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Exact(14)
end{aligned} Since B is small enough, we obtain (4.3) immediately.
Now, putting the above estimates for s into (2.8) and taking small enough, we obtain (2.16).
Choosing suitable which is small enough, we obtain by Lemma 2.6 that (235).
Choosing ε small enough, we obtain Phi y)rightarrow-infty quad text{as } |y|rightarrow +infty.
Now, Taking arbitrarily with and large enough, we obtain that (4.14).
Choosing ε small enough, we obtain the same estimate as above by Gronwall's inequality.
Similar(46)
Combing all the estimates of and selecting a small enough constant, we obtain (3.30).
Then, for large enough (M>0), we obtain begin{aligned}& x^{prime hat{alpha}}y^{prime hat{beta}}z^{prime hat {gamma}} leq{x^{ hat{alpha}}y^{hat{beta}}z^{hat{gamma}}exp(hat{triangle}-2M } leq{x^{hat{triangle}-2M }beta}}z^{hat{gamma}}}leq{x^{hat{alpha}}y
Combining (3.5 - 3.7 3.5 - 3.72), for large enough x, withbtain 1-F(x)sim pbigl(1-F_{k}(x)bigr) (3.8) as (xrightarrowinfty), where (F_{k}) represents the cdf of the (operatorname{LGMD}(k)), and σ and p are defined by Theorem 3.3.
Choosing ∥u∥ = ρ small enough, we can obtain I+ u) ≥ R > 0 if ∥u∥ = ρ.
When the sample number is large enough, we can obtain a joint spectrum sensing and access scheme with reduced complexity and limited performance loss.
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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