esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
For the 212 males and 229 females enrolled in our study, we found that participants with higher fecal butyrate excretion were more likely to be male (\(q\) = 0.03), to consume more dietary fiber (\(q\) = 0.03) and total calories (\(q\) = 0.05), and to have higher overall and central obesity, hypertension and, more generally, metabolic dysregulation. The latter included higher BMI (\(q\) = 0.03), percentage body fat (\(q\) = 0.04), waist circumference (\(q\) = 0.03), VLDL cholesterol (\(q\) = 0.05), triglycerides (\(q\) = 0.05), hs-CRP (\(q\) = 0.03), insulin (\(q\) = 0.03), and blood pressure (\(q\) = 0.03) among participants with higher fecal butyrate levels. Higher fecal butyrate was also associated with lower gut microbiota diversity (i.e., OTU richness; \(q\) = 0.03; Table 1). Trends for other SCFAs were similar (Table S2). Participants with higher gut microbiota diversity were more likely to be female (\(q\) = 0.04), and on average they had lower BMI (\(q\) = 0.04), waist circumference (\(q\) = 0.04), VLDL cholesterol (\(q\) = 0.04), triglycerides (\(q\) = 0.04), hs-CRP (\(q\) = 0.04), LBP (\(q\) = 0.04), and blood pressure (\(q\) = 0.04), while having _higher_ HDL cholesterol (\(q\) = 0.04) and adiponectin (\(q\) = 0.04; Table 1). Next, we found that the inverse association between fecal butyrate (and other SCFAs) and gut microbiota diversity persisted after adjustment for confounders (\(q\) < 0.0001; Figure 1, Table 2, and Figure S1). High microbiota diversity was also significantly associated with less gut permeability (i.e., lower LBP levels; \(q\) = 0.019), less adiposity (i.e., lower BMI and waist circumference; \(q\) < 0.05), and an overall improved cardiometabolic health profile, after multivariable adjustment (\(q\) < 0.05; Table 2). Interestingly, fecal SCFAs showed exactly the opposite pattern of association with adiposity and cardiometabolic indicators (\(q\) < 0.05; Figure 2, Table 2, Figures S2 and S3). Further, fecal SCFA levels were higher in individuals with diarrheic stools and lower in individuals with normal or solid stools (acetate: \(p\) = 0.06, propionate: \(p\) = 0.0003, butyrate: \(p\) < 0.0001, total SCFAs: \(p\) = 0.005), suggesting intestinal transit time is associated with fecal SCFA excretion (Table S3). Using UniFrac-based beta-diversity analyses, we then demonstrated that the phylogenetic composition of the participants' gut microbial communities was associated with multivariable-adjusted fecal SCFA concentrations (Figure S4) and adjusted indicators of cardiometabolic health (BMI, waist circumference, and blood pressure; \(q\) < 0.005 in Procrustes analysis with the weighted UniFrac distances for all outcomes; Table S4). In other words, fecal excretion of SCFAs and cardiometabolic disease indicators were associated with overall gut microbiota community composition.
## 2 Method to Extract the Rule-Based Knowledge
### PR Generation through NAAR Mining and Prolog  
This study proposes a method to generate KBs from NRARs that are concise and free from subsumption and circular redundancies. The proposed method contains four main steps and one additional step which is described here.  
1. Generate a set of FCIs and their mGFCIs using the MG-CHARM algorithm [18,19] towards a dataset.  
* Then, all possible NRARs are constructed based on the FCI and mGFCI set by following Definition 6. Along with this step, the \(CF_{AR}\) value of each rule must be calculated. At this stage, a set of NRARs can be considered PRs; yet, they still cannot be used as KBs.
* In order to consider PRs as KBs, we need to ensure that the PRs have been verified through logical testing, i.e., no subsumption or circular rules. In this step, all subsumption rules in NRARs need to be pruned in accordance with their \(CF_{AR}\) values. Hence, the remaining NRARs are called non-subsumption NRARs.
* Prune all possible circular rules contained in non-subsumptions NRARs to complete logical testing and they can be considered KBs. Henceforth, the final rule set which passed through this stage, is a set of NAARs.
* In this step, the rule of the inference process is performed by PROLOG as the prototype of ESs for a cerebrovascular examination dataset. PROLOG answers any query by a user based on KBs obtained from the proposed method.  
Basically, **Step 1** implements the MG-CHARM algorithm and **Step 2** constructs the initial rules with regard to results of the previous step. Thus, we discuss **Steps 3** to **5** in more detail in the following section. Additionally, the proposed method also contains a pre-processing step which transforms the original cerebrovascular examination dataset into a dataset with multi-level attributes before mining NRARs. In order to give a better illustration, the framework of the proposed method is depicted in Figure 2.  
Figure 2: Illustration of the proposed method for rule-based systems on a cerebrovascular examination dataset.
# Eco-Efficient Cement-Based Materials Using Biomass Bottom Ash: A Review
## 5 Biomass Bottom Ash in CTGM (Soil Cement) and Soil Stabilization
### Discussion  
The physicochemical properties of BBA and BFA condition the mechanical behavior of stabilized soils and cement-treated soils.  
The stabilizing potential of ash with a high CaO percentage, generally over 20%, has been observed, which is consistent with previous studies [40], reducing free swelling and soil plasticity, thus improving its load-bearing capacity, as indicated by the positive results obtained by [PERSON] et al. [24].  
Figure 5: Comparative between compressive strength and modulus of elasticity. (Modified from [PERSON] et al. [20]).  
Figure 6: CBR values of stabilized soil. (Plotted from [PERSON] et al. [24]).  
The pozzolanic activity of biomass ash is known to depend on Si + Fe + Al and CaO content, but also on other factors such as the specific surface area, organic matter content, and crystalline structure of the phases, for which BFA generally have been considered more reactive than BBA. [PERSON] et al. [61] showed that the use of BFA with high contents of Si + Fe + Al (50-70%) mixed with cement improves the properties of the binder, on the contrary, no positive results were obtained regarding the development of compressive strength.  
However, [PERSON] et al. [20] obtained very positive results with incorporation of 15% BBA together with incorporation of 3% and 5% cement, with increases in the compressive strength and modulus of elasticity of the samples tested, which implies that the use of BBA with a lower theoretical potential of pozzolanic reactivity can improve the mechanical behavior of the soil under optimal compaction and moisture conditions.
## 4 Performance Analysis  
This section presents detailed derivations and closed-form expressions for the probability of outage, diversity order, and throughput of the proposed IOS-aided C-HNOMA system for multiple users. Additionally, it includes the optimization of power fraction to maximize SSE under the constraints of iSIC and HWDs.
# Software-Defined Networking: Categories, Analysis, and Future Directions
## 6 SDN Simulators and Emulators
OFNET [172] is an open-source emulator to test and debug networks for analyzing network traffic. This tool does not support cloud platforms and large-scale implementation of layer-2 networks. ViNO [173] helps to create arbitrary network topologies via Open vSwitches and virtual machines. However, its scalability and OpenFlow support are not specified, and these can be helpful for experimentation and testing. The EstiNet [175] network emulator provides accuracy, quickness, repetition, and scalability and is based on a kernel-reentering simulation methodology that allows researchers to test their applications. The FS-SDN [176] simulator is developed in the Python language for realistic testing and validation of standard networks. It can be extended for debugging and tracing capabilities, which can be helpful for developing new SDN applications. The OMNeT++ [178; 179] simulator is helpful for network modeling, multiprocessors, and different distributed or parallel systems. However, its kernel is implemented in C++ and can only run with a modern C++ compiler. The NS-3 network simulator provides the capability to add new protocols and allows integration and customizability without remaking the core of the simulator. However, it is lacking a visual interface for creating a topology and for visible capability on an experimental level. This study provides a feature-based comparison of SDN simulators and emulators including a brief description, implementation language, and the strengths and weaknesses. It can be extended to conduct a performance-based comparison of these tools under specific scenarios. Finally, new simulators and emulators need to be developed for hybrid SDN, wireless networks, fog/edge computing, cloud computing, and so on, to meet the needs of future networks.
## 3 DDoS Attack in SDN
### SDN against DDoS Attacks  
DDoS assaults are getting bigger, more frequent, more severe, and more sophisticated on traditional networks. However, SDN has various capabilities that can help mitigate the effects of DDoS assaults to some extent [30; 31]: (a) network's overall perspective, (b) control plan and data plan separation, (c) software-based traffic analysis, (d) network Programability, and (e) updating dynamic network policy. The network is more secure since any dispute may be addressed remotely using a centralized control manager due to the global transparency of the SDN network architecture and the ability to modify traffic-forwarding rules in real time. Additionally, the controller centralizations', as well as its public nature, raise the possibility of security concerns; DoS and DDoS assaults are two prominent examples. The single point of failure and single point of network failure might both be the SDN controller. With the steady use of SDN technology, it is anticipated that security concerns may worsen [31].
###### Abstract  
Seismic monitoring relies on seismography. However, the high cost of seismic equipment has presented a challenge to increasing the density of seismic networks in previous decades. Due to the large station spacing and inferior coverage of stations, this situation has led to a loss of detail in many research results. Along with the improvement of technology, the problem of increasing the density of seismographic observations is no longer an impossible issue. This makes it feasible to deploy a dense seismic network for monitoring earthquakes. This study deployed a linear dense array across the Meishan Fault in west-southern Taiwan for the purpose of analyzing the shallow fault zone structure. While the 1906 Meishan earthquake occurred in a period when historic records were available, the surficial geology surveys of the Meishan Fault are challenging because farming and construction engineering have obscured the outcrop. Early surveys of the Meishan Fault were mainly seismic surveys. In recent decades, over thirty profiles have been completed. However, the reflection seismic records had poor signal-to-noise ratios because the Meishan Fault is buried under thick sediments. Thus, the shallow structure of the Meishan Fault is still not known in detail. This study applied double-beamforming tomography to a dense seismic array to obtain high-resolution images of the Meishan Fault zone. The result shows that there is a south-dipping interface near the fault trace as indicated by the Central Geological Survey of Taiwan. In addition, we observed velocity transitions of perturbation profiles that may be caused by a branch fault, the Chentsoliao Fault. This study demonstrates that the ambient noise double beamforming method is an effective tool for imaging the detailed shallow structure along with the dense seismic array.  
Meishan Fault zone; double-beamforming tomography; strike-slip fault +
Footnote †: journal: Applied Sciences
## 2 Construction Time-Cost Dynamic Optimization Model  
### Construction Time-Cost Function Relationship  
The total engineering cost consists of direct and indirect costs; according to the current budget quota and inventory pricing standards, direct costs include labor costs, material costs, machinery costs, safety and civilized construction costs, unit price measure fees, and other measure fees, while indirect costs include regulatory fees and management fees.  
According to the literature [4; 8], the relationship between the time of work \(i\) and the direct cost is a quadratic function:  
\[c_{i}=\frac{c_{iN}-c_{iM}}{t_{iN}^{2}-t_{iM}^{2}}t_{i}^{2}+\frac{c_{iM}t_{iN}^ {2}-c_{iN}t_{iM}^{2}}{t_{iN}^{2}-t_{iM}^{2}} \tag{1}\]  
In Equation (1): \(c_{i}\) and \(t_{i}\) represent the direct cost and time of work \(i\), \(c_{iN}\) and \(c_{iM}\) represent the normal and ultimate costs of work \(i\), and \(t_{iN}\) and \(t_{iM}\) represent the normal and ultimate time of work \(i\).  
The direct cost of the project (\(C_{1}\)) is the sum of the direct costs of each work \(i\), then:  
\[C_{1}=\sum_{i=1}^{n}c_{i} \tag{2}\]  
In Equation (2): \(n\) represents the number of jobs.  
The relationship between indirect cost (\(C_{2}\)) and construction time is linear, then:  
\[C_{2}=k\times T \tag{3}\]  
In Equation (3): \(k\) represents the indirect cost coefficient, and \(T\) represents the optimized construction time.  
Consider rewards for early completion of the project or penalties for project delay (\(C_{3}\)), then:  
\[C_{3}=e\times(T-T_{p}) \tag{4}\]  
In Equation (4): \(e\) represents the reward and punishment coefficient, and \(T\) represents the planned construction time.  
The total cost of the project (\(C\)) is:  
\[C=\sum_{i=1}^{n}c_{i}+k\times T+e\times(T-T_{p}) \tag{5}\]
## Materials and Methods  
### Preparation of Specimens and Microstructure Observation  
Sn50 Pb49 Sb1 solder alloy was prepared by smelting Sn, Cu and Sb metals with a purity of 99.9% in a vacuum resistant furnace and chilled to draw wire. The composition of the solder was tested by an inductively coupled plasma emission spectrometer (ICP-AES). Oxygen-free Cu was chosen as the substrate material, with the given size and cleaned surface. Sn50 Pb49 Sb1 solder alloy was soldered onto the Cu substrate by flux to prepare the solder joints. As the irradiation period was too long to carry out in situ observation, a few samples for microstructure observation were subjected to the following irradiation experiment. All samples were cut from the same solder joint to ensure the accuracy of the experiment. The microstructure of Sn50 Pb49 Sb1/Cu solder joints was observed using a scanning electron microscope (SEM) after grinding and polishing, and its chemical compositions were identified using an energy dispersive spectrometer (EDS).
## 3 Results
### Mono-Metal Adsorption Capacity
Solution pH was basic throughout the process, revealing that As\({}^{5+}\) species existed as HAsO\({}_{4}\)\({}^{2-}\)[14; 15; 17], so that electrostatic attraction with the adsorbent was weak. Furthermore, the adsorption isotherms presented in Figure 2d and \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Langmuir Model**} & \multicolumn{3}{c}{**Freundlich Model**} \\ \cline{2-7}  & **Q (mg/g)** & **b (L/mg)** & **R\({}^{2}\)** & **k (L/g)** & **1/n** & **R\({}^{2}\)** \\ \hline Sr\({}^{2+}\) & 11.820 & 0.258 & 0.945 & 2.514 & 0.404 & 0.893 \\ Mn\({}^{2+}\) & 14.306 & 0.396 & 0.977 & 2.913 & 0.604 & 0.704 \\ Pb\({}^{2+}\) & 38.314 & 1.145 & 0.839 & 19.593 & 0.709 & 0.984 \\ As\({}^{5+}\) & 12.469 & 0.009 & 0.205 & 0.126 & 1.080 & 0.888 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Isotherm model parameters of heavy metals for mono-metal adsorption. Figure 2: Adsorption isotherms of (**a**) Sr\({}^{2+}\), (**b**) Mn\({}^{2+}\), (**c**) Pb\({}^{2+}\), (**d**) As\({}^{5+}\) for mono-metal adsorption at adsorption dose 4 g/L. the simulation parameters in Table 4 show that arsenate molecules migrated to the surface of the biochar following the multilayer adsorption mechanism, described by the Freundlich model. In this case, the solid material achieved a maximum adsorption capacity for As5+ of 12.4 mg/g, which is generally higher than the values reported in the literature [15,17].
## 3 Numerical Algorithms
### Computational Grids  
Figure 6 shows the mesh used in the present study. In order to obtain the flow field around the train under high turbulence intensity, three refined areas are arranged around the train body. In order to capture detailed vortex-shedding information on the leeward side, the refined area on the leeward side is wider than that on the windward side. Subgrade mesh of the head car is shown in Figure 6a. The mesh of the train's cross-section is shown in Figure 6b. Thirty prism layers of cells are applied to the surface of the train, and the thickness of the grid close to the train body is \(5.0\times 10^{-5}\) mm. There is a high-velocity gradient at the surface of the train, so a precise boundary layer grid is set up to solve the complex flow in the boundary layer. The specific values for \(7.5\) m nose length train's three types of grids are shown in Table 1. The growth rate of the boundary layer grid is \(1.2\), and  
Figure 5: The computational domain: (**a**) boundary conditions; (**b**) 5 planes.  
the convergence in a time step is \(1.0\times 10^{-3}\) S. Each simulation used 5 nodes, each node had 48 cores, and one case took 115.72 h.  
Figure 7 show the y\({}^{+}\) distributions of the head and tail train. Table 2 shows summary of simulations performed at a 15\({}^{\circ}\) yaw angle. y\({}^{+}\) is less than 1 for both the head and tail car, which meet the calculation accuracy requirements.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Grids**} & \multirow{2}{*}{
\begin{tabular}{c} **First Layer** \\ **Mesh Thickness** \\ \end{tabular} } & \multicolumn{2}{c}{**Number of**} & **Boundary Layer** & \multirow{2}{*}{**Total Numbers**} \\  & & **Mesh Layers in** & & **Grid** & \\  & & **Boundary Layer** & & **Growth Rate** & \\ \hline Coarse & \(5.5\times 10^{-5}\) mm & 30 & 1.2 & 47.11 million \\ Medium & \(5.0\times 10^{-5}\) mm & 30 & 1.2 & 70.97 million \\ Fine & \(4.5\times 10^{-5}\) mm & 30 & 1.2 & 99.54 million \\ \hline \end{tabular}
\end{table}
Table 1: Spatial resolutions for 7.5 m nose length train’s three types of grids.  
Figure 6: The mesh of the train: (**a**) longitudinal cross-section; (**b**) lateral cross-section.  
Figure 7: Distribution of y\({}^{+}\): (**a**) head car; (**b**) tail car.  
Grid independence was verified for the model of Case 2, and the three grids in Table 3 were used to compare the aerodynamic coefficients of the head car. It was found that the results of the medium grid and the fine grid were almost the same, and the maximum difference was less than 3%, indicating that increasing the grid accuracy would not affect the calculation results.
## 4 Charging Method
### Constant Current-Constant Voltage (CC-CV) Method  
The constant current-constant voltage (CC-CV) charging method is an integrated strategy that combines elements from both aforementioned approaches. The charging process begins with CC charging during the initial phase. The procedure switches to CV charging when the voltage exceeds the highest safe threshold value. When either the charging current stops rising or the battery reaches its maximum charge, the charging procedure is deemed to be finished. The CV mode has a significant impact on capacity utilization, but the CC mode has a significant impact on charging time. The diagram presented in Figure 14 illustrates the configuration of a CC-CV charging method.  
Charging with a CC-CV charger minimizes battery drain, optimizes power delivery, and safeguards against overcharging in the CV stage. The careful management of CV stages is necessary for preventing the overcharging and overheating of the battery. Charging times could be reduced by increasing the charge current during the CC phase. Lithium plating at the end of the CC stage is a side effect of this, as is the rise in IR losses. There are several issues with this pricing strategy. When charging quickly, the anode potential drops and energy is wasted. This leads to both excessive and inadequate billing. There is a correlation between Li plating and a decline in battery life and charging efficiency. Overcharging causes permanent damage to the battery, while undercharging significantly diminishes its ability to store energy. When the charging current is high, more heat is lost during the constant current phase, leading to a higher temperature [71].
## 2 Materials and Methods
### Methods
#### 2.2.4 Adsorption Study in Batch and Continuous System  
The adsorption capacity (\(q_{e}\)) in the batch system was calculated by Equation (1), where \(C_{o}\) is the initial solute concentration, \(C_{f}\) is the final concentration after adsorption, m is the mass of adsorbent, and V is the volume of the solution.  
\[q_{e}=\frac{\left(C_{o}-C_{f}\right)\times V}{m} \tag{1}\]  
The removal efficiency (\(R\)) in batch and continuous systems was determined by Equation (2), where \(C_{o}\) and \(C_{f}\) are the initial and final solute concentrations, respectively, in mg/L. For packed-bed experiments, the sample was taken, collecting the water at the output of the column since the appearance of the first drop.  
\[R=\frac{C_{o}-C_{f}}{C_{o}}\times 100 \tag{2}\]  
The kinetic study was conducted under the best experimental conditions found, taking eight samples at different time intervals (5, 10, 15, 20, 30 min, and 1, 2, 4, 8, and 22 h) during 24 h. For this, 100 mL of solution was placed in contact with the adsorbent. The data were then fitted to the kinetic models (Pseudo-First Order and Pseudo-Second Order) using OriginPro 8.5(r) to calculate the fitting parameters and R\({}^{2}\). Adsorption equilibrium was performed for 24 h by varying the initial concentration of contaminant (50, 100, 200, 300 mg/L). [PERSON] and [PERSON]'s models were used to adjust the data.  
The quantity of metal ions retained in the fixed-bed column were determined from the experimental breakdown curves, using Equation (3), where \(q_{total}\) is the concentration of ions in the adsorbent (mg/g), \(Co\) is the initial concentration of the solution, and \(Ct\) is the output concentration of the ions studied in the aqueous solution (mg/L), \(ts\) is the time it  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Independent Variables**} & \multirow{2}{*}{**Unit**} & \multicolumn{6}{c}{**Range and Level**} \\ \cline{3-6}  & & \(-\alpha\) & \(-\textbf{1}\) & **0** & **+1** & **+\(\alpha\)** \\ \hline Temperature & \({}^{\circ}\)C & 30 & 40 & 55 & 70 & 80 \\ Adsorbent amount & g & 0.1 & 0.355 & 0.678 & 1 & 1.2 \\ Initial concentration & mg/L & 31.8 & 100 & 200 & 300 & 368.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment design: experimental ranges and various levels of the variables for the batch system tests.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Independent Variables**} & \multirow{2}{*}{**Unit**} & \multicolumn{6}{c}{**Range and Level**} \\ \cline{3-6}  & & \(-\alpha\) & \(-\textbf{1}\) & **0** & **+1** & **+\(\alpha\)** \\ \hline Temperature & \({}^{\circ}\)C & 33.8 & 40 & 55 & 70 & 76.2 \\ Bed height & Cm & 1.6 & 3.0 & 6.5 & 10.0 & 11.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment design: experimental ranges and various levels of the variables for continuous system tests.  
takes for the adsorbent to convert the initial concentration to the final concentration (min), \(Q\) is the flow rate (mL/min), and \(m\) is the adsorbent mass (g) [41].  
\[q_{total}=\frac{Q}{1000m}\ \int_{0}^{ts}Ct-Co\ dt \tag{3}\]
# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 2 Materials and Methods
### The Model of Short-Term Traffic State Prediction  
Considering the temporal and spatial features of traffic states, a short-term traffic state prediction model is designed. As shown in Figure 5, the framework is divided into five modules: input module, temporal feature module, spatial feature module, attention mechanism module, and prediction module.