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Figure 5 Packet loss rate (small and large pop).: error-free versus error-prone— ms.
Figure 3 Average queue size (small and large pop).: error-free versus error-prone— ms.
The correctness of error propagation is critical for the conclusions, but it is not reported in detail.
Numerical examples with different kinds of errors are elaborately designed to validate the stability as well as the correctness of the error analysis.
Fig. 9 Output SINR versus pointing error.
Figure 6 Transmission distance versus packet error rate.
Figure 6 CDFs of location RMSEs versus the error distance.
Fig. 4 Predicted versus true segmentation error on CASIA dataset Fig. 5 Predicted versus true segmentation error on IITD dataset.
These dichotomies (between efficiency versus correctness, efficiency versus programmer time, efficiency versus high-level, et cetera). are bogus.
Figure 5 Error versus noise.
Figure 3 Error versus iteration.
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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