esa-sceva/satcom-chunk-collection huggingface dataset:
# Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
## 4 Experimental
### Experiments in Low-Illumination Environments
The proposed tracker was also compared with the SOTA trackers BACF [12] (orange) and ECO [14] (cyan) at the time and the latest ADT [36] (purple). In the 165 th frame (background similarity), the LCT and ECO fail, following the car after the 320 th frame. After the 810 th frame (dark area), the LRCF, fast DSST, and BACF remain in their original positions, and only LLCT tracks the target to the last frame. Table 3 shows the average operating speed of the trackers and their performance. The LLCT-E and LLCT-S (LIME-Exact and LIME-Speed version) have the lowest processing speed of all trackers for the 720P resolution images, and the LLCT-A (accelerated version) maintains the real-time speed. The LLCT trackers have high accuracies and success rates, and the other baseline trackers except ADT fail to track in a dark environment or with similar backgrounds. Thus, the proposed method outperforms the other SOTA trackers. Figure 10: Comparison of the FPS of the algorithms and the cost of several image enhancement methods with different image sizes. Figure 12 shows that the image details obtained from the LIME [43] are excellent, the noise control is effective, and only one overexposed area occurs on the left side of the image. The proposed method does not perform well for image noise reduction, and almost all areas other than the low-illumination area are overexposed. In practical application, LIME better addresses the restoration of image details. Figures 11 and 12 indicate that the accurate enhancement method has almost the same target tracking performance as the \"rough\" enhancement method when the target is in a dark area. Additionally, the proposed enhancement method has lower computational complexity and reasonable real-time performance of the tracker. This means that the tracker does not require hyperfine samples and can track the target accurately according to the difference between the target and the background. Figure 11: Field experiment in the underground garage. The proposed method performed well in a low-illumination environment. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Tracker** & **LLCT-A** & **LLCT-E** & **LLCT-S** & **LRCF** & **f-DSST** & **LCT** & **ECO-HC** & **BACF** & **ADT** \\ \hline FPS & 38.9 & 1.00 & 4.49 & 35.6 & 146 & 48.59 & 55.69 & 40.36 & 38.04 \\ DP & 100\% 1 & 100\% & 100\% & 68.8\% & 32.5\% & 28.6\% & 28.4\% & 67.1\% & 99.4\% \\ OP & 93.2\% & 95.6\% & 96.1\% & 17.1\% & 27.4\% & 24.1\% & 17.7\% & 46.0\% & 93.1\% \\ \hline \hline \end{tabular}  
* Red: the best; Green: the second; Blue: the third. OP: average overlap score with the threshold of 0.5, DP: average center location error with the threshold of 20 pixels.  
\end{table} 
Table 3: The average operating speed and performance. Figure 12: This is a figure. Schemes follow the same formatting.
# Research and Progress on Truing and Sharpening Process of Diamond Abrasive Grinding Tools
## 3 Dressing Method of Diamond Abrasive Grinding Tools
### Sharpening Technology of Diamond Abrasive Grinding Tools
#### 3.2.2 Consolidated Dressing Tool Sharpening Method  
Consolidation dressing tool sharpening methods include the oilstone sharpening method, the ordinary grinding wheel grinding sharpening method, and the silicon carbide (SiC), or corundum block cutting sharpening method. These methods mainly utilize the difference in hardness between the sharpening tool and the diamond abrasive grains to make the abrasive grains fall off by cutting the bond material between the abrasive grains. The dressing effect is shown in Figure 10 [34]. [PERSON] et al. [35] employed internal cooling grinding tools and an electric corrosion process to dress the abrasive grains, which could achieve the ideal sharpening effect. [PERSON] et al. [36] dressed the diamond abrasive grinding wheel through three-axis linkage circular arc interpolation movement, which had a simple structure, high dressing accuracy, and high processing efficiency. [PERSON] et al. [37] used three-axis linkage machine tools and sharpening tools, such as carbide grinding rods, to dress resin-bonded V-shaped diamond abrasive grinding wheels to achieve accurate in situ dressing. [PERSON] [38] designed a twist drilling device for dressing diamond abrasive grinding wheels with a large dressing area, high efficiency, and high accuracy. [PERSON] et al. [39] employed oilstone as a grinding tool to remove the bond material of the diamond abrasive grinding wheel and drove the oilstone to dress the end face of the grinding wheel byrotating the dressing shaft. This design was effective, low cost, and showed an ideal dressing effect.  
Through the above research at home and abroad, it was found that, compared with the free abrasive grain sharpening method, the dressing efficiency of the consolidated dressing tool sharpening method was improved to a certain extent, by approximately 35\(\sim\)45%. The surface roughness of the grinding wheel after sharpening was reduced and a large number of new abrasive grains were exposed. Although some progress has been made in the consolidation dressing tool sharpening method, it cannot be used for large cutting quantities and the sharpening efficiency is low. This is still an aspect that needs to be improved in the development of the consolidation dressing tool sharpening method in the future.
## 2 Theoretical Model
### Spectrum Average Mutual Coherence Function of Localized Wave of Bessel-Gaussian Amplitude Envelope  
To obtain the received probability distribution of vortex modes for the BGLW, now we structure the spectrum average mutual coherence function (SAMCF) of BGLW in turbulent seawater. The SAMCF of output BGLW can be given by following expression [20; 23; 25]  
\[\begin{split}\langle W(\rho,\theta_{1},\theta_{2},z;t)\rangle& =\Big{\langle}u_{p}(\rho,\theta_{1},z;t)u_{p}^{*}(\rho,\theta_{2},z;t)\Big{\rangle}_{oc}\\ &=\frac{T_{0}^{2}}{4\pi}\iint_{-\infty}^{+\infty}\exp\left[- \mathrm{i}(\omega_{1}-\omega_{2})t\right]\Gamma_{2}(\rho,\theta_{1},\theta_{2},z;\omega_{1},\omega_{2})\mathrm{d}\omega_{1}\mathrm{d}\omega_{2},\end{split} \tag{8}\]  
where \(\left\langle...\right\rangle_{oc}\) is the ensemble average of seawater turbulence, \(\Gamma_{2}(\rho,\theta_{1},\theta_{2},z;\omega_{1},\omega_{2})\) represents the two-frequency mutual coherence function (TFMCF) of BG vortex wave in seawater turbulence and should be written as  
\[\begin{split}\Gamma_{2}(\rho,\theta_{1},\theta_{2},z;\omega_{1},\omega_{2})=&\exp\left[-\frac{T_{0}^{2}}{4}(\omega_{1}^{2}+ \omega_{2}^{2})\right]\left\langle u(\rho,\theta_{1},z,\omega_{1})u^{*}(\rho, \theta_{2},z,\omega_{2})\right\rangle_{oc}\\ =&\Gamma_{2}^{0}(\rho,\theta_{1},\theta_{2},z;\omega _{1},\omega_{2})\langle\exp\left[\mathrm{i}S(\rho,\theta_{1},z,\omega_{1})- \mathrm{i}S^{*}(\rho,\theta_{2},z,\omega_{2})\right]\rangle_{oc},\end{split} \tag{9}\]  
where \(\Gamma_{2}^{0}(\rho,\theta_{1},\theta_{2},z;\omega_{1},\omega_{2})=\left\langle u _{m_{0}}(\rho,\theta_{1},z,\omega_{1})u_{m_{0}}^{*}(\rho,\theta_{2},z,\omega_ {2})\right\rangle\exp\left[-\frac{T_{0}^{2}}{4}(\omega_{1}^{2}+\omega_{2}^{2})\right]\) is the TFMCF of BG vortex wave without turbulence interference.
# KACFormer: A Novel Domain Generalization Model for Cross-Individual Bearing Fault Diagnosis
## 3 Experiments
### Task Setups  
#### 3.1.1 Task 1 on PU Dataset  
The dataset for the first cross-individual task is sourced from the PU dataset from the Paderborn University Konstruktions und Antriebstechnik (KAt) data center [26]. Vibration signals from the bearing housing are collected using piezoelectric accelerometers at a sampling frequency of 64 kHz. The experimental test platform, including a drive motor, load motor, flywheel, torque sensor, and module for testing bearing vibration data, is shown in Figure 8.  
A total of 32 different bearings are included in the PU dataset. These bearings vary in terms of their manufacturer, type, and severity of damage, aligning with the objectives of our cross-individual study. The bearing faults in the dataset are generated using two primary methods: electrical discharge machining (EDM) and life acceleration treatment. To ensure the experiments closely resemble real-world industrial scenarios, only the data obtained through life acceleration treatment is utilized. The fault positions considered in this study include Healthy (N), Outer Race (OR), and Inner Race (IR). Additionally, to avoid an imbalanced dataset, only a subset of the provided bearings is used. The chosen bearings for the training and validation sets are K001 (N), K002 (N), K003 (N), KA04 (OR), KA15 (OR), KA16 (OR), KI04 (IR), KI14 (IR), and KI16 (IR). For the testing sets, we selected K006 (N), KA22 (OR), and KI21 (IR).  
All data is collected under the same working conditions, with a rotational speed of 900 rpm, load torque of 0.7 Nm, and radial force of 1000 N. The official working condition code is \"N09_M07_F10\". Detailed information about the chosen bearings is provided on Table 1.  
Figure 8: Test platform for PU dataset.
## 4 Experimental Results  
A new model based on deep learning was developed in this study, which was conducted for the classification of fabric defects. The generated model's noise resistance was evaluated by manually adding a particular amount of noise to the data. Each technique in our investigation had the following hyper-parameter values: 0.001 learning rate, 32 batch sizes, 100 iterations, Adam optimization algorithm, and input pattern size normalized to 28 \(\times\) 28 size.
## 3 Field Geological Investigations
### Landslide Inventory  
Generally, it is difficult to identify the characteristics of a loess landslide because the sliding mass lacks overall cohesion [9]. However, the landslide substrate in the study area is a river terrace with flat terrain. After the landslide occurs, the outline of the landslide accumulation area becomes relatively obvious, and the landslide deposits can remain for several months or even years. Additionally, landslides in this area are relatively concentrated, distributed in a banded pattern along the edge of the platform, making them easily identifiable in high-resolution optical images. We collected historical satellite images embedded in Google Earth to analyze the evolution of landslides over the past 20 years. Given some discrepancies between satellite images obtained at different times, we selected the July 2003 image as the reference. We chose 22 sets of optical images between July 2003 and March 2022 for comparative analysis, prioritizing those that were easiest to identify. Given the unique geological characteristics of the loess region, some errors may be inevitable. Although we selected high-quality images that clearly show landslide changes, discrepancies in image quality could still influence the precise timing of landslides. Therefore, we used landscape changes at different time points to infer the timing of landslide occurrences, minimizing errors as much as possible. However, differences in image capture dates and quality may lead to slight deviations in the precise timing of landslides. Based on the quality of the images, 22 sets of optical images between July 2003 and March 2022 were selected for comparative analysis to visually interpret the evolution of landslides. As shown in Table 11, Appendix A, a total of 76 landslides were interpreted, including 16 flowslides, 45 slides, and 15 falls (Figure 3).  
Figure 2: Three major landslide types in the study area.
## 3 The Median Filter Method
### Improved Method of Median Filter  
The key problem with the median filtering method is determining the filter window width according to the signal characteristics. On the one hand, the window should not be too wide, or the details of useful signals will be lost; on the other hand, the window should not be too narrow, or too much impulse noise will remain. In order to filter out impulse noise without losing useful signal, the window width should be double that of the pulse width. If the sampling interval of vibration signal is \(T_{s}\) and the duration of impulse noise is \(L_{s}\), the reasonable window width \(L_{d}\) can be expressed as follows:  
\[L_{d}=2L_{s}F_{s} \tag{11}\]  
where \(F_{s}=1/T\) is the sampling frequency. According to [16], the impulse noise is mainly continuous: \((5.36\pm 2.48)\times 10^{-4}\) s. For the convenience of calculation and without loss of useful signal, \(L_{s}~{}=~{}5.00~{}\times~{}10^{-4}\) s is assumed. It can be seen from Equation (11) that the window width is adaptively adjusted with \(F_{s}\), which is more conducive to eliminating impulse noise and retaining useful signals.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 3 Factors Influencing Sticking in MXene Electrodes
### Morphology and Size of MXene Flakes
#### 3.1.4 Surface Area/Volume Ratio  
The surface area/volume ratio of MXene flakes plays a crucial role in sticking. Higher surface area allows for more interaction sites, resulting in increased adhesion forces between flakes. As a result, MXene flakes with a higher surface area/volume ratio are more prone to sticking. However, [PERSON] et al. have reported a porous MXene of nitrogen-doped Ti\({}_{3}\)C\({}_{2}\)  
Figure 3: SEM image of Ti\({}_{3}\)C\({}_{2}\) (**left**) and TEM micrograph of Ti\({}_{3}\)C\({}_{2}\)T\({}_{x}\) flakes (**right**) [45]. Reprinted from [PERSON] et al., Chin. Chem. Lett. Copyright (2020), with permission from Elsevier.  
which is conditioned during electrospinning in such a way to promote self-stacking and facilitate electrolyte penetration [49]. The as-prepared N-MXene electrodes, exhibiting leaf-like shapes (Figure 5a), demonstrated specific capacitance about three times higher than MXene. Such a high capacitance can be ascribed to the combined contributions of the surface Faraday pseudocapacitance and electric double-layer capacitance that contribute equally in the case of a porous structure. As can be seen from Figure 5b, the application of melamine-formaldehyde (MF) microspheres as a template and nitrogen source results in the formation of a 3D porous design hindering the spontaneous stacking of MXene, which is not destroyed even after electrospinning on flexible substrate.  
In the analyzed range, the distribution ratio reveals that 62.5% of the nanofibers have a size falling between 200 and 300 nm, with an average diameter of approximately 230 nm.  
Understanding the factors influencing sticking in MXene electrodes, particularly the morphology and size of MXene flakes, is essential for developing effective strategies to prevent sticking and enhance the performance of MXene-based supercapacitors. By controlling these factors, one can minimize agglomeration and improve dispersion and overall electrode stability.
## Materials and Methods
### Time Matching  
Time matching between in situ observations and satellite data is another important problem for remote sensing of lake water quality [36]. Owing to the long water exchange cycle and stable aquatic conditions in highland lakes, lake transparency does not freely change drastically in a short period of time. To evaluate the impact of time matching on the inversion accuracy of the model and to obtain a reasonable time matching window, we extracted a series of datasets to represent different time windows between in situ and satellite data, including 1, 3, and 7 days. The SD statistics of 1-day, 3-day, and 7-day time window matching data are shown in Table 2. These were used separately as the dataset for model training.
## 2 Materials and Methods
### Simulation of TDS Experiment  
The TDS experiment was simulated using holographic reconstruction. The reconstruction method was based on evaluating the Rayleigh-Sommerfeld diffraction integral by use of the fast Fourier transform. The forward propagation of a complex wave field with wavelength \(\lambda\) from a plane at \(z_{1},f(x,y,z_{1})\), to a plane at \(z_{2},f(x,y,z_{2})\) is obtained by [23; 24].  
\[f(x,y,z_{2})=\mathcal{F}^{-1}\Big{\{}F(u,v)e^{2\pi iw(u,v)(z_{2}-z_{1})}\Big{\}} \tag{1}\]  
\[w(u,v)=\sqrt{\lambda^{-2}-u^{2}-v^{2}} \tag{2}\]  
where \(F(u,v)=\mathcal{F}\{f(x,y)\}\) is the two-dimensional Fourier spectrum, and \([u,v,w(u,v)]\) are the Fourier frequencies for \((x,y,z)\) at \(z=z_{1}\). Back propagation from a given diffraction field to a source field can be obtained by the complex conjugate in Equation (1).  
The simulation of the TDS experiment was conducted by propagating the THz field in steps from the source to the detector as illustrated in Figure 4.  
Starting with a point source or a source with a Gaussian intensity distribution of width \(d_{s}\), the field is propagated to a parabolic mirror of diameter \(d_{\text{mirror}}\) at distance \(f\) corresponding to the focal length of the mirror. The ratio of \(d_{\text{mirror}}/f\) defines the numerical aperture. The field is then mirrored (by taking the complex conjugate of the field) and  
Figure 4: Simulation scheme of TDS experiments (not to scale).  
Figure 3: Resolution sample (**left**), wood sample #1; LR spruce (**middle**), wood sample #2; RT spruce from branch (**right**), scale in cm; wood directions R, T, and L are shown in the figure.  
propagated by \(f-\Delta z\) to the sample position. The field is then modified by the transmission function of the sample at position \(x\) and \(y\) and propagated by \(\Delta z\) to the focal point of the experiment. Here, a metal mask with an aperture of diameter \(d_{\text{mask}}\) is applied to the field. The field is then propagated by \(f\) to a parabolic mirror. The field is mirrored again and propagated to the detector, where the complex intensity is integrated. Parameters of the simulations are the sample position in \(x\) and \(y\), the wavelength \(\lambda\), \(d_{\text{mask}}\), and \(\Delta z\).
# A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks
## 4 System Architecture and Security Model
### Security Model  
In our security model, the base station (\(B\)) is assumed to be a trusted entity, which means it knows the transmitted information but does not leak any sensitive information. Herein, we mainly focus on the threats caused by users, \(SNS\), and \(LS\).  
Users are assumed to be dishonest and try to get unauthorized information beyond the scope of their access privileges as much as possible. Thus, by querying other users' locations in location-sharing systems, they want to infer some sensitive information (e.g., locations) of their friends or strangers such as home address.  
The social network server (\(SNS\)) is supposed to be \"honest-but-curious\", which means \(SNS\) will execute the proposed protocol honestly but try to get sensitive information such as users' actual locations from the interactive communications. Previous researches such as [6; 12] also use base stations in their system architectures. In addition, the researches above also assume that the social network server can not identify base stations by observing their IP addresses in the connections. However, if a system insider (e.g., an employee) colludes with \(SNS\), the approximate region of users who send requests for location-sharing services can be figured out since the locations of base stations are fixed. Thus, with the help of insiders, we assume that \(SNS\) can infer the approximate region of users since users have to use base stations to submit location-sharing services.  
The location server (\(LS\)) is also assumed to be \"honest-but-curious\", which means \(LS\) will execute the proposed protocol honestly but try to get sensitive information such as users' social network topology or identities.  
However, the collusion between \(SNS\) and \(LS\), which means they collude together to gain users' sensitive information at the same time, is beyond our security assumption. In addition, information leakage and eavesdropping during transmission are also beyond the scope of our scheme.
## 4 Distributed Clustering Algorithm for the UAV Swarm
### Selection of the Best Switch Operation
```
0: The best switch operation selection \(\sigma_{k,l^{*}}(\mathcal{P}_{s^{*}})\) of UAV \(i\) // Selection of candidate switch set \(\mathcal{S}\)
1: Initialize candidate switch set with \(\mathcal{S}=\{i\}\)
2: Find neighbors of \(i\) belongs to the same cluster and the same group, i.e., \(\widehat{\Omega}_{i}(C_{k})=\left\{f^{\prime}\middle|\tilde{f}^{\prime}\in \Omega_{i}\cap C_{k},\tilde{g_{i}}=g_{i}\right\}\)
3:for each \(j\in\widehat{\Omega}_{i}(C_{k})\)do
4:\(\mathcal{P}_{j}=\{j\}\cup\widehat{\Omega}_{j}(C_{k})\)
5:\(\mathcal{S}^{\prime}=\mathcal{S}\cup\mathcal{P}_{j}\)
6:// Selection of switch operation set \(\mathcal{O}\)
7: Initialize switch operation set with \(\mathcal{O}=\emptyset\)
8:for each \(\mathcal{P}_{s}\in\mathcal{S}\)do
9:if\(C_{k}\backslash\mathcal{P}_{s}\) satisfies the constraints then
10:for each neighbor cluster \(C_{l}\) of \(C_{k}\)do
11:if\(C_{l}\cup\mathcal{P}_{s}\) satisfies the constraints and \(\mathcal{R}(\sigma_{k,l}(\mathcal{P}_{s}))>0\)then
12:\(\mathcal{O}=\mathcal{O}\cup\sigma_{k,l}(\mathcal{P}_{s})\)
13:// Selection of the best switch operation \(\sigma_{k,l^{*}}(\mathcal{P}_{s^{*}})\)
14:if\(\mathcal{O}\
eq\emptyset\)then
15:\(\sigma_{k,l^{*}}(\mathcal{P}_{s^{*}})=\underset{\sigma_{k,l}(\mathcal{P}_{s}) \in\mathcal{O}}{\arg\max}\mathcal{R}(\sigma_{k,l}(\mathcal{P}_{s}))\)
16:else
17:\(\sigma_{k,l^{*}}(\mathcal{P}_{s^{*}})=\emptyset\)
18:return\(\sigma_{k,l^{*}}(\mathcal{P}_{s^{*}})\)
```  
**Algorithm 1** The best switch operation selection of the \(i\)th UAV, which is in cluster \(C_{k}\) and group \(g_{i}\)  
In Algorithm 1, assuming that UAV \(i\) belongs to group \(g_{i}\) and is in cluster \(C_{k}\), we first select the candidate set of UAV \(i\). The candidate switch set consists of two types and can be selected through heuristic rules. Denote the neighbors of \(i\) in the same cluster and the same group as \(\widetilde{\Omega}_{i}(C_{k})=\left\{\tilde{f}^{\prime}\middle|\tilde{f}^{ \prime}\in\Omega_{i}\cap C_{k},\tilde{g_{i}}=g_{i}\right\}\). 1. The singleton \(\{i\}\) can be one candidate set. 2. Each \(j\in\widehat{\Omega}_{i}(C_{k})\) and its neighbors \(\widehat{\Omega}_{j}(C_{k})\) can form a candidate set. The candidate switch set should at least contain the UAV itself. The second type of candidate switch set aims for the UAVs belonging to the same task group to transfer together. After obtaining the candidate switch set, select the switch operations with positive gains that satisfy the cluster constraints to construct the switch operation set \(\mathcal{O}\). The switch operation with the largest switch operation gain is the best one, which is denoted as \(\sigma_{k,l^{*}}(\mathcal{P}_{s^{*}})\).