esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Stability Analysis
### Analysis of the Movement of the Surrounding Rock  
The surface displacements of the ore body in the Beiyi mining area at different stages of back mining are shown in Figure 10. The blue part of the figure is the part with displacement over \(1\) m, which can be regarded as the surface collapse zone, and the part with displacement over \(0.1\) m is regarded as the sinkhole zone, which is divided by color according to the specific magnitude; the part with a displacement less than \(0.1\) m is not shown. It can be seen that as the mining proceeds, the surface collapse zone gradually increases and the sinkhole range develops more rapidly.  
Figure 10: Surface displacement at different levels during mining in Beiyi mining area (unit m). (**a**) is the \(0\) m level, (**b**) is the \(-120\) m level, (**c**) is the \(-240\) m level, (**d**) is the \(-360\) m level.  
From Figure 10a, it can be seen that when mining from a +30 m to 0 m level, the collapse area of the three parts of the original side gang gradually increases, among which the collapse area of the east gang expands to the southeast to the upper elevation of 225 m. At this time, the range of surface displacement greater than 0.1 m was basically near the collapse area. At this time, the surface was not affected by a large range, and it was limited to the area around the east gang and the foot of the south and north gang slopes. After mining to a \(-\)120 m level, with a 0 m elevation at the bottom of the open pit as the reference, it can be seen that the size of the eastern caved zone in the plane increased from 285 m \(\times\) 385 m to 430 m \(\times\) 516 m. The sinkhole area obviously expanded to the east, south, and north, forming a wide range of mining disturbance area. Comparing Figure 10a,b, it can be seen that when the well was mined to \(-\)120 m, the east of the slope foot of the west gang of the open-pit side gang was a collapsed area on the east side, which was extended to 315 m.  
From Figure 10c, it can be seen that the collapse zone to the west of E9 is basically unchanged when mining reaches \(-\)240 m, which is consistent with the orebody deposit. Compared with the 0 m mining level, the surface displacement is more than 0.1 m on both sides of the open pit at the \(-\)120 m level, which is mainly due to the maximum main stress unloading caused by the mining of the ore body. The stress unloading of the rock layer then produces a large horizontal displacement. Comparing the extent of surface cave-in zone, shown in Figure 10c,d, it can be seen that the extent of surface cave-in zone basically does not change from \(-\)240 m to \(-\)360 m at the mining level, which is mainly due to the small scale of the ore body below the \(-\)240 m level in the Beiyi East quarry, and it is difficult to develop the bubble fall of the surrounding rock on the surface.
# Reversible Data Hiding in Encrypted Images Based on the Mixed Multi-Bit Layer Embedding Strategy
## 3 Proposed Scheme
### Pre-Processing
In Figure 2a,b show the two types of image blocks obtained after the first-round segmentation. (a) describes the AHB. The value of the marker position (LSB) is 1 and the block type is \(BT_{1}\). The data are embedded in 2-\(l_{1}\) bytes. (b) describes the UHB, which needs to be segmented for the second round to obtain sub-blocks \(P\) and \(Q\). The marker value is 0. \(P\) may be as (c) and (e), and \(Q\) may be as (d) and (f). There are two possible types of each sub-block. Hence, \(P\) and \(Q\) have four combinations, such as (c) and (d), (e) and (f), or other combinations, but the total length of \(P\) and \(Q\) is \(l_{1}\). The type of AHBs in \(P\) and \(Q\) is \(BT_{2}\). The data are embedded in 2-\(l_{2}\) bytes or \(l_{2}+1\)-\(l_{1}\) bytes. The specific segmentation method will affect the performance of the scheme. As shown in Figure 2, the specific location is marked, and so the marked image is obtained. In addition, the mark value and two location maps, Map1 and Map2, are generated. Among them, Map1 is used to store the marks of first-round segmentation and Map2 is used to store the marks of second-round segmentation. At the same time, the original LSB of \(x_{1}\) and the length: \(S_{1}\), \(S_{2}\), \(L_{S_{1}}\), and \(L_{S_{2}}\) are also recorded. \[L_{S_{1}}=(M\times N)/(m\times n). \tag{5}\]  
\[L_{S_{2}}=N_{2}+N_{3}=2\times((M\times N)/(m\times n)-N_{1}). \tag{6}\]  
Similarly, \(E_{2}\) of sub-blocks \(P\) and \(Q\) is obtained in the same way as \(E_{1}\). So far, all auxiliary data: \(S_{1}\), \(S_{2}\), \(E_{1}\), and \(E_{2}\), and their length: \(L_{S_{1}}\), \(L_{S_{2}}\), \(LE_{1}\), and \(LE_{2}\) are obtained.
## 3 Results and Discussion
### Oxide Layer Adhesion Results
Commonly reported limitations of hydroxyapatite-based coatings on titanium alloys include their inherent brittleness and instability [18; 19; 74]. However, the Mg-doped hydroxyapatite and tricalcium phosphate anodization coatings on the CPTi and TAV alloy substrates in the present study exhibited promising adhesion strengths. Other studies have reported that hydroxyapatite coatings with low crystallinity may exhibit excessive solubility and, thus, excessive ion release, which may limit their long-term stability and biocompatibility [51; 75]. In future studies, the solubility and ion release characteristics of the promising Mg-doped hydroxyapatite and tricalcium phosphate anodization coatings will be investigated. Finally, the use of hydroxyapatite anodization coatings would only be indicated for clinical applications where long-term osseointegration is a goal. Thus, their use would not be recommended in many temporary applications that use titanium implants. Figure 8: (**A**) EDS Ca-, P-, and Mg-dopant uptake levels in the outermost two microns of each oxide group; (**B**) corresponding EDS Ca/P ratios within the outermost two microns of each oxide group. These Ca/P surface ratios ranged from 1.3 to 1.7, which are within the range of bone tissue.
# Green Synthesis Method and Application of NaP
## Results and Discussion
### XRD Result Analysis
#### 3.2.6 Influence of Crystallization Time  
Figure 2f shows the XRD patterns of the synthesized products at different hydrothermal crystallization times (6-48 h) at 24 h and 105 \({}^{\circ}\)C. It can be seen that when the crystallization time is 6 h, CGCS does not react and remains amorphous. When the crystallization time is 12-48 h, NaP zeolite becomes the main crystalline phase, because of long-term pressure of the amorphous phase, the crystallinity of zeolite decreases with time [21]. Therefore, the optimal crystallization time to reach the maximum crystallinity is 12 h.  
The above conclusions confirm that, after the CGCS and NaOH solution are thoroughly mixed, activated molecules of SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) will be formed on the surface, and the interaction between the two will cause the active ingredients in CGCS to fall off the particle surface. When the alkaline solution is heated, the active components of the unit cell grid are destroyed by OH\({}^{-}\). The Si-O-Al and Si-O-Si bonds on the surface of the particles break, and new Si(OH)\({}_{5}\)- equipotential ions are formed, causing the surface of the particles to be negatively charged. Under the action of static electricity, Na\({}^{+}\) adheres to the solid particles in the solution. In addition, the active SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) react with Na\({}^{+}\) to form a hydrated sodium aluminosilicate gel and cover the surface of the CGCS, hinder the penetration and diffusion of active components, and reduce the diffusion speed of active molecules through the coating. The silicon-aluminum ratio leads to the continuous generation of crystal nuclei in the gel layer, and a complex and ordered grid-like channel structure appears, which is conducive to the diffusion of the molecules in the inner layer and accelerates the silicate condensation and crystallization. With the extension of time, crystal particles such as Na\({}^{+}\) accumulated on the surface of CGCS finally formed a stable NaP zeolite structure [22; 23].  
Based on the above results, we selected two NaP zeolites synthesized by adjusting the silicon-aluminum ratio (SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) = 3.40) and experimental methods (SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) = 5.20), and compared their performance differences under the same experimental conditions.
## 4 Narrowing the Digital Divide? Or, It Is Expensive to Be Poor
This suggests that teens without other means of going online value internet access enough to pay for it themselves. Another issue is whether mobile communication (or mobile internet) can be seen as a necessity given that relatively impoverished people are willing to pay such a high price (expensive handsets that can access the internet plus the cost of expensive subscriptions). As noted above, one way to test this would be to calculate an Engel curve that would compare the expenditures on internet for different income levels. This would, in all likelihood, show that the more impoverished people spend a higher portion of their income on the internet than those with higher income. In reality richer teens would probably also pay less in absolute terms since they often have alternative forms of access (via a PC paid for by their parents or eventually at a job). The willingness of poor people to pay for internet access underscores the idea that net access is not just \"nice to have\" but that it is increasingly seen as a necessity. While the calculation of an Engel curve would likely show this, the metrics for measuring this are very messy. Indeed it is not possible to make this calculation using the data from the Pew study. First, income for teens is difficult to calculate. Some teens have their own jobs; others are completely subsidized by their parents. Thus, there is no simple metric for individual income. A proxy for this could be household income. Again, however, there are problems since the household income per person dwelling in the home is not immediately available. Another set of issues arises when thinking about money spent on internet access. For some teens, this is provided free of charge by their parents via a PC and an internet subscription. For others, the teen must buy their own access terminal, such as a smart phone, and then pay for their internet traffic. There is a range of alternatives between these two poles. While we have many items that suggest that impoverished teens pay more for inferior internet access, we cannot specifically calculate the elasticity of this demand curve. Thus, while this paradox indicates a potential means of narrowing the digital divide, the cost is absorbed by those who can least afford it: Low income and minority teens. As discussed above, the digital divide is not always a simple matter of access or no access. Instead, it is a complicated series of fissures dividing people by quality and type of access, in addition to types and competency of use. The present paper suggests that purchasing the computer necessary for cheaper internet access is prohibitively expensive for households with lower incomes. Opting for the initially cheaper mobile access, the poor ultimately pay more for the internet, as mobile internet subscriptions are more expensive in the long run. Thus, the equipment divide is not narrowed. In sum, it is expensive to be poor.
# Cooperative Multi-UAV Collision Avoidance Based on a Complex Network
## The Proposed Collision Avoidance System Architecture  
Many algorithms in the collision avoidance system support its operation. The core step of the system is to process the information of each trajectory input, which is the four-dimensional (three-dimensional (3D) position + time) information of each UAV, and an analysis on whether any threat is present in the next time interval. If a threat is detected, the system immediately reacts to calculate a suitable CA strategy. The instructions are subsequently communicated to each UAV to synergistically resolve the current threat.  
The distributed dynamic optimization approach (DDOA) is used to generate suitable direction changes for the UAVs such that the problem of CA can be mitigated. The core of this approach consists of two parts: The conflict detection (CD) module is used to forecast the threats, and the conflict resolution (CR) module is applied to resolve the detected conflicts. The complex network (CN) aims to avoid collisions by choosing the suitable UAV and the corresponding UAV with a local optimization scope. The CN algorithm only allows for one UAV to perform conflict resolution at one time, which is the principle of DDOA.
# Bilinear and linear forms  a = dot(grad(v), grad(u))*dx + k*djump(v)*djump(u)*dc  L = v*f*dx
## 5 Examples
### Three-dimensional elasticity  
For a linear elasticity problem with homogeneous Dirichlet boundary conditions and traction-free discontinuity surfaces, the bilinear and linear forms read:  
\[a\left(\boldsymbol{v}_{h},\boldsymbol{u}_{h}\right) =\int_{\Omega\setminus\Gamma_{d}}\
abla\boldsymbol{v}_{h}:C:\
abla^ {s}\boldsymbol{u}_{h}\,d\Omega, \tag{25}\] \[L\left(\boldsymbol{v}_{h}\right) =\int_{\Omega}\boldsymbol{v}_{h}\cdot\boldsymbol{f}\,d\Omega, \tag{26}\]  
where \(C\) is the elasticity tensor, \(\
abla^{s}\left(\cdot\right)=\left(1/2\right)\left(\
abla\left(\cdot\right)+ \left(\
abla\left(\cdot\right)\right)^{T}\right)\) is the symmetric gradient and \(\boldsymbol{f}\) is the body force vector. For this problem, the relevant finite element space reads  
\[V=\left\{\boldsymbol{v}\in\left(L^{2}\left(\Omega\right)\right)^{d}\cap\left(H _{0}^{1}\left(\Omega\backslash\Gamma_{d}\right)\right)^{d},\boldsymbol{v}|_{ \Omega_{e}}\in\left(P^{k}\left(\Omega_{e}\backslash\Gamma_{d}\right)\right)^{ d}\right\}. \tag{27}\]  
The form compiler input for this problem in three dimensions using linear tetrahedral elements is shown in Figure 5. The input is dimension independent; \"tetrahedron\" can simply be changed to \"triangle\" to change from three dimensions to two dimensions.  
A computation has been performed for a unit cube \(\Omega=\left(0,1\right)\times\left(0,1\right)\times\left(0,1\right)\), with two discontinuity surfaces. The displacement is prescribed on the top and bottom faces of the cube (\(\boldsymbol{u}_{h}=\boldsymbol{0}\) for \(z=0\) and \(\boldsymbol{u}_{h}=\left(0,0,1\right)\) for \(z=1\)) and all other faces are traction-free, no body force is applied (\(\boldsymbol{f}=\boldsymbol{0}\)), [PERSON]'s modulus \(E=2\times 10^{4}\) and [PERSON]'s ratio \(\
u=0.2\). As with the previous example, descriptions of the discontinuity surfaces can be found in the supporting material [12]. The surfaces and the computed displacement contours are illustrated in Figure 6. As expected, the presence of the discontinuity surfaces is reflected in the contours of the magnitude of the displacement field across the discontinuity surfaces.  
Figure 5: Form compiler input for a three-dimensional linear elasticity problem with discontinuous \(\boldsymbol{u}\).
## 3 Procedures of InSAR Image Co-registration
### Extraction of Feature Points for Tie Point Matching  
Generally, the points with special features, such as intersection and airports, are possibly selected as tie points and if feature points evenly distributes in the image will improve the reliability of co-registration. However, such feature points can not always be found in an image. Therefore, the connotation of feature points should be enlarged. Some methods have been used to extract feature points from images. One of the methods is Interest Operators [69], which is based on the maximum gradient to extract feature points as shown in Figure 8(a). However, each pixel gradient is calculated based on the original whole image. Therefore, too much calculation is needed.  
Figure 8: Feature points extracted by different ways. (a) feature points extracted by Interest Operators; (b) feature points extracted by wavelet transform.  
Wavelets can also be used to extract feature points from images [70]. Therefore, an enlarged connotation of feature points in wavelet domain for InSAR image co-registration is developed by [PERSON] [71]. A method to extract feature points with wavelet transform to serve as tie points for co-registration is implemented shown in Figure 8(b). The extraction of feature points is based on the wavelet gradient modulus maximum. Compared with Interest Operators, with wavelets the wavelet gradient is implemented not in the original image but at its highest level so that it reduces the complexity of computation and makes the processing fast. In order to make the co-registration more reliable, the feature points extracted by wavelet and the points selected in grid form are combined together used as tie points [71]. Good experimental results have been achieved. Therefore, this can be considered as a recommended operation for feature extraction.
## 1 Introduction  
Life expectancy around the world has increased in the last years [1], which is good news, but it also implies an increase in medical care costs. For example, in Mexico, in 1930, people lived on average 34 years; 40 years later, in 1970, this indicator stood at 61, and by 2013, it was 76 years, [2]. In the same way, life expectancy in the U.S. has increased significantly; the number of adults between 60 and 80 years of age is expected to double in 2050 compared to the number registered in 2000 [3].  
Life expectancy is not the only important factor for better medical care, e.g., traffic accidents also call for better and opportunistic emergency health services. Adequate and immediate medical assistance after an accident can minimize the possibilities of permanent injury or even death. Traffic accidents in Mexico are one of the main causes of injuries according to INEGI 2011, and are the second leading cause of death [4]. Table 1 shows the main external injury causes of death in Mexico in 2011.  
Figure 1 shows leading causes of death in Mexico from 2006 up to 2011. Traffic accidents and homicides are the leading causes of death that are unrelated to illnesses of internal organs.  
Therefore, availability of an efficient Wireless Body Sensor Network (WBSN)-based platform, capable of reporting data remotely to a medical center, is very important to increase the chances of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Injury** & **Rate of Men** & **Rate of Women** & **Total Rate** \\ \hline Homicides & 41 & 4.8 & 23.3 \\ Traffic accidents & 22.2 & 6.1 & 14.4 \\ Suicides & 7.8 & 1.9 & 4.9 \\ Falls & 3.2 & 0.8 & 2 \\ Drowning & 3.2 & 0.6 & 1.9 \\ Burns & 0.8 & 0.3 & 0.5 \\ Poisonings & 1.8 & 0.5 & 1.2 \\ Total injuries & 80 & 15 & 48.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main indicators of external injury causes. INEGI, 2011 mortality rate per 100,000 inhabitants.  
Figure 1: Main causes of death in Mexico, 2006–2011. Source: Death registry INEGI, 2011.  
survival. These networks are capable of transmitting data from images, vital signs and video. Information is acquired from sensors and cameras and upgraded through an electronic process for transmission and processing in a hospital using a friendly interface to the user. A platform example, is the _Emergency Remote Pre-Hospital Assistance_ (ERPHA), a telemedicine system to deliver medical care to the patient at the location of an accident [5]. To successfully implement the ERPHA platform, the requirements for the provision of remote health services in real time are valuable information for the successful integration of mobile devices and wireless communication networks.
## 3 Results and Discussion
### Partial Least Square Regression (PLSR)

Table 3: The partial least square regression (PLSR) model developed for predicting anthocyanins content in rice using short wave infrared hyperspectral technique by applying the different pre-processing methods. accuracy (\(R^{2}_{p}=0.94\), RMSE = 0.0046 mg/g) by PLS with smoothed spectra [40]. Our result also revealed that model performance based on the powder was slightly better, denoted by a higher correlation coefficient of calibration (\(Rc^{2}\)) and validation (\(Rv^{2}\)) (0.94 and 0.93, respectively), and lower standard error on both calibration and validation datasets. This result was understandable due to powder samples being more homogenous than seed samples. Hence, the result will open up the door for using the NIR-HSI system to separate rice seeds based on the anthocyanins content. The bands which contributed to the model development can be identified using the beta coefficient curve (Figure 3C,D). The specified bands of the seed and powder sample were similar, around 1140 to 1350 nm, which can be associated with the CH second overtone, and around 1450 nm representing the CH combination from the aromatic structure. Both bands were in the range of influential bands used for predicting anthocyanins in Jaboticaba fruit (_Myricaria jaboticaba_ (Vell.) [PERSON]) [41]. The previous study by [40] also reported three important wavebands similar to our finding (1050, 1250, and 1400 nm), which contributed significantly to the development model predicting anthocyanins in wine grapes using NIR hyperspectral imaging. This range of wavebands between 1415 and 1512 nm is also reported as essential wavebands for flavonoid constituents [42].
# Experimental Study on Heat Transfer Performance of Vacuum Tube Heat Collector with Thermal Storage
## 4 Result and Discussion  
### Working Condition in Daytime of Transitional Season  
Figure 6 shows the transient solar radiation intensity in daytime. As can be seen from Figure 6, the solar radiation was strong on that day. The maximum radiation appeared during the daytime between 12:00 and 13:00, up to 850 W/m\({}^{2}\). The radiation value rose steadily in the morning. There were nearly 200 W/m\({}^{2}\) fluctuations at 11:30. The radiation value was very small after 16:00 and was below 100 W/m\({}^{2}\). Figure 7 shows the change of outdoor air temperature with time in daytime. From Figure 7, we can see that the peak temperature reached 33 \({}^{\circ}\)C and the lowest outdoor temperature was 18 \({}^{\circ}\)C. The average air temperature during the daytime was 27 \({}^{\circ}\)C.  
The temperature variations of different position during the period of accumulating solar energy are shown in Figure 8 to explore the heating effect induced by PCM on the water inside the U-tube. As is shown by the graph, the paraffin had not yet melted by 09:00. Then, the top part of the paraffin, facing the sun, went into the molten state and the temperature climbed slowly. At the same time, the temperature of the paraffin inside and at the bottom of the tube was still rising. After 10:40, the paraffin on the top completely melted into liquid and the slope of the temperature curve increased obviously. As a result, the temperature of the paraffin on the top was much higher than that of the paraffin in the middle and at the bottom of the tube. At 11:00, the paraffin in the middle of the tube turned into the molten state. At 11:30, the paraffin at the bottom of the tube entered into the molten state. Due to the natural convection, the heat transfer inside the tube was enhanced. The paraffin temperature difference between the middle and bottom was decreasing after the paraffin had melted. Here, the amount of heat absorbed by PCM in the daytime of the transitional season is estimated to be 2666.2 kJ.