## 3 WPT Model Decomposition Approach Validation and Optimization  
Once the individual S matrices are calculated for all sub-models, they are exported from Ansys HFSS and saved to separate files. Then, a custom MATLAB R2019b script developed by the authors is used to calculate the overall S parameters (the S parameters of the whole WPT system's model) by combining the pre-calculated individual S matrices. The empty waveguide sections joining the sub-models are analyzed analytically using the waveguide mode propagation constants computed using Ansys HFSS. To verify the validity of the proposed structure decomposition method, the numerical results calculated by employing the method were obtained for various WPT system configurations with different parameter values. In fact, a variety of models were verified via full-wave simulations. However, due to the page limit, the results of this quite comprehensive comparative study are presented only for two. Figure 11a shows the absolute values  
Figure 10: The coupling coefficients between the TEM mode of the Yagi-like antenna and waveguide modes TE\({}_{01}\), TM\({}_{21}\), and TE\({}_{21}\) against the transmitting dipole length, calculated at \(L_{\text{feed}}=70\) mm and \(d_{\text{dip-dir}}=145\) mm (**a**) and transmitting antenna feed line length, calculated at \(L_{\text{dipole}}=62\) mm and \(d_{\text{dip-dir}}=130\) mm (**b**) with a conducting plate placed behind the antenna. of the resonant cavity-based WPT system's scattering parameters (TEM mode parameters) against frequency, calculated using both the decomposition method and directly (without dividing the WPT system's model into several sub-models) with Ansys HFSS for two WPT system models with different parameter values. The reflector lengths of the Tx and Rx antennas in both models are identical and equal to 170 mm. Figure 11b shows the corresponding phase responses of the WPT systems (phases of S\({}_{11}\) and S\({}_{21}\) against frequency). For both examined models, the amplitude and phase responses are in excellent agreement with the full-wave simulation results. The results are almost indistinguishable in graphical accuracy. The parameters of the two models, whose responses are shown in Figure 11a,b, are presented in Table 2.  
To maximally mitigate the PTE sensitivity to the Rx antenna position, which is of primary importance in the smart suit charging application, it is highly desirable to maximize the size of the high PTE region--the region in the closet's interior where high PTE values are attained. To that end, various antenna parameter value combinations should be examined to find the one giving a PTE versus Tx-to-Rx separation distance curve with the widest, flattest, and highest maximum--the high PTE region. In practice, various optimization algorithms are employed to make the optimal configuration-finding process less computationally expensive. The optimization algorithms reduce CPU time by reducing the number of objective function evaluations and by choosing model parameter value combinations (trial solutions) efficiently based on the objective function values from previous iterations [37]. The local optimization methods \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter (Case 1)** & **Value** & **Parameter (Case 2)** & **Value** \\ \hline Feed line length (TX) & 75 mm & Feed line length (TX) & 75 mm \\ Dipole length (TX) & 70 mm & Dipole length (TX) & 90 mm \\ Director length (TX) & 160 mm & Director length (TX) & 140 mm \\ Director-to-dipole separation (TX) & 195 mm & Director-to-dipole separation (TX) & 195 mm \\ Feed line length (RX) & 70 mm & Feed line length (RX) & 70 mm \\ Dipole length (RX) & 65 mm & Dipole length (RX) & 65 mm \\ Top-to-TX distance & 34 mm & Top-to-TX distance & 40 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 2: WPT model parameters used for the validation of the model decomposition method.