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The intensity of change of the molecular characteristics is lowered along with the increase of samples' thickness, as soon as the basic molecular transformations occur in a thin film layer of the samples' surface.
However, SVM is more powerful for the problem with small sampling, nonlinear and high dimension, and the increase of samples will waste more time and decrease the classification performance [ 35, 36, 37].
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We also provide detailed theoretical analysis of the PSD of these two sampling algorithms with the increase of sampling intervals.
It is clear that with the increase of sample thickness, the shielding effectiveness is enhanced.
Moreover, with the increase of sample thickness, the shielding effectiveness is also enhanced.
The increasing content of phosphoric acid is also reflected in the increase of sample density.
The lateral fuel diffusion rate decreased with the increase of sample width, streamwise location and flow velocity.
Note that the amplitude of foliage clutter faded with the increase of sample time.
With the increase of sample length, the estimation of R i+n is more accurate, and the proposed beamformer has better SINR evaluation and smaller vectorial angle error.
This leads to that the cognitive radios operating on such spectrum resources are price prohibitive due to the increase of sampling rate.
For example, when D=0.3, N1=3,N2=5, the data rate R(D ≈1,350, while with the increase of sampling pairs to N1=3,N2=11, then R(D ≈1220, which is much smaller.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com