esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Algorithm
### Visibility Test  
In theory, cameras can be placed anywhere in 3D space as continuous variables. However, due to the limitations of memory size and computational complexity, an approximation of the surveillance area by 3D grid points is widely used [17]. In this study, we assume that cameras are deployed at discrete grid points. This approach quantizes the search space into many finite candidate placements and searches for the best configurations of combinatorial problems that optimize a given cost function. Cameras are constrained to be placed only at these discrete grid points with the minimum distance between camera grid points equal to \(w_{s}\); further, the coverage of cameras is calculated based on grid points with the minimum distance between surveillance target grid points being equal to \(w_{o}\). Also, the azimuth and elevation are quantized into eight and five equiangular positions to reduce the search space.  
The minimum distance between the two grid points corresponding to a surveillance target and a camera is determined by the size of the surveillance area and the requirements related to the computation time and solution quality. If the minimum distance between two grid points decreases, the probability of finding better solutions and the search space of the camera placement problem increase. In addition, to reduce the search space efficiently, we define the minimum level of coverage for each grid point of the camera. If a grid point of the camera does not satisfy the minimum level of coverage, the point is excluded from the visibility matrix. The visibility test consists of two parts: the FoV test and occlusion test. The detailed procedures are described below:  
1. From geographic information, identify all available camera positions and surveillance regions as grid points.
2. Perform the FoV test: Calculate covered points for all available camera positions, types, azimuths, and elevations.
3. Perform the occlusion test: Adjust coverage points by considering occlusions for all available camera positions.
4. Obtain an adjusted visibility matrix based on the data.  
After performing the FoV test and occlusion test, a visibility matrix with camera placements--including possible positions and poses--is generated. In this study, a camera position in the visibility matrix is called a candidate camera placement, which is a combination of four parameters: position in 3D space (5), camera type, azimuth, and elevation. Before finding a solution for the camera placement problem, some of the points covered by a camera in the visibility matrix should be excluded because some planes of surveillance targets can restrict the FoV of cameras. This step is designed to find invisible areas and update the visibility matrix with these occlusions.
## 4 Conclusions  
In summary, we studied THz see-through imaging for NDT applications. For the study, we constructed a reflection-type, raster-scan-based frequency-tunable THz imaging system using a telecentric f-\(\theta\) lens. The effect of spatial filtering, frequency, and broadband interferometric imaging technique has been comprehensively studied. With a set of coordinate transformation equations, geometric image distortion was corrected and the sub-millimeter resolution was demonstrated at 0.67 THz. False contrast and image artifacts due to the interference were observed, which was removed by the broadband imaging. To overcome the material loss, we adopted a homodyne amplification effect that amplifies the signal intensity by more than eight times.  
Conceptualization, K.M. and K.H.P; methodology, K.M.; software, K.M. and I-M.L.; validation, K.M., I-M.L., E.S.L., and K.H.P.; formal analysis, K.M.; investigation, K.M. and E.S.L.; resources, K.H.P.; data curation, K.M.; writing--original draft preparation, K.M.; writing--review and editing, K.M.; visualization, K.M.; supervision, K.M. and K.H.P.; project administration, K.H.P.; funding acquisition, K.H.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the ICT R&D program of MSIP/IITP (2017-0-00619, Development of system for terahertz nondestructive evaluation system), Ministry of Trade, Industry, and Energy (MOTIE) and Ministry of SMEs and Startups (MSS) of the Korean Government (S2524372) and Electronics and Telecommunications Research Institute (ETRI) (20 ZB1130, 20 ZH1121).  
The authors declare no conflict of interest.
Figure 13: **(a)** Magnified view of the printed circuit board (PCB) in the commercial laser pointer. **(b)** THz image of the PCB board area in **(a)**, obtained after removing the plastic lid. The metallic patterns of the PCB board are viewed as bright lines. **(c)** THz image of the same area after closing the lid. As noted by the arrows, some parts of the metallic patterns are shown as dark lines, indicating that the image contrast is reversed. **(d)** Broadband THz imaging of the same area after closing the lid. The contrast of the metallic patterns is restored. The number of pixels is reduced in the broadband image.  
Figure 14: THz–time-domain spectroscopy (TDS) measurement of the plastic lid. **(a)** THz pulse transmitted through the plastic lid (red) and the reference pulse (blue) obtained without any sample. **(b)** Fast Fourier transform (FFT) spectra of the TDS results. **(c)** The refractive index of the plastic lid (real part). **(d)** The absorption coefficient of the plastic lid.
## 3 Results
### The Analysis of AA Content among Different Grades of MKH  
As the principal non-volatile compound in tea, free AAs mainly contribute fresh and sweet taste to the flavour quality of Congou black tea. Therefore, absolute quantifications of 19 free AAs were applied to determine the characteristic AA that could help to distinguish among the grades of MKH as quality reference. The results showed that theanine accounted for the major proportion of AAs, followed by Ser and Val. In detail, the total content of AAs in MKH-0 was significantly higher than in the other grade samples, which was consistent with the sensory evaluation results that this tea sample possessed a fresher and sweeter taste than the others. As for the other MKH tea samples, the content of AAs fluctuated and decreased along with the downgrading.  
As shown in Figure 3, 19 kinds of AAs could be clustered into three categories. The first categories included eight kinds of AAs including Asn, Arg, Ile, Thr, Thea, Ser, Pro, and Val. These eight compounds all showed a maximum content in MKH-0, and their content gradually decreased along with the downgrading. Moreover, there also existed a significant difference in specific AAs among the four grades of tea. Secondly, a total of four kinds of AAs made up the next categories, which were Ala, Trp, Met, and Glu. In this category, there was little difference among the different grades of tea. The final categories consisted of seven kinds of AAs, namely, Leu, Asp, Phe, Tyr, Gln, GABA, and His. The changing trend in this category was similar to the first category. However, most of the compounds in this part possessed less content, and there existed less difference among different grades of MKH.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compound/mg/g** & **MKH-0** & **MKH-1** & **MKH-2** & **MKH-3** \\ \hline C & 8.991 \(\pm\) 0.570 \({}^{\text{a}}\) & 6.448 \(\pm\) 0.254 \({}^{\text{b}}\) & 5.408 \(\pm\) 0.102 \({}^{\text{c}}\) & 5.490 \(\pm\) 0.122 \({}^{\text{c}}\) \\ EC & 12.105 \(\pm\) 1.471 \({}^{\text{a}}\) & 6.701 \(\pm\) 0.449 \({}^{\text{b}}\) & 3.521 \(\pm\) 0.236 \({}^{\text{c}}\) & 4.932 \(\pm\) 0.222 \({}^{\text{bc}}\) \\ EGC & 76.807 \(\pm\) 4.638 \({}^{\text{a}}\) & 57.608 \(\pm\) 2.281 \({}^{\text{b}}\) & 29.617 \(\pm\) 2.653 \({}^{\text{c}}\) & 25.538 \(\pm\) 1.098 \({}^{\text{c}}\) \\ EGCG & 133.316 \(\pm\) 8.235 \({}^{\text{a}}\) & 83.953 \(\pm\) 1.394 \({}^{\text{b}}\) & 33.508 \(\pm\) 3.130 \({}^{\text{c}}\) & 33.732 \(\pm\) 4.198 \({}^{\text{c}}\) \\ ECG & 25.650 \(\pm\) 1.199 \({}^{\text{a}}\) & 16.652 \(\pm\) 0.857 \({}^{\text{b}}\) & 7.993 \(\pm\) 0.355 \({}^{\text{d}}\) & 11.597 \(\pm\) 1.377 \({}^{\text{c}}\) \\ Caffeine & 24.455 \(\pm\) 1.416 \({}^{\text{a}}\) & 22.576 \(\pm\) 0.598 \({}^{\text{ab}}\) & 21.081 \(\pm\) 1.081 \({}^{\text{b}}\) & 22.427 \(\pm\) 1.357 \({}^{\text{ab}}\) \\ \hline \hline \end{tabular} Note: different lowercase letters (a-d) represent significant differences at \(p\) ¡ 0.05.  
\end{table}
Table 1: Abundance (mg/g, DW) of catechin and caffeine caffeine in relation to different grades of MKH.
## 2 Formulation of the Hybrid Method
### Scattered Field Generated by the TDPO Region  
The primary scattered field generated by the asymptotic region is calculated through the TDPO method. The TDPO method assumes that the scattered field is only generated from the tangent plane of each point on the geometric illumination side of the object, while the field is zero in the shadow area of the object [37]. Therefore, the TDPO method can be used to solve the scattering field of a large-scale perfect electrically conducting (PEC) object without considering the creeping wave.  
We can obtain the approximate surface current density distribution \(\overset{\rightarrow}{\int}_{\text{rs}}(R,t)\) excited by the transient source:  
\[\overset{\rightarrow}{\int}_{\text{rs}}^{\text{TDPO}}\left(\overset{ \rightarrow}{R},t\right)=\left\{\begin{array}{l}2\hat{n}\times\overset{ \rightarrow}{H}_{\text{inc}}\left(\overset{\rightarrow}{R},t\right)\\ 0\end{array}\right. \tag{9}\]  
According to the surface current density of the solution area defined by Equation (9), the computation of the scattered field is simplified as an integral over the Green's function:  
\[\overset{\rightarrow}{H}_{\text{TDPO}}^{\rightarrow\text{scat}}\left( \overset{\rightarrow}{R},t\right)\approx-\frac{1}{4\pi Rc}\hat{a}_{R}\times \iint_{st}\frac{\partial\overset{\rightarrow}{f}_{\text{rs}}^{\text{TDPO}} \left(\overset{\rightarrow}{R},t\right)}{\partial t}dSt \tag{10}\]  
\[\overset{\rightarrow}{E}_{\text{TDPO}}^{\rightarrow\text{scat}}\left( \overset{\rightarrow}{R},t\right)\approx-\frac{\eta_{0}}{4\pi Rc}\iint_{st} \frac{\partial\overset{\rightarrow}{f}_{\text{rst}}^{\text{TDPO}}\left( \overset{\rightarrow}{R},t\right)}{\partial t}dSt \tag{11}\]  
where  
\[\overset{\rightarrow}{f}_{\text{rst}}^{\text{TDPO}}\left(\overset{ \rightarrow}{R},t\right)=\overset{\rightarrow}{f}_{\text{rs}}^{\text{TDPO}} \left(\overset{\rightarrow}{R},t\right)-\left(\overset{\rightarrow}{f}_{ \text{rs}}^{\text{TDPO}}\left(\overset{\rightarrow}{R},t\right)\cdot\hat{a} _{R}\right)\hat{a}_{R} \tag{12}\]  
The scattered electric field \(\overset{\rightarrow\text{scat}}{E}_{\text{DGTD}}(r,t)\) can be solved through (5), and \(\overset{\rightarrow}{E}_{\text{TDPO}}^{\rightarrow\text{scat}}\) solved by (9) and (11).  
\[\overset{\rightarrow}{E}_{\text{TDPO}}^{\rightarrow\text{scat}}\left( \overset{\rightarrow}{R},t\right)\approx-\frac{\eta_{0}}{4\pi Rc} \tag{13}\]  
where \(\overset{\rightarrow}{R}\) is the distance between the excitation source point and the integration points on the TDPO region's surface; \(\overset{\rightarrow}{R}\) is the distance between observation points and the integration points on TDPO region surface. The terms \(\eta_{0}\) and \(c\) are the wave impedance and light speed in free space, respectively. Assume that the TDPO region's surface \(St\) is discretized into \(p\) triangular cells. Equation (14) represents the integral in (13) numerically:  
\[\overset{\rightarrow}{E}_{\text{TDPO}}^{\rightarrow\text{scat}}\left( \overset{\rightarrow}{R},t\right)\approx-\frac{\eta_{0}}{4\pi Rc} \tag{14}\]
## 3 Ventilated Supercavitation Achievement  
Cavitation is produced by a local pressure drop. When the speed of an object is high in the water, cavitation occurs to cover the body. The dimensionless number representing this physical phenomenon is the cavitation number. The cavitation number (\(\sigma_{\infty}\)) for the influent flow is as follows.  
\[\sigma_{\infty}=\frac{P_{\infty}-P_{V}}{\frac{1}{2}\rho V_{\infty}^{2}}, \tag{22}\]  
where \(P_{\infty}\) is the pressure around the object or inflow, \(P_{V}\) is the vapor pressure of water (fluid), \(\rho\) is the density of water (fluid), and \(V_{\infty}\) is the velocity in the direction of travel around the object or inflow. In general, the lower the cavitation number, the larger the size of the cavities. As \(V_{\infty}\) increases, \(\sigma_{\infty}\) decreases to achieve supercavitation. Achieving supercavitation by this method is called a natural supercavitation method.  
Another way to reduce the cavitation number is to reduce the difference between inflow pressure and another. If gas is artificially injected, it can give a pressure change for cavitation production. The case of achieving supercavity by this method is called vetilated superacavitation, and the cavitation number can be defined as follows.  
\[\sigma_{c}=\frac{P_{\infty}-P_{c}}{\frac{1}{2}\rho V_{\infty}^{2}} \tag{23}\]  
where \(P_{c}\) is the pressure value in the cavitation formed by the artificially injected air, and when \(P_{c}\), the pressure in the cavitation formed through the artificial injection, increases, \(\Delta P\) decreases and, as a result, \(\sigma_{c}\) decreases. This enables supercavitation to be achieved even at low inflow velocity conditions. Normally, when the cavitation number is the same, the shape characteristic of the cavitation by natural cavitation and ventilated cavitation is known to be the same [5].  
Therefore, in order to achieve supercavitation conditions by ventilation, gas injection is required. As the injection amount of gas increases, the ventilated cavitation number decreases and the cavitation shape fits the supercavition. At this time, the injection condition is related to the ventilated gas flux coefficient (Cq), and can be written as follows. In the case of the natural supercavitation, \(\sigma_{\infty}\) was reduced by increasing the thrust of the underwater vehicle.  
\[Cq=\frac{\dot{Q}}{V_{\infty}d_{C}^{2}}, \tag{24}\]  
where \(\dot{Q}\) is the supply flow rate and \(d_{c}\) is the cavitator diameter.  
In this paper, we investigated the relationship between the ventilated cavitation resulting from the ventilated gas flux coefficient, the angle of attack of the cavitator and body velocity.
# Energy-Efficient Resource Provisioning Strategy for Reduced Power Consumption in Edge Computing +
## 5 Resource Provisioning Strategy
### Resource Provisioning Strategy
```
1:\(N_{m}\), \(N_{s}\), C\({}_{m}\), \(C_{s}\), \(T\), \(C_{t}\)
2:Scheduling scheme for tasks
3:if\(N_{m}=0\)then
4:\(result\leftarrow\) MasterMaxTolerateTime
5:if\(result=true\)then
6:return send task to master device
7:endif
8:\(result\leftarrow\)SlaveMaxTolerateTime
9:if\(result=true\)then
10:return send task to slave device
11:endif
12:return send task to cloud
13:else
14:\(\gamma\leftarrow\frac{C_{m}\cdot N_{s}}{C_{s}\cdot N_{m}}\)
15:if\(\gamma<=1\)then
16:\(result\leftarrow\)SlaveMaxTolerateTime
17:if\(result=true\)then
18:return send task to slave device
19:endif
20:\(result\leftarrow\)MasterMaxTolerateTime
21:if\(result=true\)then
22:return send task to master device
23:endif
24:\(result\leftarrow\)SlaveMaxTolerateTime
25:if\(result=true\)then
26:return send task to slave device
27:endif
28:\(result\leftarrow\)SlaveMaxTolerateTime
29:if\(result=true\)then
30:return send task to slave device
31:endif
32:return send task to cloud
33:endif
34:endif
35:functionMasterMaxTolerateTime
36:returnMaxTolerateTime(C\({}_{m}^{r}\), \(T_{m}\), \(C_{m}\), \(N_{m}\));
37:endfunction
38:functionSlaveMaxTolerateTime
39:returnMaxTolerateTime(C\({}_{s}^{r}\), \(T_{s}\), \(C_{s}\), \(N_{s}\))
40:endfunction
41:functionMaxTolerateTime(C\({}_{r}\), \(T_{r}\), \(C\), \(N_{r}\))
42:add\(C_{t}\) and \(T\) into arrays \(C_{r}\) and \(T_{r}\);
43:\(i\gets 0\) ;
44:\(N_{r}\gets N_{r}+1\);
45:while\(i<N_{r}\)do
46:if\(D_{c}[i]>T_{r}[i]\)then
47:return\(false\)
48:endif
49:\(i\gets i+1\)
50:endwhile
51:return\(true\)
52:endfunction
```  
**Algorithm 3** Resource Provisioning Strategy
The functions we used in Algorithm 3 are shown in lines 27 to 39. We calculate task delay after adding the scheduled task to the device in lines 31 to 39. We first add the computing capacity and the max tolerate delay required form the scheduled task to the device in lines 32 to 33, and increase the number of the running tasks by 1 in line 34. We update the computation delays \(D_{c}[i]\) for the tasks on the device, where \(D_{c}[i]=\dfrac{C_{r}[i]\cdot N_{r}}{C}\). Then, we compare whether they exceeded their maximum tolerated delay \(T_{r}[i]\) in line 36. If any of the tasks exceed its maximum tolerated delay, the function will return false which indicates that the task cannot be added into the device. Otherwise, the function returns true. We use two functions to encapsulate the respective calculation parameters of the master and slave devices in lines 27 to 30.
# Review of Power System Resilience Concept, Assessment, and Enhancement Measures
## 2 Impacts of Extreme Events on Power Systems
### Characteristics of Extreme Event Impacts on Power Systems  
These rare events have a catastrophic impact on the power system, resulting not only in significant performance degradation but also affecting other related infrastructure, greatly increasing the difficulty of system recovery. Resilient power systems are more capable of withstanding interruptions associated with extreme weather, natural disasters, man-made events, and combinations of these events compared to traditional systems. Such power system interruptions exhibit the following characteristics [10, 53, 54]:  
* The prediction of the disaster and its progression are uncertain.
* Power system components and other critical infrastructure are severely damaged.
* Spatial and temporal impacts are associated with the power system performance.
* The process of repair and recovery is difficult, resulting in significant power outages.  
The impacts of extreme events on the power grid differ from those caused by more commonplace failures and are related to their own peculiar characteristics. For example, floods primarily disrupt the equipment of substations and distribution stations rather than the distribution network. Restoring stations submerged due to floods also requires more time. This indicates that power systems designed for typical power outages will face more challenges in recovering from power outages caused by natural disasters [55]. Additionally, the various types of damage caused by extreme events severely affect the reliability of the system, and mitigating these damages requires adopting different, and sometimes conflicting, measures. When the infrastructure is designed to withstand one type of disruption, it becomes more vulnerable when faced with another type. Extreme robustness in fact leads to system britteness, resulting in vulnerability to cascading events. Resilience also depends to a large extent on the type of damage, as improving resilience to one type of damage may reduce resilience to other types [17, 56].
# Thermomechanically Induced Precipitation in High-Performance Ferritic (HiperFer) Stainless Steels
## 3 Results and Discussion
### Impact of Deformation Parameter Variation  
#### 3.2.1 Temperature  
As demonstrated, the microstructure and mechanical properties of HiperFer alloys are readily adjustable using thermal and/or thermomechanical processing. For this reason, the impact of systematic variations in processing parameters (cf. Table 2) on the mechanical properties of the materials was studied. The temperature of deformation was varied from 800 \({}^{\circ}\)C to 950 \({}^{\circ}\)C. Figure 7a displays the corresponding changes in particle density and area fraction, derived from image analysis, and illustrative inlay micrographs. At 800 \({}^{\circ}\)C, the particles were clearly arranged in arrays, which was less apparent at 950 \({}^{\circ}\)C. While the area fraction of the Laves-phase precipitates remained stable (within the range of scatter), the particle density decreased significantly with increasing deformation temperature (deformation degree \(\varphi\) = 0.5, holding time t\({}_{\rm H}\) = 60 s). This was caused by a pronounced particle coarsening at the higher processing temperature (950 \({}^{\circ}\)C). Figure 7b displays the variation in size distribution of particles with increasing deformation temperature. At 800 \({}^{\circ}\)C, the largest ECD measured was less than 180 nm, with a plateau from values around 20 up to 80 nm. With increasing temperature during TMP0.5/60s processing, the maximum ECD value increased to about 400 nm. Most of the particles found had ECD sizes of around 125 nm and the plateau in distribution disappeared. A higher processing temperature implies higher diffusion velocities of Nb and W [35; 36], the main Laves-phase-forming elements, which boosts precipitation and subsequent particle growth kinetics. Despite the stable area fraction, the increase in particle diameter caused by higher deformation temperature led to a reduction in tensile strength but an increase in ductility (Figure 7c). This demonstrates how important it is  
Figure 6: SEM (**left**, arrows indicate plane of TEM view)/TEM (**right**) micrographs: intragranular precipitate morphology (IG\({}_{\rm c}\)) of MSP1 (**top**) and TMP800C/0.5/80s (**bottom**) materials.  
to not draw conclusions based exclusively on particle area fraction results. For decisive evaluation, a comprehensive analysis of both area fraction and size distribution is required.
# Main Issues in Big Data Security
## 3 Systematic Mapping Study
### Summary
#### Integrity and Reactive Security  
Integrity  
Integrity has traditionally been defined as the maintenance of the consistency, accuracy, and trustworthiness of data. It protects data from unauthorised alterations during its lifecycle. Integrity is considered to be one of the three basic dimensions of security (along with confidentiality  
Figure 6: Main topics as regards integrity and reactive security.  
and availability). Ensuring integrity is critical in a Big Data environment, and authors agree as to the difficulty of achieving the proper integrity of data when attempting to manage this problem.  
For example, they propose an external integrity verification of the data [53] or a framework to ensure it during a MapReduce process [54].  
Attack Detection  
As occurs with all systems, Big Data may be attacked by malicious users. Some authors, therefore, take advantage of the inherit characteristics of Big Data and suggest certain indicators that may be a sign that the Big Data environment is under attack.  
For instance, in [55] the authors develop a computational system that captures the provenance data related to a MapReduce process. There are also researchers who propose an intrusion detection system especially intended for the specific characteristics of a Big Data environment [56].  
Recovery  
The main purpose of this topic is to create particular policies or controls in order to ensure that the system recovers as soon as possible when a disaster occurs. Many organisations currently store their data in Big Data systems, signifying that if a disaster occurs the entire company could be in danger.  
We have found only a few papers that cover this problem. For example, in [57] there are some recommendations regarding what can be done to recover from a desperate situation.  
Summary  
In this section, the main topic discussed by researchers would appear to be the integrity of data. In order to secure that integrity, they propose various kinds of verification to ensure that the data has not been modified. This section also covers the possibility of detecting the attacks that a Big Data system may undergo. There is a lack of papers dealing with the possibility of disaster occurring in a Big Data system, and how to recover from it. This is probably a consequence of the high availability that a Big Data system usually achieves, but this topic should not be overlooked.