How does Huber in book 'Robust statistical procedures' in chapter 1 compute the variance of certain statistical functions? He defines the mean square deviation to be $$s_n = \sqrt{\frac{1}{n} \sum \left(x_i - \bar{x} \right)^2}$$ and the mean absolute deviation as $$d_n = \frac{1}{n} \sum \| x_i - \bar{x} \|.$$ Then he goes on to obtain the asymptotic relative efficiency expression ARE($\epsilon$) as shown in the photo. (So, we should have at least for arguments in the ARE(), namely, ARE($\epsilon;n,x_i,x$),right? even if $n \to \infty.$ What does ARE() look like before we send $n \to \infty$? How on earth does one obtain the expression in the 2nd step?)
What is $var(s_n), E[s_n]$? How to obtain these expressions?
Finally, why does he state that just two bad observations in 1000 suffice to offset the 12% advantage of the mean square error? Is is not 5 bad observations? See 
in the photo, values for $\epsilon = 0.005$ is 1.198 which is the (12%) I guess?
