Figure (a) compares the overall MUE of method 4 with various 𝑚𝑚 values. The functional parameters are optimized for each 𝑚𝑚 value. As an overall trend, MUE decreases as 𝑚𝑚 increases, with 𝑚𝑚 = 1 (i.e., method 3) having the lowest MUE. For practical purposes, it is desirable to have an 𝑚𝑚 value that is low in MUE while having a numerically stable gradient. By analyzing Figures and(a), we empirically selected 𝑚𝑚 = 0.96 as the best-compromise 𝑚𝑚 value. Figure shows that the MUE does not improve very much in going from m = 0.96 to m =1, while Figure shows 𝑓𝑓(𝑛𝑛) is reasonably smooth at 𝑛𝑛 = 1 when m = 0.96. We therefore chose new method 4 with m = 0.96 as our new functional, and we name it DC24.