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The confidence region of the random mechanical energy transferred outside the excitation band is shown as a function of η for several levels of model and data uncertainties.
The flaring index, f, as a function of η for five different cases of t*/ β.
The variation of the velocity and concentration profiles is plotted as a function of η for some values of λ in Figure 1.
Fig. 5 The BER as a function of η for rate (frac {1}{N_{c}}) system with BPSK modulation and imperfect channel state information.
In Fig. 1, we illustrate the flaring index, f, as a function of η for the five cases of the wind parameters t*/ β between 1 × 108 yr and 1 × 1012 yr.
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For this reason, we studied the variation of λ max as a function of η and q for both EE models and for different number of users.
Interferometric processing is able to display the radiated intensity as a function of η and α even for moving sources such as trains.
We used f as an effect size measure for the ANOVA's, with f being calculated as a function of η 2 (see Cohen, 1988, p. 284), and Cohen's d for Tukey HSD post-hoc contrasts between instruction conditions.
When considering the variability of estimated parameters as a function of η OUT, the CV among k OUT and α OUT appears to be uncorrelated as above, with a CV below 15%and2.5%5%, respectively; conversely, η OUT exhibits a linear increase of CV, up to 28% for the tested values of varied η OUT (see Figure 3C).
For RT effects and self-report responses, effect sizes (f) were calculated as a function of η 2 (see Cohen, 1988, p. 284).
Define a function of η as f = η T i + ( 1 − η ) T o OA = ( T i − T o OA ) η + T o OA.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com