esa-sceva/satcom-chunk-collection huggingface dataset:
# Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals
## 3 System Design
### Carrier Selection
Compared with subcarrier 28, subcarrier 17 shown in Figure 9b is smoother and more stable; specifically, there are no spike signals around the peak of the CSI amplitude fluctuations that reflect human head motion, indicating that subcarrier 17 can more accurately detect human head motion (i.e., the green triangle in the figure) and greatly reduce the possibility of false detection. In conclusion, the joint standard deviation and kurtosis sensitivity metric proposed by Wi-Senser can better select the optimal subcarrier, which has the highest sensitivity to human head motion and is insensitive to irrelevant environmental changes. Figure 8: The results of sensitivity calculation under different metrics. Figure 9: The optimal subcarrier selected under different metrics. (**a**) Optimal subcarrier selected based on variance metric (subcarrier 28); (**b**) optimal subcarrier selected based on joint metric (subcarrier 17).
## 3 Results  
### Deletion of TRPV4 Exacerbates Aortic Diameters in CaCl\({}_{2}\)-Induced Aortic Injury  
To evaluate the role of TRPV4 in vascular calcification, we subjected WT and TRPV4-KO mice to CaCl\({}_{2}\)-induced abdominal aortic injury. The daily body weights of each mouse were meticulously documented over two weeks following the surgical procedure. The corresponding percentage changes in body weight are visually depicted in Figure 1. The CaCl\({}_{2}\)-treated groups exhibited a slight decrease in average body weights. However, no statistically significant variances were observed among the experimental groups (WT sham vs. WT CaCl\({}_{2}\): \(p=0.96\), TRPV4-KO sham vs. TRPV4-KO CaCl\({}_{2}\): \(p=0.28\)).  
Following the sacrifice of the mice, the infrarenal aorta were meticulously dissected and photographed using a predetermined scale for precise morphometric analysis. As anticipated, both wild-type (WT) and TRPV4-knockout (TRPV4-KO) mice exhibited a noteworthy augmentation in the outer diameter in response to the CaCl\({}_{2}\)-induced aorta injury model, as illustrated by representative images in Figure 2A (WT CaCl\({}_{2}\) vs. WT sham: \(p<0.0001\), TRPV4-KO CaCl\({}_{2}\) vs. TRPV4-KO sham: \(p<0.0001\)). Interestingly, the aorta of the TRPV4-knockout (TRPV4-KO) mice demonstrated a marked increase in dilation when contrasted with those of the WT-CaCl\({}_{2}\) mice (TRPV4-KO CaCl\({}_{2}\) vs. WT CaCl\({}_{2}\): \(p<0.005\)), as morphometrically illustrated in Figure 2B. This intriguing observation suggests a potential involvement of TRPV4 in averting aortic aneurysm formation, raising the possibility that TRPV4 plays a crucial role in maintaining aortic structural integrity under conditions of
## 5 Example Analysis
### Analysis of Icing Prediction Method Result
#### 5.3.2 Example 2: Model Experiment  
To verify the superiority of the prediction model, _Robust STL-BTSF_ and IBWO-LSSVR are simulated and compared with a variety of models, and the iterative adaptation curves are shown in Figure 11.  
Analysis of Figure 11 reveals that the proposed model has a strong optimization ability in the pre-iteration period, with lower adaptation values compared to the other four models at the same number of iterations. Furthermore, the optimization ability of this model significantly improves during the middle of the iteration process. A clear superiority can be observed compared to the other models, reaching optimum performance at 30 iterations. In contrast, the other models have not yet achieved their best fitness values at this point.
## 2 Problem Formulation  
The problem can be formulated as follows: if \(y(t)\) represents the total active electrical power used in a system at the instant \(t\), and we indicate with \(y_{i}(t)\) the active power absorbed by the appliance with index \(i\) at the same time, the total load can be expressed as the sum of the absorptions of the individual devices and of an unmeasured part:  
\[y(t)=\sum_{i=1}^{N}y_{i}(t)+e(t)\quad\forall t\in(0,T) \tag{1}\]  
where \(N\) is the number of appliances considered and \(e(t)\) is the unidentified residual load.  
The problem is to get the values of \(y_{i}(t)\) when only the measure of \(y(t)\) is known, that is to get an approximation of \(F(y(t))\):  
\[[y_{1}(t),y_{2}(t),\ldots,y_{i}(t),\ldots,y_{N}(t)]=F(y(t)) \tag{2}\]  
where \(F\) is the operator that, when applied to the total active power, returns \(N\) distinct values that are the best estimate of the power absorbed by individual appliances. Note that in general the \(y_{i}(t)\) does not represent the totality of household appliances, but a fraction of all those present in a house. The term \(e(t)\), not known, therefore takes into account the loads due to unmonitored appliances.  
The problem of finding an approximation of the \(F\) operator can be set as a supervised learning problem when simultaneous measurements of the aggregate load and consumption of individual equipment are available.  
If, as in our case, we are primarily interested in the cumulative consumption and activation times, the estimated \(\hat{y}_{i}(t)\) consumption of the individual appliances can be approximated with a function that is constant during the activation period of the device:  
\[\hat{y}_{i}(t)=p_{i}\hat{a}_{i}(t) \tag{3}\]  
where \(p_{i}\) is the average consumption of the appliance \(i\) and the \(\hat{a}_{i}(t)\) is an estimate of the state of activation of the individual appliance at the time \(t\), which has a unit value if the appliance is in operation and is consuming energy and zero value otherwise.  
Therefore, the method we propose seeks to obtain the most accurate possible estimate of the state of activation of the appliances starting from the aggregate load,  
\[[\hat{a}_{1}(t),\hat{a}_{2}(t),\ldots,\hat{a}_{i}(t),\ldots,\hat{a}_{N}(t)]= F_{a}(y(t)) \tag{4}\]  
and obtains an estimate of consumption using the (3) after knowing the average nominal consumption of the appliances examined.
# Efficient and Robust Image Communication Techniques for 5G Applications in Smart Cities
## 3 Model Description  
### FFT-OFDM System Model  
The incoming data stream is first encoded by using the source coding techniques to represent the data efficiently. The source encoded data is then sent to the channel encoding block with additional bits to the incoming data to ensure that the received data's errors can be detected and corrected at the receiver end. The encoded data is then modulated using the M-PSK modulation; the modulated data is then forwarded to the inverse fast Fourier transform (IFFT) block. The IFFT block maps the modulated data onto the orthogonal subcarriers, as presented in Figure 1. The nth element of discrete-time complex OFDM symbol can be written as:  
\[\mathrm{X_{n}}=\frac{1}{\mathrm{N}}\sum\
olimits_{k=0}^{\mathrm{N-1}}\mathrm{ X_{k}}\mathrm{e}^{\mathrm{j}\frac{2\pi\mathrm{kn}}{\mathrm{N}}}0\ \leq\ n\ \leq\ N-1,\ 0\ \leq\ k\ \leq\ N\ -\ 1 \tag{1}\]  
Figure 1: Block Diagram of OFDM System.  
Here, Equation (1) represents the discrete-time complex OFDM symbol. N represents the number of samples, \(\rm X_{k}\) represents the input signal, and \(\rm X_{n}\) represents the OFDM symbols obtained after the Fourier transform. After the orthogonal mapping of the data, the addition of guard interval is being done, ensuring that the received data would be free from the ISI effects. At the receiver end, the entire process is reversed to extract the original information.
# Understanding the Seismic Resilience of Metallic Cylindrical Tanks Through Parametric Analysis
## 4 Discussions on FE Results and Parametric Sensibility
### Tank H/R = 6.0  
As observed in Figure 10a, the pressure profiles for the five different \(R/t\) ratios show a similar trend. For the more slender tanks, large pressure peaks occurred at the top of the wall due to convective motion waves both for rigid and flexible wall tanks. The largest peak occurred for the flexible tank (\(R/t\) = 250) at approximately 0.9 normalized height (\(z/H\)), and this peak decreased as the rigidity of the wall increased, reaching its lowest point at (\(R/t\) = 125). In the lower part of the tank, the pressure profiles remained relatively regular, except for the most flexible wall (\(R/t\) = 400), which exhibited very large peaks due to the low resistance of the shell to horizontal excitations. It is clear that the \(R/t\) = 400 configuration tank failed through the classical elastic-plastic elephant foot buckling mechanism just above the base of the wall.  
This case behaves similarly to the \(H/R\) = 5 case study, and the comparison between the numerical results and standard codes is shown in Figure 10b. As in previous analyses, the predictions made using international regulations underestimated the envelope of pressures  
Figure 10: Horizontal pressure distribution of \(H/R\) = 6.0 tank ratio considering different \(R/t\) thickness ratios: (**a**) FE simulations and (**b**) predictive pressures computed according the international standards compared with FE simulations.  
Figure 9: Horizontal pressure distribution of \(H/R\) = 3.0 tank ratio considering different \(R/t\) thickness ratios: (**a**) FE simulations and (**b**) predictive pressures computed according the international standards compared with FE simulations.  
observed in the FE analyses. These results further demonstrate the limitations of current design codes in accurately predicting the pressure distribution especially in cases where large convective waves and buckling mechanisms significantly affect the tank's behavior under seismic loading.
# Two-Dimensional Topology Optimization of Headtube in Electric Scooter Considering Multiple Loads
## 2 Design Optimization
### Pixel-Based FE Modeling of Electric Scooter Headtube  
Figure 2a shows the FE model of the headtube of an electric scooter (Ninebot Max G30, Segway Ninebot, Beijing, China). The headtube serves as the connection between the handle support and the deck. To represent the headtube, the design area was modeled as a rectangle measuring 274 mm in length and 350 mm in height (Figure 2b). The maximum density is represented as black pixels in Figure 2a, and hollowness is assigned a minimum density (white pixels). For topology optimization, the headtube connecting the handle support and footrest was designated as the design area, excluding the sections that would interfere with the front wheel. Owing to the scooter's symmetrical structure, maximum stress during a frontal plane collision occurs along the sagittal (longitudinal) plane. To reflect this characteristic and structural behavior and enhance topology optimization, the 3D shape was simplified into a 2D FE model.  
Figure 1: Flowchart of the topology optimization algorithm.  
Figure 2: Electric scooter (Ninebot MAX). (**a**) Detailed specifications in 2D and 3D diagrams (dimensions shown in millimeters), including loading and boundary conditions. (**b**) Design area.  
The FE model was built using 23,975 pixel-based bilinear elements, each measuring 2 mm \(\times\) 2 mm. The pixel- (2D) or voxel- (3D) based approach superimposes a grid of equally sized rectangular elements on the target structure and has been successfully used in computational biomechanics to construct micro-FE models [32; 33; 34].  
The load points were selected at the top-left and bottom-right corners of the handle support, and the applied forces from the user and front wheel were used. All degrees of freedom in all nodes at the left end of the board were restrained. Equation (1) represents the modulus of elasticity of each element obtained by converting the element density based on solid isotropic material penalization. The Poisson ratio of all components was 0.3 [35; 36].  
\[E_{i}(\rho_{i})=\rho_{i}^{3}E_{0} \tag{1}\]  
where \(E_{i}\) is the reference modulus of elasticity of the \(i\)-th FE, \(\rho_{i}\) is the relative density of the \(i\)-th FE, and \(E_{0}\) (equal to 1) is the modulus of elasticity when the relative density is 1.
## 4 Ruthenium
### Halogenation  
The Kharasch reaction involves the addition of halogenated compounds to an alkene, in general catalyzed by a ruthenium complex in the presence of a radical initiator such as AIBN (azoisobutyronitrile) [26]. [PERSON] and [PERSON] have shown that this reaction can be carried out in water, without the addition of AIBN, with the help of a polymer-supported ruthenium catalyst, thus affording an atom-economic and 'green' functional group transformation [27].  
**Scheme 8.** The Kharasch reaction, catalyzed by a polymer-bound ruthenium species in water.  
The catalytic system consisted of a ruthenium(III)(pentamethylcyclopentadienyl)dichloride attached to a PS-PEG backbone via a polymer-supported phosphine. A variety of different styrene derivatives were investigated with good results (Scheme 8), affording the addition products in up to 98% yield. Aliphatic alkenes could also be used under these conditions, albeit with a somewhat lower yield. Recycling of the catalyst did not affect the catalytic activity to any extent.
## 2 Study Area and Data Sets  
### Study Area  
The Xinfengjiang River is located in the southern mountainous area of China, which is a subtropical monsoon climate zone with a mild climate, abundant sunshine and abundant rainfall (Figure 1). The XRRB catchment is selected as the research area in this study, and it has a watershed area of 5734 km\({}^{2}\) above the Xinfengjiang Hydraulic Power Station. The dam site is located in Heyuan city, Guangdong Province, 9.2 km away from the mouth of the Xinfengjiang River.  
With the Jialian Mountains to the northwest and the Dengta Basin to the southeast, the terrain of the XRRB drops from northwest to southeast. There are four major rivers in the basin, including the Lianping River, Daxi River and Funatang River [28]. The annual mean precipitation over the XRRB is 1774 mm and is mainly affected by the subtropical monsoon climate. Approximately 80% of the annual precipitation occurs from April to September and mainly includes frontal rain and typhoon rain. Heavy rainfall with a high intensity and a long flood season are observed. The incoming water during the flood season accounts for approximately 80% of the annual incoming water. The average flow of the XRRB is 190 m\({}^{3}\)/s, and the average water inflow is 6 billion m\({}^{3}\). The total storage capacity is 13.896 billion m\({}^{3}\)[29].  
Figure 1: Elevation map of the Xinfengjiang River reservoir basin.
## 3 Control Law Design and Stability Analysis
### Adaptive Neural Network Finite-Time Dynamic Surface Control Law Design
\begin{array}{l}\dot{V}_{i,n}\leq\dot{h}_{i,n}e_{i,n}u_{i}(t)+e_{i,n}(\Phi_{ i,n}^{*})^{T}\zeta_{i,n}+\Xi_{i,n}|e_{i,n}|+\frac{e_{i,n}z_{i,n-1}}{\tau_{i,n-1}}+ \Pi_{i,n-1}|z_{i,n-1}|\\ \qquad+\frac{\Pi_{i,n-1}^{2}z_{i,n-1}\tau_{i,n-1}}{\sqrt{(\Pi_{i,n-1}z_{i,n-1} )^{2}+\eta^{2}}}-\frac{z_{i,n-1}^{2}}{\tau_{i,n-1}}-\frac{\dot{\Pi}_{i,n-1}^{2 }}{\sqrt{(\Pi_{i,n-1}z_{i,n-1})^{2}+\eta^{2}}}\\ \qquad+\frac{1}{\Gamma_{i,n}}\widetilde{\Xi}_{i,n}\dot{\Xi}_{i,n}+\frac{1}{ \Upsilon_{i,n}^{T}}\widehat{\Phi}_{i,n}+\frac{1}{\mathbb{N}_{i,n-1}}\widehat{ \[PERSON]}_{i,n-1}\end{array}  \tag{55}\]  
Let  
\[\Phi_{i,n}^{T}\Psi_{i,n}=(\Phi_{i,n}^{*})^{T}\zeta_{i,n}+\frac{\widehat{\[PERSON]}_{ i,n}e_{i,n}}{\sqrt{\varepsilon_{i,n}^{2}+\eta^{2}}} \tag{56}\]  
where  
\[\Phi_{i,n}=\big{[}\Phi_{i,n}^{*},1\big{]}^{T} \tag{57}\]  
\[\Psi_{i,n}=\big{[}\zeta_{i,n},\hat{\[PERSON]}_{i,n}e_{i,n}\Big{/}\sqrt{\varepsilon_ {i,n}^{2}+\eta^{2}}\,\Big{]}^{T} \tag{58}\]  
Using Lemma 6, one obtains  
\[\Xi_{i,n}|e_{i,n}|\leq\frac{\Xi_{i,n}e_{i,n}^{2}}{\sqrt{\varepsilon_{i,n}^{2}+ \eta^{2}}}+\eta\Xi_{i,n} \tag{59}\]  
\[\Pi_{i,n-1}|z_{i,n-1}|\leq\frac{\widehat{\Pi}_{i,n-1}^{2}z_{i,n-1}^{2}}{\sqrt{ \big{(}\dot{\Pi}_{i,n-1}z_{i,n-1}\big{)}^{2}+\eta^{2}}}-\widetilde{\Pi}_{i,n-1} |z_{i,n-1}|+\eta \tag{60}\]Design the actual control law \(u_{i}(t)\) as  
\[u_{i}(t)=\mathcal{N}(\varsigma_{i,n})\beta_{i,n} \tag{61}\]  
\[\dot{\varsigma}_{i,n}=e_{i,n}\beta_{i,n} \tag{62}\]  
\[\beta_{i,n}=c_{i,n}e_{i,n}^{2u_{i}-1}+\dot{\Phi}_{i,n}^{T}\Psi_{i,n}+\frac{z_{ i,n-1}}{\tau_{i,n-1}}+\frac{\dot{\Pi}_{i,n-1}^{2}z_{i,n-1}}{\sqrt{\left(\dot{ \Pi}_{i,n-1}z_{i,n-1}\right)^{2}+\eta^{2}}} \tag{63}\]  
where \(c_{i,n}\) is a designed positive constant. Design adaptive laws \(\dot{\underline{\dot{\Sigma}}}_{i,n}\), \(\dot{\Phi}_{i,n}\), and \(\dot{\Pi}_{i,n-1}\) as  
\[\dot{\underline{\dot{\Sigma}}}_{i,n}=\frac{\Gamma_{i,n}e_{i,n}^{2}}{\sqrt{e_{ i,n}^{2}+\eta^{2}}}-\gamma_{i,n}\underline{\dot{\Xi}}_{i,n} \tag{64}\]  
\[\dot{\Phi}_{i,n}=e_{i,n}\Upsilon_{i,n}\Psi_{i,n}-\rho_{i,n}\dot{\Phi}_{i,n} \tag{65}\]  
\[\Phi_{i,n}=\big{[}\Phi_{i,n}^{*},1\big{]}^{T} \tag{57}\]0  
where \(\gamma_{i,n}\), \(\rho_{i,n}\), and \(\omega_{i,n-1}\) are designed positive constants. Substituting (56) and (59)-(63) into (54) and using the adaptive laws (64)-(66) yields  
\[ \begin{array}{l}\dot{V}_{i,n}\leq(\dot{h}_{i,n}\mathcal{N}(\varsigma_{i,n}) +1)\dot{\varsigma}_{i,n}-c_{i,n}e_{i,n}^{2u_{i}}-\frac{z_{i,n-1}^{2}}{\tau_{i,n-1}}-\frac{\gamma_{i,n}}{\Gamma_{i,n}}\underline{\widetilde{\Sigma}}_{i,n} \underline{\widetilde{\Sigma}}_{i,n}-\frac{\rho_{i,n}}{\tau_{i,n}}\widetilde{ \Phi}_{i,n}^{T}\widetilde{\Phi}_{i,n}\\ -\frac{\omega_{i,n-1}}{\mathbb{N}_{i,n-1}}\hat{\Pi}_{i,n-1}\Pi_{i,n-1}+\eta \Xi_{i,n}+\eta\end{array}  \tag{67}\]
## 3 Numerical Examples
### Single Bubble  
The bubble exists between two parallel plate electrodes; it is equivalent to a sphere. The uniform electric field intensity is about 2 MV/m. The potential on the upper plate is 50,000 V, and the lower plate is grounded. The uniform electric field intensity is about 2 MV/m. The boundary condition of the side walls is shown as Equation (8). The 3D sketch is shown in Figure 4.  
\[\epsilon\frac{\partial\varphi}{\partial n}=0 \tag{8}\]  
where \(n\) represents the unit normal vector at the interface.  
Figure 5 shows the mesh grid of CFEM. The whole solution domain is a 15 mm \(\times\) 15 mm \(\times\) 25 mm hexahedron. There is a sphere representing the bubble in the center of the hexahedron. Its radius is assumed to be 1.5 mm. The relative permittivity of liquid nitrogen is 1.4 and that of gas is 1. Table 1 compares the number of nodes and elements generated by two methods. CFEM needs to consider the interface of different materials. Improved XFEM can eliminate this process, simplifying the mesh. The number of nodes and elements is reduced, which makes the calculation process faster.  
Figure 3: (**a**) The level set function of the first interface; (**b**) The level set function of the second interface.  
Calculate the potential distribution of the single-bubble model with CFEM and XFEM. A plane parallel to the YOZ plane is made through the center of the bubble sphere. Draw a potential distribution cloud on this section. The potential sectional at \(x\) = 7.5 mm of XFEM is shown in Figure 6. It can be seen that the electric field around the bubble is distorted, which is consistent with the actual situation and the CFEM calculation results.  
The comparison results of electric potential at the same coordinate position show that the XFEM method has high accuracy. The average relative error of 1159 nodes is 0.39% and the maximum relative error is 7.79%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Node Number** & **Element Number** & **Calculation Time** \\ \hline CFEM & 1400 & 7062 & 57 s \\ XFEM & 1159 & 5626 & 37 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contrast of number of nodes and elements.  
Figure 4: 3D single bubble model in liquid nitrogen.  
Figure 5: (a) 3D single bubble mesh grid of CFEM; (b) 3D single bubble mesh grid of XFEM.  
The electric field of the node near the interface changes rapidly. Around the surface of the sphere, select the 10 nodes closest to the sphere to compare the calculation results, shown in Table 2.
## 3 Results and Discussion
### Effects of Fretting and Torsional Parameters on Dissipation Energy Characteristics
#### 3.2.1 Hysteresis Loops of Tangential Force versus Displacement Amplitude
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Contact Load, \(F_{nt}\) (N)**} & \multicolumn{3}{c}{**Torsion Angle, \(\theta\) (\({}^{\circ}\))**} & \multicolumn{3}{c}{**Crossing Angle, \(\beta\) (\({}^{\circ}\))**} \\  & **30** & **40** & **50** & **60** & **\(\pm\)1** & **\(\pm\)1.5** & **\(\pm\)2** & **\(\pm\)2.5** & **90** & **70** & **45** & **26** \\ \hline Max. tangential force (N) & 14.4 & 16.2 & 18.8 & 20.0 & 13.8 & 15.2 & 16.2 & 16.6 & 16.2 & 19.0 & 19.4 & 20.1 \\ Relative slip (\(\upmu\)m) & 78.4 & 58.5 & 25.0 & 23.4 & 43.3 & 58.0 & 58.5 & 60.8 & 58.5 & 48.6 & 59.1 & 61.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Tangential forces and relative slips between steel wires during tension–torsion cyclic loading at loading cycles 20,000 with different tension–torsion cyclic loading parameters. Effect of torsion angle  
Figure 7 shows that the hysteresis loop changes from parallelogram to ellipse in the case of each torsion angle, which indicates the fretting contact state changing from gross slip to partial slip. An increase of torsion angle induces increases in loading cycles required for the transition of hysteresis loop from parallelogram to ellipse, which indicates more difficult change of fretting contact state from gross slip to partial slip. Table 5 shows that the maximum tangential force and relative slip increases with increasing torsion angle at loading cycles 20,000. In cases of torsion angles of \(\pm 1^{\circ}\), \(\pm 1.5^{\circ}\), \(\pm 2^{\circ}\) and \(\pm 2.5^{\circ}\), at loading cycles 20,000, dissipation energies are
## 3 Results  
### Study Selection  
From the databases and other sources, the initial search resulted in a total of 98 record, resulting in 58 after duplicates removal. Following title and abstract screening, 23 studies were selected for full-text evaluation. Nevertheless, after full-text eligibility assessment, 18 studies were excluded (in detail in Supplementary Table S2). Therefore, the meta-analysis was performed on the basis of 5 articles. The flow chart of study selection together with reasons for exclusion is provided in Figure 1.