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The approach is generally applicable to any liquid-phase decomposition, provided that gaseous products can be quantitatively analyzed.
Better understanding can be gained from the examples of possible decomposition provided by the grid search approach (Fig. 7c, d), documenting the non-uniqueness of the decomposition.
This decomposition provided the opportunity to filter the noise at a desired level and the seismic data were able to be reconstructed back from its filtered coefficients.
Though this is seemingly a more restrictive notion, the existence of this one-partition version is equivalent to that of a general paradoxical decomposition, provided that (mathcal {S}) contains the unit (of ({mathcal A}) or (widetilde{{mathcal A}})).
Also the use of the Kitagawa decomposition provided insight into temporal changes in SGA rates.
Furthermore, singular value decomposition provided a consistent increase in precision and recall, which makes it an important preprocessing step when dealing with noisy data.
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Moreover, the Freeman decomposition provides three scattered powers, and the Yamaguchi decomposition provides four scattering mechanisms.
Note that chirplet decomposition provides significant data compressibility.
Cloud decomposition provides scattering entropy, scattering angle, and inverse entropy.
Among them, the Pauli decomposition provides three scatter intensities, and the Krogager decomposition provides the three components of three scattering.
The decomposition provides a very informative illustration of the reduction of elements.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com