update

entropy.tex

@@ -348,7 +348,7 @@ The capacity of the binary symmetric channel is given by:
 where $H_2(p) = -p \log_2(p) - (1-p)\log_2(1-p)$ is the binary entropy function.
 As $p$ increases, uncertainty grows and channel capacity declines.
 When $p = 0.5$, output bits are completely random and no information can be transmitted ($C = 0$).
-As already shown in \autoref{fig:graph-entropy}, an error rate over $p > 0.5$ is equivalent to $ 1-p < 0.5$,
+As illustrated in \autoref{fig:graph-entropy}, an error rate over $p > 0.5$ is equivalent to $ 1-p < 0.5$,
 though not relevant in practice.
 
 Shannon’s theorem is not constructive as it does not provide an explicit method for constructing such efficient codes,