## 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 & 94 & 98.5 & **97** & 97 & **98.5** & **100** & 98.5 & **98.5** & 97 \\  & MAG & **97** & 94 & 82.1 & 97 & **98.5** & 97 & 97 & 95.5 & 97 \\  & SUN & 95.5 & 95.5 & 65.7 & **100** & 94 & 92.5 & 95.5 & 94 & 92.5 \\  & ACTS & 91 & 95.5 & 82.1 & 92.5 & **98.5** & 89.6 & 98.5 & 91 & 91 \\ \hline \multirow{4}{*}{Slew} & GYRO & 95.5 & 97 & 74.6 & 92.5 & 97 & 95.5 & 98.5 & 95.5 & 83.6 \\  & MAG & 86.6 & 97 & 95.5 & 83.6 & 95.5 & 97 & 95.5 & 92.5 & 94 \\  & SUN & 94 & 91 & 56.7 & 89.6 & 91 & 95.5 & 91 & 92.5 & 97 \\  & ACTS & 92.5 & **100** & 59.7 & 98.5 & **98.5** & 92.5 & 95.5 & **98.5** & 94 \\ \hline \multirow{4}{*}{No Control} & GYRO & 95.5 & 94 & 83.6 & 98.5 & 91 & 98.5 & 98.5 & 94 & 97 \\  & MAG & 95.5 & 95.5 & 91 & 95.5 & 97 & 97 & 98.5 & **98.5** & **98.5** \\ \cline{1-1}  & SUN & 91 & 89.6 & 76.1 & 98.5 & **98.5** & 98.5 & **100** & **98.5** & **98.5** \\ \cline{1-1}  & ACTS & - & - & - & - & - & - & - & - & - \\ \hline \hline \end{tabular} \({}^{1}\) Results reported in bold are the highest accuracies reached by each classifier in this example.  
\end{table} 
Table 7: Binary classifiers testing with multiple faults: test batch % accuracy \({}^{1}\) on a 2-class classification. Each sensor in each attitude mode is validated and tested on a balanced dataset of 672 total entries. Results are not totally unexpected: the first two rows refer to the four-class detection exactly equivalent to the classic variance-based one, the middle ones are binary fault/non-fault detectors, while the last ones are the more sophisticated diagnostic agents showcased in the example of Table 8. What can be observed is a general increase in processing speed in trained Neural Networks with respect to SVMs, which is good to know, given that for each example, they were built case by case to be completely interchangeable, maintaining the same inputs and outputs. On another note, Gaussian SVMs maintain their unpredictability and low consistency also in terms of computational resources used. Binary detectors take overall less time with respect to four-class classifiers, which is expected due to the lower internal complexity; four-class diagnostic agents are not too far from the first type of simple detectors in terms of computational time. This can be extremely convenient, since they turned out to be the most accurate and they are usually activated only once classic detection has first assessed a fault occurring. What might be unexpected to notice are the report results in Table 10, concerning the baseline threshold-based detection, which was present and fully active in the same example simulation of the slew maneuver reported above. This outcome, however, is easily explained: timing is way higher on the classic detectors portrayed in Figure 3; also, their Total Time implicitly contains a lot more signal processing, and thresholding operations with a variance signal, compared instant by instant: any of the above classifier was summoned only about a hundred times in 100 simulated seconds, and its task was simply to gather input features and give back a flag by applying already trained weights.