esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Distribution IoT Tasks Online Scheduling Algorithm Based on Cloud-Edge Dependent Microservice
## 3 Task Scheduling Model for Distribution Stations under Cloud-Edge Collaboration
### Mapping of Microservices to Containers  
According to the correspondence between microservices and containers, each microservice can correspond to a container to handle it. The mapping relationship between microservices and containers can be represented as \(\mathrm{V_{total}}\rightarrow\mathrm{C_{total}}\), where \(\mathrm{V_{total}}\) is the set of all microservices and \(\mathrm{C_{total}}\) is the set of all containers. For any \(\mathrm{v}\in\mathrm{V_{total}}\) and \(\mathrm{c}\in\mathrm{C_{total}}\), \(\mathrm{map}(\mathrm{v})=\mathrm{c}\) represents the microservice \(\mathrm{v}\) that will be processed by container \(\mathrm{c}\).  
Assuming that the cloud and edge have images of all containers, there are three scenarios when microservices are processed by the corresponding containers. (1) The container corresponding to \(\mathrm{v}\) is running (\(\mathrm{c_{State}}=1\)). Assuming that each container can only process one microservice it is responsible for at a certain time period, when a large number of similar microservices arrive, \(\mathrm{v}\) needs to enter the container waiting queue to wait for execution. (2) The container corresponding to \(\mathrm{v}\) is not running and the edge device is not resource constrained (\(\mathrm{c_{State}}=0\wedge\mathrm{A}<\mathrm{M}\)). Disregarding the container initialization configuration time, \(\mathrm{v}\) can be executed immediately. (3) The container corresponding to \(\mathrm{v}\) is not running and the edge device is resource constrained (\(\mathrm{c_{State}}=0\wedge\mathrm{A}=\mathrm{M}\)). \(\mathrm{v}\) needs to wait for the rest of the containers to enter the idle state to release the edge device resources before being executed. Assuming that the resources of the cloud are sufficient, all three cases need to be considered in the edge device, and only the first two cases need to be considered in the cloud.
## 4 Applications of InSAR in Landslide Monitoring
### Recent Advances in Landslide Monitoring with InSAR
#### 4.2.3 Integration of InSAR for Landslide Failure Time Prediction
Recently, data-driven models and mathematical approaches have shown promising accuracy in predicting landslide displacement based on InSAR measurements [71; 72; 94; 95; 96]. This extends possibilities to integrate displacement prediction with failure time forecasting. That is, failure time predicting models could be applied based on predicted displacement patterns, offering practitioners more time to plan and implement preventive measures. However, the feasibility of this approach has yet to be explored. This method would depend more on the accuracy of displacement predictions, particularly in the accelerated deformation stage.
## Framework  
### Introduction of Framework  
The proposed framework for image fusion shown in Figure 1 has three main steps. All image patches are clustered into different groups in the first step. Then each image patch group is learned by a sub-dictionary using the SCC algorithm [24] and these sub-dictionaries are combined into an integrated dictionary. Finally, the learned dictionary is used for image fusion. The details of each algorithm and method will be explained in the following paragraphs.
## 3 Simulation Results and Analysis
### Distribution of Mises Equivalent Stress for Shaft  
As depicted in Figure 13, the distribution of Mises equivalent stress along the shaft wall was analyzed for MSDs of the horsehead roadway's roof at 0.5 m, 1.0 m, and 1.5 m. The results show that the Mises equivalent stress on the shaft wall does not exceed 50 MPa, regardless of the subsidence displacement magnitude. This observation correlates with the material properties of the C50 concrete lining employed in the shaft wall construction, which has an approximate strength of 50 MPa (refer to Section 2.2). Hence, when the Mises stress reaches the threshold of 50 MPa, it signifies the onset of plastic deformation and potential failure at that location of the shaft wall [12; 39; 42]. Consequently, the analysis indicates that the widest yield range in the shaft wall corresponds to a subsidence displacement of 1.5 m, with a plastic yield state extending up to 63 m above the horsehead roadway.  
Figure 12: Three directional stress field of shaft (MPa) (\(d_{\text{max}}\) = 1.5 m).  
Figure 13: Distribution of Mises equivalent stress along the shaft wall.  
Upon reaching subsidence displacements of 1.0 m and 1.5 m in the horsehead roadway, a more pronounced intermittent pattern of plastic yield emerges along the upper portion of the shaft wall, with exceptions noted in the intervals at 35-40 m, 45-62 m, and above 65 m from the roadway's roof, where no plastic failure is observed. Conversely, plastic failure is evident at 40-45 m and 62-65 m from the horsehead way, which, as per Table 1, corresponds to mudstone strata (highlighted in bold in the table). This suggests that the weaker mechanical properties and lower load-bearing capacity of these mudstone layers contribute to significant deformation, heightening the risk of shaft wall damage. Notably, the extent of the plastic yield intervals is reduced when the subsidence displacement reaches 1.5 m compared to 1.0 m. This observation substantiates the premise that greater subsidence displacement of the horsehead roadway's roof correlates with an expanded damage zone within the shaft.
# Main Issues in Big Data Security
## 4 Analysis of Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Availability**} & \multicolumn{2}{c}{**Confidentiality**} & \multicolumn{2}{c}{**Integrity**} & \multicolumn{2}{c}{**Privacy**} & \multicolumn{2}{c}{**Other**} \\ \cline{2-11}  & **Prob.**1 & **Sol.**2 & **Prob.**1 & **Sol.**2 & **Prob.**1 & **Sol.**2 & **Prob.**1 & **Sol.**2 & **Prob.**1 & **Sol.**2 \\ \hline
**Infrastructure Security** & 16 & 74 & 7 & 15 & 11 & 33 & 11 & 50 & 27 & 56 \\ Security for Hadoop & 1 & 16 & 1 & 7 & 1 & 11 & 0 & 14 & 11 & 28 \\ Availability & 13 & 46 & 5 & 3 & 6 & 6 & 4 & 6 & 0 & 0 \\ Architecture Security & 0 & 8 & 0 & 3 & 1 & 9 & 3 & 17 & 10 & 8 \\ Authentication & 1 & 1 & 1 & 2 & 2 & 5 & 2 & 7 & 4 & 12 \\ Communication Security & 1 & 3 & 0 & 0 & 1 & 2 & 2 & 6 & 2 & 8 \\
**Data Privacy** & 5 & 7 & 13 & 45 & 6 & 17 & 36 & 108 & 10 & 43 \\ Cryptography & 0 & 2 & 2 & 11 & 1 & 5 & 4 & 25 & 4 & 18 \\ Access Control & 0 & 2 & 1 & 4 & 0 & 1 & 6 & 17 & 5 & 21 \\ Confidentiality & 5 & 3 & 10 & 28 & 5 & 9 & 2 & 9 & 0 & 0 \\ Privacy-preserving queries & 0 & 0 & 0 & 1 & 0 & 2 & 7 & 18 & 0 & 4 \\ Anonymization & 0 & 0 & 1 & 0 & 0 & 2 & 25 & 0 & 0 \\ Privacy of Social Networks & 0 & 0 & 0 & 0 & 0 & 13 & 5 & 1 & 0 \\ Differential Privacy & 0 & 0 & 0 & 0 & 0 & 2 & 9 & 0 & 0 \\
**Data Management** & 5 & 7 & 4 & 8 & 3 & 5 & 29 & 34 & 9 & 16 \\ Security at Collection or Storage & 3 & 5 & 2 & 5 & 2 & 4 & 8 & 15 & 5 & 8 \\ Policies, Laws or Government & 0 & 0 & 0 & 1 & 0 & 0 & 19 & 11 & 2 & 3 \\ Sharing Algorithms & 2 & 2 & 2 & 3 & 1 & 1 & 2 & 8 & 2 & 5 \\
**Integrity and Reactive Security** & 6 & 8 & 5 & 9 & 13 & 39 & 9 & 8 & 2 & 8 \\ Integrity & 6 & 6 & 5 & 9 & 13 & 37 & 6 & 5 & 0 & 0 \\ Attack Detection & 0 & 0 & 0 & 0 & 0 & 2 & 3 & 2 & 2 & 7 \\ Recovery & 0 & 2 & 0 & 0 & 0 & 0 & 1 & 0 & 1 \\ \hline \hline \end{tabular} 1  
\end{table} 
Table 2: Combined results. Figure 7: Papers grouped by main categories. solve the issue by applying variations of traditional techniques that have been adjusted to the inherent characteristics of Big Data. The other two main topics found are researched to a far lesser extent than those mentioned above. While integrity is well covered, there are only a few papers dealing with the problem of recovering the system in the case of a total failure. Furthermore, we discovered that the topic of data management is not dealt with as frequently as it should be. For example, we have not found any security government frameworks that would make it possible to manage the security of a Big Data system throughout its entire life cycle. We believe that this is crucial if the correct deployment of Big Data technology is to be achieved. Related with the quantity of studies found per year, we have detected how the number of papers written by the researchers has been continuously increasing until the year 2015. This can be a consequence of the maturity reached by the Big Data technology. Figure 8 contains a graph with the evolution in the quantity of papers found during the considered period.
# Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
## 4 Joint Resource Allocation in Cognitive LEO SatComm System  
In this section, we will formulate and solve the resource allocation schemes to maximize the sum rate of all IoT users under some constraints. First, we will first investigate the optimal IoT receive power and the optimal joint legacy satellite use and IoT user receive powers. Then, based on the solved optimal power, we will exploit the optimal number of IoT users. Finally, we will discuss the effect of the non-synchronous between the primary and secondary systems.
## 3 Experimental Results
### Experimental System  
As shown in Figure 4a, the experimental system mainly consists of an LED display that is 4 \(\times\) 4 cm\({}^{2}\) for transmitting hidden data while displaying image/video, and the smartphone's CMOS camera with a capture frame rate of 30 fps for VLC signal capturing and data reception. As shown in Figure 4b, the experimental system was tested in a dim environment to avoid interference from other light sources, and the main hardware configurations of the experimental system are listed in Table 1.  
The Arduino microcontroller is used to encode the hidden data via the ABRC scheme and embed it in the VLC signal. The modulated VLC signal is then emitted from an LED display driven by the integrated 74 HC595 8-bit Shift Register. In our experiment, the LED display with 16 \(\times\) 16 LED display points is controlled to display a \"parking\" pattern, and four adjacent LED display points at the bottom right corner of the LED display are selected as a clustered 2 \(\times\) 2 LED-DCC point group, whose area is 12.56 mm\({}^{2}\).  
On the receiver side, an 8-megapixel HUAWEI P30 is employed as a receiver, which captures modulated VLC signals via its CMOS camera and recovers the hidden data with the demodulator APP. The lightweight, back-forth, fast image processing algorithm and the ABRC decoding information are integrated into the demodulator APP of the smartphone. In addition, the exposure mode of the smartphone's CMOS camera is fixed in the demodulator APP to an ISO value of 50 and an exposure time of 1/150 s in the experimental setup.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Hardware** & **Configuration** \\ \hline  & MCU & ATmega328P-PU \\  & Clock Speed & 16 MHz \\  & LED Display Panel Resolution & 16 \(\times\) 16 \\ LED-DCC & LED Display Panel Size & 4 \(\times\) 4 cm\({}^{2}\) (2.2-inch) \\ Transmitter & Area of a LED-DCC Point & 3.14 mm\({}^{2}\) \\  & Area of Clustered 2 \(\times\) 2 LED-DCC & 12.56 mm\({}^{2}\) \\  & Point & 150 fps \\  & LED Display Panel Driver & 74 HC595 8-bit Shift Register \\ \hline  & Smartphone Model & HUAWEI P30 \\ LED-DCC & Operating System & Android 10 \\ Receiver & Processor & Kirin 980 @ 2.6 GHz \\  & Camera Resolution & 3840 \(\times\) 2160 \\  & Capture Frame Rate & 30 fps \\ \hline \hline \end{tabular}
\end{table}
Table 1: The experimental system hardware configuration.  
Figure 4: (**a**) The schematic diagram and (**b**) photography of the long-distance, real-time LED-DCC system experimental setup.
# A Literature Review of Adhesive Systems in Density: Key Components and Their Clinical Applications
## 5 Resin-Dentin Bonding
### Resin Adhesive Application  
The field of application of adhesive systems is very large, and their contribution to the development of more conservative, aesthetic, and biocompatible therapies is obvious.  
These systems are currently in widespread use and not limited to the following applications:  
* Restoration of traumatic and various lesions of classes I, II, III, IV, V, and VI.
* Repair existing restorations (amalgam, composite, crown).
* Bonding fractured fragments of anterior teeth.  
* Sealing of cracks.
* Sealing pits and fissures.
* Desensitization of exposed root surfaces.
* Bonding of ceramic restorations (all-ceramic crowns, inlays, onlays, veneers).
* Improved retention of porcelain fused to metal crowns.
* Bonding of molded or prefabricated metal and fiber posts.
* Reinforcing fragile endodontically treated roots internally.
* Bonding of orthodontic brackets.
* Bonding splints in periodontology [99; 100].
## 3 Results
### Data Analysis in Time Domain  
The diagrams below (Figure 4A,B) present the medians of the stabilographic values in the time domain--the mean COP velocity in the AP direction and the range of COP movement in the AP direction.  
The values of the COP velocity in the AP direction (Figure 4A) increased statistically significantly after a participant closed their eyes and after the introduction of disturbances in VR. Statistically significant differences were present while comparing the OE measurements with measurements using VR and while comparing the CE measurements with tests in the virtual environment, except for 1.4_2, 0.7_before, and 1.4_before. No statistically significant differences occurred in the comparison of values obtained during the measurements conducted in VR. The diagram (Figure 4B) shows an increase in the values of the COP movement range in the AP direction with the application of disturbances in VR in comparison with the values obtained in the real-life environment. An increase occurred in the case of the median values, interquartile distribution, and data distribution. In the case of the median value, statistically significant differences were observed between the OE measurements and the measurements performed in VR, except for 1.4_2, 0.7_before, and 1.4_before. An analogous situation occurred between the CE measurements and measurements with the oscillating scenery 0.7_1.4 and 1.4_1.
## 3 Results and Discussion  
### Accuracy Verification of the Optimized DDARF Program  
#### 3.1.1 Model Parameters and Loading Mode  
This experiment simulates the Brazilian disc-splitting experiment; the model geometric parameters and loading mode of the simulation experiment are shown in Figure 4. The model diameter of the Brazil disc is 150 mm, and a 10 mm-thick steel plate is added to the lower part of the model. The steel plate is fixed with three fixed points. The top of the disc is loaded with concentrated force P.  
The boundary conditions are as follows: a steel plate is placed at the bottom of the Brazilian disc, the steel plate is fixed, and a concentrated force P is applied at the top of the Brazilian disc, which linearly and slowly increases from 0 until the calculation finished and the other parts of the Brazilian disc are free.  
Mechanical parameters of the Brazilian disc model are shown in Table 1: the elastic modulus of the lower steel plate is 100 GPa and the Poisson's ratio of the steel plate is 0.3.  
The optimized DDARF program is used for modeling and calculation. In order to observe the initiation, expansion and penetration of splitting cracks, the grids in the middle part of the Brazilian disc model are dense, while the sparse grid is still used on both sides of the model to improve the calculation efficiency, as shown in Figure 2. A concentrated force, P, was applied to the top of the Brazilian disc model, and its value increased linearly from 0. With the increase in P, the failure process of the Brazilian disc model could be observed, as shown in Figure 5. The convergence condition of the calculation is that the displacement generated in one step is less than 0.001 of the displacement generated in the previous step, which is convergence [11].  
Figure 3: Technical flowchart of optimized DDARF generation of heterogeneous grids.
## 6 Conclusions  
1. A new method for calculating the stress of three-dimensional nonconforming rolling contact between rolling elements and the variable diameter raceway was developed. The equations for analyzing the stress components were established. It was determined that the variable diameter raceway structure, spindle speed, friction coefficient and stick-slip coefficient are the main influencing factors of stress distribution. In  
Figure 14: Strain curve of rolling element exiting variable diameter raceway.  
Figure 13: Strain curve of rolling element entering variable diameter raceway.  
addition, it was determined that the contact area is composed of two parts: the stick region and the slip region, the stress mutation often occurs at the boundary. The error of the new method for calculating is less than 15%. The correctness of the calculation method and the accuracy of the three-dimensional stress distribution model are verified, which can predict and characterize the contact stress distribution between the rolling elements and the variable diameter raceway.
2. As the radius of the curvature \(r_{i}\) increases, the contact stress distribution gradually changes from the downward curve of the opening to the upward curve of the opening. When \(R_{i}=25.19\) mm and \(r_{i}=1.5\) mm, the contact stress in the conventional raceway and the variable diameter raceway is equal. As the spindle speed increases, the maximum stress of stress distribution occurs in the reducer raceway. Therefore, the larger \(r_{i}\) should be selected in the design of the variable diameter raceway. It is recommended that the rotation speed below 13,950 r/min is beneficial to reduce the stress impact during the process of rolling elements passing through the variable diameter raceway.
3. With the decrease in the friction coefficient \(\mu\) and the increase in the stick-slip coefficient \(k\), the stress mutation on the contact area of the rolling elements and variable diameter raceway reduces, which reduces the wear between the rolling element and variable diameter raceway. From the point of view of the discrete action, reducing \(\mu\) and increasing the stick-slip coefficient \(k\) appropriately is beneficial to increase the service life of the variable diameter raceway.  
Conceptualization, Y.Z.; methodology and validation, Y.Z. and Q.W.; software, Q.W. and M.W.; formal analysis, Q.W.; investigation, Q.W. and M.W.; resources, Y.Z.; data curation, M.W. and Q.W.; writing--original draft preparation, Q.W.; writing--review and editing, Y.Z. and Q.W.; visualization, Q.W.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 51875142), China.  
Not applicable.  
Please contact the corresponding authors.  
The authors declare no conflict of interest.
# Tensile Properties and Tensile Failure Criteria of Layered Rocks
## 4 Modified Anisotropic Tensile Failure Criterion
### Modified SPW Criterion (MSPW Criterion)  
To some degree, the SPW criterion can reflect the failure modes of layered rocks under direct tension. However, the failure modes may be different to the actual failure modes under the direct tension test when the dip angle is greater than the critical angle, \(\theta^{*}\). Therefore, based on the failure modes of the layered rocks under the direct tension test, the SPW criterion is modified in this paper.  
The saw-toothed failure mode due to direct tension can be seen when the dip angle is higher, as shown in Figure 16c. The tension-fractured rock material is taken for the analysis. The rock material is not only subjected to the force generated by the normal stress, \(\sigma_{1}\), but is also subjected to the force generated by the shear stress of the bedding plane. The rock material will be tension fractured as long as the total stress on the rock material reaches its tensile strength, \(\mathrm{T}_{90}\); meanwhile, the tensile strength, \(\sigma_{1}\), of the layered rock specimen can be obtained:  
\[\mathrm{T}_{90}=\ \sigma_{1}+2\sigma_{1}\sin^{2}\theta\cos\theta \tag{5}\]  
\[\sigma_{1}=\ \sigma_{\mathrm{t}}=\frac{\mathrm{T}_{90}}{1+2\sin^{2}\theta\cos\theta} \tag{6}\]  
Therefore, the modified SPW (MSPW) criterion can be written as:  
\[\mathrm{T}(\theta)=\left\{\begin{array}{l}\frac{\mathrm{T}_{0}}{\cos^{2} \theta}\ 0^{\circ}\ \leq\ \theta\ \leq\ \theta^{*}\\ \frac{\mathrm{T}_{90}}{1+2\sin^{2}\theta\cos\theta}\ \theta^{*}<\theta\ \leq\ 90^{\circ}\end{array}\right.. \tag{7}\]  
where the critical angle, \(\theta^{*}\), is given by:  
\[\theta^{*}\ \cong\ \arctan\left(\sqrt{\frac{\mathrm{T}_{90}}{\mathrm{T}_{0}}}-1\right) \tag{8}\]
# Medium Transmission Map Matters for Learning to Restore Real-World Underwater Images
All these methods showed better results than the traditional physical designs, as shown in Figure 1. However, the quality improvement was limited due to the ignoring of other factors, such as distance-dependent attenuation and scattering. Considering the underwater imaging process, these factors can be considered by utilizing the semantics contained in the medium transmission map [9], such as the design proposed in this paper. By analyzing the results in Figure 1g, the improvement by the medium transmission map can be fully reflected by producing more visually pleasing results in terms of color, contrast, and naturalness. In this work, we eliminate the influence of light scattering and attenuation on underwater images in real time to support intelligent underwater perception systems. Inspired by the depth-guided deraining model by [PERSON] et al. [14], we introduce the medium transmission map (MT) and formulate an MT-guided restoration framework. Specifically, a multitask learning network is designed to generate both the MT and rest  
Figure 1: Comparison of the results of different methods for processing a real underwater picture. Our method restores the chromatic aberration and enhances the contrast. (**a**) Raw, (**b**) UDCP [2], (**c**) WaterNet [5], (**d**) Ucolor [9], (**e**) UGAN [11], (**f**) FUniE-GAN [6], (**g**) Ours, (**h**) Ground Truth. A multilevel (including both feature level and output level) knowledge interaction mechanism is proposed for better mining the guidance from the MT learning space. Furthermore, to maximally reduce the computational burden caused by the MT learning branch, parameters in some specific stages are shared across these two related tasks, thus enabling real-time processing of the underwater images. In summary, this work makes the following contributions:  
* We improved the use of the medium transmission map. We can obtain good results by relying on the RGB map alone using various preprocessing and color embedding, proving that the MT map is of great significance for learning in a more powerful real-world underwater image restoration network. * A multitask learning framework is formulated for leveraging the MT map, and a novel multilevel knowledge interaction mechanism is proposed for better mining the guidance from the MT learning space. * A comparative study on two real-world benchmarks demonstrated the superiority of our MTUR-Net over the state-of-the-art in terms of both restoration quality and inference speed. The rest of our paper is organized as follows. Section 2 presents the proposed underwater image enhancement algorithm. The experimental results are reported in Section 3, followed by the conclusion in Section 4.