esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 5 Simulation Results
### UC1: Rural Environment
#### 5.1.1 Rural Coverage  
Figure 8 compares the impact of environmental and EN location on user coverage and network utilisation, and shows that the user coverage of the system depends on the EN locations. Neither FA (93.6%) nor LP scenario (95.5%) could support the required 99% user coverage.  
The rain impacts the user coverage, reducing it by 2% for light rain and 5% for heavy rain. Fortunately, for FL, the user coverage can be maintained above the required limit of 99%, at the expense of the required ENs. Thus, their usage increments by 6% in light rain and 15% in heavy rain. The vegetation (summer) reduces the user coverage by 3% compared to no leaves (winter). The worst case scenario, heavy rain in summer, requires 20% more ENs/km\({}^{2}\) (108 EN/km\({}^{2}\)) than the best case with only 89 EN/km\({}^{2}\), being able to serve up to 75 homes/km\({}^{2}\), leading to a ratio of 1.4 ENs per served CPE.
# Model-Free Identification of Nonlinear Restoring Force with Modified Observation Equation
## 3 Numerical Application
### Building Structure Equipped with SMA and MR Damper  
To investigate the performance of the proposed approach for the identification of the structure with multi-type nonlinear characteristics, the six-story building structure with SMA damper and MR damper was considered. All of the parameters for the six-story structure, SMA damper and MR damper were the same as the previous two examples. The SMA damper and MR damper were installed on the first and fourth floor of the building structure, respectively. A random excitation was applied to the top floor, and the structural responses were calculated by the Runge-Kutta method with the time interval of 0.001 s. In this example, the unknowns include \(\mathbf{Z}~{}=~{}\left[x_{1},\ldots,x_{6},\dot{x}_{1},\ldots,\dot{x}_{6},k_{1 },\ldots,k_{6},\alpha,\beta\right]^{T}\) and the NRFs provided by SMA damper and MR damper. The initial values were chosen to be 50% of the corresponding actual ones.  
Based on the proposed approach, the unknown parameters could be identified as shown in Table 3. Clearly, the relative errors were quite small. Figure 7 gives the identified \(k_{1}\) and \(k_{4}\) as examples. It can be seen that the identified results can converge to the actual ones fast. Moreover, the NRFs provided by SMA damper and MR damper are identified and plotted in Figure 8 as dashed lines, whereas the theoretical ones are shown as solid lines. It can be observed the identified NRFs are all close to the actual ones.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Parameters** & **Actual** & **Identified** & **Error (\%)** & **Parameters** & **Actual** & **Identified** & **Error (\%)** \\ \hline \(k_{1}\) (kN/m) & 180.00 & 179.96 & \(-0.02\) & \(k_{5}\) (kN/m) & 180.00 & 179.98 & \(-0.01\) \\ \(k_{2}\) (kN/m) & 180.00 & 179.94 & \(-0.03\) & \(k_{6}\) (kN/m) & 180.00 & 179.86 & \(-0.08\) \\ \(k_{3}\) (kN/m) & 180.00 & 179.95 & \(-0.03\) & \(\alpha\) & 0.2644 & 0.2642 & \(-0.07\) \\ \(k_{4}\) (kN/m) & 180.00 & 179.92 & \(-0.05\) & \(\beta\) & \(2.578\times 10^{-3}\) & \(2.589\times 10^{-3}\) & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The identified results of structural parameters equipped with SMA and MR damper.  
Figure 6: Comparison of SMA damper force: (**a**) hysteretic loop; (**b**) time history of the identified error.
## 2 Micro-Strip Patch Antenna and DR Process
### Feature Extraction (FE)
#### 2.2.2 Auto-Encoder (AE)  
While PCA sometimes faces its limitation in non-linear data analysis [26], auto-encoder (AE) is not restricted by the drawback of data linearity. As a special case of the artificial neural network (ANN) [42], AE handles the DR by converting the raw data. AE generally includes three different layers, which are the input, output, and hidden layer, respectively. AE also contains a reconstruction process of the raw data in the output layers. The data will be transformed with a bias and a weight function in the hidden layer. To reduce the gap between the raw and newly recreated data, AE tries to minimize the mean squared error corresponding to the reconstruction [43]. The reconstruction error (\(r_{e}\)) is evaluated by the squared-error cost function,  
\[r_{e}=0.5\|\sum_{k=1}^{n}W_{k}^{\top}\!\!\left(\sum_{k=1}^{n}W_{k}x_{k}+b\right) +b^{T}-X\|\, \tag{9}\]  
where \(W\), \(T\), \(x\), and \(b\) refer to the weights, transpose of the respective vector or matrix, input units, bias, and input vector, respectively. Here, \(X\) stands for input vector. A sparsity constraint exploits to minimize the total number of hidden units, so that AE can be conducted as the DR. Owing to the constraint, AE has a bottleneck shape. Some of AE's neurons will be inoperative by applying the sparsity constraint to the hidden units. The hidden layer's average activation can be defined by  
\[\Psi_{j}=\frac{1}{n}\!\!\sum_{i=1}^{n}\!\!\left[a_{j}^{(k)}(x^{(i)})\right]\!, \tag{10}\]  
where \(a_{j}^{(k)}\) denotes the activation of hidden layer \(k\) and the hidden unit \(j\). To promote the sensitivity accuracy, an extra penalty term (\(P\)) is considered.  
\[P=\sum_{j=1}^{s}\rho\log\frac{\rho}{\Psi_{j}}+(1-\rho)\log\frac{1-\rho}{1-\Psi _{j}}, \tag{11}\]  
where \(s\) and \(\rho\) represent hidden layers and the sparsity parameter, respectively. When the activation value becomes zero, a relation of \(\Psi=\rho\) is acquired.
## 5 System Model
### Optical SNR  
The SNR for a communication system is a key indicator for how well the link will perform--the higher the SNR, the better. The SNR for UOWC is generally estimated as the ratio of signal power to the sum of noise power by ambient light, dark current (the leakage current in the PD), shot noise (a portion of signal that contributed to noise), and thermal noise (from passive electronic devices such as the preamplifier circuits). Given that the objective of this study is to shed light on the considerations from a physical layer perspective for maximizing the SNR of UOWC, the inclusion of noise by the preamplifier circuitry was not included and the resulting SNR was determined as an O-SNR. This is due to the preamplifier effects being significantly determined by an engineer's design choices, which is difficult to standardize for such a model.  
The noise due to ambient light (\(\sigma_{BG}^{2}\)) may be expressed with Equation (30), where \(q\) is the electronic charge (1.602 \(\times\) 10\({}^{-19}\) C), \(S_{r}(\lambda)\) is the spectral variation in the responsivity of the PD in A/W, and \(B\) is the signaling bandwidth in Hz. The effect of dark current (\(\sigma_{DC}^{2}\)) is expressed in Equation (31), where \(I_{DC}\) is the dark current, retrievable from the datasheet of the PD, in amperes. The shot noise (\(\sigma_{SS}^{2}\)) is given in Equation (32). The signal bandwidth will be limited to a 3 dB frequency bandwidth of the PD, as it suggests the upper limit possible for any modulation signal, either by a LED or a laser, and is given by Equation (33), where \(t_{R}\) is the rise time of the PD as available from the datasheet.  
\[\sigma_{BG}^{2}(S,\lambda,z)=2 qS_{r}(\lambda)P_{BG}(S,\lambda,z)B \tag{30}\]  
\[\sigma_{DC}^{2}=2 qI_{DC}B \tag{31}\]  
\[\sigma_{SS}^{2}(S,\lambda,z)=2 qS_{r}(\lambda)P_{r}(S,\lambda,z)B \tag{32}\]  
\[B\approx\frac{0.35}{t_{R}} \tag{33}\]  
Therefore, the overall optical noise \(\left(\sigma_{Opt}^{2}\right)\) can be expressed with Equation (34), and the final O-SNR with Equations (35) and (36).  
\[\sigma_{Opt}^{2}=\sigma_{BG}^{2}+\sigma_{DC}^{2}+\sigma_{SS}^{2} \tag{34}\]  
\[O\text{-}SNR=\frac{(S_{r}(\lambda)P_{r}(S,\lambda,z))^{2}}{\sigma_{Opt}^{2}(S,\lambda,z)} \tag{35}\]  
\[O\text{-}SNR=\frac{S_{r}(\lambda)^{2}P_{r}^{2}(S,\lambda,z)}{2 qB(I_{DC}+S_{r}( \lambda)P_{r}(S,\lambda,z)+S_{r}(\lambda)P_{BG}(S,\lambda,z))} \tag{36}\]
## 3 Results and Discussion
### Characterization
The strong and broad  
Figure 1: X-ray diffraction (XRD) patterns of non- and acid-modified clinoptilolite-rich tuffs. band at 1051 cm\({}^{-1}\) was assigned to the asymmetric stretching of Si-O; (from SiO\({}_{4}\)). The weak bands of the symmetric stretching vibration observed for the O-Si-O bonds occur at 792 cm\({}^{-1}\). A weak signal can be observed at 608 cm\({}^{-1}\) corresponding to bending vibrations between tetrahedrons, particularly to double ring vibrations. Finally, the band at 457 cm\({}^{-1}\) corresponds to bending vibration T-O, T=Al, Si, and is related to the pore opening. These results agree well with those reported by other authors [40]. A broadening of the O-H band at 3446 cm\({}^{-1}\) occurred as the acid concentration increased (from 0.1 M to 1.0 M), The bands corresponding to the Si-O and O-Si-O vibrations at 1079 cm\({}^{-1}\) and 790 cm\({}^{-1}\), respectively, are more intensive. The data obtained confirm that the dealumination of clinoptiolite takes place after the acid treatment through the band near 600 cm\({}^{-1}\) which was modified when the clinoptiolite was treated with the H\({}_{2}\)SO\({}_{4}\) solutions indicating the break of the bond Al-O- from the zeolite network [41]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Si/Al**} & \multicolumn{8}{c}{**Weight, \%**} \\ \cline{3-10}  & & **O** & **Na** & **K** & **Ca** & **Mg** & **Si** & **Al** & **Fe** \\ \hline Nat-CLI & 4.37 & 64.37 & 0.56 & 1.17 & 1.65 & 0.21 & 25.77 & 5.90 & 0.37 \\ HCLI-0.1 M & 5.31 & 66.67 & 0.00 & 0.95 & 0.92 & 0.17 & 26.08 & 4.91 & 0.29 \\ HCLI-0.2 M & 7.91 & 66.6 & 0.00 & 0.59 & 0.29 & 0.00 & 28.70 & 3.63 & 0.19 \\ HCLI-0.5 M & 9.04 & 64.06 & 0.00 & 0.49 & 0.52 & 0.00 & 31.37 & 3.47 & 0.09 \\ HCLI-1.0 M & 9.52 & 68.99 & 0.00 & 0.46 & 0.22 & 0.00 & 27.32 & 2.87 & 0.07 \\ \hline \hline \end{tabular} Note: Na\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\) and Mg\({}^{2+}\) are considered the exchangeable ions from the zeolite (clinoptiolite) network.  
\end{table} 
Table 1: Chemical composition (% weight) of non- and acid-modified clinoptiolite-rich tuffs. Figure 2: Scanning electron microscopy (SEM) images of (**a**) Nat-CLI; (**b**) HCLI-0.5 and (**c**) HCLI-1.0 M samples. As the concentration of the acid increases (0.1 M to 1.0 M) the band due to vibration of the O-H groups, is widened and is shifted at 3436 cm\({}^{-1}\). Additionally, the band at 1051 cm\({}^{-1}\) is the most affected, this also appears widened and shifted to 1079 cm\({}^{-1}\), this behavior has been attributed to a decrease in the relative content of Si/Al ratio in zeolite framework with the increase in the length of the bonds Si-O-Si(Al), and with the formation of structured effects. The other band affected is the corresponding to bending vibration T-O, T=Al, Si, this is shifted to 462 cm\({}^{-1}\), which indicates a decrease of pore opening. The observed behavior suggests that the dealumination of Nat-CLI take place after its acid treatment.
## 5 Results and Discussion  
In this section, the results are presented and analyzed. Most of the components are a function of the parameters such as rotor diameter and rated power, as shown in Figure 10. The relationship shows that the rated power also has a strong impact on COE. Thus, the optimization procedures considering rated power or not (set as 2.1 MW) are shown.  
Figure 9: Flowchart of the optimization process.  
After the design procedure is finished, the iterative course of the optimization process by considering rated power is shown in Figure 11. It can be seen that the optimal results converge when the number of iteration steps reach 80.
# _EntangleNet:_ Theoretical Reestablishment of Entanglement in Quantum Networks +
###### Abstract  
In the practical context of quantum networks, the most reliable method of transmitting quantum information is via teleportation because quantum states are highly sensitive. However, teleportation consumes a shared maximally entangled state. Two parties [PERSON] and [PERSON] located at separate nodes that wish to reestablish their shared entanglement will not send entangled qubits directly to achieve this goal, but rather employ a more efficient mechanism that ensures minimal time resources. In this paper, we present a quantum routing scheme that exploits entanglement swapping to reestablish consumed entanglement. It improves and generalizes previous work on the subject and reduces the entanglement distribution time by a factor of \(4k\) in an arbitrary scale quantum network, where \(N=4k-1\) is a required number of quantum nodes located between source and destination. In addition, \(k\) is the greatest positive integer considered by [PERSON] or [PERSON], such that afterwards they choose \(N\) quantum switches.  
Keywords:quantum teleportation; quantum network; quantum routing; entanglement swapping +
Footnote †: journal: Applied Physics Letters B
# S-Band Doppler Wave Radar System
## 5 Discussion  
The comparison of the non-directional wave height spectra shows that the amplitude and shape are consistent, with the spectra correctly showing the change in wave height caused by the cold front. Moreover, during rainy weather, MORSE is still capable of monitoring the ocean and collecting data, which indicates that MORSE is less affected by water in the air. There are good results which prove the ability of MORSE to obtain non-directional wave height spectra. But the non-directional wave height spectra obtained with MORSE show overestimation in low frequency in almost all the examples and frequency shift also appears in some examples such as Figure 10a,b. We have noticed those unsatisfying performance and dug into other data we collected besides that presented in this paper. Unfortunately, we haven't find the root cause. In the future, we will continue to conduct experiments in different locations and environment. We will also try to get the directional wave height spectra obtained by Waverider MKIII and make a comparison between MORSE and Waverider MKIII.  
The accuracy of the significant wave height measured by MORSE, which is shown in the comparison, is good and the trends are consistent. However, when the wave height is low, such as in the afternoon and at midnight on March 27 and from March 29 to March 31, the results fluctuate because of the low SNR of the echoes.  
The results of the average wave period comparison are not as satisfying as those of the significant wave height. The MORSE results are approximately 1 s higher when the sea state is low. This bias is also confirmed with the frequency shift in non-directional wave height spectra. After examining the raw MORSE data, we speculate that the cause of the two dips between the afternoon and midnight of March 27, is the ultra-low SNR. The algorithm indicates that few data are valid. The wind velocity corresponding to the two dips is 3 m/s, which could cause SNR deterioration.  
Moreover, data processing performed before the comparison could also introduce additional errors. The data period used in analysis is 18 min for MORSE and 20 min for the buoy, are similar but not identical, which could also affect the results. The different data collecting method between MORSE and Waverider MKIII is also a potential factor that contributes to the error. The MORSE switches antenna every 3 min and every antenna's pointing angle are different. So in other words, the 18-min data collected by MORSE is including all frequency but with one different direction every 3 min. The data collected by Waverider MKIII is including all frequency and all direction. In some case, that may lead to bias.  
Figure 15: Scatter plot of average wave period measured by MORSE and the buoy.
# Measurement of High Numerical Aperture Cylindrical Surface with Iterative Stitching Algorithm
## 2 Principles
### Stitching Algorithm for the Cylindrical Lens
Here, for the first sub-aperture (\(i=1\)), the first part of the right side of Equation (5) is zero, whereas for the \(N\)th sub-aperture (\(i=N\)), the second part of the right side of Equation (5) is zero. The \(f_{i,i\pm 1}\) in Equation (5) is expressed as:  
\[f_{i,i\pm 1}=[\cos\Theta,\sin\Theta,-X\sin\Theta,X\cos\Theta]^{T} \tag{6}\]  
where \(X\) and \(\Theta\) are related to \(a\) and \(b\) in the overlapping region as defined above, respectively. Then, a 4 \(\times\) 4 matrix \(Q_{i,i\pm 1}\) is built with \(f_{i,i\pm 1}\):  
\[Q_{i,i\pm 1}=f_{i,i\pm 1}{}^{T}f_{i,i\pm 1} \tag{7}\]  
Combining Equations (4)-(7) and choosing the \(m\)th sub-aperture (\(m\in[1,\,N]\)) as a benchmark sub-aperture, it is possible to rewrite Equation (3):  
\[\left[ \begin{array}{c}P_{1}\\ P_{2}\\ P_{m-1}\\ P_{m+1}\\ P_{N}\end{array} \right]=\left[ \begin{array}{cccc}-Q_{1,2}&Q_{1,2}&\\ Q_{1,2}&-Q_{1,2}-Q_{2,3}&Q_{2,3}&\\ \hskip 142.26378 pt\vdots&\\ \hskip 142.26378 ptQ_{m-2,m-1}&-Q_{m-2,m-1}-Q_{m-1,m}&\\ \hskip 142.26378 ptQ_{m,m+1}&-Q_{m,m+1}-Q_{m+1,m+2}\\ \hskip 142.26378 pt\vdots&\\ \hskip 142.26378 pt-Q_{N-1,N}&Q_{N-1,N}\end{array} \right]\left[ \begin{array}{c} R_{1}\\ R_{2}\\ R_{m-1}\\ R_{m+1}\\ R_{N}\end{array} \right] \tag{8}\]  
The right side of Equation (8) is a product of the \(4(N-1)\times 4(N-1)\) matrix by the \(4(N-1)\times 1\) vector, where the benchmark sub-aperture is not included. By calculating Equation (8), it is possible to find the position error \(R_{i}\). After that, the stitching map of the whole surface \(\phi\) can be performedas the right array division by dividing each element of the sum of the corrected sub-apertures by the corresponding element of the sum of mask:  
\[\phi(X,\Theta)=\sum_{i=1}^{N}(\Phi_{i}+\Delta z_{i}\cos\Theta+\Delta y_{i}\sin \Theta-\Delta\gamma_{i}X\sin\Theta+\Delta\beta_{i}X\cos\Theta)/\sum_{i=1}^{N}mask _{i} \tag{9}\]  
where \(mask_{i}\) is a matrix with \(M\times K\) elements that equals 1 or 0. Positions of the nonzero elements coincide with the positions of the measured values \(\varphi_{iM,L}\) in \(\Phi_{i}\). For example, the \(mask_{1}\) is:  
\[mask_{1}=\left[ \begin{array}{ccccccccccccc}1&1&\cdots&1&0&\cdots&0&0&0& \cdots&0&0&0\\ 1&1&\cdots&1&0&\cdots&0&0&0&\cdots&0&0&0\\ \ldots&\ldots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots& \cdots&\cdots\\ 1&1&\cdots&1&0&\cdots&0&0&0&\cdots&0&0&0\end{array} \right].\]
## 2 Problem Statement
### Surface Radiation  
When surface radiation among the solid walls is taken into account, the walls are assumed as diffuse and gray, and the air is regarded as a non-participating media due to its slight effect. Therefore, the thermal boundary condition at the hot solid-air interfaces turns into the following:  
\[\theta_{\rm s}=\theta_{\rm f}\text{ and }-\frac{\partial\theta_{\rm s}}{ \partial X}=-\frac{1}{kr}\frac{\partial\theta_{\rm f}}{\partial X}+NrQ_{\rm rad} \tag{11}\]  
where \(Nr\) is the radiation to conduction number (defined as \(Nr=\frac{\sigma T_{\rm h}^{4}l}{k_{\rm c}(T_{\rm h}-T_{\rm c})}\)) and \(Q_{\rm rad}\) is the dimensional radiation heat flux defined by  
\[Q_{\rm r}(r_{\rm k})=\varepsilon_{\rm k}\bigg{(}1-\frac{1}{G}\bigg{)}^{4} \bigg{(}\theta_{\rm k}(r_{\rm k})+\frac{1}{G-1}\bigg{)}^{4}-\varepsilon_{\rm k }\sum_{\rm j=1}^{4}\int\limits_{S_{\rm j}}J_{\rm j}(r_{\rm j})dF_{S_{\rm k}-S_ {\rm j}} \tag{12}\]  
where \(G=\frac{T_{\rm h}}{T_{\rm s}}\), \(\varepsilon_{\rm k}\) is the emissivity of the solid walls, \(F_{S_{\rm k}-S_{\rm j}}\) is the view factor from the surface \(s_{\rm k}\) to \(s_{\rm j}\) and \(J_{\rm j}\) is the dimensionless radiosity of the solid walls.  
The local Nusselt number of the hot solid-air interfaces is employed for describing the heat transfer characteristic near the interfaces, and the convection and radiation local Nusselt numbers are defined, respectively, as,  
\[Nu_{\text{conv}}=\left.\frac{\partial\theta}{\partial X}\right|_{\text{wall}} \tag{13}\]  
\[Nu_{\text{rad}}=NrQ_{\text{rad}} \tag{14}\]  
The average convective, radiative and total Nusselt numbers of the hot solid-air interfaces are defined, respectively, as  
\[\overline{Nu}_{\text{conv}}=\int_{0}^{1}Nu_{\text{conv}}dX \tag{15}\]  
\[\overline{Nu}_{\text{rad}}=\int_{0}^{1}Nu_{\text{rad}}dX \tag{16}\]  
\[\overline{Nu}_{\text{t}}=\overline{Nu}_{\text{conv}}+\overline{Nu}_{\text{ rad}} \tag{17}\]
## 2 Materials and Methods
### Surface Roughness Assessment  
A surface roughness evaluation was performed using a contact profilometer (Hommel-werke with a linear unit LV-50 and T8000 controller, Hommelwerke GmbH, Germany). The authors proposed a new approach to evaluate surface roughness in dental acrylic resins: Pa (mean profile height), skewness (Psk) and kurtosis (Pku) [17]. Pa is a parameter directly extracted from the primary profile of a surface, and it is considered an extension of Ra within the profile. Both metrics represent the deviation of each point's height from the arithmetical mean in different profiles. From a clinical perspective, Pa is relevant, as it specifically characterizes the actual profile of the resin surface [15; 17; 21]. Considering that the value of Pa represents an arithmetic mean, it is possible for distinct surfaces to present the same value, despite displaying different textures.  
Parameters such as skewness (Psk) and kurtosis (Pku) are precisely employed to allow this distinction [15]. Per definition, skewness describes the symmetry of the amplitude distribution curve at the profile mean line, and kurtosis quantifies the sharpness of the probability density distribution within the profile [15; 17; 21].  
The surface of the specimen was transversed by a diamond tip with a constant load following a straight measuring length of 4.8 mm for 10 s. A TK300 pick up with a vertical range of measurement of \(\pm\)300 \(\upmu\)m, a tip radius of 5 \(\upmu\)m and a cone angle of 90\({}^{\circ}\) was used. Three measurements were obtained on each specimen on randomly selected measurement lines. The mean roughness value (Pa), skewness (Psk) and kurtosis (Pku) derived from the average of these three measurements were then determined.  
This study employs descriptive statistics to summarize skewness (Psk) and kurtosis (Pku). Non-parametric tests (Mann-Whitney U and Kruskal-Wallis (H)) were selected to compare differences between groups. Pa roughness values (mean and standard deviation), skewness (Psk) and kurtosis (Pku) were represented using mean and standard deviation. High kurtosis values indicate a sharp amplitude distribution with large peaks and valleys. A negative skewness suggests a concentration of the material near the top of the profile and a plateau-like surface. Pairwise comparisons were conducted using the Mann-Whitney U test to compare Pa values across different resin types within each polishing technique. The Kruskal-Wallis (H) test was utilized to assess the significance of disparities in mean Pa values between mechanized polishing, conventional polishing and the control group. Statistical analyses were conducted using IBM SPSS(r) Statistics software, version 27. A significance level of \(p\leq 0.05\) was employed for all statistical inferences.