esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Methodology
### Study Selection and Data Extraction  
ACN systems are composed of many components that offer various technologies for detection, connection, and data processing. A study selection phase is needed to focus only on the most relevant and complete works that fit our requirements. This research was conducted using electronic databases such as Scopus and Google Scholar, including only documents from 2011 to 2023. In a primary analysis, 69 papers were selected for full-text reading. Among them, only 37 papers were considered suitable for the data extraction process. In the data extraction process, we focused on the proposed RQs, and for each of them, after a full-text read of the documents, we selected a group of features as suitable for the aim of our work:  
* **Sensing layer**--mobile application, car application, GPS, camera.
* **Communication layer**--WiFi, Bluetooth, GPS, cellular, IoT.
* **Cloud layer**--cloud, ML model, ERS system.  
These features are the most relevant for answering the proposed RQs and for finding the best ACN architecture.
# Influence of a Landslide on a Tunnel in Loess-Bedrock Ground
## 5 Conclusions  
Based on the laboratory model test combined with numerical simulations, the deformation characteristics of a loess-bedrock landslide through a tunnel were studied, and the lateral and contact surface deformation of the sliding landslide were monitored. The main conclusions are as follows:  
(1) The anti-sliding force of the loess-bedrock landslide around the tunnel comes from the maximum friction force of the contact surface, the cohesion force of the soil particles, and the resistance of the tunnel. The force required to cause a landslide is relatively large, and the landslide slip resistance decreases after the initial slip, so that subsequent soil displacement slips are more prone to occur during the landslide.  
(2) The tunnel changes the distribution characteristics of the soil pressure within the landslide, whereby the soil pressure at the top of the tunnel increases, which has a negative impact on the tunnel structure. There are three types of landslides based on the three different slip zones: the loess-bedrock landslide, the slide above the tunnel, and the shallow slide at the sides of tunnel.  
(3) The deformation process of a loess-bedrock landslide is sudden, and is a process of energy accumulation and release. After the sliding of the existing sliding body below the tunnel, parallel cracks will be formed on both sides of the tunnel, and the soil will tend to gather in the middle.  
Conceptualization, Z.S. and X.Y.; methodology, S.L.; software, X.Y.; validation, Z.S., Y.C. and P.L.; investigation, Y.C.; data curation, S.L.; writing--original draft preparation, Z.S.; writing--review and editing, P.L.; visualization, Y.C.; supervision, P.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2018 YFC0809600, the National Natural Science Foundation of China, grant number 51978019 and the Beijing Natural Science Foundation, grant number 8222004.  
Not applicable.  
The relevant data are all included in the paper.  
The authors declare no conflict of interest.
# Multi-Resolution SPH Simulation of a Laser Powder Bed Fusion Additive Manufacturing Process
## 2 Computational Framework
### Material Model  
During the melting/re-solidification process in LPBF, a significant amount of energy is released/absorbed as the substance undergoes a change of state. This energy is also known as the latent heat associated with the phase change. [PERSON] and [PERSON] (2018) modified the heat capacity coefficient to address this important issue. In their modification, an apparent heat capacity of the form:  
\[c_{p}(T)=\begin{cases}c_{p}^{\text{S}}&T<T_{m}-\delta T\\ \dfrac{c_{p}^{\text{S}}+c_{p}^{\text{L}}}{2}+\dfrac{\mathcal{L}}{\delta T}&T_{ m}-\dfrac{\delta T}{2}<T<T_{m}+\dfrac{\delta T}{2}\\ c_{p}^{\text{L}}&T_{m}+\delta T<T\end{cases} \tag{21}\]  
is defined as a function of temperature, where \(c_{p}^{\text{S}}\) and \(c_{p}^{\text{L}}\) are the solid and liquid heat capacities, \(T_{m}\) the melting temperature, \(\mathcal{L}\) the latent heat of melting, and \(\delta T\) is the size of a phase-change temperature bandwidth. Together with this adjustment, a temperature-dependent state variable, \(s(T)\), is defined to specify the phase of the material at temperature \(T\). Similar to the formulation in ([PERSON] _et al._, 2018), the state of a given SPH particle reads:  
\[s(T)=\begin{cases}0&T<T_{m}-\dfrac{\delta T}{2}\\ \dfrac{T-(T_{m}-\delta T/2)}{\delta T}&T_{m}-\dfrac{\delta T}{2}\leq T\leq T_{ m}+\dfrac{\delta T}{2}\\ \\ 1&T_{m}+\dfrac{\delta T}{2}<T\end{cases} \tag{22}\]  
with \(s=0\) representing a solid state, \(s=1\) a liquid state, and \(0\leq s\leq 1\) a two-phase state between the solid and liquid phase.
# Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
## Materials and Methods
### Sentinel-1 Data and DInSAR Methodology
#### 2.1.4 Three-Dimensional Decomposition  
The resulting displacement was still represented in the LOS to the satellite. To compute the north-south (\(d_{N}\)), east-west (\(d_{E}\)), and vertical (\(d_{U}\)) components of the deformation vector in the LOS (\(d_{LOS}\)), the following well-known relation (e.g., [42]) was used:  
\[d_{LOS}=d_{N}\sin\alpha\cos\theta-d_{E}\cos\alpha\sin\theta+d_{U}\cos\theta \tag{2}\]  
where \(\alpha\) stands for the clockwise angle between the north direction and the satellite flight direction and \(\theta\) stands for the radar incidence angle at the centre of the resolution cell. Components of the deformation vector can be determined with at least three values \(d_{LOS}\) obtained from different satellite geometries for the same resolution cell. However, only ascending \(d_{LOS}^{d}\) and descending \(d_{LOS}^{d}\) deformation values were available for our study area. Since the sensibility of the deformation measurement of Sentinel-1 is low due to its almost polar orbit (e.g., [43]), most authors [15] have set up \(d_{N}\) to be equal to zero while decomposing. This approach was additionally justified in our case study area because the deformation basin is elongated in the north-south direction, following the coal extraction direction. Therefore, the dominant deformation components in our study area were horizontal in the east-west direction and vertical when the deformations in the north-south direction were close to zero.
# Effects of Aerosols on Gross Primary Production from Ecosystems to the Globe
## 4 Discussion  
### Model Improvement by Incorporating the Effects of Diffuse Radiation  
The traditional ecosystem models typically do not consider the impact of diffuse PAR [47], and have a certain deviation in simulated GPP under clear and cloudy conditions [48]. However, the changes in diffuse radiation under natural conditions are often caused by changes in aerosols, and our results found that aerosols can significantly increase diffuse PAR. [PERSON] et al. [30] hypothesized that aerosols would increase NPP due to the increased quantum yield of diffuse light. However, the effects of diffuse light on GPP have not been quantified globally. In addition, we also found that when there was a higher D\({}_{\text{f}}\), aerosols weakened plant photosynthesis by reduced direct PAR. Therefore, compared with previous modeling studies that only considered the total incident PAR [49; 50], our research results revealed that the relative effects of diffuse PAR and direct PAR on plant growth should be considered in GPP models.  
Most LUE models define GPP as the product of PAR and LUE absorbed by the vegetation canopy [2]. When PAR is affected by aerosol and changes, maximum LUE will also change [51]. In the estimation of GPP based on remote sensing, the GPP estimation caused by the structural changes of LUE can also be considered as a key component of the total error budget [21; 52; 53]. The LUE models like EC-LUE typically use a constant LUE independent of the biological community [48], and assume that LUE is independent of the diffuse PAR [54], which leads to the underestimation of GPP on cloudy days with more diffuse radiation.  
In this research, through the AOD-LUE model, we quantified the maximum LUE parameter of PAR\({}_{\text{diff}}\) and PAR\({}_{\text{dir}}\) (3.61 and 1.68, respectively). The maximum LUE of PAR\({}_{\text{diff}}\) is much higher than that of PAR\({}_{\text{dir}}\) mainly because canopy photosynthesis tends to use light more effectively under diffuse light than direct light [55]. Most of the modeling and observational studies also proved that LUE increases when the D\({}_{\text{f}}\) increases [56; 57]. The regression relationship between the GPP estimated by the EC tower data and the GPP simulated by the AOD-LUE model has a slope of 0.68, which is higher than the slope for the  
Figure 7: Percentage changes in GPP caused by aerosols globally and in different biomes: Crop (CRO). Closed Shrub (CSH), Deciduous Broadleaf Forest (DBF), Evergreen Broadleaf Forest (EBF), Evergreen Needleleaf Forest (ENF), Grassland (GRA), Mixed Forest (MF), Open Shrub (OSH), Wetlands (WET), Woody Savanna (WSA).  
original EC-LUE model (Figure 5a). This means that the model with the effects of aerosols added further shows the spatial heterogeneity of global GPP [27; 58].
# Prediction of River Sediment Transport Based on Wavelet Transform and Neural Network Model
## 3 Results and Discussion
After discrete wavelet decomposition, monthly average data of rainfall and runoff from 1982-2009 were selected as the training set and used to determine the model parameters. The data from 2010 were selected as the test samples for the prediction model. Figure 11 shows the comparison between the predicted and the observed sediment by wavelet transform and neural network model, in which the predicted sediment is indicated by the \"-\" line and the observed sediment is indicated by the red \"*\" sign. For predicting model effects, the Root Mean Squared Error (RMSE), the fit rating (S) and the Correlation Coefficient (r) are commonly used to determine the accuracy of the model's predictions. The RMSE represents the distribution of data points around the best-fit line. For a perfect match between the observed and predicted values, the root mean square error is zero. The closer the observed and predicted values are the smaller the RMSE value [27]. The RMSE can be calculated by using the following relationship:  
\[\text{RMSE}=\sqrt{\frac{\Sigma(Q_{m_{i}}-Q_{c_{i}})^{2}}{M}}\]  
Figure 11: Observed and predicted values. Figure 10: Decomposition sequence of rainfall. where \(Q_{m_{i}}\) is the measured sediment on month \(i\) and \(Q_{c_{i}}\) is the predicted sediment on month \(i\); \(M\) is the total number of values. The general form of the fit rating (S) proposed by [PERSON] [28] is expressing as:  
\[S=1-\frac{\Sigma(Q_{m_{i}}-Q_{c_{i}})^{2}}{\sum\bigl{(}\bigl{|}Q_{m_{i}}- \overline{Q_{m_{i}}}\bigr{|}+\bigl{|}Q_{c_{i}}-\overline{Q_{c_{i}}}\bigr{|} \bigr{)}^{2}}\]  
where \(\overline{Q_{m_{i}}}\) is the measured average sediment on month \(i\); \(\overline{Q_{c_{i}}}\) is the predicted average sediment on month \(i\). If the S is close to 1, it indicates that the error between the predicted and measured results is small. The Correlation Coefficient (r) demonstrates the closeness between the observed and predicted values:  
\[r=\frac{\sum\bigl{\{}\bigl{(}Q_{m_{i}}-\overline{Q_{m_{i}}}\bigr{)}\bigl{(}Q_ {c_{i}}-\overline{Q_{c_{i}}}\bigr{)}\bigr{\}}^{2}}{\sqrt{\sum\bigl{(}Q_{m_{i} }-\overline{Q_{m_{i}}}\bigr{)}^{2}}\sqrt{\sum\bigl{(}Q_{c_{i}}-\overline{Q_{c _{i}}}\bigr{)}^{2}}}\]  
Based on the prediction results, the Root Mean Squared Error (RMSE), the fit rating (S) and the Correlation Coefficient (r) are 0.05, 0.98 and 0.99, respectively, indicating that the prediction model can predict the overall trend and volatility of sediment load more accurately.
## 3 Experimental Study
### Prediction Results
Compared to ensemble methods constructed by the internal and external features-based compared algorithms, the ensemble model of the proposed method showed more accurate performance in MAPE by 25.2% up to 34.5%. For the MAE results, our prediction scheme for peak periods achieved improved accuracy by 15.4% up to 23.8% compared to the other methods. For the SMAPE and RMSE results, the proposed ensemble method outperformed the compared methods by reducing 24.0% up to 30.8% of SMAPE and by 10.5% up to 22.2% of RMSE, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**MLR**} & \multicolumn{2}{c}{**LASSO**} & \multicolumn{2}{c}{**SVVR**} & \multicolumn{2}{c}{**XGBoost**} & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**LSTM**} & \multicolumn{2}{c}{**Proposed**} \\ \cline{2-13}
**Centers** & \multicolumn{2}{c}{**MAPE MAE**} & \multicolumn{2}{c}{**MAPE MAE**} & \multicolumn{2}{c}{**MAPE MAE**} & \multicolumn{2}{c}{**MAPE MAE**} & \multicolumn{2}{c}{**MAPE MAE**} & \multicolumn{2}{c}{**MAPE MAE**} & \multicolumn{2}{c}{**MAPE MAE**} & \multicolumn{2}{c}{**MAPE MAE**} & \multicolumn{2}{c}{**MAPE MAE**} & \multicolumn{2}{c}{**MAPE MAE**} \\  & \multicolumn{2}{c}{**SMAPERMSE**} & \multicolumn{2}{c}{**SMAPRMSE**} & \multicolumn{2}{c}{**SMAPRMSE**} & \multicolumn{2}{c}{**SMAPRMSE**} & \multicolumn{2}{c}{**SMAP RMSE**} & \multicolumn{2}{c}{**SMAP RMSE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**SMAP RMSE RMSE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**RMSE**} \\ \hline Mail Center & 8.700 & 176,927.2 & 8.800 & 183,267.9 & 8.300 & 168,968.4 & 7.000 & 143,238.6 & 5.960 & 120,928.2 & 9.581 & 198,052.7 & 8.200 & 166  
Figure 8 shows the prediction performance of the proposed ensemble method using the enriching dataset based on VAE. We have experimented by replacing the MLP models in the proposed method with other compared prediction algorithms to show that algorithms used in the proposed method work well together. From Figure 8, we can see that the VAE-based data enrichment improved the average MAPE of all versions of weighted ensemble methods successfully. Moreover, the proposed MLPs-based weighted ensemble method learned from the enriched training dataset outperformed all weighted methods based on the MLR, LASSO, SVR, XGBoost, RF, and LSTM. In particular, our prediction scheme for peak periods demonstrated better performance compared with other predictive models by 21.9% on average, as shown in Figure 8.  
As a result of the experimental study on Korean postal parcel datasets, the proposed method improved the prediction performance in terms of the MAPE reduction rate up to 59.6% compared to other methods during peak periods, such as the Lunar New Year and Korean Thanksgiving Day, when demand for logistics services increases sharply.
# Recycling Aged Asphalt Using Hard Asphalt Binder for Hot-Mixing Recycled Asphalt Mixture
## 2 Experiments
### Test Methods  
#### 2.2.1 Dynamic Modulus Test  
The objective of the dynamic modulus test is to determine the optimal TLA content in hard asphalt binder considering the improvement of hard asphalt binder on the dynamic modulus of recycled asphalt mixture (RHMA). The LPC Bituminous Mixture Design Guide requires that the dynamic modulus of the high-modulus asphalt mixture is over 14,000 MPa under the conditions of a temperature of 15 \({}^{\circ}\)C and a load frequency of 10 Hz [7]. On the other hand, considering that TLA is more expensive than ordinary asphalt, the minimum TLA content that meets the above requirements is the optimal TLA content in hard asphalt. Therefore, the optimal TLA can be determined when the modulus of RHMA reaches 14,000 MPa under 15 \({}^{\circ}\)C, 10 Hz. A UTM-35 test machine was used, and the investigated frequency was 0-25 Hz. The specimens were prepared with EME-20@50%RAP as it was previously introduced in Table 7, wherein the TLA content in hard asphalt binder was 30%, 40%, 50%, and 70%. The RHMA specimens were compacted using a gyration compactor. The diameter was 150 mm, and the height was 170 mm. The specimens were cored to a diameter of 100 mm and a height of 150 mm, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Item** & **HA** & **Specification** \\ \hline Penetration at 25 \({}^{\circ}\)C (0.1 mm) & 31 & 20–40 \\ \hline Softening point (\({}^{\circ}\)C) & 59 & \(\geq\)54 \\ \hline Ductility at 15 \({}^{\circ}\)C (cm) & 45 & - \\ \hline Density (g/m\({}^{3}\)) & 1.42 & 1.3–1.5 \\ \hline After TFOT (163 \({}^{\circ}\)C, 5 h) & Mass loss (\%) & 0.22 & \textless{}2.0 \\ \hline \end{tabular}
\end{table}
Table 6: Typical characteristics of used hard asphalt binder.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Mixtures** & **Newly-Added** & **Optimal** & \
\begin{tabular}{c} **Newly-Added** \\ **Binder** \\ \end{tabular} &
\begin{tabular}{c} **Binder/Aggregate Ratio** \\ **Mass of Recycled Mixture** \\ \end{tabular} \\ \hline AC-20 & VA & 4.20\% & 0 \\ EME-20@50\%RAP & HA & 5.50\% & 2.79\% \\ EME-20@100\%RAP & HA & 6.10\% & 1.36\% \\ \hline \end{tabular}
\end{table}
Table 7: Asphalt contents of the three mixtures.  
Figure 2: Gradation of the aggregates in the three RAP classifications.
# Highly Controlled Integration of Graphene Oxide into PAN Nanofiber Membranes
## 4 Conclusions  
Simple modification of the GO filler significantly improved its dispersity in a polymer precursor solution during electrospinning, allowing for the systematic integration of GO fillers into PAN nanofibers without notable defects. The resulting composites offered the possibility of understanding  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & \begin{tabular}{c} **Biggest Pore Size (nm)** \\ **<Bubble Point>** \\ \end{tabular} & \begin{tabular}{c} **Smallest Pore** \\ **Size (nm)** \\ \end{tabular} & \
\begin{tabular}{c} **Avg. Pore Size** \\ **(nm)** \\ \end{tabular} &
\begin{tabular}{c} **Thickness** \\ **(nm)** \\ \end{tabular} & **Porosity (\%)** \\ \hline \hline Bare PAN & 664 & 167 & 207 & \(80\pm 1\) & \(54\pm 2\) \\ PAN-GO4 & 559 & 311 & 334 & \(83\pm 1\) & \(53\pm 1\) \\ PAN-mGO4 & 457 & 223 & 260 & \(84\pm 2\) & \(51\pm 3\) \\ PAN-mGO10 & 484 & 251 & 279 & \(89\pm 2\) & \(49\pm 3\) \\ PAN-mGO20 & 499 & 279 & 302 & \(91\pm 3\) & \(48\pm 4\) \\ PAN-mGO30 & 514 & 310 & 332 & \(92\pm 2\) & \(48\pm 2\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Integrity of bare Bare polyacrylamide(PAN) and PAN-GO composite membranes as a function of graphene oxide (GO) content.  
Figure 3: Stress-strain curves of bare PAN, and the series of PAN-GO composite membranes as a function of GO content.  
their structural and mechanical properties as well as membrane integrity as a function GO content well beyond typical limits. In particularly, the loading of surface-modified GO filler into polymer nanofiber membranes highly enhanced its mechanical stress and notably reduced water contact angel which are key properties applicable to water treatment systems. Thus, the proper modification of fillers can allow for a greater capability of integrating fillers into polymer nanofibers with minimal structural defects.  
[PERSON] and [PERSON] designed the experiments. [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] wrote the manuscript.  
This research is supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2018R1A2B6008854).  
The authors declare no conflict of interest.
### Enzymes Involved in Nucleotide Metabolism
#### Enzymes Synthesizing 5\({}^{\prime}\)-NDPs and Their Analogs  
Most 5\({}^{\prime}\)-(d)NDPs are synthesized from 5\({}^{\prime}\)-(d)NMPs by NMP kinases (NMPKs) in a reversible reaction, using 5\({}^{\prime}\)-(d)NTP as the phosphate donor (Figure 1). NMPKs are typically specialized for substrates or substrate groups, such as adenylate kinase or UMP-CMP kinase [49]. Unlike (d)NKs, NMPKs mainly do not differentiate between ribo- and deoxyibo-substrates [50]. However, a small group of dNMPKs, such as those found in bacteriophages T4 and T5, are specific to deoxynucleotide substrates while accepting a broad range of nucleobases [51]. Viral and parasitic NMPKs often exhibit a broad substrate range. For example, thymidylate kinase from _Plasmodium falciparum_ phosphorylates dGMP, dUMP, dIMP, and GMP, in addition to thymidine-5\({}^{\prime}\)-monophosphate (TMP). Similarly, the thymidylate kinase from _Vaccinia virus_ has a wide substrate spectrum, phosphohydatingTMP, UMP, halogenated UMP derivatives, and bulkier substrates like dGMP or privudine monophosphate [22; 52].  
The interconversion of 5\({}^{\prime}\)-NDPs to 5\({}^{\prime}\)-dNDPs, specifically at the 2\({}^{\prime}\)-hydroxyl group, is catalyzed by RNRs (Figure 1). RNRs play a crucial role in DNA synthesis across all domains of life and are present in many viruses as well [53].
## 2 Basic Concept  
In key-agreement and authentication protocols, various techniques and methods are utilized to implement security policies and ensure essential security features. In our presented method, the encryption system theories were utilized during the protocol process to implement the security policies. These theories are subsets of the ECC cryptosystem, each offering unique security features. Each of these is detailed in this section of the article.
## 3 Processing Method of Vibration Piezoelectric Signal
### Sampling Rate Selection  
The signal acquired by the piezoelectric sensor includes the time-domain response generated by collisions between the grains and the drying tray. These signals inherently contain high-frequency noise components resulting from transient impacts. Specifically, the collision events may induce sharp, short-duration high-frequency signals due to the sudden transfer of mechanical energy.  
Additionally, the vibratory system may generate harmonic components--frequencies that are integer multiples of the fundamental frequency. In this setup, the highest harmonic frequency can reach approximately 5 to 10 times the fundamental frequency, corresponding to a range of 26-52 Hz.  
According to [PERSON]'s sampling theorem, the sampling rate must be at least twice the maximum frequency component present in the signal to prevent aliasing and ensure accurate signal reconstruction.  
\[f_{s}\geq 2\times f_{max}\]  
\(f_{\text{max}}\) is the highest frequency of the signal.  
If only the fundamental frequency signal (5.2 Hz) is considered, the sampling rate should be at least  
\[f_{s}\geq 2\times 5.2\text{ Hz}=10.4\text{ Hz}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Total Mass of Corn Before** & **Mass of Grain Loss (g)** & **Grain Loss Rates (\%)** & **Drying Time (min)** \\ \hline
7.0 & 54.9 & 0.78 & 65 \\
7.5 & 59.9 & 0.79 & 76 \\
8.0 & 62.1 & 0.77 & 91 \\
8.5 & 63.2 & 0.74 & 110 \\ \hline \end{tabular}
\end{table}
Table 7: Relationship between water loss and drying time of maize seeds.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Seed and** & \multirow{2}{*}{**Serial**} & \multicolumn{3}{c}{**Traditional Box Drying Method**} & \multicolumn{3}{c}{**New Vibrating Tray Heating Method**} \\ \cline{3-7}
**Kernel Classification** & & **Number** & **Number of Grain Loss/pc** & **Mass of Grain Loss/g** & **Number of Grain Loss/pc** & **Mass of Grain Loss/g** & **Grain Loss Rate/\%** \\ \hline \multirow{8}{*}{Kernels of corn} & 1 & 560 & 210 & 1.70 & 145 & 54 & 0.45 \\  & 2 & 580 & 220 & 1.80 &  
Considering the high-frequency noise generated by collisions, assuming that the highest frequency may reach 5-10 times the fundamental frequency (i.e., 26-52 Hz), the sampling rate should be at least  
\[f_{s}\geq 2\times 52\;\mathrm{Hz}=104\;\mathrm{Hz}\]  
To ensure signal integrity and avoid aliasing, a higher sampling rate is recommended. However, excessively high sampling frequencies may introduce data processing delays, thereby compromising the system's real-time performance. To balance accuracy and responsiveness, the sampling frequency in this study was set between 104 Hz and 200 Hz.