esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study  
### Project Background  
The Xingye Express (southern section), situated in Xiangzhou District, Zhuhai, China, is a project managed by CCCC Tunnel Engineering Co., Ltd. (Beijing, China). Table 2 presents the primary shield parameters, which have been estimated based on comprehensive project data and detailed design information furnished by the aforementioned company. The main shield begins from the working shaft of Yinhua road and ends at the working shaft of Banzhang mountain (Figure 3). The length of the line is approximately 4.77 km, and the length of the main shield section is 1739 m. The outer and inner diameters of the main-line shield tunnel are 15.2 and 13.9 m, respectively. The segment thickness is 650 mm, and the ring width is 2.0 m. The minimum and maximum buried depths of the tunnel are 9.8 and 41.3 m, respectively. The minimum horizontal curve radius is 599.5 m, and the section is V-shaped. The minimum vertical curve radius is 1500 m, and the maximum vertical curve radius is 2500.0 m.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Length of shield machine/m & 15.80 \\ Exavation diameter/m & 15.76 \\ Cutter speed/rpm & 0–1.33–3.0 \\ Cutter form & With pressure and atmospheric pressure \\ Minimum turning radius/m & 599.5 \\ Aperture opening ratio/\% & 30 \\ Maximum total thrust/kN & 277,200 \\ Cylinder stroke/mm & 4200 \\ Number of stroke sensors (built-in) & 56 \\ Number of advanced cylinder partitions & 6 \\ Maximum propulsion speed/(mm/min) & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mud–water equilibrium shield parameters.  
Figure 3: Tunnel engineering diagram.  
The propulsion system of the shield machine comprised 56 sets of built-in travel sensors, which were arranged according to the segments. The jack cylinder was divided into six zones (Figure 4). The propulsion cylinders in each section of the propulsion system can be individually controlled and selected and were fitted with a set of built-in stroke and pressure measurement sensors. The oil pressure was manually adjusted on the control console, and the stroke and pressure of each cylinder and each zone were displayed in real time on the display screen in the control room.  
The main strata through which the main-line shield passes are silty clay, silty soil, gravel sand, gravel clay, fully weathered granite, and moderately weathered granite. The main shield section traverses the main strata as follows: The livable gravel sand layer (approximately 256 m long); a clay and fully weathered rock section (approximately 550 m long); a downslope section with soft and hard strata on the upper part (strongly weathered) and a lower part (breezy rock--the lowest part of the main-line shield was the entire section of breezy rock) (approximately 100 m long); and the lower part (breezy rock, approximately 460 m long). The end section is a full-section breezy rock formation (approximately 100 m long). The physical characteristics of the main soil layers are shown in Table 3[28].
## 2 An Overview on the SGCPs
### Function Approximation by the SGCPs  
Consider a function \(u(z)\) defined on \([0,\mathscr{L}]\), and it can be approximated using SGCPs as follows:  
\[u(z)=\sum_{j=0}^{N}c_{j}G_{j}^{(\sigma)}(z)=\mathbf{C}^{T}\Psi(z). \tag{16}\]  
Then, the coefficients \(c_{j}\) have the following form:  
\[c_{j}=\frac{1}{h_{j}^{\sigma}}\,\int_{0}^{\mathscr{L}}w(z)\,u(z)\,G_{j}^{( \sigma)}(z)\,dz,\quad j=0,1,\cdots, \tag{17}\]  
and  
\[\mathbf{C}^{T}= [c_{0},c_{1},\cdots,c_{N}], \tag{18}\] \[\Psi(z)= [G_{0}^{(\sigma)}(z),G_{1}^{(\sigma)}(z),\cdots,G_{N}^{(\sigma)} (z)]^{T}. \tag{19}\]
# Parameter Calibration of Discrete Element Model of Wine Lees Particles
## 4 Conclusions  
1. This paper utilizes EDEM discrete element simulation software to specifically analyze the lees particles in a winery. The Hertz-Mindlin with JKR contact model is selected to simulate the lees particles with a moisture content of 20.4%. Through the screening process using Plackett-Burman Design, the contact parameters and model parameters that significantly affect the angle of repose of the lees particles were identified. The results indicated that the particle-particle coefficient of recovery and the particle-plate coefficient of recovery had the most significant impact.
2. The steepest climb test was employed to determine the optimal value ranges for significance parameters and surface energy. A regression model between the angle of repose of lees particles and these significance parameters as well as surface energy was established and optimized using the Box-Behnken Design response surface analysis test. Analysis of variance (ANOVA) revealed that, in addition to significance parameter and surface energy, the quadratic term of surface energy between particles also had a significant effect on the angle of repose of lees particles.
3. By taking into account the actual angle of repose as an objective response value, optimization and solution techniques were applied to obtain three optimal parameter values: a particle-particle recovery coefficient of 0.603, a particle-plate recovery coefficient of 0.595, and a JKR value of 0.083.
4. By substituting the optimal values of the discrete element parameters for the simulation test of the angle of repose of lees particles, it was found that the relative error with the experimental angle of repose is 0.18%. This result proves that the method of obtaining optimal parameter values by solving the angle of repose as the response value is effective. The comparative validation test results also demonstrate that there is no significant difference between the simulated rest angle of lees particles and the actual rest angle, thus confirming the accuracy and reliability of the calibrated discrete element model parameters for lees particles.  
Conceptualization, X.Z. and R.W.; software, B.W. and X.W.; validation, J.C. and X.Z.; formal analysis, R.W. and B.W.; investigation, X.Z.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z., R.W. and B.W.; supervision, R.W.; project administration, X.Z. and R.W. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Comparison of simulation and physical test. (**a**) Physical test stacking image. (**b**) Simulation test stacking image.
# Bonding Evaluation of Asphalt Emulsions used as Tack Coats through Shear Testing
## 3 Methodology
### Performance  
In order to evaluate the performance of the asphalt test samples, the mechanical behavior of each sample was tested via LCB shear testing at 25 \({}^{\circ}\)C. For this testing, slow (SS) and rapid (RS) setting cationic asphalt emulsions were used, and these emulsions were made with PG 64-22 asphalt cement under the quality control of N-CMT-4-05-004/08 [32]. Following the NLT-382/08 standard, cylindrical specimens (101.6 mm diameter) of a hot mix asphalt were made using the Marshall procedure established in N-CMT-4-05-003/08 [33] and M-MMP-4-05-034 [34], combining the asphalt binder residue content and the dosages shown in Tables 1 and 2. Additionally, the asphalt test samples were cured for 1, 3, and 5 days and were then tested via LCB shear testing [30] using a loading press. The maximum load applied and the deformation until the softening load were recorded.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emulsion Type** & **Proportion of** & **\% Asphalt** & **Emulsifier Per 2 Liters** \\  & **Emulsifier (kg/Ton)** & **Resolute** & **of Asphalt Emulsion (g)** \\ \hline \multirow{3}{*}{Slow Setting} & & 50\% & 40.00 \\  & 10.00 & 55\% & 44.40 \\  & & 60\% & 50.00 \\  & & 50\% & 12.00 \\ Rapid Setting & 3.00 & 55\% & 13.33 \\  & & 60\% & 15.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proportions of the emulsifiers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emulsion Type** & **Sample Area** & **Application** & **Tack Coat Rate** & **Dosage for Each** \\  & **(m\({}^{2}\))** & **Temperature (\({}^{\circ}\)C)** & **(L/m\({}^{2}\))** & **Sample (g)** \\ \hline \multirow{3}{*}{Slow Setting} & & 0.30 & 2.44 \\  & & 0.50 & 4.02 \\  & & 0.70 & 5.63 \\  & & 0.30 & 2.44 \\ Rapid Setting & 0.008136 & 60.00 & 0.50 & 4.02 \\  & & 0.7 & 5.63 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proportions of the emulsifiers.  
A total of 72 asphalt test samples were prepared and tested on LCB shear equipment, and the load-deformation charts were detailed to analyze the fracture energy from the calculated areas under the curves. The results of the SS and RS asphalt emulsions were compared in terms of the displacement-load graphs and the relationship between the emulsion dosage, asphalt residue content, and curing time and the maximum applied load and fracture energy.
# Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density
## 5 Experimental Evaluation
### Contrast Algorithm  
The DSNE algorithm proposed in this paper uses community density to detect the overlapping community structure in the bipartite network starting from node pairs. LOCDalgorithm uses the similarity of nodes to detect the overlapping community structure in the bipartite network from the graph's topology. CBG&BEN algorithm uses the Bi-EgoNet, a micro-network model to detect the bipartite network's overlapping community structure based on the complete dichotomous graph. These three methods have certain similarities, and the LOCD algorithm and CBG&BEN algorithm are relatively novel and excellent in community detection results. Therefore, comparing the DSNE algorithm with these two algorithms can better illustrate the DSNE algorithm's superiority. We briefly describe the two algorithms below.  
[PERSON] et al. [9] proposed LOCD algorithm for selecting the right partitions in bipartite networks in 2017. This method contains two parameters; one parameter is used to represent the similarity between nodes of the same types, and the other parameter is used to represent the similarity between nodes of different types. The two types of nodes are processed separately. Firstly, extend the sub-community with the first parameter, which is the similarity between nodes of the same type. The similarity between nodes of different types is used to merge different types of nodes with the sub-community, and many sub-communities are obtained. Then, the sub-communities are merged according to some specific merge rules to obtain the global community structure.  
In 2019, [PERSON] et al. [30] proposed a community detection algorithm, CBG&BEN, which is based on the micro-network model (Bi-EgoNet) and complete bipartite graph (CBG). First, the algorithm constructed the micro bipartite network model Bi-EgoNet for each node pair. Second, it used the complete binary graph to extract the basic complete binary graph from each Bi-EgoNet, and the set of local communities was obtained by merging rules. Third, according to specific merging rules, the global bipartite network community structure is obtained. Finally, the network's divergent nodes are collected and allocated to the corresponding global community, and the final global community structure is finally obtained.
# Imperceptible-Visible Watermarking to Information Security Tasks in Color Imaging
## 4 Experimental Results
### Image Quality Degradation and Imperceptibility Analysis  
In an efficient watermarking algorithm, the image quality degradation is performed by the PSNR, which is strongly related with the imperceptibility that in terms of SSIM and NCD performs similarly between the original and its corresponding watermarked image. To obtain this evaluation, the binary watermark patterns FI, QR, and UNAM are embedded individually into the 1338 images contained within the UCID dataset, and the PSNR, SSIM, and NCD of the images with the same mean value \(0\leq\mu_{R}\leq 255\) in the embedding region are averaging. For the FI watermark, the average values of PSNR, SSIM, and NCD with respect to the mean value \(\mu_{R}\) are shown in Figure 10a-c, respectively, those with respect to the QR watermark are shown in Figure 10d-f, respectively; and finally, those with respect to the UNAM watermark are shown in Figure 10g-i, respectively.  
Generally, if \(PSNR\geq 37\) dB, the watermarked image quality is acceptable, and the watermark is considered invisible to the HVS [39]. In this way, Figure 10a,d,g show the average of the performance quality metric with a value greater than 37 dB. Considering all the images of the mentioned dataset, the average of quality for each embedded watermark pattern FI, QR, and UNAM is greater than 37 dB, 43.50 dB, and 53 dB, respectively; in this context, the general quality value (GQV) of the proposed algorithm is 37 dB \(\leq GQV\geq 53\) dB.  
In the experiment, if \(SSIM\geq 0.93\) and \(NCD\leq 0.05\), the original image and its corresponding watermarking are considered similar, and the embedded watermark is considered as imperceptible to the HVS. Figure 10b,c,e,f,h,i show the imperceptibility metric SSIM and NCD with respect to the mean value \(\mu_{R}\) of the appropriate embedding region. As can be observed, the obtained SSIM and NCD values are greater than 0.93 and smaller than 0.05, respectively. Similar to the GQV, a general similarity value (GSV) and general imperceptibility value (GIV) for watermarks FI, QR, and UNAM are obtained. A survey of the general performance for the UCID dataset is shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Watemark** & **F1, 150 \(\times\) 150 (22,500 Bits)** & **QR code, 116 \(\times\) 116 (13,456 Bits)** & **UNAM, 134 \(\times\) 39 (5226 Bits)** \\ \hline \multirow{2}{*}{Host Image} & \multicolumn{2}{c}{Largest homogeneous region} & \multicolumn{2}{c}{Largest homogeneous region} \\  & Ensuring & Non-ensuring & Ensuring & Non-ensuring & Ensuring \\  & vacid00146\(\pm\)fif & vacid0016\(\pm\)fif & vacid0044\(\pm\)fif & vacid00775\(\pm\)fif & vacid00036\(\pm\)fif & vacid00262\(\pm\)fif \\ \hline \end{tabular}
\end{table}
Table 4: Visual quality degradation and imperceptibility of non-attacked watermarked images.
## 3 Test Results and Analyses
### Stress Development  
The earth pressure cells S1 to S5 were kept almost constant during the loading test, indicating that they were not influenced by the surcharge. The earth pressure cell S8 varied consistently with the applied surcharge. Thus, they were excluded from the analysis. Figure 4 shows the variation of the earth pressure cells S6 and S7. The earth pressures of S6 and S7 increased fast with the increase of the surcharge, and reduced sharply or became stable after reaching a peak value. As the slip plane passed through the zones near S6 and S7, the backfills experienced plastic shear displacement with the surcharge load, resulting in a reduction in earth pressure. Thus, the decline in earth pressure can also be used as an indication of the slope failure.  
Figure 4: _Cont._  
Figure 3: Variation of lateral displacement of the slope with its vertical displacement.  
Figure 5 shows the surcharge load at the peak earth pressure. The surcharge load corresponding to the peak earth pressures at S6 were generally smaller than those at S7. In addition, increasing the rainfall intensity and duration decreased the surcharge load corresponding to the peak earth pressure. It is indicated that the rainfall caused a decline of the shear strength of the backfill.
## 2 Methods
### Preprocessing Techniques  
In this research, three key preprocessing techniques were implemented across all three models to ready the image data: grayscale conversion via OpenCV, data augmentation, and image background removal using REMBG.  
Grayscale conversion: This process was conducted using OpenCV, converting the original-colored images into grayscale. The objective of this conversion was to lessen the computational burden and to avoid inconsistencies arising from the color variance in the images. By standardizing the images to grayscale, the models could focus on distinguishing patterns and features without the additional complexity of color variations.  
Data augmentation: To enhance the robustness of the models and combat potential overfitting, data augmentation techniques were applied. These included image manipulations such as rotation, zooming, and horizontal flipping. By introducing these transformations, the model was exposed to a wider variety of data scenarios, helping it generalize better to unseen data and improve its predictive power.  
Image background removal: The third preprocessing step was the use of REMBG to remove the backgrounds from the images. By doing so, the model could concentrate solely on the crucial features of the retina, potentially improving its accuracy in predicting the severity of diabetic retinopathy.  
These preprocessing steps were instrumental in enhancing the effectiveness and performance of the models in distinguishing and categorizing various levels of diabetic retinopathy severity. The outcome of these preprocessing steps can be visualized in Figures 3-6.  
Figure 3: Normal picture.  
Figure 4: Grayscale conversion using OpenCV.  
Figure 5: Data augmentation.  
Figure 6: Image background removal using REMBG.
## 5 Evaluation of ICN Forwarding Strategies Utilizing the Developed Questionnaire  
Utilizing the questionnaire developed in Section 4, the questions are evaluated for the ICN forwarding strategy presented in Section 3.1. From the main ICN explanation in Section 3, it is known that there is no naming structure for ICN names and that there is no defined distance between two particular names. Hence, for all forwarding strategies that use ICN names without modifications, Question 3 is unsatisfied. This applies for Smart Flooding (SF) and Smart Flooding with MAP-Me enhancements (SF MM).  
Section 3 also states that, in the ICN architecture, the mapping of ICN names is done to interfaces. From the discussion in Section 2.1, this would mean that the figure \(F\) would be the ICN names and their ground \(G\) would be the interfaces. In ICN, these interfaces are normally labeled. Even if the naming of interfaces used numbers, which would satisfy the metric space definition, the scope of the naming would be local to a particular node. This means that, for all forwarding strategies that use the basic ICN naming, mapping ICN names to interfaces via the PIT and FIB, without modifications, Question 4 is unsatisfied. This applies to SF and SF MM.  
SF has the NACK PDU to avoid attempts to forward PDUs via interfaces through which there is historical information that suggests that a particular ICN name will not be found. Since NACK PDUs update the FIB over time, there is an update of the location of ICN names over time in SF. Although SF does not pay special attention to chunks, due to all non-aggregatable ICN names being tracked in the FIB, Question 2 is satisfied.  
SF MM attempts to speed up the update of ICN name to interface mapping by ensuring certain interfaces have a green state for future Interest PDUs. In the update, only the prefix of all the ICN names provided by the producer are updated, pointing all matching ICN names to one given interface, possibly undoing the work done by the SF's NACK PDUs. We have established in Section 4 that what needs to be tracked over time are the chunks related to an ICN name. Due to this conflicting update, Question 2 is only partially satisfied.  
Section 3.3.1 briefly described how 3 NA links Smart Flooding to 3N names via the use of specialized PDUs when using its SF 3N forwarding strategy. 3 NA does not directly modify the ICN namespace, meaning that Question 3 continues to not be satisfied. Although the assigning of names in 3 NA is done maintaining particular namespace properties, the ICN namespace does not map to 3N names. 3 NA could permit the creation of metric spaces for all ICN namespaces as the 3N namespace covers the whole network. This makes the 3N namespace a good candidate as the definition of a ground for place specification. Considering these points, Question 4 is only partially satisfied. With some minor modifications, it is possible for this question to be satisfied. This possibility will be leveraged in Section 6.2 when discussing modifications to the network naming schemes. SF 3N leverages SF, satisfying Question 2.
## 3 Results and Discussion
### Analytical Expressions for Limited Growth Based on the Verhulst Model
Explicit expression for \(\dot{N}\), the inflection time \(t_{inf}\), as calculated from \(d^{2}N/dt^{2}=0\) where \(\dot{N}\) shows its maximum denoted as \(\dot{N}_{max}\equiv N^{\prime}_{max}\), as shown in Table 1 for \(V_{n1}\), \(V_{n}\) and \(V_{n2}\). Reducing the risk, \(1/\tau\), by imposing rules always results in a delay and lowering of the peak because \(t_{inf}\propto\tau\); \(\dot{N}_{max}\propto 1/\tau\) and results in less medical burden. The first row in Table 1 shows the logistic model [2] with the rate equation, \(dN/dt\equiv\dot{N}\equiv N^{\prime}\), its solution \(N(t)\equiv N\) and initial condition \(N(0)\equiv N_{0}\). The second row shows the results, code \(V_{n}\), as in the first row but for the time-dependent risk on contamination as:  
\[\left(1-\left(N(t)/M\right)^{1/n}\right)/\tau, \tag{10}\]  
which is only a recast of \(V_{n1}\)[3], after the substitution \(N^{1/n}\equiv T\), which is a function of time similar to N. Lowering risk, \(1/\tau\), delays and lowers the peak because \(t_{inf}\) depends on \(n\), \(\tau\) and the ratio \(M/N_{0}\). The peak height, \(\dot{N}_{max}\equiv N^{\prime}_{max}\), depends on \(M\), \(\tau\) and \(n\) but not \(N_{0}\). Imposing behaviour rules (on top of existing ones) is a qualitative way to flatten the curve by lowering \(M\). The most effective and quantitative way to reduce \(M\) and the peak height is by vaccination. The third row shows the \(V_{n2}\) case with \(n=2\) used in the simulations. The rationale for the power \(n>1\) is not always clear, but it is inspired by the real data that shows that \(\tau_{reconv}/\tau_{group}\) is larger than 1, which means that \(n\), in the \(Vn\) model, is given by \(n\) = \(\tau_{reconv}/\tau_{group}\) and must be larger than one. The correct value for \(n\) should not concern us, only the fact that it is larger than 1. The choice for \(n=2\) is inspired by the pandemic curves with \(n\geq 1\) often. The risk is reduced by the increase in the average distance, \(a\), between uninfected people and freshly infected. Hence, \(1/\tau\propto 1/a\) and the distance, \(a\), between the 'items' is inversely proportional as:  
\[a\propto 1/\sqrt{S}, \tag{11}\]  
where \(S\left[km^{-2}\right]\) represents the surface concentration of the not-yet-infected people and still active spreaders of the COVID-19. Crowded streets result in a high local value of \(S\) and a smaller distance. That may make the factor: \(\left(1-\sqrt{N/M}\right)/\tau\) plausible in \(V_{n2}\). The rows in Table 1 show the coordinates of the maximum in the epidemic curve, \(t_{inf}\); \(\dot{N}\left(t_{inf}\right)\equiv\dot{N}_{max}\), and the value \(N\left(t_{inf}\right)\). In the models, \(V_{n1}\), \(V_{n}\) and \(V_{n2}\), the peak height of the epidemic curve, \(\dot{N}_{max}\), and \(N\Big{(}t_{inf}\Big{)}\) is independent of \(N_{0}\). The peak position, \(t_{inf}\), slightly (logarithmically) shifts to a later time for lower \(N_{o}\) values.