Thermal diffusion in almost Lorentzian mixtures.
Approximation method for thermal diffusion factor of almost Lorentzian gas mixture with better convergence properties than Chapman-Enskog approximation
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Approximation method for thermal diffusion factor of almost Lorentzian gas mixture with better convergence properties than Chapman-Enskog approximation
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A relatively simple equation has been found to express with fair accuracy, variation in manifold-charge temperature with charge in engine operating conditions. This equation and associated curves have been checked by multi cylinder-engine data, both test stand and flight, over a wide range of operating conditions. Average mixture temperatures, predicted by the equations of this report, agree reasonably well with results within the same range of carburetor-air temperatures from laboratories and test stands other than the NACA.
A technique was developed to improve spectral mixture analysis estimates of snow-covered-area in alpine regions through the use of multiple snow endmembers.
Knowledge on geographical location and distribution of crops at global, national and regional scales is an extremely valuable source of information applications. Traditional approaches to crop mapping using remote sensing data rely heavily on reference or ground truth data in order to train/calibrate classification models. As a rule, such models are only applicable to a single vegetation season and should be recalibrated to be applicable for other seasons. This paper addresses the problem of early season large-area winter crop mapping using Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) time-series and growing degree days (GDD) information derived from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product. The model is based on the assumption that winter crops have developed biomass during early spring while other crops (spring and summer) have no biomass. As winter crop development is temporally and spatially non-uniform due to the presence of different agro-climatic zones, we use GDD to account for such discrepancies. A Gaussian mixture model (GMM) is applied to discriminate winter crops from other crops (spring and summer). The proposed method has the following advantages: low input data requirements, robustness, applicability to global scale application and can provide winter crop maps 1.5-2 months before harvest. The model is applied to two study regions, the State of Kansas in the US and Ukraine, and for multiple seasons (2001-2014). Validation using the US Department of Agriculture (USDA) Crop Data Layer (CDL) for Kansas and ground measurements for Ukraine shows that accuracies of greater than 90% can be achieved in mapping winter crops 1.5-2 months before harvest. Results also show good correspondence to official statistics with average coefficients of determination R(exp. 2) greater than 0.85.
A problem of using mixture-of-Gaussian models for unsupervised texture segmentation is that multimodal textures cannot be well represented by a single Gaussian cluster.
The top-of-atmosphere (TOA) path radiance generated by an aerosol mixture can be synthesized by linearly adding the contributions of the individual aerosol components, weighted by their fractional optical depths. The method, known as linear mixing, is exact in the single-scattering limit.
A new technique, called multiple endmember spectral mixture analysis (MESM) was developed and tested in the Santa Monica Mountains, using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acqired in the fall of 1994 to map California chaparral.