>[2] http://aksw.org/Projects/Palmetto.html</p> <p>[3] https://dumps.wikimedia.org/nlwiki/20151102/</p> <p>[4] https://www.esciencecenter.nl/project/dilipad</p>
Oscillation test results for different Vdd values at positions X10Y90 and X44Y108
<p>16 files (X10Y90_X_XXV.bin and X44Y108_X_XXV.bin), where X_XX refers to the value of Vdd = oscillation counts generated by a TERO TRNG at placements X10Y90 and X44Y108 on a Xilinx Spartan 6 FPGA, with different voltage levels. Used in above mentioned paper in Figure 3.</p>
Dataset of KEMAR-BRIRs measured at several positions and head orientations in a real room
<p>Measurement of BRIRs at 25 positions (1-25) in a reverberant room for two sound sources (A,B). The dummy head is rotated by 360° counterclockwise in 5° steps, respectively, at every position. The measuring points are arranged at a distance of 0.5m in x and y axis, respectively. The sound sources are arranged at a distance of 1.0m from the measuring grid.</p> <p>A whole description of the measurement can be found in:<br> http://opac.lbs-ilmenau.gbv.de/DB=1/SET=1/TTL=2/SHW?FRST=1<br> or contact stephan.werner@tu-ilmenau.de</p> <p>Sketch of the measuring grid:</p> <p> A<br> |<br> |<br> 5 - 4 - 3 - 2 - 1<br> | | | | |<br> 10 - 9 - 8 - 7 - 6<br> | | | | |<br> B - -15 - 14 - 13 - 12 - 11<br> | | | | |<br> 20 - 19 - 18 - 17 - 16<br> | | | | |<br> 25 - 24 - 23 - 22 - 21</p>
High resolution images for 'Identification of "BRAF-positive" cases based on whole-slide image analysis'
<p>This archive contains high resolution images to accompany the article 'Identification of “BRAF-positive” cases based on whole-slide image analysis' by V. Popovici, A. Krenek and E. Budinska.</p>
Age-dependent positivity bias in children's perception of emotion terms
<p>Data set of Bahn, D., Vesker, M., Garcia Alanis, J. C., Schwarzer, G., & Kauschke, C. (in press). Age-dependent positivity bias in children’s perception of emotion terms. <em>Frontiers in Psychology</em>. doi: 10.3389/fpsyg.2017.01268</p>
Electrical impedance tomography - Depency of cardiac related impedance change amplitudes on body position and reconstruction algorithm
<p>Dataset of a publication investigating the influence of body position and reconstruction algorithm on the amplitudes of cardiac related impedance changes in healthy adult volunteers.</p>
Preliminary Breakdown Process of Winter Positive Cloud-to-Ground Lightning Flash and Its relation to the Following First Return Stroke
<p>The file <em>+CG statistics.xlsx</em> contains various statistical parameters for 60 +CG events.</p> <p>The files <em>3D_UPB.dat</em>, <em>3D_DPB.dat</em>, <em>3D_IRPB1.dat</em>, and <em>3D_IRPB2.dat</em> provide the 3D location results for the four example events discussed in the main text. Each file includes data organized in four lines, representing time (ms), x (m), y (m), and z (m), respectively.</p> <p><strong> </strong></p>
WiFi RTT RSS dataset for indoor positioning
<p>This is the first batch of WiFi RSS RTT datasets with LOS conditions we published. Please see <code>https://doi.org/10.5281/zenodo.11558792</code> for the second batch.</p> <h2><strong>Please do use version 2 for better quality.</strong></h2> <p>We provide publicly available datasets of three different indoor scenarios: building floor, office and apartment. The datasets contain both <strong>WiFi RSS and RTT signal measures</strong> with <strong>groud truth coordinates</strong> label and <strong>LOS condition</strong> label.</p> <p>1.Building Floor</p> <p>This is a detailed WiFi RTT and RSS dataset of a whole floor of a university building, of moare than 92 x 15 square metres. We divided the area of interest was divided into discrete grids and labelled them with correct ground truth coordinates and the LoS APs from the grid. The dataset contains WiFi RTT and RSS signal measures recorded in 642 reference points for 3 days and is well separated so that training points and testing points will not overlap.</p> <p>2. Office</p> <p>Office scenario is of more than 4.5 x 5.5 square metres. 3 APs are set to cover the whole space. At least two LOS AP could be seen at any reference point (RP). </p> <p>3.Apartment</p> <p>Apartment scenario is of more than 7.7 x 9.4 square metres.Four APs were leveraged to generate WiFi signal measures for this testbed. Note that AP 1 in the apartment dataset was positioned so that it could had an NLOS path to most of the testbed. </p> <p> </p> <h2><strong>Collection methodology</strong></h2> <p>The APs utilised were Google WiFi Router AC-1304, the smartphone used to collect the data was Google Pixel 3 with Android 9.</p> <p>The ground truth coordinates were collected using fixed tile size on the floor and manual post-it note markers. </p> <p>Only RTT-enabled APs were included in the dataset.</p> <h2><strong>The features of the datasets</strong></h2> <p><strong>The features of the building floor dataset are as follows:</strong></p> <blockquote> <p>Testbed area: 92 × 15 m2</p> <p>Grid size: 0.6 × 0.6 m2</p> <p>Number of AP: 13</p> <p>Number of reference points: 642</p> <p>Samples per reference point: 120</p> <p>Number of all data samples: 77040</p> <p>Number of training samples: 57960</p> <p>Number of testing samples: 19080</p> <p>Signal measure: WiFi RTT, WiFi RSS</p> <p>Collection time interval: 3 days</p> </blockquote> <p><strong>The features of the office dataset are as follows:</strong></p> <blockquote> <p>Testbed area: 4.5 × 5.5 m2</p> <p>Grid size: 0.455 × 0.455 m2</p> <p>Number of AP: 3</p> <p>Reference points: 37</p> <p>Samples per reference point: 120</p> <p>Data samples: 4,440</p> <p>Training samples: 3,240</p> <p>Testing samples: 1,200</p> <p>Signal measure: WiFi RTT, WiFi RSS</p> <p>Other information: LOS condition of every AP</p> <p>Collection time: 1 day</p> <p>Notes: A LOS scenario</p> </blockquote> <p><strong>The features of the apartment dataset are as follows:</strong></p> <blockquote> <p>Testbed area: 7.7 × 9.4 m2</p> <p>Grid size: 0.48 × 0.48 m2</p> <p>Number of AP: 4</p> <p>Reference points: 110</p> <p>Samples per reference point: 120</p> <p>Data samples: 13,200</p> <p>Training samples: 9,720</p> <p>Testing samples: 3,480</p> <p>Signal measure: WiFi RTT, WiFi RSS</p> <p>Other information: LOS condition of every AP</p> <p>Collection time: 1 day</p> <p>Notes: Contains an AP with NLOS paths for most of the RPs</p> </blockquote> <h2><strong>Dataset explanation</strong></h2> <p>The columns of the dataset are as follows:</p> <p>Column 'X': the X coordinates of the sample.</p> <p>Column 'Y': the Y coordinates of the sample.</p> <p>Column 'AP1 RTT(mm)', 'AP2 RTT(mm)', ..., 'AP13 RTT(mm)': the RTT measure from corresponding AP at a reference point.</p> <p>Column 'AP1 RSS(dBm)', 'AP2 RSS(dBm)', ..., 'AP13 RSS(dBm)': the RSS measure from corresponding AP at a reference point.</p> <p>Column 'LOS APs': indicating which AP has a LOS to this reference point.</p> <p>Please note:</p> <ul> <li>The RSS value -200 dBm indicates that the AP is too far away from the current reference point and no signals could be heard from it.</li> <li>The RTT value 100,000 mm indicates that no signal is received from the specific AP.</li> </ul> <h2><strong>Citation request<br></strong></h2> <p>When using this dataset, please cite the following two items:<br><br><code>Feng, X., Nguyen, K. A., & Luo, Z. (2024). WiFi RTT RSS dataset for indoor positioning [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.11558192" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11558192</a></code><br><br><code>@article{feng2023wifi, title={WiFi round-trip time (RTT) fingerprinting: an analysis of the properties and the performance in non-line-of-sight environments}, author={Feng, Xu and Nguyen, Khuong an and Luo, Zhiyuan}, journal={Journal of Location Based Services}, volume={17}, number={4}, pages={307--339}, year={2023}, publisher={Taylor \& Francis} }</code></p>
The Reactivation of Decayed Positive Leader with Sudden Channel Elongation in Laboratory Long Spark
<p>The data support the manuscript entitled "The Reactivation of Decayed Positive Leader with Sudden Channel Elongation in Laboratory Long Spark". The *.rar file contains all data used therein and can be decompressed and opened, where these file folders contain the data corresponding to their respective Figures, as they are named. The data can be used freely for scientific purposes with appropriate citation.</p>
Extracting positive descriptions and exploring landscape value using text analysis in the Cairngorms National Park
<p>The dataset was retrieved August 31, 2023, from <a href="http://data.geograph.org.uk/dumps/">http://data.geograph.org.uk/dumps/</a></p><p>Geograph project require all submitters to adopt a Creative Commons Attribution-ShareAlike licence on their photographic submissions.<br><a href="http://creativecommons.org/licenses/by-sa/2.0/">http://creativecommons.org/licenses/by-sa/2.0/</a></p>
Efficient spherical quadrature rules with positive weights
<p>The function "SphQuadrat.m" returns efficient quadrature rules on the sphere in a 3-dimensional space. The quadrature rules consist of the coordinates of the samling points and the (positive) weights of each point. Cartesian and spherical coordinate systems are available; please note that the notation of elevation angles <em>θ</em> in the spherical coordinate systems differs from that in MATLAB. That is, the function returns vertical angles <em>θ</em> measured from the z-axis.</p> <p>Available exactitude degrees of the quadratures are 0,2,4,...,100.</p> <p>MAT files containing the points and the weights of the quadratures are named as "xxx-yyyyy.mat", where "xxx" denotes the degree of exactitude and "yyyyy" denotes the number of points.</p> <p>By default, the weights are normalized as sum(weight)=1, while the function also provides 4pi-normalization sum(weight)=4*pi and the-number-of-points-normalization sum(weight)=numel(weight).</p> <p>Reference: Manuel Gräf, <a href="https://www-user.tu-chemnitz.de/~potts/workgroup/graef/quadrature/papers/Gr13Diss.pdf">Efficient Algorithms for the Computation of Optimal Quadrature Points on Riemannian Manifolds</a>, Ph.D. thesis, Technische Universität Chemnitz, (2013)</p>
Position error-free control of magnetic-domain wall devices via spin-orbit torque modulation
<p>Magnetic domain-wall devices such as racetrack memory and domain-wall shift registers facilitate massive data storage as hard disk drives with low power portability as flash memory devices. The key issue to be addressed is how perfectly the domain-wall motion can be controlled without deformation, as it can replace the mechanical motion of hard disk drives. However, such domain-wall motion in real media is subject to the stochasticity of thermal agitation with quenched disorders, resulting in severe deformations with pinning and tilting. To sort out the problem, we propose and demonstrate a new concept of domain-wall control with a position error-free scheme. The primary idea involves spatial modulation of the spin-orbit torque along nanotrack devices, where the boundary of modulation possesses broken inversion symmetry. In this work, by showing the unidirectional motion of domain wall with position-error free manner, we provide an important missing piece in magnetic domain-wall device development.</p>
Main output data used in "Coupling the regional climate MAR model with the ice sheet model PISM mitigates the melt-elevation positive feedback" (Delhasse et al., 2024)
<p>Outputs used in:</p> <p><em>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</em></p> <p>MAR-PISM coupling experiments outputs over 1991-2200. The main experiments are:</p> <ul> <li>MAPI-2w: 2-way coupling, consideration <em>online</em> of the melt-elevation feedback (evolving topography in MAR).</li> <li>MAPI-1w: 1-way coupling, consideration of the melt-elevation feedback only with the <em>offline</em> correction (Franco <em>et al.</em>, 2012) of the MAR outputs (fixed topography in MAR).</li> <li>MAPI-0w: 0-way coupling, no consideration of the melt-elevation feedback (fixed topography in MAR and no correction during interpolation).</li> </ul> <p>MAR files contain yearly SMB (surface mass balance) and ST (surface temperature) interpolated (with correction) on the PISM-4.5km grid. Gradients used for the correction of the melt-elevation feedback are also given for both variables. SMB and ST are the two required MAR fields to couple MAR with PISM. </p> <p>PISM files contain yearly ice thickness (THK) and ice mask (MASK) as simulated by PISM for each of the three experiments. </p> <p>The MAR code used in this dataset is tagged as v3.11.3 on https://gitlab.com/Mar-Group/MARv3# (last access: 23 January 2024) (MARTeam, 2024). The PISM code used is tagged as PISMv1.2.2 on <a href="https://github.com/pism/pism/releases/tag/v1.2.2" target="_blank" rel="noopener noreferrer">https://github.com/pism/pism/releases/tag/v1.2.2</a> (last access: 23 January 2024). Other coupling scripts are also available upon request by email (<a href="mailto:alison.delhasse@uliege.be" target="_blank" rel="noopener noreferrer">alison.delhasse@uliege.be</a>).</p> <p>If you need other variables from MAR or PISM, send us an email (alison.delhasse@uliege.be, johanna.beckmann@monash.edu) and we will be glad to help you. We will also be happy to share the scripts we have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR-PISM outputs.<br><br>Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgments should be similar to the one below that contains information related to MAR and PISM. To document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact me to add their works to the list of MAR-related publications. </p> <p>"We thank A. Delhasse and J. Beckmann, as well as the MAR and PISM teams which make available the model outputs. We also thank agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR-PISM simulations. "</p> <p>You should also refer to and cite the following paper in its latest version:</p> <p><em>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</em></p> <p>Reference</p> <p><em>Franco, B., Fettweis, X., Lang, C., and Erpicum, M.: Impact of spatial resolution on the modelling of the Greenland ice sheet surface mass balance between 1990–2010, using the regional climate model MAR, The Cryosphere, 6, 695–711, https://doi.org/10.5194/tc-6-695-2012, 2012.</em></p> <p><em>MARTeam: MARv3.11, GitLab [data set], <a href="https://gitlab.com/Mar-Group/MARv3" target="_blank" rel="noopener">https://gitlab.com/Mar-Group/MARv3#</a> (last access: 28 May 2022), 2021.</em></p>
Position error-free control of magnetic-domain wall devices via spin-orbit torque modulation
<p>Magnetic domain-wall devices such as racetrack memory and domain-wall shift registers facilitate massive data storage as hard disk drives with low power portability as flash memory devices. The key issue to be addressed is how perfectly the domain-wall motion can be controlled without deformation, as it can replace the mechanical motion of hard disk drives. However, such domain-wall motion in real media is subject to the stochasticity of thermal agitation with quenched disorders, resulting in severe deformations with pinning and tilting. To sort out the problem, we propose and demonstrate a new concept of domain-wall control with a position error-free scheme. The primary idea involves spatial modulation of the spin-orbit torque along nanotrack devices, where the boundary of modulation possesses broken inversion symmetry. In this work, by showing the unidirectional motion of domain wall with position-error free manner, we provide an important missing piece in magnetic domain-wall device development.</p>
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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.