Search results for " hybrid mode"

showing 4 items of 4 documents

Tailoring Third-Harmonic Diffraction Efficiency by Hybrid Modes in High-Q Metasurfaces

2021

Metasurfaces are versatile tools for manipulating light; however, they have received little attention as devices for the efficient control of nonlinearly diffracted light. Here, we demonstrate nonlinear wavefront control through third-harmonic generation (THG) beaming into diffraction orders with efficiency tuned by excitation of hybrid Mie-quasi-bound states in the continuum (BIC) modes in a silicon metasurface. Simultaneous excitation of the high-Q collective Mie-type modes and quasi-BIC modes leads to their hybridization and results in a local electric field redistribution. We probe the hybrid mode by measuring far-field patterns of THG and observe the strong switching between (0,-1) and…

bound states in the continuumMechanical Engineeringall-dielectric metasurface bound states in the continuum high-Q metasurface hybrid mode Third-harmonic diffraction wavefront controlhybrid modeBioengineeringSettore ING-INF/02 - Campi Elettromagnetici02 engineering and technologyGeneral Chemistryhigh-Q metasurface021001 nanoscience & nanotechnologyCondensed Matter Physics01 natural scienceswavefront controlall-dielectric metasurface0103 physical sciencesThird-harmonic diffractionThird-harmonic diffraction; all-dielectric metasurface; bound states in the continuum; high-Q metasurface; hybrid mode; wavefront controlGeneral Materials Science010306 general physics0210 nano-technology
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Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow.

2021

In support of cropland monitoring, operational Copernicus Sentinel-2 (S2) data became available globally and can be explored for the retrieval of important crop traits. Based on a hybrid workflow, retrieval models for six essential biochemical and biophysical crop traits were developed for both S2 bottom-of-atmosphere (BOA) L2A and S2 top-of-atmosphere (TOA) L1C data. A variational heteroscedastic Gaussian process regression (VHGPR) algorithm was trained with simulations generated by the combined leaf-canopy reflectance model PROSAILat the BOA scale and further combined with the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) atmosphere model at the TOA scale. Establishe…

010504 meteorology & atmospheric sciencesMean squared errorScienceReference data (financial markets)MathematicsofComputing_GENERAL0211 other engineering and technologieshybrid model02 engineering and technologyAtmospheric model01 natural sciencessymbols.namesaketop-of-atmosphere reflectanceKrigingLeaf area indexGaussian process021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsRemote sensing2. Zero hungerQbiophysical and biochemical traits; top-of-atmosphere reflectance; Sentinel-2; variational heteroscedastic Gaussian process regression; hybrid modelvariational heteroscedastic Gaussian process regressionVegetation15. Life on landsymbolsGeneral Earth and Planetary Sciencesbiophysical and biochemical traitsSentinel-2Scale (map)Remote sensing
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Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression

2021

Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time seri…

2. Zero hungerland surface phenology (LSP)010504 meteorology & atmospheric sciencesScienceQGoogle Earth Engine (GEE)0211 other engineering and technologiesGaussian Process Regression (GPR)02 engineering and technology15. Life on land01 natural sciencescrop traitsGeneral Earth and Planetary Sciencesland surface phenology (LSP); Google Earth Engine (GEE); Gaussian Process Regression (GPR); Sentinel-2; gap-filling; crop traits; hybrid modelsSentinel-2gap-filling021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote Sensing
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The performance investigation of viscoelastic hybrid models in vehicle crash event representation

2011

Aurthor's version of a chapter published in the book: Proceedings of the 18th IFAC World Congress 2011. Also available from the publisher at: http://dx.doi.org/10.3182/20110828-6-IT-1002.00284 This paper presents application of physical models composed of springs, dampers and masses joined together in various arrangements to simulation of a real car collision with a rigid pole. Equations of motion of those systems are being established and subsequently solutions to obtained differential equations are formulated. We start with a general model consisting of two masses, two springs, and two dampers, and illustrate its application to represent fore-frame and aft-frame of a vehicle. Hybrid model…

VDP::Mathematics and natural science: 400::Mathematics: 410::Applied mathematics: 413VDP::Technology: 500::Mechanical engineering: 570vehicle crash energy absorbers hybrid models kinematics modeling
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