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The histogram of the residuals between the NGDC-720 values and the R-SCHA estimations is shown in Fig. 4. The distribution of the residuals is centered on zero and the residual mean square for the 2000 caps lie between 4.9 and 51.3 nT. Figure 5 shows the 2000 regional spectra of the NGDC-720 model (grey lines), together with their average (red line) and the NGDC-720 SHA power spectrum (blue line).
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The results from the iterative nonlinear SUR estimations are shown in the Appendix.
Column 3 is the output of the proposed method, where pixels with incorrect disparity estimations are shown in pink.
Selected results of these estimations are shown in Table 6, where only our variables of interest, track and grades, are displayed.
When SNR varies from 0 to 30 dB, the RMSEs of the azimuths, elevations, and range estimations are shown in Figure 3.
When the range of the second source varies from 0.2λ to 0.5λ, the angular and range estimations are shown in Figure 12.
When the range of the near-field source varies from 0.22λ to 0.58λ, the RMSEs of DOAs and range estimations are shown in Figure 7.
A comparison of the annual differences of the observatory monthly means and model estimations are shown in Figs. 5 and 6 for the 16 selected observatories indicated by red symbols in Fig. 1.
Pairwise FST estimations are shown in Table 3.
Parametric estimations are shown in Table 4. Statistically significant results were found continuously (Student's t-test, α = 0.05), the equation was consistent (Fisher's F-test), and the goodness of fit coefficient of determination was higher than 0.98.
Furthermore, the convergence path for PIC-MF with SAGE ML estimation is shown as a dashed line, and the receiver is estimated to converge in five iterations.
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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