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Oxygen's wobbly start comes as such a surprise because so much was expected of the network.
72 holds for all σ w 2 > 0, and hence the link adaptation controller can simply assign P T = P T ( 1 ) and R = ln 1 + P T ( 1 ) h ̃ 2 without explicitly considering the variance of the CSI error σ w 2 for achieving the maximum expected of the network.
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Most of the renewals were expected because of the network's large store of hit shows, so it is no surprise to see dramas like "NCIS" and "Criminal Minds" on the list, nor comedies like "The Big Bang Theory" and "How I Met Your Mother.
In all the evaluated scenarios, the results of the OPNET simulations agreed with the expected behaviour of the network from what was informed by resource availability model through the values of the encp.
In all the presented results, the OPNET simulations confirmed the expected behaviour of the network from what was indicated by the resource availability model through the values of the encp.
Although our mathematical model is not exact and it does not make any explicit guarantees in terms of the delay behaviour, it is very useful as an approximation of the expected behaviour of the network, and it can be used as a conservative indication of the availability of the network for call admission decisions.
It is important to stress that our mathematical model is an approximate prediction of the expected behaviour of the network based on the assumptions made, and therefore it cannot be taken as an exact model to predict the behaviour of the network.
The simulation results have confirmed the effectiveness of our mathematical model and they have shown that our model has the ability to capture the resource availability for a WLAN and provide a fairly good indication of the expected behaviour of the network.
Nevertheless, the relationship is weaker than linear, suggesting that other factors, like maximum synaptic strength, number of supersynapses, etc., may have a strong influence on the expected size of the network.
This number is approximately proportional to the expected degree of the network, which follows a Zipf distribution, making it independent of network size (Szállási et al., 2006).
Furthermore, choosing a node for duplication or loss in inverse proportion to its degree favors an event in inverse relation to its expected disruption of the network.
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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