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Each subplot shows the output spectrum for a different value of the voltage (V_{mathrm{phase}}), which controls the phase shift ϕ induced on the feedback field u.
This guess solution can be the solution for a different value of the parameters or the solution for the linear problem.
We give the same remarks using Table 12 where we compute the threshold of (IT + RS) decoding and ML decoding for a different value of N1.
Each subplot shows the output power spectrum (in dB) for a different value of the voltage (V_{mathrm{phase}}) that controls the phase shift ϕ induced in the feedback field u.
However, it is possible that the results will improve for a different value of the parameter.
In the case of pure competition, i.e. all interaction coefficients α ij negative (see Method section), we obtain distributions like the ones shown in Figure 1, which represent in each row the evolution for a different value of σ: σ = 0.2, σ = 0.15 and σ = 0.1 in rows 1, 2 and 3, respectively.
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The computed numerical solutions for this example are presented in Figure 2(a - f) for different va - foforhe parameters.
Figure 1 plots over s for a few different values of N. In each curve the estimated mutation rate decreases as the selective coefficient rises.
a For different values of α with β=2.5 and μ=0 and σ=1.
Open image in new window Fig. 4 The behavior of Sagdeev potential ψ with respect to the electrostatic potential ϕ, a for different values of electron suprathermality index κ e with f = 0.8, b for different values of ion concentration f with κ e = 3.
Figure 3 plots the lower and upper boundaries of support of eigenvalues for a triangular window for different values of c.
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