esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
As the carbon black content increases in a composite material, the electromagnetic shielding efficiency generally increases. This is because carbon black is a conductive material that can absorb and dissipate electromagnetic radiation [29]. The increase in carbon black content provides more conductive pathways for the electromagnetic waves to be absorbed and dissipated, thus increasing the shielding efficiency [30]. However, the relationship between carbon black content and electromagnetic shielding efficiency may not always be linear. At higher carbon black loadings, the shielding efficiency may begin to level off or even decrease due to factors such as agglomeration, loss of dispersibility, or changes in the material's physical properties. The optimal carbon black loading for achieving maximum electromagnetic shielding efficiency depends on various factors, such as the type of carbon black, the composite matrix, and the operating frequency range [31]. In this study, the dielectric properties of epoxy matrix composites containing graphite and carbon black were investigated, and their EMI shielding performances were determined at low filler ratios. Different graphite and carbon black additive ratios with epoxy matrix combinations have been tried and compared with each other. This type of composite can be molded and produced in plate form. It can be applied as a low-cost solution with a suitable adhesive to the walls of a fixed structure where shielding is needed.
# Seismic Response Analyses of a Large-Span Powerhouse Cavern Considering Rock-Structure Interaction
## 3 Rock Mass Properties  
The cavern has been excavated in a schistose calcschist complex at a depth of 250 m below the ground surface, in a zone where the rock mass is nearly impervious (Figure 2). The structure is more or less foliated and laminated depending on the content of phyllosilicates and graphite, which contributes to the rock mass fissility along the planes of schistosity.  
According to research on systematic discontinuity, the rock mass conditions in the cavern can be divided into two zones: Zone 1, chainage 0-25 m, good rock mass quality with RMR 70-75; and Zone 2, chainage 25-50 m, fair rock mass quality with RMR = 56-64. Moreover, the joints and the faults detected are few and most are narrow and closed, without any water flow.  
To obtain the physical and mechanical characteristics of the rock mass, a series of surveys were carried out, including laboratory tests on rock cores, in situ rock properties testing, and in situ stress measurements. The main results obtained from the uniaxial compression tests are summarized in Table 1, which also indicates the unit weight, sonic velocity, and Brazilian tensile strength.  
Figure 1: Illustration of the hydroelectric scheme of concern.  
Figure 2: Schematic illustration of the hydro-geological model.  
The corresponding parameters of the Hoek-Brown model [18] were calibrated from the triaxial tests, as shown in Table 1.  
The deformability of the site was determined by plate load and flat-jack tests near the cavern's south wall; the plate load tests resulted in a deformational modulus Ed for the undisturbed rock mass in the range of 20 to 30 GPa, during loading for stress level between 4 and 12 MPa. From the flat-jack tests, which are representative of disturbed rock mass conditions, the corresponding modulus Ed is in the range of 14 to 20 GPa.  
The in situ state of stress in the rock mass surrounding the cavern was determined by hydro-fracturing tests performed in a vertical bore drilled to reach the location of the cavern [19; 20]. It could be inferred that the higher principal stresses are approximately normal to the schisty planes. The state of stress in the horizontal plane is shown to be nearly isotropic with the horizontal stresses being 6.1 MPa, and the vertical stress equal to 10.6 MPa is shown to be slightly greater than the geostatic stress. Accordingly, the stress ratio could be obtained as K = 0.57. The in situ stress is summarized in Table 2.
# Cross-Domain Access Control Model in Industrial IoT Environment
## 3 Distributed Access Control Model Based on a Master-Slave Chain
### Dynamic Selection Strategy of Master Chain Nodes Based on Reputation Mechanism
#### 3.2.1 Reputation Mechanism Process  
The reputation values of edge nodes are impacted by their behavior during the selection process of master chain nodes. Moreover, the slave chains will also record the behavior of the nodes and finally affect the final reputation value of the nodes. The block structure of the slave chains is shown in Figure 2. Edge nodes exhibiting good behavior tend to have higher reputation values. Consequently, edge nodes with high reputation values have a greater probability of becoming master chain nodes.  
During the reputation-based selection process of master chain nodes, the behavior of edge nodes will be recorded when they participate in the consensus process of the slave chain. Subsequently, the blockchain will evaluate the node's reputation value through the reputation calculation model based on each edge node's behavior before the consensus's end. The reputation calculation model will be further elaborated in the following section. Ultimately, the 10% of edge nodes with the highest reputation will be selected as the master chain nodes.
## 2 Materials and Methods
### Model Evaluation  
The models were evaluated utilising the metrics R\({}^{2}\) and mean absolute percentage error (MAPE). The R\({}^{2}\), or coefficient of determination, ranges from 0 to 1; a value closer to 1 indicates better performance in predicting the data. MAPE measures the absolute error between the predicted and reference values in percentage form, making it easy to interpret as the error is expressed as a percentage. A model performs better when the R\({}^{2}\) value is larger and the MAPE value is smaller. The formulae for R\({}^{2}\) and MAPE are as follows:  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_ {i}-\hat{y}_{i})^{2}} \tag{14}\]  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\frac{|y_{i}-\hat{y}_{i}|}{y_{i}}\times 1 00\% \tag{15}\]  
where \(y_{i}\) represents the true parameter value, \(\hat{y}_{i}\) is the predicted parameter value from the model, and \(n\) denotes the number of data samples. The interpretation of the MAPE value is shown in Table 3 and indicates the level of prediction accuracy [40].  
Figure 10: Illustration of CNN-2 model diagram.
## 2 Materials and Methods
### Sample Analysis  
Total solids (TS), volatile solids (VS), total suspended solids (TSS) and volatile suspended solids (VSS) of samples were measured according to the American public health association standard method 2540 (APHA 1999) [18]. Total and soluble COD of feed samples were also measured according to the APHA standard (method 5220 D). Sample pH was measured using a Beckman 300 pH meter equipped with Sentix-82 pH electrode. Ammonium-nitrogen content (NH\({}_{4}\)+-N) was measured according to APHA 4500-NH\({}_{3}\). Both COD and NH\({}_{4}\)+-N concentrations were measured using commercially available test kits and Spectroquant Pharo 300 spectrophotometer (Darmstadt, Germany). Total and individual VFA (volatile fatty acid) content of samples were measured using an Agilent gas chromatography flame ionization detector (GC-FID). Sample characterization results are provided in Table 1 and VFA concentrations are provided in Table 3. Total and volatile solids contents of granular sludge were also measured as a mass percentage (% _w/tv_) according to AMPTS II manual (Bioprocess control 2016) [19].  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{1}{c}{**Sample**} & \multicolumn{1}{c}{**Sample Description**} & \multicolumn{1}{c}{**Feed (mL)**} & \multicolumn{1}{c}{**Granule**} & \multicolumn{1}{c}{**Total**} & \multicolumn{1}{c}{**Headspace**} \\ \multicolumn{1}{c}{**Name**} & \multicolumn{1}{c}{**Sample Description**} & \multicolumn{1}{c}{**Feed (mL)**} & \multicolumn{1}{c}{**(mL)**} & \multicolumn{1}{c}{**(mL)**} & \multicolumn{1}{c}{**(mL)**} \\ \hline RF1 & Raw feed parallel 1 & 75 (sample) & 200 & 275 & 200 \\ RF2 & Raw feed parallel 2 & 75 (sample) & 200 & 275 & 200 \\ RF3 & Raw feed parallel 3 & 75 (sample) & 200 & 275 & 200 \\ RF-CEL1 & Raw feed and cellulase & 1.2 g cellulase + 75 (sample) & 200 & 275 & 200 \\  & parallel 1 & & & & \\ RF-CEL2 & Raw feed and cellulase & & & & \\  & parallel 2 & 1.2 g cellulase + 75 (sample) & 200 & 275 & 200 \\ RF-CEL3 & Raw feed and cellulase & & & & \\  & parallel 3 & 1.2 g cellulase + 75 (sample) & 200 & 275 & 200 \\ CF1 & Centrifuged feed parallel 1 & 75 (sample) & 200 & 275 & 200 \\ CF2 & Centrifuged feed parallel 2 & 75 (sample) & 200 & 275 & 200 \\ CF3 & Centrifuged feed parallel 3 & 75 (sample) & 200 & 275 & 200 \\ CF-CEL1 & Centrifuged feed and & & & & \\  & cellulase parallel 1 & 1.2 g cellulase + 75 (sample) & 200 & 275 & 200 \\ CF-CEL2 & Centrifuged feed and & & & & \\  & cellulase parallel 2 & 1.2 g cellulase + 75 (sample) & 200 & 275 & 200 \\ CF-CEL3 & Centrifuged feed and & & & & \\  & cellulase parallel 3 & 1.2 g cellulase + 75 (sample) & 200 & 275 & 200 \\ BLANK1 & Blank parallel 1 & 75 (distilled water) & 200 & 275 & 200 \\ BLANK2 & Blank parallel 2 & 75 (distilled water) & 200 & 275 & 200 \\ BLANK-CEL & Blank and cellulase & 1.2 g cellulase + 75 & 200 & 275 & 200 \\  & parallel 1 & (distilled water) & 200 & 275 & 200 \\ \hline \multicolumn{1}{c}{RF: Raw Feed; CF: Centrifuged Feed; CEL: Cellulase; BLANK: Distilled water and inoculum.} \\ \end{tabular}
\end{table}
Table 2: Sample preparation for batch test initial conditions.
# Potential Possibilities of Using Peat, Humic Substances, and Sulfurous Waters in Cosmetology
## 3 Peat
### Potential Uses of Peat and Humic Compounds  
#### 3.5.1 Types of Peat Preparations  
Oral administration of a preparation containing humic acids influenced fermentation in the colon and positively modified the intestinal microbiota. This phenomenon can be used in the production of preparations used to rebuild the intestinal microflora, e.g., after antibiotic therapy, inflammation of the digestive tract, or metabolic diseases [63; 64]. Peat preparations are, however, mainly used as products for external use in cases of hematoma, phlebitis, dehiscence, mygelosis, arthrosis, polyarthritis, osteochondrosis, and osteoarthritis, among others [24; 39]. In addition to unprocessed peat (peat pulp), the following are used for therapeutic purposes: peat patches, ointments, hydrogels, and gynecological tampons [61]. An interesting example of the use of humic acids is the production of active polymeric transdermal hydrogel materials. It was shown that the addition of up to 5% humic acids increased the strength and improved the structure of the polymer, which can be used in the production of transdermal systems [65]. Peat is also used in cosmetic products and is particularly popular in dermatocsmetics and natural cosmetics [61].  
**Peat in Cosmetics**  
The popularity of treatments using peat has remained high in Poland but also in other regions of Europe for many years. Currently in Poland, also cosmetics containing peat are very popular. These include preparations such as gels, ointments, pastes for compresses, toothpastes, creams, dental gels, bath emulsions, shampoos, and peat biriquettes [47]. Although drugs containing Tolpa peat have been withdrawn from the pharmaceutical market, the Tolpa company has remained one of the leading producers of cosmetics in Poland for the face, body, and hair and oral hygiene since 1989 [66]. Concentrated peat extract has been produced by Torf Corporation on the basis of a patented technology developed by Prof. [PERSON] and his co-workers. The production technology involves the alkaline extraction of peat, acidification to remove a humic acid fraction, and concentration connected with thermal processing [59]. These are \"dermocsmetics\" and \"ecocosmetics\" available for individual customers but also specialist cosmetics intended for professional treatments in spa and wellness centers and health resorts. Peat and humic acids as a cosmetic raw material also appear in the recipes of cosmetics produced in other countries, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
## 3 Results  
Dependence on the Frequency of the Gain in Step-Size for the Periodic LMS Algorithm with Sequential Partial Updates  
In order to figure out the dependence of gain in step-size on (a) the frequency, (b) the decimating factor \(N\), and (c) finally, the length of the filter \(M\) given by Equation (29), we carried out two experiments described in the upcoming subsubsections.  
1.1 Gain in Step-Size vs. Frequency for a Fixed Value of the Decimating Factor (\(N=2\)) and Variable Length of the Filter \(M\)  
In this experiment, we worked with a single-tone discrete-time signal. Digital frequency was set by normalizing analog frequency by a sampling frequency \(F_{\mathrm{s}}=8000\frac{samples}{s}\).  
\[x(n)=A\cos\left(2\pi\frac{F_{0}}{F_{\mathrm{s}}}n\right). \tag{40}\]  
Because we want to deal with periodic signals (one may consider that a discrete-time sinusoidal signal only shows a periodic--in samples--behavior under certain conditions), we have considered a range of values for the period \(P\) from 512 down to 8 samples (step 8 samples), corresponding, respectively, to analog frequencies \(F_{0}\) from 15,6 to 1000 Hz. The length of the filter has been set to \(M=\{40,\ 80,\ 160,\ 240,\ 320\}\) coefficients. The decimating factor of the sequential partial updates strategy has been set to \(N=2\).  
Thus, from signal \(x(n)\) and its decimated version \(x_{d}(n)=x(Nn)\) we have derived matrices \(G\) and \(G_{d}\), respectively from Equations (14) and (24). These matrices are necessary to implement the full updates and the partial-update adaptive strategies described, respectively, in Sections 2.1 and 2.2. Then, the ratio between the bounds of the step-size given by Equation (29) has been obtained and drawn. In so doing, we have the gain in step-size affordable for every frequency, shown in Figure 4.  
Figure 4: Gain in step-size for a single tone when using the matrix-based version (from [PERSON] et al.) of the periodic LMS algorithm with seq. partial updates. Decimation factor is set to \(N=2\). The length of the filter \(M\) varies from 40 to 320 taps.  
1.2 Gain in Step-Size vs. Frequency for a Fixed Value of the Length of the Filter (\(M=160\)) Coefficients and Variable Declinating Factor \(N\)  
In this experiment, whose results appear in Figure 5, we repeat the main idea of the previous subsection but set the length of the filter to \(M=160\) coefficients, whereas the decimating factor varies as \(N=\{1,\ 2,\ 4,\ 8\}\).  
The signal used is the same single tone given by Equation (40), sampled at \(F_{s}=8000\frac{samples}{s}\). The range of values for period \(P\) varies from 512 down to 32 samples (in steps of 8 samples), corresponding, respectively, to analog frequencies \(F_{0}\) from 15,6 to 250 Hz.  
From Figures 4 and 5 one may infer that the gain in step tends asymptotically toward the squared decimating factor \(N^{2}\) as frequency increases. Moreover, the gain in step-size shown in these figures does not present frequency-dependent notches with the matrix-based formulation of the P-LMS.
# A Segmentation-Based Optimal Seamline Generation Method for SAR Image Mosaic
## 4 Conclusions  
This paper introduces an SOSG method for SAR image mosaicking, aiming to enhance the integrity of feature targets in mosaic images and realize seamless mosaicking. The proposed method, designed with a consideration of SAR image characteristics, involves generating parcels through joint segmentation. Analysis is performed on the parcels extracted by joint segmentation, and the consistency-stable parcels, namely homogeneous areas, are extracted and assigned different weights. The consistency-stable parcels with higher weights contain obvious ground objects. By constructing a cost matrix combining  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Indicator** & \begin{tabular}{c} **[PERSON] et al. [20]** \\ **Method** \\ \end{tabular} & \
\begin{tabular}{c} **[PERSON] et al. [25]** \\ **Method** \\ \end{tabular} &
\begin{tabular}{c} **SOSG Method** \\ \end{tabular} \\ \hline \multirow{3}{*}{1} & Number of crossings of visible feature targets & 83 & 67 & 16 \\  & Time (s) & 30 & 58 & 273 \\ \hline \multirow{3}{*}{2} & Number of crossings of visible feature targets & 251 & 246 & 47 \\  & Time (s) & 157 & 704 & 1215 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical results of different methods.  
segmentation and pixel characteristics, the optimal seamline is selected in the areas with lower weights, and the obvious ground object target is successfully bypassed. The SOSG method proves effective in enhancing feature target information in SAR mosaic images, as is evident in the experimental results. While successfully avoiding obvious feature targets, it improves both the visual quality and quantitative performance of SAR mosaic images. However, it is acknowledged that the time efficiency of joint segmentation may impact the overall efficiency of the SOSG method, and future work will focus on addressing this limitation. Furthermore, this holistic method presents significant advantages for subsequent SAR mosaic image interpretation.  
Conceptualization, R.L.; methodology, R.L. and J.Z.; validation, R.L.; formal analysis, R.L. and N.J.; investigation, R.L.; resources, R.L.; data curation, R.L.; writing--original draft, R.L.; writing--review and editing, J.Z., N.J. and Y.C.; visualization, R.L.; supervision, H.Y.; project administration, N.J. and H.Y.; funding acquisition, N.J. and H.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Future Star Foundation of Aerospace Information Research Institute, Chinese Academy of Sciences, under Grant Number E3Z108010F.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality.  
The authors declare no conflicts of interest.
## 5 Performance Evaluation
### Single User  
For \(K=1\), we considered a DNN with one input layer, multiple hidden layers and one output layer. There was one neuron in the input layer, where we input the channel parameter \(|h_{1}|\). The output layer consisted of two neurons that represented \((P^{*},\tau_{0}^{*})\). Choosing the number of hidden layers and number of neurons in hidden layers was a crucial part of designing the DNN. We performed experiments for different numbers of hidden layers (i.e., \(l=1\), \(2\), \(3\), \(4\), \(5\), \(6\)) and different numbers of neurons (i.e., \(N=50,100,150,200\)). The best and simplest network architecture that we found had three hidden layers with 100 neurons in each hidden layer. We obtained a minimum MSE with less complexity when we chose an architecture with ReLU activation at every hidden layer. We set a batch size of 100 and used 100 training epochs.  
We trained the DNN for a single user on 6000 samples and validated the performance on 2000 samples in all training epochs. Figures 4 and 5 show the optimal value for power and downlink-time allocation, respectively, obtained from the DNN and SPCA methods, and show that the DNN achieved the same accuracy with less complexity. Figure 6 shows the MSE convergence for training and validation. This validation performance proves the accuracy of mapping the input and output. Table 1, shows that the time required to find the optimal solution to Equation (7) from our proposed scheme is much less than the SPCA method. We performed testing on 2000 data samples, and our proposed scheme obtained an MSE of \(1.802\times 10^{-7}\) for optimal testing power.
# An Optimized Planning Tool for Microwave Terrestrial and Satellite Link Design
## 3 Tool Description
### Satellite Links
#### 3.5.5 QoS Parameters  
Here, the user must specify the maximum bit-error ratio (BER) to be reached, as well as the annual maximum time percentage of the outage target. These characteristics have an impact on both the estimates for the link budget as a whole as well as the path loss calculations (particularly the rain attenuation). In order to offer an updated result, these elements are recalculated in response to the changes in this step.
# Time-Domain Aeroelasticity Analysis by a Tightly Coupled Fluid-Structure Interaction Methodology
## 3 Structural Model and Solution Method
### AGARD 4445.6 Wing  
Based on Rayleigh-Reiz method, the generalized structural dynamics equations can be given in the form of second-order ordinary differential equations:  
\[[\mathbf{M}]\big{\{}\ddot{\mathbf{q}}(t)\big{\}}+[\mathbf{D}]\big{\{}\dot{\mathbf{q}}(t)\big{\}} +[\mathbf{K}]\{\mathbf{q}(t)\}=\{\mathbf{F}(t)\} \tag{11}\]  
where \(\{\mathbf{q}(t)\}\) is generalized displacement vector, \([\mathbf{M}]\), \([\mathbf{D}]\) and \([\mathbf{K}]\) are generalized mass matrix, damping matrix, and stiffness matrix of the structure, respectively. The generalized force is determined as:  
\[\mathbf{F}(t)=\frac{1}{2}\rho_{\infty}V_{\infty}{}^{2}\iint C_{p}(\mathbf{x},\mathbf{y}, \mathbf{z},t)\mathbf{\Phi}(\mathbf{x},\mathbf{y},\mathbf{z})ds \tag{12}\]  
where \(\mathbf{\Phi}\) is Ritz basis function satisfying the displacement boundary conditions and having sufficient continuity, and \(C_{p}\) is the aerodynamic load coefficient on the structure. The generalized force is a key factor coupling the structural model and the fluid model, and it depends merely on the fluid model if the linear structural model is adopted.  
When the generalized displacement is solved, the actual displacement of the structure can be determined as:  
\[\mathbf{w}(\mathbf{x},\mathbf{y},\mathbf{z},t)=\sum_{i=1}^{n}q_{i}(t)\phi_{i}(\mathbf{x},\mathbf{y}, \mathbf{z}) \tag{13}\]  
Taking the generalized normal mode displacement as the Ritz basis function for the generalized structural dynamic equations and Equation (11) can be transformed into linear equations:  
\[\Big{\{}\dot{\mathbf{S}}\Big{\}}+[\mathbf{A}]\{\mathbf{S}\}=\{\mathbf{Q}\} \tag{14}\]  
where  
\(\mathbf{\omega}\) and \(\mathbf{\varsigma}\) are modal frequency and damping vector respectively. To be tightly coupled with the fluid model, the linear Equation (14) is discretized into a pseudo time-stepping scheme:  
\[\begin{array}{c}[\mathbf{B}]\{\Delta\mathbf{S}\}=-(1+\phi)\big{\{}\mathbf{S}^{p}\}+(1+ 2\phi)\big{\{}\mathbf{S}^{n}\}-\phi\big{\{}\mathbf{S}^{n-1}\}-\Delta t\big{\{}\dot\mathbf{{S}}^{p}\big{\}}\\ \qquad\qquad\qquad[\mathbf{B}]=\left[\begin{array}{cc}1+\phi&-\Delta t\\ \Delta t\omega^{2}&1+\phi+2\Delta t\omega\varsigma\end{array}\right]\\ \qquad\qquad\qquad\{\Delta\mathbf{S}\}=\Big{\{}\mathbf{S}^{p+1}\Big{\}}-\{\mathbf{S}^{p}\} \end{array} \tag{15}\]  
when \(\phi=0.5\) and \(p\to\infty\), Equation (15) performs second-order time accuracy.