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Figure 6 Distribution of SINR values obtained from the evaluated scenario.
Regarding each evaluated scenario, the data gathered is relative to 30 participants and was analyzed using a 2 (scene groups) × 4 (TMOs) mixed factorial ANOVA.
Note also that, as shown in Figure4, isolated nodes were not able to properly track the emitter in the evaluated scenario.
With the above-presented results, we get one solution with minimal power, and one solution with minimal area requirement per evaluated scenario.
Table 1 also shows the values for some diversity and Wright F statistics in each evaluated scenario.
All scenarios were evaluated over a five-year time horizon and we also evaluated Scenario 1 over a ten-year time horizon.
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Details of all remaining evaluated scenarios can be found in [10].
For all evaluated scenarios, separate optimizations for instruction memory and data memory were performed.
Figure 7 shows the number of control messages of each type for all evaluated scenarios.
To sum up, the eOLLA outperforms the traditional OLLA in the whole set of evaluated scenarios.
After, we evaluated scenarios with several different types of service requests.
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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