esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The TLBO Optimization Method
### Original TLBO Formulation
#### 4.1.1 Teacher Stage  
Individual \(i\) within the population is represented by vector \(X(i)\). Therefore, \(X(i,j)\) refers to variable \(j\) of individual \(i\). The teacher stage aims to assess the function for each member of the population considering its own parameters, \(f(X(i))\). The individual with a value closest to the optimum is selected as the teacher (\(X_{best}\)). Then, teacher values are used to bring his disciples closer to him by following Equation (2), which also takes into account the mean values of the population. Thus, each individual will have its new (\(X_{new}\)) solution.  
\[X_{new}(i,j)=X(i,j)+rand(0,1)(X_{best}(j)-TFactor\cdot X_{m}(j)) \tag{2}\]  
As can be observed in Equation (2), the value of \(X(i,j)\) is modified according to the distance that exists between the teacher and the mean of the population (\(X_{m}\)). The factor \(T\) is a random integer value that can be valued at 1 or 2, and whose calculation is shown in Equation (3). After changing the values of each individual, the function is evaluated again (\(f(X_{new}(i))\)). Only in the case that the evaluation offers a more optimal solution than the original one, the new values calculated for the individual \(i\) are taken.  
\[TFactor=round(1+rand(0,1)) \tag{3}\]
## 5 Discussion
### Limitations  
Despite the favorable performance in both qualitative analysis and quantitative evaluation, ASGASN still has certain limitations. The learning of the ASGASN model relies on a large number of training samples and performs poorly in classes with few training samples. In some areas, such as remotely sensed feature extraction, large amounts of data often lead to large costs. This requires our model to perform well against small sample data. In the future, it is critical to address small training samples through data enhancement techniques and semisupervised semantic segmentation techniques, with a focus on improving the accuracy of small sample categories.
## 3 Fabrication, Measurements and Discussions
### Measurement and Results
#### 3.2.2 Capacitance Ratio  
A critical parameter of the proposed RF MEMS switch is the capacitance ratio \(C_{r}\). However, the up- and down-state capacitance value \(C_{u}\) and \(C_{d}\) are difficult to measure. Measured S parameter extraction was used to obtain the capacitance ratio \(C_{r}\) in this paper.  
Figure 15: Measurement S parameters results of the proposed RF MEMS switch: (a) up-state; (b) down-state.  
Mathematically, the transmission (\(ABCD\)) parameters of the proposed circuit model can be expressed as follows:  
\[\left(\begin{array}{cc}A&B\\ C&D\end{array}\right)\ =\ M_{1}M_{2}M_{1} \tag{16}\]  
\[M_{1}\ =\ \left(\begin{array}{cc}\cos\theta&jZ_{0}\sin\theta\\ j\frac{1}{Z_{0}}\sin\theta&\cos\theta\end{array}\right) \tag{17}\]  
\[M_{2}\ =\ \left(\begin{array}{cc}1&0\\ Y_{2}&1\end{array}\right) \tag{18}\]  
\[Y_{2}\ =\ \left[(j\omega C_{s})^{-1}+j\omega L+R_{s}\right]^{-1} \tag{19}\]  
\[L\ =\ \frac{1}{\omega C_{s}} \tag{20}\]  
where \(M_{1}\) denotes the CPW transmission line part, \(M_{2}\) represents the lumped parameter model of the RF MEMS, \(\theta\) is the electric length of CPW transmission line, \(Z_{0}\) is the characteristic impedance of transmission line and \(C_{s}\) is \(C_{u}\) or \(C_{d}\) when the RF MEMS switch stays at different state. S\({}_{21}\) is:  
\[S_{21}\ =\ \frac{2}{A+\frac{B}{Z_{0}}+CZ_{0}+D} \tag{21}\]  
\(C_{u}\) and \(C_{d}\) can be calculated by using the above equations. \(C_{u}\) = 47.1 fF, \(C_{d}\) = 11.6 pF, \(C_{r}\) = 246.3, \(R_{s}\) = 0.55 \(\Omega\). A typical MEMS switch has a capacitance ratio of roughly 100. Therefore, the capacitance ratio in this paper is better than the traditional design.
# Analysis of Nanoindentation Test Results of Asphalt Mixture with Different Gradations
## 4 Conclusions  
In this paper, nanoindentation tests were carried out on asphalt mixture with different gradations and asphalt mastic with different RFA. The feasibility of nanoindentation in different types of asphalt mixes was investigated. And the test results of asphalt mastic before and after polishing were compared. Based on the findings, the following conclusions can be drawn:  
(1) The nanoindentation modulus of continuous graded asphalt mixture is much higher than that of gap-graded and open-graded asphalt mixtures. The load-depth curves of continuous dense gradation asphalt mixture have some dispersion in the maximum depth and curve form. The average modulus after removing the anomalies and outliers has a large coefficient of variation. The reliability of the nanoindentation test results is low for dense graded asphalt mixture containing a high level of fine aggregates. The nanoindentation tests on asphalt mixtures preferable to use a gradation type with less fine aggregates.  
(2) The difference in nanoindentation modulus between gap-graded and open-graded asphalt mixtures is reflected in their different RFA. The results and microstructure of asphalt mastic with different RFA showed that as the RFA increased, the nanoindentation modulus enlarged because of the interaction enhanced between asphalt and mastic. The amount of mineral powder in the asphalt mixture has a significant impact on the results of the nanoscale test.  
(3) The differences between the nanoindentation test results of asphalt mastic with different RFA before and after polishing are minor, indicating that polishing has a negligible effect on the test results of the asphalt mixture.  
The authors confirm contribution to the paper as follows: study conception and design: [PERSON], G.X., J.Y.; data collection: Y.Y., T.W.; analysis and interpretation of results: Y.Y., H.W.; draft manuscript preparation: Y.Y., J.Y., G.X., H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number No. 51778140 & No. 52078130.  
Not applicable.  
The authors acknowledge the financial support of the National Natural Science Foundation of China (No. 51778140, No. 52078130), Technology Research and Development Program of China State Railway Group Co., Ltd. (Beijing 10000, China) (P2019G030), Postdoctoral Research Fund Program at Southeast University (1121002107).  
The authors declare no conflict of interest.  
Figure 14: The dispersion state of mineral powder in the mastic.
## 3 The Dynamic Harmonic Compensation Sliding Mode Control Method
### System Structure  
The quasi-proportional-resonant sliding mode control (QPR-SMC) is integrated into the vector control of a PMSM drive system. In the two-phase stationary coordinate system, the control targets the harmonics with high harmonic content in the stator current of the PMSM. The fundamental angular frequency varies with the motor speed. The system control block diagram is shown in Figure 6. The system consists of a speed loop based on a PI regulator, a current loop based on dynamic harmonic compensation sliding mode control, and a coordinate transformation module.  
Based on the current error-proportional resonant-sliding mode control switching surface equation shown in Equation (16), its structural block diagram can be derived, as illustrated in Figure 7. Taking the d-axis sliding mode switching surface as an example, its input is the current error, and its output is the new sliding mode control surface that combines the current error and harmonic compensation terms. The 5 th and 7 th harmonic currents in the stator current interacting with the fundamental magnetic field of the rotor permanent magnet will produce 6 th-order torque ripples. Thus, the constructed current error-proportional resonant-sliding mode control switching surface effectively suppresses harmonics in the current by attenuating the 6 th-order harmonics in the input current error.
# Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey
## 3 DNNs in CPS-Based Spatial-Temporal Data
### The Problem Space  
As denoted by Figure 3, six targets are defined to represent the fundamental research objectives that the DNNs have been used to handle spatial-temporal data in CPS. The targets emerged from the adopted research strategy described above and the careful consideration of the various goals achieved by the research conducted. For example, traffic speed, flow, and congestion prediction are achieved by processing road traffic data in transportation CPS. Research with these kinds of goals is categorized under spatial-temporal data processing. While those to detect or prevent the occurrence of attacks are categorized under anomaly detection. Real-time decisions go to autonomous vehicles or industrial control scenarios. However, in manufacturing CPS, for example, predicting equipment failures are classified as predictive maintenance, while the allocation of production logistics counts under resource allocation. Researchers that use data from various sources of different types are categorized under multi-modal data fusion.  
The defined targets are elaborated further below:  
* Spatial-temporal Data Processing (\(Y_{1}\)): It involves using DNNs to process and analyze spatial-temporal data in CPS, including feature extraction, classification, regression, and future event predictions.
* Anomaly Detection (\(Y_{2}\)): It involves using DNNs to detect anomalies based on spatial-temporal data in CPS, such as faults, attacks, or intrusions.
* Predictive Maintenance (\(Y_{3}\)): It involves using DNNs to predict the performance and maintenance needs of CPS according to spatial-temporal data, such as predicting the failure of smart manufacturing systems.
* Resource Allocation (\(Y_{4}\)): It involves using DNNs to optimize the allocation of resources in CPS, such as allocating network resources in communication networks and production logistics in manufacturing CPS.
* Real-time Decision (\(Y_{5}\)): It makes real-time decisions with spatial-temporal data, controlling the trajectory of autonomous vehicles.  
Figure 3: Problem Space for DNNs in CPS Spatial-Temporal Data.  
* Multi-modal Data Fusion (\(\gamma_{6}\)): It includes employing DNNs to process data from multiple sources in CPS, such as combining data from sensors and communication networks to provide insightful information to the decision process.
## 3 Results
### Characterization
#### 3.1.3 Thermal Analysis  
Thermal analyses were conducted to determine the amount of anchored syrenesulfonic acid in the catalyst as well as to assess the catalyst's thermal stability under an inert atmosphere of nitrogen gas. In Figure 5 are reported the superimposed TGA, DTA, and derivative-TGA (DTG) curves of SBA-15-Br and SBA-15-PSSA; relevant data are listed in Table 1. SBA-15-Br decomposition occurs basically in three temperature intervals. The first step (25-157 \({}^{\circ}\)C, mass loss of 0.81%) is generally due to a drying process; in other words, volatile compounds such as adsorbed water and gaseous molecules are released in the mentioned temperature range. However, as no clear peaks in this temperature range are present in the DTA curve. The second decrease in weight occurs at 157-319 \({}^{\circ}\)C, with an estimated weight loss of 6.24% and an exothermic peak at 249 \({}^{\circ}\)C in the DTA curve ascribed to the attached initiator. The appearance of the TGA/DTG thermogram in the third temperature interval (up to 700 \({}^{\circ}\)C) was indicative of the stepwise volatilization of small molecules (total mass loss of 3.40%) with an endothermic peak at 395 \({}^{\circ}\)C and an exothermic peak at 494 \({}^{\circ}\)C corresponding to the thermal degradation of the remaining organic components, including APTES fragments. Thermal degradation of the catalysts can also be inferred to occur in three stages, as can be evinced from Figure 5 and Table 1. The first stage corresponds to the loss of adsorbed volatile compounds (mass loss of 2.87%, DTG 66 \({}^{\circ}\)C, and DTA-endothermic \(\sim\)71 \({}^{\circ}\)C). The second stage corresponds to the multistep degradation of intermediates such as amines, alkyls, carbon-oxides, organo-halides, and water (total mass loss of 8.69%). The third stage, above 458 \({}^{\circ}\)C, clearly indicates one exothermic heat transition at 556 \({}^{\circ}\)C (DTG at 545 \({}^{\circ}\)C), with the major weight loss belonging to the polymer. An endothermic heat transformation was observed to occur at temperatures above 700 \({}^{\circ}\)C, which is due to phase changes in the residual materials.  
Figure 4: Fourier-transform infrared spectra of SBA-15, SBA-15-NH\({}^{2}\), SBA-15-Br, and SBA-15-PSSA. SBA-15: pore-expanded mesoporous silica; SBA-15-PSSA: SBA-15-supported polystyrene sulfonic acids.  
The results of TGA data analysis were used to estimate the catalyst's polymer contents. The estimated organic-based contents were 9.64 wt% in SBA-15-Br and 22.50 wt% in SBA-15-PSSA. The 12.86 wt% difference is an approximation of the polymer contents of SBA-15-PSSA; therefore, 0.70 mmol of styrenesulfonic moieties were present per gram of catalyst which, by assuming complete conversion of sulfonates into sulfonic acids, equals the number of active sites (-SO\({}_{3}\)H or H\({}^{+}\)) as well (Table 2).
## 3 Results
### Model Simulations of GPP, Re, and NEP under All Sky Conditions from 2003-2017
#### 3.1.4 Annual Sum Simulations of GPP, Re, and NEP  
The annual sum simulations of GPP, Re, and NEP were calculated and shown in Figures 7-9. Similar phenomena and estimates were also found for their annual sums as for the monthly sums. The mean annual ratios were 1.93, 1.04, and 1.11 for \(\mathrm{GPP_{cal}}/\mathrm{GPP_{obs}}\) under three atmospheric conditions from 2003-2017, respectively, 1.06 for \(\mathrm{Re_{cal}}/\mathrm{Re_{obs}}\) under the realistic atmospheric conditions, and 0.473, 0.599, and 0.650 for \(\mathrm{NEP_{cal}}/\mathrm{NEP_{obs}}\) under three atmospheric conditions. Thus, the empirical models also performed better simulations for Re (with a relative bias of 5.52%) and underestimated annual sums of NEP by 34.99% under realistic atmospheric conditions. The empirical models captured the peaks of GPP and Re in 2006, 2010, and 2016. The simulations of annual sums of NEP exhibited similar variations for polluted and realistic atmospheric conditions; the observed GPP and NEP in 2016 had some measurement problems, which are reflected by the simulations [40; 41]. In general, the empirical models of GPP, Re, and NEP better reproduced their interannual variations.  
The ratio \(\mathrm{GPP_{cal}/GFP_{obs}}\) of 1.93 and GPP decreases with the increase in S/Q (representative of all atmospheric GLPs) [40], which reveals that a clean atmosphere is more beneficial to large GPP production and storage in plants than a polluted atmosphere. A similar phenomenon is also reported where fire pollution causes a GPP reduction [83]. Therefore, we strongly recommend controlling the increase of all kinds of GLPs in the atmosphere (e.g., GHGs, as well as air pollutants, aerosols, and VOCs (volatile organic compounds)) so as to mitigate climate warming. In the future, with the extensive implementation of the aforestation strategy in China and other countries in the world, more and more BVOCs will be emitted from vegetation, resulting in fast increases of ozone, secondary organic aerosols (SOA), and other new GLPs, i.e., the increases of atmospheric GLPs [65; 84; 85; 86]. So, a stronger control of GLP emissions and production than before is suggested. This point of view is supported by a study that a clear positive and non-linear relationship exists between air temperature and total atmospheric GLPs for some representative stations, Qianyanzhou, Ankara (39\({}^{\circ}\)58\({}^{\prime}\)21.7\({}^{\prime\prime}\) N, 32\({}^{\circ}\)51\({}^{\prime}\)49.3\({}^{\prime\prime}\) E), Sodankyla (67.367 N, 26.630 E) in the Arctic, and Dome C (75\({}^{\circ}\)06\({}^{\prime}\) S, 123\({}^{\circ}\)21\({}^{\prime}\) E) in the Antarctic, indicating an important mechanism that air temperature will decrease with the decrease of all types of chemical compositions in the atmosphere [87].
## 3 Results  
### TEM and SAED and SEM and EDX  
The structural properties of the material were investigated using transmission electron microscopy selected area electron diffraction (SAED) and scanning electron microscope. Energy-dispersive X-ray was used for elemental analysis. In Figure 1, images (a), (b), (c), and (d) are TEM images. Image a shows the spherical nanoparticles with 5.5 nm average diameter monodispersed on the 2D sheets clearly visible in image b. This is in agreement with the TEM obtained when the avocado seed was used instead of the seed husk [10]. Image c shows the lattice fringes with the lattice spacing of 0.24 nm indicatingthe crystallinity of the material as confirmed by the SAED image d and XRD patterns in Figure 2. SEM in image (e) suggests the surface morphology of the material to be rough. EDX confirmed the presence of the jarosite and H\({}_{3}\)O-jarosite seed EDX shows K and P as extra elements due to the presence of the extract.  
Figure 1: (**a–c**) TEM images showing the shape of the particles, the lattice fringes, and the triangular sheets, respectively. (**d**) SAED (**e**) SEM image displaying the surface morphology and EDX spectrum showing the elemental composition of the prepared hydronium jarosite husk.
## 4 Results and Discussion
### Prediction of Optimal Planting Zones for Se-Rich Crops
#### 4.2.3 Model Comparison and Discussion  
In the process of predicting and delineating optimal planting zones for Se-rich crops in the study area, three models were employed: FWofE, BP-NN, and SVR. The Se-rich crop thresholds used by these models were 0.040 mg/kg, 0.045 mg/kg, and 0.044 mg/kg, respectively. A statistical comparison of the study areas classified as Se-low crop planting zones, second-class Se-rich crop planting zones, and first-class Se-rich crop planting zones, along with the number of Se-rich crop samples within each zone, is presented in Table 2.  
As shown in Table 2, the percentage of Se-rich crop samples within the Se-rich crop planting zones delineated by the FWofE, BP-NN, and SVR models were 74.75%, 86.67%, and 74.68%, respectively. The BP-NN exhibited the highest spatial concordance between the predicted Se-rich crop planting zones and the distribution of Se-rich crop samples. The FWofE model predicted smaller Se-rich crop planting zones, whereas the SVR model predicted larger zones. Based on the statistical and comparative analysis, the BP-NN was determined to be the most effective model for predicting and delineating optimal planting zones for Se-rich crops in the study area.  
In Se-deficient regions, applying Se fertilizers has proven effective in increasing Se crop content [5; 56]. For areas where Se crop content is low, applying Se fertilizers could be a viable solution. The graded results indicate that certain areas in the study region have low Se crop content. By applying Se fertilizers in Se-low crop planting zones and optimizing the use of Se-rich crop planting zones, the yield and quality of local Se-rich  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & **FWofE** & **BP-NN** & **SVR** \\ \hline \multirow{4}{*}{Se-low crop planting zones} & Number of Se-rich crops & 25 & 8 & 20 \\  & Proportion of Se-rich crops (\%) & 25.25 & 13.33 & 25.32 \\  & Area (km\({}^{2}\)) & 159.49 & 116.97 & 121.46 \\  & Area Proportion (\%) & 42.44 & 31.12 & 32.32 \\ \hline \multirow{4}{*}{II Se-rich crop planting zones} & Number of Se-rich crops & 50 & 40 & 36 \\  & Proportion of Se-rich crops (\%) & 50.51 & 66.67 & 45.57 \\  & Area (km\({}^{2}\)) & 188.37 & 214.18 & 176.80 \\  & Area Proportion (\%) & 50.12 & 56.98 & 47.04 \\ \hline \multirow{4}{*}{I Se-rich crop planting zones} & Number of Se-rich crops & 24 & 12 & 23 \\  & Proportion of Se-rich crops (\%) & 24.24 & 20.00 & 29.11 \\ \cline{1-1}  & Area (km\({}^{2}\)) & 27.97 & 44.72 & 77.60 \\ \cline{1-1}  & Area Proportion (\%) & 7.44 & 11.90 & 20.65 \\ \hline \end{tabular}
\end{table}
Table 2: Statistical summary of prediction results of the FWofE, BP-NN, and SVR models.  
crops can be enhanced, thereby promoting the development of Se-rich agricultural products in the region.
## 3 Path-following Control Based on Improved LOS
### Improved LOS
#### 3.2.1 Adaptive Acceptance Circle Strategy  
The turning radius of the robot is related to the flight speed during the path change, and the minimum turning radius is determined by the design parameters of the robot itself. As shown in Figure 6, given the minimum turning radius, \(R_{min}\), and path angle, \(\theta_{i}\), the robot must change its direction at point A by switching from path segment \(P_{i-1}P_{i}\) to \(P_{i}P_{i+1}\). If the reference turning radius of the robot at point \(P_{i}\) is \(R\), then the radius of the admittance circle at \(P_{i}\) is  
\[R_{0,i}=R+k\cdot sigmoid(a\cdot V+b\cdot\theta_{i}) \tag{4}\]  
where \(a\) and \(b\) are, respectively, the coefficients of velocity and the angle of rotation. If the reference turning radius, \(R=R_{o}\), is set to reduce the following error, when the path angle is \(\theta\in(\pi/2,\pi)\), the following error will oscillate with an increase in \(\theta\). To solve this problem, an angular threshold is defined to adjust the acceptance circle radius based on the path segment angle and the current speed.  
Figure 5: Schematic diagram of improved LOS.  
Figure 6: Arc transition method.  
Considering the correspondence between the radius of the acceptance circle; the angle of the path segment, \(\theta_{i}\); and the current velocity, \(V\), the adaptive acceptance circle is designed as follows:  
\[R_{0,i}=\max\biggl{\{}\begin{matrix}R_{0}+k\cdot sigmoid(a\cdot V+b \cdot\theta_{it})\\ R_{min}\\ \end{matrix} \tag{5}\] \[\mathit{sigmoid}(x)=\frac{2}{1+\epsilon^{-2}}-1\] \[\theta_{it}=\pi-2*\theta_{i}\] \[\forall\ \theta_{i}\in(0,\pi],\ \ \ \ \ \ \mathrm{i}=1,2,\cdots,\mathrm{n}\]  
where \(R_{0}\) is the standard acceptance circle radius, which represents the acceptance circle radius in straight flight, and \(\theta_{i}\) is the angle in waypoint \(P_{i}\). Parameter \(k\) is the scaling factor.  
The following can be deduced from Equation (5), which shows the adaptive acceptance circle radius.  
If \(R_{0}+k\cdot sigmoid(a\cdot V+b\cdot\theta_{it})>R_{min}\), \(R_{0,i}\) is set to \(R_{0,i}\)=\(R_{0}+k\cdot sigmoid(a\cdot V+b\cdot\theta_{i})\) and the robot completes the turn using the reference turn radius determined by the path segment angle and the current velocity. This is done to prevent following error oscillation.  
If \(R_{0}+k\cdot sigmoid(a\cdot V+b\cdot\theta_{it})\leq R_{min}\), \(R_{0,i}\) is set to \(R_{0,i}=R_{min}\). The robot completes the turn with the minimum turning radius, which can effectively reduce the following error.
# An Ensemble Extreme Learning Machine for Data Stream Classification
###### Abstract  
Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN). Because ELM has a fast speed for classification, it is widely applied in data stream classification tasks. In this paper, a new ensemble extreme learning machine is presented. Different from traditional ELM methods, a concept drift detection method is embedded; it uses online sequence learning strategy to handle gradual concept drift and uses updating classifier to deal with abrupt concept drift, so both gradual concept drift and abrupt concept drift can be detected in this paper. The experimental results showed the new ELM algorithm not only can improve the accuracy of classification result, but also can adapt to new concept in a short time.  
extreme learning machine; data stream classification; online learning; concept drift detection 11