esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### Parallel Genetic Algorithm to Determine the Community Structure of the Network
#### 3.2.2 Parallelizing the Population and Calculating the Fitness Function  
The initialized population was then parallelized using the _parallelize_ method into populationRDD. The entire population was divided into segments, and each segment of the population was assigned to an individual node in the distributed environment to process as depicted in Figure 4. The island model was used to parallelize the genetic algorithm in which the entire population was subdivided into finite populations. After mapping the parallel population segments with a fitness function, each population partition was assessed in parallel. Modularity [34] was employed as the fitness function to optimise the solutions until the network achieved the optimal community structure. Modularity is a metric that is used to analyse the different communities in a network, and the ideal  
Figure 3: Parallel FCM learning [60].  
community structure has the maximum modularity value. Modularity exists within the range \([-1,1]\). Equation (4) can be used to accurately estimate the modularity.  
\[Q=\frac{1}{2w}{\sum_{i=1}^{N}\sum_{j}^{N}\left(w_{ij}-\frac{w_{i}^{out}w_{j}^{in} }{2w}\right)}\delta\left(C_{i}C_{j}\right), \tag{4}\]  
where  
\[w_{i}^{out}=\sum_{j}w_{ij}, \tag{5}\]  
\[w_{j}^{in}=\sum_{i}w_{ij}, \tag{6}\]  
\[2w=\sum_{i}w_{i}^{out}=\sum_{j}w_{j}^{in}=\sum_{i=1}^{N}\sum_{j}^{N}w_{ij}, \tag{7}\]  
where the Kronecker delta function \(\delta\left(C_{i}C_{j}\right)\) is 1 if there is a link between vertex \(i\) and \(j\), and it is 0 otherwise. Following that, the evolution operations were carried out.
# Physiological and Behavioral Changes of Passive Fatigue on Drivers during On-Road Driving
## 4 Discussion
### Research Question 1: Eye Movement Patterns and Driving Performance under Passive Fatigue  
The results of eye movement patterns in the current study centered on the eye tracking method. The results found that gaze dispersion grew larger both horizontally and vertically after about 90 min of driving, which echoed with findings from the previous studies  
Figure 10: Vehicle dynamics under three driving stages. (Note: (**a**) represented analysis of drivers’ mean speed; (**b**) represented analysis of drivers’ speed compliance; (**c**) represented analysis of drivers’ the standard deviation of steering angle; (**d**) represented analysis of drivers’ steering hold frequency. Two asterisks (**) indicate \(p\) < 0.01, and three (***) indicate \(p\) < 0.001).  
focusing on autonomous and sleep-deprived driving situations [35]. When tasks are monotonous and boring, drivers would possibly experience passive fatigue and decrease attention allocation as illustrated in the capacity model of attention [13], leading to driver inattention or a status similar to mind wandering [14].  
Moreover, the current study found that pupil diameter increased as the driving task went on. However, the robustness of pupil diameter indicating fatigue level is relatively low [36], with multiple factors influencing the change of pupil diameters, such as workload, alertness, and sleepiness level [3]. A driving scenario that elicits passive fatigue might have a combined influence on pupil diameter.  
For driving performance, the current study showed a significantly faster driving speed and declined speed compliance at driving stage III. According to the capacity model of attention, the above observation might be a result of drivers' passive fatigue, leading them to gradually lowering their standard to obey with the research protocol and posted speed requirements (i.e., maintaining a constant driving speed). Regarding steering wheel control, the current study demonstrated that the steering wheel control was impaired more at stage II than stage I (i.e., larger _SD_ of steering angles and longer steering hold frequency), but the same trend was not found at stage III.
# Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain
## 2 System Model
### Modulation Schemes Analysis  
In the proposed system, intensity modulations with the direct detection (IM/DD) of different modulation schemes have been studied. The IM/DD is a cost-effective strategy, where LED intensity is modulated by the input signal and then demodulation is achieved through direct detection, which produces a current proportional to the received light using a photodetector. Various modulation techniques are considered: OOK, SC-BPSK, L-PPM, and L-I-PPM (where L is equal to 2\({}^{n}\) and n is an integer = 1, 2, 3,... ). In order to accomplish reliable data communication, **BER** must be less than or  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Location/Model** & **a** & **b** \\ \hline Marshall and Palmer & 0.365 & 0.63 \\ Carbonneau–France & 1.076 & 0.67 \\ Japan & 1.58 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Attenuation parameters due to rain.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Snow Type** & **a** & **b** \\ \hline Wet & 0.000102 \({}^{*}\)\(\lambda\) + 3.79 & 0.72 \\ Dry & 0.0000542 \({}^{*}\)\(\lambda\) + 5.50 & 1.38 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Attenuation parameters due to snow.  
equal to \(10^{-6}\)[24]. Therefore, calculating the necessary **SNR** to achieve the target **BER** is important. The **SNR** is obtained as [13]  
\[\textbf{SNR}=\frac{\mathbf{r}^{2}\mathbf{P}_{\mathbf{rx-los}}^{2}(\mathbf{t})}{ \mathbf{\sigma}_{\mathbf{tot}}^{2}}. \tag{19}\]  
The **BER** for the **OOK** scheme is actually obtained by the same equation as in the 2-PPM case [24], which is given by  
\[\textbf{BER}_{\mathbf{ook}}=\textbf{Q}\left(\sqrt{\textbf{SNR}}\right) \tag{20}\]  
where  
\[\textbf{Q}(\mathbf{x})=\frac{1}{2\pi}\int_{\mathbf{x}}^{\infty}\mathbf{e}^{- \frac{\mathbf{r}^{2}}{2}}\textbf{dy}. \tag{21}\]  
The **BER** performance for **SC-BPSK**, **L-I-PPM**, and **L-PPM** can be obtained as [24]  
\[\textbf{BER}_{\mathbf{SC-BPSK}}=\textbf{Q}\left(\sqrt{\frac{\textbf{SNR}}{2}}\right) \tag{22}\]  
\[\textbf{BER}_{\mathbf{L-PPM}}=\textbf{Q}\left(\sqrt{\textbf{SNR}}\ \sqrt{\log_{2}\mathbf{L}}\right) \tag{23}\]  
\[\textbf{BER}_{\mathbf{I-L-PPM}}=\textbf{Q}\left(\sqrt{\textbf{SNR}}\ \sqrt{\frac{\log_{2}\mathbf{L}}{\mathbf{L-1}}}\right). \tag{24}\]  
Using the Q-function table, the minimum SNR values for each modulation scheme can be evaluated as shown in Table 4. These values will be considered as threshold values for each modulation technique.
# A Linear Fitting Algorithm Based on Modified Random Sample Consensus
## 2 Common Linear Fitting Algorithm
### Maximum Likelihood Estimation  
The MLE utilizes a probabilistic model aimed at estimating and determining the optimal parameter settings for the fitting model by maximizing the likelihood of the observed data given the model parameters [12].  
The core idea of MLE revolves around the parameter estimation problem. Specifically, it estimates an unknown non-random constant \(\theta\) by observing \(n\) sample data points \((x_{1},x_{2},x_{3},\ldots,x_{n})\), with the estimated value of \(\theta\) maximizing the probability of the occurrence of the sample data.  
Suppose there exist \(n\) sample data as \((x_{1},y_{1}),(x_{2},y_{2}),\cdots,(x_{n},y_{n})\) which follows  
for the maximum likelihood estimation method. Thus, by solving the WLS, the optimal model parameters can be obtained.
# Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Exoregions
## 2 Materials and Methods
### Variables for Estimating Emissions
#### 2.2.4 Emission Factor  
The emission factor is the amount of a compound released in grams per kilogram of fuel consumed (g/kg). The data in [41] have been widely used, but they are at the global level; thus, in this work, we used emission factors that were measured during a campaign in Mexico as part of the Megacity Initiative Local and Global Research Observation (MILAGRO) project, which was developed by the US Forest Service and Mexican agencies, including CONAFOR [42]. For this study, those results [42] were complemented with the work of [43], since the MILAGRO project did not consider two types of vegetation involved in the prescribed burnings.
## 3 Analysis of High Ground Temperature Monitoring Results
### Monitoring Plan  
Monitoring points were set according to the on-site situation. The temperature of the jointed rock mass was measured using temperature monitoring probes, and the temperature monitoring range was 9 m. The monitoring instruments were set at the arch waist of the tunnel so that the probes can monitor all the temperatures of the rock mass at the positions of 0 m, 3 m, 6 m, and 9 m away from the tunnel wall. A total of eight monitoring groups were arranged. Prior to use, the monitoring probes and cables were subjected to high-temperature and waterproof treatments. The on-site monitoring layout is shown in Figure 5.
## 2 Research Method  
### Mathematical Modeling  
#### 2.1.1 Damping Force Model  
The MR damper generates a magnetic field when a current is applied to the internal coil, and the MR fluid in the chamber generates yield stress. The generated yield stress causes a pressure drop between the upper and lower chambers, resulting in a damping force corresponding to the movement of the piston. The damping force of the proposed MR damper with an additional flow path can be expressed as follows:  
\[F_{Damper}\equiv F_{viscosity}+F_{MR}+F_{accumulator} \tag{1}\]  
Figure 1: Cross section of the proposed MR damper.  
Figure 2: Floor section of the proposed MR damper.  
The total damping force of the proposed MR damper with an additional flow path is defined as \(F_{Damper}\), the viscous damping force due to fluid viscosity is defined as \(F_{viscosity}\), the damping force due to the yield stress generated by the MR fluid is defined as \(F_{MR}\), and the elastic force of the MR damper accumulator is defined as \(F_{accumulator}\). \(F_{accumulator}\) can be interpreted as an experimental measurement equation, which is formulated as follows:  
\[F_{accumulator}=k_{e}x_{piston} \tag{2}\]  
In Equation (2), \(k_{e}\) is the accumulator coil spring constant, and \(x_{piston}\) is the piston displacement. \(k_{e}\) can be indicated as in Equation (3).  
\[k_{e}=\frac{A_{r}^{2}}{C_{g}}=\frac{P_{0}\kappa}{V_{0}}A_{rod}^{2} \tag{3}\]  
In Equation (3), \(A_{rod}\) is the area of the silver piston rod, \(P_{0}\) is the initial pressure, \(V_{0}\) is the volume of the accumulator, \(C_{g}\) is the compliance of the accumulator gas, and \(\kappa\) is the specific heat ratio. However, the elastic force of the MR damper accumulator is not included in \(F_{Damper}\) because \(F_{MR}\) has an effect similar to that of the elastic element of the nitrogen accumulator. Therefore, \(F_{Damper}\), the total damping power of the MR damper with an additional flow path used in this study, is assumed as follows.  
\[F_{Damper}\cong F_{viscosity}+F_{MR} \tag{4}\]
# Speech Emotion Recognition through Hybrid Features and Convolutional Neural Network
## 4 Experimental Results and Discussion
### Results of Experiment I  
In this experiment, the derived MFCCT features were utilized as inputs for five distinct ML classifiers, including k-NN, RF, J48, NB, and SVM, as well as a CNN. The weighted accuracies of these classifiers for three datasets are demonstrated in Figure 4. The CNN model outperformed the classifiers, obtaining a 97% weighted accuracy for the Emo-DB dataset. Additionally, the CNN model for SER achieved 92.6% and 91.4% weighted accuracy for the SAVEE and RAVDESS datasets, respectively. Among the other five ML classifiers, an irregular pattern was observed in weighted accuracy. The SVM and RF classifiers for SER achieved the highest weighted accuracy (80.7% and 86.9%) on the Emo-DB dataset SER model, compared to other three ML classifiers. Moreover, the SER models for the SAVEE  
\begin{table}
\begin{tabular}{c} \hline \hline
**CNN Configuration** \\ \hline Conv\_1 (5, @64), activation function = “ReLU”, Dropout Rate (0.2), \\ MaxPooling\_1 (4), Conv\_2 (5, @128), activation function= “ReLU”, \\ Dropout Rate (0.2), MaxPooling\_2 (4), Conv (5, @256), activation function= “ReLU”, \\ function= “ReLU”, Dropout Rate (0.2), Flatten, Dense (7 neurons), \\ Softmax (7 emotions) \\
**Parameters:** Adam Optimizer, 100 epochs, 0.0001 learning rate \\ \hline \hline \end{tabular}
\end{table}
Table 2: Layer structure of the lightweight CNN model.  
and RAVDESS, RF, and k-NN classifier achieved the highest weighted accuracy (74% and 54.1%, respectively) compared to the other four ML classifiers. In all the analysis, the NB classifier achieved the lowest weighted accuracy.  
In summary, the CNN model for SER demonstrated superior performance over the other ML classifiers in achieving improved recognition accuracy using all datasets. Furthermore, the ROC diagrams for the three datasets achieved through the CNN model are presented in Figure 5. Figure 5a illustrates the ROC diagram for Emo-DB. The performance of boredom and disgust emotions is slightly better than the anger emotion. This could be because several analysis techniques, such as formant and pitch, are less precise for high-pitch emotions as compared to the low-pitch emotions. Figure 5b illustrates the ROC diagram for all 7 emotions, indicating acceptable recognition accuracy for all emotions. Figure 5c shows the ROC diagram for all emotions in RAVDESS dataset, demonstrating reasonable recognition accuracy for all emotions.  
Figure 4: Weighted accuracies of the classifiers for the three datasets.  
Figure 5: ROC Curves of the (**a**) EMO-DB, (**b**) SAVEE, and (**c**) RAVDESS Models.
# Design of an Emulgel for Psoriasis Focused on Patient Preferences
## 3 Results
### HP-\(\beta\)-CD Hydrogel  
The specific recognition and binding of CDs and various guest molecules have been used as a strategy to alter the release profiles of soluble and poorly soluble active ingredients from hydrogels. Although the natural CDs are hydrophilic, their aqueous solubility is limited, as is the case with \(\beta\)-CD. The physicochemical properties and, consequently, the inclusion capacity of native CDs have been improved over time through the chemical modification of their hydroxyl groups, giving rise to different CD derivatives, such as Me-\(\beta\)-CD and HP-\(\beta\)-CD. These substituted CDs have better water solubility and are suitable for the formation of inclusion complexes with poorly water-soluble \"guest\" molecules. As the incorporation of a BD-cyclodextrinrin inclusion complex into the hydrogel matrix can enhance the aqueous solubility of the corticosteroid drug, the solubilizing potential and complexing ability of Me-\(\beta\)-CD and HP-\(\beta\)-CD with BD was evaluated using a phase solubility method [25]. Phase-solubility study is a very efficient method and involves an examination of the effect of cyclodextrin on the substrate (BD). Thus, the equilibrium concentrations of BD were plotted against the concentration of each CD and in this way the phase solubility diagrams were established. The aqueous solubility of BD increased linearly as a function of CDs showing, in both cases, an \(A_{L}\)-type solubility profile [26]. As the slope of the plot is less than unity, a 1:1 molecular complex is formed for the BD and CDs [26]. The determination of the stability constant (\(K_{S}\)) is a useful index to estimate the binding strength of the host-guest and the changes in the physicochemical properties of the guest in the complex. The \(K_{S}\) value of the complexation was calculated from the phase solubility diagram according to (1) (see Materials and Methods). The stability constants, \(K_{S}\), calculated for Me-\(\beta\)-CD and HP-\(\beta\)-CD inclusion complexes, were 6603.7 \(\pm\) 325.0 M\({}^{-1}\) and 5990.0 \(\pm\) 275.4 M\({}^{-1}\), respectively. Phase solubility studies indicate that the solubility of BD is considerably improved in the presence of CDs. Considering the data found and that HP-\(\beta\)-CD is nowadays one of the most versatile excipients among the cyclic oligosaccharides, being used in oral, dermal, and parenteral formulations, this CD was selected for the preparation of the hydrogel. Although encapsulation in cyclodextrin has been shown to be effective in increasing BD solubility, it was not possible to achieve the active pharmaceutical ingredient dosage commonly found in marketed formulations for topical application. In fact, during the hydrogel preparation, complete solubilization of BD was not obtained (it was only possible to dissolve about 15 mg of the 64 mg of BD), meaning that HP-\(\beta\)-CD complexation capacity does not sufficiently enhance the solubility of the poorly soluble drug. New techniques and approaches are currently under study in order to improve the aqueous solubility of BD.
## 6 Conclusions  
In this study, we explored the spatial and temporal evolution of ecosystem WUE in the Li River Basin, 2001-2020, in combination with different analytical tests, to understand the inter-annual trend in WUE and the impacts of different driving factors on the change in WUE. We found the following:  
1. The average value of WUE in the Li River Basin from 2001 to 2020 was 1.8251 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\) and showed a decreasing trend at a rate of 0.0072 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\). The WUE of the various land cover types from high to low were forest > shrubland > grassland > cropland; spatially, the south side of the basin was the main distribution area of the high values of WUE, while the low values were mostly located in the central part of the basin.
2. By using the correlation analysis method to determine the relationship between different influencing factors and WUE, it was found that ET was the primary influencing factor in the change in WUE; in 67.22% of the total area of the basin, precipitation was positively correlated with WUE; in 92.67% of the total study area, air temperature was negatively correlated with WUE.
3. The analysis showed that the NDVI, LAI, and WUE were all negatively correlated with WUE, and the percentages of negatively correlated areas were similar, both between 60 and 70%, and the growth of vegetation inhibited the increase in WUE in the basin to a certain extent. Regarding VPD, the proportions of positive and negative correlation areas with WUE were similar, accounting for 49.58% and 50.42%, respectively.
4. Based on the SPEI, we found that the occurrence of drought events and their enhancement resulted in a continuous increase in WUE in the basin; for different vegetation types, the correlation of the SPEI and WUE from strongest to weakest was grassland > cropland > forest > shrubland.  
[PERSON] wrote the main manuscript text; [PERSON] and [PERSON] were responsible for the polishing of the article. All authors have read and agreed to the published version of the manuscript.  
This study was jointly financed by the natural science foundation of China GuangXi (Xuemei Zhong-2020 GXNSFAA297266). The authors are grateful for the funding for this research. At the same time, the authors are grateful to the anonymous reviewers and editors for their input and constructive comments.  
The dataset will be provided if requested.
# Attention Block Based on Binary Pooling
## 5 Conclusions  
This paper proposes a novel attention block for image classification. Compared to previous research on attention, our work utilizes more advanced pooling operations. Additionally, to enhance the extraction capability of feature vectors, fully connected and point convolution layers are adopted to aggregate feature maps maximally. Experiments on the ImageNet demonstrate the effectiveness and superior performance of the proposed model. The performance of the model is not limited to ImageNet alone. It is believed to exhibit promising outcomes in medical applications such as disease diagnosis, organ segmentation, and skin anomaly detection. Due to limitations in acquiring datasets, the efficacy of this model in medical imaging will be our focus in future research. However, there is still room for further research. For example, to avoid information loss and noise amplification, the dimensionality of operation is roughly chosen, which significantly increased the number of parameters in the attention block, although the increased computational complexity is acceptable. There are considerable optimization techniques in the fully connected layers. Furthermore, our proposed attention block is channel-based, but incorporating spatial attention may yield even better results. Exploring spatial attention mechanisms is thus an area for future research.  
Conceptualization, C.C.; methodology, C.C.; software, C.C.; validation, C.C.; formal analysis, C.C.; investigation, C.C.; resources, H.Z.; data curation, C.C.; writing--original draft preparation, C.C.; writing--review and editing, C.C. and H.Z.; visualization, C.C.; supervision, H.Z.; project administration, C.C.; funding acquisition, H.Z. 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 under Grant U1809206.  
**Institutional Review Board Statement:** Not applicable.  
Figure 4: Attention weight distribution map. The redder the color, the higher the attention weight. These pictures well prove that using proposed attention block can bring good accuracy to the model.
# Walking-Age Estimator Based on Gait Parameters Using Kernel Regression
## 3 Results
### Results of Kernel Regression Model Without Height and Weight  
We also estimated the ages of walkers using kernel regression analysis with only the gait parameters, excluding self-reported height and weight. As mentioned in Section 1, our method is intended for monitoring the physical fitness of users; hence, it is reasonable that some information is provided by the users to estimate their walking age. However, to widen the potential applications of the age-estimation method, knowing the accuracy of the age estimation only by gait parameters is important. As listed in Table 5, after variable selection, six gait parameters remained as explanatory variables. The mean correlation coefficient between the estimated and actual ages was 0.67. The mean absolute error \(\pm\) S.D. was \(12.97\pm 0.97\) years. The lowest estimated age was below zero, \(-12.42\) years old, and the highest estimated age was 87.69 years old. Without the self-declared information, the estimation accuracy of the kernel regression analysis was comparable to that of the multiple-regression analysis with the self-declared information, as in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameters** & **Weight** & **Minimum Foot Clearance** & **Anterior Velocity of CoM** & **Step Width** & **Stride Length** \\ \hline Sensitivity & 0.95 & 0.96 & 0.91 & 0.87 & 0.83 \\ \hline \end{tabular}
\end{table}
Table 4: Sensitivity of explanatory variables. Gradients of regression lines between the actual and estimated ages.  
\begin{table}
\begin{tabular}{c c} \hline
**Model** & **Explanatory Variables** \\ \hline Kernel regression analysis (before variable selection) & Height \\ Weight \\ Minimum foot clearance \\ Mediolateral velocity of CoM \\ Anterior velocity of CoM \\ Step width \\ Stride length \\ Cadence \\ Swing duration  
\end{table}
Table 5: Differences in explanatory variables by the estimation method.