## 5 Results
### Targeted Fault Diagnosis and Computational Cost
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Attitude** & \multirow{2}{*}{**Sensor**} & \multicolumn{4}{c}{**SVM**} & \multicolumn{4}{c}{**ANN**} \\
**Mode** & & **Linear** & **Quadratic** & **Cubic** & **Gaussian** & **Narrow** & **Medium** & **Large** & **Bilayered** & **Trilayered** \\ \hline \multirow{4}{*}{Pointing} & GYRO & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\  & MAG & 100 & 100 & 100 & 97.9 & 97.9 & 100 & 97.9 & 100 & 95.8 \\  & SUN & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 97.9 \\  & ACTS & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 97.9 \\ \hline \multirow{4}{*}{Slew} & GYRO & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\  & MAG & 100 & 100 & 100 & 95.8 & 97.9 & 97.9 & 100 & 97.9 & 97.9 \\  & SUN & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 97.9 \\  & ACTS & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 97.9 \\ \hline \multirow{4}{*}{No Control} & GYRO & 100 & 100 & 100 & 100 & 100 & 97.9 & 97.9 & 100 & 100 & 97.9 \\  & MAG & 97.9 & 97.9 & 100 & 95.8 & 97.9 & 100 & 100 & 97.9 & 95.8 \\ \cline{1-1}  & SUN & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \cline{1-1}  & ACTS & - & - & - & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Autonomous diagnostics agents: test batch % accuracy on a 4-class classification. Each sensor in each attitude mode is validated and tested on a balanced dataset of 480 total entries. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Type** & \multirow{2}{*}{**Time**} & \multicolumn{4}{c}{**SVM**} & \multicolumn{4}{c}{**ANN**} \\
**Mode** & & **Linear** & **Quadratic** & **Cubic** & **Gaussian** & **Narrow** & **Medium** & **Large** & **Bilayered** & **Trilayered** \\ \hline \multirow{2}{*}{4-class detection} & Total Time & 0.008 & 0.017 & 0.016 & 0.226 & 0.005 & 0.004 & 0.004 & 0.004 & 0.004 \\  & Self Time & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 \\ \hline \multirow{2}{*}{Binary detection} & Total Time & 0.002 & 0.004 & 0.003 & 0.080 & 0.004 & 0.004 & 0.004 & 0.004 & 0.004 \\  & Self Time & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 \\ \hline \multirow{2}{*}{4-class diagnosis} & Total Time & 0.007 & 0.024 & 0.016 & 0.079 & 0.005 & 0.004 & 0.004 & 0.004 & 0.004 \\  & Self Time & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Profiler-Times Report (in seconds) on a slew maneuver run with all classifiers active. Total simulation time: 16.455 s.  
Training times are a slightly different matter with respect to simulation times of the single classifiers. While the latter concern actual performances and are pivotal for the quickness of the algorithms, training times are a one-time-only cost to pay during the implementation process. They were not reported in each instance, due to their non-recurring nature, but most importantly, for their average order of magnitude, which despite their slightly fluctuating values, is consistent and explained here. To be specific, on the hardware employed for this study, consisting in a 12 th generation Intel(r) Core(tm) 17-1255U, 1700 MHz (10 cores), mounted on a 16 Gb RAM pc, the training times of all algorithms were well below 10 s: SVMs settled at around 2 s each, while Neural Networks required on a rough average 6 s for their training, in all cases. Important outliers are Cubic SVMs and Trilayered networks, which required up to 8 s of training. These results derive once again from choosing very simple architectures for the two classifier families, and confirm once again the quickness of ML approaches when compared to other strategies, such as Deep Learning. It is easy to see how, even with datasets made of hundreds of entries, training times can be considered minimal in an actual SIL or HIL implementation, and even more in real mission planning scenarios, where they are totally negligible.