## 7 The Experimental Results and Discussion
### The Reconfigurability-Reliability Dependency Based on the Improved Fault Coverage
 \tag{28}\]where \(FC_{DSaF}\) represents the cumulative FC for double SaFs, \(FC_{SaF2}\) denotes the individual fault coverage for double SaFs, and, respectively, \(G_{1}\), \(G_{2}\), and \(G_{3}\) designate the generator polynomial functions used in the RBILBO. In the third batch of SaFs, the RBIBLO approach detected 15 Sa0 and Sa1 faults out of 32 test scenarios, for an initial FC of 46.87% using only the \(G1\) polynomial function. Using an additional set of 32 test vectors and switching to the \(G2\) polynomial resulted in an improvement in \(FC\) of 81.24%, with 26 detections. However, during the third simulation run, employing the \(G3\) polynomial function, the FC only increases by 12.51%, leaving 6.25% \(ALS\) as a residual, as seen in Equation (29):  
\[ \begin{array}{l}FC_{TSaF}=FC_{SaF3}(G_{1})+FC_{SaF3}(G_{2})+FC_{SaF3}(G_{3} )+ALS\\ =46.87\%+34.37\%+12.51\%+6.25\%=93.75\%+6.25\%,\end{array}  \tag{29}\]  
where \(FC_{TSaF}\) represents the cumulative FC for triple SaFs, \(FC_{SaF3}\) denotes the individual fault coverage for triple SaFs, and, respectively, \(G_{1}\), \(G_{2}\), and \(G_{3}\) designate the generator polynomial functions used in the RBILBO. As a result, the overall FC for all 96 test instances was 93.75%, indicating 30 successful identifications of 32 probable Sa0 and Sa1 fault scenarios. In conclusion, the global fault coverage for all 264 experimental trials was assessed at an average of 98%, with only 2% of ALS occurring during the simulation. In the context of computer systems and digital communication, aliasing refers to the phenomenon where two different signals produce the same output when sampled or quantized by a digital system. This can occur due to limitations in the sampling or quantization process [80]. The experimental results indicate that the proposed RBILBO architecture enhances initial fault coverage by an average of 53%, demonstrating its feasibility as a low-cost and adaptable design for efficient fault detection implementation. Following, by addressing the polynomial function \(G_{1}\), the corresponding \(DR\) values can be computed as in Equation (30):  
\[\left\{ \begin{array}{l}DR_{SaF_{1}}(G_{1})=FC_{SaF_{1}}(G_{1})\cdot N_{P}=3 7.50\%\cdot 32=12\\ DR_{SaF_{2}}(G_{1})=FC_{SaF_{2}}(G_{1})\cdot N_{P}=50\%\cdot 32=16\\ DR_{SaF_{3}}(G_{1})=FC_{SaF_{3}}(G_{1})\cdot N_{P}=46.87\%\cdot 32=15\end{array}  \right., \tag{30}\]  
where \(N_{P}=32\) and each \(FC\) variable is substituted by the values computed in Equations (27)-(29). Regarding the polynomial functions, \(G1\) and \(G2\), the corresponding \(DR\) values can be computed as in Equation (31):  
\[\left\{ \begin{array}{l}DR_{SaF_{1}}(G_{1},G_{2})=FC_{SaF_{1}}(G_{1},G_{2}) \cdot N_{P}=84.37\%\cdot 32=26.99\\ DR_{SaF_{2}}(G_{1},G_{2})=FC_{SaF_{2}}(G_{1},G_{2})\cdot N_{P}=79.16\%\cdot 3 2=25.33\\ DR_{SaF_{3}}(G_{1},G_{2})=FC_{SaF_{3}}(G_{1},G_{2})\cdot N_{P}=81.24\%\cdot 3 2=25.99\end{array} \right., \tag{31}\]  
where \(N_{P}=32\) and each \(FC\) variable is represented by the values computed in Equations (27)-(29). Regarding the polynomial functions \(G1\), \(G2\), and \(G3\), the corresponding \(DR\) values can be computed as in Equation (32):  
\[\left\{ \begin{array}{l}DR_{SaF_{1}}(G_{1},G_{2},G_{3})=FC_{SaF_{1}}(G_{1},G_ {2},G_{3})\cdot N_{P}=100\%\cdot 32=32\\ DR_{SaF_{2}}(G_{1},G_{2},G_{3})=FC_{SaF_{2}}(G_{1},G_{2},G_{3})\cdot N_{P}=100 \%\cdot 32=32\\ DR_{SaF_{3}}(G_{1},G_{2},G_{3})=FC_{SaF_{3}}(G_{1},G_{2},G_{3})\cdot N_{P}=93. 75\%\cdot 32=30\end{array} \right., \tag{32}\]  
where \(N_{P}=32\) and each FC variable is replaced by the values computed in Equations (27)-(29). The \(DR\) values, computed from Equations (30)-(32), serve as an indicator of the identification of S-a-0 and S-a-1 faults through the fault diagnostic process, concerning the overall number of test patterns (NP = 32). Utilizing Equation (12), the calculation of the _STF_ parameter can be facilitated based on these derived values, employing the \(G_{1}\) polynomial function:  
\[\left\{