esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Statement and Data Analysis
### Data Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Order**} & \multirow{2}{*}{**Series**} & \multirow{2}{*}{**Test**} & \multicolumn{4}{c}{**Critical Value**} & \multirow{2}{*}{\(p\)**Value**} & \multirow{2}{*}{**Stationarity**} \\  & & & **Statistic** & & & & & **Value** & **(5\%)** \\ \hline \multirow{4}{*}{Zero} & \multirow{4}{*}{RB} & CLOSE & \(-1.889\) & \multirow{4}{*}{\(-3.431\)} & \multirow{4}{*}{\(-2.862\)} & \multirow{4}{*}{\(-2.567\)} & \multirow{4}{*}{\(0.337\)} & no \\  & OPEN & & \(-1.872\) & & & & & 0.345 & no \\  & HIGH & & \(-1.891\) & & & & & 0.337 & no \\  & LOW & & \(-1.934\) & & & & & 0.334 & no \\ \cline{2-8}  & \multirow{4}{*}{HC} & CLOSE & \(-1.927\) & \multirow{4}{*}{\(-3.431\)} & \multirow{4}{*}{\(-2.862\)} & \multirow{4}{*}{\(-2.567\)} & \multirow{4}{*}{\(0.312\)} & no \\  & OPEN & & \(-1.933\) & & & & & 0.317 & no \\  & HIGH & & \(-1.925\) & & & & & 0.337 & no \\  & LOW & & \(-2.067\) & & & & & 0.258 & no \\ \hline \multirow{4}{*}{One} & \multirow{4}{*}{RB} & CLOSE & \(-67.71\) & \multirow{4}{*}{\(-3.431\)} & \multirow{4}{*}{\(-2.862\)} & \multirow{4}{*}{\(-2.567\)} & \multirow{4}{*}{\(0.000\)} & yes \\  & OPEN & & \(-227.87\) & & & & & 0.000 & yes \\  & HIGH & & \(-45.67\) & & & & & 0.000 & yes \\  & LOW & & \(-48.87\) & & & & & 0.000 & yes \\ \cline{2-8}  & \multirow{4}{*}{HC} & CLOSE & \(-64.35\) & \multirow{4}{*}{\(-3.431\)} & \multirow{4}{*}{\(-2.862\)} & \multirow{4}{*}{\(-2.567\)} & \multirow{4}{*}{\(0.000\)} & yes \\  & OPEN & & \(-148.11\) & & & & & 0.000 & yes \\ \cline{1-1}  & HIGH & & \(-55.819\) & & & & & 0.000 & yes \\ \cline{1-1}  & LOW & & \(-49.22\) & & & & & 0.000 & yes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ADF test on the price data of RB and HC. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Residual**} & \multirow{2}{*}{**ADF Test**} & \multicolumn{4}{c}{**Critical Value**} & \multirow{2}{*}{\(p\)**-Value**} & \multirow{2}{*}{**Conclusion**} \\  & & **Statistic** & & & & & \\ \hline \multirow{2}{*}{\(et\)} & \(-4.52\) & \(-3.431\) & \(-2.862\) & \(-2.567\) & \(0.0001\) & cointegration \\ \cline{2-8}  & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: ADF test on the residual series. To ensure the effectiveness of our fitting process, we conducted a stationarity test on the fitted spread data. Table 4 demonstrates that the fitted spread data are a stationary time series at a 5% confidence level, indicating that our fitting process is robust and reliable. The time series plot of the fitted data for the closing price spreads is displayed in Figure 2.
###### Abstract  
The use of digital image correlation for modal analysis is becoming an appealing option thanks to its non-contact and full-field measurement process. However, frequency response function (FRF) estimation can be challenging due to the limited number of time domain data and heavy measurement noise. Thereby, the present work aims to propose a method which improves the estimation accuracy of point-wise FRFs. Firstly, a Gaussian-process-based spatial-frequency model is proposed, which makes use of the intrinsic properties of the FRF and the local spatial information of field measurements. Then, a Bayesian solution is developed, which is enforced by a stable and efficient numerical procedure. Finally, the effectiveness of the proposed method is verified by making a comparison with the spectral estimator through the use of simulated data, and it is further validated based on an experimental application.  
frequency response function; Gaussian process; digital image correlation; spatial-frequency model; Bayesian inference +
Footnote †: journal: Applied sciences
# On-Demand Maintenance Method Using Fault Prediction to Reduce Elevator Entrapment
## 3 Prediction of Elevator Entrapment Incidents
### Prediction Results  
To provide more intuitive prediction results of elevator entrapment failures and guide maintenance enterprises in preventive measures, an elevator entrapment risk index has been developed. It is a quantitative assessment metric designed to evaluate the probability of elevator entrapment incidents occurring within a 30-day period. The elevator entrapment risk index is derived from the entrapment probability values generated by the predictive model. The predicted entrapment probability value is the raw numerical output of the prediction results, while the prediction index is an alternative representation of this probability value. Whether the predictive index shows a red code or green code is determined by the magnitude of the predicted elevator entrapment probability. Empirical validation with City H's data determined 0.010 as the optimal high-risk threshold, where an elevator entrapment risk index \(\geq\) 0.010 classifies elevators as high-risk (red code) and an elevator entrapment risk index < 0.010 indicates operational normality (green code).  
The system retrieves the results from the database to visualize the prediction and the precise localization of elevators at risk of entrapment. It extracts relevant elevator data, including equipment details, location, and maintenance provider information, to generate a monthly elevator entrapment risk index report. This report, presented in a tabular format, records each elevator's predicted safety status and basic information, categorizing them based on their entrapment risk index, as shown in Table 9.  
The table records multiple critical data elements about the elevator, specifically, the unique elevator number used for identification; model-generated entrapment risk probability values; the red and green code indices based on the threshold division; the district, county, and street where the elevator is located; detailed address information; and information about the enterprise responsible for the elevator's maintenance. This approach facilitates the rapid identification of the elevator's risk level, improves elevator safety monitoring effectiveness, and supports subsequent on-demand maintenance work.
## 5 Data Analysis Method
### Signal Processing Approach  
All results presented in the following sections were processed using MATLAB. To ensure compatibility between the frequency ranges of both vibration and Doppler data, a common sampling frequency was selected. The recommended sampling frequency for the accelerometers is 20 kHz. Since the Doppler data were saved as a WAV file at Audacity's sampling rate of 44.1 kHz, the Doppler data had to be resampled to 20 kHz. With this sampling rate, a one-second record size (20,000 samples) and 50% overlap, the Doppler and acoustic data could be used for PSD and spectrogram analysis.  
The focus for this research was to ensure the radar data were matched as best as possible to the acoustic accelerometer data. This meant using the same sampling frequency as the accelerometers. It was recommended that a sampling rate of 20 kHz should be used for the accelerometer data to yield a frequency resolution of 1 Hz. Therefore, the same sampling rate was used for the radar data. MATLAB's spectrogram function was used for convenient processing for taking the STFT and matching the recommended parameters of the accelerometers. MATLAB's spectrogram function took inputs for the number of FFTs (NFFTs), number of overlapped samples, sampling rate, and a specified cyclical frequency vector. NFFT was set to 20 k, as well as the sampling rate to ensure a 1 Hz frequency resolution. The number of overlapped samples was chosen to be 10 k to achieve 50% overlap, which is typically a good practice to ensure that every part of the signal contributes equally to the spectrogram. Since the accelerometer data only recorded positive acoustic frequencies, a single-sided frequency vector was chosen for the radar micro-Doppler data as well. As mentioned previously, since this type of vibration was assumed to be inherently symmetrical motion, the negative Doppler frequency components were ignored for comparison with the acoustic data. Although a Hanning window is generally recommended to minimize spectral leakage, a rectangular window was used because there was concern that any other windowing scheme would lose important spectral information.
## 4 Materials and Methods
### Processing of Mass Spectrometry Data  
Raw data were processed following an untargeted metabolomic workflow. Both GC-MS and LC-MS raw data were processed using the MetAlign software [54] for baseline correction, peak picking (S/N > 3) and alignment of the mass signals. Mass signals that were present in less than three samples were discarded, considering that three biological replicates were analysed per variety. Subsequently, mass features were reconstructed to potential compounds using the MSClust workflow [55].  
Volatile metabolites were identified based on matching of the reconstructed mass spectra and calculated RIs with authentic reference standards and using the NIST17 Mass Spectral library and in-house databases. Non-volatile metabolites were putatively identified based on their molecular ion mass match with the online databases KnaSAck [56], Dictionary of Natural Products [57] and mzCloud [58] within a mass deviation of 5 ppm. In addition, MS/MS fragmentation patterns of selected compounds were manually compared to those in literature. Having analysed in negative ESI mode, the main detected ions were [M\(-\)H]\({}^{-}\), its formic acid adduct [M\(+\)FA\(-\)H]\({}^{-}\), and for a few compounds also [2M\(-\)H]\({}^{-}\). The in-source and MS/MS fragments for most compounds comprised a loss of hexose (neutral loss of 162.05282), deoxyhexose (neutral loss of 146.05791), pentose (neutral loss of 150.052823), a methyl group (neutral loss of 14), CO\({}_{2}\) (neutral loss of 44), and the aglycone of the related metabolite. All these indicative masses were used, when possible, to verify the annotations of the identified compounds. The levels of identification given follow the Metabolomics Standards Initiative suggestions [24]; level 1 identified compounds have been verified with authentic reference standards, level 2 identified compounds are those with a high match of mass spectrum (and RI for VOCs); level 3 are compounds with low match of mass spectrum (or RI for VOCs); level 4 are compounds that the match of mass spectrum (and RI for VOCs) is too low to provide a putative annotation.
# Analysis of Human Body Shadowing Effect on Wireless Sensor Networks Operating in the 2.4 GHz Band
## 4 Discussion
These results show that in the indoor environment where multipath propagation occurs, the path loss depends on the spatial orientation of people. This effect can be studied with the proposed model of the human body. The body shadowing effect was also observed in experimental measurements conducted in a typical building with concrete walls. In this case, the additional path loss introduced by the presence of a person was equal to 10 dB, while with two people standing close to each other and fading the transmitter path loss increased by approx. 15 dB. These measurement results correspond well with the results of simulations using ray-based software and the proposed human body model based on six-wall prisms. The observed differences between absolute values of path loss obtained with the measurements and the simulations may be caused by the simplification of the building structure that was made in the XGtd(tm) program. The room was modelled with four walls, a floor, and ceiling made of concrete with the same thickness and material properties. In a real building, the structure of the walls is different than the structure of the ceiling and the floor. With those simplifications, it was possible to analyze the influence of the human body on the path loss by the relative change of this parameter.
## 4 Application
### Design Evaluation  
The functionality of structural components was evaluated concerning von Mises values. Von Mises stress is a scalar value used to predict the yields of materials under multi-axial loading conditions. If this value exceeds the yield strength of the material, plastic deformation starts. The concept components generated with the generative design model were autonomously analyzed with a script in the CATIA V5 software, and von Mises values were determined. The macro script prepared for this purpose converts the mesh format workpieces into a solid model and performs structural analysis by defining the boundary conditions.  
Firstly, mesh-formatted data are taken into the CAD environment, and surface defects are removed. In the next stage, mesh structures are converted into surface and solid models, respectively (Figure 6). This solid model is transferred to the simulation module after holes are drilled in the part. At this stage, structural analysis is performed under a linear force with standard boundary conditions (fixed supports on small holes, bearing load on big hole). In the final stage, the performance score (von Mises stress) and model volume are saved in an Excel file. This structural analysis process was applied for all 3D models generated by the GAN model. The data preparation and structural analysis process took, on average, 36.2 s per component. The evaluation time of all models was approximately 5 h.  
At each structural analysis stage, 500 3D models were evaluated, and 250 with relatively superior mechanical performance were included in the dataset. The amount of data was kept constant by randomly removing 250 3D models from the initial dataset. The GAN model was retrained using the training dataset created with the newly added data. At the end of each training step, the training data were updated by evaluating the mechanical performance of the 3D models produced. This iterative process was completed after six cycles. In the last cycle, approximately 20.3% of the training set consisted of data generated by the GAN model. Table 3 shows some of the GAN models generated by the 3 DGAN model at the end of the sixth iteration. The physical and mechanical properties of these models are also in the table. The table shows that some high-volume 3D models were subjected to high stresses due to their thin sections.  
Figure 6: Preparation of 3D model and structural analysis process.
# Detecting Suspicious Texts Using Machine Learning Techniques
## 5 Experiments
### Evaluation Results
#### 5.2.1 Statistical Evaluation
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Classifier** & **FE** & **Features** & **A (\%)** & **P (\%)** & **R (\%)** \\ \hline \multirow{8}{*}{LR} & & F1 & 82.28 & 79.46 & 91.91 \\  & & F2 & 81.64 & 77.24 & 94.99 \\  & BoW & F3 & 78.07 & 72.18 & 98.58 \\  & & F4 & 82.07 & 79.72 & 90.88 \\  & & F5 & 82.21 & 79.57 & 91.52 \\ \cline{2-6}  & & F1 & 84.00 & 81.14 & 92.81 \\  & & F2 & 81.50 & 77.36 & 94.35 \\  & tf-idf & F3 & 77.85 & 72.22 & 97.81 \\  & & F4 & 83.85 & 80.75 & 93.19 \\  & & F5 & 83.92 & 80.84 & 93.19 \\ \hline \multirow{8}{*}{DT} & & F1 & 76.00 & 76.78 & 81.51 \\  & & F2 & 77.78 & 77.14 & 85.36 \\  & & F3 & 74.57 & 69.24 & 97.68 \\  & & F4 & 75.57 & 76.67 & 80.61 \\  & & F5 & 75.50 & 77.38 & 79.07 \\ \cline{2-6}  & & F1 & 77.92 & 78.24 & 83.56 \\  & & F2 & 79.57 & 77.85 & 88.44 \\  & tf-idf & F3 & 76.14 & 70.69 & 97.56 \\  & & F4 & 75.35 & 75.71 & 82.02 \\  & & F5 & 76.71 & 76.93 & 83.05 \\ \hline \multirow{8}{*}{RF} & & F1 & 83.21 & 79.43 & 94.22 \\  & & F2 & 80.50 & 78.68 & 89.08 \\  & & F3 & 76.00 & 70.49 & 97.81 \\  & & F4 & 82.14 & 79.29 & 91.91 \\  & & F5 & 83.20 & 79.82 & 93.45 \\ \cline{2-6}  & & F1 & 83.71 & 78.54 & 97.30 \\  & & F2 & 81.57 & 78.22 & 96.68 \\  & tf-idf & F3 & 77.92 & 72.16 & 98.20 \\  & & F4 & 83.21 & 78.14 & 96.91 \\  & & F5 & 83.71 & 78.90 & 96.53 \\ \hline \multirow{8}{*}{MNB} & & F1 & 81.57 & 79.10 & 90.88 \\  & & F2 & 79.00 & 74.37 & 94.99 \\ \cline{1-1}  & & F3 & 65.50 & 61.84 & 99.22 \\ \cline{1-1}  & & F4 & 81.14 & 77.77 & 92.55 \\ \cline{1-1}  & & F5 & 81.00 & 77.14 & 93.58 \\ \cline{1-1} \cline{2-6}  & & F1 & 83.78 & 81.29 & 92.04 \\ \cline{1-1}  & & F2 & 80.35 & 76.25 & 93.96 \\ \cline{1-1}  & tf-idf & F3 & 73.21 & 67.84 & 98.58 \\ \cline{1-1}  & & F4 & 83.85 & 80.55 & 93.58 \\ \cline{1-1}  & & F5 & 83.50 & 80.17 & 93.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison for different feature combinations where F1, F2, F3, F4, and F5 means unigram, bigram, trigram, a combination of unigram and bigram, and a combination of unigram, bigram, and trigram features, respectively. A, P, and R denotes accuracy, precision, and recall, respectively.
# Sequential and Comprehensive Algorithm for Fault Detection in Semiconductor Sensors
## 2 Materials and Methods
### Data Preprocessing  
The dataset used in this study was provided from Samsung Electronics Co., Ltd. (Suwon, Korea). It contained the sensor data for wafer preparation, which was recorded every 0.1 s, of virtually 30,000 wafers. Our dataset involved 25 processes, and each process took about 2 min. The total duration for the data collection including the time for initialization between processes was approximately 1 h 19 min and 12 s, and the whole data amount of each sensor was 47,520. The dataset contained 600 sensors named V001 to V600, which were categorized into two groups. One group was a constant sensor group associated with keeping the process condition for the safety. The whole process could stop as the output streams of the constant sensors discontinue. The other group was a non-constant sensor group, representing the current physical quantities of each process. This non-constant sensor group was also classified into peak and step sensors according to their output shapes. However, the step sensor group contained the sensors with too little variability. When any sensor value varies within \(\pm\)0.2%, it was considered to be the constant sensor and its variation could be ignored as a noise, because the state with the variation within \(\pm\)0.2% is defined as a steady state in a control system. Consequently, we classified the dataset into three groups: constant sensor, peak sensor, and step sensor (Figure 2A). The numbers of the sensors in each group are 535 (constant sensor), 22 (peak sensor), and 43 (step sensor). In this study, we focused on the peak sensor, because we could simply declare the faults by judging whether it was in a steady state or not for the constant and step sensors. However, there were no repetitive characteristics in the peak sensor group; in other words, a peak sensor represented the different outputs at each point of measurement. Such a difference could be generated due to the changes in other physical quantities; hence, we chose the relational analysis, such as the regression and clustering method. For the analysis of the peak sensors, we preprocessed the peak sensor data by extracting the peak value, which could be the most important value in the peak sensor. Algorithm 1 was developed to collect the peak data.  
Figure 1: Flow chart describing our proposed algorithm. The algorithm consisted of three steps: data preprocessing, individual and relational inspection, and clustering and regression analysis.
## 4 Numerical Experiments and Applications
### Weights of the Experts by Model (4)  
Next, we intend to study what the difference between our method and the existing methods is in determining the weights of experts.  
For the same overall score matrix of the alternatives from each expert, we will compute the weights of experts by Model (4) in this paper and Model (6) in [8], respectively. In Table 5, we first fix an overall score matrix, which is the same as in [8], to make a comparison between our method and that in [8].  
Using Algorithm 1 to solve Model (4), we obtain the weights of experts:  
\[w=(0.4823,0.3325,0.3333)^{T}.\]  
With the function \"fmincon\" in MATLAB to solve Model (6), the weight vector of experts is:  
\[\bar{w}=(0.3342,0.0300,0.4877)^{T}.\]  
Corresponding to \(w\) and \(\bar{w}\), we compute the errors of the experts between the actual vector of evaluations and the ideal one for all of the experts. The error vector of the three experts by our Model (4) is:  
\[(0.5933,0.5929,0.5957)\times 10^{-3}.\]  
\begin{table}
\begin{tabular}{c|c c c} \hline \hline
**Alternatives** & \(\mathbf{DM_{1}}\) & \(\mathbf{DM_{2}}\) & \(\mathbf{DM_{3}}\) \\ \hline \(\mathbf{A_{1}}\) & 0.3378 & 0.3420 & 0.3817 \\ \(\mathbf{A_{2}}\) & 0.3335 & 0.3429 & 0.3725 \\ \(\mathbf{A_{3}}\) & 0.3360 & 0.3390 & 0.3333 \\ \(\mathbf{A_{4}}\) & 0.3353 & 0.3295 & 0.3532 \\ \(\mathbf{A_{5}}\) & 0.3382 & 0.3360 & 0.3408 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Score matrix of alternatives by each expert.  
\begin{table}
\begin{tabular}{c|c c c} \hline \hline
**Alternatives** & \(\mathbf{DM_{1}}\) & \(\mathbf{DM_{2}}\) & \(\mathbf{DM_{3}}\) \\ \hline \(\mathbf{A_{1}}\) & 0.3058 & 0.2983 & 0.3009 \\ \(\mathbf{A_{2}}\) & 0.3239 & 0.3030 & 0.3066 \\ \(\mathbf{A_{3}}\) & 0.3176 & 0.3052 & 0.3338 \\ \(\mathbf{A_{4}}\) & 0.3573 & 0.3385 & 0.3196 \\ \(\mathbf{A_{5}}\) & 0.3085 & 0.3122 & 0.3277 \\The error vector by Model (6) is:  
\[(0.6005,\underline{0.2751},\underline{0.7328})\times 10^{-3}.\]  
The ranges of the two error vectors are \(2.8\times 10^{-6}\) and \(4.577\times 10^{-4}\), respectively. This indicates that by our method, the obtained overall scores for each alternative can reflect the evaluations of the experts as many as possible.  
However, the different weights of experts obtained by the above two methods do not seriously affect the final scores of the alternatives for the fixed initial score matrix in Table 5. Actually, Table 6 shows that the ranking is the same by the two methods.