esa-sceva/satcom-chunk-collection huggingface dataset:
# Through-Floor Vital Sign Searching for Trapped Person Using Wireless-Netted UWB Radars
## 4 Results  
### Simulated Result  
In this section, the simulation is performed to illustrate the proposed vital sign searching concept in Section 3. According to the abovementioned \"searching path design\" method, as shown in Figure 6a, the 2D observation aperture is divided into 6 \(\times\) 6 grids and there is one observation point in each grid (marked by green star-circle). Then, in Figure 6b, four wireless-netted UWB life-detection radars (marked by four red pentagrams) can be deployed around one observation point. Note that only one observation point should be selected during one measurement. The observation point can be selected randomly for the first measurement. Subsequently, the observation point for the next measurement should be determined by the vital sign searching procedure. Finally, after several measurements, the vital sign of the trapped person can be searched, and the spatial location can be obtained. As shown in Figure 6c, the vital sign searching path is composed of four observation points, and the vital sign of the trapped person (marked by the black diamond) is searched nearby  
Figure 5: The vital sign searching procedure.  
the fourth observation point. The position of the simulated trapped person is set as (3.75 m, \(-\)3.75 m, 3.75 m).  
The detailed results in such a vital sign searching process are listed in Table 2. The first observation point is \"observation point 1 (\(-\)3.75 m, 4.583 m, 0 m)\", and the detection results obtained from no. 1-no. 4 radars around the observation point are 13.4 m, 12.61 m, 13.89 m, and 12.28 m, respectively. The corresponding output indicates the next observation point might be \"observation point 2 (0.42 m, 2.9 m, 0 m)\". Subsequently, the final observation point might be \"observation point 4 (3.75 m, \(-\)2.917 m, 0 m)\", and the output result of (3.72 m, \(-\)3.75 m, 3.765 m) should be the spatial location of the vital sign of the trapped person, which is in accordance with the simulated setup. This process can also be illustrated by Figure 7. The red dotted arrow points to the next possible observation point, and the light-blue dotted frame indicates the possible spatial location of the vital sign. Finally, the vital sign searching path composed of four observation points can be formed (marked by  
Figure 6: The schematic of searching a trapped person along a searching path. (**a**) The available observation points in the 2D observation aperture; (**b**) the wireless-netted radars are deployed around the observation point; (**c**) the vital sign searching path.  
the blue dotted arrows). The judgment rule can be summarized as: (1) the virtual elements located in this red dot-line frame should be considered as the next possible observation point; (2) one virtual element is selected as the next observation point which is nearest to one solution represented by a red dot; (3) the searching process represented by the blue dot-line arrow is completed when the rectangular suspected area is small enough and contains only one same virtual element by at least two iterations.
###### Abstract  
The use of underwater sensor networks (UWSNs) offers great advantages in many automatic observation services such as water monitoring (ocean, sea, etc.) and registering of geological events (landslides, earthquakes). However, UWSNs have many more limitations than terrestrial sensor networks (smaller bandwidth, higher delays, etc.) with new requirements such as low power consumption by nodes or being able to select appropriate routes in a dynamic topology due to water currents and movements. To cope with these problems, the use of a routing protocol is very important. In this paper we propose a routing technique that adapts to changes in the network topology, avoiding multiple retransmissions that would affect its overall performance. This protocol is energy-efficient and is implemented using a fuzzy analytical hierarchical process (FAHP) under multi-criteria decision making (MCDM) to make an intelligent routing decision based on objectives, criteria and alternatives. To select the next node on the route, several comparison matrices are used: number of hops, distances to the sink node, and number of neighbors. The results show that the proposed setup behaves similarly to other existing underwater sensor network routing schemes using fuzzy schemes such as SPRINT.  
fuzzy analytical hierarchy process; UWSNs; routing protocols 2022228930  
24 October 2022  
16 November 2022  
18 November 2022  
###### Abstract  
The use of underwater sensor networks (UWSNs) offers great advantages in many automatic observation services such as water monitoring (ocean, sea, etc.) and registering of geological events (landslides, earthquakes). However, UWSNs have many more limitations than terrestrial sensor networks (smaller bandwidth, higher delays, etc.) with new requirements such as low power consumption by nodes or being able to select appropriate routes in a dynamic topology due to water currents and movements. To cope with these problems, the use of a routing protocol is very important. In this paper we propose a routing technique that adapts to changes in the network topology, avoiding multiple retransmissions that would affect its overall performance. This protocol is energy-efficient and is implemented using a fuzzy analytical hierarchical process (FAHP) under multi-criteria decision making (MCDM) to make an intelligent routing decision based on objectives, criteria and alternatives. To select the next node on the route, several comparison matrices are used: number of hops, distances to the sink node, and number of neighbors. The results show that the proposed setup behaves similarly to other existing underwater sensor network routing schemes using fuzzy schemes such as SPRINT.
## 3 Analysis of the Tensile Strength
### Peak Stress  
According to the above stress-strain curves, the peak stress of sandstone with different bedding angles and different bedding strengths was extracted. It can be seen from Figure 4 that the peak stress of sandstone is controlled by the bedding angle \(\theta\) and the bedding strength when the bedding strength is smaller than the matrix. With the increasing bedding dip angle, the peak stress of ultra-weak sandstone and weak sandstone first declines and then climbs, forming an asymmetric \"U\" shape as a whole, with the smallest value at 60\({}^{\circ}\). Peak stress exhibits a distinct decreasing pattern from 15\({}^{\circ}\) to 60\({}^{\circ}\) and a sluggish increasing trend from 60\({}^{\circ}\) to 90\({}^{\circ}\); when the sandstone's bedding strength surpasses its strength, the bedding angle has no effect on the peak stress.  
The reason for this phenomenon is that the bedding planes of the above two specimens are both weak, and their existence will weaken the bearing capacity of the rock mass and make its failure stress level lower than that of the intact rock mass (i.e., the strength of the bedding and the matrix is equal). When \(\theta=0^{\circ}\)and 15\({}^{\circ}\), the damage model of the rock mass is the tensile damage that occurs through the bedding and along the bedding rupture, and the bearing capacity of the rock mass at this time mainly depends on the strength of the rock matrix, so its peak is slightly smaller than that of the intact rock mass. When \(\theta=30^{\circ}\), the damage type is mainly tension damage along with the bedding; local penetration rupture is produced, and its bearing capacity is affected by the joint influence of bedding strength and matrix strength. When \(\theta=60^{\circ}\), the damage model is tension damage completely along with the bedding, and its load-bearing capacity depends entirely on the strength of the bedding surface, so the peak value is the lowest. When \(\theta=45^{\circ}\), 75\({}^{\circ}\), or 90\({}^{\circ}\), the rock body occurs completely along with the bedding tension damage, and the bearing capacity mainly depends on the bedding strength, while the degree of influence of its matrix strength on the bearing capacity varies with the change in the dip angle. In addition, the interaction  
Figure 4: Peak stress curves of different sandstone specimens.  
between cracks can also affect its carrying capacity, and the peak stress is larger than when \(\theta=60\). When the strength of the bedding reaches the strength of the matrix, the peak stress does not affect the bedding angle. When the strength of the bedding increases within a certain range, the peak stress does not change with the strength of bedding. This is because even if the bedding strength is increased, the thickness of the bedding surface is very thin compared to the matrix, and the properties of the two are relatively similar at this time; the rock mass is a homogeneous material, and the mechanical properties are isotropic, so the bearing capacity will not be affected by anything.
## 5 Results
### Results of the PNR Algorithm for Fall Detection  
Data was transmitted throughout the network and to monitoring software after each event. However, the mobile node only relayed the values provided by the accelerometer to the monitoring software every 500 ms. Figure 9 provides the results obtained from the 40 falls, where the four types of falls (the two types of side falls were categorized as one) captured by the monitoring software are shown. The monitoring software was reset for each kind of fall to assure more precise data. The tests reveal that the mobile node transmitted a total of 1,446 entries for the four types of falls to the monitoring software, which were then processed, analyzed and graphed. The black line in the graph reveals that the SMV remains lower than 1.8 g for normal everyday activities (walking, standing, standing up, sitting down, _etc._). However, when the user falls, the line rises to levels above 1.8 g, which causes the mobile to alert both the network and the monitoring software of a possible fall. After the simulated fall, subjects remained immobile on the ground, simulating being hurt. This was reported by the high peak of the black line in the graph. Significantly, when the black line does not exceed 0.13 g between entries for more than 30 s the mobile device sends a critical fall alert to both the network and the monitoring software.  
In total, 56 possible falls and 41 critical falls were detected in the four tests, meaning that a total of 97 events were created. These events were then relayed across the network to the fall detection software. Only one false alarm occurred, which means that the PNR algorithm identified falls within the margin of error of 2.5%. Table 3 shows the generated DATA packets by falls.  
Figure 9: (**a**) Results from backwards falls; (**b**) Results from side falls; (**c**) Results from forward falls; (**d**) Results from knee falls.
## 5 Discussion
### Implications for Indoor-Outdoor Campus Navigation App Design
#### 5.4.7 Consistency  
The consistency between the user interface (UI) and the real world is crucial and should be addressed when designing maps and UI for navigation systems [95]. High interface consistency is expected to result in higher user satisfaction, fewer errors, and faster task completion [96]. To ensure consistency, it is important to match the elements of the application to those in the environment by understanding the naming conventions, landmarks, labeling, zones, icons, color codes, images and signage of buildings and places [73]. According to [95; 97], more attention should be paid to designing consistency between the real world and digital presentation when designing indoor maps because the shapes, colors, and materials of the environment vary from building to building.  
However, consistency in navigational signage used in the real world is also important to aid wayfinding in complex environments with multiple buildings. [PERSON] et al. [98] also recommended using consistent naming protocols for places with multiple buildings, such as educational institutions, although it is suggested to avoid alphanumeric coding as it is less memorable as compared to place names. However, it is also important to provide consistency within the alpha-numeric coding system (such as Room B3.7) when using it as a naming protocol. Moreover, ensuring consistency in sign types is also important to identify and recognize signage, for example, consistent colors, logos, and typeface; consistent graphic layouts; consistent materials and construction; and consistent overall appearance [98]. In the present study, participants reported some challenges related to university signage issues highlighting inconsistency in naming, numbering, and labeling for rooms, floors, and buildings. The suggestions for improvement focused on the need to ensure consistency, some of the comments included: \"There should be consistent numbering of floors\", \"Same buildings names in different platforms/navigation tools\", \"It is possible to mark every building by their name and number both\".
## 5 Example Analysis
### Example 1
#### 5.1.2 Stability Analysis
The stability factor calculated by Geo is the largest, the stability factor calculated by PLAXIS 3D is the second, and the stability factor calculated by this algorithm is the smallest. The stability factor calculated by this method, respectively, differs from the results simulated by Geo and PLAXIS 3D finite element software by 7.1% and 4.6%. By comparing Figure 18 with Figure 21, it can be seen that the position of the most dangerous slip surface obtained by this algorithm is close to that obtained by Geo. Although the location of the most dangerous slip surface cannot be directly obtained in Figure 20, the possible shape of the slip surface can also be seen from the range of areas that may be damaged. The shape of the slip surface reflected in Figure 20 is similar to that of the most dangerous slip surface in Figures 18 and 21, which fully shows that the slip surface search method in this paper  
Figure 21: Example 1 calculation results of the stability of Geo. Figure 20: Total displacement increment cloud map of 1:0.5 model. is reliable. The stability factor of this algorithm is smaller than that of two finite element software, which also shows that this algorithm has a certain security reserve.
# The Stationary Thermal Field in a Multilayer Elliptic Cylinder
###### Abstract  
An analytical-numerical method for determining the two-dimensional (2D) thermal field in a layer-inhomogeneous elliptic cylinder (elliptical roller) was developed in the article. A mathematical model was formulated in the form of a boundary problem for Poisson equations with an external boundary condition of the third kind (Hankel's). The conditions of continuity of temperature and heat flux increment were assumed at the inner boundaries of material layers. The eigenfunctions of the boundary problem were determined analytically. Hankel's condition was subjected to appropriate mathematical transformations. As a result, a system of algebraic equations with respect to the unknown coefficients of the eigenfunctions was obtained. The above-mentioned system of equations was solved numerically (iteratively). As an example of an application of the aforementioned method, an analysis of the thermal field in an elliptical electric wire was presented. The system consists of an aluminum core and two layers of insulation (PVC and rubber). In addition to the field distribution, the steady-state current rating was also determined. The thermal conductivities of PVC and rubber are very similar to each other. For this reason, apart from the real model, a test system was also considered. Significantly different values of thermal conductivity were assumed in individual layers of the test model. The temperature distributions were presented graphically. The graphs showed that the temperature drop is almost linear in the insulation of an electrical conductor. On the other hand, in the analogous area of the test model, a broken line was observed. It was also found that the elliptical layer boundaries are not isothermal. The results obtained by the method presented in this paper were verified numerically.  
Keywords:field theory methods; steady-state temperature field; multilayer elliptic cylinder; current-carrying capacity +
Footnote †: journal: Applied Sciences
# A Systematic Control Design Method with Active Damping Control in Voltage Source Converters
## 2 System Description and Modeling  
Figure 1 shows the principal scheme of the studied system, which includes primary energy sources (i.e., the solar power plant or the wind turbine systems), the power grid, back-to-back three-phase voltage source converter (VSC), an LCL output filter, and a control system. The power grid is represented by a Thevenin model seen from the point of common coupling (PCC).  
The grid inductance (\(L_{g2}\)) can also vary widely to mimic a weaker or stronger connection point. The remaining parameters in Figure 1 are as follows: \(i_{f}\), \(i_{g}\), \(v_{f}\), \(v\), \(v_{g}\), \(v_{dc}\), and \(i_{dc}\) are the converter and grid currents, voltage across the filter capacitor, inverter and grid voltages, dc-link voltage, and dc-side current, respectively. Moreover, \(L_{f}\), \(L_{g1}\), \(C_{f}\), \(L_{g2}\), and \(C_{dc}\) represent the converter and grid-side filter inductances, filter capacitance, equivalent grid inductance, and dc-link capacitance, respectively; \(\delta\) is the phase angle obtained by the PLL; and the subscript _ref_ gives the reference values for different control loops.
## 4 Results and Discussion
### Case 2: 6000 W NedStack PS6 Stack  
In the second case, Nedstack PS6 PEM stack, 65 fuel cells are in series with a rated power of 6 kW. The detailed technical characteristics are explained in Table 1. As procedures carried out for the first case study, the results obtained show that the best value of SSE over the 400 iterations is via the proposed COA. The best fitness is 2.10941 which is lower than the corresponding SSE obtained by the selected competitive methods SSA, SCA, MFO, and PSO. Moreover, the results, also, have been compared to the recent literature IAEO [44] and CFSO [45]. The results presented in Table 5 emphasize that COA outperforms all reported methods. The optimal parameters of the generated model using the optimization methods are presented in Table 5. The generating model used the estimated parameters have been used to construct the polarization (I-V) curve, as shown in Figure 7. Moreover, the effect of various losses is shown in Figure 7b. The high closeness between generated model and the data-set (I-V) confirms the accuracy of the proposed COA. Again, the convergence rate of COA is compared to others, confirming the effectiveness of the proposed CO
# An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline
## 4 Detection Algorithm and Its Applicability
### Performance Measurement
#### 4.2.2 Frechet Inception Distance (FID)  
The generated synthetic images are close to real images and could be difficult to distinguish them by human visual perception. FID is one of the most recommended and widely used metrics for calculating the similarities between real and synthetic images. It does so by calculating the Wasserstein-2 distance between the two images, real and synthetic, using the Inception-v3 neural network feature space. A lower FID value means higher quality and diverse image.  
\[FID=\big{|}\big{|}\mu_{r}-\mu_{g}\big{|}\big{|}^{2}+Tr\left(\sum_{r}+\sum_{g}-2 \left(\left(\sum_{r}\sum_{g}\right)^{\frac{1}{2}}\right)\ \right) \tag{15}\]  
where \(\mu_{r}\) and \(\mu_{g}\) represent the mean of the real and synthetic images, respectively. \(\sum_{r}\) and \(\sum_{g}\) represent the covariance matrices of the real and synthetic images, respectively.
# Profile Control Using Fly Ash Three-Phase Foam Assisted by Microspheres with an Adhesive Coating
## 3 Results and Discussion
### Plugging Performance
#### 3.4.3 Influence of Permeability Ratio  
As shown in Figure 7, the three-phase foam can remarkably plug the high-permeability channel and achieve satisfactory profile control, while exhibiting indistinctive damage to the low-permeability zone. The profile control efficiencies are about 0.89 and 0.87 for the two tests, respectively. The larger the permeability ratio is, the greater the fractional flow difference is in the parallel heterogeneous sand packs, and the worse the profile control performance is.
# Modeling GPR Wave Propagation in Complex Underground Structures Using Conformal ADI-FDTD Algorithm
## 2 Methodology
### UPML Absorbing Boundary Condition  
GPR electromagnetic wave propagation in underground structures is an open domain problem. Thus, it is necessary to set reasonable absorbing boundary conditions at the truncated boundary of the computational region. In this paper, the uniaxial anisotropic absorption layer (UPML) with easy programming and simple iterative formula is used as the absorption boundary [38]. The Maxwell curl equation in the UPML medium can be written as:  
\[\
abla\times H=j\omega\overline{\mathcal{S}}\cdot E \tag{36}\]  
\[\
abla\times E=-j\omega\mu\overline{\mathcal{S}}\cdot H\]  
where \(\overline{\mathcal{S}}\) has the characteristics of a uniaxial anisotropic medium, expressed as:  
\[\overline{\mathcal{S}}=\left[ \begin{array}{cccc}s_{y}s_{z}/s_{x}&0&0\\ 0&s_{z}s_{x}/s_{y}&0\\ 0&0&s_{x}s_{y}/s_{z}\end{array} \right]. \tag{37}\]  
Here, \(s_{i}=\kappa_{i}+\sigma_{i}/j\omega\epsilon_{0}\) (\(i=x,\,y,\,z\)) is the diagonal tensor of a uniaxially anisotropic medium (for a 2D GPR wave, \(s_{z}\) is 1), \(\sigma_{i}\) is the attenuation factor of the UPML region, and \(\kappa_{i}\) is used to absorb the modulated wave that reaches the UPML layer. For the 2D TM wave, \(E_{x}=0\), \(E_{y}=0\), and \(H_{z}=0\), and the ADI-FDTD formula under the UPML boundary conditions is shown below. When \(D_{z}=\varepsilon_{y}E_{z}\), the time advance formula of \(H_{x}\), \(H_{y}\)\(\rightarrow\)\(D_{z}\) is as follows:  
\[\frac{\partial H_{y}}{\partial x}-\frac{\partial H_{x}}{\partial y}=\kappa_{x} \frac{\partial D_{z}}{\partial t}+\frac{\sigma_{x}}{\epsilon_{0}}D_{z}, \tag{38}\]and the time advance formula of \(D_{z}{\rightarrow}E_{z}\) is:  
\[\frac{\partial D_{z}}{\partial t}=\varepsilon_{1}\kappa_{y}\frac{\partial E_{z}} {\partial t}+\varepsilon_{1}\frac{\sigma_{y}}{\varepsilon_{0}}E_{z}, \tag{39}\]  
When \(B_{x}=\mu_{1}H_{x}/s_{x}\) and \(B_{y}=\mu_{1}H_{y}/s_{y}\), the time advance formulae for \(E_{z}{\rightarrow}B_{x}\), \(B_{y}\) are as follows:  
\[\frac{\partial E_{z}}{\partial y}=-\kappa_{y}\frac{\partial B_{x}}{\partial t }-\frac{\sigma_{y}}{\varepsilon_{0}}B_{x}, \tag{40}\]  
\[\frac{\partial E_{z}}{\partial x}=-\kappa_{x}\frac{\partial B_{y}}{\partial t }+\frac{\sigma_{x}}{\varepsilon_{0}}B_{y}, \tag{41}\]  
and the time advance formulae for \(B_{x}\), \(B_{y}{\rightarrow}H_{x}\) and \(H_{y}\) are as follows:  
\[\kappa_{x}\frac{\partial B_{x}}{\partial t}+\frac{\sigma_{x}}{\varepsilon_{ 0}}B_{x}=\mu_{1}\frac{\partial H_{x}}{\partial t}, \tag{42}\]  
\[\kappa_{y}\frac{\partial B_{y}}{\partial t}+\frac{\sigma_{y}}{\varepsilon_{ 0}}B_{y}=\mu_{1}\frac{\partial H_{y}}{\partial t}, \tag{43}\]  
setting  
\[ \begin{array}{ll}ax=2\kappa_{x}/\Delta t+\sigma_{x}/2\varepsilon_{0}&ay=2 \kappa_{y}/\Delta t+\sigma_{y}/2\varepsilon_{0}&er=2/(\varepsilon_{1}\Delta t )\\ bx=2\kappa_{x}/\Delta t-\sigma_{x}/2\varepsilon_{0}&by=2\kappa_{y}/\Delta t- \sigma_{y}/2\varepsilon_{0}&hr=\Delta t/(2\mu_{1})\end{array}