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Figure 7e shows the cache hits as a function of the cache size.
Figure 7f represents the mean percentage of redirection hits as a function of the cache size.
Figure 6f represents the mean percentage of redirection hits as a function of the Zipf slope.
Figure 4e represents the percentage of cache hits as a function of the mean time between requests.
The mean percentage of redirection hits as a function of the mean TTL of the documents is shown in Figure 5f.
Finally, we can mention that only COOP performs better than CLIR for high-density networks in terms of timeouts (Figure 3d). Figure 3e illustrates the percentage of cache hits as a function of the number of nodes.
Similar(52)
Thus, hits differed as a function of prosody.
The observed correction for maximum overlap of AR-0.73 and ROCS hit lists as a function of shape Tanimoto appears to be linear.
Figure 2 shows both the byte hit rate as a function of the number of cache clients in this experiment (Fig. 2a) and the required cache size to achieve this byte hit rate (Fig. 2b).
A linear regression of the byte hit rate as a function of the logarithm of the cache size results in an adjusted (R^2) between (0.91) and (0.95) with errors statistically independent and normally distributed in all workloads, both for optimistic and pessimistic scenarios.
Previous research [ 16] found differentials in hit rate as a function of the number of dots being displayed.
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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