esa-sceva/satcom-chunk-collection huggingface dataset:
# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 2 Materials and Methods
### Regression Metrics  
To evaluate the performance of the proposed methods for forecasting, three metrics were used: root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R\({}^{2}\)). These metrics are often used for evaluation in time series forecasting.  
Root mean square error (RMSE): Used to evaluate the performance of the model in regression problems. It is an error measure calculated by taking the square root of the Mean Squared Error (MSE). RMSE is used to evaluate the magnitude of the errors. It is a very common and reliable measure for evaluating the performance of the prediction.  
Mean absolute error (MAE): It is an error measure that calculates the average of the absolute values of the differences between the actual values and the predicted values. A low MAE value indicates that the model is successful in predictions.  
Correlation coefficient (R\({}^{2}\)): is a performance metric that measures how good a model's predictions are. R\({}^{2}\) shows how close the predicted values are to the true values and takes a value between 0 and 1.  
R\({}^{2}\) = 1: The model made excellent predictions.  
R\({}^{2}\) = 0: The model performed no better than predicting the average of the actual data. R\({}^{2}\) < 0: The model performed very poorly in predictions.
## 3 Validation Study Based on Hemisphere Model
### Results of the Proposed Registration Strategy Using the Hemisphere Model  
To evaluate the proposed registration method, the initial condition (coarse registration effect) was set to coincide with the origin of the hemisphere model composed of the point sets in the medical image and patient space. The rotation matrix was calculated using the ICP optimization process due to the mismatch between the points in each space. The SRE and TRE values were calculated by transforming the points in patient space (Figures 4 and 5). Compared to the initial condition, the SRE was reduced by approximately 1.3% and 33% for the conventional and proposed method, respectively. As the number of points in the medical image increases, the overall trend of SRE decreases. Overall, the error when the proposed algorithm is applied is reduced by an average of about 0.4 (0.2) mm compared to the error when the conventional registration method is used. When the registration results of the proposed method are visualized, the number of points in patient space increases, but the overall error value decreases (Figure 4).  
RE using the conventional registration protocol and the proposed registration protocol is demonstrated in Figure 5. Depending on the number of points in the medical image space (8 cases), the TRE varies by about 0.6 mm for the conventional method, and the registration using the proposed protocol was found to be 0.2 (0.1) mm smaller on average. In addition, as the target was located at an inferior position, the conventional registration method showed a larger TRE. However, there was almost no difference in TRE (0.075-0.083 mm) according to the target location.
# Springback Behavior of AA 7075-T6 Alloy in V-Shaped Bending
## 4 Analysis of Variance and Regression Analyses of Springback Angle and Maximum Load  
The regression equation is  
\[\text{Springback angle (}^{\circ}\text{) = 2.33 - 0.0216 T - 5237 t + 0.0667 BA + 1.661 BR} \tag{1}\]  
Table 5 presents the results of the ANOVA conducted to evaluate the influence of key parameters--namely, forming temperature, sheet thickness, bending angle, and bending radius--on springback behavior. The total sum of squares was used to quantify the relative contribution of each factor, with higher values indicating a stronger influence on the response variable. These values were further utilized to calculate the percentage contribution of each parameter. As shown in Table 5, the bending radius emerges as the most dominant factor, accounting for 45.2% of the variation in springback angle. This is followed by sheet thickness, contributing 28.1%, and forming temperature, with a 19.9% influence. In contrast, the bending angle has a minimal effect on springback, contributing only 1.3%. The combined effect of these parameters on the maximum load can be described by a regression model, represented in Equation (2), which shows a strong correlation with an R\({}^{2}\) value of 0.98, indicating excellent predictive accuracy.  
\[\text{Max. Load (N) = 4460 - 4.11 T + 5285 t - 66.5 BA - 310.2 BR} \tag{2}\]  
Table 6 shows the results of ANOVA Table 5 shows that sheet metal thickness (84.21%) is the most significant factor in maximum load, and the second factor is bending angle (6.88%). The influence of the bending radius is 4.65%. Temperature has a much lower impact among other bending parameters (2.12%).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variance Source** & **DF** & **Adj SS** & **Adj MS** & **F-Value** & \(p\)**-Value** & **Contribution Rate (\%)** \\ \hline Temperature, T, (\({}^{\circ}\)C) & 1 & 67.884 & 67.884 & 21.97 & 0.003 & 19.9 \\ \hline Thickness, t, (mm) & 1 & 95.823 & 95.823 & 31.01 & 0.001 & 28.1 \\ \hline Bending angle, BA, (\({}^{\circ}\)) & 1 & 4.500 & 4.500 & 1.46 & 0.273 & 1.3 \\ \hline Bending radius, BR, (\({}^{\circ}\)) & 1 & 154.180 & 154.180 & 49.90 & 0.000 & 45.2 \\ \hline Error & 6 & 18.540 & 3.090 & & & 5.4 \\ \hline Total & 10 & 340.927 & & & & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 5: ANOVA results.
###### Abstract  
For time-critical precise applications, one popular technology is the real-time precise point positioning (PPP). In recent years, there has been a rapid development in the BeiDou Navigation Satellite System (BDS), and the constellation of global BDS (BDS-3) has been fully deployed. In addition to the regional BDS (BDS-2) constellation, the real-time stream CLK93 has started to support the BDS-3 constellation, indicating that the real-time PPP processing involving BDS-3 observations is feasible. In this study, the global positioning performance of real-time PPP with BDS-3/BDS-2 observations is initially evaluated using the datasets from 147 stations. In the east, north and upward directions, positioning accuracy of 1.8, 1.2 and 2.5 cm in the static mode, and of 6.7, 5.1 and 10.4 cm in the kinematic mode can be achieved for the BDS-3/BDS-2 real-time PPP, respectively, while the corresponding convergence time with a threshold of 10 cm is 32.9, 23.7 and 32.8 min, and 66.9, 42.9 and 69.1 min in the two modes in the three directions, respectively. To complete this, the availability of BDS-3/BDS-2 constellations, the quality of BDS-3/BDS-2 real-time precise satellite products, and the BDS-3/BDS-2 post-processed PPP solutions are also analyzed. For comparison, the results for the GPS are also presented.  
BDS-3; BDS-2; real-time; precise point positioning +
Footnote †: journal: _Sensors_
## 2 Envelope Analysis to Detect Defects in Rotating Machinery
### Degraded Detection Performance by Impulsive Noise  
In the previous section, a general method of detecting defects through envelope analysis was introduced for cases in which rotating machinery has a periodic defective signal. However, various types of noise exist in an environment in which the fault detection of rotating machinery is implemented in practice. Unpredictable background noise causes the accuracy of envelope analysis to degrade. In particular, impulsive noise affects a wide frequency band, making it difficult to detect faults in envelope analysis. In the present study, we investigate the effect of impulsive noises on envelope analysis and propose an improvement method. Equations (10) and (11) represent the normal and defective signals in the presence of the impulsive noises \(i(t)\). \(i(t)\), given by Equation (12), is modeled as a rectangular wave with short time \(\tau\).  
\[x^{\prime}_{normal}(t)=d(t)+w(t)+i(t) \tag{10}\]  
\[x^{\prime}_{fault}(t)=d(t)+s(t)+w(t)+i(t) \tag{11}\]  
Figure 4 shows the normal model as a situation in which impulsive noise is contained at one time at 0.1 s. The signal to which the band-pass filter is applied and its envelope are shown in Figure 4a. Figure 4b shows the envelope spectrums. Compared to Figure 3b, which does not include the impulsive noise, the baseline of the spectrum is increased, and the peak of the spectrum due to the defective signal \(s(t)\) is less distinct. Figure 5 shows the envelope spectrums for the case where the impulsive noises are contained in four times. Red dotted line means the faulty signal without impulsive noise as the reference. Compared with Figure 4b, in which the impulsive noise is mixed in one time, it can be seen that the peak of the spectrum representing the defective signal is more difficult to distinguish from that of the normal signal. It is very hard to differentiate the peak magnitude of the defective signal without impulsive noise (red dotted line) and that of the normal signal with four impulsive noises (green line). This ambiguity occurs because not only the increase of the baseline by the impulsive noise, but also the periodicity of the impulsive noise is considered.  
Impulsive noises are likely to be contained in several times in a short moment unless specific facilities such as anechoic chamber are installed for measurement. In this section, it has been confirmed that it is hard to distinguish between the normal and fault conditions when there are many impulsive noises where defects in rotating machinery are detected. In the next section, we propose a method to detect the signature of defect generated from rotating machinery even in the presence of such impulsive noises.  
Figure 4: (**a**) Filtered normal signal model with one impulsive noise at around 0.1 s. (**b**) Envelope spectrum of the normal and faulty model with one impulsive noise at around 0.1 s.
# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
## 7 Experimentation Results
### Discussion
#### 7.4.2 Advantages and Limitations of PPFS  
The present research has six main advantages.  
* PPFS methods are competitive and efficient FS methods.
* The four PPFS methods are different but provide similar results. The user can either apply all of them and choose the suitable result or use only one of them.
* PPFS methods can be used directly through EPP-Lab software.
* The EPP-Lab can be used to avoid the parameter setting of the optimization methods.
* PPFS methods provide a list of outliers, and their frequency allows validating the non-relevant features.
* EPP-Lab is an open-source desktop application, downloadable in any environment. In spite of the previous advantages, PPFS have two limitations.
* PPFS failed to find the non-significant features when most of the features are highly correlated or include noise.
* PPFS methods do not work well when the dataset is binary (the values of features' vectors are either 0 or 1)
###### Abstract  
The relative motion and coupled dynamics between individual units in a Multiple AUVs in Tandem Connection (MATC) system make speed and inter-unit distance control particularly challenging, especially in large-scale configurations. This study proposes a novel hybrid thrust allocation method for steady straight-line sailing in MATC systems, addressing thrust constraints and unit coordination. First, the motion model of the MATC system was established based on [PERSON]'s second law. Second, an improved Genetic Algorithm (GA) was developed to optimize thrust values for each unit in smaller configurations. Third, to address the computational challenges of thrust allocation in large MATC systems, an offline model training method was introduced, combining the Harris Hawks Optimization (HHO) algorithm with a BP neural network. Simulations were conducted for MATC configurations with 5 and 30 AUV units. The results demonstrate that, under current disturbances, the inter-unit distances and overall speed for the 5-unit MATC system quickly converged to target values of 0.12 m and 1.5 knots, respectively, without exceeding the 3.5 N thrust constraint. For the 30-unit MATC system, the proposed method achieved rapid convergence to target values, with a 56% reduction in straight-line speed deviation compared to using the improved GA alone. These findings validate the effectiveness of the proposed approach in enhancing control accuracy and scalability in MATC systems, offering significant potential for large-scale underwater applications.  
multiple AUVs; tandem connection; straight-line sailing; genetic algorithm; Harris Hawks Optimization
## 3 Results and Discussion
### Internal Flow Field
Thus, the position with the high EPR peak moves toward the leading edge, which is consistent with the direction of the initial position movement of the TLV. The position of the peak almost coincides with the initial position of the TLV. In addition, the decrease in discharge leads to a decrease in EPR. However, under the low-discharge condition, the flow field from the impeller inlet to the leading edge of blade with a remarkably higher EPR compared with the other two conditions is affected by RFV and leading-edge overflow. The blade load can accurately reflect the turbocharging performance and is associated with the characteristics of the TLV. The static pressure coefficient, \(C_{p}\), is introduced to analyze the blade load. \[C_{p}=\frac{p-\overline{p}}{0.5\rho u_{out}}, \tag{8}\]  
where \(p\) is the static pressure, \(\overline{p}\) is the static pressure of the inlet, and \(u_{out}\) is the impeller circumferential velocity. The blade load under different conditions is shown in Figure 12. The \(C_{p}\) distribution law of the 0.05 span is in accordance with that of the 0.50 span, and the distribution lines almost coincide. However, the static pressure near the tip clearance is remarkably lower than that at mid-span and near the hub. Thus, the blade load near the shroud is reduced. In addition, the static pressure of the suction surface no longer increases gradually but first decreases and then increases near the initial position of the TLV, which is shown in the green circles. The blade load distribution varies depending on the condition. Under the 1.6 \(Q_{d}\) condition, \(C_{p}\) of the pressure surface near the tip at the blade inlet is approximately the same as the suction surface. Thus, a zero load appears on the blade surface. This phenomenon explains why the streamline flows along the tip clearance in Figure 7a. Under the BEP  
Figure 12: Blade load of different span planes: (**a**) 1.6 \(Q_{d}\); (**b**) 1.0 \(Q_{d}\); (**c**) 0.7 \(Q_{d}\). condition, \(\Delta C_{P}\) first increases and then decreases. The load at the middle of the blade chord is larger than at other positions. Under the 0.7 \(Q_{d}\) condition, \(\Delta C_{P}\) and blade load decrease but tend to remain stable along the radial direction. As a result, the leakage flow intensity decreases.
# Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
## 3 Results and Discussion
### Application of NDVI and TBI to Wavelength and Water Status
#### 3.5.1 Ndvi  
With regard to WC assessment, a region corresponding to a higher coefficient of determination is observed with \(\lambda_{1}\) near 1400 nm and with \(\lambda_{2}\) at approximately 1450-1650 nm (Figure 9). Better NDVI regression model performance is obtained with LOG, where the model is \(y=87.995x+85.649\), the coefficient of determination for calibration is \(R^{2}=0.6080\), the coefficient of determination for validation is \(r^{2}=0.3696\), and the root-mean-squared error is \(RSME\) = 1.44 (Table 2) at the wavelengths \(\lambda_{1}\) = 1370 nm and \(\lambda_{2}=1610\) nm. For MC assessment, better NDVI (Figure 9) regression model performance is obtained with RAW, when \(\lambda_{1}\) = 1300 nm and \(\lambda_{2}=1310\) nm, where the model is \(y=317.11x-0.3247\) (\(R^{2}=0.8042\), \(r^{2}=0.6467\), RSME = 0.2689).  
Figure 10: Two-band index (TBI) of tomato wavelength and leaf water status. (**a**) RAW and WC; (**b**) RAW and MC; (**c**) LOG and WC; (**d**) LOG and MC; (**e**) DIFF and WC; (**f**) DIFF and MC.
# Efficient Method for Enhancing Reverse-Time Migration Images Using Vertical Seismic Profiling Data
## 5 Discussion  
The CRRTM method proposed in this paper effectively images seismic data in VSP observation systems while maintaining a low noise level in the final imaging results. This improvement is advantageous for subsequent applications, such as hydrocarbon reservoir exploration.  
Similar to traditional W-VSP RTM imaging methods, CRRTM is sensitive to the accuracy of the velocity model. Future research could focus on developing more accurate velocity models, such as through full-waveform inversion methods. Furthermore, with the continuous advancements in machine learning technologies, there is potential to integrate velocity inversion with machine learning to achieve even more precise velocity models ([PERSON] and [PERSON] 2023) [31].  
The quality of seismic data and wavefields plays a significant role in determining imaging quality, particularly when using elastic waves as the excitation source. This is also a critical factor influencing the quality of RTM imaging in practical research. Unlike acoustic waves, elastic waves comprise both P-waves and S-waves, which makes signal separation more complex. Therefore, it is essential to remove noise from the raw seismic data, such as direct waves and multiples. Additionally, separating S-waves and P-waves in the raw seismic data is necessary to enhance imaging accuracy. Currently, most signal separation methods damage the shallow reflection part of seismic data for direct wave removal. The methods used for wave separation also lead to a loss in seismic data quality. Therefore, implementing high-quality seismic signal separation methods can significantly enhance imaging quality.  
Furthermore, the CRRTM proposed in this paper and the ETC-IC can be combined with least-squares reverse-time migration (LSRTM). This combination can be further extended to LS-CRRTM, reducing the negative impact of the forward operator transpose and improving the imaging quality.
# Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach
###### Abstract  
Blasting is routinely carried out in urban quarry sites. Residents or houses around quarry sites are affected by the ground vibrations induced by blasting. Peak Particle Velocity (PPV) is used as a metric to measure ground vibration intensity. Therefore, many prediction models of PPV using experimental methods, statistical methods, and Artificial Neural Networks (ANNs) have been proposed to mitigate this effect. However, prediction models using experimental and statistical methods have a tendency of poor prediction accuracy. In addition, while prediction models using ANNs can produce a highly accurate prediction results, a large amount of measured data is necessarily collected. In an urban quarry site where the number of blastings is limited, it is difficult to collect a lot of measured data. In this study, a new PPV prediction method using Weighted Non-negative Matrix Factorization (WNMF) is proposed. WNMF is a method that approximates a non-negative matrix (including missing data) to the product of two low-dimensional matrices and predicts the missing data. In addition, WNMF is one of the unsupervised learning methods, so it can predict PPV regardless of the amount of data. In this study, PPV was predicted using measured data from 100 sites at the Mikurahana quarry site in Japan. As a result, the proposed method showed higher accuracy when using measured data at 60 sites rather than 100 sites, and the root mean square error for PPV prediction decreased from 0.1759 (100 points) to 0.1378 (60 points).  
blasting; Peak Particle Velocity (PPV); prediction models; urban quarry vibrations; Weighted Non-negative Matrix Factorization (WNMF) +
Footnote †: journal: Applied Sciences  
Article
## 2 Methodology
### Relevant Basic Theories  
#### 2.2.1 Seasonal Decomposition STL Algorithm  
The STL algorithm is a locally weighted linear regression (LOESS)-based seasonal decomposition algorithm proposed by [PERSON] et al. [34], which uses an additive model to decompose the time series data \(Y_{t}\) into a trend component \(T_{t}\), a seasonal component \(S_{t}\), and a residual component \(R_{t}\).  
\[Y_{t}=T_{t}+S_{t}+R_{t},\ t=1,2,3,\cdots,n \tag{5}\]  
The STL decomposition method is widely used in time series analysis and seasonal adjustment, especially for data with nonlinear trends and seasonal patterns. In the data analysis, this paper identifies that the data from the eight building groups involved in the study exhibit complex trend variations and pronounced 24 h seasonal changes. Therefore, using the STL algorithm will effectively extract the trend and seasonal components of the building heat load. The decomposition principle is divided into two parts, the inner loop and the outer loop; the inner loop is mainly for fitting the trend and calculating the seasonal components, and the outer loop is mainly for calculating and updating the robustness weights of each sample point.  
Mark \(T_{t}^{k},S_{t}^{k}\) as the trend component and seasonal component at the end of the k-1 st time of the inner cycle, \(T_{t}^{0}=0\) at the first trial. Denote the number of inner cycles as \(n_{i}\), the number of outer cycles as \(n_{o}\), and the period as \(n_{p}\).  
The inner cycle is divided into the following six steps:  
1. Detrending. The time series \(Y_{t}\) is de-trended from the last iteration \(T_{t}^{k}\) to obtain a new \(Y_{t}\gets Y_{t}-T_{t}^{k}\), where, initially, \(T_{t}^{0}=0\).
2. Cyclic subsequence smoothing. Each subsequence in step (1) is processed using LOESS regression, extending one cycle each before and after, with the smoothing parameter \(n_{s}\), and obtaining a smoothing result denoted as \(C_{t}^{k+1}\).
3. Low flux filtering of subsequences. The smoothing result \(C_{t}^{k+1}\) in (2) is sequentially subjected to a sliding average of lengths \(n_{p}\), \(n_{p}\), 3 and then a LOESS regression with parameter \(n_{l}\) is performed to obtain a sequence \(L_{t}^{k+1}\) of length N.
4. Removing seasonal trends. Obtain the seasonal component \(S_{t}^{k+1}=C_{t}^{k+1}-L_{t}^{k+1}\).
5. De-seasonalization. The seasonal component of de-seasonalization is \(Y_{t}-S_{t}^{k+1}\).
6. Trend smoothing. The series obtained in step (5) is subjected to LOESS regression to obtain the trend component \(T_{t}^{k+1}\). Determine if there is convergence; if there is convergence, output the result; otherwise, return to (1).  
The outer loop is mainly used to adjust the robust weight \(\rho_{t}\) in the LOESS regression to minimize the effect of outliers on the regression values.  
\[\rho_{t}=\frac{B(|R_{t}|)}{h} \tag{6}\]  
where \(h\) is:  
\[h=6*median(|R_{t}|) \tag{7}\]  
\(B(x)\) is the weight function, defined as follows:  
\[B(x)=\left\{\begin{matrix}(1-x^{2})^{2},&0\leq x<1\\ 0,&x>1\end{matrix}\right. \tag{8}\]