esa-sceva/satcom-chunk-collection huggingface dataset:
# A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks
## 2 System Model
### Mobility Model  
Due to the complexity of satellite networks, we adopted the BPP model as the representative for satellite networks in this study. The BPP model has been proven by [PERSON] [32] to be an effective model for satellite networks.  
**Proposition 1**.: _For a point in homogeneous BPP, the azimuth angle is uniformly distributed between \(0\) and \(2\pi\), i.e., \(\phi_{\text{BPP}}\sim U[0,2\pi]\), and the cumulative distribution function (CDF) of each point's polar angle (of the spherical coordinate), \(\theta_{\text{BPP}}\), follows_  
\[F_{\theta_{\text{BPP}}}(\theta)=\frac{1-\cos\theta}{2},0\leq\theta_{\text{ BPP}}\leq\pi, \tag{1}\]  
Figure 1: Network model.  
and \(\theta_{BPP}\) can be generated by,_  
\[\theta_{BPP}=\arccos(1-2\mathcal{U}[0,1]),0\leq\theta_{BPP}\leq\pi. \tag{2}\]  
Note that the BPP given in subsequent parts of this paper means the homogeneous BPP unless otherwise stated. The distribution of user terminals is modeled randomly.  
Considering the relativity of motion, we assumed that the satellite topology remains unchanged, assuming that ground terminals have a random initial direction and speed of movement for simulation. When a ground user moves into the coverage area of a satellite, they initiate a content request to the satellite. To facilitate simulation, we set up a scheme for each time slot that calculates the positions of ground users before the start of each time slot, and within each time slot, the network topology is relatively static. The speed of ground users is distributed randomly to simulate the heterogeneous and diverse user characteristics in a real environment. For the \(t\)-th time slot, the current satellite, user, and content are denoted as \(s^{t}\), \(u^{t}\), and \(o^{t}\), respectively.
## 2 Related Works  
There are other simulators, presented in Table 1, already available for the simulation of SpaceWire and SpaceFibre networks. Among them, Modelling of SpaceWire Traffic (MOST) [10], developed by Thales Alenia Space (TAS), is probably the most used. MOST is based on the toolkit OPNET, hence it requires an annual license to be used, while OMNeT++ requires a license only for commercial applications and it is free for academic and research projects. Thanks to its long-term development, MOST provides a wide library of SpaceWire-related products, such as the SMCS1165 pW, the SMCS332 SpW, the Remote Terminal Controller (RTC) and the SpW-10X Switch. It also supports Spacefibre endpoints in its OPNET version. In comparison, SHINe provides only ideal components. Currently, TAS is porting MOST from OPNET to NS-3, which does not require the payment of a license. It appears that support on SpaceFibre has been recently added [11], however, no detailed public information are available. Another important simulator is SANDS [12], developed by Saint-Petersburg University of Aerospace Instrumentation (SUAI). SANDS aims to support the topology design for SpaceWire networks taking into account several parameters, such as the required fault-tolerance level, total network mass including cables and nodes, and power consumption. SANDS also allows simulating the network, using its SystemC engine, either at bit level or at packet level, as well as providing support for the generation of the scheduling tables for scheduling Quality of Service of STP-ISS [13] transport protocol running on top of SpaceWire.  
While each simulator has its own strength, SHINe is the only one supporting both SpaceFibre and SpaceWire that is free, making it a very good product to help the development of such protocols.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulator** & **Toolkit/Language** & **SpaceWire/SpaceFibre Support** \\ \hline SHINe & OMNeT++ (C++) & SpW/SpFi \\ MOST (NS-3) & NS-3 (C++) & SpW/SpFi \\ MOST (OPNET) & OPNET (C) & SpW/SpFi \\ SANDS & Own/SystemC & SpW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the main SpaceWire/SpaceFibre network simulators available.
## 5 Conclusions  
In this paper, the low-cycle cyclic tests of three gypsum-concrete dense-column composite boards with external thermal insulation systems were conducted, with the seismic performance characterized and compared with similar boards.  
1. On the basis of composite boards only having vertical gypsum cavities, the influence of external thermal insulation system was considered, and a new kind of gypsum-concrete dense-column thermal-insulation composite board was developed. Under the influence of tensile-compressive cyclic load, the repetitive open-close feature of gypsum cracks between concrete dense columns can effectively absorb earthquake energy, which is beneficial to the improvement of seismic performance.
2. Horizontal earthquake action is directly applied on the core load-bearing component of the composite board. The deformation results in the specimen facade (length or width of crack, partial fall-off of gypsum, etc.) are more sensitive than those in the backface. The thermal insulation board and gypsum guard board can bear some load and resist the earthquake action together with the composite board.
3. Based on the load-strain analysis of composite board, it is demonstrated that when the typical zone is stretched, the material strain is relatively large, which becomes relatively small in compressed zones. Thus, it is implied that brittle materials such as concrete and gypsum enter the elastoplastic deformation easier under tensile stress than under compressive stress. In the early stage of cracking, fibers in the gypsum board present some tensile resistance.
4. By comparing to similar composite boards of the literature [22] without thermal insulation system, the load bearing capacity and ductility of the board developed in this study are increased by 8.59% and 76%, indicating that this composite board has favorable seismic performances.  
[PERSON] performed the tests, analyzed the data and wrote the paper. [PERSON] and [PERSON] conceived and supervised the work. All authors contributed to conclusion of the thesis. All authors have read and agreed to the published version of the manuscript.  
The authors would like to express heartful gratitude to the financial support by the Science Technology of the Ministry of Housing and Urban-Rural Development (No. 2018-K9-065), China Postdoctoral Science Foundation funded project (No. 2018M632805), Foundation of He'nan Educational Committee (18A560007), and key scientific and technological project of Henan Province (No. 182102210234).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Cytokine Secretion (Production)
#### 3.6.1 TNF-\(\alpha\) (Tumor Necrosis Factor-\(\alpha\) Assay)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Concentration** (\(\mu\)g/mL)} & \multicolumn{2}{c}{**Oragans of _M.crystallinum_ (Mean \(\pm\) S.D.)} & \multirow{2}{*}{**F-Value (\(p\))**} \\ \cline{2-2} \cline{4-4}  & **Cotyledon** & & & \\ \hline
0 & 10.72 \(\pm\) 0.35 & 10.72 \(\pm\) 0.35 \({}^{\text{\text{C}}}\) & 10.72 \(\pm\) 0.35 \({}^{\text{\text{D}}}\) & 0.000 (1.000) \\
10 & 10.04 \(\pm\) 0.90 & 9.41 \(\pm\) 0.49 \({}^{\text{\text{B}}}\) & 9.59 \(\pm\) 0.65 \({}^{\text{\text{C}}}\) & 0.636 (0.561) \\
30 & 10.08 \(\pm\) 1.01 & 9.04 \(\pm\) 0.50 \({}^{\text{\text{B}}}\) & 8.91 \(\pm\) 0.35 \({}^{\text{\text{BC}}}\) & 2.680 (0.147) \\
50 & 9.70 \(\pm\) 0.52 & 9.01 \(\pm\) 0.24 \({}^{\text{\text{B}}}\) & 8.92 \(\pm\) 0.40 \({}^{\text{\text{BC}}}\) & 3.379 (0.104) \\
100 & 10.08 \(\pm\) 0.88 \({}^{\text{\text{b}}}\) & 7.97 \(\pm\) 0.10 \({}^{\text{\text{AA}}}\) & 8.36 \(\pm\) 0.13 \({}^{\text{\text{AB}}}\) & 14.337 (0.005) \({}^{\text{\text{**}}}\) \\
300 & 9.51 \(\pm\) 0.79 \({}^{\text{\text{b}}}\) & 7.26 \(\pm\) 0.54 \({}^{\text{\text{AA}}}\) & 6.57 \(\pm\) 0.13 \({}^{\text{\text{AA}}}\) & 22.900 (0.002) \({}^{\text{\text{**}}}\) \\ \hline F-value (\(p\)) & 0.857 (0.537) & 26.385 (0.000) \({}^{\text{\text{***}}}\) & 39.398 (0.000) \({}^{\text{\text{***}}}\) & \\ \hline \hline \end{tabular}  
* Means with different letters in each column (common letter (a–c): the level of significance for each organs) and row (capital letter (A–D): the level of significance for each treatment with ug/mL) are significantly different by [PERSON]’s multiple range test (\(p<0.05\)). Values are mean \(\pm\) SD (n = 3). \({}^{\text{\text{*}}}p<0.01\), \({}^{\text{***}}p<0.001\) compared to the control by ANOVA. Values are mean \(\pm\) SD (n = 3).  
\end{table} 
Table 7: Effects of ethanol extract according to each ice plant organs on the IL-6 levels in LPS-induced RAW 264.7 cells.
# Post-synthesis Tellurium Doping Induced Mirror Twin Boundaries in Monolayer Molybdenum Disulfide
## 2 Materials and Methods  
It is well known that Te-based TMD materials such as MoTe\({}_{2}\) and WTe\({}_{2}\) are sensitive to air and aqueous solution exposures due to oxidization and decomposition [26; 27]. To address this side effect, a special growth-doping strategy was developed in our experiments. Specifically, monolayer MoS\({}_{2}\) films were first grown onto graphene-supported molybdenum-based TEM grids (prepared according to [28]) via the standard chemical vapor deposition (CVD) process. The as-formed graphene-supported MoS\({}_{2}\) samples were then loaded into another clean quartz tube for subsequent tellurium doping, prior to being loaded into the TEM chamber for further ADF-STEM characterizations. Such a process minimized air exposure and was free of any wet-chemistry-involved transfer process, which ensured the operability and validity of the main contents of this study.  
Graphene-supported molybdenum-based TEM grids were mounted onto a custom-built ceramic carrier, facing down above a boat containing MoO\({}_{2}\) precursors (Sigma-Aldrich, 99%) and then loaded into the CVD system for the MoS\({}_{2}\) growth. The furnace was heated to 780 \({}^{\circ}\)C and kept at that temperature for an additional 25 min for the MoS\({}_{2}\) growth. About 15 min after the furnace temperature reached 780 \({}^{\circ}\)C, heating of the sulfur source (Aladdin, 99.999%) started with its temperature reaching 180 \({}^{\circ}\)C in 2 min and being held there for the next 10 min. The whole system was cooled down to room temperature naturally after the growth. During the whole process, high-purity argon carrier gas with a flow rate of 100 sccm (standard-state cubic centimeters per minute) was used.  
Tellurium doping was conducted in a two-furnace CVD system where the graphene-supported TEM grid containing as-grown MoS\({}_{2}\) monolayers was loaded into a one-end sealed quartz tube inside the furnace located downstream. The tellurium powders (Alfa Aesar, 99.999%) were mounted in another furnace upstream, with its temperature set to be around 550 \({}^{\circ}\)C. The grid-containing furnace was ramped to 350 \({}^{\circ}\)C in 15 min, then heated to 750 \({}^{\circ}\)C in another 13 min and kept there for an additional 2 min for the tellurization (see details in Figure S1). A higher doping temperature of 850 \({}^{\circ}\)C was also used for comparison experiments. A mixed gas flux of 135 sccm Ar and 15 sccm H\({}_{2}\) was used as the carrier gas. After natural cooling, the sample-containing TEM grid was loaded into the microscope chamber without any further treatment.  
ADF-STEM characterization was conducted with a probe-corrected STEM (FEI Titan G\({}^{2}\)-ChemiSTEM). The detailed operation parameters are as follows: acceleration voltage of 200 kV, probe convergence semi-angle of 21.4 mrad, collection semi-angle of 43.4-200 mrad, and a probe current of 70 pA.
## 3 Optimization Framework
### POD-Based Dimension Reduction Technique and an Adaptive Sampling Method  
#### 3.4.1 POD-Based Dimension Reduction Technique  
The POD-based optimization method includes choosing snapshot sample points and building a reduced order model (ROM). The sample points have a great impact on the final optimization results and can be obtained in a variety of ways, such as selecting from initial optimization results and obtaining by DOE. The procedure of building ROM based on POD is as follows.  
Given the data matrix \(\mathbf{X}=\left[x^{(1)}\cdots x^{(i)}\cdots x^{(n)}\right]\), where \(\mathbf{x}^{(i)}=\left[x_{1}{}^{(i)},x_{2}{}^{(i)},\cdots x_{m}{}^{(i)}\right]^{t}\) is the \(i\)th sample, \(m\) is the number of design variables.  
Then, solve the eigenvector function:  
\[\mathbf{XX}^{t}\mathbf{U}=\mathbf{U}\mathbf{\Lambda}. \tag{1}\]  
It equivalent to solve the following function:  
\[\mathbf{XX}^{t}\mathbf{\varphi}^{(i)}=\lambda_{i}\mathbf{\varphi}^{(i)},i=1,\cdots,n, \tag{2}\]  
where \(\mathbf{U}\) stands for eigenvectors matrix of \(\mathbf{XX}^{t}\), and the columns of \(\mathbf{U}=[\mathbf{\varphi}^{1}\cdots\mathbf{\varphi}^{(i)}\cdots\mathbf{\varphi}^{(n)}]\) are basis eigenvectors. \(\mathbf{\Lambda}\) represents the diagonal matrix of eigenvalues \(\lambda_{i}\).  
Using the above method, mapping the data from the original design space to projected design space:  
\[\mathbf{X}_{\mathbf{proj}}[m,n]=\mathbf{U}^{T}[m,n]\mathbf{X}[m,n]. \tag{3}\]  
By retaining only the most significant components after POD analysis, an ROM of original design space is constructed:  
\[\mathbf{X}_{\mathbf{ROM}}[k,n]=\mathbf{U}^{T}[k,n]\mathbf{X}[m,n]. \tag{4}\]  
Given a new point \(\mathbf{X}^{\prime}_{\mathbf{proj}}\) in the projected design space:  
\[\mathbf{X}^{\prime}_{\mathbf{proj}}[m,1]=\mathbf{X}^{\mathbf{opt}}_{\mathbf{proj}}[m,1]\mathbf{\mu}[1, m]. \tag{5}\]  
The correspondent point in the original design space is:  
\[\mathbf{X}[m,1]=\mathbf{U}[m,n]\mathbf{X}^{\prime}_{\mathbf{proj}}[m,1], \tag{6}\]  
where \(\mathbf{\mu}[1,m]=\left[\mu^{1},\mu^{2},\cdots,\mu^{k},1,1,\cdots 1\right]^{T}\) are POD coefficients.  
The POD coefficients act as the new design variables. The number of design variables is reduced as the number of POD coefficients is much less than original parameters (\(k\) is far less than \(m\)).
# Intelligent Dynamic Power Control with Cell Range Expansion for Small-Cells in 5G HetNets
## 5 Conclusions  
In this paper, we proposed an intelligent DPC scheme with CRE to increase the performance of both SUEs and CUEs for the DL of 5G HetNets. In the proposed DPC scheme, MBSs first found some MUEs that have serious interference from neighboring SBSs and then offloaded them to SBSs to reduce the load of MBSs. In addition, SBSs divided their SUEs and CUEs into two groups, i.e., inner and outer groups, to assign different subchannels and dynamically allocate the appropriate transmission power to increase the performance of both SUEs and CUEs. Through simulation results, it is shown that the proposed DPC scheme increased the mean capacities of SUEs and CUEs by up to 119% with the same performance of the mean MUE capacity and reduced the outage probability of MUEs and SUEs by 191% and 32%, respectively, compared to other DPC schemes with CRE. As a result, the proposed DPC scheme reduced the load of MBSs using CRE and increased the capacity of SUEs and CUEs using the power control of SBSs. We think that the proposed DPC scheme with CRE can be used in 5G advanced and 6G systems if the HetNet is introduced. For future work, we plan to extend the proposed DPC scheme using reinforcement learning to improve the system performance of 5G HetNets.  
Conceptualization, I.B. and S.-J.K.; methodology, I.B. and S.-J.K.; software, I.B.; validation, I.B. and S.-J.K.; formal analysis, I.B. and S.-J.K.; investigation, I.B. and S.-J.K.; resources, I.B. and S.-J.K.; data curation, I.B.; writing--original draft preparation, I.B.; writing--review and editing, S.-J.K.; visualization, I.B. and S.-J.K.; supervision, S.-J.K.; project administration, S.-J.K.; funding acquisition, S.-J.K. All authors have read and agreed to the published version of the manuscript. This study was supported by research fund from Chosun University, 2022.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.
## 4 Case Studies
### An Experimental Case Study on Complex-Shaped Metallic Turbine Blades
#### 4.1.2 Reference MD Space Based on Defective Samples  
Another perspective in classification is to construct a reference MD space based on defective samples. The same configuration as the dataset allocation in Section 4.1.1 is adopted here. The classification criteria, in essence, are to differentiate between the healthy and defective samples by evaluating their similarity to the defective population (rather than to the healthy population, as in Section 4.1).  
The classification results are shown in Figure 7 for the training and testing datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training Accuracy** & **Testing Accuracy** & **Number of Features** \\  & **(\%)** & **(\%)** & **(-)** \\ \hline \(\text{IMCS}_{\text{PSO}}^{\text{BPSO}}\) & 98.81 \(\pm\) 0.39 & 97.06 \(\pm\) 1.35 & 5.50 \(\pm\) 0.73 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification performance of the algorithm over 20 independent runs using a reference MD space based on healthy samples.  
Figure 6: (**a**) Classification on training pool dataset; (**b**) classification on the testing dataset.  
The IMCS algorithm shows 82.73% and 62.37% classification accuracies for training and testing datasets, respectively. There are seven screened features \(f_{1},f_{3},f_{5},f_{7},f_{12},f_{13},f_{14}\approx\) (3.9, 7.0, 10.7, 19.1, 24.5, 25.5, 25.9) kHz. The results clearly indicate that constructing an MD space based on defective samples deteriorates the classification performance significantly. The immediate reason behind this observation is found in the high sensitivity of MD to even slight variations [31]. Furthermore, there are various kinds of defects in the first-stage turbine blades, such as (i) microstructure changes due to overheating, (ii) airfoiled cracking, and (iii) intergranular erosion (corrosion) [16]. Additionally, each of these defect types may vary at different MD levels and, as such, different distributions. This obviously makes it difficult to construct a good reference MD space in which the MD population is well clustered. A further challenge arises from the fact that the number of defective samples is too sparse in the feature space to construct a good reference MD space.  
Classification performance using IMCS over 20 independent runs is summarized in Table 2. Apart from the low mean values of accuracies, the standard deviation increased significantly, indicating poor classification performance.
# Dilated Filters for Edge-Detection Algorithms
## 3 Methodology
### Canny Algorithm  
The Canny edge-detection algorithm is a classical and robust method for edge detection in gray-scale images. The edge-detection algorithm is widely used due to its short operation time and relatively simple calculation process.  
The traditional Canny algorithm has the following steps: (1) smooth the image with a Gaussian function, (2) apply the first-order operator, (3) non-maximum suppression of the magnitude of the gradient and (4) double threshold is used for edge connections. The classic Canny algorithm is presented in Algorithm 5.  
Non-maximum suppression is an important step for the Canny algorithm. The purpose of it is to find the local maximum value of the pixels, and set the gray value corresponding to the non-maximum point to zero, so that a large part of non-edge points can be eliminated. It is a technique through which the edges are made thinner by checking, for each edge pixel, if the adjacent pixels which fall between the gradient angle have a smaller value. If not, the edge pixel is suppressed [13].  
After the non-maximum suppression phase using the two thresholds, we double threshold and edge link by hysteresis the edge points. If an edge pixel's gradient value is higher than the high threshold it is set as a strong edge pixel. Similarly, if an edge pixel's gradient value is smaller than the high threshold value but larger than the low threshold value, it is marked as a weak edge pixel. If an edge pixel's gradient value is smaller than the low threshold value, it will be suppressed. At the end, the remaining \"weak\" and \"strong\" pixels are connected as long as there is one strong edge pixel that is involved in the blob.  
```
Input: RGB image Output: Binary edge map Parameters: Sigma value (\(S\)), Low Threshold (\(TL\)), High Threshold (\(TH\)),
```
1:for edge operator do
2: Convert image to gray-scale image
3: Apply Gaussian filter smoothing
4: Apply convolution with kernels on the \(x\) and \(y\) axes
5: Calculate gradient magnitude with their kernels on the \(x\) and \(y\) axes
6: Apply non-maximum suppression, as in [13]
7: Apply edge tracking by hysteresis using double threshold
8:endfor ```
**Algorithm 5** [PERSON] Operator steps.
# Search-Space Reduction for S-Boxes Resilient to Power Attacks
We follow the goal, not to obtain good S-boxes in the sense of trade-off classical resistance and the resistance against power attacks, but to provide a novel formal framework for the actual S-box design context. This result can be applied to search over the Hamming weight class space instead of searching over the entire S-box space, which entails reducing the space and improving the performance of the search process. Taking into account that all S-boxes belonging to the same Hamming weight class have, by the way the classes are defined, the same hypothetical leakage according to the Hamming weight leakage model, it is intuitively expected that all of them have the same resistance to power attacks, when quantified using the confusion coefficient variance metric, since this metric is based on that leakage model. Based on this idea, it was experimentally verified that the value of the variance of the confusion coefficient is constant within each Hamming weight class, and, to explain these experimental results, this property was theoretically demonstrated. However, to present the content in a more logical sequence, the document presents first the theoretical proof and then its experimental and statistical confirmations: (1) effective attack on 1000 S-boxes that belongs to the Advanced Encryption Standard S-box class; and (2) constant confusion coefficient variance value on S-boxes in the same class. In both cases, we generated the S-boxes using our new randomized algorithm. The paper is structured as follows. Section 2 includes the necessary basics concepts. Section 3 presents the contributions about the new equivalence relationship; the Hamming weight equivalence classes and their representatives; the algorithm to generate the S-boxes into each class; and the theoretical demonstration and experimental verification that random S-boxes of a class have the same resistance to power attacks. The number of classes and the number of S-boxes within each class are calculated, showing that, as \(n\) increases, the class space dimension is an ever-smaller fraction of the space of S-boxes, which significantly reduces the space of search of S-boxes resilient to power attacks, when the search is performed from class to class. Finally, Section 4 provides concluding remarks.