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Fig. 8 Using fault-injection and testing to measure robustness to specific failure sequences.
Bit error rate is employed to measure robustness of the zero-watermarking system, BER = B MN 100%% (10).
To measure robustness of a network, the deformation of individual points of the network is portrayed by strain.
This includes a detailed discussion on how to account for design uncertainties and how to measure robustness (i.e., how to evaluate robustness).
For the testing data, they used the same sequences, but applied an artificial light source of varying intensity, as the goal of their evaluation was to measure robustness to illumination variations.
In order to measure robustness, indexes relative to the overall travel time of a network when links fail are introduced for two different cases: without-bridging interruptions and with-bridging interruptions.
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Measuring robustness is critical to understand and estimate the consequences that architectural decisions may have on the robustness of the system.
By contrast with the simulation techniques, measuring robustness does not require to know the dependencies between components nor the rules that govern failure propagation.
A framework is presented for measuring robustness in this way, and implications for system modelling and acceptable levels of robustness are discussed.
Taguchi measures robustness in terms of the signal-to-noise (S/N) ratio and the quality loss function (QLF), estimated through full or fractional factorial experimental designs.
Finally, measuring robustness for different partitions of the variables (all 13 characteristics; 5 essentials; 8 common) lets us reach conclusions about which of these partitions may be more meaningful and in that sense better.
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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