## 5 Experiments
### Test of the Proposed Method
#### 5.1.2 Application of the Method to the Scenarios
Initially, the experts were given a detailed overview of the goals for each scenario given in Section 5.1.1, such as low-cost or high-performance objectives. The experts then engaged in a discussion to evaluate the relative importance of each criterion in achieving these goals. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Product ID** & **Product Name** & **MTBF** & **Processing** & **Supported AI** & **Power Consumption** & **Weight** & **Size** & **Flash** & **RAM** & **Cost** (-) \\ \hline \multirow{4}{*}{EdgeHW\_11} & STM32 HP Series (STM32 HP7 Series (STM32 HP7 Series [63]) & ARM Cortex-M7 & PyTorch and @480 MHz & \begin{tabular}{c} TensorFlow, \\ Cortex-M7 \\ Scikit-learn \\ via ONNX \\ \end{tabular} & \multirow{4}{*}{\(\sim\)0.3} & TensorFlow, & \multirow{4}{*}{\(\sim\)0.3} & 24 \(\times\) & \multirow{4}{*}{2 MB} & \multirow{4}{*}{1 MB} & \multirow{4}{*}{515} \\  & & & & & & & & & \\ \hline \multirow{4}{*}{EdgeHW\_12} & STM32 MP1 Series (STM32 HP7 Series [63]) & Dual-core Cortex-A7 & \begin{tabular}{c} TensorFlow, \\ Cortex-M7 \\ Scikit-learn \\ via ONNX \\ \end{tabular} & \multirow{4}{*}{2 MB} & \multirow{4}{*}{1 MB} & \multirow{4}{*}{515} \\  & & & & & & & & & \\ \hline \multirow{4}{*}{EdgeHW\_13} & Xilinx Zynq & \multirow{4}{*}{1000 K+} & Quad-core Cortex-A7 & \begin{tabular}{c} Caffe, \\ PyTorch, \\ \end{tabular} & \multirow{4}{*}{\(\sim\)20} & 200 \(\times\) & \multirow{4}{*}{300} & 200 \(\times\) & \multirow{4}{*}{4 GB} & \multirow{4}{*}{53250} \\  & & & & & & & & & \\ \cline{1-1}  & ZCU102 [64] & & & & & & & & & \\ \hline \multirow{4}{*}{EdgeHW\_14} & Xilinx Zynq & \multirow{4}{*}{1000 K+} & Quad-core Cortex-A53 + FPGA & \
\begin{tabular}{c} Caffe, \\ PyTorch, \\ Scikit-learn \\ \end{tabular} & \multirow{4}{*}{\(\sim\)25} & \multirow{4}{*}{400} & 200 \(\times\) & \multirow{4}{*}{512 MB} & \multirow{4}{*}{4 GB} & \multirow{4}{*}{51700} \\  & & & & & & & & & \\ \hline \multirow{4}{*}{EdgeHW\_15} & BeagleBone Black [66] & \multirow{4}{*}{50 K+} & ARM Cortex-A8 & Darknet (YOLO), Keras, & \multirow{4}{*}{2-3} & 90 \(\times\) & \multirow{4}{*}{4 GB} & \multirow{4}{*}{512 MB} & \multirow{4}{*}{4 GB} & \multirow{4}{*}{51720} \\  & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & & \\ \hline \multirow{4}{*}{EdgeHW\_16} & BeagleBone & \multirow{4}{*}{50 K+} & Quad-core Cortex-A8 &
\begin{tabular}{c} TensorFlow \\ Percept-M7 \\ Scikit-learn, \\ OpenCV \\ \end{tabular} & \multirow{4}{*}{2-3} & \multirow{4}{*}{40} & \multirow{4}{*}{40} & 90 \(\times\) & \multirow{4}{*}{4 GB} & \multirow{4}{*}{512 MB} & \multirow{4}{*}{555} \\  & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & \\ \hline \multirow{4}{*}{EdgeHW\_16} & BeagleBone AI-64 [66] & \multirow{4}{*}{50 K+} & Quad-core Cortex-A7 & Darknet (YOLO), Keras, & \multirow{4}{*}{7-10} & 100 \(\times\) & \multirow{4}{*}{60 \(\times\)} & 16 GB & \multirow{4}{*}{4 GB} & \multirow{4}{*}{5145} \\  & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & & \\ \hline \hline \end{tabular}  
* Values for the criteria are retrieved from the products’ datasheets and web pages. Metrics that could not be obtained from product datasheets or web pages are retrieved from various manufacturers’ pages.  
\end{table} 
Table 3: _Cont._* Expert Scoring: Following the focus session, each expert was asked to assign scores to each criterion, typically on a scale of 1 to 10, where 1 represents the least importance and 10 represents the highest importance. Experts provided their individual opinions on how critical each criterion is for the success of the UAV design in the specific scenario. * Aggregation of Experts Scores: After the individual expert scores were collected, the scores were aggregated to obtain a consensus weight for each criterion. The aggregation process involved calculating the mean and standard deviation (SD) of the expert scores for each criterion.