Due to the closeness to the optimal 𝜆 opt and least uncertainty, we identified the GCV and mGCV methods as the two most accurate approaches amongst all methods. Therefore, the nominal regularization level, 𝜆 0 , in , which is analogous to 𝜆 in RR, was selected, leveraging the optimal, GCV, and mGCV scores. To compare these three methods against the corresponding hyper-𝜆 method, we generated a synthetic experiment (N = M = 81) with 𝜎 𝑛 exp = 0.2 Ω for the single ZARC, each 2×ZARC, the PWC, and the Gerischer models. Then, we recovered the DRTs and impedances, leveraging the iterative algorithm described elsewhere .