esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Open Resonator Measurements  
MW measurements were performed using the open resonator technique, which utilizes the fact that the resonance frequency and the Q-factor of a resonator are affected in a known way by the presence of a dielectric material in the resonant cavity.  
The open resonator employed was of the plano-concave type, shown in Figure 1, which uses fundamental-mode Gaussian beam resonances for measurement. Samples were positioned on the plane mirror, and a Vector Network Analyzer (VNA) (Rohde and Schwarz ZVA50) was used to record the complex transmission coefficient. The Q-factors and resonant frequencies were then obtained by fitting a resonance model to the data. Measurements of permittivity and loss angle were obtained by the fixed-frequency tunable length method [29]. For each sample, measurements were made at three resonant modes close to 36 GHz, set by changing the VNA frequency, to check that measurements are not affected by coincident higher-order modes. The Q-factor of the empty cavity was approximately 130,000. The technique requires that sample thickness be approximately an integer number of half wavelengths in the medium of the material. Uncertainties were evaluated from contributions associated with dimensions, Q-factors, and theory. The Gaussian beam is highly polarized, which allows the two axes of uniaxial Y-cut quartz crystals to be measured at different specimen rotations. For Z-cut quartz, measurements are expected to be independent of the specimen rotation.  
Note that MW measurements typically yield values of real permittivity \(\varepsilon^{\prime}\) and loss angle \(\delta\), whereas THz TDS measures refractive index \(n\) and absorption coefficient \(\alpha\). The two sets of parameters can be inter-converted by using the relations:  
\[\varepsilon^{\prime}=\left(n^{2}-k^{2}\right) \tag{3}\]  
\[\varepsilon^{\prime\prime}=2 nk \tag{4}\]  
\[\tan\delta=\varepsilon^{\prime\prime}/\varepsilon^{\prime} \tag{5}\]  
\[k=\alpha c/4\pi\
u \tag{6}\]  
where \(k\) is extinction and \(\varepsilon^{\prime\prime}\) is the imaginary part of complex permittivity.  
For low-loss materials, such as those studied here, permittivity \(\varepsilon^{\prime}\) at MW frequencies is expected to have negligible dispersion. This is because the Kramers-Kronig relationship between the real and imaginary parts of the refractive index (\(n\) and \(k\)) requires that \(\mathrm{d}n/\mathrm{d}\
u\propto k\) (except in the vicinity of an absorption peak) [30]. When \(n\)\(>\)\(k\), \(n\) has negligible dispersion, and so does \(\varepsilon^{\prime}\).  
Figure 1: Fabry-Perot open resonator used for MW measurements.
## 3 Case Studies
### Carbon Monoxide Adsorption in Zeolites
In general terms, the relative proportion of single and dual cation sites depends on the zeolite being considered, and on the corresponding Si:Al ratio, as shown for Na-FER and K-FER in Figure 7. Zeolites having a very low Si:Al ratio (and a small unit cell) can show a more complex situation, as depicted in Figure 8 for the case of Na-A, which has the LTA structure type and a 1:1 Si to Al ratio. Figure 5: Carbon monoxide adsorption complexes in Na-FER; **(a)** single cation site and **(b)** dual cation site. Framework Al, Si and O atoms are shown in black, grey and red sticks, respectively; the C and O atoms of CO are shown in grey and red balls, and Na\({}^{+}\) atoms are depicted in violet (primary cation) or yellow (secondary cation). Distances in Å units (adapted from [65]). Figure 6: Dependence of stabilization energy (full line) and of change of the CO vibrational frequency (dotted line) on r(M-M) distance between two M\({}^{+}\) ions; calculated using a 2x(1-T) cluster model (inset). Plots for Na\({}^{+}\)–Na\({}^{+}\) and K\({}^{+}\)–K\({}^{+}\) pairs depicted in violet and green, respectively (adapted from [92]). In such a case, the vast majority of adsorption sites were found to involve either 2 or 3 Na\({}^{+}\) cations; the latter were termed _multiple cation sites_. VTIR spectroscopy, combined with DFT calculations [71], showed values of \(\Delta H^{0}\) in the range of \(-20\) to \(-30\) kJ mol\({}^{-1}\) for CO adsorption on the dual sites, and \(-14\) to \(-28\) kJ mol\({}^{-1}\) in the case of multiple cation sites.
## 2 Materials and Methods
### Test Methods
#### 2.3.2 Soluble Salt Content Test  
Barium ions and sulfate ions react to form an insoluble barium sulfate precipitate under acidic conditions (chemical Equation (3)). Therefore, the soil leaching solution of the treated sample could be prepared, which could react to form barium sulfate precipitation within a certain time by adding barium chloride solution. Furthermore, the change in the sulfate ion content in the soil before and after the improvement was revealed according to the weight of barium sulfate precipitation (Figure 3).  
\[\text{SO}_{4}^{2-}+\text{Ba}^{2+}=\text{BaSO}_{4}\downarrow \tag{3}\]  
The test process was as follows. First, the cured sample was sifted with a 0.075 mm sieve after it was dried and ground with a mortar. Second, a soil sample with a weight of 50.00 g was poured into a 500 mL dry shake flask, and 250 mL of distilled water was added. The shake flask was placed on a reciprocating electric oscillator for continuous shock for 5 min. After standing for 24 h, the soil leaching solution of the sample was obtained by extracting the supernatant liquid. Then, the soil leaching solution was poured into a 400 mL beaker, to which was added diluted hydrochloric acid until the solution turned exactly red after dropping the methyl red indicator. After that, the solution was heated to near-boiling in the sand bath, and then 40 mL 0.02 mol/L barium chloride solution was quickly added, after which the solution was stirred intensely for 2 min. When the solution was cooled to room temperature, a little barium chloride solution was dropped into it to test whether the precipitation was complete. Next, the solution containing the precipitation of barium sulfate was pumped through the filtration device. After the suction filtration was completed, the precipitation was placed in the crucible (the mass of which was known) and put in the oven at 110 \({}^{\circ}\)C for drying. During the period, the mass of the crucible was weighed every 1 h until the difference between the two masses was less than  
Figure 3: Schematic illustration of the CLI polymer reacting with sulfate saline soil to produce calcium sulfate precipitation.  
Figure 2: TSZ-1 full-automatic triaxial shear equipment.  
0.0001 g. Finally, the mass of the barium sulfate precipitation was calculated according to Equation (3).
# Analysis of Erosion Minimization for a Slurry Pump Using Discrete Phase Model Simulations
## 3 CFD Analysis  
### DPM Analysis Modeling  
A slurry pump consists of a casing, an impeller, and a throat bush, as shown in Figure 2. The throat bush of a slurry pump from Warman, Australia, was selected as the target model in this study, and an analysis model was designed accordingly. Figure 2a depicts the throat bush used in the slurry pump that was subjected to erosion wear by slurry particles. The simulation model comprised a casing, impeller, and throat bush (Figure 2b).  
Mesh independence is an important part of a simulation study to guarantee that the numerical solution independent of the size of the mesh [26]. For this reason, three meshes, i.e., fine mesh, normal mesh, and coarse mesh, were generated. The total elements for fine mesh, medium mesh, and coarse mesh are 418,173, 291,421, and 260,052 respectively, with all three meshes shown in Figure 3. In this study, simulations were performed using a fine mesh. Table 1 shows the number of grids with different grid types in the slurry pump.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grid Size** & **Number of Cells** & **Skewness** \\ \hline Coarse & 260,052 & 0.27198 \\ Normal & 291,421 & 0.26986 \\ Fine & 418,173 & 0.2657 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of cells with different grid types in the slurry pump.  
Figure 3: Three types of mesh slurry pump: (**a**) fine mesh; (**b**) normal mesh; (**c**) coarse mesh.  
Figure 2: Throat bush and slurry pump: (**a**) erosion by slurry particles; (**b**) boundary conditions for multiphase flow analysis model of the slurry pump with an impeller, throat bush, and casing.  
Among the analysis conditions, the three boundary conditions for the fluid region are the mass flow rate condition at the inlet, no-slip condition at the wall, and pressure condition at the outlet. The number and velocity of the particles are determined using the value obtained by dividing the mass flow rate assigned to the representative particle by the actual mass of the particle [27]. Table 2 presents the boundary conditions for the simulations. The boundary condition was set based on the operating conditions applied in a Korean thermal power plant.  
The main parameters that affect the erosion rate density include the particle diameter, gap between the main components, and impeller speed. The analysis range of the pump based on these parameters was set as the overload and underload states under actual operating conditions. Table 3 lists the ranges of parameters applied to the DPM. Figure 4 depicts the gap between the impeller and the throat bush (\(w\)).
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 3 Star Identification Algorithm
### Identification Algorithm Flow  
In the star identification, the star closest to the center of the FOV is selected as the reference star, and other stars within the FOV are considered neighboring stars. Using these neighboring stars, a star pattern feature is constructed for the reference star, including the distances \(r\) between the reference star and its neighboring stars, as well as the angles  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Star ID** & \(\theta_{1}\) & \(\theta_{2}\) &... & \(\theta_{n_{d}}\) \\ \hline
1 & 14.83 & 6.99 &... & 70.82 \\
2 & 15.88 & 18.74 &... & 35.66 \\... &... &... &... &... \\
4960 & 51.94 & 10.84 &... & 24.26 \\ \hline \end{tabular}
\end{table}
Table 2: Navigation star angle feature.  
\(\theta\) between each pair of neighboring stars. The distance \(r\) between the reference star and neighboring stars varies with changes in focal length. To reduce the dependency of the identification algorithm on an accurate focal length, the distance ratio corresponding to the angle sides is used as an identification feature.  
Through the feature extraction, multiple sets of identification features can be obtained. Each feature set serves as starting points for identification. This method overcomes the previous methods' excessive reliance on the positioning star/reference star, achieving rotational invariance and enhancing robustness against variations in focal length. By expanding from a single match to multiple matches, the overall performance of the algorithm is improved.  
After the initial identification of this feature, multiple candidate star combinations that meet the initial matching criteria can be obtained. Next, the matching degree between each candidate star and the reference star to be identified is calculated. Since different starting points may correspond to the same candidate star, multiple matching degree scores may arise. In this case, the highest matching degree score is selected as the final score for that candidate star. Ultimately, the candidate star with the highest matching degree score will be identified as the identification result for the reference star to be identified. The pseudocode is given in Appendix A.
## 2 Materials and Methods  
This section details the comprehensive framework and methodology employed in this study. It is divided into four main parts. First, we describe the system design, outlining the architecture and specific design considerations for local self-organizing network terminals and satellite communication network terminals. Next, we cover the experimental setup, detailing the equipment and configurations used to test the system. Following this, the experimental procedure is described, providing a step-by-step account of how the experiments were conducted. Finally, we present the data statistics methods used to analyze the collected data, ensuring the reliability and validity of the results.
# A Unified Approach for the Control of Power Electronics Converters. Part II: Tracking
## 2 Preliminaries  
In these preliminaries, the models considered in this paper, developed in detail in [19], are recalled. We consider systems described by  
\[\left\{\begin{array}{rcl}\frac{dx(t)}{dt}&=&(A_{0}+A_{u_{1}(t)u_{2}(t)\ldots u _{m}(t)})x(t)+(B_{0}+B_{u_{1}(t)u_{2}(t)\ldots u_{m}(t)})v_{in}(t)\\ y(t)&=&(C_{0}+C_{  
with \(\bullet_{i}=\bullet_{u_{1}(t)u_{2}(t)\ldots u_{m}(t)}\), \(\bullet=A,B,C\) where \(u_{1}(t)u_{2}(t)\ldots u_{m}(t)\) is the binary expression of \(i-1\) denoted \(bin_{M}(i-1)\)[19]. The previous model can also be written in the following more compact form considered in the sequel:  
\[\left\{\begin{array}{rcl}\dfrac{dx(t)}{dt}&=&A_{0}x(t)+B_{0}(x(t))\lambda(t) \\ y(t)&=&C_{0}x(t)+D_{0}(x(t))\lambda(t)\end{array}\right. \tag{3}\]  
where, denoting \(M=2^{m}\),  
\[B_{0}(x)=\left[\begin{array}{cccc}A_{1}x+(B_{0}+B_{1})v_{in}&\ldots&A_{M}x+( B_{0}+B_{M})v_{in}\end{array}\right]\]  
\[D_{0}(x)=\left[\begin{array}{cccc}C_{1}x&\ldots&C_{M}x\end{array}\right]\]  
Another important model is that obtained by allowing the variable \(\lambda(t)\) to belong to the set \(\Lambda_{R}=\{\lambda\in[0,1]^{M}:\sum_{i=1}^{M}\lambda_{i}=1\}\). The resulting model is known as the relaxed or embedded one [46]. In [46], it is shown that the initial value problem for (3) is dense in the solution set of initial value problems of the relaxed model for a topology (\(C^{0}\)-Whitney topology [47]) that is sufficiently general to cover almost all the associated practical problems of interest. In particular, this connection can be invoked for averaging or regularizing controls developed from (3) and applicable to the relaxed model using, for example, Pulse Width Modulation (PWM) or hysteresis comparators [19]. When \(v_{in}(t)\) is constant, the classical concept of the equilibrium point is meaningless for systems described by (3). However, for the relaxed model, the classical notion of the equilibrium point is recovered and, because of the connection between the trajectories of (3) and the ones of its relaxed version, the set of equilibria for the relaxed model is of particular interest when considering model (3). This interest can be discussed in terms of refining the notion of the \"solution\" for model (3) ([PERSON], [PERSON]'s solutions, etc.--see [48] for details). In this context, the set of equilibrium points associated with (3) is given by  
\[X_{eq}=\{x_{e}\in\mathbb{R}^{n},\;\lambda_{e}\in\Lambda_{R}:A_{0}\;x_{e}+B_{0} (x_{e})\lambda_{e}\;=0\}\]  
Under an appropriate invertibility condition, an alternate expression is  
\[X_{eq}=\left\{x_{e}\in\mathbb{R}^{n},\;\lambda_{e}\in\Lambda_{R}:x_{e}=-\left[ \sum_{i=1}^{M}\lambda_{ei}(A_{0}+A_{i})\right]^{-1}\left[\sum_{i=1}^{M} \lambda_{ei}(B_{0}+B_{i})\right]V_{in}\right\}\]
# Forest Fire Risk Forecasting with the Aid of Case-Based Reasoning
## 3 Forecasting the Risk of Forest Wildfires Using a Case-Based Approach
### Results (Employed Works)
#### 3.3.3 Conceptualization and a Case Model
In accordance with the model described, a data set was formed in the form of textual files of the CSV (comma-separated values) format intended for their further processing when creating a prototype of the case base. The structure of the description of the current case c* corresponds to the described case model. However, as the object of the situation, we considered not the forest fire but the state of each forest quarter of a territory at a certain point in time. The choice of a forest quarter as a spatial unit for data processing is due to the limited data available. In particular, data on forest type and fire hazard class of a forest area are linked to forest quarters.
## 2 Experiment Setup and Model Configuration
### Experimental Setup for Pressure Measurements  
The pressure measurement section was located at the center of the test model (Figure 3). In the present study, 46 taps were arranged around the pressure measurement section (Figure 4). The pressure taps were connected to three units of digital sensor arrays (DSA3217, Scanivalve Corp(r), Liberty Lake, WA, USA) by using polyvinyl chloride (PVC) tubes for pressure data acquisition. These PVC tubes had an internal diameter of 0.9 mm and a length of 0.5 m. The precision of the DSA 3217 Model is \(\pm\)0.05% of the full-scale range (\(\pm\)10 inches of H\({}_{2}\)O). During the surface pressure measurements, the sampling frequency and time of each pressure tap were set as 312.5 Hz and 32 s, respectively. Based on the work of [PERSON] et al. [22] and [PERSON] et al. [18], it can be expected that there are relatively small and negligible effects of the tubing system on the pressure measurements for the present test cases, such as the amplitude, attenuation and the phase lag of the instantaneous pressure signals. However, the blockage ratios were 6.67-17.85%, which correspond to the wind attack angle range of 0-20\({}^{\circ}\) in the current study. The solid blockage and the wake blockage effects of the test models on the pressure measurements were corrected, as suggested by [PERSON] et al. [23].  
Figure 3: View of the model from the downstream direction.  
The pressure coefficients distributed around the surface of the test models can be obtained by the pressure measurement system as follows:  
\[C_{pi}=\frac{p_{i}-p_{\infty}}{\frac{1}{2}\rho U^{2}} \tag{3}\]  
where \(p_{i}\) is the surface static pressure on the test model and \(p_{\infty}\) is the static pressure at the inlet of the test section.
## 4 Experimentation and Results
### Statistical Analysis
#### 4.5.1 Sampling Mechanism  
The study's dataset was meticulously selected to reflect a wide variety of retail settings, such as changes in lighting, product density, and consumer behavior. As shown in Table 10, the dataset was randomly divided into three subgroups.
## 2 Real-Time City-Scale Nonlinear Time-History Analysis
### Regional Seismic Loss Prediction and Resilience Assessment  
#### 2.5.1 Regional Seismic Loss Prediction Using Conventional Method  
Based on the damage state of each building, the seismic loss can be predicted using the conventional loss prediction method. For example, according to the National Standard of China, \"_Post-Earthquake Field Works-Part 4: Assessment of Direct Loss_\" [24], the house damage loss \(L_{h}\) and decoration damage loss \(L_{d}\) are calculated using Equations (15) and (16), respectively:  
\[L_{h}=S\times D_{h}\times P \tag{15}\]  
\[L_{d}=\gamma_{1}\times\gamma_{2}\times(\xi\times S)\times D_{d}\times(\eta \times P) \tag{16}\]  
where \(S\) is the building area (m\({}^{2}\)); \(D_{h}\) and \(D_{d}\) are the loss ratios of the house and decoration damage, given a damage state; \(P\) is the building replacement cost; \(\gamma_{1}\) is the correction factor considering different economic conditions of different regions; \(\gamma_{2}\) is the building function correction factor; \(\xi\) is the proportion of buildings with mid-to-high-quality decoration; and \(\eta\) is the ratio of the building decoration cost to the building construction cost. The values of \(\gamma_{1}\), \(\gamma_{2}\), \(\xi\), \(\eta\) can be found in Table A.1-A.4 in GB/T 18208.4 [24].
# Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction
## 2 Related Background
### RGB Color Space and Color Stacking
#### 2.2.2 Color Stacking in RGB Space  
In a typical VSS system, the decoder needs to stack shares in order to decode the secret bits. Such stacking is equivalent to logic OR operation if shares are assumed to be printed on transparencies. Let \(a\sqcap d\) represents the logic OR operation between two bits \(a\) and \(d\), then we have \(0\sqcap 0\to 0\), \(0\sqcap 1\to 0\), \(1\sqcap 1\to 1\), \(1\sqcap 0\to 0\). For any two colors from the eight colors \(\mathcal{C}=\{\mathsf{R},\;\mathsf{G},\;\mathsf{B},\;\mathsf{C},\mathsf{M},\; \mathsf{Y},\;\mathsf{W},\;\mathsf{K}\}\), the stacking operation can also be represented as component-wise OR between color vectors. Let \(\mathsf{a}=[a_{r},\;a_{g},\;a_{b}]^{T}\) and \(\mathsf{d}=[d_{r},\;d_{g},\;d_{b}]^{T}\) be two colors in \(\mathcal{C}\), then the stacking of \(\mathsf{a}\) and \(\mathsf{d}\) is  
\[\mathsf{a}\sqcap\mathsf{d}=[a_{r},\;a_{g},\;a_{b}]^{T}\sqcap[d_{r},\;d_{g},\;d _{b}]^{T}=[a_{r}\sqcap d_{r},\;a_{g}\sqcap d_{g},\;a_{b}\sqcap d_{b}]^{T}. \tag{3}\]  
For example, the stacking of \(\mathsf{R}\sqcap\mathsf{B}\) is  
\[\mathsf{R}\sqcap\mathsf{B}=[1\;0\;0]^{T}\sqcap[0\;0\;1]^{T}==[1\;0\;1]^{T}= \mathsf{M}. \tag{4}\]  
The stacking operation can be extended to more than two shares. Some examples of stacking of three shares are illustrated in Table 1.
## 4 Experimental Results  
A total of 51 subjects were recruited for this experiment, 34 males and 17 females. All of them were aged between 20-65 years old, with their driving experience ranged from 1 to 20 years with educational levels being at the university and research institute level. A total of 31 of them used cars as their main means of transportation. The four methods for judging the minimum safe distance for these 51 subjects (A1: general side-view mirror, A2: patented side-view mirror with distance marking, A3: Dutch Reach, and A4: stands outside the vehicle door) were used for the minimum safe distance behavioural observation experiment, and statistical analysis was carried out. The descriptive statistics are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solutions** & **General** & **Patent** & **Dutch Reach** & **Outside** \\ \hline Mean & 14.2379 m & 15.0399 m & 12.9732 m & 13.9586 m \\ Standard Error & 0.33941 & 0.31411 & 0.35922 & 0.3698 \\ Median & 13.8 m & 15 m & 13 m & 12.9 m \\ Standard Deviation & 4.19831 & 3.88534 & 4.44327 & 3.9487 \\ Variance & 17.626 & 15.096 & 19.743 & 15.592 \\ Kurtosis & 0.542 & \(-\)0.093 & \(-\)0.231 & \(-\)0.261 \\ Skewness & 0.698 & 0.124 & 0.395 & 0.409 \\ Range & 21.50 & 18.30 & 20.80 & 17.30 \\ Minimum & 6.50 m & 6.00 m & 5.10 m & 6.20 m \\ Maximum & 28.00 m & 24.30 m & 25.90 m & 23.50 m \\ Sum & 2178.40 m & 2301.10 m & 1984.90 m & 1533.90 m \\ Count & 153 & 153 & 153 & 114 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of the four solutions.