esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Feature Metrics  
In the framework of landslide evaluation, the impact factors are devoted to represent the region. The noise caused by some factors may lead to the degradation of performance, so it is necessary to check and exclude the impact factors with low predictive ability or repeatability. Therefore, three statistical methods were carried out to screen the characteristics.  
The Pearson correlation coefficient is the quotient of the covariance and standard deviation between two variables, which can reflect the degree of linear correlation between two random variables [55]. The value of \(r\) is between \(-1\) and \(1\). When the value is \(1\) or \(-1\), it represents perfect positive or negative correlation between two random variables, respectively. When the value is \(0\), it means that the sample correlation coefficient between two random variables is linearly independent, which is denoted as \(r\):  
\[r=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt{\sum_{i= 1}^{n}(x_{i}-\overline{x})^{2}}\sqrt{\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}} \tag{13}\]  
where \(n\) is the number of samples; and \(x_{i}\) and \(y_{i}\) are the observed values of point \(i\) corresponding to variables \(x\) and \(y\), respectively, while \(\overline{x}\) and \(\overline{y}\) are the average values of the samples.  
Multi-collinearity diagnosis is an effective tool for determining the linear correlation between two or more variables in a dataset to help select the appropriate characteristic factors [73]. Currently, the method has been applied for a variety of purposes including landslide susceptibility, soil erosion susceptibility, groundwater potential mapping, and so on. The multi-collinearity of the impact factors for landslide susceptibility may lead to the failure of the prediction function. Therefore, multi-collinearity diagnostics was carried out in the impact factor. When tolerance (TOL) is greater than \(0.1\) or the variance inflation factors (VIF) is less than \(10\), it indicates that there is no serious collinearity problem in the factor. The formula is as follows:  
\[\text{TOL}=1-R_{j}^{2} \tag{14}\]  
\[\text{VIF}=\frac{1}{TOL} \tag{15}\]  
where is explained the determination coefficient of variable \(j\) for auxiliary regression model. In this study, the multi-collinearity of \(12\) impact factors were examined.  
The information gain ratio (IGR) evaluates and ranks the importance of input variables due to the wide range of applications in selecting impact factors [74], which can be expressed as follows:  
\[E(Y)=-\sum_{i=1}^{n}P(Y_{i})log_{2}(P(Y_{i})) \tag{16}\]  
\[E(Y|X_{i})=-\sum_{i=1}^{n}P(Y_{i})\sum_{i=1}^{n}P(Y_{i}|X_{i})log_{2}(P(Y_{i}| X_{i})) \tag{17}\]  
\[IG(Y,X_{i})=E(Y)-E(Y|X_{i}) \tag{18}\]  
\[IGR(Y,X_{i})=\ \frac{IG(Y,X_{i})}{E(Y)} \tag{19}\]  
where \(E\) is defined as the entropy value of the impact factor \(X_{i}\) (with \(n\) classes) corresponding to the output type \(Y\) (landslide and non-landslide). \(P(Y_{i})\) and \(P(Y_{i}|X_{i})\) represents the prior probability of \(Y\) and the posterior probability of \(Y\) corresponding to \(X_{i}\), respectively.
## 4 Security
### Open Issues  
The selected standards are resistant against most of the considered attacks except for continuous jamming at all frequencies, collision and flooding attacks as well as traffic analysis. However, solutions against such particular kind of jamming and collision attacks are particularly difficult to find because the data transmission is made impossible in both cases. The only solution would necessitate human interventions to eliminate the interference source(s), as soon as a long-term communication breakdown is detected. Flooding of _connection request_ is more delicate to solve, as regular nodes must still be able to send such request in order to join the network and authenticate themselves. Filtering the connection requests by a list of devices susceptible to join the network would not solve the problem, as the network manager would have to receive the requests in order to determine the sender identity. A solution remains therefore to be found. Even if payload encryption provides a partial solution to traffic analysis, it is not sufficient. Indeed, an analysis of the routing paths can be sufficient to determine the traffic scheme. Countermeasures to this analysis could be to insert fake packets and/or randomly distribute the traffic. However, the routing would not be optimized and the network performances including end-to-end delay would be degraded. Moreover, insertion of additional packets would drag the energy budget of the nodes down.  
The existing security mechanisms are theoretically sufficient to cover the main attacks. However, the standards only provide specifications and leave design and implementation to the users. For example, the roles of each security key are described, but their management scheme has to be developed by the users, according to their requirements and respecting the standard specifications. To the best of our knowledge, [PERSON] _et al._ are the first to have proposed a design and an implementation of a security manager adapted to the WirelessHART standard in [47]. The investigation of similar proof of concept for additional standards e.g. the promising ISA100.11a may be an interesting track to follow in order to provide already evaluated implementations that the users can tune to meet their requirements.
## 3 Flow Control Devices for Gust Load Alleviation
### Summary of the Characteristics of These Flow Control Devices  
Table 1 gives a summary of the advantages and limitations of the flow control devices presented above. Although the traditional control surfaces have been applied to aircraft for gust load alleviations, these devices do not meet the requirements of fuel efficiency for future aircraft design due to their large size and structure weight. Although passive control technologies have been tested in the laboratories, it is still a challenge to improve the robustness and reliability. Active flow control using fluidic actuators show high efficiency and have great application potential for gust load alleviation. However, as it is still a novel technology, there is a lack of comprehensive understanding of flow control mechanism and in-depth research on their applications in the actual flight environment of aircraft.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Flow Control Category** & **Typical Devices** & **Advantages** & **Limitations** \\ \hline \multirow{6}{*}{Active flow control} & Traditional control surfaces: Trailing-edge flaps and Spoilers & Robustness for a large range of incoming flow speeds & large size and heavy low-frequency responses \\ \cline{2-4}  & & & decreases in gust load alleviation effects with increasing incoming flow speeds \\ \hline \multirow{2}{*}{Passive flow control} & wingtip twist and flexible wing-fold devices & no extra energy required simple structural mechanism & Poor robustness away from design point \\ \cline{2-4}  & & & decreases in gust load alleviation effects with increasing incoming flow speeds \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of the characteristics of the flow control devices for gust load alleviation.  
Figure 7: The influence of spanwise load distributions due to CC jet blowing. (a) \(M_{\infty}=0.3\); (b) \(M_{\infty}=0.7\).
## 6 PV System Analysis Using PVsyst Tools
The average ratio is 0.803. The PV system's nominal power output is 380 kW. Due to various factors such as site location and system losses, the power capacity of the system was reduced, as shown in Figure 2. The energy yield was calculated based on the south-facing array surface. The expected plant production for different probability scenarios (the probability of meeting a generation value) is also presented in Figure 2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Unit Nom Power & 100 kWac \\ Number of inverters & 3 units \\ Total power & 380 kWac \\ Operating voltage & 630–1000 V \\ P\({}_{\text{nom}}\) ratio (DC:AC) & 1.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Installed inverter details. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline PV module size & 470 Wp \\ Number of modules & 810 units \\ Nominal (STC) & 380 kWp \\ Modules & 81 strings \(\times\) 10 in series \\ P\({}_{\text{mpp}}\) & 707 V \\ U\({}_{\text{mpp}}\) & 501 A \\ Module area & 1751 m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: PV module details. The yield factor was defined to be a factor consolidating all the system losses that occurred across this power plant. The major losses that occurred during the operation of the solar PV power plant were temperature loss, module mismatch loss, and DC to AC conversion losses. Figure 3 presents the loss diagram over the whole year. The diagram represents the energy flows in the system and the losses from every parts. Figure 3 shows that the largest losses came from temperature with 7.77%, while the inverter loss was 1.47%, which is expected from the manufacturing d \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **GlobHor** & **DiffHor** & **T\_Amb** & **GlobInc** & **GlobEff** & **EArray** & **E\_Grid** & **E\_Grid** \\  & **kWh/m2** & **kWh/m2** & \({}^{\circ}\)C & **kWh/m2** & **kWh/m2** & **MWh** & **MWh** & **MWh** \\ \hline JAN & 133.6 & 79.58 & 27.21 & 140.4 & 137.0 & 44.38 & 43.10 & 0.806 \\ FEB & 131.6 & 81.85 & 27.84 & 135.4 & 132.0 & 42.72 & 41.55 & 0.806 \\ MAR & 153.8 & 88.56 & 28.27 & 153.6 & 149.9 & 48.24 & 46.89 & 0.802 \\ APR & 142.7 & 73.33 & 27.86 & 138.6 & 135.0 & 43.44 & 42.15 & 0.799 \\ MAY & 144.8 & 77.78 & 28.71 & 136.7 & 132.5 & 42.88 & 41.59 & 0.799 \\ JUN & 133.0 & 70.24 & 28.20 & 124.4 & 120.7 & 39.19 & 37.98 & 0.802 \\ JUL & 134.4 & 81.91 & 28.20 & 126.7 & 122.7 & 39.98 & 38.77 & 0.804 \\ AUG & 136.7 & 82.95 & 28.09 & 131.4 & 127.7 & 41.52 & 40.27 & 0.805 \\ SEP & 133.6 & 69,76 & 27.38 & 132.1 & 128.8 & 41.45 & 40.20 & 0.799 \\ OCT & 140.5 & 86.62 & 27.67 & 143.0 & 139.6 & 45.20 & 43.88 & 0.806 \\ NOV & 124.6 & 69.35 & 26.85 & 130.2 & 127.4 & 41.12 & 39.87 & 0.804 \\ DEC & 121.3 & 67.82 & 27.27 & 128.1 & 125.0 & 40.37 & 39.13 & 0.802 \\ YEAR & 1630.5 & 929.75 & 27.80 & 1620.5 & 1578.1 & 510.49 & 495.39 & 0.803 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Balance and main results. Figure 2: Normalized production (per installed kWp).
## 5 Mitigation Countermeasures against Building and Cropland Damages in Coal Mining Subsidence Areas
### Mining-Techniques-Based Damage Mitigation Measures  
Reasonable options for mining techniques and measures can effectively control surface displacement and deformation, so as to reduce the surface building and cropland damage severities. The coal-mining-induced surface damages can be mitigated through the following mining techniques. (1) The mining and backfilling method: in this way, coal miners backfill the goat after their coal mining work. Through the solid waste or the prepared filler left in the goat [43,44], fillers and coal pillars interact with each other; based on such mechanical mechanism [45,46], they can jointly support the stress from the upper overlying rock mass, so as to control the overlying rock mass displacement and deformation, and to mitigate surface subsidence [47]. The application of mining and backfilling technology can play a significant role in the control of surface deformation, mining-induced solid waste treatment and green coal mining in the \"three-under\" coal deposit area. (2) Strip mining method: with this method, miners divide the coal seam to be mined into several strips for partial mining, and the pillars are left untouched so as to support the roof rock mass and to thereby mitigate surface subsidence; however, the disadvantage of this method is the low coal yield rate [48,49]. (3) Coordinated and controlled mining: through the rational coal mining layout, miners control the mining speed and the regional coal seam mining thickness [50], so as to mitigate the influence of coal mining on surface buildings and croplands. In addition, the surface displacement and deformation can be controlled from the aspects of the grouting into overlaying but separated rock mass, the roof management and other methods, so as to mitigate the coal-mining-induced damage.  
Based on the forecast of building and cropland damage in the coal-mining-impacted area, the conflict between coal mining and ecological and environmental protection can be mitigated through surface building and cropland enhancement, or reasonable coal mining technology adjustment underground, so as to achieve sustainable coal mining.
# An Audio Distortion Dynamic Range Index for Evaluating the Performance of Audio Systems
## 5 Discussion
Similar to the uneven sensitivity to different distortions of human ears, the distorted components farther away from the fundamental wave generally have less influence, and this can be compensated with a weighting function on the spectrum before the calculation of ADDR plot.
# Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
## 3 Method
### Detection of Anomalies  
Based on indexes recorded in the resampled index table, the corresponding embedding vectors can be extracted from the codebook to yield the normal quantized latent code. After that, the decoder maps this quantized latent code back to the image space to reconstruct the restored image \(\tilde{x}_{r}\). Reconstruction-based methods typically calculate the reconstruction error of the input and output the anomaly score, with higher scores representing more likely anomalies. However, we chose the \(\ell^{2}\)-distance between the VQ-VAE directly reconstructed image \(\tilde{x}_{d}\) and the restored image \(\tilde{x}_{r}\) which is defined in Equation (6), as the anomaly score to perform anomaly detection:  
\[\mathcal{A}(i,j)=\left\|\tilde{x}_{r}(i,j)-\tilde{x}_{d}(i,j)\right\|_{2} \tag{6}\]  
We argue that the VQ-VAE model is applied to compression, the reconstruction may only lose some details, but the semantics of the raw image is not changed. In addition, anomalies are typically small in both size and proportion to the image being processed [1], and VQ-VAE has enough generalization capability to reconstruct these unseen anomalies. As resampling operation is applied only to components with very low likelihood values in the index table, the results calculated using Equation (6) will only have high anomaly scores in certain regions, reducing the possibility of false alarms. We verified the proposed anomaly score in the experimental section, and the results also show that ours is better than using traditional detection methods.  
After getting the residual map, we apply a smoothing post-processing using bilateral filtering. The smoothed anomaly score map shows the anomaly score for each pixel, and the final segmentation can be obtained by binarization operation to identify the location of the anomaly.  
Some visual examples of the above process are given in Figure 3. Since images are mapped to a unified latent space that is quantized as a discrete and solidified codebook, PixelSNAIL can easily determine whether to resample from the distribution of normal images based on a fixed threshold, followed by the decoder generating an anomaly-free sample closest to the input. Figure 3c shows restored images decoded from resampled index tables. Comparing with the original images in Figure 3a, one can see that the decoder only regenerates for the parts that have anomalies, but not for the whole image. As shown in Figure 3d, VQ-VAE can still reconstruct unseen anomalies, maintaining the structure of the original  
Figure 2: Diagram of the resampling condition.  
images. By comparing these two reconstructed images, it is possible to obtain areas that are considered anomalous as shown in Figure 3b.
# Bearing Fault Diagnostics Based on the Square of the Amplitude Gains Method
###### Abstract
The model of a square of amplitude gains has also been implemented for shaft diagnostics during their operation [89]. The authors showed the effectiveness of the damage map to verify the different types of shaft damage in simulation and experimental research. In the presented article, the authors adapted the square of amplitude gains method to detect bearing damage (outer race, inner race, bearing ball damage at different depths). Firstly, a recorded signal of shaft displacement is divided into two observation periods. Then, the analytical autocorrelation functions are determined and approximated by a polynomial. On their basis, the Fourier transform is used to convert to the spectral (frequency) form. Thanks to this, it is possible to determine the parameters of the model to which fixed statistical diagnostic thresholds are assigned. The last step is to convert the model parameters into color damage maps that are easy to analyze and interpret. At this stage, the proposed method allows for early detection of bearing technical conditions (damaged or undamaged). However, based on the parameter values, the authors cannot indicate the type of damage. The article is presented as follows: The introduction is presented in Section 1. Section 2 describes the mathematical procedure for determining the model's parameters and obtaining color damage maps of the bearings. Section 3 presents the measurement stand and describes the objective and scope of the tests. Section 4 presents the experimental results of the tests carried out and compares the results obtained (by using FFT analysis and a square of the amplitude gains method) for the undamaged and damaged bearings. Conclusions and discussions are in Section 5.
## 4 Results and Discussion  
### Experiment Description  
In the field experiment, onboard measuring sensors were used to collect data from Oranienburg to Frankfurt, France. The environment is urban and open country, as depictured in Figure 7. Figure 8 shows the German ATL train used for data acquisition. The test train operated in Germany is a diesel-driven ICE test train, called Advanced TrainLab (ATL). The train consists of four segments, two traction units and two mid-segments. The diesel-electric traction unit permits it to run at a maximum speed of 200 km/h as shown in Figure 9. The ATL has a specific mounting rack for the flexible installation of perception and localization sensors. The inertial sensor used was IMAR's iMAR iNAT-RQI-4001-3, as shown on the left in Figure 10, with the parameters and performance shown in Table 1. The OD uses the DF16 series photoelectric speed sensor, as shown on the right in Figure 10. Its speed range is 0 to 1500 rpm (round per minute) or 0 to 3000 rpm. It is a piece of  
During the experiment, the train's trajectory is shown in Figure 8, and the train's speed profile was captured using an OD as shown in Figure 9. The train wants to remain stationary for 100 s, and then because the train needs to keep turning, the train will keep  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Sensors**} & \multicolumn{1}{c}{**Contents**} & \multicolumn{1}{c}{**Parameter Settings**} \\ \hline \multirow{4}{*}{IMU} & \multirow{2}{*}{Gyroscope} & Angular rate bias stability & 0.004\({}^{\circ}\)/h \\  & & Angular rate range & \(\pm\)400\({}^{\circ}\)/s \\  & & Random walk & \textless{}0.005 deg/sqrt (h) \\ \cline{2-3}  & \multirow{2}{*}{Accelerometer} & Accelerometer bias stability & 0.05 mg \\  & & Accelerometer range & \(\pm\)20 g \\  & & Random walk & \textless{}12 ug/sqrt (Hz) \\ \hline \hline \end{tabular}
\end{table}
Table 1: INS iMAR iNAT-RQT-4001-3 parameters.  
Figure 10: Sensors used in experiments: (**a**) iMAR iNAT-RQT-4001-3 and (**b**) DEUTA DF16 series photoelectric speed sensor.  
Figure 9: Train’s speed profile was captured using an OD.  
accelerating and decelerating, and finally the train keeps a uniform speed of 45 m/s. We utilized IMAR's high-precision iNAT-RQT-4001-3 to obtain IMU measurement data and the German company's DEUTA DF16 series photoelectric speed sensor to obtain high-precision speed and mile data. These data were stored first, and we used post-processing to analyze and validate our proposed algorithm according to Section 3.
# Optical Cable Lifespan Prediction Method Based on Autoformer
## 2 Factors Impacting OPGW Lifespan  
### Environmental Impacts on OPGW Lifespan  
The persistent operation of fiber optic cables post installation is contingent on various factors, with temperature fluctuations emerging as a crucial determinant of the cable's longevity. Environments experiencing considerable temperature shifts can induce thermal expansion and contraction within the cable, leading to cyclic stress and subsequent material aging and degradation. Elevated temperatures accelerate material aging, thereby increasing the risk of optical attenuation and signal loss, while lower temperatures render the cable more brittle and prone to breakage. Prolonged temperature fluctuations may alter the cable's internal microstructures, potentially causing micro-cracks or damage, ultimately compromising the cable's performance and lifespan [10; 11].  
In addition to temperature, wind and ice loads surpassing the cable's weight significantly impact the longevity of OPGWs. Concurrently, the high-frequency, low-amplitude vibrations induced by wind forces influence both the cable's fatigue performance and the transmission characteristics of the optical fibers. Furthermore, events like short circuits and lightning currents can generate excessive heat, potentially exceeding the temperature threshold of the optical fiber coating and causing material damage [12].
## 4 Comprehensive Experiments
### Benchmark Test
It can be seen from the data in Table 3 that IWOA had the best optimization performance in MK01, MK02, MK04, MK07, and MK08, obtaining the optimal value of the optimal objective function. In MK06 and MK10, although the optimal solution was not obtained, the solution was better than the comparison algorithm. Among the three benchmark instances MK03, MK05, and MK09, the accuracy of the proposed IWOA was slightly lower than that of IWOA(1), WOA-LEDE, and WOA(2). According to the above analysis, it is not obvious that the proposed algorithm is significantly better than the other three comparison algorithms, but compared with the other three improved whale optimization algorithms, the proposed algorithm has strong competitiveness. In terms of algorithm stability, the proposed algorithm had the best robustness performance on the five instances MK01, MK05, MK06, MK09, and MK10, and the obtained standard deviation was the smallest. The IWOA designed in this paper has certain advantages in solving accuracy and stability, the solution results were significantly better than those of NSGA-II algorithm, and it performed well in most of the 10 instances. In Figure 5, the IWOA obtained the minimum value of _APRD_ in seven out of 10 instances, which further proves that the proposed model has superior performance and can obtain relatively optimal and stable solutions on different scale shop scheduling problems. In summary, the optimization comparison results based on benchmark examples verified the feasibility and effectiveness of the proposed algorithm. In addition, we will further verify the superiority of the algorithm in Section 4.3, detailing the workshop example simulation.