Here https://stats.stackexchange.com/a/313138 @whuber describes a beautiful solution to generating a correlated vector to an existing one. The thing i cant figure out is $SD()$ in following expression: $$X_{Y;\rho} = \rho\, \operatorname{SD}(Y^\perp)Y + \sqrt{1-\rho^2}\,\operatorname{SD}(Y)Y^\perp.$$ And also this sentence:
("$\operatorname{SD}$" stands for any calculation proportional to a standard deviation.)
The exact question is: what does standard deviation of two orthogonal vectors or something proportional to it have to do with finding suitable linear combination of them with intended $\theta$?
EDIT2: another way of asking, what is wrong with this equation?: $$X_{Y;\rho} = \rho\, Y \frac{1}{\begin{Vmatrix}Y\end{Vmatrix}} + \sqrt{1-\rho^2}\,Y^\perp \frac{1}{\begin{Vmatrix}Y^\perp\end{Vmatrix}}.$$
Or if it is supposed to be correct, how $\operatorname{SD}(Y^\perp)$ is related to $\frac{1}{\begin{Vmatrix}Y\end{Vmatrix}}$, and $\operatorname{SD}(Y)$ to $\frac{1}{\begin{Vmatrix}Y^\perp\end{Vmatrix}}$ ?