## 2 Materials and Methods
### Initial Results and Method Evaluation  
To evaluate the suggested methodology, some preliminary results of this work are compared against existing data from the literature. This comparison leads to a very interesting observation that, to a large extent, defines a large part of the approach of this study and strengthens the motivation.  
Figure 3: Parameters Spearman rank correlation coefficient heatmap, used to investigate potential correlations between the acquired data.  
Indicatively, as shown in Figure 4 and Table 3, the in-house generated trendline for the \"wingspan vs. GTOW\" dataset is compared against the trendline calculated in Ref [24]. Note that the two results achieve a very high prediction accuracy and are in very good agreement with each other. Any minor deviations are attributed by the authors to the different dataset entries, since the two studies do not have a common database. As a next step, the authors applied these trendlines to predict the wingspan parameter for a given GTOW value, for several UAVs that belong in different NATO categories. Some typical results are shown in Table 4. Note that the exact same trendlines that, as described above, adequately represent the general sample are not as accurate for all NATO categories alike. For example, although a single-digit accuracy is achieved for the mini category, the predictions deviate considerably for larger UAVs, reaching as much as 40% for the tactical category.  
As discussed in the introduction, it is highly likely that, by applying a regression to all the data alike, the individual information in-between is \"smoothed-out\", rendering the general trendlines insufficient. However, when it comes to developing a new fixed-wing UAV, the mission requirements (e.g., useful payload capacity) will usually point to a range  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**UAV** & **Region** & **GTOW** & **Span (Actual)** & **Span (Ref.)** & **Span (Current)** & **Error (Ref.)** & **Error (Current)** \\ \hline Stardust 2 & Chile & 5 & 1.5 & 1.50 & 1.57 & 0\% & 4\% \\ Yasir & Iran & 25 & 2.36 & 2.72 & 2.85 & 15\% & 21\% \\ HERTI & UK & 340 & 11.99 & 7.16 & 7.50 & \(-\)40\% & \(-\)37\% \\ CH5 & China & 3100 & 21 & 16.2 & 17.05 & \(-\)23\% & \(-\)19\% \\ Mantis & UK & 9000 & 20 & 24 & 25.32 & 20\% & 27\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Indicative results for UAVs of different NATO categories (“Current”) and comparison with the actual values (“Actual”) and the reference literature [24] (“Ref.”).  
Figure 4: Span as a function of GTOW for the general sample category.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **R2** & **A** & **B** & **_n_** & **Type of Fitting** \\ \hline Reference [24] & 0.76 & 0.828 & 0.37 & 836 & Power \\ Current study & 0.811 & 0.962 & 0.342 & 202 & Power \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reference dataset trend factor results and comparison of the corresponding literature [24].  
of NATO categories, if not to a specific one. Therefore, the corresponding design engineer (or design team) will not be looking for \"fixed-wing UAV design trends\" in general, but for the trends and data that apply to their specific design case.  
Summing up, based on these preliminary results, the reader is advised to evaluate each chart separately and not to only rely on the generic UAV trendlines. Dot plots and box plots are also provided as a statistical means to show the dispersion of the data [37], which, in some cases, is also a very important metric to better define the corresponding design space and evaluate the fitting (apart from R\({}^{2}\)).