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For the first term, using (5.7), the embedding property and η = ∥ L t ∥ < 1, the term can be estimated as ∥ L t k n u 0 − P L t k n u 0 ∥ ≤ M h θ ∥ u 0 ∥ 2. (5.38).
For the first term, using (5) we obtain for all (Tleqtau^{ast}) I_{1}leq C int_{0}^{T}e^{-varepsilon^{-4}w T-tau }biglVert psi(tau )bigrVert _{sigma},dtauleq Csup_{tauin[0,tau ^{-varepsilon^{-4}w T-tau }biglVert_{sigma} int _{0}^{-varepsilon^{-4}w T-tau }biglVerteq C varepsilon^{-varepsilon^{-4}w T-tau }biglVertnition of (tau^{ast}).
Hence, by the additivity of the expectation we get Now we can rewrite the first term using the independence property as follows: which is the expected accuracy of σ1 in the sequence s1.
You can factorize these by factorizing the first term, using each factor as the first term of each binomial factor, then finding two factors of c that can be used as the second terms of the binomial factors.
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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