esa-sceva/satcom-chunk-collection huggingface dataset:
# IoTBystander: A Non-Intrusive Dual-Channel-Based Smart Home Security Monitoring Framework
## 7 Experimental Results
### Effectiveness of IoTBystander
#### 7.2.2 Performance of Audio-Based Monitoring  
The audio-based event recognition (AER) component effectively recognizes most events produced by dumb devices that generate unique audio signals, as shown in the AER columns in Table 8. For example, it achieves very high scores for events like \"clock-alarm\", \"door-ring\", \"kettle-heating\", and \"range-hood-on\", with F1-scores around 0.95 to 1.00. This performance reflects the ability of the audio channel to accurately identify these events based on distinct acoustic signatures. However, AER's performance slightly dips for events with more subtle or shorter sound patterns, such as \"mouse-click\", whose precision, recall, and F1-score are 0.76, 0.97, and 0.85, respectively. Our analysis of the results indicated that the main cause of low precision and high recall could be that some short, abrupt sounds in the environment, such as collision or tapping sounds, were mistakenly identified as \"mouse-click\" due to their similarities in acoustic characteristics. In addition, AER has very high performance for smart device events like \"gas-detected (XG)\" and \"gas-alarm-stop (XG)\", with all metrics 0.97. The overall accuracy of audio-based monitoring on audible events is 96%. The audio channel cannot detect inaudible events such as \"plug on/off (for HP/XP)\", \"switch on/off (GP)\", and \"camera on/off (XC)\", which is the Achilles heel of audio-based monitoring.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Event Type** & \multicolumn{3}{c}{**Precision**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**F1-Score**} \\  & **(Device Model)** & \multicolumn{1}{c}{TER} & **AER** & **DCA** & **TER** & **AER** & **DCA** & **TER** & **AER** & **DCA** \\ \hline \multirow{8}{*}{Dumb Device} & clock-alarm & – & 0.95 & 0.95 & – & 0.91 & 0.91 & – & 0.93 & 0.93 \\  & door-ring & – & 1.00 & 1.00 & – & 0.99 & 0.99 & – & 1.00 & 1.00 \\  & faucet-on & – & 0.95 & 0.95 & – & 0.90 & 0.90 & – & 0.92 & 0.92 \\  & gas-stove-on & – & 0.99 & 0.99 & – & 0.97 & 0.97 & – & 0.98 & 0.98 \\  & kettle-heating & – & 0.98 & 0.98 & – & 0.99 & 0.99 & – & 0.98 & 0.98 \\  & keyboard-typing & – & 0.92 & 0.92 & – & 0.94 & 0.94 & – & 0.93 & 0.93 \\  & microwave-heating & – & 0.98 & 0.98 & – & 0.98 & 0.98 & – & 0.98 & 0.98 \\  & mouse-click & – & 0.76 & 0.76 & – & 0.97 & 0.97 & – & 0.85 & 0.85 \\  & range-hood-on & – & 0.99 & 0.99 & – & 0.99 & 0.99 & – & 0.99 & 0.99 \\  & toilet-flush & – & 0.95 & 0.95 & – & 0.89 & 0.89 & – & 0.92 & 0.92 \\  & vacuum-cleaner & – & 1.00 & 1.00 & – & 0.95 & 0.95 & – & 0.97 & 0.97 \\  & washing-on & – & 0.94 & 0.94 & – & 0.91 & 0.91 & – & 0.93 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._
## 5 Experimental Evaluation
### Results and Discussion
#### 5.4.3 Performance Evaluation under the Number of Users  
In Figure 5, the performance of the caching schemes when the number of active users is varied is observed. The LFRU scheme dropped the initial good performance to average performance in this assessment. The best scheme for ensuring high hit rates with increasing number of active users is the proposed DPoM caching scheme. The FIFO and TLRU are the worst-performing schemes in this analysis.  
Figure 4: Performance of request rate variation on hit rate, latency, and backhaul traffic.  
The latency assessment realizes good performances from the proposed, LFRU and the Greedy schemes. The sharp decrease in latency, which is an advantage, is not realized here. In a cooperative device-to-device caching network, an increase in the number of active devices would have caused a decline in latency. Device-to-device caching is not considered in this work, as the underlying security features required to ensure fully functional device-to-device cooperative caching are not foundationally built. The issue of transmission power of the devices, bandwidth consumption among others further pushes the realization of this technology to a more distant future.  
The third sub-figure of Figure 5 depicts that the backhaul traffic is directly proportional to the number of active users for all the caching schemes. The TLRU and FIFO are the best-performing schemes in this comparison. This can be attributed to the fact that these schemes transmit less data because of their caching policies, which do not involve content popularity considerations or fractional caching. Rather, entire files are cached and only replaced during cache replacements.
## 3 A Prototype System for Pedestrian Positioning
### Particle Filtering
#### 3.9.2 Implementation of the Particle Filtering Algorithm  
The implementation of the particle filtering algorithm for processing the positioning data is as follows:  
1. **Initialization**. At the algorithm outset for \(k\)=1, all the particle states \(\mathbf{x_{i}}(1)\) are randomly initialized according to a given distribution. The weights \(w_{i}(1)\) are assigned equal values of \(\frac{1}{L}\).
2. **Prediction**. Based on the transition Equation (50), new particle states are predicted. \[\mathbf{x}_{i}(k)=\mathbf{f}(\mathbf{x}_{i}(k-1),\mathbf{u}(k-1),\mathbf{v}_{ i}(k-1))\] (50) \(\mathbf{u}(k-1)\) is a driving vector, \(\mathbf{v}_{i}(k-1)\) is a noise vector introduced to the state due to the error of \(\mathbf{u}(k-1)\), where each particle is perturbed with an individually generated vector \(\mathbf{v}_{i}(k-1)\), \(\mathbf{f}(\cdot)\) is a transition function that calculates a new state based on the previous one, driving signals and errors thereof.
3. **Measurement update**. Each measurement \(\mathbf{y}(k)\) updates the weights of the particles by Equation (51). \[w_{i}(k)=w_{i}(k-1)\cdot p(\mathbf{y}(k)|\mathbf{x}_{i}(k))\] (51) \(p(\mathbf{y}(k)|\mathbf{x}_{i}(k)\) is a conditional probability density of measuring \(\mathbf{y}(k)\), given the particle state \(\mathbf{x}_{i}(k)\). In other words, \(p(\mathbf{y}(k)|\mathbf{x}_{i}(k)\) characterizes error distribution of a given data source. Particles that diverge in the long run from measurements will assume small weights \(w_{i}(k)\).
4. **Weights' normalization**. The weights are normalized so that they sum up to 1--Equation (52). \[w_{i}(k):=\frac{w_{i}(k)}{\sum\limits_{i=1}^{L}w_{i}(k)}\] (52)
5. **State estimation**. The most probable system state is estimated, e.g., by the first moment (53): \[\overline{\mathbf{x}}(k)=\sum\limits_{i=1}^{L}\mathbf{x}_{i}(k)\cdot w_{i}(k)\] (53)
6. **Resampling**. After a number of algorithm iterations, all but a few particles have negligible weights and therefore do not participate in the simulation effectively. This situation is detected by calculating the so-called degeneration indicator, expressed by Equation (54), which is an inverse of the second moment reflecting the dispersion of the weights. \[d(k)=\frac{1}{L\sum\limits_{i=1}^{L}w_{i}^{2}(k)}\] (54) If all particles have the same weights of \(L^{-1}\) then \(d(k)\) reaches its maximum value of 1. As the weights start to differ, the \(d(k)\) indicator decreases. If \(d(k)\) falls below a given threshold, then a process called resampling is introduced and a new set of particles is created. The probability of copying a particle to the new set is proportional to its weight \(w_{i}(k)\). Therefore particles that poorly approximate the system state are superseded by more \"accurate\" particles. Then all particles are assigned the same weight \(L^{-1}\).
7. **Go to point 2**.
## 4 Predict the Evader Position Two Steps Forward in Time Considering Noisy Environments
### Estimate \(\mathbf{r}_{k}^{e}\) and \(\mathbf{r}_{k+1}^{e}\)  
We discuss how to estimate \(\mathbf{r}_{k}^{e}\), \(\mathbf{r}_{k+1}^{e}\), and \(\mathbf{r}_{k+2}^{e}\). Let \((x(k),y(k))\) denote the evader's location measured at time index \(k\).  
Recall we assumed that the evader's motions are smooth, so the evader's trajectory curves are derivative. Curve fitting methods are used to predict the target's position within two steps in the future. We utilize curve fitting methods for recent measurements: \((x(k-K+1),y(k-K+1))\), \((x(k-K+2),y(k-K+2))\), \(...,(x(k),y(k))\). Here, \(K>2\). This implies that we require more than two measurements.  
Recent \(x\) coordinate measurements are as follows. \(x(k-K+1),x(k-K+2),...,x(k)\). We fit \(x(k-K+1),x(k-K+2),...,x(k)\) using the second order polynomials \(x(n)=a_{x}*n^{2}+b_{x}*n+c_{x}\). This second order polynomials represent the \(x\) coordinate trajectory of the evader within the recent \(K\) time indexes. (Similarly, we can use higher order (3 or more) polynomials to represent the \(x\) coordinate trajectory of the evader within the recent \(K\) time indexes. But, using higher order polynomials does not assure accurate prediction of the evader's position).  
To solve this fitting problem, we utilize the following matrix form.  
\[A*S=B. \tag{28}\]  
\[\text{Here, }A\ =\ \left(\begin{array}{cccc}(k-K+1)^{2}&k-K+1&1\\ (k-K+2)^{2}&k-K+2&1\\...&...&...\\ k^{2}&k&1\end{array}\right),\ B\ =\ \left(\begin{array}{cccc}x_{k-K+1}&x_{k-K+2}&...&x_{k} \end{array}\right)^{T}\text{, and }S=\left(\begin{array}{cccc}a_{x}&b_{x}&c_{x} \end{array}\right)^{T}\text{. We solve for }S\text{ using pseudo-inverse methods.}\]  
\[S=(A^{T}*A)^{-1}*A*B. \tag{29}\]  
Let \(\mathbf{\hat{r}}_{k}^{e}[i]\) denote the \(i\)th element in \(\mathbf{\hat{r}}_{k}^{e}\). We estimate the \(x\) coordinate of \(\mathbf{r}_{k}^{e}\).  
\[\mathbf{\hat{r}}_{k}^{e}[1]=Q_{1}*S \tag{30}\]  
where \(Q_{1}=\left(\begin{array}{cccc}k^{2}&k&1\end{array}\right)\). In addition, we estimate the \(x\) coordinate of \(\mathbf{r}_{k+w}^{e}\) where \(w\leq 2\).  
\[\mathbf{\hat{r}}_{k+w}^{e}[1]=Q_{2}*S \tag{31}\]  
where \(Q_{2}=\left(\begin{array}{cccc}(k+w)^{2}&k+w&1\end{array}\right)\).  
Similarly, we estimate the \(y\) coordinates of \(\mathbf{r}_{k}^{e}\), \(\mathbf{r}_{k+1}^{e}\), and \(\mathbf{r}_{k+2}^{e}\) using the second order polynomials \(y(n)=a_{y}*n^{2}+b_{y}*n+c_{y}\). Recall that this curve fitting method requires that we have more than two measurements, i.e., \(K>2\).  
If we have only one measurement, then we set  
\[\mathbf{\hat{r}}_{1}^{e}=\mathbf{\hat{r}}_{2}^{e}=(x(1),y(1)). \tag{32}\]If we have only two measurements, then we set  
\[\tilde{\mathbf{r}}_{2}^{\varepsilon}=(x(2),y(2)). \tag{33}\]  
In addition, we set  
\[\tilde{\mathbf{r}}_{3}^{\varepsilon}=(2*x(2)-x(1),2*y(2)-y(1)). \tag{34}\]  
Ref. (34) implies that we fit two measurements using the first order polynomials.
## 4 Results and Discussion
### Dataset Description and Assessment
#### 4.1.1 Quantitative Analysis  
In order to accurately reflect the performance of our proposed method, we conducted a quantitative evaluation and selected the Daxing Airport loop, with a length of about 6 km, as the validation set for this time. The intersection over union (IOU) and the overall accuracy (OA) and accuracy per class are used to evaluate the classification results. The ratio of the intersection and set is the ratio of their intersection to the real scene, which is the accuracy of detection IOU, as shown in Equation (3). mIOU is the average intersection ratio of each class.  
\[\text{IOU}=\frac{\text{Prediction result}\,\cap\,\,\text{Real scene}}{\text{ Prediction result}\,\cup\,\,\text{Real scene}} \tag{3}\]  
where the prediction result is the prediction result, and the Real scene is the real scene. Overall accuracy OA indicates how close the prediction is to the simulated label.  
Figure 5: (**a**) Self-developed mobile LiDAR system; (**b**) experimental data collection area; (**c**) cloud data of road field points of interest.  
Mean Intersection over Union (mIOU) calculates the average ratio of intersection and union of all categories, Equation (4).  
\[\text{mIOU}=\frac{1}{k+1}\sum_{i=0}^{k}\frac{\text{TP}}{\text{FN}+\text{FP}+ \text{TP}} \tag{4}\]  
where true positive (TP) means correct prediction, prediction = true = positive class; false positive (FP) means wrong prediction, prediction = positive class \(\
eq\) true = negative class; false negative (FN) means wrong prediction, prediction = negative class \(\
eq\) true = positive class.  
Overall accuracy (OA) indicates how close the prediction is to the simulated label. Accuracy is a single class indicating the degree of approximation between prediction and simulation label in Equation (5),  
\[\text{Accuracy}=\frac{\text{Total correct class}}{\text{Total seen class}} \tag{5}\]  
where the entire correct class is the completely correct class, and the total seen class is all the classes.
## 4 Experimental Evaluation
### Channel Estimators with Iterative Frequency-Domain Equalization  
Aiming to achieve even better estimation of the TIBWB-OFDM original bit stream, we considered the use of the IB-DFE equalizer and CSI obtained with Algorithm C, with results presented in Figures 6-8 for different values for the preamble average power. For low SNR values, the channel would maintain massive influence in mean-square-error calculation; for high SNR values, the opposite effect should happen. It should also be expected to achieve, at each IB-DFE iteration, gradually better performance. However, results showed that, even for the worst case (ZC sequence with the same average power of the data is used), fine CSI estimation did not significantly affect the overall system performance. This can be justified by the good ZC performance on dispersive channels and how ineffectively the  
Figure 5: BER results for TIBWB-OFDM over a dispersive channel employing Algorithm A for a ZC preamble sequence with power amplification.  
interference affected the preamble sequence. Also, the data did not present these good characteristics for channel estimation. So, the algorithm's estimated weight was enhanced by the first estimation performed by the ZC preamble, i.e., by Algorithm A.  
Thus, although slight performance improvement was achieved, the Algorithm C complexity did not justify this additional estimation procedure. However, the IB-DFE receiver showed some performance evolution from iteration to iteration, and influence in increasing the power of the preamble. Figure 8 shows the effectiveness of Algorithm A combined with IB-DFE since, with just two iterations, it could perform better than the MMSE with perfect CSI could, and a further 1 dB gain could be obtained if 5 iterations were considered.  
Figure 6: BER results for TIBWB–OFDM with IB–DFE over a dispersive channel employing Algorithm C for a ZC preamble having the same average power as data symbols.  
Figure 7: BER results for TIBWB–OFDM with IB–DFE over a dispersive channel employing Algorithm C for a ZC preamble sequence with 3 dB higher average power than the data symbols.
# Cable Force Health Monitoring of Tongwamen Bridge Based on Fiber Bragg Grating
## 2 Monitoring Method and FBG Vibration Sensor  
### Measurement Method  
According to the theory of string vibration, the relation between tension \(T\) acting on a two-end fixed tightened string and the characteristic frequency of the string vibration can be written as:  
\[T=\frac{4 ml^{2}f_{n}^{2}}{n^{2}} \tag{1}\]  
where \(l\), \(m\) and \(f_{n}\) are length, linear density and \(n\)-th order characteristic frequency of the string, respectively.  
Since the physical size of the suspension cable is known, cable force can be obtained as long as the vibration frequency of the suspension cable can be measured in real time. The actual cross section of the suspender is comprised of 19 thin wires connected in parallel, which form one suspension cable with a diameter of 70 mm. A PVC protection tube tightly wraps all suspension cables. Compared to the cable, rigidity of PVC tube is small, so its linear density can be ignored during the calculation of cable force using Equation (1). For this purpose, according to their vibration frequency characteristics, FBG vibration sensors for Tongwamen bridge were developed, as described in detail in the next section.  
On each side (north and south) of Tongwamen bridge, there are 19 suspension cables, among which 8 were selected for monitoring. Accordingly, a total of 16 FBG vibration sensors were mounted on Tongwamen bridge. As Figure 1a shows, on both north and south sides of the bridge, sensors are located on the 3 rd, 5 th, 7 th, 9 th, 11 th 13 th, 15 th and 17 th suspension cables, in the direction from Shipu to Dongmen island. As shown in Figure 1b, sensors on the south and north sides are numbered as S1-S8 and N1-N8, respectively. Sensors on either side were concatenated through a single-mode optical fiber, which were then connected to the control room. A homemade FBG demodulator (channels: 8, acquisition frequency: 0-5000 Hz, accuracy: 3 pm, resolution: 0.1 pm) is used to record the wavelength of the sensor at a sampling frequency of 200 Hz. The bandwidth of light source in the interrogator is 80 nm (1525-1605 nm), where each channel can accommodate at least 20 FBG vibration sensors with a different central wavelength.
# Classification of Faults Operation of a Robotic Manipulator Using Symbolic Classifier
## 2 Materials and Methods
### GPSC Evaluation Methods
#### 2.6.1 Evaluation Metrics  
The accuracy, area under received operating characteristics curve (AUC), recall and precision values, and F1-score were used to evaluate the obtained models. When discussing classification problems, there are four types of outcomes: true positive (TP), true negative (TN), false positive (FP), and false negatives (FN). The TP is an outcome where the ML model correctly predicts the positive class. In the case of binary classification, there are two classes defined as positive (class labeled as 1) and negative class (class labeled as 0 or \(-\)1). The TN is an outcome where the ML model correctly predicts the negative class. The FP is an outcome where the ML model incorrectly predicts the positive class. The FN is the outcome where the ML model incorrectly predicts the negative class.  
The classification accuracy [32] is the fraction of prediction the ML model got right. The classification accuracy is defined as correct predictions and total values ratio. \(ACC\) can be written in the following form:  
\[ACC=\frac{TN+TP}{FP+FN+TP+TN}. \tag{5}\]  
_AUC_ is one of the EMs used in this research that computes the area under the curve which defines the ratio of true positive and false negative predictions [33]. The precision metric value [34] gives information on how many positive classifications were correct, The _Precision_ may be expressed as:  
\[Precision=\frac{TP}{TP+FP}. \tag{6}\]  
\begin{table}
\begin{tabular}{c c} \hline
**GPSC Hyperparameter** & **Range** \\ \hline PopSize & 100–1000 \\ \hline NumGens & 100–300 \\ \hline TourSize & 100–300 \\ \hline InitDepth & 3–12 \\ \hline Cross & 0.001–1 \\ \hline SubMute & 0.001–1 \\ \hline HoistMute & 0.001–1 \\ \hline PointMute & 0.001-1 \\ \hline StopCrit & \(1\times 10^{-7}\)–\(1\times 10^{-6}\) \\ \hline MaxSamp & 0.99–1 \\ \hline ConstRange & \(-\)10,000–10,000 \\ \hline ParsCoef & \(1\times 10^{-5}\)–\(2\times 10^{-4}\) \\ \hline \end{tabular}
\end{table}
Table 3: The range of GPSC hyperparameters.  
The recall metric value [34] provides information on how many actual members of the positive class were identified correctly by the trained ML model. The \(Recall\) is calculated as:  
\[Recall=\frac{TP}{TP+TN} \tag{7}\]  
The \(F1-Score\), according to [35], can be written as:  
\[F1-Score=\frac{2\cdot Recall\cdot Precision}{Recall+Precision}. \tag{8}\]  
All evaluation metric values are expressed in the range of \([0,1]\) with the higher score representing better performance.
## 3 Driving Behavior Characterization for Fuel-Consumption Scenarios
### Selection of Relevant Features

Table 2: Driving behavior signals and Pearson correlation coefficients (PCCs) of fuel consumption with relevant features. Mean values and variances are computed using 8 s analysis windows. positive correlation with fuel consumption, while the positive X-axis acceleration presents moderate correlations, as can be seen. On the other hand, BP and negative X-axis acceleration, both mean and variance, exhibit negative correlation coefficients. This means that an increase in BP or in X-axis deceleration is associated with a decrease in fuel consumption. Although these features are meaningful concerning driving behavior analysis, their correlations with fuel consumption are rather weak. Afterwards, we chose the most fuel-demanding sections of the Uyanik route, those that ran through highway and motorway, in order to develop the assessment system. Moreover, sections with traffic jams and slow traffic (i.e., mean speed below 60 km/h) were discarded with the aim of avoiding outliers during the training process. After limiting the type of road, the PCCs, as presented in Table 2, varied slightly. The most noticeable changes were a moderate reduction of the fuel consumption correlations with VS and a remarkable increase in the fuel consumption correlations with the mean and variance of positive X-axis acceleration. In view of these results, the latter features were also taken into account in a preliminary round of SOM training experiments. Thus, the following variables were selected as candidate features: mean values [VS, PGP, ERPM, GP, Pos XACC] and variances [PGP, Pos XACC]. A comprehensive series of training experiments revealed that a reduced subset of only four features is able to model the relationship between fuel consumption and driving behavior in a very satisfactory way. These features were mean PGP, mean ERPM, mean GP, and Pos XACC variance.
## 3 Results
### Correlation of Microorganisms with Protein, Carbohydrates and VOCs  
During storage, microbial metabolism of carbon source compounds and nitrogen source compounds plays an important role in fish spoilage. Carbon source compounds mainly come from glycogen, glucose and lactic acid in muscle, while nitrogen source compounds mainly come from protein in fish. In particular, the increase of free amino acids, TVB-N and TCA- soluble peptides is related to the degradation of protein. Figure 6 shows the correlation of microorganisms with protein and carbohydrates during storage. The glucose, lactic acid and total sugar were negatively correlated with _Yersinia_, _Hafnia-Obesumbacterium_, _Thauera_, _Mycobacterium_ and _Planifilum_; the proportion of these microorganisms were relatively high in the early stage, but they gradually decreased in the later stage. TVB-N and TCA-soluble peptides were positively correlated with _Pseudomonas_, _Shewanella_, _Brochothrix_, _Vagococcus_, _Myroides_ and _Acinetobacter_, and these microorganisms increased greatly in the later stage. The results showed that carbohydrates were preferentially utilized by microorganisms in the early stage of storage, and protein was mainly consumed in the later period.  
Figure 6: Correlation analysis of microbial species with protein and carbohydrates in samples.  
The correlation between VOCs and microorganisms is shown in Figure 7. _Pseudomonas_, _Acinetobacter_, _Hafnia_, _Thuera_ and _Yersinia_ were positively correlated with VOCs. _Planifilum_ was negatively correlated with VOCs. _Pseudomonas_ plays a major role in the production of alcohols [40]. It occupied the main dominant position in samples. _Pseudomonas_ was positively correlated with phenylethyl alcohol, 3-methyl-1-butanol and acetic acid (_p_ < 0.05). This was the opposite of _Mycobacterium_. _Mycobacterium_ and Thuera were positively correlated with n-hexadecanoic acid and methylene chloride. _Acinetobacter_ showed a significant positive correlation with acetone (_p_ < 0.05). _Planifilum_ was significantly associated with 1-butanol (_p_ < 0.05). _Hafnia_ and _Yersinia_ were positively correlated with toluene (_p_ < 0.01), carbon disulfide, phenol, p-xylene, ethyl acetate and styrene (_p_ < 0.05).