esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 3 Experiments and Analyses
### Neural Network Training
#### 3.2.2 Training Result  
We chose 14 different activation functions (elliotsig, hardlim, hardlims, logsig, netinv, poslin, purelin, radbas, radbasn, satlin, satlin, satlin, satlin, stofmax, tansig, and tribas) using the first layer. Additionally, the activation function of the second layer was purelin. After training, the best ten neural networks' distributions of error for the first step in Section 2.3.2 were gathered to be shown in Figure 5, and their parameters are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Group ID** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline
**target number** & 30 & 40 & 50 & 60 & 70 & 80 & 90 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for satellite 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Parameter** & **Value** \\ \hline \multirow{6}{*}{Orbit Parameter} & Semimajor Axis (m) & 6,878,000 \\  & Eccentricity & 0 \\  & Inclination (\({}^{\circ}\)) & 0 \\  & RAAN (\({}^{\circ}\)) & 0 \\  & Argument of Perigee (\({}^{\circ}\)) & 0 \\  & True Anomaly (\({}^{\circ}\)) & 230 \\ \hline \multirow{6}{*}{Attitude Parameter} & Agile & Y \\  & Max. Maneuver Angle & 45 \\  & Angular Velocity & 1 \\  & Angular Acceleration & 0.1 \\  & Maneuver Mode & Trapezoid Method \\ \hline \multirow{2}{*}{Payload Parameter} & Imaging Width (km) & 4 \\  & Image Size per Frame (Mb) & 6.4 \\ \hline \multirow{6}{*}{Communication Parameter} & Frequency Band & S \\  & Antenna Number & 4 \\  & Bandwidth (M) & 10 \\ \cline{1-1}  & Modulation Mode (M) & PSK \\ \cline{1-1}  & Spread Spectrum Mode (M) & CDMA \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for satellite 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Group ID** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline
**bandwidth usage** & 60\% & 65\% & 70\% & 75\% & 80\% & 85\% & 90\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation value of bandwidth usage.
## 2 Materials and Methods
### Characterizations  
In this study, an Ocean Optics MAYA-pro 2000+ optical fiber spectrometer with the spectral range of 200-1100 nm and the resolution of 1.3 nm was employed to measure the ethanol vapor microwave plasma at atmospheric pressure. Two sets of spectra were measured: the first one was for Ar-H\({}_{2}\) plasma, and the other one was for Ar-Ethanol-H\({}_{2}\). The composition from the decomposition of ethanol in the plasma emits spectral lines at a specific frequency, which are detected through analysis of the spectrum line differences between these spectral lines. The decomposition rates of the ethanol precursor at different temperatures, with and without plasma, were measured by an Agilent 6890N gas chromatograph to measure the decomposition ability of the atmospheric pressure microwave plasma. A scanning electron micrograph (SEM; Hitachi S-5000 20 kV) and a high-resolution transmission electron microscope (HRTEM; FEI Tecnai G2 F20 200 kV) were used to examine the morphology and the microstructure of the CNTs. The composition and the contents of the samples were measured by X-ray photoelectron spectroscopy (XPS; Thermo Fisher Escalab Xi+) and Raman spectroscopy (Thermo Fisher DXD).
# The Contributions of Climate and Human Activities to Water Use Efficiency in China's Drylands
## 2 Materials and Method
### Method
#### 2.3.4 Multiple Linear Regression and Residual Analysis  
In this study, residual trend analysis was employed to distinguish the effects of climate variability and human activities on WUE. This method, proposed by [PERSON] et al. [43], has been widely applied and is based on the principle of establishing a model between vegetation WUE and climatic factors. The impact of human activities on vegetation is characterized by a variance that cannot be explained by the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pattern Name** & **Trend** & \\ \hline Pattern A & \(slope_{WUE}<\)0; \(slope_{GPP}<\)0; \(slope_{ET}<\)0 & GPP dominant reduced WUE \\ Pattern B & \(slope_{WUE}<\)0; \(slope_{GPP}<\)0; \(slope_{ET}>\)0 & GPP, ET reduced WUE \\ Pattern C & \(slope_{WUE}<\)0; \(slope_{GPP}>\)0; \(slope_{ET}>\)0 & ET dominant reduced WUE \\ Pattern D & \(slope_{WUE}>\)0; \(slope_{GPP}<\)0; \(slope_{ET}<\)0 & ET dominant increased WUE \\ Pattern E & \(slope_{WUE}>\)0; \(slope_{GPP}>\)0; \(slope_{ET}<\)0 & GPP, ET increased WUE \\ Pattern F & \(slope_{WUE}>\)0; \(slope_{GPP}>\)0; \(slope_{ET}>\)0 & GPP dominant increased WUE \\ \hline \hline \end{tabular} Note: \(slope_{WUE}\), \(slope_{GPP}\), and \(slope_{ET}\) represent the slopes obtained from Sen’s trend analysis for WUE, GPP, and ET, respectively.  
\end{table}
Table 2: The classification of WUE variation patterns.  
In past residual analyses, the selected meteorological factors were mostly focused on Tem, Prec, and Solra. Given the significant controlling effect of VPD on WUE, this paper adds VPD to the residual analysis on top of the three meteorological factors. This analysis helps quantify the impact of climate change and human activities on WUE's temporal variations in China's drylands, elucidating WUE's temporal drivers. The relevant calculation formula is as follows:  
\[WUE_{cli}(i,t)=\text{a}\times Tem(i,t)+\text{b}\times Pre(i,t)+\text{c}\times Solar (i,t)+\text{d}\times VPD(i,t)+e \tag{5}\]  
\[WUE_{res}(i,t)=WUE_{obs}(i,t)-WUE_{cli}(i,t) \tag{6}\]  
where \(WUE_{cli}(i,t)\) is WUE predicted value by regression of climate factors, representing WUE value under climate change, and Tem, Pre, Solra, and VPD are four climate factors. \(WUE_{obs}(i,t)\) is the observed value of WUE; \(WUE_{res}(i,t)\) is the residual value, defined as the WUE value under the influence of human activities.  
Next, the slopes of observed value, predicted value, and residual value were recorded as Slope_WUEobs_, Slope_WUEcli_, and Slope_WUEres_. The three factors were used to calculate the contribution rate of climate change and human activities to WUE in China's drylands. The calculation method is shown in Table 3.
# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 5 Discussion  
The proposed model effectively combines a variational method with deep learning through algorithm unfolding, significantly reducing computational costs. By using reasonable assumptions and constraints, it limits the solution space, avoiding unnecessary calculations and improving efficiency without sacrificing performance or accuracy. This is particularly advantageous for real-time applications, where speed is critical. While this trade-off may slightly reduce flexibility and precision in certain cases, it maintains a strong balance between performance and computational cost.  
Regarding data requirements, our model excels with smaller datasets. For instance, when handling linear blur, we only need 400 training images and can generate an additional 1200 through random cropping, achieving optimal results without the need for tens of thousands of images. In contrast, DUBLID [22] also uses algorithm unfolding for small datasets, but its \(L_{1}\) regularizer struggles to estimate blur features and its FFT-based restoration method fails to preserve image details, resulting in lower quality and significant detail loss. Although the SFNet [25] model also uses a variational method, it is still influenced by the dataset due to the embedding of certain modules within the U-Net framework, and its model training process is not fully transparent. Deep learning models [6; 7; 8; 24] generally struggle with small datasets as they rely on large amounts of data for effective learning and generalization. With limited data, these models often overfit or underfit, failing to capture complex patterns, which leads to suboptimal restoration, especially in high-quality image restoration and detail retention.
## 3 Finite Element Model Creation
### Material Properties  
The slope rock bodies in the study area are mainly the waste dump and bedrock, and the strength parameters of the slope rock and soil bodies are determined experimentally to determine the initial values. Experimental material was taken from the Bang Nau dump at the initial discharge in June 2020 (not experiencing rainfall). The specific parameters are shown in Table 1.  
Many studies have shown that the strength of the rock mass decays with the wetting-drying cycles. In this study, the cohesion, angle of friction, and permeability coefficient of the waste dump were tested for 5 cycles, and the results are shown in Table 2. Cohesion showed a slow decrease with the number of wetting-drying cycles, and the angle of friction showed no discernible pattern. The permeability coefficient showed a tendency to increase, stabilize and finally decrease with the number of wetting-drying cycles. Here, it is divided into three stages. The first stage of seepage will expand the existing fissures and form a large fissure with a rapid increase in the permeability coefficient ws. In the second stage, water will flow through the large fissure and only a small number of new fissures will be created, and the permeability coefficient ws increases slowly. In the third stage, water will break down the geotechnical body, blocking the seepage channels, and the permeability coefficient ws eventually decreases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Poisson’s Ratio** & \begin{tabular}{c} **Density/** \\ **(g-cm\({}^{-3}\))** \\ \end{tabular} & \begin{tabular}{c} **Cohesion/** \\ **(kPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Angle of** \\ **Friction/(\({}^{\circ}\))** \\ \end{tabular} &
\begin{tabular}{c} **Permeability** \\ **Coefficient/(m-s\({}^{-1}\))** \\ \end{tabular} \\ \hline Waste dump & 0.31 & 1.83 & 47.23 & 17.32 & \(8.7\times 10^{-8}\) \\ \hline Bedrock & 0.29 & 2.05 & 104.54 & 26.21 & \(9.8\times 10^{-10}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameters of slope rock mass.  
Figure 2: Slope wetting–drying cycle numerical simulation program.
## 3 Results
### Phosphate Adsorption in Batch Experiments  
#### 3.3.1 Effect of pH  
Figure 7a shows the effect of pH on phosphate adsorption onto the BS-based adsorbents. Phosphate exists in different ionic species, including H\({}_{3}\)PO\({}_{4}\), monovalent H\({}_{2}\)PO\({}^{-}\), divalent HPO\({}_{4}\)\({}^{2-}\), and trivalent PO\({}_{4}\)\({}^{3-}\) ions, depending on the pH of the solution [36]. At pHs greater than 2, phosphate mainly exists in the anionic forms of H\({}_{2}\)PO\({}_{4}\)\({}^{-}\), HPO\({}_{4}\)\({}^{2-}\), and PO\({}_{4}\)\({}^{3-}\).  
BS has a negligible adsorption ability to phosphate at all pH ranges. Phosphate adsorption by the aminated BS (BS-GMA-EDA and BS-GMA-PEHA) was influenced by varying the pH. The adsorption capacity was higher at acidic conditions than the adsorption capacity at alkaline conditions. At lower pHs, the protonation of the amine makes the adsorbents positively charged, providing electrostatic attraction to the anionic phosphate molecule, thus resulting in a high adsorption performance. At a pH of 4, BS-GMA-EDA and BS-GMA-PEHA have the highest phosphate adsorption capacity. Below pH 4, the dominant species was transformed to H\({}_{2}\)PO\({}_{4}\)\({}^{-}\) and neutral H\({}_{3}\)PO\({}_{4}\), causing the adsorption capacity decrease. At higher pHs, the adsorption capacity decreased due to the de-protonation of the amine and the competition of OH\({}^{-}\).  
The zeta potential of BS-GMA-EDA and BS-GMA-PEHA explain the pH-dependent adsorption, as shown in Figure 7b. The zeta potential was higher in acidic conditions than those in alkaline conditions. Thus, the positive value in acidic conditions was favorable for phosphate adsorption. To increase the phosphate adsorption performance of the modified BS in a broader pH range, BS-GMA-EDA and BS-GMA-PEHA were quaternized.  
Unlike BS-GMA-EDA and BS-GMA-PEHA, the phosphate adsorption capacity of BS-GMA-EDA and BS-GMA-PEHA-Q was independent of pH. Moreover, the zeta potential was also very high in alkaline conditions. The electrostatic attraction strength between the quaternary ammonium salt group and the phosphate remained unchanged. Thus, the BS-GMA-EDA-Q and BS-GMA-PEHA-Q can absorb phosphate at a broad pH, which is more favorable for a wide range of applications.
## 4 Methods
### Basic Property Tests of Conductive Mortar  
This study begins with basic property tests of the suggested conductive mortar at different substitution ratios. Basic property tests measured the flexural strength, compression strength, bonding strength under standard and thermal cycling conditions, permeability, water absorption coefficient, moisture permeation resistance, and length variation ratio. The selected test criteria were based on the standards for testing polymer cement mortar for concrete structure repair, outlined in KS F 4042 [34]. Note that before the tests and application of the developed conductive mortar, workability was tested through flow tests. Similar measures are slump tests for concrete [35].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Activated Carbon Mix Design (OPC/S = 1.45)**} \\ \hline \multicolumn{5}{c}{**Admixture Ratio: 0\%**} & \multicolumn{2}{c}{**Admixture Ratio: 5\%**} \\ \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Weight (g)**} & **Percentage by** & **Weight (g)** & **Percentage by** \\  & & **Volume (\%)** & & **Volume (\%)** \\ \hline Cement & 348.46 & 85.28 & 348.46 & 85.28 \\ \hline \(\alpha\)-gypsum hemihydrate & 4.73 & 1.16 & 4.73 & 1.16 \\ \hline Expansion agent & 15.76 & 3.86 & 15.76 & 3.86 \\ \hline LI-T & 2.24 & 0.55 & 2.24 & 0.55 \\ \hline Latex resin & 6.31 & 1.54 & 6.31 & 1.54 \\ \hline Silica fume & 28.57 & 6.99 & 28.57 & 6.99 \\ \hline Polycarboxylic acid-based polymer & 1.13 & 0.28 & 1.13 & 0.28 \\ \hline PVA & 1.22 & 0.3 & 1.22 & 0.3 \\ \hline Dispersing agent & 0.16 & 0.04 & 0.16 & 0.04 \\ \hline Air-entraining agent & 0.03 & 0.01 & 0.03 & 0.01 \\ \hline Total powder & 408.61 & 100 & 408.61 & 100 \\ \hline Sand \#5 & 116 & 19.59 & 116 & 19.59 \\ \hline Sand \#6 & 476 & 80.41 & 446.4 & 75.41 \\ \hline Activated carbon & 0 & 0 & 29.6 & 5.00 \\ \hline Total sand & 592 & 100 & 592 & 100 \\ \hline Mortar & 1000 & 100 & 1000 & 100 \\ \hline Water & 180 & 18 & 180 & 18 \\ \hline Total & 1800 & - & 1000 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mix design for mortar at different substitution ratios (in the table, the admixture ratio is defined as the volume of admixture divided by the volume of admixture and sand).
# Adaptive Thermal-Aware Routing Protocol for Wireless Body Area Network
## 4 Performance Evaluation
### Simulation Results  
#### 4.1.1 Analysis of Temperature Rise in Node  
Figure 14 provides the comparison between ATAR, SHR, TARA and MRRP. We ran the simulation at the time period of 3000 s. The high-temperature reading coming from MRRP, TARA and SHR protocol indicates that it is not suitable for implanted nodes. Finally, MRRP, TARA and SHR give temperature peaks while transmitting data of sensor nodes; thus, ignoring the temperature factor in its working; whereas the ATAR takes temperature factor in its routing in the implanted nodes and it maintains the temperature below 0.04 \({}^{\circ}\)C to avoid tissue damage. The low-temperature peaks for ATAR in the simulation are due to the reason that ATAR always selects the forward with minimum available temperature. In case of temperature rise of the forward, ATAR selects another forward within the next ring level to forward obtained sensor readings.  
Figure 15 shows the simulation results of temperature variations at different times. The graph represents the variations in temperature over the course that our simulation runs. At the initial phase of simulation, the temperature values rose initially, i.e., ATAR rose up to 0.0002 \({}^{\circ}\)C, TARA rose up to 0.025 \({}^{\circ}\)C and SHR rose up to 0.037 \({}^{\circ}\)C and MRRP rose up to 0.05 \({}^{\circ}\)C. From the graph, we can argue that if we select an appropriate forwarding node as in the case of ATAR, the difference in the temperature can be controlled as compared to the MRRP, SHR and TARA protocol. When the simulation ends, i.e., reaches our target of 6000 s, the changes of the temperature were 0.00012 \({}^{\circ}\)C, 0.080 \({}^{\circ}\)C, 0.010 \({}^{\circ}\)C and 0.0106 \({}^{\circ}\)C of the ATAR and TARA, MRRP and SHR, respectively. Also from the graph below, it can be seen that once the network reaches the threshold value in the ATAR protocol then change in the temperature is negligible. Similarly, the TARA, SHR and MRRP have a higher count as the protocol does not take temperature into consideration.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**Simulation Setup**} \\ \hline Simulation Area & 3 m \(\times\) 2 m \\ Number of implanted nodes & 9 \\ Sink & 1 static \\ Initial temperature of a node & 35 \({}^{\circ}\)C/0.035 \({}^{\circ}\)C \\ Size of a Packet & 296 bits \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation setup.
## 4 Results and Discussion
Figure 13 illustrates the effect of the global Reynolds number \(Re\) on the distributions of the concentration in copper nanoparticles along the tube for \(d_{np}=200\) nm, \(\varphi=0.016\) and two Reynolds numbers \(Re=600\) and \(1600\) at three radial locations \(r/R=0.98\) (near the top wall), \(r/R=0\) (pipe axis) and \(r/R=-0.98\) (near the bottom wall). Even for this large nanoparticles, the concentration of nanoparticles along the tube axis remains almost constant. Throughout the pipe length, the variation of \(\varphi\) is 0.13% for \(Re=1600\) and 0.75% for \(Re=1600\). At \(r/R=0.98\), \(\varphi\) decreases by a factor 2 then 4 for \(Re=1600\) and \(Re=600\), respectively. It results in a huge increase in nanoparticle concentration at the  
Figure 12: Axial variations of the volume fraction \(\varphi\) at three radial locations: \(r/R=0.98\) (**a**,**d**,**g**); \(r/R=0\) (**b**,**e**,**h**); and \(r/R=-0.98\) (**c**,**f**,**i**). Results obtained for \(Cu\) and \(Al_{2}O_{3}\) nanoparticles of three different diameters \(d_{np}=42\) nm (**a**–**c**); 100 nm (**d**–**f**); and 200 nm (**g**–**i**) with \(\varphi=0.016\) and \(Re=1600\). bottom of the pipe (Figure 13c): \(\varphi\) increases by 87.5% and 137.5% for \(Re=1600\) and 600, respectively. Almost all the copper nanoparticles are sedimented and there are only few copper nanoparticles suspended in pure water at the top of the tube for \(Re=600\). The Reynolds number plays an important role to keep the nanoparticles well dispersed in the base fluid and reduce the sedimentation process, inducing a better stability of the nanofluid and higher resulting heat transfer.
###### Abstract  
This paper discusses the results of simulations relating to the performances of turbo codes, low density parity check (LDPC) codes, and polar codes over an additive white Gaussian noise (AWGN) channel in the presence of inter symbol interference, denoting the disturbances that altered the original signal. To eliminate the negative effects of inter symbol interference (ISI), an equalizer was used at the level of the receiver. Practically, two types of equalizers were used: zero forcing (ZF) and minimum mean square error (MMSE), considering the case of perfect channel estimation and the case of estimation using the least square algorithm. The performance measure used was the modification of the bit error rate compared to a given signal to noise ratio; in this sense, the MMSE equalizer offered a higher performance than the ZF equalizer. The aspect of channel equalization considered here is not novel, but there have been very few works that dealt with equalization in the context of the use of turbo codes, especially LDPC codes and polar codes for channel coding. In this respect, this research can be considered a contribution to the field of digital communications.  
equalization; Zero Forcing; minimum mean square error; least square
# Smart Healthcare Network Management: A Comprehensive Review
## 6 Architecture Implementation Plan
### Supporting the Architecture with Software-Defined Networking and Deep Learning
A DL-enabled SDN architecture for healthcare is presented in Figure 7. Beyond SDN, Network Function Virtualization (NFV) and Management and Orchestration (MANO) play critical roles in modernizing healthcare network infrastructures. NFV virtualizes network functions, decoupling them from dedicated hardware and running them as software instances, thereby reducing dependency on specialized networking equipment, improving scalability, and lowering operational costs [48]. This shift is particularly beneficial in smart healthcare systems, where the rapid deployment and adaptation of network services are essential for real-time patient monitoring and emergency response scenarios. The MANO framework further enhances NFV by automating the deployment, configuration, and lifecycle management of virtualized network functions (VNFs). This automation reduces manual intervention, minimizes errors, and ensures efficient resource allocation, leading to higher flexibility and reliability in healthcare networks. Moreover, chaining VNFs through MANO enables dynamic service adaptation, allowing healthcare applications to seamlessly scale based on demand, thus enhancing quality of service (QoS) and patient care outcomes [49].
# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 5 Results and Discussion
### Prediction Model Results  
SPSS V.22 program was used to implement a prediction model to assess the rock-fall occurrence-rock-fall influencing factors relationship. The parameters of the model and their significant probabilities are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Independent Variable** & **Coefficient (\(\beta\))** & **Std. Error** & **Wald** & **Significant Probability** \\ \hline Slope-angle & 0.306 & 0.132 & 5.419 & 0.020 \\ Rainfall-rate & 0.425 & 0.165 & 6.669 & 0.010 \\ Temperature variation & 0.915 & 0.421 & 4.712 & 0.030 \\ \hline \hline \end{tabular} Std. Error = standard error of coefficient, Wald = Wald statistics.  
\end{table}
Table 2: Prediction model parameters.  
Figure 10: ALARP thresholds triangle.  
Figure 9: Rock-fall risk probability.  
From the data in Table 2, the standard error values (Std-Error) were less than 0.5, which were deemed tiny, indicating how similar the forecasts were to the eventual results. The likelihood of importance is another parameter that emerged in Table 2. This parameter confirmed the independent causes that had a significant effect on a rock-fall. If the significant likelihood was less than 0.05, this meant that the independent variable in rock-fall was statistically significant [21]. The findings revealed that slope angle, precipitation rate, and temperature variation with large likelihood values in the range of 0.010 to 0.030 had a statistical effect on the frequency of rock-fall. The three hypotheses were included in the model (rainfall rate, slope angle, and temperature variation). Depending on three useful predictor variables and their parameters, the prediction model was built. To implement the prediction model, independent variable coefficient values in Table 2, \(\beta_{1}=0.306\), \(\beta_{2}=0.425\), and \(\beta_{3}=0.915\), and the intercept of model, \(\beta_{0}=37.859\), were substituted in Equation (3). The result is shown in Equation (12):  
\[p(r)=\frac{e^{0.306x_{1}+0.425x_{2}+0.915x_{3}-37.859}}{1+e^{0.306x_{1}+0.425x_{ 2}+0.915x_{3}-37.859}} \tag{12}\]  
where p(r) = probability of rock-fall occurrence, x1 = slope angle (degree) (range 20-60), x2 = rainfall rate (mmh-1) (range 0-46), and x3 = temperature variation (co) (range 0-21). Using the aforementioned prediction model coefficients, the likelihood of rock-fall events was estimated with values ranging from (0.014) to (0.951).