esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Photocatalysis for Congo Red Dye Removal
#### 3.2.5 Kinetics of Photocatalysis  
The kinetics of heterogeneous photocatalysis obey the Langmuir-Hinshelwood model. If the initial concentration of the dye is low, the model could be simplified to a pseudo-first order equation [16; 28; 29]:  
\[\mathrm{ln}\bigg{(}\frac{\mathrm{c}}{\mathrm{c}_{0}}\bigg{)}=-\mathrm{k}_{ \mathrm{app}}\mathrm{t} \tag{2}\]  
where \(\mathrm{c}_{0}\) is the initial dye concentration (mg/L), \(\mathrm{c}\) is the concentration of the dye at time \(\mathrm{t}\) (mg/L), \(\mathrm{t}\) is the irradiation time (min) and \(\mathrm{k}_{\mathrm{app}}\) is the apparent rate constant (min\({}^{-1}\)).  
The representations of \(-\mathrm{ln}(\mathrm{c}/\mathrm{c}_{0})\) as a function of time are presented as insets in Figures 7 and 8, which present the experimental results obtained in different initial condi  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **c\({}_{0}\) (mg/L)** & **Solid:liquid (g/L)** & **Efficiency (\%)** & **k\({}_{\mathrm{app}}\)\(\cdot\)10\({}^{2}\) (min\({}^{-1}\))** & **R\({}^{2}\)** \\ \hline \multirow{6}{*}{EthylAmAcid} & 25 & 1 & 98.6 & 1.69 & 0.9796 \\  & 50 & 1 & 95.3 & 1.18 & 0.9771 \\  & 100 & 1 & 86.0 & 0.802 & 0.9737 \\  & 50 & 0.5 & 93.1 & 1.01 & 0.9774 \\  & 50 & 2 & 92.2 & 1.00 & 0.9608 \\ \hline \multirow{6}{*}{BenzylAmAcid} & 25 & 1 & 83.1 & 0.661 & 0.9430 \\  & 50 & 1 & 85.1 & 0.761 & 0.9596 \\ \cline{1-1}  & 100 & 1 & 73.1 & 0.569 & 0.9760 \\ \cline{1-1}  & 50 & 0.5 & 81.2 & 0.533 & 0.9114 \\ \cline{1-1}  & 50 & 2 & 92.7 & 1.09 & 0.9525 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CR removal in different conditions.  
Figure 8: Influence of solid catalyst amount when using EthylAmAcid as the photocatalyst (**a**); BenzylAmAcid as the photocatalyst. Inset: kinetics of photocatalysis (**b**). \(\mathrm{c}_{0}=50\) mg/L. Inset: kinetics of photocatalysis.  
tions of photocatalysis. In Table 2 are given the calculated values of the constant k\({}_{\text{app}}\) for the presented experiments. It could be noticed that the kinetic constants are higher when material EthylAmAcid is utilized as a photocatalyst.
## 3 Results and Discussions
### Modeling of Tensile Strength by ANOVA and RSM
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & \
\begin{tabular}{c} **F-** \\ **Value** \\ \end{tabular} &
\begin{tabular}{c} _p-Value** \\ \end{tabular} & **Remarks** \\ \hline Model & 3894.31 & 17 & 229.08 & 49.84 & \(<\)0.0001 & significant \\ A-A & 396.49 & 1 & 396.49 & 86.27 & \(<\)0.0001 & \\ B-B & 170.13 & 1 & 170.13 & 37.02 & \(<\)0.0001 & \\ C-C & 532.36 & 1 & 532.36 & 115.83 & \(<\)0.0001 & \\ D-D & 35.19 & 1 & 35.19 & 7.66 & 0.0171 & \\ AB & 19.58 & 1 & 19.58 & 4.26 & 0.0613 & \\ AC & 53.22 & 1 & 53.22 & 11.58 & 0.0052 & \\ AD & 79.39 & 1 & 79.39 & 17.27 & 0.0013 & \\ BC & 30.31 & 1 & 30.31 & 6.59 & 0.0246 & \\ BD & 64.64 & 1 & 64.64 & 14.06 & 0.0028 & \\ CD & 3.84 & 1 & 3.84 & 0.8358 & 0.3786 & \\ A\({}^{2}\) & 22.04 & 1 & 22.04 & 4.80 & 0.0490 & \\ B\({}^{2}\) & 40.06 & 1 & 40.06 & 8.72 & 0.0121 & \\ C\({}^{2}\) & 237.86 & 1 & 237.86 & 51.75 & \(<\)0.0001 & \\ D\({}^{2}\) & 98.26 & 1 & 98.26 & 21.38 & 0.0006 & \\ ABD & 24.40 & 1 & 24.40 & 5.31 & 0.0399 & \\ A\({}^{2}\)C & 124.23 & 1 & 124.23 & 27.03 & 0.0002 & \\ AB\({}^{2}\) & 73.36 & 1 & 73.36 & 15.96 & 0.0018 & \\ Residual & 55.15 & 12 & 4.60 & & & \\ Lack of Fit & 29.65 & 7 & 4.24 & 0.8304 & 0.6037 & not significant \\ Pure Error & 25.50 & 5 & 5.10 & & & \\ Cor Total & 3949.46 & 29 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: ANOVA for the reduced cubic model.
## 2 CFD Numerical Model  
### Governing Equations and Turbulence Model  
The crosswind flow field around a moving train performs three-dimensional complicated and unsteady features, which usually can be described by the large eddy simulation (LES), detached eddy simulation (DES) and Reynolds-Average Navier-Stokes (RANS) numerical methods. However, the LES and DES are very computationally intensive for the work presented in this research associated with a moving train due to their requirements for the high-quality and high-density grid in the calculation. In fact, the RANS turbulence model is the dominant approach in simulating the flow evolution characteristics of the railway rolling stock under cross winds, owing to its efficiency and accuracy. In order to correctly capture the flow structure on the train surface, the SST (shear stress transport) \(k\)-\(\omega\) turbulence model based on RANS two equations was adopted in this study, and it is assumed that the flow is incompressible. Thus, the continuity and momentum conservation equations in tensor form are respectively expressed as follows:  
\[\frac{\partial}{\partial x_{i}}(\rho u_{i})=0 \tag{1}\]  
\[\frac{\partial(\rho u)_{i}}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(} \rho u_{i}u_{j}\big{)}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial}{ \partial x_{i}}\Bigg{(}\mu\frac{\partial u_{i}}{\partial x_{j}}-\rho\overline{ u_{i}^{\prime}u_{j}^{\prime}}\Bigg{)}+F_{i} \tag{2}\]  
where \(\rho\) is the air density; \(u\) and \(u^{\prime}\) are the mean and fluctuating speed components, respectively; \(p\) is the air pressure; \(F\) is the force of unit mass; the subscripts \(i\), \(j=1\), \(2\), \(3\) present the \(x,y,z\) directions, respectively.
The key driver for auctioning more mid-band spectrum, particularly for markets that have not yet introduced commercial 5G services, is the popularity of 3.5 GHz in 5G deployments. The European Union has taken several actions to guarantee a dependable rollout of 5G across European countries. The allocation of the 5G spectrum among the major European countries and a few others is shown in Table 2.  
5G mobile wireless communication technology has many advantages, but many obstacles must be overcome before its many advantages can be fully embraced. Among these challenges are high path loss, high penetration loss, interference, and cyber security risk. To address this issue of high path loss, an accurate channel model is required to accurately predict path loss; aid network planners and researchers in designing, analyzing, and evaluating various proposed wireless technology solutions; and enhance coverage for existing and future deployments. Meanwhile, the use of different bands of frequencies for 5G introduces a new challenge for accurate path loss prediction [8,9]. The high-band frequency spectrum has been highlighted as one of the crucial technologies for reaching extremely high data speeds in 5G mobile networks. Nonetheless, because of the shorter wavelength, the signal suffers from air absorption, increased attenuation loss, and obstruction [10]. The high-band frequency spectrum in the order of millimeter wave (mm-wave) is suitable for 5G mobile system communication in small cell access and backhaul since the frequency spectrum between 28 and 38 GHz demonstrates negligible air absorption in short-range links [11]. Path loss prediction using empirical and deterministic models occasionally leads to undesirable results [12]. Each of the models has its benefits and drawbacks in terms of accuracy, applicability, and computing effectiveness. This paper aims to review current cellular empirical models and machine learning-based path loss modeling for mid-band and high-band channels in rural, urban, and indoor environments. As a result, the following is a summary of the paper's significant contributions. * We compared the applicability of machine learning models against existing current 5G empirical models at the high-band frequency spectrum. * We evaluated the applicability of these models in the context of coexistence studies in the selected mid-band and high-band spectrums. * Considerations for potential scenarios involving additional environmental factors were considered when examining the study of frequency band propagation analysis that are candidates for 6G systems. The remainder of this paper is organized as follows: Section 2 delves into mid-band and high-band channel propagation. Section 3 examines empirical and machine learning models. Performance metrics used to evaluate the accuracy and effectiveness of path loss models are explored in Section 4. Section 5 conducts a review of empirical-based path loss models. Section 6 reviews machine learning-based path loss models. Section 7 addresses research gaps and future directions. Finally, Section 8 offers concluding remarks and discusses future work.
## Materials and Methods
### Data Model Accuracy Evaluation Index  
To evaluate the predictive performance of the prediction model, the normalized mean absolute error \(e_{NMAE}\), normalized mean-square-error \(e_{NRMSE}\), and Theil coefficient (TIC) are used as the performance evaluation indicators of the prediction model.  
\[e_{NMAE}=\frac{1}{P_{r}}\frac{1}{n}\sum\limits_{i=1}^{n}\lvert\hat{y}_{i}-y_{i} \rvert\times 100\%, \tag{18}\]  
\[e_{NRMSE}=\frac{1}{P_{r}}\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\bigl{(}\hat{y} _{i}-y_{i}\bigr{)}^{2}}\times 100\%, \tag{19}\]  
\[\textit{Theil IC}=\frac{\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\bigl{(}y_{i}- \hat{y}_{i}\bigr{)}^{2}}}{\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}y_{i}^{2}+ \sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\hat{y}_{i}^{2}}}}. \tag{20}\]  
where \(y_{i}\) is the actual observed value, \(\hat{y}_{i}\) is the predicted value, \(\overline{y}_{i}\) is the total average of the observed values, \(n\) is the number of samples, and \(P_{r}\) is the rated installed capacity of the PV power plant. The Hill unequal coefficient is always between 0 and 1. The smaller the value, the smaller the difference between the fitted value and the true value, which means the prediction accuracy is higher. When it is equal to 0, it means a 100% fit.  
Figure 3: Combination Forecasting Model Based on VMD-ARMA-DBN. PV: photovoltaic; IMF: Intrinsic Mode Function; VMD: Variational Mode Decomposition; DBN: Deep Belief Network.
## 7 Application Examples of the SDI on Real Concrete Surfaces  
The index proposed in this study underwent practical application on real concrete surfaces to assess its potential utility and the accuracy of its defect quantification capabilities. The selection of specific examples for the application of the SDI was methodically determined, as depicted in Table 6. Each chosen example involved careful consideration of different defect rates and represented a distinct quality of concrete surfaces, ranging from pronounced defects to minor imperfections. Specifically, the defects ranged from the most pronounced on surface S1 to moderate defects on surface S2 and minimal defects on surface S3. This selection was intended to provide a pertinent assessment of the SDI's versatility across different conditions of concrete surfaces. Two frames were positioned on samples of concrete surfaces. The frames delineated specific areas affected by surface defects for calculating the percentage of the defects' volume.  
Figure 12: Relationship between the volume of defects (surface area, depth) and the SDI.  
After the application of the RMP to the concrete surfaces inside both frames, the results displayed a significant variation. Surface indices of 68.58%, 15.03%, and 1.61% were obtained for surfaces S1, S2, and S3, respectively, reflecting the differing levels of defects encountered. This variation underscores the sensitivity of the index to nuances in surface quality. The index of 15.03% represents a surface with characteristics distinct from those with higher or lower indices. This range of results highlights the SDI's sensitivity in discerning and quantifying defects at different levels of defect severity, showcasing its effectiveness in capturing the variability in concrete surface conditions.  
The results reveal a direct correlation between the SDI values and the quality of the concrete surfaces. A higher index value (68.58%) corresponds to a surface with a higher volume of defects, suggesting potentially lower surface quality in terms of durability, while a lower value (1.61%) indicates a surface with a minimal defect volume, suggesting higher surface quality and, by extension, increased durability of the concrete. Notably, the surface with an index of 15.03% represented a moderate level of defects, indicating a moderate level of surface quality. This direct relationship showcases the efficacy of the SDI as a quantitative measure for defect characterization. The results specify the SDI as a valuable tool for distinguishing between varying levels of surface quality, contributing to accurate defect quantification and objective quality evaluation. By quantifying defects accurately, the SDI provides a meaningful approach to predicting the strength and durability of concrete, thus supporting more reliable durability assessments in the construction industry.
# Path Loss Measurements and Model Analysis in an Indoor Corridor Environment at 28 GHz and 38 GHz
###### Abstract  
This paper examines the large-scale path loss models for an indoor corridor environment at frequencies of 28 and 38 GHz. The measurement environment consists of an indoor corridor with both line-of-sight (LOS) and non-line of sight (NLOS) scenarios using vertical-vertical (V-V) and vertical-horizontal (V-H) antenna polarizations. The single-frequency close-in (CI), floating intercept (FI), free space large-scale path loss models and measured data from the measurement campaign were used to evaluate the performance analysis. The paper also focuses on various parameters, such as standard deviation, path loss exponent (PLE), accuracy, simplicity, and stability of the models. The analysis focuses on the peculiarity of the effect of the wall proximity on the path loss parameters as well as comparisons with the parameters in some of the reviewed literature studies. The FI and CI models produce comparable results for both antenna polarizations and clearly fit with the measured path loss. The PLE, with the highest value of 3.33 at 38 GHz (V-H), is much higher in the NLOS scenario with V-H polarization due to the signal degradation along the path from the transmitter (Tx) to the receiver (Rx). This is because there is no direct LOS between the Tx and Rx antennas. The Rx only relies on signal diffractions and reflections from obstacles as it transmits through the path from the Tx antenna. The path loss measurements and model analysis presented here are useful in designing 5G wireless communication systems for indoor environments, particularly for power budget calculations.  
28 GHz; 38 GHz; indoor environment; line of sight; millimeter wave; non-line of sight; path loss; path loss models; wave propagation +
Footnote †: journal: _Sensors_
## 4 Results and Discussion
### Noise Analysis before and after Blockage of Permeable Asphalt Pavement  
The rutting tester and noise meter are used to test the change of the road noise during the silting process of the PAC-13 asphalt pavement, and record the value of the road noise after each silting is completed, as shown in Table 10.  
From the data in the table, it can be seen that the noise reduction effect of the permeable pavement is significantly reduced after being blocked. As the degree of congestion deepens, road noise gradually increases. The noise of the heavily congested road is about 4 dB higher than that of the road before it is not congested. In order to more intuitively observe the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Blocking Times**} & \multirow{2}{*}{**Blockage Quality (g)**} & \multirow{2}{*}{**Mass Growth Rate (\%)**} & \multicolumn{2}{c}{**Pavement Noise**} \\  & & & **Average (dB)** \\ \hline
0 & 0 & 0 & 74.36 \\
1 & 1000 & 1.85 & 75.27 \\
2 & 1000 & 3.70 & 75.98 \\
3 & 1000 & 5.56 & 77.47 \\
4 & 1000 & 7.41 & 78.02 \\
5 & 493 & 8.32 & 78.46 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Noise changes during blockage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Type of Rut**} & \multicolumn{1}{c}{**Noise (dB)**} & \multicolumn{1}{c}{**Average Value (dB)**} \\ \hline AC-13 & 81.0 & 80.2 & 80.3 & 80.5 \\ SMA & 80.6 & 79.8 & 79.9 & 80.1 \\ PAC-13 & 78.0 & 79.1 & 78.4 & 78.5 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Tire noise of various types of mixture.  
relationship between the change in noise and the change in quality, the ratio of the noise increase value to the initial value is defined as the noise increase rate, and it is plotted with the quality growth rate in a unified line graph, as shown in Figure 13.  
As can be seen from the broken line graph, the noise reduction effect of the permeable pavement gradually decreases as the road surface is gradually blocked when it is put into use. There is a certain quantitative relationship between the size change of road noise and the degree of road blockage. Therefore, the degree of blockage of the permeable road can be detected according to the change of the noise level of the road. We quantitatively evaluated the blockage of the road surface to achieve the goal of non-destructive and accurate detection of blockage.
## 3 Results and Discussion
### Effect of Working Potential  
If the electrochemical detection of the products of enzymatic reaction takes place at a constant value of the applied potential, the magnitude of the working potential will play the most important role in every amperometric biosensor application. The dependence of height of reduction current on working potential is shown in Figure 4. The resulting curve has the highest reduction response at \(-0.25\) V. Therefore, it was selected as the optimum, which corresponds to the previously reported value (\(-0.24\) V) for amperometric detection (reduction) of \(p\)-quinone [17]. Moreover, the negative working potentials more than \(-0.30\) V have a negative influence on stability of baseline and background noise of amperometric signal due to oxygen reduction.  
Figure 4: The dependency of the amperometric response on the magnitude of the working potential applied at the CPE/MWCNTs (curve _a_) compared to CPE/MWCNTs/TYR/Nafion® (curve _b_); analyte 500 μmol\(\cdot\)L\({}^{-1}\) Trolox; supporting electrolyte 0.01 mol\(\cdot\)L\({}^{-1}\) PB of pH 7.0; stirring speed of 400 rpm.
## 3 Results and Analysis
### Analysis of Cavern Development  
The cavern contours at the vertical section (X = 0 m) and the different cross sections are depicted after each leaching stage in Figures 20 and 21. Initially, the solution cavity reaches a height of approximately 12 m. By the second stage, it expands upwards by about 7 m, roughly 2/3 of its initial height. In the third stage, the previously formed cavities continue to expand upwards, albeit at a diminishing rate. Continuing leaching results in no outward expansion for the first-stage cavity, reaching a maximum height influenced by the injection flow. Thus, the previously formed cavities expand at a gradually decreasing rate in the subsequent stages.  
The results indicate that the solution cavity's height in the first stage surpasses that in the subsequent four stages, with the latter stages maintaining a consistent height. The reflux cavity measures approximately 9 m in height and 15 m in length. Despite the variations in brine concentration and fluid flow, the reflux cavities at the inlet section retain a consistent height and shape after each leaching stage.  
As shown in Figure 22, the outlet brine concentration and cavern volume evolve with the leaching time. In the first stage, the outlet brine concentration quickly increases, with the rate of rise decelerating over time. The concentration experiences a significant initial surge during the first leaching stage, followed by a sharp decrease, and then a gradual increase in the subsequent stages. The fluctuations in brine concentration become progressively milder. At start of each stage, there is a slight drop in concentration, which then slowly climbs, with the later stages showing higher concentrations than the earlier ones. As the cavern expands, the outlet brine concentration eventually stabilizes. Once the leaching rate stabilizes, the outlet concentration also gradually stabilizes, indicating that the changes in outlet concentration directly influence the leaching rate. For the cavern volume, despite rapid expansion initially, the cavern volume grows slowly due to the small contact area between the salt rock wall and brine.  
Figure 19: Contours of brine displacing concentration at vertical section (X = 0 m) of each stage (black lines represent brine concentration contour lines).
# Synthesis of C/SiC Mixtures for Composite Anodes of Lithium-Ion Power Sources
###### Abstract  
Nowadays, research aimed at the development of materials with increased energy density for lithium-ion batteries are carried out all over the world. Composite anode materials based on Si and C ultrafine particles are considered promising due to their high capacity. In this work, a new approach for carbothermal synthesis of C/SiC composite mixtures with SiC particles of fibrous morphology with a fiber diameter of 0.1-2.0 \(\upmu\)m is proposed. The synthesis was carried out on natural raw materials (quartz and graphite) without the use of complex equipment and an argon atmosphere. Using the proposed method, C/SiC mixture as well as pure SiC were synthesized and used to manufacture anode half-cells of lithium-ion batteries. The potential use of the resulting mixtures as anode material for lithium-ion battery was shown. Energy characteristics of the mixtures were determined. After 100 cycles, pure SiC reached a discharge capacity of 180 and 138 mAh g\({}^{-1}\) at a current of C/20 and C, respectively, and for the mixtures of (wt%) 29.5C-70.5 SiC and 50 Si-14.5C-35.5 SiC discharge capacity of 328 and 400 mAh g\({}^{-1}\) at a current of C/2 were achieved. The Coulombic efficiency of the samples during cycling was over 99%.  
lithium-ion battery; silicon carbide; electrodeposited silicon; composite material; anode material; Coulombic efficiency +
Footnote †: journal: Physics Letters B
## 4 Simulation and Analysis  
To evaluate the performance of the proposed algorithm, consider a two-dimensional scenario with an unknown and time-varying number of targets observed in clutter over the surveillance region \([0,3000\text{ m}]\times[0,3000\text{ m}]\). The sensor is located at the origin and the clutter is molded as a Poisson RFS with intensity \(\kappa_{k}=10\) (i.e., 10 clutter returns over surveillance region each scan). Each target has survival probability \(\textbf{P}_{5,k}=0.99\) and follows a linear Gaussian dynamic motion with constant velocity. The target birth process is assumed to be a Poisson distribution with  
Figure 4: Detection probability changes with distance.  
\(\gamma_{k}(x)=\sum\limits_{i=1}^{4}\omega_{r,k}^{(i)}\mathrm{N}\Big{(}x:\mathbf{m} _{r,k}^{(i)},\mathbf{P}_{r,k}^{(i)}\Big{)}\), with \(\omega_{r,k}^{(i)}=0.03\), \(\mathbf{m}_{r}^{(1)}=[300;0;400;0]\), \(\mathbf{m}_{r}^{(2)}=[630;0;800;0]\), \(\mathbf{m}_{r}^{(3)}=[500;0;1700;0]\), \(\mathbf{m}_{r}^{(4)}=[1200;0;200;0]\), and \(\mathbf{P}_{r}^{(i)}=\mathrm{diag}[100;100;100;100]\). The gate threshold is set to \(\gamma=9\), which corresponds to a two-dimensional gating probability of \(P_{g}=0.989\) and sampling interval \(T=1\) s. The Gaussian mixture filter has a pruning threshold \(10^{-5}\), merging threshold 4, and limitation on number of Gaussians \(J_{\mathrm{max}}=100\). The state parameters of targets are shown in Table 1. The trajectory of targets are shown in Figure 5. The red circle represents the starting position of the target, and the blue box indicates the end position of the target, and the dotted line indicates the circle of the distance from the origin. The following simulation and analysis are all in the same scenario show in Figure 5.