esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In 2018, in order to improve the system performance, iterative per-vector equalization for OSDM was proposed [7]. In 2019, a low-complexity equalization of OSDM was proposed [19]. Compared with vector OFDM, the OSDM using the Kronecker product and rows of DFT matrix has a better mathematical form. When the OSDM system's subcarrier number N increases to the maximum, that is to say, only one bit is transmitted on each subcarrier, the system becomes an OFDM system. When the OSDM system's subcarrier number N decreases to 1, the system becomes asingle-carrier communication system. In general, the emerging OSDM technology is an interesting modulation scheme which can realize SC-FDE modulation, OFDM modulation and different vector length OSDM modulation by changing the vector length M.  
The OSDM modulation technology has potential application prospects in underwater acoustic hybrid single- and multi-carrier modulations and adaptive modulation. For this reason, a pseudo-noise (PN) sequence-based underwater acoustic OSDM communication system (UWAOSDM/PN) is proposed in this paper. A data frame structure with PN sequence is designed to solve the multipath and Doppler effect of underwater acoustic channel. On the basis of the PN sequence, a compressive sensing method based on the orthogonal matching pursuit (OMP) algorithm and the minimum mean square error (MMSE) algorithm is designed for channel estimation and equalization. On the basis of the system construction, the relationship among the OSDM vector length M, the OSDM subcarrier number N, and the underwater acoustic channel length is further studied for adaptive modulation of underwater acoustic communication. The paper is organized as follows. In Section 2, the system design of UWAOSDM/PN is described. In Section 3, the key modules are introduced. In Section 4, the simulation results are introduced and discussed. In Section 5, the system parameters and performance are compared and in Section 6 the conclusions are summarized.
# Sharp Interface Capturing in Compressible Multi-Material Flows with a Diffuse Interface Method
## 6 Numerical Experiments: Two-Material Test Cases  
### Two-Dimensional (2D) Air-Helium Shock-Bubble Interaction: Experimental Validation  
We focus here on the comparison of the numerical results produced with our method for the case of an air-helium shock-bubble interaction with one of the classical experiments shown in [47]. The goal, however, is not to have a precise quantitative comparison, but a qualitative one, to show that the method provides physically relevant results with a good resolution of the interfaces, especially when the coarse meshes are used. To study the case, a suitable computational domain is designed (see Figure 8). The dimensions of the computational domain are given in Table 1.  
Figure 6: Application of Formula (48) to 1D linear advection of a smooth + hat initial condition with \(\beta=1\) and \(\theta=0.5\) (MINMOD type) and \(\beta=2\) and \(\theta=1\) (MLP-UB).  
Figure 7: Reformulated advection step with the application of MUSCL+MLP-UB on primitive form of the scalars \(\Pi(\alpha_{\phi},\rho_{\phi},e_{\phi})\).  
observe that the values of cell volume fraction (\(\alpha\)) for both air and helium are in the range \([0,1]\) and thus bounded. Some slight differences in the topology could be observed between the numerical results and experimental graphs. This difference can be explained by the absence of a surface tension force in the current numerical model.
# A Novel Visual System for Conducting Safety Evaluations of Operational Tunnel Linings
## 2 The Fuzzy-AHP Evaluation Model and Its Programming
### Programming of Fuzzy-AHP Model
#### 2.2.3 Determination of Membership Function  
The calculation of the membership matrix in MATLAB is one of the important aspects. The traditional manual membership matrix is very complicated to solve. When there are dozens of sections, the calculation difficulty and complexity are very large, and the accuracy of the traditional calculation cannot be guaranteed at all. Therefore, this article uses MATLAB to program the solution of the membership matrix, which can solve the membership matrix quickly and precisely. The program is divided into four parts, namely, lining cracking, lining weakening, water leakage, and lining cave. The membership matrix can be calculated by the membership function (Formulas (1)-(3) described below).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criterion Layer** & **Index Layer** & **Weight Values** \\ \hline \multirow{3}{*}{Lining cracking A1} & Crack length and width A11 & 0.4109 \\ \cline{2-3}  & Lining deformation A12 & 0.3781 \\ \cline{2-3}  & Lining peeling A13 & 0.2110 \\ \hline \multirow{3}{*}{Lining weakening A2} & Strength index A21 & 0.5622 \\ \cline{2-3}  & Thickness index A22 & 0.4378 \\ \hline Leakage water A3 & Leakage water state A31 & 1.0000 \\ \hline \multirow{3}{*}{Lining cave A4} & Cave depth A41 & 1.0000 \\ \cline{2-3}  & & \\ \end{tabular}
\end{table}
Table 1: Weight values of evaluation indexes for all layers.  
According to the membership function determination rule, the \"ridge-shaped distribution function\" was selected. There are three main types of ridge-shaped distribution:  
1. Partial-small type \[R(x)=\left\{\begin{array}{rl}0&x\leq a_{1}\\ \frac{1}{2}-\frac{1}{2}\sin\frac{\pi}{a_{2}-a_{1}}(x-\frac{a_{1}+a_{2}}{2})&a_{ 1}<x\leq a_{2}\\ 0&x>a_{2}\end{array}\right.\] (1)
2. Inter-mediate type \[R(x)=\left\{\begin{array}{rl}0&x\leq a_{1}\\ \frac{1}{2}+\frac{1}{2}\sin\frac{\pi}{a_{2}-a_{1}}(x-\frac{a_{1}+a_{2}}{2})&a_ {1}<x\leq a_{2}\\ \frac{1}{2}-\frac{1}{2}\sin\frac{\pi}{a_{2}-a_{1}}(x-\frac{a_{1}+a_{2}}{2})&a_ {3}<x\leq a_{4}\\ 0&x>a_{4}\end{array}\right.\] (2)
3. Partial-large type \[R(x)=\left\{\begin{array}{rl}0&x\leq a_{1}\\ \frac{1}{2}+\frac{1}{2}\sin\frac{\pi}{a_{2}-a_{1}}(x-\frac{a_{1}+a_{2}}{2})&a_ {1}<x\leq a_{2}\\ 1&x>a_{2}\end{array}\right.\] (3)  
In Formula (1)-(3), where \(a\) means the grading standard of the evaluation index, based on both actual engineering and relevant specifications, membership \(R(x)\) indicates the degree where \(x\) belongs to \(R\): the closer \(R(x)\) is to 0, the smaller the degree where \(x\) belongs to \(R(x)\); the closer \(R(x)\) is to 0.5, the fuzzier the degree to where \(x\) belongs to \(R\); the closer \(R(x)\) is to 1, the greater the degree where \(x\) belongs to \(R(x)\).
# Study on the Effect of Anchor Cable Prestress Loss on Foundation Stability
## 3 Numerical Simulation
### Analysis of Numerical Simulation Results
#### 3.4.4 Horizontal Displacement and Bending Moment of the Retaining Pile  
Under different prestress loss rates, the horizontal displacement of the retaining pile under different working conditions is shown in Figure 9. The horizontal displacement is positive in the direction of the foundation pit. As can be seen from the figure, the horizontal displacement of the retaining pile in the upper soil layer is significantly greater than that in the rock layer due to the significant difference in the strength of the rock and soil masses. With the increase in the prestress loss rate, the horizontal displacement of the retaining pile increases linearly. When the excavation is completed, compared with the condition without prestress loss, the maximum horizontal displacement of pile body increases from 12.3 mm to 15.0 mm under the condition of 40% prestress loss rate, an increase of about 22%, which is basically the same as the maximum settlement growth rate of the rock and soil mass.  
Figure 8: Plastic zone distribution of the rock and soil mass under different prestress loss rates. (a) \(\beta=0\%\); (b) \(\beta=10\%\); (c) \(\beta=20\%\); (d) \(\beta=30\%\); (e) \(\beta=40\%\).  
Figure 9: Horizontal displacement of the retaining pile under different prestress loss rates.  
Under different prestress loss rates, the bending moment of the retaining pile at the completion of excavation is shown in Figure 10. It can be seen from the figure that the maximum bending moment of the retaining pile is in the soil layer at a depth of 8.5 m, and it keeps increasing with the increase in the prestress loss rate. When the loss rate increases from 0% (bending moment of 507.4 kN\(\cdot\)m) to 40% (bending moment of 722.6 kN\(\cdot\)m), the maximum bending moment increases by 42.4%, which greatly affects the safety of the retaining pile.
## 3 Materials and Methods  
Satellite images of Sentinel missions acquired between July and October 2018 are considered for this study. The Sentinel missions are a group of five satellites part of the European Space Agency (ESA) Copernicus program, for the observation and monitoring of the Earth's surface, and the development of operational applications for environmental monitoring. All data gathered by the Sentinel satellites are provided for free, full and open access to users. More specifically, data from three of the Sentinel missions are used in this study.  
* Sentinel-1 (S1), operating all-weather, day and night and performing C-band synthetic aperture radar imaging, enabling them to acquire imagery regardless of weather condition with a spatial resolution of 10 m.
* Sentinel-2 (S2) with the on-board Multispectral Instrument (MSI) provides high-resolution optical imaging over land and coastal waters. It measures the Earth's reflected radiance in 13 spectral bands, at visible and mid-infrared wavelengths and at various spatial resolutions (10, 20, 60 m).
* Sentinel-3 (S3) that makes use of multiple sensing instruments, of which data acquired by OLCI (Ocean and Land Colour Instrument) are used in this study. It is a medium-resolution imaging spectrometer with 21 spectral bands with wavelengths ranging from the optical to the near-infrared at approximately 300 m.  
All of these missions consist of a constellation of two twin polar-orbiting satellites.
## 3 Results and Discussions
### Effect of Fibers Content on Composite Thermal Conductivity and Thermal Diffusivity  
Table 4 presents the effective thermal conductivities, k\({}_{\text{eff}}\), and diffusivities, \(\alpha\), of the samples reinforced with different fiber loadings. We can see that the thermal conductivities decrease with increasing the fiber mass ratio. In fact, the values decreased from 0.521 W/m\(\cdot\)K to 0.171 W/m\(\cdot\)K when the percentage of Juncus maritimus fibers mass ratio increases from 0% to 10%. This reduction could be explained by the low thermal conductivity of the Juncus maritimus fiber compared to mortar matrix. Another explanation point out that the introduction of the Juncus maritimus particle in the mortar matrix leads to increase the porosity of composite samples. Similarly, the thermal diffusivity decreases simultaneously with the increase of fiber content by up to 37%.  
Table 5 illustrates a comparison between the thermal properties and the densities of Juncus maritimus/ mortar composites which are investigated in this work and other materials used for the thermal insulation in buildings. It is clear that the thermal conductivity for Juncus maritimus (JM) /mortar composites (10 wt%) are lower than the mortar composites filled with other natural fibers such as wood shavings (30 wt%) [28], and date palm mesh (DPF) (51 vol.%) [12] while the thermal  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fibers loading (wt%)** & **k\({}_{\text{eff}}\) (W/m-K)** & **\(\alpha\) (m\({}^{2}\)/s)** \\ \hline
0 & 0.521 + 0.0036 & 2.6 \(\times\) 10\({}^{-7}\) \(\pm\) 0.015 \\
2 & 0.445 \(\pm\) 0.005 & 2.53 \(\times\) 10\({}^{-7}\) \(\pm\) 0.006 \\
5 & 0.236 + 0.001 & 2.01 \(\times\) 10\({}^{-7}\) \(\pm\) 0.1 \\
7 & 0.197 + 0.0004 & 1.73 \(\times\) 10\({}^{-7}\) \(\pm\) 0.06 \\
10 & 0.171 + 0.00223 & 1.623 \(\times\) 10\({}^{-7}\) \(\pm\) 0.016 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Thermal conductivity and thermal diffusivity of composite samples for different mass fraction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **JMC0** & **JMC1** & **JMC2** & **JMC3** & **JMC4** \\ \hline Mass ratio of Juncus maritimus to & & & & \\ mortar mixture (\%) & 0 & 2 & 5 & 7 & 10 \\ Juncus maritimus volume fraction (\%) & 0 & 3.1 & 6.6 & 8.8 & 11 \\ Dry density (kg/m\({}^{3}\)) & 1933 & 1540.76 & 1302.6 & 1236.52 & 1081.96 \\ Calculated porosity \(\phi\) (\%) & 27 & 39 & 43 & 47.77 & 52.22 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical properties of composite samples; JMC0, JMC1, JMC2, JMC3, JMC4 are the Juncus maritimus/mortar samples corresponding to the Juncus maritimus mass fractions (0, 2%, 5%, 7%, and 10%), respectively.  
diffusivity is almost constant for these three composites. The JM/mortar composites exhibit also a good thermal performance compared to mortar/cork composites [32]. It is interesting to highlight that the thermal conductivity of the JM/ mortar are close to the Hemp concrete which is available on the market (0.17 W/m\(\cdot\)K \(<\) k\({}_{\text{eff}}\) \(<\) 0.48 W/m\(\cdot\)K) [33].
# Analyzing Physics-Inspired Metaheuristic Algorithms in Feature Selection with K-Nearest-Neighbor
## 3 Results and Discussion
### Parameter Settings  
No algorithm may perform well without optimizing its parameters. However, finding the right parameters for an algorithm is itself an optimization problem. To obtain the maximum of each algorithm, several combinations of parameters were tried, in addition to using the combinations suggested by the authors of these algorithms in their original papers. It was also kept in mind that the number of iterations or function evaluations remains the same for all algorithms. The parameter settings used for each algorithm are presented in Table 2.  
To assure consistency in results, each algorithm was run 20 times in a row on all datasets. Furthermore, each dataset was split into training and test sets, where 80% of samples were used for training, and 20% of samples were used for testing. For classification, the \(k\)-Nearest Algorithm (KNN) was used. The KNN classifier is a well-liked wrapper method due to its straightforward implementation and the fact that, in comparison to other classifiers, it only requires one parameter, \(k\). The value of \(k\) is tuned by performing several experiments, and it was found that \(k=5\) is the most suitable value.  
\begin{table}
\begin{tabular}{l l l} \hline
**Algorithm** & **Parameter** & **Value** \\ \hline \multirow{3}{*}{Common for all algorithms} & \(k\) & 5 \\  & Maximum iterations (\(t_{\text{max}}\)) & 200 \\  & Number of search agents (\(N\)) & 30 \\  & Number of independent runs & 20 \\  & Ratio of validation data & 0.2 \\ \hline \multirow{3}{*}{Simulated Annealing} & Cooling rate (\(c\)) & 0.93 \\  & Initial temperature (\(T_{0}\)) & 100 \\ \hline \multirow{3}{*}{Gravitational Search Algorithm} & Initial gravitational constant (\(G_{0}\)) & 100 \\  & Constant, (\(\alpha\)) & 20 \\ \hline \multirow{2}{*}{Sine Cosine Algorithm} & Constant, (\(\alpha\)) & 2 \\ \hline \multirow{2}{*}{Atom Search Optimization} & Depth weight, (\(\alpha\)) & 50 \\  & Multiplier weight, (\(\beta\)) & 0.2 \\ \hline \multirow{6}{*}{Henry Gas Solubility Optimization} & Number of gas types & 2 \\  & Influence of other gas, (\(\alpha\)) & 1 \\  & \(\beta\) & 1 \\  & \(l_{1}\) & 0.05 \\  & \(l_{2}\) & 100 \\  & \(l_{3}\) & 0.01 \\ \hline \multirow{3}{*}{Equilibrium Optimizer} & \(a_{1}\) & 2 \\  & \(a_{2}\) & 1 \\ \cline{1-1}  & Generation probability & 0.5 \\ \cline{1-1}  & \(V\) & 1 \\ \hline \end{tabular}
\end{table}
Table 2: Parameter settings of all algorithms.
## 3 Materials and Methods
### Methods of Analysis
#### 3.4.2 ROC Analysis  
Statisticians use cutoff values to estimate optimal decision thresholds. Determining ROC curves is a non-parametric procedure designed to define the optimal point in a curve. The research covered in this paper applied ROC analysis in SPSS 22 to obtain ROC curves for street-length density and active mobility. The ROC curve, and the amounts of sensitivity and specificity were calculated for street-length density around homes and active mobility in commuting and non-commuting trips. In addition, ROC analysis was generated for street-length density around workplaces and active mobility. Each ROC curve plot is the true positive rate (sensitivity) against the false positive rate (1-specificity) [45]. Finally, we calculated the Youden Index for each ROC analysis based on sensitivity and specificity [45].  
\[\text{Younden Index}=\text{sensitivity}+\text{specificity}-1 \tag{1}\]  
Higher Youden Index values are better than lower ones. The maximum amount of the Youden Index can therefore reveal the optimal cutoff point. This meant obtaining the corresponding amount of street-length density for the optimal cutoff point for each analysis, which would show that active mobility behavior has changed as a result of the corresponding amount of street-length density. For this last step, we implemented three chi-square tests to determine changes in active mobility behavior based on the corresponding street-length density of optimal cutoff points.
## 3 Results and Discussion
### Study on Combined Remediation Mechanism
This shift was linked to the introduction of organic acids and salts from plant root exudates into the soil, which formed stable organically bound complexes with Cd, reducing its bioavailability [60]. Figure 8B shows the As 3d XPS spectra. Before and after remediation, the binding energies for As (V) were observed at 45.67 eV and 45.58 eV, and for As (III) at 44.87 eV and 44.78 eV, respectively [61]. The post-remediation spectra indicated a decrease in As (III) binding energy and peak area, alongside an increase in both parameters for As(V). This suggested oxidation of As (III) to As(V) facilitated by soil oxidants like Fe (III) and Mn (IV), reducing As bioavailability [62]. Figure 8C illustrated the Fe 2p XPS spectra, where pre-remediation peaks at 707.78 eV and 720.43 eV, and post-remediation at 710.17 eV and 722.93 eV, were indicative of Fe (III) oxide. The peaks at 710.78 eV and 723.34 eV pre-remediation, and 713.08 eV and 725.54 eV post-remediation, corresponded to FeOOH [63]. The oxidation of As (III) to As(V) by Fe (III) resulted in less toxic As(V), which then underwent adsorption and co-precipitation with hydroxylated iron, thereby reducing As activity and increasing the binding energy of Fe (III) in the soil [64; 65]  
\[\text{Fe}_{2}\text{O}_{3}+\text{AsO}_{3}{}^{3-}+4\text{H}^{+}\rightarrow 2\text{Fe}^{2+}+\text{AsO}_{4}{}^{3-}+2\text{H}_{2}\text{O} \tag{7}\]  
\[\text{FeO(OH)(H}_{2}\text{O)}_{1+\text{x}}+\text{AsO}_{4}{}^{3-}\rightarrow \text{AsO}_{4}{}^{3-}\cdot\text{FeO(OH)(H}_{2}\text{O)}_{1+\text{x}} \tag{8}\]
## 3 Results and Discussion
### Effect of NaSt on the Compressive Strength and Water Absorption Rate of AASRSS Mortars
#### 3.1.2 Water Sorptivity  
Figure 3 shows the results of the water sorptivity for each AASRSS mortar group at 28 days. It can be seen that the AASRSS control group exhibited a value of water sorptivity as high as 6.09 \(\times\) 10\({}^{-1}\) mm/min\({}^{0.5}\), which is about 10 times larger than that of an OPC sample with a similar W/B or the same compressive strength level. Encouragingly, the water sorptivity of AASRSS was reduced to 2.36 \(\times\) 10\({}^{-1}\) mm/min\({}^{0.5}\) and 1.24 \(\times\) 10\({}^{-1}\) mm/min\({}^{0.5}\), respectively, when 0.5 wt.% and 1.5 wt.% of NaSt was added. The water sorptivity of AASRSS was significantly reduced by up to approximately 80%, showing that NaSt does effectively reduce the water absorption rate of AASRSS.  
To further explore the mechanisms behind these findings of compressive strength and water sorptivity, the reaction process, reaction products and pore structure were carefully examined, and the results are presented in the following sections.
# Model Reference Adaptive Control of Vehicle Slip Ratio Based on Speed Tracking
## 3 Model Reference Adaptive Control for Slip Ratio Control  
In this section, a model reference adaptive controller based on a road condition observer is designed to facilitate vehicle speed tracking control. The control system diagram is shown in Figure 3. The basic idea of the model reference adaptive controller is to design an adaptive control law so that the tracking error between the reference model and the plant asymptotically converges to zero by automatically adjusting the adaptive parameters. The reference model is obtained by a vehicle speed observer. Based on the road condition observer, the model reference adaptive controller has stronger anti-disturbance capability and adaptability.  
Figure 3: Diagram of slip ratio MRAC system.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 5 Performance Evaluation
### Overall Result Discussion  
The proposed work is evaluated with sky motes by considering the metrics such as total power consumption, delay, frequent number of parent changes in routing, packet delivery ratio, etc. It is highly important to analyze the working of RPL by varying the application scenarios. Here, the IoT-Fog-based forest environment is taken for analysis. The proposed CAA-ERPL protocol improved the features of RPL by adding energy as an objective function and achieved a solution to the load imbalance problem by routing through region-based aggregators. The experiments were done with various network sizes  
Figure 8: Average number of parent changes vs. data rate.  
Figure 9: Average end-to-end delay vs. average hop count.  
such as 10, 20, 30, 40, and 50 nodes. It is seen that the power consumption and average CPU power consumption of CAA-ERPL decrease with an increasing number of nodes as compared to E-RPL. In CAA-ERPL, the average hop count decreases, as it processes first data aggregation and then routing. Stability in the network is maintained for CAA-RPL with varying network size as compared to E-RPL. The overhead of CAA-ERPL and E-RPL is 500 and 460, respectively, for the network size of 50 nodes. The PDR of CAA-ERPL is 70%, whereas for E-RPL, it is 20% for 50 nodes setup. When the end-to-end delay is considered for the performance, it increases maximum up to 250 ms in CAA-ERPL, whereas, it is 280 ms in E-RPL. Lastly, the average number of parent changes is low in CAA-ERPL when compared to the E-RPL. In summary, the overall comparison of the proposed CAA-ERPL minimized a large number of control overheads, thereby increasing the PDR, reducing the data packet delay in the network with efficient energy consumption.
# ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks
###### Abstract  
Wireless Sensor Networks (WSNs) have been around for over a decade and have been used in many important applications. Energy and reliability are two of the major problems with these kinds of applications. Reliable data delivery is an important issue in WSNs because it is a key part of how well data are sent. At the same time, energy consumption in battery-based sensors is another challenge. Therefore, efficient clustering and routing are techniques that can be used to save sensors energy and guarantee reliable message delivery. With this in mind, this paper develops an energy-efficient and reliable clustering protocol (ERCP) for WSNs. First, an efficient clustering technique is proposed for sensor nodes' energy savings considering different clustering parameters, including the link quality metric, the energy, the distance to neighbors, the distance to the sink node, and the cluster load metric. The proposed routing protocol works based on the concept of a reliable inter-cluster routing technique that saves energy. The routing decisions are made based on different parameters, such as the energy balance metric, the distance to the sink node, and the wireless link quality. Many experiments and analyses are examined to determine how well the ERCP performs. The experiment results showed that the ECRP protocol performs much better than some of the recent algorithms in both homogeneous and heterogeneous networks.  
wireless sensor networks; clustering; routing; reliability; energy balance 2022228950