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97 results for “PSI”
X-PSI Parameter Recovery for Temperature Map Configurations Inspired by PSR J0030+0451
<p>Posterior sample files associated with the preprint "X-PSI Parameter Recovery for Temperature Map Configurations Inspired by PSR J0030+0451 " by Vinciguerra et al. (2023; <a href="https://doi.org/10.48550/arXiv.2209.12840">arXiv</a>; almost submitted to for publication in ApJ) and Jupyter notebook scripts to reproduce the corresponding figures.</p> <p>Also included are examples of model modules in the Python language using the X-PSI framework.</p> <p>Please refer to the READme for detailed information.</p>
Systematic PSI processing over Santorini volcano: the 2011-2012 unrest period
<p>In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions Of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation.</p> <p>The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the <strong>period 2011-2012, based on 12 descending ASAR Envisat images</strong>. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc. </p>
Systematic PSI processing over Santorini volcano: the 2012-2013 post unrest period
<p>In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation. The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the period 2012-2013, based on 25 descending TerraSAR-X images. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc. </p>
Systematic PSI processing over Santorini volcano: the 1992-2000 pre-unrest period
<p>In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions Of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation.</p> <p>The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the <strong>period 1992-2000, based on 37 descending ERS-1,2 images</strong>. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc. </p>
Systematic PSI processing over Santorini volcano: the 2015 post unrest period
<p>In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions Of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation.</p> <p>The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the <strong>period 2015, based on 30 ascending COSMO-SkyMed</strong> <strong>images</strong>. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc. </p>
Systematic PSI processing over Santorini volcano: the 2012-2016 post unrest period
<p>In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions Of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation.</p> <p>The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the <strong>period 2012-2016, based on 20 descending Radarsat-2 images</strong>. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc. </p>
FRACTESUS_PSI_A533B (JRQ)_MC_T0TEM input
<div>Fractesus project. Fracture test mini-CT. Master curve input JRQ. PSI. </div> <div> <div> <p> </p> </div> </div>
FRACTESUS_PSI_A533B (JRQ)_T0_MCT_output
<div>Fractesus project. Fracture test mini-CT. Master output JRQ. PSI. </div> <div> <div> <p> </p> </div> </div>
Supporting dataset for the article 'Unique structural attributes of the PSI-NDH supercomplex in Physcomitrium patens'
<p><strong>Figure </strong><strong>1</strong><strong>:</strong> <strong>Separation and identification of pigment-protein complexes from </strong><strong>Physcomitrium patens</strong><strong> and structural characterization of PSI-NDH/NDH by single particle electron microscopy. a</strong><strong>) </strong>CN-PAGE separation of pigment-protein complexes from the TM from Pp solubilised by 0.5% β-DDM. The image shows the selected gel line scanned at room temperature in transmission mode (colour image) and in fluorescence mode (black and white image). <strong>b)</strong> Immunoblot identification of the NdhH subunit on a PVDF membrane with transferred proteins from the second-dimension denaturing PAGE. Two horizontal bands of the marker (blue colour) designate the molecular weight of proteins at these positions. <strong>c</strong><strong>–</strong><strong>e</strong>) Electron density maps of two forms of PSI-NDH sc and NDH monomer from Pp revealed by single particle electron microscopy (stromal view). The 2D class average of (<strong>c</strong>) the typical form of PSI-NDH sc consisting of 2566 particles, (<strong>d</strong>) the shifted form consisting of 1098 particles, and (<strong>e</strong>) the NDH monomer composed of 2725 particles. Abbreviations: PSII - photosystem II, PSI - photosystem I, LHCII - light-harvesting complex of photosystem II, mc - megacomplex, cc - core complex, mono - monomer, trim – trimer, B1 - C<sub>2</sub>S<sub>2</sub>M<sub>2</sub> band, B2 – C<sub>2</sub>S<sub>2</sub>M band, B3 – C<sub>2</sub>S<sub>2</sub>/C<sub>2</sub>SM band, B4 – PSII cc + PSI band.</p> <p><strong>Figure </strong><strong>2</strong><strong>:</strong> <strong>Structural characterisation of PSI-NDH supercomplex and NDH monomer in </strong><strong>Physcomitrium patens</strong><strong>. a) </strong>Structural model of the PSI-NDH sc in Pp, which corresponds to the arrangements in the PSI-NDH sc from At. <strong>b) </strong>Structural model of the PSI-NDH sc, where PSI is rotated clockwise by 35° in comparison to its position in 2a). <strong>c) </strong>Superposition of the Pp PSI-NDH model with the rotated PSI from image 2b) (in colour) over its un-rotated form from 2a) (grey colour) with pictorial color-coded legend of individual LHCA antennae. <strong>d)</strong> Structural model of the NDH monomer fitted by the truncated At NDH. <strong>e)</strong> Comparison of the Pp NDH model (in colour) versus the complete At NDH model (grey) with highlighted subunits absent in Pp NDH (limon). Structural models of PSI-NDH scs viewed from the stromal side were obtained by a fit of the NDH monomer from At (PDB ID: 7WG5, Su et al., 2022) and PSI complex from Pp (PDB ID: 7KSQ, Gorski et al., 2022). Prior to fitting, the subunits of NDH unencoded in Pp genome were removed from the At NDH structure, namely the PnsB2, PnsB3, and the subunits of SubL. PSI subunits and NDH subcomplexes and selected subunits are color-coded. PSI: forest green - core; pink, white, chocolate, yellow and magenta – LHCA1, LHCA2a, LHCA2b, LHCA5 and LHCA3, respectively. NDH: orange – SubM, cyan – SubB (PnsB1, PnsB4, PnsB5), blue – SubA and NdhT, lime – SubB (PnsB2, PnsB3) and SubL.</p> <p><strong>Supporting Figure S1: Single particle electron microscopy analysis of PSI-NDH supercomplex in <em>Physcomitrium patens</em>. </strong>Representative transmission EM micrographs of the <strong>a)</strong> B1, <strong>b)</strong> B2 and <strong>c)</strong> B3 band containing PSII scs (un-circled particles), PSI-NDH scs (in blue circles) and NDH monomers (in yellow circles); <strong>d)</strong> 2D classification of the PSI-NDH and NDH particles extracted from all micrographs of B1 and B2 bands with the given numbers of particles in each class. The 2478 and 20 454 particles were extracted for B1 and B2 band, respectively, and were further classified into 15 classes.</p> <p><a name="_Hlk177648374"></a><strong>Supporting Figure S</strong><strong>2</strong><strong>: <a name="_Hlk174966851"></a>Relative content of selected proteins in analysed bands (B1–B3). </strong><strong>a) </strong>Relative protein abundance of individual LHCI proteins (including LHCB9) evaluated to the protein content of PSI core (represented by the sum of PsaA and PsaB) in three different bands: B1, B2, and B3.<strong> </strong>The relative protein content of all isoforms representing one LHCA protein (see Table S2) were summarised and subsequently evaluated as one for reducing results complicity. <strong>b)</strong> The relative protein content of PSI (represented by sum of PsaA and PsaB) and PSII (represented by sum of PsbA(D1) and PsbD (D2)) evaluated to the sum of all proteins in specific bands.<strong> c) </strong>The SubA/PSI ratio calculated as the sum of relative protein abundance of SubA NDH subunits divided by the sum of relative protein content of PsaA and PsaB in bands B1 and B2. The relative content (relative PG iBAQ values (riBAQ) in ppm format) of individual LHCA isoforms, LHCB9, SubA subunits, PsaA, PsaB, PsbA (D1) and PsbD (D2) was determined by mass spectrometry in samples prepared from gel bands excised from CN-PAGE (Fig 1a). The presented values are means ± SD from 3 replicates.<strong> </strong></p> <p><strong>Supporting Figure S2 (xlsx format): </strong>Selected mass spectrometry source data for Figure S2(a-c).</p> <p><strong><a name="_Hlk177648454"></a>Supporting Figure S3: Impact of PSI rotation within Pp PSI-NDH sc on the distance between the putative ferredoxin (Fd)-binding sites in NDH and PSI. </strong>Comparison of the distance between Fd-binding site in NDH and PSI in typical (a) and shifted (b) form of PSI-NDH sc. The subunits forming Fd-binding site in NDH according Laughlin et al. (2020): NdhO, NdhI, NdhH (excluding NdhS and NdhV absent in model) and in PSI according Caspy et al. (2020): stromal PsaC-E (excluding PsaA, PsaF) are labelled and colour coded as follows: NDH: limon, blue, orange – NdhO, NdhH, NdhI, respectively; PSI: yellow, violet, and firebrick –PsaC, PsaD, PsaE, respectively, with the rest of the subunits in PSI and NDH in grey colour. The binding positions of the Fd in NDH (O-site) and PSI are illustrated as brown circles. Structural models of PSI-NDH scs viewed from the stromal side were obtained by a fit of the NDH monomer from At (PDB ID: 7WG5, Su et al., 2022) and PSI complex from Pp (PDB ID: 7KSQ, Gorski et al., 2022). Prior to fitting, the subunits of NDH unencoded in Pp genome were removed from the At NDH structure, namely the PnsB2, PnsB3, and the subunits of SubL. Two headed arrows determine the distance between approx. centre of Fd-binding sites in NDH and PSI in two different structures with estimated values in Angstroms. <strong> </strong></p> <p><strong>Supporting Table S1:</strong> <strong>A summary of the identified PSI-NDH supercomplex subunits in </strong><strong>Physcomitrium patens</strong><strong>. </strong>MS analysis of the B2 band (in 3 technical replicates) from CN-PAGE used for transmission EM analysis. The subunits of the PSI and NDH complexes identified in the band after digestion with trypsin and assigned according to UniProt database records are listed. Proteins highlighted in green are not encoded in the Physcomitrium patens genome or do not have any record in the UniProt database, proteins highlighted in orange were not identified in our dataset.</p> <p><a name="_Hlk177048123"></a><strong>Supporting Table S2: The list of all LHCI proteins (including LHCB9) and their isoforms identified in four analysed bands B1, B2, B3 and B4 by mass spectrometry.</strong> Individual rows represent different isoforms of LHCI antennae detected in specific bands by MS, accession numbers according UniProt database in one row represent the same isoform of protein. Letters “Y” and “N” indicate whether the specific protein was detected or undetected in MS dataset of individual bands, respectively. The label “Low” was assigned to those protein that were detected, however, whose abundance was close to the detection limit or they had overall low and negligible abundance relatively to PSI core. For band B1–B3 and B4 three and four technical replicates were analysed, respectively. </p>
SNAPPING PSI surface motion measurements over selected sites presented in MDPI Remote Sensing paper "SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping"
<p>SNAPPING PSI surface motion measurements over selected sites as presented in the paper with the title "SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping" by Michael Foumelis, Jose Manuel Delgado Blasco, Fabrice Brito, Fabrizio Pacini, Elena Papageorgiou, Panteha Pishehvar and Philippe Bally on Remote Sensing Open Access Journal.</p> <p>Whenever using this dataset, please cite its original paper (<a href="https://doi.org/10.3390/rs14236075">https://doi.org/10.3390/rs14236075</a>) and include the reference to this dataset (<a href="https://doi.org/10.5281/zenodo.7369653">https://doi.org/10.5281/zenodo.7369653</a>).</p> <p>This dataset includes average Line-of-Sight velocities for the following sites and dates:</p> <table> <tbody> <tr> <td><strong>Site name</strong></td> <td><strong>Country</strong></td> <td><strong>Period</strong></td> <td><strong>Relative orbit</strong></td> <td><strong>Orbit direction</strong></td> </tr> <tr> <td>Cap-Haïtien</td> <td>Haiti</td> <td>Jan-2017 / Dec-2019</td> <td>106</td> <td>ascending</td> </tr> <tr> <td>Gran Renaissance Ethiopian Dam</td> <td>Ethiopia</td> <td>Jan-2019 / Jun-2021</td> <td>50</td> <td>descending</td> </tr> <tr> <td>La Palma Volcano</td> <td>Spain</td> <td>Jun-2019 / Dec-2021</td> <td>169</td> <td>descending</td> </tr> <tr> <td>Santorini Volcano</td> <td>Greece</td> <td>Apr-2015 / May-2021</td> <td>29</td> <td>ascending</td> </tr> <tr> <td>San Francisco</td> <td>USA</td> <td>Jan-2016 / Dec-2020</td> <td>115</td> <td>descending</td> </tr> <tr> <td>Thessaloniki International Airport (SKG)</td> <td>Greece</td> <td>Apr-2015 / Dec-2020</td> <td>102</td> <td>ascending</td> </tr> </tbody> </table>
Pre-seismic Sentinel-1 PSI surface motion measurements for the area affected by the February 2023 Türkiye–Syria earthquakes
<p>We have processed Copernicus Sentinel-1A data from 01/2019 to 01/2023 (descending track 21) over the broader area (approx. 48400 sq. km) affected by February 6, 2023, M7.8 and M7.5 earthquakes in Türkiye and Syria, utilizing the SNAPPING Persistent Scatterers Interferometry (PSI) medium resolution service of the Geohazards Exploitation Platform (GEP; <a href="https://geohazards-tep.eu">https://geohazards-tep.eu</a>).</p> <p>Measurements contain average Line-of-Sight (LoS) velocities, corresponding uncertainties, and the complete displacement time series. Please note that the original dataset of about 2M point measurements was split into parts, each containing 200k points, to facilitate easier manipulation and visualization.</p> <p>References</p> <p>[1] Foumelis, M.; Delgado Blasco, J.M.; Brito, F.; Pacini, F.; Papageorgiou, E.; Pishehvar, P.; Bally, P. SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping. Remote Sens. 2022, 14, 6075. <a href="https://doi.org/10.3390/rs14236075">https://doi.org/10.3390/rs14236075</a></p> <p>[2] SNAPPING – Surface motioN mAPPING Sentinel-1 on-demand processing service, Online tutorial, <a href="https://docs.terradue.com/geohazards-tep/tutorials/Snapping.html">https://docs.terradue.com/geohazards-tep/tutorials/Snapping.html</a>.</p>
Psi - Greek letter
This is a 3D model of psi, which is a character found in the Greek alphabet. Source: Objaverse 1.0 / Sketchfab
psi bar meter
psi bar meter scanned with moto G 100. photogrammetry made 3D live scanner images are to survey media art PhD project Source: Objaverse 1.0 / Sketchfab
PSI Santorini Volcano Unrest (Greece)
<p>Persistent Scatterer Interferometry (PSI) dataset over Santorini volcano (Greece) for the unrest period of March 2011 - March 2012 (ESA ENVISAT mission).</p> <p>Foumelis, M., Trasatti, E., Papageorgiou, E., Stramondo, S. & Parcharidis, I., 2013. Monitoring Santorini volcano (Greece) breathing from space. Geophysical Journal International 193 (1), 161-170, doi: 10.1093/gji/ggs135.</p> <p> </p>
McStas simulated data of elliptic neutron lens in DMC beamline at PSI (x- & y-axis rotation)
<p>A dataset of ~12000 jpg images created from McStas simulations: elliptic adaptive optics neutron lens in DMC beamline.</p> <p>x- and y-axis of the lens are misaligned within the range \pm 1.5^o</p> <p> </p>
McStas simulated data of elliptic neutron lens in DMC beamline at PSI (x-axis rotation)
<p>A dataset of 13000 jpg images created from McStas simulations: elliptic adaptive optics neutron lens in DMC beamline.</p> <p>x-axis of the lens is misaligned within the range \pm 1.5^o</p>
McStas simulated data of elliptic neutron lens in DMC beamline at PSI (y-axis rotation)
<p>A dataset of ~16000 jpg images created from McStas simulations: elliptic adaptive optics neutron lens in DMC beamline.</p> <p>y-axis of the lens is misaligned within the range \pm 1.5^o</p>
Mycenaean Figurine, Psi-Type
Mycenaean female terracotta figurine, Psi-type. The original artifact was discovered in Peloponnese/Greece. Dating: Mycenaean Culture, late 14th to 12th century BC. Source: Objaverse 1.0 / Sketchfab
1. FRACTESUS_PSI_JRQ_KJc_MCT_-117_02
<div>Fractesus project. Fracture test mini-CT. Raw data JRQ. PSI. </div> <div> <div> <p> </p> </div> </div>
1. FRACTESUS_PSI_JRQ_KJc_MCT_-113_20
<div>Fractesus project. Fracture test mini-CT. Raw data JRQ. PSI. </div> <div> <div> <p> </p> </div> </div>
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.