esa-sceva/satcom-chunk-collection huggingface dataset:
# Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs
## 5 Proposal of Spiral Mobility Based on Optimized Clustering Routing Protocols
### Network Settling Phase  
The energy dissipation of CHs is higher than member nodes because of the extra processing and communication responsibilities. Therefore, the energy consumption of the CH is the bottleneck to finding the lifetime of the network. As we mentioned earlier, two types of nodes \"normal nodes and advanced nodes\" are deployed in the network. To find the average number of normal and advanced nodes in one cluster, we calculate respectively:  
\[N_{n}=\frac{A}{\pi R^{2}} \tag{6}\] \[N_{a}=N_{i}\frac{\pi R^{2}}{A}\]where \(N_{n}\) are normal nodes and \(N_{a}\) are advanced nodes in the cluster, while \(A\) is the area and \(\pi R^{2}\) is the radius of the cluster.  
In order to select the CHs between normal and advanced nodes, two types of threshold values are calculated. As per the energy level of nodes, the threshold value of advanced nodes will always be greater than the threshold value of normal nodes. This higher value of the threshold creates more chances for advanced nodes to become CHs. \(P_{n}=\frac{1}{P_{optimum}}(1-n)\) is used to calculate the probability for normal nodes, and \(P_{a}=\frac{1}{P_{optimum}}(1-a)\) is used to calculate the probability for advanced nodes. After the probability calculation of both types of nodes, the threshold values are calculated by the following equations.  
\[T(normal)=\begin{cases}\frac{P_{n}}{(1-P_{n}*(mod\frac{1}{P_{n}})\times d)},& \text{if }n\in N_{n}\\ 0,&\text{otherwise}\end{cases} \tag{7}\]  
\[T(advance)=\begin{cases}\frac{P_{a}}{(1-P_{a}*(mod\frac{1}{P_{n}})\times d)},& \text{if }a\in N_{a}\\ 0,&\text{otherwise}\end{cases} \tag{8}\]  
After calculating the threshold for normal and advanced nodes, random number \(R_{n}\) will be generated for all nodes \(N_{i}\). If the threshold value of the specific node is greater than the random number, then this node will be selected as the CH.  
Apart from the sink mobility pattern, the path length of the sink in the cluster is \(2\times R\), since it should go by the center of the cluster. Moreover, the CH must be within a distance range of \(r\) from the sink path. Therefore, the region where CHs are located will be approximately equal to \(4\times R\times r\). In order to find the number of CHs and clusters in the network, we calculate respectively:  
\[N_{CH}=N_{a}-N_{i}-1=(\pi R^{2}-4 Rr)\frac{N_{a}}{A} \tag{9}\]  
and:  
\[N_{c}=\frac{4 Rr}{\pi R^{2}}\times N_{CH}-1=(4 Rr)\frac{N_{CH}}{A}-1 \tag{10}\]
## 3 Current Developments
### Data Quality Analysis
#### 3.1.3 Data Trust and Vandalism
Several researchers presented the first approaches on how the volunteers could act as a sort of quality measure. [PERSON] and [PERSON] [69] discussed a possible solution for VGI projects based on trust ratings in a social network that acts as an indicator for user reputation. In the case of the OSM project, this approach would not be suitable, due to the lack of a social network structure [119]. [PERSON] _et al_. [70] specified different patterns that can be used to determine trust values based on the history of contributed objects. In a second study, it was shown that for a test area in Germany, the approach can provide useful information for potential data users, even without a reference dataset, and the researchers pointed out that for further analysis, the reputation of each contributor should be considered as an important factor [120]. Although the OSM project showed some promising developments in recent years, the increasing popularity also comes with a number of caveats, especially in the form of cases of vandalism, similar to developments seen in Wikipedia [121]. While [PERSON] [122] summarized some of the first methods on how to validate contributors and their spatial information, [PERSON] _et al_. [52] developed the first prototype to automatically detect vandalism in VGI projects and revealed that within a timeframe of one week, at least one case of vandalism or accidentally destroyed objects by new or inexperienced members can be detected in the OSM database each day.
## 2 Theory
### THM Coupling Equation of CO\({}_{2}\) Displacement CBM
#### 2.2.4 Porosity and Permeability  
The adsorption and desorption of gas in the seepage and diffusion field, compression of the stress field on the coal seam, and thermal expansion of the temperature field are transformed into the influence on porosity, which is defined as follows [40]:  
\[\varphi=1-\frac{1-\varphi_{0}}{1+\mathrm{e}}(1+\frac{\Delta V_{S}}{V_{S0}}) \tag{19}\]  
where \(\varphi_{0}\) is the initial porosity of coal; \(\mathrm{e}\) is the volumetric strain of coal; \(V_{S0}\) is the initial volume of coal skeleton; \(\Delta V_{S}\) is the coal skeleton volume changes under the comprehensive action of pore pressure compression, thermal expansion, and gas absorption expansion.  
The effects of gas pressure and coal temperature on porosity are considered:  
\[\varphi=1-\frac{1-\varphi_{0}}{1+\mathrm{e}}(1+\frac{\Delta V_{SP}}{V_{S0}}+\frac {\Delta V_{ST}}{V_{S0}}) \tag{20}\]  
where \(\Delta V_{SP}\) is bulk expansion and deformation of coal caused by pressure; \(\Delta V_{ST}\) is bulk expansion and deformation of coal caused by temperature change, and the equation is as follows:  
\[\left\{\begin{array}{l}\frac{\Delta V_{SP}}{V_{S0}}=-\frac{\Delta P}{K_{S}} \\ \frac{\Delta V_{ST}}{V_{S0}}=\beta\Delta T\end{array}\right. \tag{21}\]  
Volumetric deformation resulting from gas adsorption and desorption is given as follows [41]:  
\[\varepsilon_{p}=\frac{2\rho_{c}RTa_{i}}{3V_{m}K}\ln(1+\sum\
olimits_{i=1}^{2} b_{i}P_{i}) \tag{22}\]  
The porosity equation can be obtained by integrating the porosity factors:  
\[\varphi=1-\frac{1-\varphi_{0}}{1+\mathrm{e}}(1-K_{Y}\Delta P+\beta\Delta T+ \frac{\frac{2\rho_{c}RTa_{i}}{3V_{m}K}\ln(1+\sum_{i=1}^{2}b_{i}P_{i})}{1- \varphi_{0}}) \tag{23}\]  
The relationship between porosity, permeability, and particle size distribution in porous media is as follows [29; 33]:  
\[k=\frac{d_{e}^{2}\varphi^{3}}{72(1-\varphi)^{2}} \tag{24}\]  
where \(d_{e}\) is the effective diameter of particle. According to Equation (22), we obtain:  
\[\frac{k}{k_{0}}=\left(\frac{\varphi_{0}}{\varphi}\right)^{3}\left(\frac{1- \varphi_{0}}{1-\varphi}\right)^{2} \tag{25}\]  
The second term on the right tends to be consistent when porosity is substantially lower than 1. The relationship between porosity and permeability is as follows [42; 43]:  
\[k=k_{0}\left(\frac{\varphi}{\varphi_{0}}\right)^{3} \tag{26}\]  
where \(k_{0}\) is the initial permeability (m\({}^{2}\)), and coal seam permeability is as follows:  
\[k=k_{0}\left(\frac{1-\frac{1-\varphi_{0}}{1+\mathrm{e}}(1+\frac{\Delta V_{SP }}{V_{S0}}+\frac{\Delta V_{ST}}{V_{S0}})}{\varphi_{0}}\right)^{3} \tag{27}\]
# A Frequency-Reconfigurable Dual-Band RF Crossover Based on Coupled Lines and Open Stubs
## 3 Simulation  
To simulate the proposed design, the physical dimensions of the ports, CLs, and open stubs are required. These dimensions were obtained by utilizing LineCalc, which is a tool provided in ADS. The electrical parameters of a CL are the even mode impedance, odd mode impedance, frequency, and electrical length; they were converted to width, length, and separation between the two lines. As for a TL, the impedance, frequency, and electrical length are used to find the physical width and length. Then HFSS was used to simulate and further optimize the design, especially because the position of the varactor diode along the open stubs affects the S-parameters, resonant frequencies, and tuning range proportion between the two bands [39]. It can be noticed that there is a considerable difference between \(Z_{\mathrm{el}}\) and \(Z_{\mathrm{o1}}\), and as previously mentioned, this can lead to high coupling, which can negatively affect the results. This was proven when the design was simulated. Thus, the patterned ground technique [41] was employed, and two slots were etched at the ground plane to reduce the coupling effect. After that, the design was simulated, and Figure 4 shows the S-parameters of the proposed design.  
When the capacitance of the varactor diodes was set to 2.67 pF, the dual-band frequencies obtained were 2.09 GHz and 5.44 GHz. Then, when the capacitance was decreased, the operating frequencies increased. Until reaching 0.63 pF, the frequencies were continuously tuned until reaching 2.49 GHz and 5.80 GHz. The tuning range was calculated using Equation (23). For the lower band, the tuning range is 17.5%. Whilst for the higher band, the tuning range is 6.4%. In both bands along the whole tuning range, the insertion loss \(\left|\mathrm{S_{31}}\right|_{\mathrm{dB}}\) is less than 1.4 dB. For the lower band, \(\left|\mathrm{S_{11}}\right|_{\mathrm{dB}}\) is above 12 dB, \(\left|\mathrm{S_{21}}\right|_{\mathrm{dB}}\) is greater than 16 dB, and \(\left|\mathrm{S_{41}}\right|_{\mathrm{dB}}\) is better than 13 dB. Going to the higher band, \(\left|\mathrm{S_{11}}\right|_{\mathrm{dB}}\) is higher than 24 dB, \(\left|\mathrm{S_{21}}\right|_{\mathrm{dB}}\) is above 13 dB, and \(\left|\mathrm{S_{41}}\right|_{\mathrm{dB}}\) is more than 20 dB.  
\begin{table}
\begin{tabular}{c c c c} \hline \(Z_{\mathrm{e1}}\) & 198 \(\Omega\) & \(Z_{0}\) & 50 \(\Omega\) \\ \hline \(Z_{\mathrm{o1}}\) & 77 \(\Omega\) & \(Z_{1}\) & 65 \(\Omega\) \\ \hline \(Z_{\mathrm{e2}}\) & 64 \(\Omega\) & \(\emptyset\) & 90 \(\Omega\) \\ \hline \(Z_{\mathrm{o2}}\) & 52 \(\Omega\) & C (SMV1405) & 0.63 pF–2.67 pF \\ \hline \end{tabular}
\end{table}
Table 1: Circuit parameters of the dual-band frequency-reconfigurable crossover.
## 4 Lunar Antenna Types
### Wire Antennas
#### 4.4.2 Monopole Antennas  
Monopole antennas are the most basic type of RF antenna, consisting of a single linear element with a length equal to a quarter wavelength. It is created by substituting one half of a dipole antenna with a ground plane at right angles to the remaining half. If the ground plane is large enough, the monopole acts precisely like a dipole, as though its reflection in the ground plane forms the dipole's missing half. A typical monopole antenna feed is a coaxial wire with its inner conductor linked to the vertical monopole element via a hole in the ground plane and its outer conductor attached to the ground plane via a flange [72; 73; 74].  
The monopole antenna is used in a moon mission by China. One notable application that utilizes monopole antennas is the Chang'e-3 spacecraft's lunar penetrating radar (LPR), which is a key scientific instrument [75]. Its scientific objectives include surveying lunar regolith and detecting underlying geologic features. LPR is a type of carrier-free nanosecond imaging radar that operates in the time domain. The LPR system sends a pulse signal into the lunar subsurface, which reflects and scatters back to the radar upon encountering uneven layers, interfaces, rocks, or other objects. By analyzing the reflected and scattered signals, the thickness and distribution of the lunar regolith and the geological structure of the lunar subsurface can be determined along the rover's path.  
The LPR system has two detection channels: CH1 and CH2. CH1 operates in the 40-80 MHz range and has a depth resolution of several meters. The selection of the CH1 antennas for the lunar rover took into account various technical requirements, including the size and shape of the rover and possible installation locations. Two broadband monopole antennas grounded on the rover's body were used for this purpose, as shown in Figure 14a. As illustrated in Figure 14b, the LPR's CH1 transmitting and receiving antennas are installed  
Figure 13: Small-sized active dipole antenna: (**a**) prototype, (**b**) antenna frontal projection. Adopted from “The first detection of the solar U+ III association with an antenna prototype for the future lunar observatory” by [PERSON] et al. Research in Astronomy and Astrophysics, (2021) [71]. Reproduced by permission of RAA. All rights reserved.  
via an extension mechanism on the two bottom edges of the lunar rover's top board to enable installation. Once the lunar rover lands on the moon's surface, a command signal is transmitted from the ground base to activate the release mechanisms, which deploy the antennas along a pre-determined path to the back of the rover. The antennas are then precisely pointed in the X direction for optimal communication and data transmission, as shown in Figure 14a. Since this antenna is used in real missions, the antenna is mounted inside a radome for mechanical support and thermal stability.
## 4 Simulation Results
### Impact of Clipping  
As aforementioned in the previous section, the high PAPR of DCO-OFDM leads more LED nonlinearity, thus drastically deteriorating the overall system performance. Clipping is favorable in LED non-linearity mitigation, which is an easy and simple approach and  
Figure 9: BER performance of the proposed scheme with/without CP.  
can be directly employed in time-domain DCO-OFDM signal. Nonetheless, the noise is accompanied by deep clipping, which deteriorates the BER performance and corrupts the signal spectrum. Figure 10 depicts the BER curves of the proposed scheme over LOS and NLOS \(U_{1,5}\) when the clipping with CR = 8 dB is applied. When the tested SNR is greater than 10 dB, the advantage brought by clipping is dominant in the system compared with the disadvantage brought by the accompanied clipping noise because an appropriate clipping can relieve the nonlinear distortion caused by the LED. Therefore, the proposed scheme is more robust to the nonlinear clipping noise. The reason for the superior performance is that the clipping procedure is similar to the upper saturation effect of LEDs so that the characteristics of the nonlinear clipping can be also learned in the training stage, which suggests the robustness of the proposed scheme against nonlinear clipping distortions while the system could enjoy the benefits of clipping.  
In summary, we can conclude that the proposed DL-NPE is beneficial to the channel impairment compensation and can achieve excellent BER performance improvement from the above simulations and discussions.
## 4 Numerical Simulation Results
### Stress-Strain Curves  
Figure 6 shows the stress-strain curves of the numerical specimen with different RFCAs, with the confining stresses set at 0.2 MPa and 0.6 MPa. As shown in Figure 6, the stress-strain curves of the numerical specimen under different confining stresses can generally be divided into four phases: compaction, elastic deformation, pre-peak yielding, and post-peak destruction. Under low confining stress (0.2 MPa), all numerical specimens exhibit pronounced brittle characteristics, i.e., when the load applied to the specimens reaches the maximum strength, the specimens fail, and their strength decreases rapidly. When the confining stress increases to 0.6 MPa, the stress-strain curves of the numerical  
Figure 5: Peak stress and elastic modulus for each RFCA by numerical modelling and laboratory UCTs. (**a**) Peak stress; (**b**) elastic modulus.  
specimens can be divided into two types based on the proportion of fine aggregates. Specifically, when the proportion of fine aggregates in the numerical specimen is less than or equal to 0.3, i.e., H \(\leq\) 0.3, the stress-strain curve shows a more obvious softening characteristic after reaching the peak; i.e., the strength decreases slowly with the increase in strain after the load applied to the specimens reaches the peak strength. However, when \(H\) > 0.3, the stress-strain curve still shows brittle characteristics. It can be concluded that the stress-strain curves of similar materials are jointly affected by the confining stress and the amount of fine aggregate. In other words, an increase in confining stress and a decrease in the amount of fine aggregate will both weaken the brittle properties of similar materials.
# Metamaterial Integrated Folded Dipole Antenna with Low SAR for 4G, 5G and NB-IoT Applications
## 4 Metamaterial Integrated Antenna Design
Thus, there is less radiation towards the head phantom, especially at high frequency LTE and NR n78 bands. The SAR dwindling percentage for all the bands is represented in the pie diagram of Figure 23. The major portion of SAR reduction is shown for the n78 5G and LTE high frequency bands, with an average reduction of 84.2% in comparison to the operation without the metamaterial. The middle and low frequency LTE bands are reduced by an average of 21.2% and 53.6%, respectively. In addition, the SAR observed for the IoT bands are reduced by an average of 50.1% in comparison to the operation without the metamaterial. The peak SAR values observed for the antenna design with and without the metamaterial are summarized in Table 5. Hence, very small amounts of SAR are observed for all the bands, which are considerably below the maximum allowed range of 1.6 W/kg. Hence, the integrated antenna design was demonstrated to be an excellent design for SAR reduction. Figure 22: Illustration of SAR simulation setup. Figure 18:0 SAR minimization for different frequency bands. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Band No.**} & \multirow{2}{*}{**Band Name**} & \multicolumn{2}{c}{**SAR (W/kg)**} & \multicolumn{2}{c}{**SAR Minimization**} \\ \cline{3-5}  & & **Without Metamaterial** & **With Metamaterial** & **(\%)** \\ \hline B2U & & 0.6637 & 0.2907 & 56.20 \\ \hline B2D & & 0.6294 & 0.3152 & 49.92 \\ \hline B25U & & 0.6751 & 0.2871 & 57.47 \\ \hline B25D & & 0.6347 & 0.3028 & 52.29 \\ \hline B1U & & 0.6375 & 0.3071 & 51.83 \\ \hline B1D & & 0.5846 & 0.3953 & 32.38 \\ \hline
35 & & 0.6637 & 0.2907 & 56.20 \\ \hline
39 & & 0.6565 & 0.2974 & 54.69 \\ \hline
33 & \multirow{2}{*}{Low frequency LTE} & 0.6525 & 0.3006 & 53.93 \\ \cline{3-5}
37 & & 0.6483 & 0.3037 & 53.15 \\ \hline
36 & & 0.6294 & 0.3152 & 49.92 \\ \hline
34 & \multirow{2}{*}{Middle frequency LTE} & 0.6005 & 0.3290 & 45.21 \\ \cline{3-5}
40 & & 0.4853 & 0.4159 & 14.30 \\ \cline{3-5}
41 & & 0.5225 & 0.4563 & 12.66 \\ \hline
38 & \multirow{2}{*}{High frequency LTE} & 0.5243 & 0.4587 & 12.51 \\ \cline{3-5}
42 & & 0.6053 & 0.0732 & 87.90 \\ \hline
43 & \multirow{2}{*}{High frequency LTE} & 0.5723 & 0.1209 & 78.87 \\ \cline{3-5}
0.78 & & 0.6027 & 0.0850 & 85.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Peak SAR of antenna design.
## 2 Materials and Methods
### Simulated Leaching Experiment  
The on-site performance and the adsorption property of the as-fabricated magnetized zeolite were characterized by mixing the magnetized zeolite powder with 10 g of target contaminated soil with CsCl (Sigma Aldrich, St. Louis, MO, USA) acting as the simulated Cs137 source. The simulated Cs-contaminated soil, with particle size detailed in Table 3, was prepared by mixing the CsCl with kaolinite (Sigma Aldrich, St. Louis, MO, USA) (20 wt% of moisture) using a mechanical stirrer (DX234, Guangdong, China) and keeping the Cs-to-soil ratio at 1%. The details of the simulated Cs-contaminated soil can be seen in the following table. The magnetized zeolite was thoroughly mixed with the simulated contaminated soil at room temperature, and we then waited 24 h before proceeding with the separation. The magnetization separation process of the contaminated soil and the magnetized zeolite can be further realized by a self-assembled magnetic cylinder separation device with a cylinder volume of around 50 mL, as shown in Figure 3. The rotating speed of the cylinder was kept at 50 rpm, and the vibration frequency was set at 200 Hz with a vibration impulse of 0.1 N-s. The Cs concentration in the treated soil can be further determined by an X-ray fluorescence spectrometer (XRF) (PW4400, PANalytical, Almelo, The Netherland) with rhodium target X-ray tube with an energy of 60 kV and a current of 50 mA. The adsorption efficiency and recovery efficiency of the magnetized zeolite can be determined by the following Equations (1) and (2), where m\({}_{\text{r}}\) and m\({}_{0}\) stand for the mass of magnetized zeolite recovered and the magnetized zeolite initial added, and C\({}_{\text{t}}\) and C\({}_{0}\) stand for the Cs concentration for the treated soil and intact soil separately:  
\[\text{Recovery efficiency}\left(\theta\right)=\left(\text{m}_{ \text{r}}/\text{m}_{0}\right)\times 100\% \tag{1}\] \[\text{Adsorption efficiency}\left(\mu\right)=\left(\text{C}_{ \text{t}}/\text{C}_{0}\right)\times 100\% \tag{2}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Particle Size** & **Partition (\%)** \\ \hline \textless{}160 mesh & 2.97 \\ 60–160 mesh & 13.99 \\ 2 mm–60 mesh & 32.82 \\ \textgreater{}2 mm & 50.22 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Particle size and partition of simulated soil.
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 6 Conclusions  
Due to the difference in radio access technologies, the introduction of LTE to the unlicensed band often degrades Wi-Fi performance. Moreover, the lack of communication between LTE and Wi-Fi systems is even more detrimental to the two systems' coexistence. Therefore, this study uses HAP as a relay point between LTE and Wi-Fi. Integrating the two interfaces can communicate with these two different systems. Wi-Fi can be informed when LTE wants to use the unlicensed band. Transmission abortion due to interference can be avoided. In the proposed scheme, we formalize the utility function for the overall throughput and propose an algorithm to adjust the time ratio \(\delta\) of the contention period (CP) and the contention-free period (CFP) in a repetition interval such that the maximization of overall throughput can be achieved.  
We simulated several scenarios to examine the proposed scheme's performance and compare it with other methods. The simulation results show that the proposed scheme outperforms others in overall throughput. When the Wi-Fi load is low, the LTE throughput can be significantly improved by utilizing the unlicensed band. Another critical issue is how friendly the coexistence mechanism is to Wi-Fi. Most previous schemes can enhance the performance of LTE while sacrificing the performance of Wi-Fi in the unlicensed band. The proposed scheme has considered the fairness issue in the coexistence of Wi-Fi and LTE. Therefore, in the last two simulations, the results show that the proposed scheme's fairness index is better than NBS by adjusting the accessing periods of Wi-Fi and LTE. We show that the proposed scheme can improve the LTE throughput and consider the fairness of the resource allocation in the unlicensed band in the coexistence of Wi-Fi and LTE.  
Conceptualization, W.K.L. and Y.-D.Y.; methodology, W.K.L.; software, Y.-D.Y.; validation, C.-S.S. and C.-Y.T.; formal analysis, C.-Y.T.; investigation, Y.-C.L.; resources, C.-S.; data curation, Y.-D.Y.; writing--original draft preparation, Y.-C.L.; writing--review and editing, C.-S.S.; visualization, C.-S.S.; supervision, W.K.L.; project administration, W.K.L.; funding acquisition, C.-S.S. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by the Ministry of Science and Technology, Taiwan under the grant numbers MOST 111-2221-E-110-025 and MOST 111-2221-E-992-066.  
Not applicable.  
The authors declare no conflict of interest.