esa-sceva/satcom-chunk-collection huggingface dataset:
# Construction of Compact Finite Difference Schemes by Classic Differential Quadrature
## 3 Construction of Compact Difference Schemes by DQM  
### The Generalized Backward Differential Formulae  
As shown in Figure 1, we divide the interval \(x\in[x_{n},\ x_{n+1}]\) into \(s\) segments, namely the grids, in which \(h=x_{n+1}-x_{n}\), \(x(x_{n}+c_{0}h)\equiv x_{n}\), \(x(x_{n}+c_{s}h)\equiv x_{n+1}\).  
Defining  
\[v=\left\{\begin{array}{ll}(s+2)/2,&\mbox{for even $s$}\\ (s+1)/2,&\mbox{for odd $s$}\end{array}\right. \tag{14}\]  
and taking the \(v\)-th equation from Equation (12), we can obtain  
\[hf(\widetilde{x}_{v},\;y_{n+v})\equiv hf_{n+v}=\sum_{z=0}^{s}\alpha_{z}y_{n+z} \tag{15}\]  
in which,  
\[\alpha_{z}=g_{vz},\;z\in(0,\;s) \tag{16}\]  
It can be shown that the above numerical method (15) has the accuracy of \(s\)-order.  
The numerical method (15) has a similar form to the traditional backward difference formulae (BDF), but we will later show that this method has the better numerical stability then traditional BDF. Thus, for the sake of simplicity, we define this numerical method as the generalized backward difference formulae (GBDF).  
When selecting the uniform grids, the coefficient values (\(\alpha_{z}\)) of GBDF are shown in the following Tables 1 and 2.  
For the uniform grids, we can define that  
\[\overline{h}=(x_{n+1}-x_{n})/s=h/s \tag{17}\]  
therefore, the Equation (15) can be rewritten as the following formula  
\[\overline{h}f_{n+v}=\sum_{z=0}^{s}\Big{(}\frac{\alpha_{z}}{s}\Big{)}y_{n+z} \tag{18}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(s\) & \(v\) & \(\boldsymbol{\alpha_{0}}\) & \(\boldsymbol{\alpha_{1}}\) & \(\boldsymbol{\alpha_{2}}\) & \(\boldsymbol{\alpha_{3}}\) & \(\boldsymbol{\alpha_{4}}\) & \(\boldsymbol{\alpha_{5}}\) & \(\boldsymbol{\alpha_{6}}\) & \(\boldsymbol{\alpha_{7}}\) & \(\boldsymbol{\alpha_{8}}\) \\ \hline
1 & 1 & \(-1\) & 1 & & & & & & & \\
3 & 2 & \(\frac{1}{2}\) & \(-3\) & \(\frac{3}{2}\) & 1 & & & & & \\
5 & 3 & \(-\frac{1}{2}\) & \(\frac{5}{2}\) & \(-5\) & \(\frac{5}{3}\) & \(\frac{5}{2}\) & \(-\frac{1}{2}\) & & & \\
7 & 4 & \(\frac{1}{20}\) & \(-\frac{5}{15}\) & \(\frac{21}{10}\) & \(-7\) & \(\frac{2}{4}\) & \(\frac{24}{5}\) & \(-\frac{7}{10}\) & \(\frac{1}{15}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The coefficients of GBDF using uniform grids (for odd \(s\)).  
Figure 1: Schematic diagram of the grid points for generalized backward difference formulae (GBDF).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(s\) & \(v\) & \(\boldsymbol{\alpha_{0}}\) & \(\boldsymbol{\alpha_{1}}\) & \(\boldsymbol{\alpha_{2}}\) & \(\boldsymbol{\alpha_{3}}\) & \(\boldsymbol{\alpha_{4}}\) & \(\boldsymbol{\alpha_{5}}\) & \(\boldsymbol{\alpha_{6}}\) & \(\boldsymbol{\alpha_{7}}\) \\ \hline  
It can be verified from Tables 1 and 2 that, the numerical method (18) is consistent with the GBDF proposed by [PERSON] et al. [19]. In reference [21], it has been shown that the methods (18) are both A-stable numerical methods for time-domain numerical integration.
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 3 Method
### System Model
#### 3.1.2 Geometric Model--Satellite Availability  
In this model, we assume that each user is associated with its nearest satellite, i.e., the satellite that is closest to the user's zenith angle [38]. This scheme provides best channel conditions for the ground user. As such we define the contact angle, \(\varphi_{\rm o}\), as the Earth-centered zenith angle of the nearest satellite to the observer IoT device calculated as follows,  
\[\varphi_{\rm o}=\mathrm{acos}\bigg{(}\frac{r^{2}+R_{z}^{2}-d^{2}}{2 rR_{\rm o}} \bigg{)}, \tag{1}\]  
where \(r=h+R_{\odot}\); \(R_{\odot}\) is the Earth's average radius, \(h\) is the satellite altitude, and \(d\) represents the slant distance between the IoT device and the satellite. For the observer IoT device to be within the satellite footprint, the condition \(\varphi_{\rm o}<\varphi_{\rm max}\) must be met where \(\varphi_{\rm max}\) is the maximum zenith angle of the footprint. The maximum zenith angle occurs when the IoT device is at the edge of the satellite's beam, which can be represented as follows [44],  
\[\varphi_{\rm max}=\begin{cases}2\mathrm{asin}\Big{(}\frac{1}{\alpha}\sin\frac {\psi}{2}\Big{)}-\frac{\psi}{2}&\psi\leq\psi_{\rm o}\\ \mathrm{acos}\ \alpha&\psi>\psi_{\rm o}\end{cases}, \tag{2}\]  
where \(\alpha=R_{\odot}/(h+R_{\odot})\), \(\psi_{\rm o}\) is the maximum effective beamwidth, and \(\psi\) the satellite's antenna beamwidth. Figure 4 illustrates the maximum satellite footprint limited by horizon depicted as the green patches and the blue patches representing the satellite footprint when limited by the satellite antenna beamwidth. It shows that the satellite footprint is further limited by the satellite antenna beamwidth. To simplify the calculation, we assume the narrower-beam antenna from satellites to bypass this issue. After calculating the maximum zenith angle and the zenith angle, the satellite availability at that instance can be obtained by the Boolean condition of \(\varphi<\varphi_{\rm max}\), the average satellite availability which is the ratio of satellite available time per day can be calculated by the following equation,  
\[A=\frac{\int_{0}^{T_{\rm d}}\mathbb{1}\left(\exists\ x:\varphi_{x}<\varphi_{ \rm max}\right)\mathrm{d}t}{T_{\rm d}}, \tag{3}\]  
where \(\mathbb{1}\left(\exists\ x:\varphi_{x}<\varphi_{\rm max}\right)\) is the indicator function when at least a satellite \(x\) is within maximum zenith angle, and \(T_{\rm d}\) is the seconds per day, which is equal to 86,400 s. Figure 5 illustrates the comparison of the average satellite availability using practical satellite constellations. It shows that the satellite availability of the Walker-Star constellation is lower than the availability of the Walker-Delta constellation near the equator (for the same satellite number and for a narrow beamwidth), while the random satellite constellation has the lowest, and nearly constant, availability at different locations.
## 5 Model Validation, Results and Discussion
The comparison of heat fluxes exchanged and temperatures within the ventilated air layer serves to confirm the good level of agreement between behavioral modeling of a composite Trombe wall integrating PCM and experimentation. It is possible therefore to calculate the energy recovered through the energy balance of the solar wall over a given period (19-26 April 2014). This balance was calculated in the ventilated air layer using two methods validated in previous studies [22,24]. The first method is the energy balance due to the total head loss coefficient of air between the inlet and outlet of the ventilated air layer (Equation 32), while the second method is based on a direct heat flux measurement (Equation 33). Figure 14 presents a comparison of the power released from the solar wall, as calculated by the numerical model and estimated from heat flux measurements over one week (19-26 April 2014). Small differences can be observed between the two curves; their comparison indicates that the power calculation compared to experimental measurements is indeed acceptable. The thermal energy supplied by the composite Trombe wall equals, over the period considered, 4.4 kWh/m\({}^{2}\) for the calculation and 4.7 kWh/m\({}^{2}\) for the estimation derived from fluxmeter measurements; the difference between the two methods is on the order of 6.3%. As regards the results listed in Table 3, the total accumulation of measured solar energy over this 7-day period (April 19-26, 2014) is equal to 21.4 kWh/m\({}^{2}\) (per m\({}^{2}\) of solar wall). Most recently, 35.8% of the total energy, i.e., 7.7 kWh/m\({}^{2}\), was absorbed by the storage wall at its external surface. The stored energy, which was released within a 3h 04 min time delay (calculation with cross-correlation function between \(\phi_{\rm ext}\) and \(\phi_{\rm int}\)) to the ventilated air layer is estimated at 4.7 kWh/m\({}^{2}\), which equals to 60.8% of the absorbed energy and 21.8% of the energy generated from the total incident solar flux on the vertical facade of the solar wall (as measured by the pyranometer). As indicated above, the enthalpy method implemented in the composite solar wall model also provided accurate predictions for reproducing the thermal behavior of the latent heat and estimating the amount of energy released. One question worth asking is whether the latent heat storage is more or less efficient than sensible heat storage for the same volume. For the simulation of the composite  
Figure 14: Comparison of the power released by the solar wall. Power\({}_{\rm cal}\) denotes the power released according to calculation results, while Power\({}_{\rm fluxm}\) is the power released according to fluxmeter measurements. Trombe wall in the case of sensible heat storage, a model developed earlier and validated in previous studies [24] will be used; it was composed of conventional building materials (solid concrete blocks). Both these models will now serve to compare the efficiency of each of these two types of solar wall by applying the meteorological data from Section 3.
# Respiration Monitoring for Premature Neonates in NICU
## 3 Methods
### Motion Matrix Calculation  
The flowchart of the proposed system is shown in Figure 2, where motion matrix estimation is an essential step in the pipeline. We estimate dthe pattern of apparent motion including respiration of infants in videos. Motion matrix estimation can be defined as the distribution of apparent velocities of movement in successive images.  
Two flow estimation methods were employed to estimate pixel motion vectors between adjacent video frames. First, the conventional optical flow method [18] was utilized and evaluated. However,  
Figure 1: Example of an acquired video frame.  
Figure 2: Flowchart of the proposed video-based respiration monitoring system.  
the optimal flow is more sensitive to high gradient texture, whereas in our case the infant chest is either bare or covered by a blanket. Thus, in both cases, the chest area, which mostly indicates the respiratory motion, lacks gradient texture. Deep flow [19] is sensitive to the entire part of moving objects with less regard to texture information. Therefore, better performance is expected by using a flow estimation method based on deep learning.  
For both methods, the derived motion vectors contain respiration information, induced by the motion of the abdominal wall and chest wall. We only considered the vertical motion vector, since the respiration related motion is mainly in the vertical direction in the captured videos. For infants under one year of age, it is also recommended by the American Academy of Pediatrics (AAP) that they should be placed to sleep on their backs every time being laid down to sleep. This lowers the risk of sudden infant death syndrome (SIDS).
# Piecewise Function Hysteretic Model for Cold-Formed Steel Shear Walls with Reinforced End Studs
## 3 Hysteretic Model of CFS Shear Walls with Reinforced End Studs
### Hysteretic Model of Load-Displacement Curve  
#### 3.2.1 Model Proposition  
According to previous test results, typical hysteretic loops of a wall with reinforced end studs were extracted, as shown in Figure 7, where \(\delta\) is the ratio of lateral displacement \(\Delta\) to wall height \(H\);\(f\) is  
Figure 6: Simplified calculation model for the wall with reinforced end studs in shear.  
the ratio of lateral load \(F\) to shear capacity \(F_{\text{P}}\). It can be observed that a typical hysteretic loop consists of an unloading segment, a slipping segment, and a loading segment; the ascending and descending branches of the curve are essentially symmetrical; particularly, the strength deterioration in successive cycles of the same amplitude is negligible.  
Hysteretic loops for different governing deformations can be captured based on the above characteristics, and the _f-\(\delta\)_ hysteretic model of the wall can be further established, as depicted in Figure 8. As a means of simplification, strength deterioration in successive cycles of the same amplitude is not considered, each hysteresis loop has its unique slipping axis. In consideration of the symmetry of hysteretic loops, a mathematical model for the ascending branch of the curve is proposed first (see Figure 8b), and then the descending part of the curve can be obtained by origin symmetry.
# An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning
## 4 Experiments
### Experiment Settings
#### 4.1.2 Experiment Data  
We use the characteristic parameters such as pulse amplitude(PA), carrier frequency (CF), pulse width (PW), pulse repetition interval (PRI) and angle of arrival (AOA) to simulate generating the emitter data of six system-like radars. For the signal parameters, they are set at the same intermediate frequency: 10 MHz, and the sampling frequency is 100 MHz. 1000 signal samples are generated using the above five pulse description words for radar 1, radar 2 and radar 3, respectively, and a total of 3000 signal samples are as known radar emitter data corresponding to the source domain data above.  
In addition, 1000 signal samples are generated for radar 4, radar 5 and radar 6, respectively, and a total of 3000 signal samples are as unknown radar emitter data corresponding to the target domain data above. The mean values and standard deviations after normalization of the known radar emitter data and the unknown radar emitter data are significantly different, so they no longer satisfy the assumption of the same distribution, which can be used to verify the transfer learning and semi-supervised learning method. The details of the experiment data are shown in Tables 1 and 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Known Radar** & **PA** & **CF/MHz** & **PW/\(\mu s\)** & **PRI/\(\mu s\)** & **AOA/\({}^{o}\)** \\ \hline radar 1 & [6; 16] & [2019, 2020] & [1.1, 1.3] & 400/50/550 & [46; 48] \\ radar 2 & [2; 12] & [2150, 2250] & [0.3, 0.5] & 300/350/400 & [62; 64] \\ radar 3 & [16; 20] & [3121, 3333] & [7.1, 7.2] & 800/830/860 & [66; 68] \\ \hline \end{tabular}
\end{table}
Table 1: Known radar emitter data.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Unknown Radar** & **PA** & **CF/MHz** & **PW/\(\mu s\)** & **PRI/\(\mu s\)** & **AOA/\({}^{o}\)** \\ \hline radar 4 & [12; 14] & [2545, 2546] & [0.2, 0.4] & 710/730/770 & [28; 30] \\ radar 5 & [5; 8] & [2763, 2773] & [0.6, 0.8] & 240/280/320 & [52; 54] \\ radar 6 & [23; 31] & [2855, 3003] & [4.7, 4.8] & 600/640/660 & [48; 50] \\ \hline \end{tabular}
\end{table}
Table 2: Unknown radar emitter data.  
A radar signal sample is written as \(W_{i}=\left[PA_{i},CF_{i},PW_{i},PRI_{i},AOA_{i}\right]^{T}\). The distribution of the specific parameters of the radar is shown in Figure 2, (a) describes the entire data set from the perspective of parameters PA and AOA, and (b) describes the entire data set from the perspective of parameters of CF, PW and PRI.
## 2 Materials and Methods
### Fabricated Prototype  
To experimentally support the numerical approach, we also fabricated a prototype (Figure 4). In particular, both the metasurface and the RF loop were realized by using PCB technology. The metasurface was etched over the top layer of a 0.1 mm thick FR4 substrate (\(\epsilon_{r}=4.3\) and \(tan\delta=0.025\)), whereas, on the other side the evaluated capacitors were soldered according to the numerical design process. Since we used AWG single strand copper wires in the numerical design to computationally speed up the simulations, we designed the PCB printed coils according to the following relation [36] to guarantee the inductance equivalence.  
\[w=4.482a, \tag{7}\]  
where \(w\) is the printed strip width and \(a\) is the wire radius.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **C1 (pF)** & **C2 (pF)** \\ \hline RF loop 17 MHz & 1550 & 112 \\ RF loop 64 MHz & 411 & 7 \\ RF loop and Metasurface 17 MHz & 240 & N/A \\ RF loop and Metasurface 64 MHz & 7.6 & 59 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Capacitances employed in matching networks.  
Figure 4: _Cont_.  
Because the metasurface can be used to enhance the magnetic field both in transmission and reception, special attention must be paid to the capacitor's choice. Indeed, especially in transmission, relatively high power levels must be handled, therefore, appropriate ceramic capacitors have been consequently selected [37]. On the other hand, the RF active loop was etched on the top layer of a 0.8 mm thick FR4 substrate. Also in this case, we soldered on the bottom layer the components of the matching networks at the two frequencies (Figure 4). Finally, we equipped the RF loop with a 50-\(\Omega\)-micro-SMA connector to enable measurements with a vector network analyzer (Fieldfor series VNA N9918A, Keysight, Santa Rosa, CA, USA). Lastly, we realized a muscle phantom (Figure 5) by using a cylindrical plastic container (200 mm height with a circular base presenting a 100 mm diameter), filled with a mixture of water, glycine (\(C_{2}H_{5}NO_{2}\)) at 12% \(w/v\), and NaCl at 0.53% \(w/v\), following the methodologies described in [35; 38]. It is important to note that the relative positioning among RF coil, metasurface, and phantom must respect the distance values optimized during the design process to guarantee the maximum performance of the overall system.  
Figure 4: Experimental set-up: (**a**) top and bottom layer of the PCB metasurface; (**b**) top and bottom layer of the PCB driver; (**c**) measurement set-up, including RF loop, metasurface and VNA. We employed foam spacers (\(\epsilon_{r}=1\)) to control both metasurface-RF loop distance and metasurface-phantom distance.  
Figure 5: Muscle phantom sample.
## 4 Transition from Wire Antenna to Planar Antenna  
Although the wire antenna configuration is good enough for fabrication and measurements, an equivalent printed planar arc antenna would be a better option because of two reasons. Firstly, the input reactance of the wire antenna, although, can be easily compensated by putting a lumped component in series with the feed port in a simulation environment, yet it is cumbersome to achieve the same practically. Secondly the dimensions of the antenna structure should be precise to achieve maximum isotropy in measurements. Therefore, an equivalent printed planar arc antenna with the same electrical dimensions was simulated in CST to verify the performance. This was again done in two steps: a printed antenna with both dipole legs on the same side of the substrate was simulated first. Following the same technique of impedance matching proposed in Section 2, a capacitor in series is attached to the input port. The line thickness is kept at 1 mm. The design is simulated using FR4 substrate (\(\varepsilon r=4.4\), \(h=1.6\) mm). Figure 16 presents the simulation layout of port fed printed antenna, while a 3D gain plot of the same is shown in Figure 17. It can be seen that, by adding the substrate, the total gain variation has increased from \(\leq\)0.5 dB to \(\leq\)0.8 dB.  
Figure 14: Azimuth and elevation total gain plots at \(\mid f=885\) MHz.  
Figure 15: Azimuth and elevation total gain plots at \(\mid f=1110\) MHz.  
Similarly, Figure 18 presents the simulation layout of the transmission line fed printed antenna, where it can be seen that two legs of the dipole printed on the top and bottom surfaces of the substrate are fed with a microstrip transmission line with integrated stub that compensates the reactive part of the input impedance of the antenna. The width of dipole legs is increased from 1 mm to 3 mm in order to match it with that of the 50 ohms transmission line.  
Figure 19 also shows the current distribution of the printed antenna, and it can be seen that the current at the middle point of curved segment is maximum which almost entirely contributes to the horizontally polarized field. At the end of the arc, it becomes lower. Again, the small segment (\(z\)-direction) has a high current density and while the long arm (\(x\)-direction) has significantly low current density. Collectively though physically the antenna has a greater vertical length than horizontal and, therefore, is able to balance the power of vertical and horizontal polarizations providing isotropy.  
The 3D gain plot of the final printed design is shown in Figure 20, showing that the overall gain variation has now increased to \(\leq\)0.9 dB.  
Figure 16: Simulation layout of port fed printed antenna.  
Figure 17: 3D Gain of port fed printed antenna.  
Figure 19: Current distribution for the center frequency.  
Figure 18: Simulation layout of microstrip fed printed antenna.
# CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention
## 5 Conclusions  
We proposed CFAM for estimating the 3D hand pose from a single RGB image. As far as we know, we are the first to use this attention mechanism as a block in the application of 3D hand pose estimation, and the accuracy was clearly an improvement over other commonly used methods. We reasonably used the missing information in the color image by combining a 2D score map and an RGB image. In addition, we used an attention mechanism as a weighting scheme to clarify the guiding effect of the two features of color images and the 2D joint points on the 3D joint point estimation. We validated our method on the RHD and STB datasets. Multiple contrast experiments on public datasets demonstrated that our proposed method could achieve state-of-the-art accuracy, and an ablation experiment showed that an RGB image and a 2D score map could be combined to improve the result of the 3D hand estimation, which means that the information in the RGB image was also very important. In future research, we will improve the efficiency of the program and simplify the model. Our method can be used in virtual reality equipment to accurately locate joint points.  
Conceptualization, X.W.; data curation, X.W. and L.K.; formal analysis, L.K. and D.L.; funding acquisition, Y.G.; methodology, X.W., Y.G., and Y.W.; resources, Y.W.; software, X.W.; validation, Y.W.; writing--original draft, X.W.; writing--review and editing, J.J. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the National Natural Science Foundation of China (NSFC) under grant nos. 61806218, 61873274, and 61806215.  
The authors declare no conflicts of interest.