esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Catalyst Characterization  
Images from the scanning electron microscopy (SEM) were captured using a Hitachi TM 1000 tabletop scanning electron microscope (Marunouchi, Japan). Carbon tape was used as a substrate for the SEM analysis to prevent charging effects without requiring metallization.  
The X-ray diffraction (XRD) analyses were performed with a Rigaku Miniflex-600 horizontal-scan powder diffractometer (Tokyo, Japan) using Cu-K\(\alpha\) radiation, with a graphite monochromator on the diffracted beam. Scherrer's Equation (3) was used to calculate the size of the crystallites.  
\[\mathrm{D}=(\mathrm{K}\cdot\lambda)/(\beta\cdot\mathrm{cos}\theta) \tag{3}\]  
where D is the crystal size, \(\lambda\) is the X-ray wavelength (0.154 nm with the Cu K\(\alpha\) generator), K is the shape factor (0.9), \(\beta\) is the width at the half maximum of the peak (i.e., FWHM) and \(\theta\) is the Bragg angle.  
Figure 7: FSP schematic representation. Adapted from [41].  
The N\({}_{2}\) physisorption analyses were performed with an ASAP 2020 instrument by Micromerities (Norcross, GA, USA). The pretreatment before the actual analysis included the degassing of the samples, performed under a vacuum at 150 \({}^{\circ}\)C for 4 h. This pretreatment allowed us to remove any contaminants or traces of water present in the catalysts. This non-destructive analysis was performed on 200 mg of nano powder at a temperature of \(-\)196 \({}^{\circ}\)C. The data obtained were processed following the BET theory, BJH theory and t-plot analysis method.  
The diffuse reflectance (DR) UV-Vis spectra of the catalysts were recorded on a Shimadzu UV\(-\)3600 Plus (Kyoto, Japan) in the range of 200-800 nm, using an integrating sphere and BaSO\({}_{4}\) as reference standards. Using the reflectance spectra as the input data, a Kubelka-Munk elaboration was performed, according to Equation (4) [42].  
\[\mathrm{F(R\infty)=(1-R\infty)^{2}/2R_{\infty}} \tag{4}\]  
The calculated (F(R)hv)1/r (with r = 2 or 1/2 for the direct and indirect bandgaps) was plotted versus hv to obtain the bandgap of each sample [43]. To prevent the saturation of the samples that absorb too much, all of the photocatalysts were diluted one-to-one or one-to-two with BaSO\({}_{4}\), which was the reflectance standard. The shapes of the curves were not affected by blending with the BaSO\({}_{4}\), so a comparison of the absolute value of the reflectance could not be performed. The final form of the equation is reported in Equation (5).  
\[\mathrm{(F(R\infty)hv)^{\gamma}=A(hv-E_{g})} \tag{5}\]  
with the parameter F(R\(\infty\)) corresponding to the Kubelka-Munk function, A being a constant, E\({}_{g}\) being the bandgap energy of the semiconductor and hv being the energy of the incident radiation calculated using Planck's constant and the frequency of the radiation itself. The exponent \(\gamma\) can assume two values: 2 or 1/2. In the specific case of the synthetized FSP catalysts, the value of \(\gamma\) considered was 1/2, because both TiO\({}_{2}\) and WO\({}_{3}\) (monoclinic) are semiconductors with an indirect transition [44].
## 3 Experimental Results and Analysis
### Simulated Signal Experiments  
In the simulated signal experiments, we conducted a comprehensive comparative analysis involving ten algorithms. Among them, we included the widely used traditional search algorithms, namely the ARS algorithm and the MRG algorithm. Additionally, we incorporated the seven popular swarm intelligence algorithms that were previously discussed in Section 2.3.3. Furthermore, a nonlinear weighted least squares method, known as 2 SP, was utilized as a control algorithm to effectively verify the positioning performance. Two scenarios were considered: one with fine satellite geometry and another with poor satellite geometry. To maintain clarity and simplicity, assuming the receiver remained stationary, we focused only on position-time domain searches in both scenarios. Our analysis concentrated on a civilian GPS L1 signal, filtered with a 1 MHz bandwidth filter and sampled at 8.184 MHz, using real ephemeris data for simulation. Atmospheric delays were modeled: ionospheric effects were simulated using the Klobuchar model, and tropospheric delays were represented using the Hopfield model [37]. The uncertainty space of the  
Figure 5: CEC2020 iteration curve chart.  
position was defined by the typical uncertainties of assistance data provided via GSM cellular networks: a horizontal uncertainty range of \(\pm\)3000 m and a vertical uncertainty range of \(\pm\)600 m. The center of this uncertainty space deviates from the true position by 845.30 m, 759.35 m, and 871.99 m in the Cartesian space, resulting in a total positional error of approximately 1447.75 m. Given the larger uncertainty range of the clock bias relative to the position uncertainty, the clock bias search was managed separately, with an uncertainty range set to \(\pm 15,000\) m to facilitate the experiment [7]. Estimation performance was assessed using the root mean square errors of the estimated position, while computational cost was evaluated based on CPU processing time. The initial population size for each swarm intelligence algorithm was set to 100, with the remaining parameter settings kept consistent in Table 3. The grid step sizes for the MRG algorithm were summarized in Table 5[12]. The number of iterations for the ARS algorithm was set equal to the total number of points in the MRG algorithm. To reduce the impact of randomness, each algorithm was independently run 50 times.
# Analysis of TDMP Algorithm of LDPC Codes Based on Density Evolution and Gaussian Approximation
## 3 The Analysis of Density Evolution and Gaussian Approximation of the TDMP Algorithm
### The Analysis of Density Evolution of the TDMP Algorithm  
The variable messages updated [28; 29] in each layer is shown in Equation (10), then prior messages updated in the next layer [30] is shown in Equation (11).  
\[v_{j}^{(l)}=\lambda_{c}+u_{j}^{(l)}, \tag{10}\]  
\[\gamma_{j+1}^{(l)}=v_{j}^{(l)}-u_{j}^{(l-1)}=\lambda_{c}+u_{j}^{(l)}-u_{j}^{( l-1)}, \tag{11}\]  
\(l\) represents the number of iterations, and \(j\) represents the current number of layers. \(\lambda_{c}\) is the channel initial message which initializes the variable message before decoding.  
According to the symmetry condition of density evolution [24], we can get Equation (12).  
\[\gamma_{j+1}^{(l)}=\lambda_{c}+u_{j}^{(l)}+u_{j}^{(l-1)}, \tag{12}\]  
When updating to the last layer, the posterior message \(\gamma^{(l)}\) is an output of the \(l\)-th iteration decoding.  
According to the knowledge related to probability, the probability density of the sum of the two statistically independent variables \(X\) and \(Y\) is convolution of probability density of \(X\) and \(Y\). Since the variables in Equations (10) and (12) satisfy the condition of statistically independent distribution, we can obtain  
Figure 1: The message passing scheme of the Turbo Decoding Message Passing (TDMP) algorithm.  
\[P\left(v_{j}^{(l)}\right)=P(\lambda_{c})\otimes P\left(u_{j}^{(l)}\right), \tag{13}\]  
\[P\left(\gamma_{j+1}^{(l)}\right)=P(\lambda_{c})\otimes P\left(u_{j}^{(l)}\right) \otimes P\left(u_{j}^{(l-1)}\right), \tag{14}\]  
Use the Fourier transform to convert the convolution operation in Equations (13) and (14) into a multiplication operation, as shown in Equations (15) and (16)  
\[P\left(v_{j}^{(l)}\right)=F^{-1}\left(F[P(\lambda_{c})]\cdot F\left[P\left(u_{ j}^{(l)}\right)\right]\right), \tag{15}\]  
\[P\left(\gamma_{j+1}^{(l)}\right)=F^{-1}\left(F[P(\lambda_{c})]\cdot F\left[P \left(u_{j}^{(l)}\right)\right]\cdot F\left[P\left(u_{j}^{(l-1)}\right)\right] \right), \tag{16}\]  
When updating to the last layer, the probability density \(P\left(\gamma^{(l)}\right)\) of the posterior message \(\gamma^{(l)}\) is the final output probability density at the \(l\)-th iteration decoding. Thus, we can calculate BER based on Equation (5).
## 4 Equations of Motion of FG Plate Placed on Elastic Foundation
\begin{array}{l}\rm I_{1}=\int\limits_{-h/2}^{h/2}\rho(z)dz,\qquad I_{2}=\int \limits_{-h/2}^{h/2}\rho(z)zdz,\\ \rm I_{3}=\int\limits_{-h/2}^{h/2}\rho(z)f(z)dz,\qquad I_{4}=\int\limits_{-h/2} ^{h/2}\rho(z)z^{2}dz,\\ \rm I_{5}=\int\limits_{-h/2}^{h/2}\rho(z)zf(z)dz,\qquad I_{6}=\int\limits_{-h/2 }^{h/2}\rho(z)(f(z))^{2}dz.\end{array}  \tag{26}\]  
Analytical solutions will be derived for the simply supported rectangular FGM plate, wherein the boundary conditions are defined according to [54] as:  
\[ \begin{array}{l}\rm v_{0}=w_{0}=\theta_{y}=N_{xx}=M_{xx}=P_{xx}=R_{y}=0,\ \mbox{on the sides where}\ x=0\ \mbox{or}\ x=a,\\ \rm u_{0}=w_{0}=\theta_{x}=N_{yy}=M_{yy}=P_{yy}=R_{x}=0,\ \mbox{on the sides where}\ y=0\ \mbox{or}\ y=b.\end{array}  \tag{27}\]  
In order to satisfy the previously defined boundary conditions, the following [PERSON]'s solution is assumed:  
\[ \begin{array}{l}\rm u_{0}(x,y,t)=\sum\limits_{m=1}^{\infty}\sum\limits_{n=1 }^{\infty}U_{mn}\cos\frac{m\pi x}{a}\sin\frac{n\pi y}{b}e^{i\omega t},\ \ \rm v_{0}(x,y,t)=\sum \limits_{m=1}^{\infty}\sum\limits_{n=1}^{\infty}V_{mn}\sin\frac{n\pi y}{a}e^{ i\omega t},\\ \rm w_{0}(x,y,t)=\sum\limits_{m=1}^{\infty}\sum\limits_{n=1}^{\infty}V_{mn} \sin\frac{m\pi x}{a}\sin\frac{n\pi y}{b}e^{i\omega t},\\ \rm\theta_{x}(x,y,t)=\sum\limits_{m=1}^{\infty}\sum\limits_{n=1}^{\infty}T_{ xmn}\cos\frac{m\pi x}{a}\sin\frac{n\pi y}{b}e^{i\omega t},\ \ \rm\theta_{y}(x,y,t)=\sum \limits_{m=1}^{\infty}\sum\limits_{n=1}^{\infty}T_{ymn}\sin\frac{n\pi x}{a} \cos\frac{n\pi y}{b}e^{i\omega t}.\end{array}  \tag{28}\]  
Comparing the assumed form of [PERSON]'s solutions (17) and (28), it can be observed that the only difference between these forms is in the terms \(e^{i\omega t}\), where \(\omega\) is the natural frequency of the system and \(U_{mn},V_{mn},W_{mn},T_{xmn},T_{ymn}\) are parameters to be determined. By substituting (28) into (25) the following equation is obtained:  
\[\mathbf{K}=\left[\begin{array}{cccc}\rm K_{11}&\rm K_{12}&\rm K_{13}&\rm K _{14}&\rm K_{15}\\ &\rm K_{22}&\rm K_{23}&\rm K_{24}&\rm K_{25}\\ &&\rm K_{33}&\rm K_{34}&\rm K_{35}\\ &\rm sym&\rm K_{44}  
The determinant in (29) must be equal to zero value in order to get nontrivial solutions:  
\[\left|\mathbf{K}-\omega^{2}\mathbf{I}\right|=0. \tag{33}\]
## 3 Results and Discussion
### Relationship of Compressive Strength
As the strengths obtained have already included the influence of load duration on aged timber members, the influence of long-term load, \(K_{\text{Q4}}\), was not considered for aged CL, with \(K_{\text{Q4}}=1.00\). The calculated \(f_{\text{m-CABP}}\) and the test results of the log samples are compared in Table 7. The mean of the difference \(\delta_{\text{M}}\) is 28.50%, which suggests that the test results were significantly larger than the estimated values. A larger difference denotes a larger safety margin and more conservative estimate for the log samples, compared with that from [35]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample Type**} & \multirow{2}{*}{\(K_{\text{P}}\)} & \multirow{2}{*}{\(K_{\text{A}}\)} & \multicolumn{4}{c}{\(K_{\text{Q}}\)} & \multirow{2}{*}{\(K\)} \\ \cline{3-3} \cline{5-8}  & & & \(K_{\text{Q1}}\) & & \(K_{\text{Q2}}\) & & \(K_{\text{Q3}}\) & \(K_{\text{Q4}}\) \\ \hline Aged CL & 1.00 & 0.96 & 1.00 & 0.90 & 0.75 & 1.00 & 0.65 \\ New CL & 1.00 & 0.96 & 1.00 & 0.90 & 0.75 & 0.72 & 0.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Coefficients for calculating the parallel-to-grain compressive strength [35]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Serial Number**} & \multirow{2}{*}{\(f_{\text{c},\text{mean}}\) (MPa)} & \multicolumn{4}{c}{**HW (\(\lambda\) = 0)**} & \multicolumn{4}{c}{**SW (\(\lambda\) = 1)**} \\ \cline{3-10}  & & \multirow{2}{*}{\(\tau_{\sigma}\)} & \(\overline{f}_{\text{c-H}}\) & \(f_{\text{m-cal}}\) & \(\delta_{\text{M}}\) & \(\gamma_{\sigma}\) & \(\overline{f}_{\text{c-S}}\) & \(f_{\text{m-cal}}\) & \(\delta_{\text{M}}\) \\ \hline Aged CL-1 & & & & & & & & & (MPa) & (MPa) & (MPa) & (MPa) & (\%) \\ \hline Aged CL-2 & & & & & & & & & & \\ (\(t\) = 100) & 41.98 & 0.739 & 50.56 & 37.37 & 12.35 & 0.687 & 49.96 & 34.32 & 22.32 \\ Aged CL-2 & & & & & & & & & & \\ (\(t\) = 100) & 29.56 & 0.739 & 39.75 & 29.37 & 0.64 & 0.687 & 40.07 & 27.53 & 7.38 \\ Aged CL-3 & & & & & & & & & & \\ (\(t\) = 100) & 19.37 & 0.739 & 34.79 & 25.71 & 24.65 & 0.687 & 33.21 & 22.82 & 15.11 \\ New CL-4 (\(t\) = 0) & 36.76 & 0.939 & 33.92 & 31.85 & 15.40 & 0.887 & 45.30 & 40.18 & 8.51 \\ New CL-5 (\(t\) = 0) & 33.66 & 0.939 & 40.04 & 37.60 & 10.47 & 0.887 & 39.39 & 34.94 & 3.67 \\ \hline \hline \end{tabular} Note: \(\overline{f}_{\text{c-H}}\) and \(\overline{f}_{\text{c-S}}\) are the mean compressive strengths of standard specimens of HW and SW, respectively; \(f_{\text{m-cal}}\) is the calculated compressive strength of the timber log using Equation (5); the difference \(\delta_{\text{M}}\) is calculated from \(\delta_{\text{M}}=|f_{\text{m}}-f_{\text{m-cal}}|/f_{\text{m}}\%\).  
\end{table} 
Table 5: Estimation of compressive strength of the log samples using Equation (6).
# Rock Slope Instability Mechanism Induced by Repeated Mining in Mountain Mining Areas
## 4 Numerical Simulation Analysis of the Collapse
### Analysis of the Failure Process  
#### 4.2.1 The Mining of the Upper Coal Seam  
Figure 9 exhibits the fracture evolution of the slope during the excavation of the upper coal seam. When the upper coal seam was mined for 40 m, the separation was caused in the immediate roof. Due to the large span of the working face, the immediate roof first collapsed after the mining length of 80 m. Some separations were caused in the overlying strata. After the mining of S3, the overlying strata above the working face collapsed as an inverted trapezoid, and some separation fissures were caused between the marl and mudstone. At this time, the rock mass cut by the deep structural plane at the top of the slope toppled towards the inside slope, resulting in the closure of J3. When S4 was mined, the caving area continued to increase, and the mining-induced cracks on the left boundary also expanded upward. Tensile cracks appeared at the bottom of the rock bridge on the top of the slope, and J2 gradually closed. When S5 was mined, a large displacement had been caused in the overlying strata, and the marl and mudstone above the working face collapsed completely. The mining-induced cracks had been transmitted upward to the surface, and the joint rock toppled to the trailing edge of the mountain. Some cracks were induced at the foot of the slope. After the mining of S6, the range of the fracture zone above the goal was further expanded. The slope stability was significantly reduced due to the oblique tension crack in the middle of the slope.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Limestone** & **Marl** & **Silty Mudstone** & **Coal Seam** & **Muddy Silitstone** \\ \hline Porosity (\%) & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 \\ Particle density (kg/m\({}^{3}\)) & 2700 & 2650 & 2600 & 1350 & 2560 \\ Effective modulus/GPa & 10 & 8 & 7 & 1.84 & 5.6 \\ Bond normal-to-shear stiffness & 1 & 1 & 1 & 1 & 1 \\ Ratio (\(\overline{k}^{\prime}\)) & 10 & 7 & 5 & 1.84 & 4.6 \\ Bond tensile strength/MPa & 15 & 12.5 & 10.2 & 3.15 & 8 \\ Bond cohesion/MPa & 25 & 18.1 & 15.4 & 5.53 & 14.45 \\ Bond friction angle/\({}^{\circ}\) & 38 & 35 & 33.8 & 18 & 30.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters in the numerical model.  
Figure 9: _Cont._
# LightR: A Fault-Tolerant Wavelength-Routed Optical Networks-on-Chip Topology +
## 2 Background
### MRR Faults and Signal Faults  
An MRR fault can either be temporary or permanent [13]. Temporary faults are caused by environmental changes. For example, a change of \(1\,^{\circ}\mathrm{C}\) in temperature can shift the resonant wavelength of an MRR by \(0.1\,\mathrm{nm}\), which causes the MRR to resonate with a different wavelength than was intended [13; 14]. Some researchers have worked on that problem and proposed some ONoC resilience techniques, such as trimming [21], to correct the faults. On the other hand, permanent faults are caused by fabrication errors. For example, some changes in the physical dimensions, e.g., the radius of the MRRs, the width of the waveguides, and the thickness of the wafer, can affect the resonant wavelengths of the MRRs [14; 22]. These permanent faults cannot be corrected by those resilience techniques. Therefore, permanent faults, which can significantly lower the fabrication yield of WRONoC s, should be carefully considered in the design phase, not as an afterthought.  
When an MRR is permanently faulty, its resonant wavelength deviates from its designated wavelength, which causes two types of signal faults: stuck-at-zero (s-a-0) and stuck-at-one (s-a-1). The s-a-0 signal fault is that a signal fails to be coupled to the MRR, which is designed to be on-resonance with the signal. As shown in Figure 5, the MRRs do not resonate with the designated wavelength, and thus the signals cannot be coupled to the MRRs and suffer the s-a-0 faults. On the other hand, a signal suffers an s-a-1 fault when it is coupled to an MRR that is not designated to be on-resonance with the signal. For example, the MRRs designed to resonate with \(\lambda_{i}\) are now resonant with another wavelength \(\lambda_{j}\), and the signals on \(\lambda_{j}\) are coupled to the MRRs as shown in Figure 5. When a signal suffers either an s-a-0 or s-a-1 fault, it deviates from its planned propagation direction and may fail to reach its designated destination. As a result, the data carried on the signals are lost, which raises the reliability concern of WRONoC s.  
Figure 4: The first-order noise is generated (**a**) when a signal passes a waveguide crossing, (**b**) when a signal passes an off-resonance MRR, and (**c**) when a signal is on-resonance with an MRR.
## 4 Challenges
### Environmental Aspects  
At present, marine energy storage technology, though largely embryonic in its development, is undergoing significant progress. Considering the complexity of the bathymetry, the harshness of the environment, and the randomness of the seabed flow direction, the impact of underwater gas storage on marine ecology is also uncertain. A principal risk is the potential for the destruction of habitat for marine organisms and seabed microorganisms. Studies have shown that most microorganisms live 2 cm above the seabed sediment [99]. Installations of large underwater gas storage accumulators on the deep seabed are bound to occupy the habitats of the original marine organisms. At the same time, during the working process of the gas storage system, the seawater flow around the gas storage accumulator forms vortex streets and wakes, causing long-term disturbance to the microorganisms in the seabed upper sediment. At the same time, the arrangement of multiple large-scale underwater gas storage accumulators will also increase the range of influence. The construction  
Figure 5: Schematic diagram of underwater gas transportation system.  
of the seabed ecological balance is an extremely long process, and the components and physical and chemical properties are different before and after reconstruction [100]. The long-term disturbance to the seabed sediments may cause a permanent imbalance in the local ecology of the seabed. A reasonable and effective environmental assessment system of underwater gas storage systems needs to be developed. This could be a complement and extension of the existing frameworks of the ecological risk assessment of marine renewable energy [101].  
Studies have also shown that underwater systems can have a series of positive effects on the marine ecological environment, such as acting as artificial reefs and promoting local ecological diversity under the protection of closed fishing and restricted navigation areas [102].
# Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry
## 5 Results
### Impact of the Geophysical Corrections  
As mentioned above, different solutions for some of the geophysical corrections are provided with the different coastal altimetry products. This is the case in particular for the wet tropospheric delay and ocean tides correction. In order to test their relative impact on the quality of the coastal altimetry sea level data, as well as to define the best choice of corrections, we use the different possibilities in Equation (1) to derive different sea level data sets (only one term varies in each case). Concerning the wet tropospheric correction, we tested both the decontaminated solution from the radiometer [26] and the composite correction derived from ECMWF and the radiometer. Concerning the tides, we considered both FES2004 and FES2012, besides GOT4.8. We also computed a version without the DAC. Given the results obtained above, we concentrated on case 1 area. Different diagnostics are considered to evaluate the impact of the correction choice:  
1. Secular trend and uncertainty (mm/yr) computed for different retrackers (results are shown in Table 7), and
2. Standard deviation of the SLA residuals with respect to the tide gauge data (results are shown in Table 8).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SLA Series** & **ALES** & **MLE3** & **MLE4** \\ \hline Agreement & 5.12 & 4.82 & 4.88 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Deseasoned and detrended SLA standard deviation w.r.t. tide gauge sea level (cm) for case 1.  
Figure 12: Detrended and deseasoned SLA time series based on ALES, MLE3, and MLE4, with 3-month smoothing (tide gauge SLA—noted TG—is shown as reference).  
Inspection of Tables 6 and 7 indicates that the impact of the wet tropospheric correction is rather low. For the trend estimates, the uncertainties are the same but the composite wet delay results are +0.1-0.2 mm/yr larger. Some values of the standard deviations of the differences w.r.t the tide gauge data increase, but some others decrease.  
The impact of the ocean tide correction appears much more important. The use of FES tide solutions introduces slightly less residual errors than GOT 4.8. FES2012 decreases the trend uncertainties, but the trend estimate seems too large (large difference with respect to the tide gauge). Correlations for different tide solutions with respect to the tide gauge data are also shown in Table 9. All ocean tide solutions lead to excellent correlations between altimetry-based and tide gauge-based sea level. FES 2012 gives the highest correlation, FES2004 the lowest.  
Remember that we did not correct the HK tide gauge data from the DAC. Assuming that the tide gauge and altimeter roughly capture the same dynamic atmospheric effects, we should obtain better statistics if no DAC correction is applied to altimetry. It corresponds to what is observed in Table 8.  
In conclusion, we note that the choice of the retracker and of the altimetry geophysical corrections, especially the tide correction, has a very significant impact on the estimated coastal sea level trend.