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Spatiotemporal data, especially remote sensing data, are widely used in ecological, geographical, agriculture, and military research and applications. With the development of remote sensing technology, more and more remote sensing data are accumulated and stored in the cloud. An effective way for cloud users to access and analyse these massive spatiotemporal data in the web clients becomes an urgent issue. In this paper, we proposed a new scalable, interactive and web-based cloud computing solution for massive remote sensing data analysis. We build a spatiotemporal analysis platform to provide the end-user with a safe and convenient way to access massive remote sensing data stored in the cloud. The lightweight cloud storage system used to store public data and users' private data is constructed based on open source distributed file system. In it, massive remote sensing data are stored as public data, while the intermediate and input data are stored as private data. The elastic, scalable, and flexible cloud computing environment is built using Docker, which is a technology of open-source lightweight cloud computing container in the Linux operating system. In the Docker container, open-source software such as IPython, NumPy, GDAL, and Grass GIS etc., are deployed. Users can write scripts in the IPython Notebook web page through the web browser to process data, and the scripts will be submitted to IPython kernel to be executed. By comparing the performance of remote sensing data analysis tasks executed in Docker container, KVM virtual machines and physical machines respectively, we can conclude that the cloud computing environment built by Docker makes the greatest use of the host system resources, and can handle more concurrent spatial-temporal computing tasks. Docker technology provides resource isolation mechanism in aspects of IO, CPU, and memory etc., which offers security guarantee when processing remote sensing data in the IPython Notebook. Users can write complex data processing code on the web directly, so they can design their own data processing algorithm.

Spatiotemporal data, especially remote sensing data, are widely used in ecological, geographical, agriculture, and military research and applications. With the development of remote sensing technology, more and more remote sensing data are accumulated and stored in the cloud. An effective way for cloud users to access and analyse these massive spatiotemporal data in the web clients becomes an urgent issue. In this paper, we proposed a new scalable, interactive and web-based cloud computing solution for massive remote sensing data analysis. We build a spatiotemporal analysis platform to provide the end-user with a safe and convenient way to access massive remote sensing data stored in the cloud. The lightweight cloud storage system used to store public data and users' private data is constructed based on open source distributed file system. In it, massive remote sensing data are stored as public data, while the intermediate and input data are stored as private data. The elastic, scalable, and flexible cloud computing environment is built using Docker, which is a technology of open-source lightweight cloud computing container in the Linux operating system. In the Docker container, open-source software such as IPython, NumPy, GDAL, and Grass GIS etc., are deployed. Users can write scripts in the IPython Notebook web page through the web browser to process data, and the scripts will be submitted to IPython kernel to be executed. By comparing the performance of remote sensing data analysis tasks executed in Docker container, KVM virtual machines and physical machines respectively, we can conclude that the cloud computing environment built by Docker makes the greatest use of the host system resources, and can handle more concurrent spatial-temporal computing tasks. Docker technology provides resource isolation mechanism in aspects of IO, CPU, and memory etc., which offers security guarantee when processing remote sensing data in the IPython Notebook. Users can write complex data processing code on the web directly, so they can design their own data processing algorithm.

This paper presents a measurement of quantities related to the formation of jets from high-energy quarks and gluons (fragmentation). Jets with transverse momentum 100 GeV 500 MeV and vertical bar eta vertical bar < 2.5 are used to probe the detailed structure of the jet. The fragmentation properties of the more forward and the more central of the two leading jets from each event are studied. The data are unfolded to correct for detector resolution and acceptance effects. Comparisons with parton shower Monte Carlo generators indicate that existing models provide a reasonable description of the data across a wide range of phase space, but there are also significant differences. Furthermore, the data are interpreted in the context of quark- and gluon-initiated jets by exploiting the rapidity dependence of the jet flavor fraction. A first measurement of the charged-particle multiplicity using model-independent jet labels (topic modeling) provides a promising alternative to traditional quark and gluon extractions using input from simulation. The simulations provide a reasonable description of the quark-like data across the jet p(T) range presented in-this measurement, but the gluon-like data have systematically fewer charged particles than the simulation. ; CERN; ANPCyT, ArgentinaANPCyT; YerPhI, Armenia; ARC, Australia, Australian Research Council; BMWFW; FWF, AustriaAustrian Science Fund (FWF); ANAS, Azerbaijan National Academy of Sciences (ANAS); SSTC, Belarus; CNPq, National Council for Scientific and Technological Development (CNPq); FAPESP, BrazilFundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP); NSERCNatural Sciences and Engineering Research Council of Canada; CFI, CanadaCanada Foundation for Innovation; CONICYTComision Nacional de Investigacion Cientifica y Tecnologica (CONICYT); NSFC, ChinaNational Natural Science Foundation of China; COLCIENCIAS, ColombiaDepartamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias; MSMT CRMinistry of Education, Youth & Sports - Czech Republic; MPO CR; VSC CR, Czech RepublicCzech Republic Government; DNRF; DNSRC, DenmarkDanish Natural Science Research Council; IN2P3-CNRS, CEA-DRF/IRFU, France; BMBFFederal Ministry of Education & Research (BMBF); MPG, GermanyMax Planck Society; GSRT, GreeceGreek Ministry of Development-GSRT; RGC, Hong Kong SAR, ChinaHong Kong Research Grants Council; ISFIsrael Science Foundation; Benoziyo Center, Israel; INFN, ItalyIstituto Nazionale di Fisica Nucleare; MEXTMinistry of Education, Culture, Sports, Science and Technology, Japan (MEXT); JSPS, JapanMinistry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSWMinistry of Science and Higher Education, Poland; NCN, Poland; FCT, PortugalFundacao para a Ciencia e a Tecnologia (FCT); MNE/IFA, Romania; NRC KI, Russian Federation; MESTD, Serbia; MSSR, Slovakia; ARRSSlovenian Research Agency - Slovenia; MIZS, Slovenia; MINECO, Spain; Wallenberg Foundation, Sweden; SNSFSwiss National Science Foundation (SNSF); MOST, TaiwanMinistry of Science and Technology, Taiwan; TAEK, TurkeyMinistry of Energy & Natural Resources - Turkey; STFC, United KingdomScience & Technology Facilities Council (STFC); DOEUnited States Department of Energy (DOE); NSFNational Science Foundation (NSF); BCKDF; CANARIE; COST, ERC; ERDFEuropean Union (EU); Marie Sklodowska-Curie Actions, European UnionEuropean Union (EU); Investissements d' Avenir LabexFrench National Research Agency (ANR); ANR, FranceFrench National Research Agency (ANR); DFGGerman Research Foundation (DFG); AvH Foundation, Germany, Alexander von Humboldt Foundation; EU-ESFEuropean Union (EU); Greek NSRF, Greece; BSF-NSF; GIF, Israel, German-Israeli Foundation for Scientific Research and Development; CERCA Programme Generalitat de Catalunya, Spain; Royal Society of London; Leverhulme Trust, United KingdomLeverhulme Trust; NDGF (Denmark, Norway, Sweden); KIT/GridKA (Germany); INFN-CNAF (Italy); NL-Tl (Netherlands), PIC (Spain); ASGC (Taiwan); BNL (USA) [110] ; Open access article ; This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.

Narrow resonances decaying into WW, WZ or ZZ boson pairs are searched for in 139 fb(-1) of proton-proton collision data at a centre-of-mass energy of root s = 13TeV recorded with the ATLAS detector at the Large Hadron Collider from 2015 to 2018. The diboson system is reconstructed using pairs of high transverse momentum, large-radius jets. These jets are built from a combination of calorimeter- and tracker-inputs compatible with the hadronic decay of a boosted W or Z boson, using jet mass and substructure properties. The search is performed for diboson resonances with masses greater than 1.3TeV. No significant deviations from the background expectations are observed. Exclusion limits at the 95% confidence level are set on the production cross-section times branching ratio into dibosons for resonances in a range of theories beyond the Standard Model, with the highest excluded mass of a new gauge boson at 3.8TeV in the context of mass-degenerate resonances that couple predominantly to gauge bosons. ; ANPCyT, ArgentinaANPCyT; YerPhI, Armenia; ARC, AustraliaAustralian Research Council; BMWFW, Austria; FWF, AustriaAustrian Science Fund (FWF); ANAS, AzerbaijanAzerbaijan National Academy of Sciences (ANAS); SSTC, Belarus; CNPq, BrazilNational Council for Scientific and Technological Development (CNPq); FAPESP, BrazilFundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP); NSERC, CanadaNatural Sciences and Engineering Research Council of Canada; NRC, Canada; CFI, CanadaCanada Foundation for Innovation; CERN; CONICYT, ChileComision Nacional de Investigacion Cientifica y Tecnologica (CONICYT); CAS, ChinaChinese Academy of Sciences; MOST, ChinaMinistry of Science and Technology, China; NSFC, ChinaNational Natural Science Foundation of China; COLCIENCIAS, ColombiaDepartamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias; MSMT CR, Czech RepublicMinistry of Education, Youth & Sports - Czech RepublicCzech Republic Government; MPO CR, Czech RepublicCzech Republic Government; VSC CR, Czech RepublicCzech Republic Government; DNRF, Denmark; DNSRC, DenmarkDanish Natural Science Research Council; IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, GermanyFederal Ministry of Education & Research (BMBF); HGF, Germany; MPG, GermanyMax Planck Society; GSRT, GreeceGreek Ministry of Development-GSRT; RGC, Hong Kong SAR, ChinaHong Kong Research Grants Council; ISF, IsraelIsrael Science Foundation; Benoziyo Center, Israel; INFN, ItalyIstituto Nazionale di Fisica Nucleare; MEXT, JapanMinistry of Education, Culture, Sports, Science and Technology, Japan (MEXT); JSPS, JapanMinistry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science; CNRST, Morocco; NWO, NetherlandsNetherlands Organization for Scientific Research (NWO)Netherlands Government; RCN, Norway; MNiSW, PolandMinistry of Science and Higher Education, Poland; NCN, Poland; FCT, PortugalFundacao para a Ciencia e a Tecnologia (FCT); MNE/IFA, Romania; MES of Russia, Russian FederationRussian Federation; NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS, SloveniaSlovenian Research Agency - Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, SwitzerlandSwiss National Science Foundation (SNSF); Canton of Bern, Switzerland; MOST, TaiwanMinistry of Science and Technology, Taiwan; TAEK, TurkeyMinistry of Energy & Natural Resources - Turkey; STFC, United KingdomScience & Technology Facilities Council (STFC); DOE, United States of AmericaUnited States Department of Energy (DOE); NSF, United States of AmericaNational Science Foundation (NSF); BCKDF, Canada; CANARIE, Canada; CRC, Canada; Compute Canada, Canada; COST, European Union; ERC, European UnionEuropean Union (EU)European Research Council (ERC); ERDF, European UnionEuropean Union (EU); Horizon 2020, European Union; Marie Sklodowska-Curie Actions, European UnionEuropean Union (EU); Investissements d' Avenir Labex and Idex, ANR, FranceFrench National Research Agency (ANR); DFG, GermanyGerman Research Foundation (DFG); AvH Foundation, GermanyAlexander von Humboldt Foundation; EU-ESFEuropean Union (EU); Greek NSRF, Greece; BSF-NSF, Israel; GIF, IsraelGerman-Israeli Foundation for Scientific Research and Development; CERCA Programme Generalitat de Catalunya, Spain; Royal Society, United KingdomRoyal Society of London; MIZS, Slovenia; Canton of Geneva, Switzerland; Leverhulme Trust, United KingdomLeverhulme Trust; Herakleitos programme; Thales programme; Aristeia programme ; Open access journal ; This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.