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Table 3 presents estimated coefficients based on Equation (1).
We also investigate the maximum likelihood estimation for the drift coefficients based on continuous time observations.
Growth dynamics are estimated by mathematical functions with coefficients based on inventory data or yield tables.
It blends the pyramid coefficients based on a scalar weight map.
The numerical results of the buckling coefficients based on both methods show good agreement.
The novel aspect is the introduction of new representative coefficients based on HOS in time and frequency domains.
In the following section, we describe how we actually compute these coefficients based on Bayesian mean square error (MSE) predictions.
After that, we investigate the maximum likelihood estimation for the drift coefficients based on continuous time observations.
An adaptive algorithm adjusts the main filter coefficients based on some metric applied to the output error.
Synthetic unit hydrographs are derived for ungauged watershed by computing various coefficients based on the physical features of the watershed.
Hence, instead of thresholding or generating the wavelet coefficients, we filter the coefficients based on the nonlocal means approach.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com