A Statistical Characterization of Resonant Electromagnetic Interactions with Thin Wires: Variance and Kurtosis Analysis

Higher-Order Statistics for Stochastic Electromagnetic Interactions: Applications to a Thin-Wire Frame

“…The first resonance produces the largest value of σ, even though κ 4 (f 1 ) is slightly larger than 3 thereby implying a slightly wider spread than a Gaussian spread. Even though σ(f 3 ) is three times smaller than σ(f 2 ), the fact that κ 4 (f 3 ) is conversely 2.5 times larger than κ 4 (f 2 ) indicates the presence of more outliers at f 3 than at f 2 and f 1 , as explained in [12,32].…”

“…We have successfully applied this modus operandi to thin-wire problems by evaluating the first four statistical moments of the observable with the aid of quadrature algorithms. The thus obtained statistics could be post-processed using Chebychev's inequality to obtain general bounds of the distribution of the observable via its second-order moments [11] or to quantify the likelihood of observing extreme samples using the fourth-order moment, or kurtosis [12].…”

Susceptibility of Electro-explosive Devices to Microwave Interference

“…For example, the statistical indicators (i.e. mean, standard deviation and kurtosis) of the induced voltage in an undulating thin-wire over a ground plane have been obtained with a similar method denominated sparse grid 14 .…”