esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Fault Diagnosis of Rolling Bearings
## 3 Rolling Bearing Fault Detection
### Morphological Transform-Based Fault Detection Methods  
The morphological transform-based detection method is a signal processing method based on mathematical morphology theory that can capture the fault-related components in the bearing vibration signals through morphological operators, such as erosion, dilation, open, and close. [PERSON] introduced mathematical morphology as a denoising method for image processing [16], and then [PERSON] extended it to the field of signal processing [16; 17]. Given the characteristic of morphological transformation to remove signal noise, several researchers have applied it to the fault diagnosis of components of mechanical systems and conducted a great deal of research in recent years. [PERSON] et al. [18] used a morphological close operator to process vibration signals for extracting fault impulses. [PERSON] et al. [19] proposed morphological close-open transform and morphological open-close transform by cascading the close or open operator of the morphology. [PERSON] et al. [20] and [PERSON] et al. [21] calculated the gradient (difference) between the dilation and erosion operator of the morphology to obtain the vibration impact component in the signal and defined this procedure as morphological gradient transformation. Following this, some improved methods based on morphological gradient transformation were developed that integrated the close and open operators [22; 23] and the close-open and open-close operators [24; 25]. These morphological gradient transform methods typically change the negative impact to positive impact, resulting in the change of signal impact components. To ensure that the positive and negative impulses of the signal do not change after morphological transformation, [PERSON] et al. [26] and [PERSON] et al. [27] utilized the mean value operator to fuse the results of the closed and open operators. In addition, [PERSON] et al. [28] and [PERSON] et al. [29] further developed the morphological hat-transform technology, which can enhance the weak impact in the signal by subtracting the morphological transformation result from the original signal.  
Figure 4: Summary of rolling bearing fault detection methods.  
Recently, [PERSON] et al. [30] proposed a morphological gradient product method by multiplying the results of two morphological transforms through a product operator. In addition to developing new morphological transformation methods, multi-scale analysis was used to improve the efficiency of the existing morphological transformation methods [31; 32; 33; 34; 35; 36; 37; 38]. To sum up, by analyzing the available morphological transform methods and using cascade operator, gradient operator, hat-transform, product operator, and multi-scale analyses, researchers have developed a series of morphological transform-based fault detection methods with excellent performance.
## 2 Materials and Methods
### Satellite-Based Datasets
#### 2.1.2 Tropomi SIF Product  
The TropPOMI instrument is carried on-board the Sentinel 5 Precursor (S-5P) satellite ([PERSON] et al., 2018), which has a crossing time of 13:30 local solar time and a revisit time of 17 days. Available ungridded SIF data with a footprint of \(3.5\times 7\) km is available from February 2018 and gridded data with a spatial resolution of \(0.2^{\circ}\times 0.2^{\circ}\) is available from March to October 2018 ([PERSON] et al., 2018). For each \(0.2^{\circ}\) grid cell, if the footprint of the sample covered the center of the cell, this sample was used to calculate the SIF ([PERSON] et al., 2018).  
As the crossing time for TROPOMI is the same as that for TanSat and because it provides almost complete global coverage, \(0.2^{\circ}\) Tropomi SIF data were used for comparison with the continuous TanSat SIF produced in this study.
## 3 GLRT-Based Fusion Rule
### Distortion Channel: BSC  
Let \(b_{n}\) denote the binary data obtained by the quantizer (2). We assume that the distortion channels between all sensor nodes and the FC are independent, and each of them is modeled as a binary symmetric channel (BSC), _i.e._,  
\[y_{n}=\left\{\begin{array}{ll}b_{n}&\text{with probability }1-\tilde{p}\\ 1-b_{n}&\text{with probability }\tilde{p}\end{array}\right. \tag{5}\]  
where \(y_{n}\) denotes the received data given input \(b_{n}\) and \(\tilde{p}\) is the crossover probability of the BSC. Clearly, the PMF of the received data at the FC under \(H_{i}\), \(i=0\) or 1, is expressed as:  
\[P_{Y,i}(y_{n};\sigma_{i})=(1-\tilde{p})P_{B,i}(y_{n};\sigma_{i})+\tilde{p}P_{B,i}(1-y_{n};\sigma_{i}) \tag{6}\]
# Analyzing Zone-Based Registration under 2-Step Paging in Mobile Communication Network
## 2 Zone-Based Registration and 2-Step Paging
### Mobility Model for Movement among the Zones  
Assume that the zone of zone-based registration is a rectangle. In this case, the UE moves to one of the four adjacent zones.  
As shown in Figure 3, the probability of moving to one of the four zones adjacent to the current zone--top, bottom, left, or right--is assumed to be equal to \(\frac{1}{4}\). This mobility model is called a symmetric random walk model and is widely used for convenient analysis.  
By contrast, in studies dealing with 2 ZR, performance analysis has been performed based on the assumption that the probability of returning to the zone visited immediately before (\(\theta\)) is different from the probability of entering one of the remaining zones (\((1-\theta)/3\)) [9; 12; 13], as shown in Figure 4. Even in real situations, the probability of returning to the zone visited immediately before would be greater than that of symmetric random walk (\(\theta>1/4\)); hence, this assumption was validated to some extent. Although mathematical analysis became very difficult due to the introduction of the probability of returning to the zone visited immediately before (\(\theta\)), it was shown that the higher \(\theta\) was proportional to a lower location registration cost, and 2 ZR was ultimately identified to be better in most cases. However, \(\theta\) does not necessarily have to be greater than \(1/4\), and analysis is also possible for \(\theta\) less than \(1/4\). Most importantly, by introducing \(\theta\), analysis is possible no matter what value \(\theta\) has, including the symmetric random walk model.  
Figure 4: Inter-zone mobility model in 2 ZR.  
Figure 3: Symmetric random walk mobility model.  
In this study, we reflected the same movement characteristics as in 2 ZR on 1 ZR to allow for a comparison with 2 ZR under similar circumstances by setting up a proper inter-cell mobility model in a zone composed of \(n\) cells. Finally, we calculated the optimal paging cost of 1 ZR under the 2-step paging method by obtaining the steady-state probability of each cell through the Markov chain model.  
Similar to 2 ZR, if 2-step paging is adopted in the case of 1 ZR, the zone would be divided into two small areas to perform paging. Therefore, to determine the cells that should be selected from which to start paging, we need to calculate the exact probability of being in each cell. However, to calculate the probability of being in each cell, it is first necessary to model the movement characteristics between the cells in the zone. As can be seen in the inter-zone mobility model in the studies on 2 ZR, unlike the symmetric random walk model, the probability of moving to the zone visited immediately before (\(\theta>1/4\)) is different from the probability of moving to a new zone (\((1-\theta)/3\)). These characteristics must be reflected in the movement between the cells in the zone in 1 ZR.
## 3 Characteristics of Dataset
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Frequency**} & \multicolumn{3}{c}{**Ratio \%**} \\ \cline{3-5}  & & **Serious** & **Slight** \\ \hline \multirow{5}{*}{Road surface} & Dry & 264,085 & 35.1 & 64.9 \\  & Wet & 31,596 & 37.0 & 63.0 \\  & Frozen & 2041 & 31.5 & 68.5 \\  & Snow & 1083 & 33.4 & 66.6 \\ \hline \multirow{5}{*}{Road type} & At an intersection & 70,541 & 37.1 & 62.9 \\  & Near an intersection & 50,830 & 31.9 & 68.1 \\  & On a crosswalk & 11,889 & 54.5 & 45.5 \\ \cline{1-1}  & Near a crosswalk & 6037 & 47.5 & 52.5 \\ \cline{1-1}  & Over the bridge & 2916 & 32.8 & 67.2 \\ \cline{1-1}  & Single road & 151,963 & 33.7 & 66.3 \\ \cline{1-1}  & Crosswalk at an intersection & 2508 & 41.7 & 58.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Categories and frequencies of variables (road factor). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Frequency**} & \multicolumn{3}{c}{**Ratio \%**} \\ \cline{3-5}  & & \multicolumn{1}{c}{**Serious**} & **Slight** \\ \hline \multirow{5}{*}{Accident type} & Side collision & 93,841 & 32.0 & 68.0 \\  & Backup collision & 574 & 11.8 & 88.2 \\  & Head-on collision & 10,444 & 41.9 & 58.1 \\  & Rear-end collision & 66,062 & 25.3 & 74.7 \\  & Crossing & 33,684 & 58.0 & 42.0 \\  & Passing on driveway & 7261 & 41.7 & 58.3 \\  & Passing on the edge of the road & 5399 & 35.6 & 64.4 \\  & Passing on the sidewalk & 3998 & 46.5 & 53.5 \\  & Other & 77,542 & 35.2 & 64.8 \\ \hline \multirow{5}{*}{Violation of law} & Non-compliance with safe driving obligation & 158,966 & 35.0 & 65.0 \\  & Not keeping a safe distance & 44,245 & 24.5 & 75.5 \\  & Signal violation & 39,416 & 34.6 & 65.4 \\  & Pedestrian protection obligation violation & 12,384 & 29.6 & 70.4 \\  & Centerline violation & 11,297 & 43.5 & 56.5 \\  & Violation of pedestrian protection obligation & 10,754 & 51.7 & 48.3 \\  & Speeding & 455 & 78.7 & 21.3 \\  & Other & 21,288 & 32.2 & 67.8 \\ \hline \multirow{2}{*}{Perpetrator’s gender} & Male & 254,924 & 35.4 & 64.6 \\  & Female & 43,881 & 34.7 & 65.3 \\ \hline \multirow{5}{*}{Perpetrator’s age} & Underage & 10,424 & 37.4 & 62.6 \\  & Youth & 62,835 & 35.5 & 64.5 \\ \cline{1-1}  & Middle-aged & 106,285 & 36.1 & 63.9 \\ \cline{1-1}  & Old-aged & 89,769 & 34.4 & 65.6 \\ \cline{1-1}  & Elderly & 29,492 & 34.7 & 65.3 \\ \hline \multirow{2}{*}{Victim’s gender} & Male & 224,221 & 33.2 & 66.8 \\ \cline{1-1}  & Female & 74,584 & 41.7 & 58.3 \\ \hline \multirow{5}{*}{Victim’s age} & Underage & 20,436 & 37.3 & 62.7 \\ \cline{1-1}  & Youth & 70,421 & 31.4 & 68.6 \\ \cline{1-1}  & Middle-aged & 88,565 & 31.5 & 68.5 \\ \cline{1-1}  & Old-aged & 89,212 & 36.0 & 64.0 \\ \cline{1-1}  & Elderly & 30,171 & 52.1 & 47.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Categories and frequencies of variables (human factor).
# An Optimal and Stable Algorithm for Clustering Numerical Data
## 3 Proposed Clustering Algorithm with a Constant Seeding Selection
### Computational Complexity
Furthermore, with \(p>0.05\), the performance of the Zk-AMH approach is essentially comparable to those of the fuzzy c-means (\(mean=0.61,95\%\) confidence interval \([0.59,0.62]\)), fuzzy c-means++ (\(mean=0.55,95\%\) confidence interval \([0.54,0.56]\)), k-AMH numeric (\(mean=0.61,95\%\) confidence interval \([0.59,0.62]\)), and k-AMH numeric++ (\(mean=0.60,95\%\) confidence interval \([0.59,0.62]\)) algorithms in terms of average clustering performance. Based on the overall average scores for the combination of all eight datasets, the results provide evidence that the Zk-AMH algorithm is optimal and comparable to the other clustering algorithms. The box plot demonstrated the first piece of evidence in this regard, where the Zk-AMH approach yielded the median value that is almost similar to the other algorithms, such as the fuzzy c-mean and k-AMH algorithms (see Figure 3). Furthermore, the Zk-AMH scored the highest minimum score in 800 experimental runs (1 algorithm \(\times\) 100 runs \(\times\) 8 datasets). Finally, the one-way ANOVA test proved that the Zk-AMH algorithm (a) outperformed the hard clustering approaches such as the k-means and k-means++ algorithms, (b) is comparable to the fuzzy c-means and fuzzy c-means++ approaches, and (c) retained its clustering performance with the original k-AMH algorithm and k-AMH with k-means++ seeding method (k-AMH++). In addition, for the dataset comparison, the Zk-AMH algorithm clearly showed its advantages by producing the highest scores for four datasets, an approximate score for one dataset, and marginally lower values for the other three datasets (see Table 4). Figure 3: Initial comparison among the k-means, k-means++, fuzzy c-means, fuzzy c-means++, k-AMH numeric, k-AMH numeric++, and Zk-AMH algorithms for the combination of all eight datasets considered. ### Cluster Stability

Table 4 presents the performances of all algorithms using the mean, maximum, minimum, and standard deviation. The values in bold are the optimum values (the highest minimum and maximum accuracies and lowest standard deviations) obtained by a particular algorithm for each dataset. The most impressive result regarding the Zk-AMH algorithm is that its standard deviation is zero for all datasets, which means that the mean, minimum, and maximum scores are identical. Based on this result, it can be concluded that the Zk-AMH algorithm produced constant and stable clustering results for every run. The other algorithms show their variability in producing clustering results due to seeding mechanism, even though they were seeded by distinctive objects as in k-means++ seeding. In addition, the Zk-AMH algorithm yielded the highest mean scores for four of the eight datasets, specifically, datasets 1, 4, 7 and 8; approximately the same mean score for dataset
## 3 Results and Discussions
### Simulation Results of Micro Reacting Pipe and Selection of Model Parameters
#### 3.2.1 Selection of Model Parameters and the Three Design Principles
The Re can be increased by enlarging the critical dimension, however, due to the constraint of the second design principle, only increasing the critical dimension will reduce the mixing efficiency. Therefore, fully developed turbulence can only be created by increasing the cross-section's average velocity or decreasing the Re *. One specific way to increase the cross-section's average velocity is to increase the energy supply of external energy sources, such as increasing the pump's output pressure and flow rate, or installing an ultrasonic oscillator on a micro reacting pipe. Multiple pumps are used for multi-stage energy supply, and the micro reacting pipe is placed in an electric or magnetic field to accelerate or rotate charged particles in a fluid by an electric field or magnetic field force, which is the active micro reacting pipe [37]. Figure 8: Variation of the intensity of segregation with mixing time (Is = 0 represents completely homogeneous mixing). However, the active micro reacting pipes' energy consumption and overall device complexity greatly increase due to the need for additional external energy sources. Fortunately, some former studies have also shown that adding obstacles inside the channel can reduce Re * [31]. Therefore, this study used the method of creating complete turbulence by reducing the Re *. Some obstacles in the micro reacting pipe's channel were designed to generate turbulence to improve the mixing efficiency [38].
## 7 Mode-Locking Techniques
### Hard/Soft Aperture Mode-Locking
#### 7.1.1 Hard Aperture  
Figure 5a plots \(\gamma\) (calculated at the end mirror, \(EM1\)) as a function of the crystal position near the end of the first stability zone, \(\delta\lesssim\delta_{1}\). When the crystal is located near the focus, \(\gamma\) is negative, and hence, the Kerr lensing reduces the mode size for ML compared to CW operation. This enables the introduction of a physical (\"hard\") aperture near the end mirror, \(EM1\), to employ loss on the CW mode, while the smaller ML mode passes through the aperture without attenuation. Hard aperture is illustrated in Figure 4a, where the power-dependent stability limit, \(\delta_{1}\), for ML operation is pushed towards higher values of \(\delta\). Hence, the working point for mode-locking is at \(\delta\lesssim\delta_{1}\), where the CW mode is larger than the ML mode.  
**Figure 5. (a)** Kerr strength \(\gamma\) at \(EM1\) as a function of the crystal position, \(Z\), near the stability limit, \(\delta_{1}\). At \(Z=0\), the crystal center is located at a distance \(f_{1}\) from \(M1\). Positive values of \(Z\) correspond to the translation of the crystal away from mirror \(M1\). (**b**) Kerr strength \(\gamma\) as a function of the crystal position, \(Z\), at \(EM2\) near the stability limit, \(\delta_{2}\). At \(Z=0\), the crystal center is located at a distance, \(f_{2}\), from \(M2\). Positive values of \(Z\) correspond to the translation of the crystal away from \(M2\).
## 4 Discussion
### Microhardness and Light-Curing Unit (LCU) Effectiveness  
Research by [PERSON] et al. [12] and [PERSON] et al. [13] demonstrated that polywave LEDs improved the DC and Knoop Hardness Number (KHN) of materials containing TPO, while monowave LEDs showed a higher DC in CQ-based composites. However, [PERSON] et al. [14] reported that dual-peak LEDs performed similarly to single-peak LEDs and halogen LCUs for co-initiator composites, with some limitations in achieving sufficient DoC in deeper layers.  
In our study, the radiant exitance measured was 12 J/cm\({}^{2}\) for Elipar Trilight (QTH), 21 J/cm\({}^{2}\) for Demi Ultra (single-wave), 20.6 J/cm\({}^{2}\) for BluePhase Style (multi-wave), and 20 J/cm\({}^{2}\) for Valo (multi-wave). Irradiation times were in line with manufacturer's recommendations for all materials. Specimens cured with the halogen (Ha) LCU showed significantly lower microhardness values at the 4 mm depth compared to the other LCUs, except for SDR and X-tra Base (XT). This is consistent with the lower energy output of the Ha LCU, resulting in reduced energy absorption by the composite. According to the reciprocity law, the degree of cure (DoC) depends on the total energy (irradiance \(\times\) exposure time), with lower irradiance or exposure time leading to insufficient curing [6; 7; 39; 40]. For the SDR material cured with the QTH LCU, no significant differences were observed compared to the rest of the curing units, and for XT, higher values than the polywave curing units were observed. This finding could be explained due to the high translucency of these materials. Bulk-fill composites with a higher percentage of the monomer TEGDMA, such as SDR, increase the flexibility and reactivity of the organic matrix and are prone to a higher degree of conversion [17; 18; 41; 42]. In the present study, this was verified for the flowable bulk-fill composites tested, i.e., SDR, Filtek Bulk Fill, Xtra-Base, and Venus Bulk Fill, in terms of microhardness ratios higher than 80% of the top microhardness values at the 4 mm depth for all LCUs and the 5 mm depth for all LED LCUs [5; 7; 39; 40; 43]. Further, [PERSON] et al. [34] reported that a QTH LCU produced a higher bottom-to-top hardness ratio than LED LCUs when curing a 2 mm thick bleach shade of regular composite resin, while [PERSON] et al. [9] found that the Valo multi-peak LCU significantly increased the DC and microhardness in 2 mm bulk-fill composites with additional photoinitiators (e.g., Tetric Bulk Fill) compared to a monowave LCU, while composites containing only CQ did not show differences. These findings suggest that high-translucency materials can adequately cure with lower-energy LCUs, whereas polywave LCUs do not always offer an advantage for all materials.
## 3 Results and Discussion
### Results and Discussion of Experiment 2
\(\rm D_{flow}^{SIS2}=|0.134169-0.134236|=6.6999\times 10^{-5}\ vch/s\), \(\rm D_{flow}^{SES3}=|0.134236-0.133416|=0.000819\ vch/s\), and \(\rm D_{flow}^{SIS3}|0.134169-0.133416|=0.000753\ vch/s\), hence the capacities of S1 and S2 are \(\rm D_{flow}^{SIS3}=0.6999\times 10^{-5}\ vch/s\). Figure 8: Period index vs. network density: experiment 2. Figure 7: Period index vs. network speed: experiment 2.  
similar, the capacities of S2 and S3 are similar, and the capacities of S1 and S3 are similar. \(\mathrm{D_{density}^{S1S2}}=|0.018634-0.0202025|=0.0015685\) veh/m, \(\mathrm{D_{density}^{S2S3}}=|0.0202025-0.0215532|=0.00135069\) veh/m, and \(\mathrm{D_{density}^{S1S3}}|0.018634-0.0215532|=0.0029192\) veh/m, hence the critical densities of S1 and S2 are similar, the critical densities of S2 and S3 are similar, and the critical densities of S1 and S3 are dissimilar. The results presented above are summarized in Table 3. According to these results, the vehicles' top speeds distribution is a variable that can affect the MFD; the MFDs between S1 and S3 are dissimilar. Figure 10: Network density vs. network speed: experiment 2. Figure 9: Period index vs. network flow: experiment 2. In Table 4, the mean and standard deviation of the network speed, network density, and network flow for simulations S1, S2, and S3 in experiment 2 are presented across three phases. The distribution (set with speedFactor) producing the greater percentage of higher top speed values (greater than or equal to 16.66 m/s) is the one used in S1, followed by S2 and finally S3. In Figure 10, it can be observed that the DS pattern of S1 is above the DS pattern of S2, and that the DS pattern of S2 is above the DS pattern of S3. Thus, for an x-axis value (network density) the higher y-axis value (network speed) is presented by S1, followed by S2 and then S3. The observed maximum density value is similar for S2 and finally S3. Figure 11: Network speed vs. network flow: experiment 2. Figure 12: Network density vs. network flow: experiment 2.  
and S3. For S1, it is 0.0211 veh/m; for S2, it is 0.024 veh/m; and for S3, it is 0.0236 veh/m. From period 200 (the last period in which vehicles were generated), in S1 it takes 3 more periods for the network to be empty, in S2 it takes 4 more periods, and in S3 it takes 9 more periods. Figure 12 shows that the critical density is not (clearly) reached and surpassed in any simulation, so there is no congestion. Another indicator that no significative congestion occurs in any simulation is that links are cleared a few periods after the last period in which vehicles were generated. In addition, Figure 11 shows that the speed does not decrease along the flow. Figure 8 shows that the difference in the network density is evident in a range of phase three between S1 and S2 and between S1 and S3.
# No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction
## 4 Experiments and Analysis
### Performances Comparison  
We compared MSIQA with three FR-IQA methods (PSNR, SSIM [7], FSIMc [8]) and ten NR-IQA methods (BLINDS-II [9], BRISQUE [10], CORNIA [22], Kang [26], BIECON [14], Bosse [27], DeepBIQ [28], DIQA [15], Hallucinated [38], QualNet [39]). The test results of the MSIQA model on the LIVE data set and TID2013 data set are shown in Table 4. The test results of the MSIQA model for different distortion types in the LIVE data set are shown in Table 5. For each test, the best two models are shown in bold.  
It can be seen from the results that our method performs very well on the LIVE dataset. The performance on the TID2013 dataset is not very good. We analyzed some images with large prediction errors and found that there are two reasons for this: (1) the TID2013 dataset contains non-real synthetic images, and the model is not designed with the images which synthetic and semantic information lacking. (2) Some of the distortion types in the TID2013 dataset are performed by changing the color of the image. Our model believes that in the case of no distortion of image details, such distortion types have little impact on the quality of the image, but have a greater impact on the aesthetic quality of the image. But the dataset is manually annotated, and humans give lower scores to images with unreasonable colors.