## 3 Performance Metrics
Accordingly, this results in a true negative. Second, when the number of the detected errors is beyond the RS Codes' correcting capability, RS Codes detect that no correction can be applied. Third, when the number of errors is beyond the RS Codes' correcting capability, RS Codes falsely assume it is within their capability. Despite this false assumption, RS Codes could not produce any output. In such a case, the code word becomes corrupted, but in a way that there are more than one valid code word with the same minimum symbol distance to the corrupted code word. As a result, this leads to an uncertainty on providing the output, and in the case of RS Codes, uncertainty results in no output. It should be noted that, in the second and third scenarios, RS Codes can be designed in such a way to stop the transmission and switch the system to a minimum-risk state. Nevertheless, in all scenarios, the system receives warnings that are desired. This category is labeled in a shade of orange. * Data False Positive (DFP): The data are in control and determine the output, and the received output is correct albeit the detector outcome is incorrect. In such a scenario, although the received output is correct, the syndrome is nonzero, which indicates that the code word has been affected by the disturbance. This could happen when RS Codes detect that something went wrong during the transmission and correct the corrupted data. However, the receiver is unable to determine if the correction is valid and the system receives a warning. Based on the initiated assumption, in the case of a positive output, no detection should happen. Furthermore, although the output still \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Category** & **Channel Status** & **Data Status** & **Detector Status** & **Label** \\ \hline Data True Positive (DTP) & Data In Control & Uncorrupted & No Warning & **Label** \\ \hline Data True Negative (DTN) & Data In Control & Corrupted & Warning & **Label** \\ \hline Data False Positive (DFP) & Data In Control & Uncorrupted & Warning & **Label** \\ \hline Data False Negative (DFN) & Data In Control & Corrupted & No Warning & **Label** \\ \hline Channel True Positive (CTP) & Channel In Control & Uncorrupted & No Warning & **Label** \\ \hline Channel True Negative (CTN) & Channel In Control & Corrupted & Warning & **Label** \\ \hline Channel False Positive (CFP) & Channel In Control & Uncorrupted & Warning & **Label** \\ \hline Channel False Negative (CFN) & Channel In Control & Corrupted & No Warning & **Label** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An overview of the considered categories. depends on the data, it is an indication that the channel's health might degrade in the future. This category is labeled in a shade of orange. * Data False Negative (DFN): The data are in control and determine the output, the received output is incorrect, and the detector outcome is incorrect. In this case, the data producer is either hacked or interfered internally. This is a very dangerous scenario in which the received data will be used without any warning. As a result, the system is unaware that the received data word is wrong, and the system receives no warning. Although this category is detrimental to the overall system safety, it could not occur under the considered fault model as, within this paper, the data producer and the data consumer are assumed to be protected from any EMD or internal hacking. In case this category happens under different environments or setups, it should be labeled in a shade of red. * Channel True Positive (CTP): The channel is in control and determines the output, the received output is correct, and the detector outcome is also correct. As pointed out, when the channel is in control, the EMDs or any other external factor enforce a logical '1' or '0', regardless of what has been transmitted by the data producer.