esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Spatial Attention Mechanisms in Geospatial Applications  
Spatial attention mechanisms have increasingly been integrated into deep learning models to improve their capacity to focus on important spatial regions within input data [32]. Attention mechanisms are inspired by the human visual system, which selectively focuses on the salient parts of a scene. In geospatial applications, spatial attention enables models to assign varying importance to different locations, enhancing their capability to analyze complex spatial patterns in data that contain both local and global dependencies [33]. In the context of railway deformation risk prediction, spatial attention mechanisms can be applied to identify critical areas within a railway network that exhibit significant deformation signals [34]. This targeted focus enables the model to prioritize high-risk regions, enhancing the interpretability and efficiency of deformation predictions. By embedding spatial attention in a deep learning framework, models can better capture localized deformation behaviors and variations in terrain, soil type, or infrastructural elements that contribute to differential deformation risks. Research in spatial attention for geospatial applications often involves the integration of convolutional neural networks (CNNs) with attention layers to process spatial information effectively [18]. These models leverage spatial dependencies and contextual information, which are crucial in complex geospatial phenomena. For railway deformation monitoring, spatial attention helps to refine the predictive accuracy of models by focusing computational resources on high-impact areas, potentially identifying early signs of deformation that might otherwise be overlooked in traditional analysis [35]. Combined with temporal models like LSTM, spatial attention enhances the overall performance of predictive systems, offering a robust solution for continuous monitoring and risk management in large-scale railway infrastructures [36].
## 5 Dynamic Pressure Control Technology for a Roadway's Surrounding Rock  
### Basic Concepts of Surrounding Rock Control  
After the roadway was excavated, the stress field of the original rock around the roadway underwent secondary stress redistribution due to damage, as depicted in Figure 11. At the simultaneous occurrence, there was a gradual formation and development of the plastic zone in the surrounding rock, necessitating prompt support for the roadway. Anchor bolts and cables are frequently employed for the purpose of providing support to newly excavated roadways, thereby minimizing the deformation of the surrounding rock formations and guaranteeing the overall safety of the roadway. At present, the shallow section of the roadway can be effectively supported by traditional anchor bolts and cable supports to ensure a stable load-bearing structure.  
However, roadways under dynamic pressure often have to withstand one or more mining impacts during their service life. Due to the displacement of rock formations caused by intense dynamic pressure, the anchor cables frequently experience shearing, leading to support failure and localized instability in the surrounding rock. The stable bearing body formed before also begins to lose stability, eventually leading to the damage of the roadway, and even roof collapse and other phenomena [29].  
Therefore, considering the significant distortion and damage caused to the roadway under dynamic pressure, it becomes challenging for traditional anchor bolts and cables to ensure the stability of the surrounding rock. Consequently, passive supports, such as  
Figure 10: Relationship between the supporting force and plastic zone (\(\lambda=1.5\)).  
U-shaped steel frames and CFST supports, are necessary [30]. The high bearing capacity of passive supports limits the roadway displacement, achieving the purpose of controlling large deformation. Accordingly, an overall active and passive support technology comprising an anchor cable and channel steel beam-CFST column combined bracket is proposed herein to support roadways under dynamic pressure.
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
## 6 Simulation Results
### Number of Packets Received  
Figure 12 shows the cumulative number of packets received for the standard and the proposed WAC-MAC protocol. WAC-MAC protocol delivers 780 bytes of data as compared to 700 bytes of data transmitted by the standards scheme. This improvement in the number of packets sent is due to the added function of sensing before frame transmission to identify WLAN inactivity periods, thus resulting in low chances of transmission overlap with the WLAN user.
# Optimization and Mapping of the Deep Drawing Force Considering Friction Combination
## 2 Simulation of Deep Drawing Process
### Forming Limit Stress Diagram (FLSD)  
The failure at different values of the geometrical and operating parameters was investigated by using the damage initiation criteria in ABAQUS for the forming limit stress diagram (FLSD) to determine the mechanical properties of the sheet metal. The material was assumed to be anisotropic with three perpendicular similarity planes. The yield theory is presented as the following quadratic function [33; 34; 35]:  
\[2f\big{(}\sigma_{ij}\big{)}\equiv F\big{(}\sigma_{y}-\sigma_{z}\big{)}^{2}+G \big{(}\sigma_{z}-\sigma_{x}\big{)}^{2}+H\big{(}\sigma_{x}-\sigma_{y}\big{)}^{ 2}+2L\tau_{yz}^{2}+2M\tau_{zx}^{2}+2N\tau_{xy}^{2}=1, \tag{3}\]  
where f is the yield function, \(F\), \(G\), \(H\), \(L\), \(M\), and \(N\) are factors particular to the anisotropic condition of the material, and \(x\), \(y\), and \(z\) are the principal anisotropic axes. For the plane stress condition (\(\sigma z=\tau xz=\tau yz=0\); \(\sigma x\
eq 0\); \(\sigma y\
eq 0\); \(\tau xy\
eq 0\)), the yield criterion function becomes  
\[2f\big{(}\sigma_{ij}\big{)}\equiv(G+H)\sigma_{x}^{2}-2H\sigma_{x}\sigma_{y}+( H+F)\sigma_{y}^{2}+2N\tau_{xy}^{2}=1. \tag{4}\]  
The anisotropic constants \(F\), \(G\), \(H\), and \(N\) can be formulated in terms of the r-values as follows:  
\[F=\frac{r_{0}}{r_{90}(1+r_{90})},\,G=\frac{1}{(1+r_{0})},\,H=\frac{r_{0}}{(1+ r_{0})},\,N=\frac{(r_{0}+r_{90})(1+2r_{45})}{2r_{90}(1+r_{0})}. \tag{5}\]  
The r-values \(r_{0}\), \(r_{45}\), and \(r_{90}\) were 1.79, 1.51, and 2.27, respectively as presented in Table 2. When the principal directions of the stress tensor correspond with the anisotropic axes (\(\sigma x=\sigma 1\), \(\sigma y=\sigma 2\), \(\tau xy=0\)), the yield theory can be presented as the principal stress.  
\[\sigma_{1}^{2}-\frac{2r_{0}}{(1+r_{0})}\sigma_{1}\sigma_{2}+\frac{r_{0}(1+r_ {90})}{r_{90}(1+r_{0})}\sigma_{2}^{2}=\sigma_{0}^{2}, \tag{6}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Property** & **Value** \\ \hline Young’s modulus (\(E\)) & 206 GPa \\ \hline Poisson’s ratio (\(\
u\)) & 0.3 \\ \hline Density (\(\rho\)) & 7800 kg/m\({}^{3}\) \\ \hline Yield strength (\(\sigma_{y0}\)) & 167 MPa \\ \hline Anisotropy characteristic values & \(r_{0}=1.79\), \(r_{45}=1.51\), \(r_{90}=2.27\) \\ \hline \end{tabular}
\end{table}
Table 2: The material properties of sheet metal blank [1].  
Figure 3: Plastic true stress–true strain curve of mild steel [30].  
where \(\sigma_{1}\) is a major stress, \(\sigma_{2}\) is a minor stress, and \(\sigma_{0}\) is the yield stress in the rolling direction (\(\sigma_{o}\)) of the sheet metal. Figure 4 shows the forming limit stress diagram for the sheet metal with a thickness equal to 0.8 mm.
# An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data
## 2 Literature Review
### Fuzzy C-Means Method  
In addition, the clustering method is another type of imputation method. The common clustering method typically is comprised of K-means and fuzzy K-means. [PERSON] et al. [10] pointed out that the central problem in the K-means was the determination of the centroid positions of clusters. These centroid positions were iterated to update them according to the calculated distance and judgment conditions. Then, the missing value was replaced by the nearest neighbor based on its newest cluster [10]. [PERSON] et al. [43] utilized the fuzzy K-means method to estimate the missing values. The fuzziness meant that each instance did not belong to a cluster completely. The estimation results showed that a more robust clustering was obtained after numerical tests [43]. Theoretically, the main obstacle of applying the fuzzy K-means is how to determine the cluster number and the membership degree [44; 45; 46]. It should be noted that the selection of the cluster number and the determination of the membership degree cannot follow an artificial method. Thus, fuzzy K-means based on the hybrid algorithm merits further investigation.
# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
## 2 Materials and Methods
### Methods  
#### 2.3.1 Drought Indicator  
TVDI is calculated by dry and wet edge feature space constructed with LST and NDVI:  
\[\mathrm{TVDI}=\frac{\mathrm{T_{S}-T_{S_{min}}}}{\mathrm{T_{S_{max}}-T_{S_{min}}}} \tag{1}\]  
\(\mathrm{T_{S}}\) is the land surface temperature; \(\mathrm{T_{Smax}}\) is the highest land surface temperature based on the dry-edge equation; \(\mathrm{T_{Smin}}\) is the lowest land surface temperature based on the wet-edge equation.  
The wet- and dry-edge equations can be expressed as Equations (2) and (3), respectively:  
\[\mathrm{T_{S_{max}}=a+b\times NDVI} \tag{2}\]  
\[\mathrm{T_{S_{min}}=c+d\times NDVI} \tag{3}\]  
where a and b denote the coefficients of the fitted equation for the maximum value of land surface temperature corresponding to any given NDVI on the dry edge. c and d denote the coefficients of the fitted equation for the minimum value of land surface temperature corresponding to any given NDVI on the wet edge (Figure 2).  
Figure 2: Illustration of Ts-NDVI feature space (a, b, and c are three vertices).  
Landsat lacks NDVI and LST products. Therefore, we employed a thermal infrared atmospheric correction algorithm, utilizing bands 4, 5, and 10, to retrieve the land surface temperature and calculated NDVI using bands 4 and 5.  
In order to verify the drought monitoring effect of TVDI, two drought indices MCI and SRHI representing meteorological and soil water classification were selected for comparative analysis. MCI is a drought index constructed by considering the effects of precipitation and evapotranspiration on the current drought in different time periods in the previous period [49]. SRHI is an indicator calculated by measuring the water content of different depths in the soil to measure the moisture status of the soil [50]. Such three indices were divided into different dry grades with reference to meteorological drought grade, agricultural drought grade, and TVDI drought grade (Table 1) [51; 52; 53]. Considering the limitation of drought levels in TVDI, for the purpose of facilitating statistics, the extreme drought of SRHI and MCI were grouped into the severe drought category to achieve consistency with TVDI.
# Uncertainty Quantification Approach on Numerical Simulation for Supersonic Jets Performance
## 3 UQ Analysis on Nozzle Discharge Pressure  
### Sensitivity Analysis on Discharge Pressure  
The value of the discharge pressure \(\text{p}_{\text{s}}\) has a remarkable impact on the nozzle flow structure, especially in determining the position and strength of the shock wave: this is evident referring to the 'r2' condition where the shock wave is positioned at the nozzle exit section [8]. As a proof of the reliability of the CFD model, similar results have been obtained in similar conditions in the experimental case by [PERSON] et al. [22]. Figure 3 shows Mach number contours for five different \(\text{p}_{\text{s}}\) values (80 kPa \(\div\) 120 kPa) around the 'r2' condition. The flow structure and the shock wave location change significantly even with a small \(\Delta\) of 10 kPa (about 10% of the pressure range mean value).  
The performance variation of the nozzle within the above range is further confirmed by the charts in Figure 4, which represent the Mach and the Pressure Ratio (PR) along the nozzle axis (Equation (2)).  
\[PR=p_{s}/\P  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Domain Zone** & **Type** \\ \hline Nozzle Inlet & Total pressure; Total temperature \\ \hline Environment Side walls & Discharge pressure \\ \hline Nozzle walls & No slip condition; adiabatic \\ \hline \hline \end{tabular}
\end{table}
Table 1: Boundary conditions.  
Figure 3: Mach number contours for five \(\text{p}_{\text{s}}\) values: (**a**) 80 kPa, (**b**) 90 kPa, (**c**) 100 kPa, (**d**) 110 kPa, (**e**) 120 kPa; the black line identifies nozzle exit section.  
It is evident that the position of the shock wave close to the nozzle exit is affected by the small change in the environment discharge static pressure. Another important parameter, which confirms the performance variation, is the \(\Delta\text{Pt}\) (Equation (3)) i.e., the total pressure drop through the nozzle that quantifies the mechanical losses due to viscous or compressible flow effects. In Table 2, the large variation of this quantity was reported: losses tend to decrease with outlet static pressure in a non-linear way (see the difference between cases 1 and 2 compared to 2 and 3); this is related to the shock wave location (inside/outside the convergent-divergent).  
\[\Delta\text{$p_{t}$}=\text{$\text{Pt}$}_{\text{t in}}-\text{$p_{t~{}out}$} \tag{3}\]  
This off-design behavior makes the UQ analysis useful to study the component response to a non-deterministic input variable.
## 2 Literature Review
### Covariates Analysis of Rural Single-Vehicle Crashes
#### 2.1.1 Driver Characteristics  
Numerous studies have shown that driver-related characteristics have a significant influence on the severity of rural SV crashes, including lack of seat belt use, drunk driving, speeding, fatigue, driver age, and driver gender. For example, the probability of fatal crashes in rural areas due to seat belts not being used and drunk driving will increase 15.3% and 36.3%, respectively [12]. It was also suggested that fatigue is associated with severe rural SV crashes and that increasing rest breaks could reduce crash risk [13,14]. Driver age was divided into three stages--young drivers (age < 24), mid-age drivers [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65], and older drivers (age > 65)--to establish SPFs, suggesting that the probability of severe crashes varied across age stages [15]. Specifically, compared with mid-age drivers, the probability of serious crashes of young drivers was reduced by 4.8% and that of old drivers was increased by 66.2% [16].  
There is a significant correlation between driver gender and crash severity, which has been widely accepted. However, due to the differences of research objects and modeling methods, inconsistent findings have been shown. A partial proportionality odds model and a mixed logit model were established using SV run-off-road crashes to investigate the factors associated with crash severity. It was found that male drivers were more likely to be involved in fatal or severe crashes than female drivers [17; 18]. Subsequently, a hierarchical Bayesian random intercept approach and a random parameter hierarchical ordered probit approach were established and found that female drivers were more likely to be involved in serious crashes than males [19; 20]. Further, weather conditions were divided into five categories--sun, rain, snow, fog, and overcast--to capture risk factors. The regression results suggested that male drivers were more likely to be involved in fatal or severe crashes in sunny and snowy weather than females, but this finding could not be supported by rainy, foggy, and overcast weather [21]. These findings suggest that there is no definite conclusion about the impact of driver gender on crash severity. This may be related to the effect of unobserved heterogeneity on regression results.
# Fault-Tolerant Tripod Gait Planning and Verification of a Hexopod Robot
## 2 The Modeling of Hexapod Robot
### Structure of the Hexapod Robot  
The design of hexapod robot is based on the body structure and locomotion characteristics of insects. It is composed of six legs with the same structure connected to the body which is composed of two hexagonal plates. Legs are arranged symmetrically on the left side (numbered 1, 2, 3) and right side (numbered 4, 5, 6). Each leg has three revolving joints--coxa joint, femur joint and tibia joint--connecting three segments of the limb--coxa, femur and tibia.  
Figure 1: The definition of static stability margin (SSM).  
In order to describe the robot quantitatively with a mathematical model, a series of coordinate systems need to be established. A world coordinate system is established to describe the locomotion route of the robot, denoted by the symbol \(\sum O_{w}(X_{w}Y_{w}Z_{w})\). Then the body coordinate and reference coordinate systems of leg links are established, denoted by the symbols \(\sum O_{b}(X_{b}Y_{b}Z_{b})\) and \(\sum O_{0}^{i}\big{(}X_{0}^{i}Y_{0}^{i}Z_{0}^{i}\big{)}\) (i=1,2,3,4,5,6), respectively. Coordinate systems attached to the coxa joint (\(\beta_{1}^{i}\)), femur joint (\(\beta_{2}^{i}\)), and tibia joint (\(\beta_{3}^{i}\)) of leg \(i\) are established for each joint. The simplified hexapod model and coordinate systems are shown in Figure 2.
## 3 Analysis and Discussion
### Analysis of Time-Frequency Characteristic Parameters
#### 3.2.2 Influence of Inverse Spectrum Boosting and Pre-Emphasis  
Most speech recognition systems will pre-emphasize the input speech signal to filter out low-frequency interference, namely, by a first-order digital filter to preprocess the input signal. Pre-emphasis has a transfer function \(H(z)=1-\mu z^{-1}\), where the value of \(\mu\) is close to 1 and typically takes values in the range \([0.94,0.97]\). However, this may be unreliable or even negative for GM data. Because the pre-emphasis filter used in the FFT calculations can compromise the phase information, the phase spectrum directly affects the intensity and non-stationarity of seismic waves [81], as illustrated in Figure 9a. Nevertheless, the speech signal's pre-emphasis enhances Log Mel's performance, so it is retained in MFCCs.  
Figure 8: Validation accuracy of each model with different hand-crafted feature inputs (such as the legend of 3073 s-1024: a truncation length of 30.73 s is used in the preprocessing, and the first dimension of hand-crafted features is 1024).  
In addition, both low-order and high-order DCT coefficients are easily affected by noise [82]. In contrast, the middle-order coefficients are relatively much more stable, so there are also some MFCCs implemented with cepstrum-lift that aim to suppress the Mel coefficients at both ends and strengthen the middle Mel coefficients. However, the above procedure has little influence on the GM data, as shown in Figure 9a.  
In conclusion, MFCCs are extracted using \(\mu=0\) (i.e., no pre-emphasis), while no cepstrum-lift is used.
## 3 Results and Discussion
### Product Efficiency under Laboratory Conditions
Since the averaged dust fluxes presented in Table 2 were derived from the PM10 concentrations (Figure 5), they are in full correlation with the averaged PM10 concentrations and have the same  
Figure 5: PM10 concentrations recorded during the laboratory wind tunnel experiment on the diverse dust control products under wind velocities 4.0, 6.5, 8.0, and 9.5 m s\({}^{-1}\). Note, the “control” is a soil sample with no treatment. The background level measured in the wind tunnel before the test was \(\sim\)20 m m\({}^{-3}\). differences (and significance level, \(p<0.05\)) discussed above. This is relevant also for the convenience of presentation of the dust suppression efficacy of the diverse products presented in the lower part of Table 2. The dust suppression efficiency was derived as the ratio between the averaged dust flux (per wind velocity) of specific application (Brine, Lignin, Resin, Bitumen, PVA) and the averaged dust flux of the control sample (no-treatment). The values are presented in percentage. It clearly shown that the Brine is the most efficient product in dust suppression, reducing more than 95% of the PM10 dust emission. Nonetheless, the minimum efficiency was 77.7% (Bitumen at 6.5 m s\({}^{-1}\)) among all the products and wind velocities under the controlled conditions of the laboratory experiment.
## 4 Discussion
Since volatile acids and aldehydes are odorants that could affect the aroma of sesame oil, they should be given more attention in the production and quality control of fragrant sesame oil. Our colorimetric sensor was highly sensitive to such VOCs, the difference between hot-pressed sesame oil and small-milled sesame oil could be amplified. Principal component analysis (PCA) suggested that despite slight brand-to-brand variation, the processing technique (i.e., hot pressing or small milling) had the dominant effect on the color VOC fingerprint of the final oil product. In addition, the colorimetric sensor array could effectively combine the information on sample VOCs into a color fingerprint; the color changes exhibited by the receptors were clearly observable by the naked eye, and minimal data processing was thus required. These factors make the sensor a promising rapid analytical tool for quality control in the sesame oil production line. In addition to classification by the processing technique, the sensor demonstrated the potential to detect the presence of refined oil in hot-pressed sesame oil. The deviation in the color VOC fingerprint of blended sesame oil from that of pure fragrant sesame oil was closely related to the concentration of refined oil. It could thus directly monitor the dilution of aroma during the formulation and production of blended sesame oil. In addition, VOC analysis has been proposed as an alternative method to examine the authenticity of edible oil; the VOC profile could provide more useful information on authenticity when the components in a blended oil have similar triglyceride/fatty acid profiles [47]. Moreover, when refined oil is illegally added to sesame oil, the final product is considered adulterated. Since the sensor could be operated without any strict requirement on environmental conditions, it might also be used to perform on-site inspection during market surveillance, saving time and effort in sampling and transferring to a centralized laboratory. However, the sensor demonstrated in this study was developed for the purpose of rapid classification and authentication of fragrant sesame oil. Future research can thus aim at establishing the correlation between the color VOC fingerprints generated by the sensor and the concentrations of target VOCs. Moreover, since the naked eye-observable color changes exhibited by the colorimetric receptors can be easily captured by a camera, advanced image processing algorithms, such as deep convolutional neural networks (DCNNs), can be used to further enhance the user-friendliness of the sensing device.