esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Factors Affecting the Removal of 4-CP by the Combined MPHAC/PS System
#### 3.3.1 Effect of the Initial Solution pH on 4-CP Adsorption and Degradation
However, over time, PS is adsorbed to the catalyst surface of the MPHAC and sulfate radicals are produced, inducing 4-CP degradation and enhancement of 4-CP removal [16]. As indicated previously, at pH 9, PS is repelled from the catalyst surface due to its anionic property and its activation is reduced, confirming that, for this pH value, adsorption is the predominant process for 4-CP removal.
# Evaluating the High Frequency Behavior of the Modified Grounding Scheme in Wind Farms
## 2 Wind Turbine Model Description
### Frequency-Dependent Underground Cable Model  
High-frequency analysis requires an advanced cable model that has smaller impedance than the transmission lines, especially during transient overvoltages. Therefore, in this study, we use the FD line model, which has more accuracy than the Constant Parameters (CP) line model, to model the underground cable, including a wide range of frequencies greater than 2.5 kHz [35]. The three-phase single-core underground LV cables are XLPE-PVC [36].
## 4 Experiments and Results
### Feature Importance and Correlation  
The feature importance algorithm [46] gives an idea of how relevant each attribute is in indicating the level of contribution to a model that extracts some information from \begin{table}
\begin{tabular}{l l} \hline \hline
**Filter Name** & **Euclidean Distance** \\ \hline Exponential Smoother & 4,319,020.19 \\ Mean & 4,366,915.55 \\ Mode & 4,613,657.21 \\ Butterworth & 4,627,373.19 \\ Lowess Smoother & 4,697,382.31 \\ Convolution Smoother & 4,924,863.06 \\ Spline Smoother & 6,702,942.84 \\ Spectral Smoother & 7,243,313.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Filters [44] tested with the sum of euclidean distances calculated with pitch feature between cycles sorted by ascending order. Figure 3: Automatically machinery filtered by ZCR descriptor. (**a**) Spectrum, ZCR, Temporal waveform of raw data. (**b**) Spectrum, ZCR, Temporal waveform of filtered data. the features. This tool is used in the frame of a predictive model and gives, for each used feature, a value from zero to one, which is called the importance factor, and that allows knowing how useful it is for the model. Combined importance calculated as a mean of the importance of each predictor has been obtained through different models that predict the gasses levels. These models have been extracted from Scikit-learn [47] and, among them, there are: _(i)_ logistic regression, _(ii)_ decision tree regressor, _(iii)_ random forest classifier, _(iv)_ extra trees classifier. The Table 2 shows the combined importance factors relating the acoustical features with CO2 by cycle and aggregated of the three cycles. Analysing the set of cycles, the most important descriptor with a difference of 0.18 points, is the MFCC 6. After this attribute, MFCC 9, 4, 3 are the most relevant ones. All the above named features accumulate an importance of 0.5814 and will be discussed in more detail in the following subsection. Another interesting aspect to consider is the way how each descriptor relates to the others. The correlations among descriptors indicate redundancies and shared information. For this study, a matrix of correlations was calculated and it is shown in Figure 4.  
In previous studies, authors found that, when chicks are stressed, higher values of vocalNum and Pitch are found [29]; this slight positive correlation can be observed in this analysis. Also, it is observed that this two features are mainly inverse correlated with the MFCC except from the MFCC 3. Some of the most outstanding correlations are the positive ones between MFCCs. MFCC 1 is the feature with highest correlations. As seen in Table 2, it is the less important feature for detecting gas concentration, which could be related to the redundant information in similar descriptors. It is noteworthy that correlations among two consecutive MFCCs are always close to zero with the only exception of 1 st and 2 nd, whereas there is a notable positive correlation among MFCC n and MFCC \(\pm\) 2 for every n from 1 to 10.
###### Abstract  
This study identified the governing variables affecting the effective properties of the representative volume element (RVE) of nanocomposites consisting of unidirectionally aligned carbon nanotubes (CNTs) and determined the mechanism through which they act. For this purpose, multi-walled nanotubes (MWNTs) and polyvinylidene fluoride (PVDF) were selected as the components of the nanocomposites, and Monte Carlo simulations were conducted to examine the variability of the effective properties according to the CNT length. The governing variables affecting the effective properties were identified considering the conditions under which the selected CNTs can be arranged inside the RVE. Using the geometrical relationship between the RVE and CNTs, a simplified two-parameter equation that can calculate the effective properties of the RVE was derived. Using this equation and Monte Carlo simulations, this study confirmed that the characteristics of the effective properties vary with changes in the length of the RVE and the length fraction of the CNTs, and the mechanism of these changes was determined. In addition, the variation in the characteristics of the effective properties according to the coefficient of variation of the CNT length was also determined.  
CNT/PVDF nano composite; COV (coefficient of variation); effective properties; homogenization method; representative volume elements +
Footnote †: journal: Applied Sciences
## 3 Results
### Signal-to-Noise Ratio Analysis of Satellite Communication Network  
Based on the median SNR, the channel activity can be divided into four categories: positive (median SNR \(\geq\) 40), ordinary (median SNR \(\geq\) 20), negative (median SNR \(\geq\) 10), or silent (SNR \(<\) 10). Figure 8a shows the overall SNR performance of the 10 channels, and Figure 8b-e show the SNR of positive, ordinary, negative, and silent channels, respectively.  
Figure 7: (a) Number of successful and failed data packets received; (b) number of successful and failed data packets sent.  
As shown in Figure 8a, more than 50% of the channels were always active or normal active during the experiment. In all moments, the optimal SNR was close to 60, indicating that the overall performance of the satellite communication network channels met the operational requirements. As shown Figure 8b-e, there were three channels with positive activity, two channels with normal activity, two channels with negative activity, and three channels with silent activity. The activity of the 10 channels varied at different moments of the experiment, which indicates that the signal transmission conditions of the channels did not depend entirely on their activity at a given moment. It is concluded from the analysis that the overall channel activity of the working satellite communication network terminal can always meet the transmission requirements of RSMC.  
Due to the special communication environment in forested areas, there is no guarantee that there is always a line of sight to the satellite. Therefore, it is necessary to discuss whether the signal has enough SNR redundancy to compensate for the loss of signal caused by tree shading. For satellite digital communications, an SNR of 10 dB or more is usually required to ensure reliable communications. For the 10 channels shown in Figure 8, the overall SNR average value of the test is 23.46 dB, then the average SNR redundancy of all channels is 13.46 dB, and the system has enough redundancy to compensate for the loss of signal caused by tree shading.
## 4 Experiments and Results
### Robustness Analysis  
#### 4.3.1 Evaluation Indicator of Experimental Results  
In the distribution and use of BIM data, unintentional or malicious attacks may occur, necessitating the extraction of watermark information from the model for data copyright determination. To evaluate the similarity between the extracted and claimed watermark information, this study introduces normalized correlation (NC) as a quantitative index. The extracted watermark information is compared with the original watermark information, and the NC calculation can be summarized as the ratio of the number of water-mark bits with correct information extracted to the total number of watermark bits. An NC value of 1 indicates complete extraction of the correct watermark, while a value of 0 indicates no extraction at all. The higher the coefficient, the higher the watermark extraction accuracy. Different usage scenarios have different requirements for the NC value. In this study, we set the threshold value at 0.75, and considered the watermark extraction successful when the NC coefficient was greater than the threshold value.
_Applied_  
_sciences_  
_Molecular Simulation of CH\({}_{4}\) Adsorption Characteristics under the Coupling of Different Temperature and Water Content_  
**[PERSON] \({}^{1,2}\)\({}^{\copyright}\)**, [PERSON] \({}^{1,2,*}\), [PERSON] \({}^{1,2}\), [PERSON] \({}^{1,2}\) and [PERSON] \({}^{1,2}\)\({}^{\copyright}\)  
\({}^{1}\) School of Safety and Emergency Management Engineering, Taiwan University of Technology,  
Taiyuan 030024, China; [EMAIL_ADDRESS] (Y.G.); [EMAIL_ADDRESS] (J.C.); [EMAIL_ADDRESS] (S.Z.); [EMAIL_ADDRESS] (Z.L.)  
\({}^{2}\) Graduate Education Innovation Center for Sharmi Coal Mine Safety, Taiyuan University of Technology,  
Taiyuan 030024, China  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]; Tel.:+86-136-4346-9780  
**Abstract:** The adsorption characteristics of CH\({}_{4}\) have an important influence on gas content prediction, gas extraction, and hazard prevention. Therefore, we explored the mechanism of CH\({}_{4}\) adsorption under the action of water and temperature to grasp the influence of water and temperature on the adsorption characteristics of CH\({}_{4}\). In this paper, a giant, regular-system Monte Carlo method is used to simulate the CH\({}_{4}\) adsorption behavior at the molecular level under different temperatures, water contents, and the coupling of both. The results indicate that an empirical formula for the coupling effect of temperature and water content on CH\({}_{4}\) adsorption was obtained. The impact of different effects on CH\({}_{4}\) adsorption is as follows: coupling effect > single temperature effect > single water content effect. The optimal combination is at a temperature of 363 K and a water content of 8.31%. Compared with the CH\({}_{4}\) adsorption capacity without water at room temperature, the CH\({}_{4}\) adsorption capacity is reduced by 68.04% under the coupling effect of the optimal combination. Temperature has a negative effect on the adsorption of CH\({}_{4}\), and temperature changes the adsorption capacity by changing the average molecular kinetic energy of CH\({}_{4}\). The reason why the increase in H\({}_{2}\)O reduces the adsorption capacity of CH\({}_{4}\) is that the interaction between H\({}_{2}\)O and the oxygen-containing functional groups of coal is stronger than that of CH\({}_{4}\). As the water content increases, the adsorption heat decreases, thereby inhibiting the adsorption of CH\({}_{4}\). In addition, H\({}_{2}\)O has a smaller molecular dynamics radius as compared to CH\({}_{4}\); the larger the free volume and surface area in the pore structure, the more adsorption pores it occupies, resulting in a more significant reduction in the adsorption of CH\({}_{4}\).  
**Keywords:** temperature; water content; coupling; molecular simulation
# Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data
## 2 Materials and Methods
### Synchronized Floodwater Index
#### 2.4.2 Time-Series Synchronized Floodwater Index (SfWi)  
SfWi was developed by taking advantage of the similarity in behavior between the flood area from MODIS data and the flood hydrograph in the part where the water level exceeds the flood danger level (DL). SfWi is a new conditional indexing algorithm capable of flood detection with annual temporal and spatial changes using time-series MLSWI-derived flood maps with observed water level data exceeding DL in Equation (2). Based on changes in the time-series MLSWI values (0 < MLSWI < 1), SfWi was calculated using the moving average according to the gradient of MLSWI (the temporal interval is 40 days within five points) over the optimal threshold value (Th) and the threshold of the flood danger level (DL) at the three gauge stations (Sirajganj: 13.35 m, Baghabari: 10.40 m, Bhagyakul: 6.3 m).  
\[\text{SfWi}(\text{t})=\left\{\begin{array}{rl}\text{MLSWI}(\text{t}),\ x\geq\text{Th}\text{ and if }y\geq\text{DL}\\ 0,\ \text{if},\ x<\text{Th}\text{ and }y<\text{DL}\end{array}\right. \tag{2}\]  
where t is the composite-day-of-year (DOY) in the year of acquisition, \(x\) is MLSWI values ranging from 0 to 1, \(y\) is the river water level at gauge stations, Th is the optimized threshold value of MLSWI, and DL is the flood danger level of river water when a flood occurs.  
SfWi indicates the frequency of cumulated annual floodwater by the time-series MLSWI's value calculation with an unsupervised binary classification from the moving average calculation counted on the three sequential points (of MLSWI) exceeding flood danger level, at the same time utilizing existing points (of MLSWI) during periods of flooding as the major threshold value for flood extent mapping.  
\begin{table}
\begin{tabular}{c c c} \hline
**Return Period (Year)** & **Inundated Area (\%, km\({}^{2}\))** \\ \hline
2 & 20 & 29,900 \\
5 & 30 & 43,000 \\
10 & 37 & 55,000 \\
20 & 43 & 62,000 \\
50 & 52 & 75,000 \\
100 & Over 60 & 89,000 \\ \hline \end{tabular} The average of inundated area: 2-year return period = 20\%  
The 2007 flood: 20-year return period = 43\%  
The 1998 flood: 100-year return period = over 68\%  
\end{table}
Table 2: Return periods with corresponding inundated areas in Bangladesh [33; 34].
## 5 Conclusions  
In this paper, we focused on the evaluation of long-term domestic water-usage trends with economic development from typical countries. The relationship between domestic water usage and socio-economic developments on a country scale for the time period from 1900 to 2020 in 10 typical countries was demonstrated. The simulation results show that with the growth in GDP per capita, domestic water per capita showed an expanded S-curve of'slow growth-rapid growth-zero growth, or even negative growth, with three key points, which were the takeoff point, turning point, and zero-growth point, respectively. The takeoff point of the expanded S-curve was located at a GDP per capita of USD 1500-USD 5000, according to the different development levels. The turning point was located at a GDP per capita of USD 10,000-USD 13,000, and the zero-growth point was concentrated around a GDP per capita of USD 17,000-USD 22,000, consistent with the post-industrial stage. Besides, the urbanization was proven to accelerate domestic water usage, and the higher tertiary industry proportion of GDP enhanced the domestic water-usage trends. The decreased water usage was attributed to technological progress, with widely used water-saving appliances.  
The results of this research show that the expanded S-curve is applicable to the relationship between domestic water usage and economic development on a country scale. We hope this conclusion can contribute to the development of future solutions and strategies for domestic water prediction in different economies or similar economies under different development stages. However, there is still a lot of uncharted territory of the application of the expanded S-curve models. In this paper, only four of 10 typical countries were simulated in detail, and studies on a series of countries with few water usage data are still deficient. We will apply this expanded S-curve model to other countries in our future research, and hope that this model will encourage the efficient and sustainable management of water resources.  
Conceptualization, X.G. and A.W.; methodology X.G. and G.L.; validation and data curation, X.G. and B.D.; writing--original draft preparation, X.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 72088101, 71991485, and 71991480. The APC was funded by grant number 72088101.  
Not applicable.  
Figure 8: DWPC subsector proportions in (**a**) the US and (**b**) Japan.
## 4 Slip Ratio Control Strategy Based on Dynamic Adaptive Compensation Sliding Mode Control
### Design of the Slip Ratio Controller
From the slip ratio calculation formula, we obtain:  
\[\lambda_{i}=\dot{\omega_{i}}\frac{v_{i}}{\omega_{i}^{2}R}-\frac{\dot{\sigma}_{ i}}{\omega_{i}R} \tag{42}\]  
Substitute the single-wheel model into:  
\[\lambda_{i}=\left(\frac{1}{J}T-\frac{R}{J}F_{xi}\right)\frac{v_{i}}{\omega_{i} ^{2}R}-\frac{\dot{v}_{i}}{\omega_{i}R} \tag{43}\]  
Combining the sliding mode control surface with the reaching law, we obtain:  
\[S(t)=ce(t)+\dot{\lambda}_{i}=-\varepsilon\tanh\left(\frac{S}{\sigma}\right)- kS-\int_{0}^{t}\omega(\tau)S(\tau)d\tau \tag{44}\]  
Substituting Equation (43), the sliding mode control for the torque is:  
\[T=\frac{J\omega_{i}^{2}R}{v_{i}}\left(-ce-\varepsilon\tanh(S)-kS-\int_{0}^{t} \omega(\tau)S(\tau)d\tau\right)+F_{xi}\cdot R+\frac{J\omega_{i}\dot{v}_{i}}{v _{i}} \tag{45}\]  
The stability and convergence of this control are proven as follows. Construct the Lyapunov function for the control:  
\[V=\frac{1}{2}S^{2} \tag{46}\]  
Taking the derivative of Equation (46), we obtain:  
\[\dot{V}=S\dot{S}=S(ce+\dot{\lambda}_{i}) \tag{47}\]  
**Lemma 1** ([34]). : _For \(\forall S>0\), there exists \(\sigma>0\) such that the following inequality always holds: \(|s|-\tanh\left(\frac{s}{\sigma}\right)\leq\mu\sigma,\mu=0.2785\)._  
After transformation, the following is obtained:  
\[\varepsilon\tanh\left(\frac{s}{\sigma}\right)\leq-\varepsilon|s|+\varepsilon \mu\sigma \tag{48}\]  
Substituting the transformed Lemma 1 into Equation (47), we obtain:  
\[ \begin{array}{l}S\dot{S}=-\varepsilon S\tanh\left(\frac{s}{a}\right)-kS^{2} -k_{\omega}\int_{0}^{t}exp^{-\beta|e(\tau)|}d\tau\cdot S^{2}\\ \leq-KS^{2}+\varepsilon\mu\sigma=-2 KV+b\end{array}  \tag{49}\]  
where \(K=k+k_{\omega}\int_{0}^{t}\exp^{-\beta|e(\tau)|}d\tau\) is a constant determined by \(k,k_{\omega}\) and \(\beta\), and \(b=\varepsilon\mu\sigma\). The solution to the inequality \(V\leq-2 KV+b\) is:  
\[V(t)\leq e^{-2K(t-t_{0})}V(t_{0})+\frac{\varepsilon\mu\sigma}{2K}\left(1-e^{- 2K(t-t_{0})}\right) \tag{50}\]We continue solving and obtain:  
\[\lim_{t\to\infty}V(t)\leq\frac{\varepsilon\upmu\sigma}{2K} \tag{51}\]  
Therefore, the system is asymptotically stable and exponentially convergent, with the convergence rate depending on \(\varepsilon,\sigma\), and \(K\).
## 2 Materials and Methods
### Random Forest Regression (RFR)  
Random forest regression (RFR) is a supervised learning algorithm that falls within the ensemble learning family and is utilized for tasks such as regression and classification [35]. During the training phase, numerous decision trees are created. Each decision tree in the forest is constructed using a random subset of the training data and features, promoting diversity and reducing overfitting. The final output from RFR is obtained by averaging the predictions made by each tree, which enhances the accuracy and robustness. This method capitalizes on the strengths of multiple models to produce a more reliable and stable prediction compared to a single decision tree, as shown in Figure 4. RFR is widely favored for its simplicity, versatility, and superior performance in handling complex datasets and capturing non-linear relationships.
# Comparative Study of the Presence of Heavy Metals in Edible Vegetable Oils
## 2 Analysis of Publications and Trend of Heavy Metals in Edible Vegetable Oils
### Results of the Analysis of Heavy Metals in Edible Vegetable Oils
#### 2.3.2 Lead (Pb)
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Types** & **Analytical** & **Limit of** & **Limit of** & **Results** & **Region** & **Reference** \\
**of Oils** & **Techniques** & **Detection (LD)**1 & **(LQ)**1 & **Average Value**1 & **Average Value**1 & **Region** & **Reference** \\ \hline Rice oil & ICP-MS & 0.10 & - & \(<\)LD–0.127 & USA (Peoria, Illinois) & [31] \\ \hline Soybean oil & AAS & 0.12 & - & 0.31–2.35 & Bangladesh & [85] \\ \hline Rapeseed oil & ICP-OES & - & - & 1.96 & China (Shizhuyuan, Chenzhou) & [80] \\ \hline Rapeseed oil & GF-AAS & - & - & 0.012–0.100 & Poland (Lubelskié, Mazowiockie, S’laskie, Opolskie, Wielkopol- skie) & [86] \\ \hline Olive oil & ICP-MS & - & - & 0.15–1.48 & Cyprus & [60] \\ \hline Olive oil & ICP-OES & - & - & 0.984–12.33 & Iran, Italy & [81] \\ \hline Olive oil & ICP-OES & - & - & 8.546–18.783 & Iran, Italy & [81] \\ \hline Rapeseed oil & ICP-OES & \(1.00\times 10^{-3}\) & \(3.00\times 10^{-3}\) & 0.099–0.100 & Iran & [53] \\ \hline Corn oil & ICP-OES & \(1.00\times 10^{-3}\) & \(3.00\times 10^{-3}\) & 0.099–0.100 & Iran & [53] \\ \hline Olive oil & ICP-OES & \(1.00\times 10^{-3}\) & \(3.00\times 10^{-3}\) & 0.099–0.100 & Iran & [53] \\ \hline Sesame oil & ICP-OES & \(1.00\times 10^{-3}\) & \(3.00\times 10^{-3}\) & 0.092–0.099 & Iran & [53] \\ \hline Sunflower oil & ICP-OES & \(1.00\times 10^{-3}\) & \(3.00\times 10^{-3}\) & 0.098–0.100 & Iran & [53] \\ \hline Coconut oil & ICP-OES & - & - & 0.158 & United (London) & [42] \\ \hline Olive oil & ICP-OES & - & - & 0.143 & United (London) & [42] \\ \hline Rapeseed oil & ICP-OES & - & - & 0.181 & United (London) & [42] \\ \hline Sunflower oil & ICP-OES & - & - & 0.274 & United (London) & [42] \\ \hline Sesame oil & FAAS & \(3.04\times 10^{-4}\) & \(1.02\times 10^{-3}\) & 1.370–6.641 & Pakistan (Mardan) & [51] \\ \hline Olive oil & FAAS & \(3.06\times 10^{-4}\) & \(1.03\times 10^{-3}\) & 1.321–7.249 & Pakistan (Mardan) & [51] \\ \hline Rapeseed oil & FAAS & \(3.06\times 10^{-4}\) & \(1.03\times 10^{-3}\) & 1.301–6.765 & Pakistan (Mardan) & [51] \\ \hline \end{tabular}  
* The values present the decimal places provided by the authors or, failing that, approximated to three decimal places. 2 Different seasonings added to flavour the oil, such as pepper, truffle, and vegetable.  
\end{table} 
Table 4: Lead (Pb) content in edible vegetable oils, expressed in mg/kg.