esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 3 Link State Evaluation  
### Characteristics of LoRa Communication Link  
A LoRa communication link refers to the connection between the ED and the GW. Due to the complex environment in which EDs often operate and their power consumption limitations, the link state is difficult to predict. In wireless communication, signal fading is divided into large-scale fading and small-scale fading. Large-scale fading is mainly caused by path loss, which is proportional to the propagation distance; the greater the distance, the higher the loss. Small-scale fading occurs due to multipath interference, where the signal rapidly fades over short periods or short distances, making large-scale fading negligible [26].  
When the received signal experiences line-of-sight (LOS) propagation, the direct signal is the main component, and the multipath signals are superimposed on it. The envelope of  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Proposed Solution** & **Optimization Approach** & **Link** & **Link** & **Power** & **Network** & **ED** \\  & & & **Reliability** & **Stability** & **Consumption** & **T****ation** & **put** & **Latency** \\ \hline
[9; 10] & LoRaWAN ADR & Parameter Resource Optimization & ✓ & & ✓ & & \\ \hline
[13] & I-SFA & Parameter Resource Optimization & ✓ & & & ✓ & \\ \hline
[14] & CA-ADR & Parameter Resource Optimization & ✓ & & ✓ & ✓ \\ \hline
[17] & EF-LoRa & Parameter Resource Optimization & ✓ & & ✓ & & \\ \hline
[21] & K-ADR & Parameter Resource Optimization & ✓ & & ✓ & & \\ \hline
[23] & XCopy & CommunicationProcess Optimization & ✓ & ✓ & ✓ & ✓ & \\ \hline  & Proposed Algorithm & Parameter Resource Optimization & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of adaptive data rate algorithms.  
the received signal follows a Rician distribution. When there is no direct signal, i.e., non-line-of-sight (NLOS) propagation, the signal reaches the receiver through reflection, refraction, diffraction, and other paths, and the envelope follows a Rayleigh distribution [27; 28].
## 2 Possibility Analysis and Testing Method of the Structure Sound Absorption Performance
### Testing Method  
The sound absorption coefficient is an important index to evaluate the sound absorption performance of materials [24; 25; 26]. Among the many methods of acoustic performance testing, the reverberation chamber method and tube measurement method are the most commonly used methods to measure the sound absorption coefficient of materials. Tube measurement method is divided into standing wave ratio method and transfer function method. Standing wave ratio method calculates the sound absorption coefficient of the material to the vertical incident wave by measuring the maximum and minimum values of the standing wave sound pressure. Transfer function method separates the incident wave and the reflected wave by processing the signal delay to obtain the sound absorption coefficient of the material. The above two methods measure the vertical sound absorption coefficient of the material. The reverberation chamber method is used to measure the random incident sound absorption coefficient of the material, which is obtained by the reverberation time before and after the material is placed.  
There are a variety of noise sources in the tunnel, including fan noise and vehicle noise. The incident trajectory of acoustic waves is untraceable, so it cannot be measured by pipe measurement method. In addition, because of the unique structure of the tunnel, the distribution of the internal sound field is different, and it is impossible to simulate the sound field with a simple reverberation chamber. Therefore, it is necessary to seek a novel testing method, which requires accurate evaluation criteria and can be applied to tunnels. The testing method proposed in this paper refers to the loudspeaker test and typical tunnel measurements, which are the two typical methods in the existing tunnel noise research. The loudspeaker test is carried out in the tunnel by using a fixed sound source. And the fixed sound source is used to release several octave pure tone signals, then the sound pressure level changes along the longitudinal direction of the tunnel can be measured. Typical tunnel measurement is to compare the noise level before and after sound absorption treatment of the actual tunnel, and to effectively evaluate the actual effect of the sound absorption materials. This test is based on the actual measurement of a typical tunnel and combined with some ideas in the loudspeaker test, the new fashioned two test conditions Case I and Case II have been formulated. In Case I, a fixed sound source was selected to release several octave pure tones separately, and the sound pressure level changes were measured along the longitudinal direction of the tunnel before and after the installation of the aluminum foam boards. In Case II, the change of vehicle noise before and after the installation of the aluminum foam boards were measured, and the insertion loss of the closed-cell aluminum foam was analyzed and studied. The two test conditions will be described in detail below.
## 5 Simulation
### Optimization of the Ellipsoid for Initial States  
In this subsection, we provide the simulation results about the ellipsoid \(\Gamma(P_{1},P_{2})\). For ease of presentation, we consider the following two-dimensional plant borrowed from [21] with  
\[A=\left(\begin{array}{cc}-1.7741&0.4815\\ -7.68374&2.0741\end{array}\right),B=\left(\begin{array}{cc}8\\ 8\end{array}\right),C=\left(\begin{array}{cc}6&9\end{array}\right).\]  
Then, according to Theorem 3, one can obtain the parameter \(\rho_{0}\) and the gain matrices \(K,L\). With \(\epsilon_{1}=30\), \(\epsilon_{2}=17\), a group of solutions to the LMIs in Theorem 3 are \(\rho_{0}=0.2\), \(K=(-2.5738,1.2988)\),  
\[L=\left(\begin{array}{cc}-0.6418\\ 2.8786\end{array}\right),P_{1}=Q_{1}^{-1}=\left(\begin{array}{cc}1.2450&-0. 7870\\ -0.7870&0.5891\end{array}\right),\text{ and }P_{2}=\left(\begin{array}{cc}73.2137&19.5689\\ 19.5689&18.3229\end{array}\right).\]  
For convenience of plotting the ellipsoid, we consider the case that the initial state of the observer in Label (2) is \(\hat{x}(0)=0\), and correspondingly \(z(0)=-x(0)\). In this situation, the set \(\Gamma(P_{1},P_{2})\) can reduce to \(\Gamma_{0}(P_{1}+P_{2})\times\{(0,0)^{\mathrm{T}}\}\) with \(\Gamma_{0}(P_{1}+P_{2})=\{x^{\mathrm{T}}\in\mathbb{R}^{n}|x^{\mathrm{T}}(P_{1} +P_{2})x\leq 1\}\). Hence, one can evaluate the size of \(\Gamma(P_{1},P_{2})\) by \(\Gamma_{0}(P_{1}+P_{2})\) to some degree.  
More importantly, \(\Gamma_{0}(P_{1}+P_{2})\) is a two-dimensional ellipsoid and can be plotted in the plane.  
Figure 5 shows the boundary of the ellipsoid \(\Gamma_{0}(P_{1}+P_{2})\) with different \(\rho_{0}\), \(P_{1}\), and \(P_{2}\). First, we plot the ellipsoid with \(\rho_{0}=0.2\) and the original \(P_{1}\) and \(P_{2}\) obtained from Theorem 3. Then, according to Theorem 2, one can obtain the optimized version for \(\rho_{0}=0.2\), and it is plotted by the solid curve. Obviously, Theorem 2 can enlarge the ellipsoid for fixed gain matrices \(K\) and \(L\). Moreover, Figure 5 provides the curves with the parameters obtained from Theorem 2 for different \(\rho_{0}\). The results show that the size of the ellipsoid increases by decreasing \(\rho_{0}\), which indicates that there is a trade-off between the sampling performance and the admissible region for initial states such that the closed-loop system is asymptotically stable.
## 2 Theory
### \(0\
u\beta\beta\) Decay  
The computational scheme used here is presented in detail in Reference [50]. We present here key relations. Assuming light Majorana neutrino exchange [4; 50], the inverse half-life can be written as  
\[\left[t^{(0\
u)}_{1/2}(0^{+}_{i}\to 0^{+}_{f})\right]^{-1}=g_{A}^{4}G_{0\
u} \Big{|}M^{(0\
u)}\Big{|}^{2}\big{|}\big{<}m_{\
u}\big{>}\big{|}^{2}, \tag{5}\]  
where \(G_{0\
u}\) is the phase-space factor for the final-state leptons, \(g_{A}\) is the axial vector coupling constant, \(\big{<}m_{\
u}\big{>}\) is the effective neutrino mass, and \(M^{(0\
u)}\) is the nuclear matrix element (NME). The \(M^{(0\
u)}\) NME can be decomposed as follows:  
\[M^{(0\
u)}=M^{(0\
u)}_{\text{GT}}-\bigg{(}\frac{g_{V}}{g_{A}}\bigg{)}^{2}M^{( 0\
u)}_{\text{F}}+M^{(0\
u)}_{\text{T}}, \tag{6}\]  
where \(M^{(0\
u)}_{\text{GT}}\), \(M^{(0\
u)}_{\text{F}}\), and \(M^{(0\
u)}_{\text{T}}\) are the Gamow-Teller, Fermi, and Tensor components of the NME, respectively, and \(g_{V}\) is the vector coupling constant. Contribution from various multipoles constituting all the intermediate transitions is given as follows:  
\[M^{(0\
u)}_{K}=\sum_{J^{\pi}}M^{(0\
u)}_{K}(J^{\pi}), \tag{7}\]  
where \(K=\text{GT},\text{F},\text{T}\), and \(M^{(0\
u)}_{K}(J^{\pi})\) are the contributions from all the states \(i\) of the intermediate multipole \(J^{\pi}\). Each multipole contribution is, in turn, decomposed in terms of the two-particle transition matrix elements and one-body transition densities. In the pnQRPA calculations, the two-particle transition matrix element reads  
\[M_{K}^{(0v)}(J^{\pi})=\sum_{k_{1},k_{2},J^{\prime}}\sum_{pp^{ \prime},nn^{\prime}}(-1)^{j_{k}+j_{p^{\prime}}+I+J^{\prime}}\sqrt{2J^{\prime}+1 }\begin{cases}j_{p}&j_{n}&J\\ j_{n^{\prime}}&j_{p^{\prime}}&J^{\prime}\end{cases}\times\] \[\left(pp^{\prime}:J^{\prime}|O_{K}|nn^{\prime}:J^{\prime}\right) \left(0^{\prime}_{j}||[c_{p^{\prime}}^{\dagger}\varepsilon_{n}]_{I}||I_{k_{1}}^ {\pi}\right)\langle J_{k_{1}}^{\pi}|J_{k_{2}}^{\pi}\rangle\langle J_{k_{2}}^{ \pi}||[c_{p}^{\dagger}\varepsilon_{n}]_{I}||0_{i}^{+}\rangle, \tag{8}\]  
where \(k_{1},k_{2}\) label the pnQRPA solutions for a given multipole \(J^{\pi}\), starting from the final (\(k_{1}\)) and initial (\(k_{2}\)) nuclei, and \(p,p^{\prime},n,n^{\prime}\) denote the proton and neutron single-particle quantum numbers. The operator \(O_{K}\) contains the neutrino potentials, the characteristic two-particle operators for the different \(K\) components, and short-range correlation effects. The quantities \(\left(0_{j}^{+}||[c_{p^{\prime}}^{\dagger}\varepsilon_{n}]_{I}||I_{k_{1}}^{\pi}\right)\) and \(\left(J_{k_{2}}^{\pi}||[c_{p}^{\dagger}\varepsilon_{n}]_{I}||0_{i}^{+}\right)\) are the corresponding decay amplitudes, and \(\langle J_{k_{1}}^{\pi}|J_{k_{2}}^{\pi}\rangle\) is an overlap factor connecting the two branches of pnQRPA solutions for the \({}^{136}\)Cs wave functions.  
In the NSM calculations, we use one unique set of states in \({}^{136}\)Cs so that the sum over \(k_{1},k_{2}\) in Equation (8) is replaced by a sum over a single state number \(k\) and the overlap factor is not needed.
## 4 Results
### Validation
#### 4.1.1 Lid-Driven Cavity  
The lid-driven cavity flow is a standard CFD benchmark, where the flow inside a square chamber is driven by a constant velocity at the chamber top lid. Depending on the Reynolds number, different vortex structures can be observed. The characteristic length \(L\) equals the length of the chamber side (\(L=n_{y}-1\) in lattice units), the x-velocity of the top lid is the characteristic velocity \(U\) (we used \(U=0.1\) in lattice units), and y-velocity of the top lid is zero. The velocities at all other nodes are assumed to be zero. On the top wall, the constant velocity boundary condition was imposed. For other walls, we used the bounce-back boundary conditions.  
The results were compared with data from [28] for different mesh resolutions \(n_{x}\times n_{y}\), an example is shown in Figure 2. To obtain the correct Re values, the fluid viscosity was computed according to Equation (1) as \(\
u=U\cdot(n_{y}-1)/\text{Re}\). A uniform fluid density \(\rho=1\) was imposed initially. For some combinations of mesh resolution and Reynolds numbers (for small resolutions and high Re numbers, as well as for large resolutions and small Re numbers), we observed numerical instabilities. Verified simulation settings are shown in Table 1. The simulation for Re = 10,000 was stopped before fully converging (mean squared error \(\sum(v-v_{ref})^{2}/n\) was at the order of \(10^{-3}\), maximum relative error was about 30% for \(v_{x}\) at \(y=0.2813\)) and stabilised (we could still observe small, fading swirling waves), but it was slowly approaching the reference velocity profiles. We also observed that, for Re = 3200, a single reference data point at \((y=0.4531,v_{x}=0.86636)\) was significantly different than the others.
# A Novel Genetic Algorithm for the Synthetical Sensor-Weapon-Target Assignment Problem
## 2 Problem Analysis and Formulation
### The Improved S-WTA Model
#### 2.2.2 Behaviors of the Proposed Gain Factor \(\sigma\)
However, if there are many sensors assigned to the target, the detection and tracking accuracy of the target by the sensors is high enough. At this moment, increasing the number of sensors has little effect on the accuracy of detection and tracking, so the kill probability will increase very little. For example, assume that there is one sensor assigned to the \(k\)th target and the detection and tracking probability \(q_{ik}\) is 0.8, then the probability \(Q_{k}\) is 0.8. If we add an identical sensor to detect and track the \(k\)th target, then the probability \(Q_{k}\) is 0.96. If we add another identical sensor to detect and track the \(k\)th target, which means that there are three sensors assigned to one target, the probability \(Q_{k}\) is 0.996. Therefore, as more and more sensors are assigned to a target, the probability \(Q_{k}\) will increase less and less. Therefore, as we introduce the hyperbolic tangent function \(tanh(\sum\limits_{i=1}^{M}x_{ik}-1)\) to control the value of \(\sigma\), the gain of the kill probability can tally with the actual situation of the S-WTA problem. According to the above analysis, the proposed model of the gain factor \(\sigma\) in line with the practical S-WTA problem; it can effectively reflect the influence of different sensor assignment schemes on the kill probability.
## 4 Discussion
(2021) [52] demonstrated that physical activity alternates constantly between the aerobic and anaerobic systems based on the match's demands. Perhaps those are the reasons for the scarce information on lactate-guided threshold interval training in soccer players, in contrast to the vast body of literature and the ongoing promising approaches in middle- and long-distance runners [53]. Thus, the use of LT determination in soccer is mainly utilized as a determinant of fitness readiness rather than a method for setting training intensities. Further research using global positioning systems (GPSs) during official games would offer a better understanding of how improvements in vLT with training would affect external load and develop training programs to develop all energy systems involved. Additionally, LT determination could be utilized in adjusting the running thresholds set by the various GPS software. Furthermore, the results of this study demonstrated the importance of lactate profiling in youth for comparative purposes with the professional squad and their lack of agreement. The vLT in soccer is indicative of aerobic status [54], is significantly faster for higher playing standards [17], and is more sensitive to aerobic fitness adaptations of the preseason [55] and in-season periods [35]. Likewise, professional squad practitioners should integrate LT determination as a regular assessment tool for youth to monitor and prescribe endurance sessions. However, LT concepts should never be used interchangeably by both professional and youth soccer players, as they lack agreement. When evaluating the aerobic power of youth for talent identification, lactate profiling during incremental testing and RE should be evaluated, which could be more informative than the widespread field testing usually applied in youth leagues. Practitioners should carefully standardize the termination of incremental testing based on the \(\dot{V}\)O\({}_{2}\) max criteria and avoid volitional fatigue, especially in youth soccer players, as this will cause an engagement of the anaerobic system beyond what is physiologically required and thus overestimations of the LT.
## 3 Results and Discussion
The trend of the plot of H\({}_{\rm A}\) for Ce\({}_{2}\)(Fe\({}_{14-x}\)Si\({}_{x}\))B at 25 \({}^{\circ}\)C found here differs from that reported for Nd\({}_{2}\)(Fe\({}_{14-x}\)Si\({}_{x}\))B at 22 \({}^{\circ}\)C [30], where a maximum was observed at \(x\) = 0.5 (3 atom % Si). Meanwhile, no maximum was observed here for Ce\({}_{2}\)(Fe\({}_{14-x}\)Si\({}_{x}\))B. In general, Ni substitution for Fe in Ce\({}_{2}\)Fe\({}_{14}\)B results in a small overall reduction in \(H_{\rm A}\), while Al and Si substitutions showed larger reductions. Figure 5 shows composition dependence of \(T_{\rm C}\) for Ce\({}_{2}\)(Fe\({}_{14-x}\)T\({}_{x}\))B, (T = Al, Ni, and Si). Replacement of Fe by Al leads to an increase in lattice constant, c, and lattice volume, consequently increasing the distances between Fe sites. This likely leads to a weakening of the Fe-Fe magnetic interactions and \(T_{\rm C}\) falls as the concentration of Al increases. This behaviour is in agreement with previous studies on the effect of Al on other RE\({}_{2}\)Fe\({}_{14}\)B discussed in the literature [29; 43]. Ni prefers to substitute for Fe in the 16\(k_{2}\) and 8\(j_{2}\) sites, and Si prefers 16\(k_{2}\) and 8\(j_{2}\) sites in RE\({}_{2}\)Fe\({}_{14}\)B [48; 49; 50]. Fe substitution by both Ni and Si in Ce\({}_{2}\)Fe\({}_{14}\)B leads to decreases in the lattice constant, c, and the lattice volume. This reduces the distances between the Fe sites, strengthening the magnetic interactions and leading to increases in \(T_{\rm C}\). At a maximum solubility of 9 atom % Ni (\(x\) = 1.45), a \(T_{\rm C}\) of 210 \({}^{\circ}\)C was measured. We found that Ni substitution leads to an increase of about 7.5 \({}^{\circ}\)C per \(x\) = 0.17 (1 atom % Ni). Increasing Si substitution for Fe in the Ce\({}_{2}\)Fe\({}_{14}\)B compound also increases the \(T_{\rm C}\) almost linearly (about 7.2 \({}^{\circ}\)C per \(x\) = 0.17 (1 atom % Si)) and a \(T_{\rm C}\) of 252 \({}^{\circ}\)C was measured in the composition containing the maximum solubility of Si. Figure 3: The composition dependence of the saturation magnetization (\(M_{\rm S}\)) for the Ce\({}_{2}\)(Fe\({}_{14-x}\)T\({}_{x}\))B solid solutions (\(T\) = Al, Ni, Si) at 25 \({}^{\circ}\)C. Some error bars are not visible because they are smaller than the data points. Figure 4: The composition dependence of the anisotropy fields (\(H_{\rm A}\)) for the Ce\({}_{2}\)(Fe\({}_{14-x}\)T\({}_{x}\))B solid solutions (T = Al, Ni, and Si) at 25 \({}^{\circ}\)C.  
Figure 5: The composition dependence of the Curie temperatures (\(T_{\rm C}\)) for the Ce\({}_{2}\)(Fe\({}_{14-x}\)T\({}_{x}\))B solid solutions (T = Al, Ni, and Si).
# A Compact Printed Monopole Antenna for WiMAX/WLAN and UWB Applications
## 5 Conclusions  
In this paper, a design for a printed monopole antenna suitable for cable-less seismograph nodes is proposed. This antenna can work in the WiMAX (5.25-5.85 GHz) and WLAN (2.4, 5.2, and 5.8 GHz) bands. The proposed antenna satisfies the needs for miniaturization and has a relative bandwidth of 64% in the high-frequency band. The prototype with a compact overall size of \(17\:\mathrm{mm}\times 30\:\mathrm{mm}\times 1.6\:\mathrm{mm}\) achieved a measured bandwidth of 270 MHz (2.23-2.50 GHz) and 3.88 GHz (4.30-8.18 GHz) at lower and upper bands, respectively. The simulation and experimental results generally agree well with each other. The test results indicate a high-frequency bandwidth of 4.30-8.18 GHz for the proposed antenna. Therefore, the proposed simple antenna may be suitable for next-generation UWB systems. Of course, we can also improve the gain of the antenna to increase the transmission distance. Thereby, it would connect more cable-less seismograph nodes under the same system. Thus, we could reduce the number of AP (Access Port) relays.  
The data collection is completed Z.C. The research design is completed by B.L. The document retrieval and icon production are completed by Y.Z. The software part is completed by H.L.  
The research is sponsored by the Natural Science Foundation of China (No.41404097, 41074074), Science and technology development project of Jilin Province (No.20150520071 JH, 20160204065 GX, SXG)SF2017-5), China Postdoctoral Science Foundation (No.2015M571366).  
The authors declare no conflict of interest.
# Two-Dimensional Metal-Organic Frameworks and Their Derivative Electrocatalysts for Water Splitting
## 2 Fabrication Strategy of 2D MOF and Their Derivatives
### Fabrication Strategy of 2D MOF Derivative  
Generally, 2D MOF derivatives can be obtained by simple heating of 2D MOF precursors. The organic ligands of 2D MOFs decompose at appropriate temperatures under different gaseous atmospheres (N\({}_{2}\), Ar, and Air) to give the corresponding carbonized or oxidized 2D MOF derivatives [127; 128; 129; 130]. The generated MOF derivatives generally have ad  
Figure 6: (**a**) The preparation of HXP. (**b**) SEM image of HXP. (**c**) TEM image of HXP. (**d**) AFM image of HXP. Reproduced with permission [123]. Copyright 2020, American Chemical Society.  
vantages such as high porosity, high specific surface area, high stability, and good electrical conductivity. Additionally, 2D MOFs can be sulfated and phosphorylated to obtain derivatives with high electrocatalytic activity and stability, as shown by [PERSON] et al. [131], who obtained Ni-BDC@NiS derivatives with high OER activity by sulfidation of 2D Ni-BDC.  
Although 2D MOF derivatives have many advantages, their preparation has many problems. With the decomposition of organic ligands, the structures of 2D MOFs tend to collapse, leading to the destruction of the original MOF structure. The metal ions in 2D MOFs tend to aggregate, which reduces their activity. Therefore, preparing 2D MOF-derived materials still requires considerable effort from researchers.  
Table 1 provides a summary of the prevalent synthetic strategies and methods for creating 2D MOFs and their derivatives. Despite a range of electrocatalysts having been synthesized from these 2D MOFs and their derivatives, the process is not without its flaws. Hence, there is a pressing need for the development of ultra-thin 2D MOFs that feature controllable shape and size coupled with high yield.