esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Development of an Evaluation System
### Implementation of the Underwater Propagation Attenuation Model in the Low-Frequency Band
#### 5.2.2 Underwater Noises  
For underwater communication, it is necessary to consider not only the attenuation equation but also noises. In this paper, the following three equations are considered. They respectively relate to thermal noise (\(N_{th}\)), ship noise (\(N_{s}\)) and wave noise (\(N_{w}\)) [16]  
\[10\log N_{th}(f)=-15+20\log\ f \tag{6}\]  
\[10\log N_{s}(f)=40+20(s\ -0.5)+26\log\ f\ -60\log(f+0.03) \tag{7}\]  
\[10\log N_{w}(f)=50+7.5\ w+20\log\ f\ -40\log(f+0.4) \tag{8}\]  
where \(s\) represents the influence of ship, and \(w\) is wind speed (m/s). The value of \(s\) ranges between 0 (no influence) and 1 (heavy influence). The influences of these noises at different frequencies are shown in Figure 10.  
In Figure 10 the solid lines show wave noises while the dotted lines show ship noises. The ship noise is significant at frequencies between about 10 Hz and 2000 Hz. The influence of wave noise is prominent at frequencies between about 100 Hz and 100 kHz. Since the present research focuses on data transmission, it uses a frequency band of 150 kHz in order to ensure a transmission speed above a certain level. At this frequency band, the influences of the wave noise and ship noise are not significant.
# Development of a Model Material for Dynamic Geotechnical Model Tests
## 4 Experimental Results and Sensitivity Analysis
### Comparison and Verification of Raw Rock and Similar Materials  
Model rock samples were configured using the ratio in Table 8. Splitting, uniaxial and shear tests were conducted to obtain relevant physico-mechanical parameters. Figure 11 shows the required values based on the similarity ratio, the values calculated through the model formula and the values measured in the laboratory test. The model materials generally met the needs of the shaking table model test.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \(\rho\)/g\(\cdot\)cm\({}^{-3}\) & \(\sigma_{t}\)/MPa & \(\sigma_{c}\)/MPa & \(c\)/MPa & \(\varphi l^{c}\) & \(\mathbf{G_{d}}\)/GPa & \(\mathbf{E_{d}}\)/GPa \\ \hline Actual mean & 2.60 & 2.65 & 54.58 & 15.72 & 25.45 & 4.05 & 23.50 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Physico-mechanical parameters of samples of siltstone in the bar service area.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Physico-Mechanical Parameters** & \(\rho\)/g\(\cdot\)cm\({}^{-3}\) & \(\sigma_{t}\)/MPa & \(\sigma_{c}\)/MPa & \(\sigma\)/MPa & \(\varphi l^{c}\) & \(\mathbf{G_{d}}\)/GPa & \(\mathbf{E_{d}}\)/GPa \\ \hline Similar material value & 2.60 & 0.05 & 1.11 & 0.32 & 25.45 & 0.08 & 0.48 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Physico-mechanical parameters of model materials similar to the samples collected from the site.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Gypsum** & **Cement** & **Quartz Sand** & **Barite** & **Pure Iron Powder** \\ \hline Percentage & 3.80\% & 1.20\% & 26.60\% & 43.14\% & 24.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Proportions of components in the model material similar to the actual samples from the site.  
(2) Failure mode verification  
As shown in Figure 12, the failure modes of sandstone samples and samples of model materials observed in the uniaxial compression test were consistent, mainly shear failure with partial conical surfaces. The failure mode of the model material was basically consistent with that of real sandstone material in the uniaxial compression test.  
(3) Verification of failure process  
As shown in Figure 13, during the process of uniaxial compression of model materials similar to sandstone, the model material underwent an elastic stage and an elastic-plastic stage and finally reached the brittle failure stage. In the direct shear process of model materials similar to sandstone, with the increase in axial force, the peak shear strength and the residual strength increased and were much less than the peak shear force.  
Figure 11: Comparison of physical parameters.  
Figure 12: Comparison of failure modes under uniaxial compression. (**a**) Sandstone sample; (**b**) model material.
###### Abstract  
The use of rocks containing high amounts of natural zeolites (zeolitites) as soil amendment has been found as a valuable method for increasing agriculture sustainability. However, the potentialities and the effects of zeolites on the biogeochemical cycles of nitrogen (N) and carbon (C) have still not been clearly addressed in the literature. The objective of this study was therefore to investigate the N and C pools and \({}^{15}\)N distribution in an agricultural soil amended with both natural and NH4+-enriched zeolitites with the aim of understanding their effects on the soil-plant system, during sorghum cultivation, under fertilization reductions. Zeolitites were applied to an agricultural soil both at natural state (5 and 15 kg m\({}^{-2}\)) and in an enriched state with NH4+ ions from pig slurry (7 kg m\({}^{-2}\)). Both zeolitites at natural and enriched state increased soil cation exchange capacity and affected microbial biomass, causing an initial decrease of microbial C and N and then a possible increase of fungal population. N-NO3- content was lower in natural zeolitite treatments, that lead to a lower NO3- availability for denittifying bacteria. Zeolitites slightly affected the fixed N-NH4+ pool. \({}^{15}\)N turnover indicated that N from NH4+-enriched zeolitites remained in the soil until the growing season and that fertilizers partially substituted the fixed pool. Leaf \({}^{15}\)N content indicated that plants assimilated N from NH4+-enriched zeolitites and evidenced a higher fertilization recovery in natural zeolitite treatments. Organic C tended to be higher in all zeolitite treatment rhizosphere. In soils amended with zeolitites at natural state (at both application rates) sorghum yield was similar (+3.7%) to that obtained in the control while it was higher (+13.9%) in the plot amended with NH4+-enriched zeolitites.  
Zeolitite; carbon and nitrogen pools; \({}^{15}\)N natural abundance; zeolite agriculture; microbial biomass; nitrification and denitrification reduction
# A Self-Recovery Fragile Image Watermarking with Variable Watermark Capacity
## 4 Experimental Results and Comparison Analysis
### Performance of Tamper Detection and Self-Recovery  
To evaluate the tamper detection precision of the presented method, the false negative ratio (FNR) and false positive ratio (FPR) [35] are employed in this paper. The definitions of FPR and FNR can be expressed as:  
\[\text{FNR}=\frac{N_{\text{tu}}}{N_{\text{t}}},\text{FPR}=\frac{N_{\text{ut}}}{ N_{\text{u}}}, \tag{14}\]  
where \(N_{\text{t}}\) refers to the number of truly tampered blocks, \(N_{\text{u}}\) is the number of untampered blocks, \(N_{\text{tu}}\) is the number of tampered blocks that are regarded as untampered, and \(N_{\text{ut}}\) denotes the number of untampered blocks that are regarded as tampered. As the formula implies, the FNR is used to measure the proportion of the invalid blocks that are mistakenly determined as authentic blocks, while the FPR is used to measure the proportion of the authentic blocks that are falsely judged as invalid blocks. The lower FNR and FPR indicate a better tamper detection accuracy. The recovery ability is evaluated by the PSNR between the original watermarked image and the restored image.  
To test the performance of the presented method in tamper detection and self-recovery, various attacks are performed on the watermarked images. Besides, we compare the presented method with other two self-recovery fragile watermarking methods in [13; 25]. Reference [13] is a watermarking scheme with a fixed watermark capacity, and reference [25] is variable capacity watermarking scheme.
# Numerical and Parametric Analysis for Enhancing Performances of Water Photovoltaic/Thermal System
## 2 Methodology
### Heat Balance Governing Equations
#### 2.2.1 Solid Layers
Moreover, three heat exchange coefficients \(h_{s1}\), \(h_{s2}\), and \(h_{s3}\) can be expressed as follows:  
\[h_{s1}=hr_{absl,sky}=\sigma\varepsilon_{absl}F_{sky,absl}\ A\Big{(}T_{sky}+T_{ absl}\Big{)}\cdot\Big{(}T_{sky}^{2}+T_{absl}^{2}\Big{)} \tag{9}\]  
\[h_{s2}=hr_{absl,gr}\ \sigma\varepsilon_{absl}F_{absl,gr}\ A\big{(}T_{gr}+T_{g} \big{)}\cdot\Big{(}T_{gr}^{2}+T_{g}^{2}\Big{)} \tag{10}\]  
\[h_{s3}=hv_{a,absl} \tag{11}\]  
where \(\varepsilon_{absl}\), is heat transfer between the absorber and the ambient. The heat exchange coefficient \(h_{s3}=hv_{absl,a}\) could be calculated as function of the forced or the free convection regime using the following correlations considered in [29]. In case of forced convection:  
\[hv_{absl,a}=5.7\ w \tag{12}\]  
While in free convection is determined by:  
\[hv_{absl,a}=\frac{Nu_{free}\ \lambda a}{L} \tag{13}\]  
\[Nu_{free}=\left\{0.825+\frac{0.387 Ra^{1/6}}{\left[1+\left(\frac{0.492}{Pr} \right)^{9/16}\right]^{8/27}}\right\}^{2} \tag{14}\]  
For the duct formed by the two aluminum plates, the heat exchange coefficient \(h_{s}\) is due to the radiation mechanism as expressed in [30]. Table 3 shows the set of governing equations introduced in the numerical model for the solid PV/T layers.
## 3 Results
### The Effect of Protrusion Height
#### 3.1.2 Flow Structure and Pressure Drop  
Velocity magnitude contour plots in the section shown in Figure 10 are presented for various \(H_{p}/D\) and jet \(Re\) numbers in Figure 11. The CGVs direct the wall jets formed on the top faces of the CPs after the impingement of the immersed jets, first to the lateral faces and then to the base plate. By leaving a sufficient clearance of \(0.4D\) between both the top and lateral faces of a CP and its CGV, flow splitting by the CGV and excessive pressure drops are prevented in each case studied. The chosen values of the CGV and the CP upper edge fillet radii may have caused flow separation from the upper portion of the lateral face, thereby reducing heat transfer. The shapes and dimensions of both CGVs and filleted top edges of the CPs need to be optimized in a future study to avoid flow separation. In this study, CGVs are extended toward the target plate leaving a \(0.5D\) clearance between them to prevent premature flow separation from lateral faces of CPs.  
In the case of a flat target plate, the outer jet is deflected and deformed by crossflow at all jet \(Re\) numbers considered, as seen in Figure 11. On the other hand, as the height of CPs is increased so that velocities in the core of impinging jets increase and shear layers become thinner and the interactions of crossflow with the jets are mitigated, deflections and  
Figure 11: For \(D_{p}/D\) of 3.5, velocity magnitude contour plots in a planar section through the axes of the CPs at various jet \(Re\) numbers, for \(H_{p}/D\) of (**a**) 4.5; (**b**) 4; (**c**) 3; (**d**) 2.5; (**e**) 2; (**f**) 1.5; (**g**) 1; (**h**) flat target plate.  
deformations of the impinging jets decrease significantly. Furthermore, due to the decreased momentum of wall jets after being directed to the lateral faces of CPs by CGVs, the fountains formed by interacting neighboring streams on the target plate weaken compared with much more prominent ones in the case of a flat target plate.  
The pressure drops between the jet orifices and the system outlet (\(\Delta P\)) according to \(H_{p}/D\) for various jet \(Re\) numbers are presented in Figure 12. For any jet \(Re\) number considered, at \(H_{p}/D\) smaller than 4, a minor rise in \(\Delta P\) occurs compared with that of the flat target plate, the largest difference being 2%. At \(H_{p}/D\) of 4, for jet \(Re\) numbers of 5000, 10,000, 15,000, and 20,000, \(\Delta P\) are 6.5%, 6.8%, 7.3%, and 6.8% higher than those of the flat target plate, respectively. At \(H_{p}/D\) of 4.5, due to the extremely high momentum of the impinging jets, \(\Delta P\) significantly increases by 40.9%, 45.5%, 49.7%, and 50.1%, respectively, for the jet \(Re\) numbers of 5000, 10,000, 15,000, and 20,000.
# Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals
## 3 System Design
### Data Processing
#### 3.3.1 Hampel Filter  
Figure 3 shows the raw CSI amplitude sequence (i.e., 30 CSI subcarriers) collected by the Intel 5300 network card, where it can be observed that there are generally many outliers in the raw CSI amplitude sequence. To address this issue, Wi-Senser utilizes a Hampel filter with a sliding window to remove the outlier CSI amplitudes. Specifically, given the n-length CSI amplitude sequence \(C^{i}=\left\{c_{1}^{i},\ldots,c_{n}^{i}\right\}\) in the \(i^{th}\) subcarrier and the \(j^{th}\) sliding window \(W_{k}^{i}=\left\{c_{k}^{i},\ldots,c_{k+\Phi-1}^{i}\right\}(1\leq k\leq n-\Phi+1)\) with a length of \(\Phi\), if any CSI amplitude \(c_{j}^{i}(k\leq j\leq k+\Phi-1)\) in the current sliding window satisfies the condition \(\mu_{k}^{i}-\gamma\times\sigma_{k}^{i}\leq c_{j}^{i}\leq u_{k}^{i}+\gamma \times\sigma_{k}^{i}\), where \(\mu_{k}^{i}\) and \(\sigma_{k}^{i}\) represent the median and standard  
Figure 2: Wi-Senser deployment in a laboratory.  
deviation of the CSI amplitudes in the current sliding window \(W_{k^{\prime}}^{i}\), and \(\gamma\) represents an expandable factor (i.e., a constant), the Hampel filter considers it as an outlier. For any outlier \(c_{j}^{i}\), the Hampel filter replaces it with the median \(\mu_{k}^{i}\). In the experiments, we empirically set \(\gamma\) and \(\Phi\) to 1.5 and 7, respectively. Figure 4 shows the CSI subcarriers after being filtered by the Hampel filter, where it can be observed that the outliers identified in Figure 3 were effectively removed.
## 3 Results
### Seasonal and Interannual Variations in Ecosystem Carbon Fluxes and Environmental Factors
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**GPP (g C m\({}^{-2}\) a\({}^{-1}\))**} & \multicolumn{2}{c}{**NEE (g C m\({}^{-2}\) a\({}^{-1}\))**} & \multicolumn{2}{c}{**ET (mm)**} & \multicolumn{2}{c}{**WUE (g C kg\({}^{-1}\) H\({}_{2}\)O)**} \\ \cline{2-9}  & **GPP\({}_{\text{w}}\)** & **GPP\({}_{\text{a}}\)** & **NEE\({}_{\text{w}}\)** & **NEE\({}_{\text{a}}\)** & **ET\({}_{\text{w}}\)** & **ET\({}_{\text{a}}\)** & **WUE \({}_{\text{w}}\)** & **WUE \({}_{\text{a}}\)** \\ \hline
2004–2005 & 677.49 & 798.94 & \(-\)450.73 & \(-\)637.25 & 413.24 & 453.44 & 2.31 & 2.68 \\
2005–2006 & 732.80 & 750.49 & \(-\)437.35 & \(-\)545.42 & 374.23 & 376.64 & 2.34 & 2.48 \\
2006–2007 & 699.50 & 624.16 & \(-\)357.70 & \(-\)448.59 & 338.32 & 382.82 & 2.9 & 2.07 \\
2007–2008 & 726.91 & 757.11 & \(-\)464.88 & \(-\)595.65 & 403.21 & 395.86 & 2.37 & 2.63 \\
2008–2009 & 784.96 & 684.68 & \(-\)520.72 & \(-\)518.52 & 432.09 & 441.35 & 2.24 & 2.12 \\
2009–2010 * & 542.90 & 385.90 & \(-\)327.46 & \(-\)256.69 & 310.63 & 298.22 & 2.10 & 1.79 \\ Average ** & 724.33 & 723.08 & \(-\)446.28 & \(-\)549.08 & 392.22 & 410.02 & 2.43 & 2.40 \\ \hline \hline \end{tabular}
* Data statistics were from September to the next August during the 2004–2009 seasons, and from September to June in the 2009–2010 season. ** The average data were calculated for the 2004–2009 seasons.  
\end{table} 
Table 1: Annual summaries of GPP, NEE, ET, and WUE of the winter wheat and agroforest ecosystems. Figure 3: Seasonal dynamics in (**a**) GPP and (**b**) NEE of the winter wheat and agroforest ecosystems. ** and * represent a significant level of differences between the two ecosystems at \(p<0.01\) and \(p<0.05\), respectively.
## 6 Empirical Results and Analysis
### Heterogeneity Test
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** \\ \hline \multicolumn{2}{c}{**Variables**} & & & & & \\ \hline InHSRFreq1 & 0.147 *** & 0.205 *** & 0.021 & 0.118 *** & 0.104 *** & 0.073 *** \\  & (5.92) & (3.85) & (0.14) & (3.40) & (3.97) & (2.80) \\ sci\_GDP & 1.146 *** & 0.707 ** & 0.554 * & 0.633 ** & 0.602 * & 0.597 *** \\  & (3.15) & (2.26) & (1.72) & (2.03) & (1.94) & (1.99) \\ Inbooks\_100\_1 & 0.136 *** & 0.170 *** & 0.146 *** & 0.143 *** & 0.142 *** & 0.144 *** \\  & (2.92) & (3.49) & (3.16) & (3.09) & (3.07) & (3.11) \\ Inmobile1 & \(-0.086\) ** & \(-0.087\) ** & \(-0.086\) ** & \(-0.086\) ** & \(-0.086\) ** & \(-0.086\) ** & \(-0.085\) ** \\  & (\(-2.39\)) & (\(-2.41\)) & (\(-2.41\)) & (\(-2.42\)) & (\(-2.41\)) & (\(-2.40\)) \\ Fin\_GDP & \(-0.128\) ** & \(-0.120\) ** & \(-0.122\) ** & \(-0.116\) ** & \(-0.121\) ** & \(-0.122\) ** \\  & (\(-2.45\)) & (\(-2.32\)) & (\(-2.34\)) & (\(-2.20\)) & (\(-2.33\)) & (\(-2.34\)) \\ unemp\_Pop & \(-0.090\) ** & \(-0.087\) ** & \(-0.086\) ** & \(-0.085\) ** & \(-0.077\) ** & \(-0.085\) ** \\  & (\(-2.43\)) & (\(-2.31\)) & (\(-2.23\)) & (\(-2.24\)) & (\(-1.97\)) & (\(-2.25\)) \\ gyfs\_GDP & \(-0.065\) ** & \(-0.067\) ** & \(-0.068\) *** & \(-0.067\) ** & \(-0.067\) ** & \(-0.066\) ** \\  & (\(-2.52\)) & (\(-2.56\)) & (\(-2.62\)) & (\(-2.55\)) & (\(-2.58\)) & (\(-2.54\)) \\ InHSRFreq1\_sci\_GDP & \(-0.213\) *** & & & & & \\  & (\(-3.47\)) & & & & & \\ InHSRFreq1\_Inbooks\_100\_1 & & \(-0.029\) ** & & & & \\  & & (\(-2.37\)) & & & & \\ InHSRFreq1\_Inmobile1 & & & 0.011 & & & \\  & & & (0.48) & & & \\ InHSRFreq1\_Fin\_GDP & & & & \(-0.011\) & & & \\  & & & & (\(-0.96\)) & & \\ InHSRFreq1\_unemp\_Pop & & & & & \(-0.017\) & \\  & & & & & (\(-0.87\)) & \\ InHSRFreq1\_gys\_GDP & & & & & & 0.039 \\  & & & & & (1.61) & \\ Constant & \(-3.063\) *** & \(-3.165\) *** & \(-3.088\) *** & \(-3.092\) *** & \(-3.086\) *** & \(-3.088\) *** \\  & (\(-12.54\)) & (\(-12.66\)) & (\(-12.73\)) & (\(-12.72\)) & (\(-12.73\)) &
## 4 Results and Comparisons with Other Surface Water Products  
The following results and comparisons involve the optimized version of the NN classification with five input parameters (an equalization of water and non-water pixels, and the output threshold is 0.85). In Section 4.1, the SAR-predicted surface water maps are calculated for two test areas in the Mekong Delta, and compared to Landsat-8 surface water maps over the Tonle Sap Lake in Cambodia and over the Mekong river in Vietnam (see test dataset in Table 1). Other regions were tested but the results are not shown here. Due to the lack of in-situ local inundation maps at the time of this study, we do not have a reference dataset to confirm the accuracy of the Landsat-8 based maps. Therefore, an inter-comparison between Sentinel-1 estimate and other existing estimates is the only way to evaluate the new wetland product based on SAR Sentinel-1 data. First, the results are evaluated with respect to the floodability map derived mainly from the HydroSHEDS topography dataset [33], developed by [34] (Section 4.2). Second, time series of the SAR-derived surface water over the Mekong Delta is compared to the MODIS/Terra-derived inundation maps based on the methodology described by [14], for 2015 (Section 4.3)
## 4 Factors Affecting the Successful Introduction of New-Age Nanotechnologies in Pavement Engineering
### Hydrophobic Characteristics of an Alkoxy-Silane Modification and/or Treatment  
The treatment of stone/aggregate/soil using an alkoxy-silane or alkoxy-silane modified binder changes the surfaces of the material to become hydrophobic as shown in Figure 1. The power of the hydrophobic (water repellent) effect achieved is directly related to the size of the alkyl group attached to the silicon atom [1]. The water-repellent nature of material is usually observed through a leading effect [14], which is measured by the contact angle of the water with the material (Figure 4).  
When the contact angle is less than 90\({}^{\circ}\) the material will be subject to water ingress and a positive absorption will be observed with time, resulting in capillary rise. Material samples placed in water will show a wetness of the material appearing above the waterline, showing a positive water absorption rate (Cos of the angle (\(\theta\)) is positive between 1 and 0). When the contact angle is between 90\({}^{\circ}\) and 180\({}^{\circ}\), the material is hydrophobic, not allowing water to penetrate the material. Samples of the material placed in water will show no wetness above the water line, indicating a negative capillary rise (Cos of the angle (\(\theta\)) is negative between 0 and \(-1\)). The practical pavement engineering effects of the hydrophilic and hydrophobic effects and capillary rise, caused by the nano-silane modification of bitumen emulsion stabilisation of materials, are shown in Figure 5a,b.  
Figure 3: Comparative surface area versus reduction in dimensions [13].  
Figure 4: Beading effect: illustrating the hydrophobic (water repellent) nature of a material indicating the water-repellent nature of the material.  
Figure 5: Beading effect (hydrophobic) in (**a**) indicating the water-repellent nature of nano-silane modification of bitumen emulsion and the negative capillary rise shown in (**b**) resulting from the nano-silane modification [15].  
Compatibility Aspects of Alkoxy-Silane Modifications to Binders That Will Influence the Successful Application and Use Thereof
# Key Distribution Scheme for Optical Fiber Channel Based on SNR Feature Measurement
## 2 Key Generation Scheme for Optical Fiber Communication
### Key Generation Process  
The key quantization optimization process is shown in Figure 3. [PERSON] and [PERSON] extract channel characteristics of the SNR, quantify and code to generate the key, and generate the key with excellent consistency and security after post-processing. The specific steps are as follows:  
Step 1: [PERSON] calculates the channel's SNR through loopback measurements;  
Step 2: The characteristic information SNR is quantized to generate a consistency key by encoding. The specific steps are shown in Equation (2);  
Step 3: The key's consistency is judged, and the consistency factor is set as \(\varepsilon=0.95\). If \(KCR>\varepsilon\), it will proceed to the next step; otherwise, the quantization factor \(\alpha_{q}\) will be updated and it will go back to the beginning;  
Step 4: After the key is post-processed, [PERSON] transmits the quantization coefficient \(\alpha_{q}\) and consistency factor \(\varepsilon\) to [PERSON] through the public channel;  
Step 5: To increase the security of the generated key, data with length 0 or length 1 are discarded, and the key sequence KB with good randomness and consistency is obtained;  
Step 6: [PERSON] shares the optimized parameters \(\alpha_{q}\) and \(\varepsilon\) with [PERSON], and [PERSON] generates the key sequence KA in the same way.