## 5 Results
Figure 21 portrayed the performance of second-order bandpass Butterworth filter with gradient correction, seismic noise filtration, and FFT verification. Figure 19: Histogram of data gathered at two unique geolocations in five calendar months (80,000 samples). Figure 20: FFT of data gathered at two unique geolocations in five calendar months (80,000 samples). The processed data set after the filter is given in Figure 16. After running the PDS and APMS functions in Figure 22 dataset, SWEDA ran the SCAGS loop to store unique structured wave patterns. SCAGS detected only six unique waves that it searched in the STA window. Wave patterns increase the probability of percentage similarity with a dynamically generated EPS. In Figure 23, a 3D spectrogram is shown. It gives a clear picture of the cross verification of outputs of SWEDA. The orange shade shows the maximum strength of our gradient signal is near 0, as interpreted by SCAGS. In Figures 16 and 17, it is very obvious that the 0.2 peak-to-peak distance is the least re-occurring cluster which is proof of a seasonality impact in our SHM data set shown in the yellow legend. The blue legend is scattered in the form of small segments and regular intervals, showing the stationarity in the data set. Figure 21: Filtered signals with Butterworth (band-pass) filter (order = 2) for two sites. Figure 22: Peak detection sequence (PDS)/autoregressive pattern mapping sequence (APMS) for SCAG for extracted signals for two sites (structured waves mapped = 6). In Figure 24, the final output of SWEDA is displayed for the QU bridge. Every time, SWEDA generated a different EPS for the different nodes at runtime to demonstrate its live characterization capability given as a red line. The dataset being highly stationary and only LTAs existed due to the seasonality impact made it very challenging to map STAs for P, S, and surface waves, even though as per live data it generated its EPS separately for separate data streams from the sensors. The EPS values (0.09, 0.00, 0.05, 0.02) show that for the current situation the percentage resemblance with seismic waves was 0.00 as there was no STA detected from the six waves stored in the SCAGS. However, due to the LTA, there is an extremely negligible chance of an earthquake which can be stated as \"no chance\". Figure 24: Earthquake probabilistic sequence (EPS) for the assumed dataset (down-sampled to 400). Figure 23: 3D spectrograms in the time domain from two SHM sites (QU Bridge and QU Lab). In Figure 25, the final output of SWEDA is displayed for the QU Bridge site. The EPS values (0.01, 0.00, 0.00, 0.00) show that for the current situation the percentage resemblance with seismic waves is 0.00 as there was not a single STA detected from the six waves stored SCAGS. The higher or longer the length of the LTA, the closer the signal to zero. At any point when red (EPS) intersects with blue (signal) and the part resembles even 73%, it maps an STA. The absence of STAs shows neither P, nor S, nor surface waves were detected. In fact, it may also reflect that the stored wave is an anomaly and may or may not be any seismic wave in the worst case. In Figures 14-25 SWEDA detected no signs of earthquake. In Figure 26, an induced earthquake was tested in-situ test bench for sensor nodes clusters set has been selected to test the performance of our SWEDA. Keeping an account of Equation (1), STA was triggered at 10 s and recorded eleven p-waves, second was triggered at 21 s and recorded 5 s-waves, likewise third was triggered and stored 5 surface waves. Figure 17:0 EPS for the assumed dataset (down-sampled to 400). Figure 17:1 EPS for the assumed dataset (down-sampled to 400). PDS gave a scaling resolution command to ADCs from 16-bit to 22-bit to achieve that the CANopen baud rate of 250 kb/s using 0x03 was changed to 1 Mb/s using 0x00 SDO. The node, to be purely Industry 4.0 compliant, must maximize programmable and configurable hardware parameters.