## 3 Results
### General Results: Key Geometrical, Aerodynamic and Performance Parameters  
This section is dedicated to the analysis of the key technical parameters. For every parameter, the results are initially presented for all UAVs alike, followed by the NATO categories and regional classifications. More specifically, in the first charts of Section 3.1, the technical parameters of wingspan and weights are discussed, along with the critical weight design ratios. Concerning the latter, the fundamental relation shown in Equation (7) is used as a reference point [19; 33]. \[W_{0}=\frac{W_{\text{crew}}+W_{\text{payload}}}{1-\left(W_{f}/W_{0}\right)- \left(W_{e}/W_{0}\right)} \tag{7}\]  
Note that W\({}_{0}\) stands for GTOW, the ratios \(W_{f}/W_{0}\) and \(W_{e}/W_{0}\) for the fuel and empty weight fractions and \(W_{\text{crew}}\) is the crew weight that in UAV design is, by definition, set to zero. Correlations regarding the payload weight and empty weight are investigated. Moreover, the empty weight ratio (\(W_{e}/W_{0}\)) is also analyzed as one of the most critical parameters that need to be estimated during conceptual design [19]. The analysis of the fuel weight ratio, however, is not as straightforward, since a common UAV operator strategy is to exchange between payload (for maximum effectiveness and/or sensing) and fuel (for maximum flight time), depending on the mission. Therefore, the approach of [24] is  
Figure 5: Fixed-wing UAV missions. employed, and the parameter of \(W_{p}\cdot\text{E}\) is discussed, where E stands for endurance (flight time). Finally, the cruise speed and service ceiling parameters are also plotted as a function of GTOW. Note that, as mentioned in Section 2.4, the authors opted to use dot plots to highlight the large sample dispersion and to provide as much information to the reader as possible. Figure 6 shows the wingspan as a function of GTOW with respect to the NATO classification, while Table 5 sums up the corresponding statistical and correlation results. As also discussed in Section 2.4, the trendline fitting is sufficient in the general category, achieving an R\({}^{2}\) of 0.8. However, when each NATO category is individually investigated, the fitting changes considerably. This can be attributed to the inherently large dispersion inside the NATO UAV categories, which is smoothed-out when examining the general dataset. Although a statistical fit might not seem good for a statistics expert, in the field of UAV design this should not be considered a negative result. Instead, in the eyes of a design engineer, this translates to a larger design space for the given category, which would not be available if only the general trendline was taken into account. Figure 7 shows the wingspan as a function of GTOW with respect to region of origin. The data dispersion regarding the wingspan as a function of GTOW correlation for different regions of origin is very high. However, this chart can be used to extract certain market conclusions regarding each region. For example, the largest UAVs in terms of wingspan \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\mathbf{R^{2}}\) & \(\mathbf{A}\) & \(\mathbf{B}\) & \(\boldsymbol{n}\) & **Type of Fitting** \\ \hline All & 0.81 & 0.962 & 0.342 & 202 & Power \\ Micro-Mini & 0.47 & 0.866 & 0.464 & 40 & Power \\ Small & \textless{}0.2 & 0.619 & 1.473 & 65 & Logarithmic \\ Tactical & \textless{}0.2 & 0.633 & 0.408 & 45 & Power \\ MALE & 0.37 & 0.871 & 0.376 & 29 & Power \\ HALE-Strike & 0.34 & 0.836 & 0.371 & 23 & Power \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical data regarding the trend fitting for the wingspan vs. GTOW plots. Figure 6: Wingspan as function of GTOW with respect to the NATO classification. Dark red color is used for the trendline of the entire sample, while the rest of the colors are used for the respective classes and coincide with the bullets color code, as explained in the legend.