esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Finite Element Simulation and Verification
### Finite Element Model Verification
#### 3.2.4 Earth Pressure  
The peak \(p\) (\(p_{\text{u}}\)) of different cases were derived based on the locations of the earth pressure measurement points in Figure 5a, as illustrated in Figure 15. Solid and hollow symbols are used to symbolize the \(p_{\text{u}}\) obtained from the test (\(p_{\text{u-TEST}}\)) and FEM (\(p_{\text{u-FEM}}\)), respectively. It is found that the distribution patterns of \(p_{\text{u-FEM}}\) and \(p_{\text{u-TEST}}\) are similar. Under push \(u_{\text{t}}\), the \(p_{\text{u-FEM}}\) of \(S_{4}\) is the largest, while values at the other measurement points are nearly zero. Under pull \(u_{\text{t}}\), the \(p_{\text{u-FEM}}\) of \(S_{5}\) is the largest, the \(p_{\text{u-FEM}}\) values of \(S_{3}\), \(S_{2}\), and \(S_{1}\) decrease gradually, and the \(p_{\text{u-FEM}}\) of \(S_{4}\) is nearly zero. \(p_{\text{u-FEM}}\) is larger than \(p_{\text{u-TEST}}\). This may be due to the presence of voids in the sand at the start of the test, which could increase the sand compression space. In contrast, the FEM usually assumes the sand to be a continuous medium without initial voids.  
Figure 14: Sand strain contour plots: **(a)** H21-Push; **(b)** H21-Pull; **(c)** H31.5-Push; **(d)** H31.5-Pull; **(e)** H63-Push; and **(f)** H63-Pull. (The densely contoured regions with a bluish hue and reddish hue, denoted by dashed lines, represent compressive strain and tensile strain of sand, respectively).  
Figure 15: \(p_{\text{u}}\) obtained from tests and FEM: **(a)** Push; **(b)** Pull.
# An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments
## 4 Case Study
### Case Description
A wide range of methods allowing high detection rates have been proposed, and recently, there are research works for achieving a real-time detection process in order to run this ECG analysis applications into wearable biosensors [90; 91; 92; 93]. \(\mathfrak{t}_{5}\): _Feature extraction_: This is the core tasks of the ECG analysis. Any information extracted from the heartbeat used to discriminate its type maybe considered as a feature. Using the wavelet transforms is a suitable method for extracting features from the ECG signal from both frequency and time domains [97; 98]. \(\mathfrak{t}_{6}\): _Anomaly detection_: this task consists in inferring the corresponding user's heart condition: healthy, heart failure, myocardial infarction, etc. In this field, there also exists intensive research work [50; 51]. Recently, machine learning methods offer a good tradeoff between accuracy and computational cost [94; 99]. \(\mathfrak{t}_{7}\): _Visualization_: Visualization of the data and results. This task can show the information in real-time or on-demand. The medical staff can obtain aggregate data from the team of players. \(\mathfrak{t}_{8}\): _Store Data_: The data and the results of analysis can be stored in the computer for further processing. For example, conducting aggregate big data analysis. \(\mathfrak{t}_{9}\): _Raise alarm_: In case where the user is in a risk situation for health, the system can raise an alarm to the medical staff and to the user itself. The relations between the tasks are defined according to the data flows depicted in the figure. Some tasks can be processed sequentially and others can be performed in parallel. The results obtained can be stored, displayed on the mobile device and/or an alarm can be raised if necessary.
# An Object Detection and Localization Method Based on Improved YOLOv5 for the Teleoperated Robot
## 2 Method
### Object Detection
#### 2.1.2 Improvement of YOLOv5 Network Structure  
Feature Fusion Network Structure Design  
In the YOLOv5 model, a feature pyramid module combining feature pyramid network (FPN) [30] and path aggregation network (PAN) [31], is used in the Neck network to achieve deep fusion of feature maps at multiple scales, as shown in Figure 2. Greater location information exists in the bottom-level feature maps, which is helpful for locating objects, whereas stronger semantic data exists in the high-level feature maps, which is helpful for classifying objects. The FPN structure enhances the semantic information of the predicted feature maps, establishing a top-down path, and the low-level features are fused with high-level features through upsampling. The PAN structure improves the location information of the predicted feature maps, which establishes a bottom-up path, and the high-level features are fused with the low-level features through downsampling. The FPN network and the PAN network make the predicted feature maps have both high semantic information and location information, which greatly improves the accuracy of the object detection task.  
Although the FPN structure and the PAN structure can fuse multi-scale features, the feature maps are simply superimposed, and all input features are treated equally without distinction when fusing different input feature maps. However, different input features have varying levels of resolution, and they typically make up the output feature inequitably. [PERSON] et al. [32] developed a straightforward and effective bidirectional feature pyramid network (BiFPN) as a solution to this problem, as shown in Figure 2. The BiFPN network repeatedly applies top-down and bottom-up multi-scale feature fusion and learns the importance of different input features using learnable weights to achieve a weighted  
Figure 1: The input terminal, the Backbone network, the Neck network, and the head output terminal are the four parts of the classic YOLOv5 network structure.  
feature fusion mechanism. The feature pyramid structure of the Neck network in the classic YOLOv5 model is changed to the BiFPN network structure in our work to improve the feature expression ability of image features and the model's detection accuracy.
# Temperature Condition Analysis for Freezing on Tunnel Lining Back Side According to Tunnel Length
## 2 Background
### [PERSON]'s Law  
Thermal conduction is presented with the energy flow caused by the temperature difference between materials. Heat is transferred from high temperatures (T\({}_{1}\)) to low temperatures (T\({}_{2}\)), and energy equilibrium between materials appears after a certain period, as shown in Figure 2a. Figure 2b shows that the heat transfer occurs inside a tunnel between the warm surrounding ground heat and the cold air temperature inside a tunnel in the winter (T\({}_{1}\)\(\rightarrow\)T\({}_{2}\)). The rate of heat transfer is proportional to the area and the temperature gradient (\(dT/dx\)) according to [PERSON]'s law [14].  
The rate of heat transfer can be expressed as in Equation (1). The heat flows from high temperature to low temperature and has a negative sign according to the second law of thermodynamics [15]. The railway tunnel for drainage was designed in the order of concrete lining, waterproofing membrane, and shotcrete. The freezing problems in the tunnel can be solved by the LUSAS Thermal Program with the correlations of the air temperature inside the tunnel, the ground temperature, and the tunnel structures.  
\[\dot{Q}=-kA\frac{dT}{dx} \tag{1}\]  
Here, \(\dot{Q}\): The rate of heat transfer; \(k\): Thermal conductivity of the material; \(A\): Area; \(dT\): Temperature difference; \(dx\): Thickness.  
Figure 1: Freezing sequence inside the tunnel. (**a**) Step 1: Inflow of outside air into the tunnel; (**b**) Step 2: Heat transfer to the back of the waterproofing membrane.  
Figure 2: Conduction heat transfer inside the tunnel according to [PERSON]’s law. (**a**) Fourier’s law [14]; (**b**) Conduction heat transfer inside the tunnel.
###### Abstract  
Vehicle to everything (V2X) is widely regarded as a critical application for future wireless communication networks. In V2X, large relative speeds between vehicles may severely deteriorate the performance of communication between vehicles. Orthogonal time frequency space (OTFS) modulation, which converts time- and frequency-selective channels into non-selective channels in the delay-Doppler (DD) domain, provides a solution for establishing reliable wireless communications in V2X scenarios. However, in the complex multi-scattering scenarios, the channel also suffers from a serious inter-Doppler interference (IDI) problem, which poses a great challenge to the accurate demodulation of OTFS receiver signals. To address the above problems, this paper considers the variation of Doppler sampling points within one symbol when deriving the channel model, which effectively overcomes the IDI problem, and employs a basis expansion model (BEM) to convert the channel estimation into a sparse recovery problem for the basis coefficients. In addition, to better utilize the sparse nature of the OTFS channel, a generalized approximate message passing-sparse Bayesian learning (GAMP-SBL)-based algorithm is employed to estimate the basis coefficients of the channel. The complexity of this algorithm is greatly reduced compared to the conventional SBL algorithm. Finally, system simulation results are reported to verify the superiority of the proposed scheme.  
V2X; OTFS; delay-Doppler; inter-Doppler interference; sparse Bayesian learning; channel estimation 2023
## 5 Neural Networks and a Classical Image Identification Algorithm
### Radial Network Used in Research  
An RBF is a one-way neural network with a radial basis function. Such networks are applied in classification problems, where they recreate the density function of the distribution of variables, but most often, they are used in the tasks of non-linear approximation of numerical variables. The RBF neural network is used in studies that require regression tasks. Among others, article [41] proposes the use of the radial base function of the artificial neural network to predict trihalomethanes levels in real distribution systems. In turn, a combination of multilayer perceptrons and radial neural networks with a base function (RBF-NN) was proposed in [42]. A multilayer RBF network was designed for regression problems.  
The process of learning radial networks consists of two stages:  
(a) designation of centers representing the training data space and parameters of the shape of base functions,  
(b) selecting the neuron weights used in the approximation of target values.  
A radial basis function is a function that changes radially around a fixed center point \(c\) and is non-zero only around this point. The parameters \(x\) and \(c\) stand for two points lying in the space of the distribution of functions. Point \(c\) is the fixed center point of this space, while point \(x\) is the floating point. The general form of the radial basis function can be represented by the formula [43]:  
\[\phi(x)=\varphi(||x-c||). \tag{1}\]  
From Equation (1), we can conclude that the argument \(x\) of the \(\phi(x)\) radial function is the distance of a given point \(x\) from the center \(c\). However, the formula for the \(\varphi(x)\) function may include an additional \(\sigma\) parameter to specify the center width.  
From a practical point of view, the use of a large number of training sets \(p\) in the expansion of base functions is unacceptable. Usually, the number of \(p\) patterns is very large, which leads to high computational complexity of the algorithm. As in multilayer networks, weight reduction is needed; in radial networks, a reduction in the number of base functions is employed. In a space with smaller dimensions, while limiting the basic functions to \(K\), an approximate solution with sufficiently high accuracy can be expressed by the formula [43]:  
\[F(x)=\sum_{i=1}^{K}w_{i}\varphi(||x-\mathbf{c_{i}}||),\text{for}\ K<p, \tag{2}\]  
where:  
\(K<p\)--number of base functions,  
\(p\)--number of patterns,  
\(\mathbf{c_{i}}(i=1,2,...,K)\)--a set of centers to be designated,  
\(x\)--a variable point lying in the space of function distribution,  
\(\varphi(||x-\mathbf{c_{i}}||)\)--distance between the point and the center.
## 2 Constrained Application Protocol (CoAP)
Therefore, the resource observation avoids continuous client polling in resources and final devices can send data when they are available combining it with other tasks like the sleeping mode. On the other hand, clients can receive asynchronous data updates without polling resources. An example of a observe communication is given in Figure 2. The communication starts with a GET request to the resource of interest by a CoAP client, a temperature sensor resource in this case, with the observe option established. Once the CoAP server has processed the request, it saves the client in the list of observers and sends observe updates until the client sends an RST message. Resource observation can also be optimized with conditional observations [11] where clients can establish a configurable criteria to receive resource notifications.
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 3 Calculation Model and Method of the Main Circuit Parameters
### Calculation Formulas of Main Circuit Parameters
#### 3.2.5 Calculation of the Reactive Power Consumptions  
The reactive power consumption at the rectifier side \(Q_{\text{dR}}\) can be calculated as:  
\[Q_{\text{dR}}=P_{\text{dR}}\frac{\sin(2\alpha)-\sin(2\alpha+2\mu_{\text{R}})+ 2\mu_{\text{R}}}{\cos(2\alpha)-\cos(2\alpha+2\mu_{\text{R}})} \tag{38}\]  
The reactive power consumption at the inverter side \(Q_{\text{dI}}\) can be calculated according to (26).
# COX2-Inhibitory and Cytotoxic Activities of Phytoconstituents of _Matricaria chamomilla_ L.
## 3 Results
### Molecular Docking In Silico

Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Compounds** & **LBE (kcal/mol)** & **pKi (\(\upmu\)M)** & **Pharmacophore** \\ \hline
26 & Citronellol & \(-6.01\pm 0.01\) & 39.50 \(\pm\) 6.52 & ILE124, ASP125, **THR129**, **THR149**, ARG150, ASN375, ARG376, ILE377, ALA378, PHE529 \\ \hline
27 & P-Cymene & \(-6.01\pm\) \(<\)0.01 & 39.57 \(\pm\) \(<\)0.01 & ALA202, THR206, TYR385, HIS388, LEU390, LEU391, ALA199, GLLN203, THR206, HIS207, PHE210, TYR385, TRP387, HIS388, LEU390, LYS97, ASN104, GLN350, TYR355, HIS356, LYS358 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 3: Molecular docking analysis of 7 compounds derived from chamomile (_Matricaria chamonilla_) and celecoxib to COX2 (5F1A). They were bound to different pockets within the same domain. The three red circles indicate the different binding sites. The low binding energy (Kcal/mol) values significantly correlate with the predicted inhibition constant pki (\(\upmu\)M) (\(r=0.574\); \(p=7.53\times 10^{-20}\)), as shown on the left graph. The 2D and 3D illustrations (Lines 2–5) show the interaction of celecoxib, apigenin, \(\upbeta\)-amyrin, \(\upbeta\)-eudesmol, \(\upbeta\)-sitosterol, daucosterol, farnesol, and myricetin with different amino acids of COX2.
## 3 Numerical Simulation  
### Simulation Setup  
The dynamic magnetic field in the working region of the MPTs can be regarded as being uniform along the direction of the wires. Thus, for simplification, a two-dimensional model was built in COMSOL Multiphysics (COMSOL 5.3a, COMSOL Inc., Stockholm, Sweden) as shown in Figure 5. The coupling layer between the magnetostrictive patch and aluminum plate was neglected as it was non-conducting and non-magnetic. The coils of the two layers sealed in the FPC were made of copper. The material of the magnetostrictive patch was Fe-Co alloy. Permalloy, silicon steel, pure iron and Mn-Zn ferrite were the optional materials with a high relative permeability for the SMP. Considering the eddy-current loss, which may be caused by the dynamic magnetic field, the Mn-Zn ferrite with low conductivity was selected. In Figure 5, the real sizes of different parts are marked. The symbols represent the dimensions in the model (l: Length; t: Thickness; g: Gap distance) and the subscripts denote different parts (a: Aluminum plate; m: Magnetostrictive patch; c: Coil; s: SMS). Icc was the interval length of wires in the same coil. The material properties are listed in Table 1. In this study, the generation and detection of the zeroth shear horizontal mode (SH0 mode) was considered to have the advantage of non-dispersion. To avoid the interference of high order modes, the excitation frequency should be below the cutoff frequency of the SH1 mode (312 kHz) in the aluminum plate (the thickness was 5 mm). With the consideration of the high attenuation of wave modes at high frequencies and low detection sensitivity at low frequencies in field tests, the frequency range was determined as 55-150 kHz. The frequency tests were carried out with increasing steps of 5 kHz using the FPCs named MF64 and MF128, which were designed at the main frequencies of 64 kHz and 128 kHz, respectively (MF64 for 55-90 kHz, MF128 for 95-150 kHz). Two coils were designed in one FPC for wave direction controlling as shown in Figure 3, but only one was used to avoid channel crosstalk in experiments. The simulations used the same setup and the current directions are shown in Figure 6. The red solid lines in Figure 5 are reference lines with the length of Im to observe the magnetic intensities. For wave generation, the alternating currents with the amplitude 1A were loaded into the coils and the magnetic intensity along the \(x\)-axis of Line 1, which is in the middle of the magnetostrictive patch, was observed. In wave detection, the alternating magnetic fields with the amplitude 1 A/m\({}^{2}\) are applied into the magnetostrictive patch along the \(x\)-axis and the magnetic intensity in Line 2, which was across the middle of the coils near the magnetostrictive patch, was observed.  
Figure 5: Numerical model for dynamic magnetic field simulation of magnetostrictive patch transducers.
## 5 Methodology
### SWOT Analysis  
The appraisal of SWOT analysis is founded on the internal (strengths and weaknesses) and external (opportunities and threats) measures. Originally, the SWOT technique was applied in the business and marketing environments. Today, it is been applied in other research disciplines including the field of energy [23]. Widely accepted in energy-related research in evaluating salient factors is the SWOT analysis tool. Several authors have applied this technique in eliciting findings in the field of energy. Some include a study by [PERSON] et al. [24], which applied the SWOT technique in evaluating the empowerment of the renewable energy sector in Pakistan. [PERSON] et al. [25] used SWOT analysis as a tool in assessing the advancement and potential of green energy in South Korea, Japan, and Taiwan. [PERSON] et al. [9] applied the tool in the assessment of Nigeria's nuclear power exploration planning. The tool was also used by [PERSON] et al. [26] in analyzing the key issues in the Tunisian energy sector. The application of SWOT analysis was conducted by [PERSON] et al. [27] in assessing Africa's photovoltaic solar power in comparison to China. The deployment of SWOT analysis is advantageous on the grounds that it can be conducted with little or no cost. It also lays emphasis on crucial factors that could affect the smooth execution of a business or project. The data for this study were sourced from relevant, existing literature and working documents on nuclear energy development in South Africa and around the world. The significance of this study is to explore and ensure strategic decision making regarding nuclear energy options in South Africa that are expected to be of immense value to the decision makers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Technology** & **Levelized Cost of Energy Components (\$/MWh)** \\ \hline
**Alternative Energy** & **Low End** & **High End** \\ \hline Solar PV–Rooftop Residential & 151–160 & 245–267 \\ Solar PV–Community & 67–73 & 135–145 \\ Solar thermal tower with storage & 78–98 & 164–181 \\ Fuel cell & 45–103 & 83–152 \\ Geothermal & 46–71 & 76–111 \\ Wind & 23–29 & 45–56 \\ \hline
**Conventional Energy** & & \\ \hline Gas peaking & 108–152 & 145–206 \\ Nuclear & 87–112 & 162–189 \\ Coal & 41–60 & 110–143 \\ Gas combined cycle & 16–41 & 47–74 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Levelized Cost of Energy Components: Low and High End [10].
# Shock Initiation of a Satellite Tank under Debris Hypervelocity Impact
## 3 Initiation Criterion for the Liquid Hydrazine Tank
### Pressure Duration  
The shock wave pressure must be equal to or greater than the C-J pressure of liquid hydrazine in order to trigger an explosion. Otherwise, if the pressure is lower than the C-J pressure, the shock wave will not play a role in the explosion. The period from the moment when the shock wave is generated in liquid hydrazine to the moment when the shock wave pressure decays to the C-J pressure is the pressure duration. The C-J pressure of liquid hydrazine is \(P_{CJ}=1.39\times 10^{10}\) Pa [18], the pressure duration \(t\) in liquid hydrazine can be calculated as follows:  
\[\frac{\rho_{l}a_{l}u_{p}}{1+(3C_{D}\rho_{l}u_{p}/8\rho_{p}r_{p})t}+\frac{ \rho_{l}b_{l}u_{p}^{2}}{\left[1+(3C_{D}\rho_{l}u_{p}/8\rho_{p}r_{p})t\right]^ {2}}=1.38\times 10^{10} \tag{25}\]  
The power density \(P_{d}\) is expressed in the integral form:  
\[P_{d}=\int_{0}^{t}P^{2}(t)\mathrm{d}t=\int_{0}^{t}\left\{\frac{\rho_{l}a_{l} u_{p}}{1+(3C_{D}\rho_{l}u_{p}/8\rho_{p}r_{p})t}+\frac{\rho_{l}b_{l}u_{p}^{2}}{ \left[1+(3C_{D}\rho_{l}u_{p}/8\rho_{p}r_{p})t\right]^{2}}\right\}^{2}\mathrm{ d}t \tag{26}\]  
Equations (5)-(26) constitute the initiation criterion for the liquid hydrazine tank under hypervelocity impact.