esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results
### ECA-ATCNet on MI-EEG Classification
#### 4.1.4 Comparison with State-of-the-Art Models  
The proposed ECA-ATCNet model is tested on the BCI-2a dataset for individual experiments. The MI-EEG data of nine subjects were trained separately, and validation was performed on the test set. The results are shown in Table 2, where the ECA-ATCNet model is compared with SOTA models such as ATCNet [32], EEG-TCNet [56], D-ATCNet [34], and CSANet [33]. The results show that our improvements significantly enhanced model  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attention Layer** & **Accuracy \%** & \(\kappa\) \\ \hline None & 85.38 & 0.80 \\ Efficient Spatial Attention & 87.40 & 0.83 \\ Efficient Spectral Attention & 84.43 & 0.79 \\ Efficient Spatial Attention + Efficient Spectral Attention & 87.89 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation Analysis of Attention in the ECA-ATCNet Model.  
performance, achieving an overall accuracy of 87.9%. Compared to the unmodified ATCNet model, the accuracy improved by 2.5%. Compared to other models, the accuracy of ECA-ATCNet increased by 0.8% to 4.1%. This result indicates that the proposed model, through improvements in MI decoding, demonstrates superior learning and prediction capabilities compared to existing models. In our proposed model, the standard deviation of accuracy between different subjects is only 6.05%, significantly lower than that of other models. This result indicates that the proposed ECA-ATCNet model exhibits high stability in within-subject classification performance. The \(\kappa\)-score is 0.08, outperforming the current state-of-the-art research results.  
In addition to the BCI-2a dataset, we performed between-subject classification tasks on the PhysioNet MI-EEG dataset using the proposed ECA-ATCNet model. The proposed model achieved the highest accuracy of 71.88%, as shown in Table 3. This result indicates that the proposed model has strong generalization ability in MI-EEG decoding tasks.
# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 4 Detection Coverage Simulation Experiment
In contrast, the EDDM-MSC-MMP method proposed in this paper achieves a detection coverage rate of 95.35% even when \(P_{\min}=95\%\), which is excellent detection performance. We provide a summary of the detection coverage of the eight detection methods at varying minimum acceptable probabilities. The comprehensive performance is ranked from high to low as follows: EDDM-MSC-MMP > Ratio+ELP > Ratio > CN0 > ELP > Slope+Delta > Delta > Slope. The MSC-MMP method proposed in this paper exhibits superior detection performance to that of other methods in terms of spoofing detection across diverse scenarios, including varying powers, code phase offsets, and carrier offsets. Figure 5: Detectable areas of the various detection methods at \(P_{\min}=80\%\). From top to bottom, the rows of the subplots correspond to \(C/N_{0}\), ELP, Delta, Ratio, Slope, Slope+Delta, Ratio+ELP, and the EDDM-MSC-MMP. The yellow indicates detectable areas, while the dark blue indicates undetectable areas.
## 4 Results  
### Influence of Height Difference of Half-Clutch Magnets  
There are various research results available for couplings with both identical half-clutches. It is preferable to apply magnetic couplings with different magnets' heights in different half-clutches for some applications. Minimization of weight or volume of one half-clutch is the criteria in some industrial designs. The example used is application of magnetic couplings in the structure of the food blender [15]. Further, there are analyzed and presented dependencies of the maximum torque and the maximum axial force on the half-clutch magnets' height difference parameter \(\Delta h_{m}\) (see Figure 6) when the total volume of coupling's magnets is constant.  
The height of permanent magnets on different half-clutches during the simulation was set as follows (see Figure 6)  
\[h_{m1}=h_{m}+\Delta h_{m}\text{ and }h_{m2}=h_{m}-\Delta h_{m}. \tag{9}\]  
The obtained dependencies of the maximum axial torques on magnets' height difference parameter \(\Delta h_{m}\) for the cases of the couplings with ferromagnetic and non-ferromagnetic backings are presented in Figure 7a,b, respectively.  
The dependencies of the maximum axial force on \(\Delta h_{m}\) for the cases of the couplings with ferromagnetic backings and with non-ferromagnetic backings are presented in Figure 8a,b, correspondingly.  
The relationship of the maximum torque to \(\Delta h_{m}\) is nonlinear. The sensitivity of the maximum torque to half-clutch height difference is larger in the upper range of \(\Delta h_{m}\). Dependencies of the relative maximum torque \(T^{\prime}{}_{m}/T^{\prime}{}_{m}\), (where \(T^{\prime}{}_{m}\) is the maximum torque for the case of coupling with identical half-clutches (\(\Delta h_{m}=0\)), and \(T^{\prime}{}_{m}\) the torque for cases of coupling with non-identical half-clutches (\(\Delta h_{m}\
eq 0\))) on \(\Delta h_{m}\) are very close for  
\begin{table}
\begin{tabular}{c c} \hline \hline Modelling environment & “COMSOL Multiphysics 4.4” \\ Space dimensions & 3D \\ Physics & AC/DC module \\ Interface & Magnetic Fields, No currents \\ Study type & Stationary \\ Mesh type & Free Tetrahedral \\ Maximum element size & 1 mm \\ Minimum element growth rate & 1.5 \\ Curvature factor & 0.6 \\ Resolution of narrow regions & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model details.  
Figure 6: Magnets’ height difference parameter \(\Delta h_{m}\).  
the all considered air-gap values. There is a slightly higher relative torque sensitivity to \(\Delta h_{m}\) for the coupling with non-ferromagnetic backings.  
The dependencies of the maximum axial force, as well as dependencies of the maximum torque, on \(\Delta h_{m}\) are nonlinear. Sensitivity of the maximum axial force to half-clutch height difference is larger in the upper range of \(\Delta h_{m}\).  
The maximum axial force of the coupling with ferromagnetic backings indicates stronger dependence on air-gap length, especially for the cases with higher half-clutch diversity.
# Intelligent Monitoring of Tunnel Fire Smoke Based on Improved YOLOX and Edge Computing
## 5 System Design and Case Study
### Case Study  
To assess the feasibility of intelligent tunnel fire and smoke monitoring using the improved YOLOX model and edge computing, this study conducted experiments at the construction site of Nanjing Metro Line 3, specifically from Mozhou East Road Station to Hefeng Road Station. The construction work for the third phase of the Nanjing Metro Line 3 project under the general contracting D.003.3-TA01 covers the following sections: Mozhou East Road Station (excluding) to Hefeng Road Station, Hefeng Road Station, and the interval from Hefeng Road Station to Moling Street Station. This interval is further divided into an open-cut section (shield hoisting well) and a shield section. The open-cut  
Figure 11: Fire and Smoke Intelligent Monitoring System Interface.  
Figure 10: Fire monitoring system workflow.  
section is located to the south of the existing section of Mozhou East Road Station and runs along the north-south direction of Ailing Road. The design starting point for the right line is K44 + 871.848, with the interval connection channel at K44 + 995.000. The shield hoisting well is situated in the green belt on the west side of the road. The open-cut section is designed as an underground frame structure with a standard section width ranging from 11.6 to 16.68 m, while the shield hoisting well has a width of 21.9 m. The total length of the open excavation section is 130.652 m, with the bottom plate buried at depths ranging from 17.7 to 20.85 m. The hoisting well reaches a depth of 20.85 m, and the connection channel follows the shield section. The shield section begins from the shield shaft at the southern end of the K45 + 002.500 open-cut section and proceeds south along Shuanglong Avenue to the northern end of K46 + 659.660, Hefeng Road Station.  
To ensure the safety of the construction site during the experiments, dry ice was employed to simulate the smoke effect within the tunnel. It is important to note that dry ice, due to its low temperature, cannot entirely replicate the large quantity of high-temperature flames produced in an actual fire event, nor can it mimic the black or differently colored smoke generated by the combustion or incomplete combustion of specific substances. Additionally, the smoke from dry ice dissipates rapidly.  
The conclusive experimental results are presented in Figure 12. These results unequivocally demonstrate that the model remains capable of detecting smoke targets within subway tunnel construction even when lighting conditions are insufficient, and the smoke target is relatively small. In the captured video, which comprises 1080 frames, approximately 400 frames show the presence of smoke. Remarkably, the model successfully detected smoke in about 370 of these frames, achieving a detection rate of approximately 93%. This underscores the reasonableness and effectiveness of the tunnel fire smoke intelligent detection method proposed in this paper, which leverages deep learning and edge computing.
## 3 Results and Validation
### Atmospheric Correction Results
The dates of these images from top to bottom respectively are (**a,b**) 29 May 2015, (**c,d**) 1 December 2016, (**e,f**) 19 February 2017, (**g,h**) 23 March 2017, and (**i,j**) 11 June 2017. Based on the above two examples and manual checking of more than 1000 atmospheric-corrected MHSR images, it can be concluded that the proposed method is capable of atmospherically correcting most of the MHSR imagery in arid areas with bight and stable surfaces with large varied aerosol loadings and with unevenly aerosol distribution (see line (a, b), (c, d) and (g, h) in Figure 4) and the spatial and temporal consistency after atmospheric correction is excellent.
## 4 Performance Evaluations
### Simulation Results  
We conduct simulations in MATLAB (Jinling Institute of Technology, Nanjing, Jiangsu, China, version R2016b) and compare the performances of our proposed method with those of the previous methods, including DV-hop, Amorphous, MDS-MAP, PDM, and LSVR. The communication range of all of the sensor and anchor nodes is identical, and these nodes are deployed in a 1000 m \(\times\) 1000 m square region for all simulations. We assume two types of distribution: regular distribution and random distribution. In each type of distribution, we considered three types of networks: C-shaped, L-shaped, and D-shaped. The networks used in the following simulations are presented in Table 1. Specially, we use the symbol '' to indicate the anchor nodes and 'o' to denote sensor nodes.  
All of the reported results are the average over 100 trials. We used the ALE to evaluate the performances of all of the compared methods. The definition of ALE can be briefly formulated as  
\[ALE=\frac{\sum\limits_{i=1}^{N}LocationError_{i}}{N} \tag{14}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **C-Shaped** & **L-Shaped** & **D-Shaped** \\ \hline Random distribution & & \\ Regular distribution & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The simulation scenarios.  
where \(N\) is the number of unknown nodes. The location error of sensor node \(i\) can be formulated as:  
\[LocationError_{i}=\frac{\|\hat{\mathbf{x}}_{i}-\mathbf{x}_{i}\|}{R}\times 100\%, \tag{15}\]  
where \(\mathbf{x}_{i}\) and \(\hat{\mathbf{x}}_{i}\) denote the real and estimated location of node \(i\), respectively. \(R\) is the communication range of nodes and \(\|\bullet\|\) represents the Euclidean distance.  
The assumptions and simulation parameters are listed in Table 2.
## 3 Validation on a Case Study
### Geometry, Domain, Boundary Conditions and Mesh  
The geometry of the numerical simulations consists of an ITER-like vessel. The major radius of the Tokamak is 6.2 m, while the minor radius is 2 m [35]. The internal vessel has been built according to the poloidal section of ITER, while the external structure has been placed just to represent a solid approach and it does not play any role in the numerical simulation. The geometry of the numerical simulation is shown in Figure 1.  
The plasma used for the numerical simulation has been built applying a circular equilibrium type, widely used in the literature for the investigation of polarimetry performances. The electron density and temperature of the plasma have a parabolic profile. Defined by the dimensionless minor radius \(\rho(r,z)=\left(r^{2}+z^{2}\right)^{0.5}/a\), where \(r\) is the minor radius coordinate and \(a\) is the minor radius of the plasma, the electron density can be written as \(N_{e}(r,z)=N_{0}\left(1-\rho(r,z)^{2}\right)\) and the electron density is \(T_{e}(r,z)=T_{0}\left(1-\rho(r,z)^{2}\right)\), where \(N_{0}\) is the electron density on the magnetic axis of the plasma. The toroidal field, defined by its value \(B_{0}\) on the major radius \(R_{0}\), has the form \(B_{t}(R)=B_{0}\frac{R_{0}}{R}\). The profile of the plasma current density has a parabolic profile as well and it is calculated from the plasma current (\(I_{p}\)) as \(j(r,z)=\frac{2I_{p}}{\pi a^{2}}\left(1-\rho(r,z)^{2}\right)\). The poloidal field is calculated from the integration of the current density and it is \(B_{p}(r,z)=\frac{R_{0}I_{p}}{2\pi a}\frac{1-\left(1-\rho(r,z)^{2}\right)^{2}} {\rho(r,z)}\). Then, the refractive index field is computed (see Section 2, Equation (7)). Figure 2 shows the field of the electron density, toroidal and poloidal magnetic field, and of the differential refractive index, defined as the vacuum refractive index minus the plasma refractive index.  
Figure 1: Geometry schematics of the numerical simulation.  
Figure 2: Property field of the plasma used for the numerical simulation.
## 2 Modelling of PV Systems under PSC
### PSC Effect  
In general, the PV system needs to ensure a certain output voltage; however, a single PV cell can only output extremely low voltage (almost 0.6 V). Hence, PV cells are always connected with each other in a string to improve the output voltage. At the same time, when the array is shaded for some reason, the output voltage of the PV cells in the shaded part will be lower than that of the unshaded PV cells, due to the decline of received solar irradiation. Consequently, the shaded PV cells will consume a part of the generated power. This phenomenon causes large loss of output power in the PV string. In addition, it also leads to hot spots in the location of the shaded PV cells, which will greatly decrease the service life of PV cells [27].  
To solve this issue, the shaded PV cells are usually bypassed by bypass diodes. Figure 1a demonstrates the operation in a PV array with parallel strings. Although adding bypass diodes can effectively solve the issues mentioned above in shaded PV cells, they also result in a new problem, e.g., they will distort the original \(P\)-\(V\) characteristic curves of PV cells and form a two-peak curve. In particular, such a situation turns thornier when a few PV strings are connected in parallel for the sake of obtaining a larger output current. Generally, when the number of shaded PV cells on each string changes, each string will generate various PV curves. Because of the parallel connection, those PV curves with multiple peaks are usually combined to produce a multi-peak curve illustrated in Figure 1b Hence, in order to determine the maximum solar energy from the PV array, the PV systems ought to operate at the GMPP all the time. Only in this way, the large amount of energy will not be lost at LMPP.
## 5 Conclusions  
The study shows that the loss of forest land is a serious environmental issue in the northern area of Pakistan. This research has shed light on how the vegetation cover has changed in the Malam Jabba region during the past forty years. The different categories are high to very high, medium, and low to extremely low vegetation cover. Based on the background, random forest appeared to be a promising technique for mapping LULC from satellite imagery. Three distinct LULC classes, including forest, woodland forest, and other land cover, were mapped from Landsat MMS, TM, ETM+, and OLI images using the random forest classifier in this research. To optimize the RF settings, we used methods based on the out-of-bag (OOB) estimate of error while also considering the impact of each of Landsat's spectral bands. We tested a range of tree numbers and RF input settings and found 50 trees to be optimal. Malam Jabba's total forest land area in 1980 was roughly 236 km\({}^{2}\), which shrank by 154 km\({}^{2}\) in 2020, and the overall rate of forest cover loss was 32 percent. Woodland cover loss from 1980 to 2020 was 18 km\({}^{2}\), or around 27.43 percent, with a 2.1 km\({}^{2}\)/yr annual deforestation rate. The overall accuracy, kappa values, and F1-score were all between 91 to 96%, while kappa was 0.90 to 0.96 and the F1-score was 0.87 to 0.93 between 1980 to 2020, respectively. Policymakers must develop effective strategies to reduce these changes in how land is used. The remaining forest will be lost if no action is taken to stop the rapid pace of deforestation that is currently occurring. The FAO's statistics on forest cover, which show that the loss of forest is very high and significant, are consistent with the conclusion, even though the annual rate of deforestation figure differs from other estimates. The main causes of deforestation in the nation are anthropogenic activities including overgrazing, urbanization, road construction, firewood collection, and subsistence farming. This is a matter of fact; if proper management, planning, and strategies are not put into place to improve and maintain the high rate of deforestation currently present, then any remaining forests will not be sustained for a longer period of time and this will negatively affect the socioeconomic situation of the area.  
Conceptualization, A.I. and S.Z.; Methodology, M.J. and M.S.; Software, M.S.; Validation, M.J. and M.S.; Formal analysis, M.J. and A.K.; Investigation, M.J.; Resources, M.J., M.S. and S.Z.; Data curation, A.I. and A.K.; Writing--original draft, M.J. and M.S.; Writing--review & editing, M.J. and M.S.; Visualization, M.S.; Supervision, J.S.; Project administration, M.J.; Funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the science and technology plan of Gansu Province. Research on Key Technologies of automatic monitoring of surface cover change in Gansu Province (20 yf3 ga013).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Design of Multi-Objective Fuzzy Continuous Damping Controller
### Multi-Objective Fuzzy Continuous Damping Controller
#### 3.2.1 Fuzzification Process  
The main function of fuzzification is to process accurate variables and transform them into corresponding universe to form fuzzy variables.  
The inputs of fuzzy control are rollover index (RI; i.e., the value of the LTR mentioned previously) and yaw angle rate. The outputs are the damping reference coefficients of each wheel that generates a corresponding damping force through PID control. The objective is to reduce the vibration of passengers under normal driving conditions and to achieve stability and rollover control in the case of rollover or instability.  
The process of fuzzification needs to complete the operation of accurate clear quantity into fuzzy quantity. The fuzzy universe of each variable should be defined at first, which can realize the transformation from physical clarity to fuzziness by quantization factors. The fuzzy universe of RI is defined as [-1, +1], the fuzzy universe of yaw angle rate is defined as [-8, +8] and the fuzzy universes of the damping reference coefficients of each wheel are defined as [0, +1]. In order to correspond the actual variables to the normalized universe, the quantization factors are defined as \(N_{RI}\) = 1, \(N_{yaw}\) = \(\delta\)/\(r_{max}\)], \(N_{u}\) = 1.  
Taking RI as an example, the fuzzy language variable is used to describe the input variable RI [45]. After fuzzifying, the fuzzy set \(T\)(RI) of the RI can be expressed as  
\[T(RI)=\left\{\begin{array}{cc}Negative\;Big\;\;\;\;\;Negative\;\;Small\;\; \;Zero\;\;\;\;Positive\;\;Small\;\;\;Positive\;Big\;\;\;\;\right\}\]  
It can be abbreviated as  
\[T(RI)=\left\{\begin{array}{cc}NB&NS&ZO&PS&PB\\ \end{array}\right\}\]  
Similarly, fuzzy sets of the yaw angle rate and the damping reference coefficient can be obtained. The fuzzy set \(T\)(r) of the yaw angle rate can be expressed as  
\[T(r)=\left\{\begin{array}{cc}NB&NM(Negative\;Middle)&NS&ZO&PS&PM(Positive\; \;Middle)&PB\\ \end{array}\right\}\]  
Figure 7: Multi-objective fuzzy continuous damping control structure.  
The fuzzy set \(U(dc)\) of the damping reference coefficient of each wheel can be expressed as  
\[U(dc)=\left\{\begin{array}{cc}SS(Very\ Small)&S(Small)&M(Middle)&B(Big)&BB(Very\ Big) \end{array}\right\}\]
###### Abstract  
The instantaneous frequency (IF) of a non-stationary signal is usually estimated from a time-frequency distribution (TFD). The IF of heart rate variability (HRV) is an important parameter because the power in a frequency band around the IF can be used for the interpretation and analysis of the respiratory rate but also for a more accurate analysis of heart rate (HR) signals. In this study, we compare the performance of five states of the art kernel-based time-frequency distributions (TFDs) in terms of their ability to accurately estimate the IF of HR signals. The selected TFDs include three widely used fixed kernel methods: the modified B distribution, the S-method and the spectrogram; and two adaptive kernel methods: the adaptive optimal kernel TFD and the recently developed adaptive directional TFD. The IF of the respiratory signal, which is usually easier to estimate as the respiratory signal is a mono-component with small amplitude variations with time, is used as a reference to examine the accuracy of the HRV IF estimates. Experimental results indicate that the most reliable estimates are obtained using the adaptive directional TFD in comparison to other commonly used methods such as the adaptive optimal kernel TFD and the modified B distribution.  
Keywords:time-frequency analysis; heart rate variability; respiratory signal; instantaneous frequency +
Footnote †: journal: Applied Sciences
# Distributed Coordination of Space-Ground Multiresources for Remote Sensing Missions
###### Abstract
Section 3 transfers and decomposes the optimization problem into subproblems to be solved by the RSS systems and DRS systems distributedly. In Section 4, the ADMM-based distributed coordinated multiresource scheduling algorithm is developed for the remote sensing missions in integrated networks. Simulation results and conclusion are given in Sections 5 and 6, respectively.