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Figure 4 Correlation difference output for preamble 1 (or equivalently 2).
Table 2 Comparative experimental results Original frame Ground truth MoG NP-KDE Temporal median Frame difference Output FP 21.5%2.3%6.9%21.5%FN12.9%11.5%8%13.4%8% 13.4%.
In Table4, all possible inputs of the last round's S-box layer for each difference output of the last round's S-box layer are shown (remember that difference input can be a ∈ {1,2,4,8}).
To find out the nibble of S 31 which is involved in the faults, the adversary may reverse the difference output of P layer (P layer−1(C ⊕ D)) following the approaches presented in Algorithms 1 and 2 to obtain one nibble of S 31.
For each faulty cipher text, we have got a difference output of the last round's S-box layer, and we can determine possible inputs of the last round's S-box layer by searching in the differences distribution table of the S-box.
As above, the construct which generated the BORROW output (GFP) and that which generated the DIFFERENCE output (RFP) were co-transformed into E. coli cells.
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Given in Table5, we see that the sets of S-box difference outputs for each specific input are different.
In Table5, all S-box difference outputs for each S-box input, with one bit difference in S-box input, are depicted.
Hence, at the end of Algorithm 1, we expect to receive three difference outputs of last round's S-box layer for each nibble.
Algorithm 1 arranges the faulty cipher texts and correct the cipher text in 16 groups of difference outputs of last round's S-box layer, i.e., ΔB 31 N j for 1 ≤ j ≤ 16.
Having women in key editorial roles makes a difference to output.
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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