esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation
### CASE 1 (Observers Performance Comparison)
#### 5.1.1 Domestic Observers Comparison  
To verify the effectiveness of the proposed fuzzy logic rules for adaptively adjusting the observer gain to cope with disturbances with different amplitudes, comparative simulations are conducted for a quadrotor UAV manipulator performing a hovering flight with the manually designed scalable disturbance (39) between FASTESO and ASTESO combined with a backstepping controller (BAC). The ASTESO is defined as a simplified version of FASTESO, which is not equipped with adaptive gain under fuzzy logic rules. Their coefficients are given as FASTESO: \(\alpha_{1}=diag(0.4,0.4,0.5)\); \(\alpha_{2}=diag(0.7,0.7,0.9)\); \(e=0.0001\); \(k_{e}=0.05\). ASTESO: \(\alpha_{1}=diag(0.4,0.4,0.5)\); \(\alpha_{2}=diag(0.7,0.7,0.9)\). BAC: \(K_{b1}=diag(5,5,8)\); \(K_{b2}=diag(3,3,4)\). The simulations are divided into two groups classified by different scalars of disturbances, \(a=10,20\). The corresponding results could be found in Figures 6-9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Coefficients** & **Description** & **Value** \\ \hline \(m_{b}\) & Mass of the quadrotor base & 1.8 kg \\ \(j_{bx}\) & Pitch inertia & \(1.319\times 10^{-2}\) kg \(\cdot\) m\({}^{2}\) \\ \(j_{by}\) & Roll inertia & \(1.319\times 10^{-2}\) kg \(\cdot\) m\({}^{2}\) \\ \(j_{bz}\) & Yaw inertia & \(2.283\times 10^{-2}\) kg \(\cdot\) m\({}^{2}\) \\ \(l\) & Quadrotor base size & 0.205 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quadrotor base coefficients used in simulation.  
As shown in Figure 6, both FASTESO and ASTESO have good estimation performance under scalar \(a=10\). The resultant observer gain of FASTESO is described in Figure 7. Nevertheless, the ASTESO starts to break down when the disturbance is larger, which could be found in Figure 8. Under \(a=20\), the ASTESO fails to follow the larger disturbance, but the proposed FASTESO still performs well. This is because the fixed parameter of ASTESO is not big enough to estimate the disturbance with the amplitude in Figure 8. As shown in Figure 9, the adaptive gain of FASTESO also shows why it is effective under disturbances with different amplitudes. To summarize, this part shows that the proposed FASTESO could estimate a wider range of disturbance thanks to the fuzzy logic rules for adaptive observer gain.
# Dual-Frequency Microwave Plasma Source Based on Microwave Coaxial Transmission Line
## 3 Experiment Results
The spectrum at only 700 W 2450 MHz microwave that was inputted from Port 1 acquired by the OES method is presented in Figure 4.  
Figure 4: Spectrum of the coaxial line plasma driven by the 2450 MHz microwave from Port 1 at a power of 700 W and pressure of 80 Pa.  
Figure 3: Effect of the microwave inputted from port 2 measured by the Langmuir probe: (**a**) electron temperature; (**b**) electron density. The spectrum result presents two main particles in plasma: nitrogen molecule (N\({}_{2}\)) and Ar. The N\({}_{2}\) comes from air residue. In the experiment, the intensity of the Ar I line group was much higher than the others. In the Ar I line group, the characteristic peaks at Ar I 750.3869 nm, and Ar I 811.5311 nm were quite obvious with the highest intensity. The OES data at the 200 W-915 MHz microwave that was inputted from Port 2 and at the 200 W-2450 MHz microwave that was inputted from Port 2 are presented in Figure 5a,b, respectively. In Figure 5, it is obviously seen that the intensity of the Ar I characteristic peaks was sufficiently improved when both 915 and 2450 MHz microwaves were inputted, and different Ar I characteristic peaks show different changes. The intensity of the two highest Ar I characteristic peaks (750.3869 and 811.5311 nm) at the 915 MHz microwave that was inputted from Port 2 were higher than that of the 2450 MHz microwave that was inputted from Port 2. Meanwhile, the intensity of other Ar I characteristic peaks (such as 696.5431 nm, 706.7218 nm, 772.3760 nm) at the 915 MHz microwave that was inputted from Port 2 are lower than that of the 2450 MHz microwave that was inputted from Port 2. The intensity changes of several Ar I characteristic peaks versus microwave power that were inputted from Port 2 are shown in Figure 6.  
Figure 5: Spectrum of microwave inputted from Port 2 at the power of 200 W: (**a**) 915 MHz microwave; (**b**) 2450 MHz microwave. Figure 6: Intensity changes of Ar I characteristic peaks versus microwave power inputted from port 2 (**a**) 750.3869 and 811.5311 nm; (**b**) 696.5431, 706.7218 and 772.3760 nm. The OES results show that when the intensity of the characteristic peaks is higher at only 700 W 2450 MHz that was inputted from Port 1, the intensity increase is higher at 915 MHz that was inputted from Port 2 than at 2450 MHz that was inputted from Port 2 (Table S1, Figure S1). This indicates that particles at different energy levels presented different responses to different frequencies. In addition, the particles at different energy levels contributed different ratios in plasma electron density and electron temperature, thus, different frequencies could selectively control the plasma characteristics.
# Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks
## 3 IEMACO Routing Algorithm
### Generation of Pheromone  
Energy is the bottleneck in mobile Ad Hoc networks. Once the node is exhausted, its path is immediately invalid, resulting in data transmission failure and higher end-to-end delay. This paper introduces the path minimum residual energy, average energy and energy consumption rate into the initialization of each node pheromone in the path. The remaining lifetime of nodes (RLT\(n\)) and the remaining lifetime of links (RLT\(l\)) are defined based on the prediction of the state of the network, while the remaining energy and the average energy are based on the metrics of the current network state.
## 2 Current Research on Data Acquisition, Refinement, and Quality Evaluation
### AI Research Imitating Human Visual Information
All tasks have a proper program tool that is not yet automatized, because the tasks cannot be immediately completed. The most frequently used tasks during refinement are changing the image size, adjusting the resolution, and changing the name of the file; labeling can be carried out again after these tasks are completed. During these procedures, manpower is constantly deployed. Furthermore, some cases may require expert knowledge on refinement and labeling depending on the research field, such as construction, civil engineering, and medical care. However, research results of AI in specific fields show low performance or are delayed because highly educated personnel are costly and limited. Figure 4: (**a**) Classification (0 click); (**b**) Classification + Localization (0 click); (**c**) Object Detection (8 clicks); (**d**) Instance Segmentation (58 clicks). A typical artificial intelligence method to perceive images. The number in the parentheses represents the number of mouse clicks. License plates and human faces were mosaicked or blurred for privacy. Following the development of the ability to perceive various objects on one image, the number of mouse clicks as well as the refinement time increased, whereas the volume of the training dataset decreased, leading to quickened learning speed and, thus, the need for comparative studies on this topic.
# Modelling and Analysis of Neuro Fuzzy Employee Ranking System in the Public Sector +
## 5 Discussion  
### Time Complexity of ANFIS  
The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a computational model that is often used to model complex nonlinear relationships between inputs and outputs. The time complexity of ANFIS can vary greatly based on a variety of factors, such as the size of the dataset, the number of inputs and outputs, the complexity of the fuzzy rules, and the optimisation algorithm employed.  
In general, ANFIS is considered to possess a high time complexity as a result of its iterative optimisation procedures, such as gradient descent or the Levenberg-Marquardt algorithm, which are used to minimise the error between the actual and predicted outputs. In terms of computational cost, the forward pass, or prediction step, of ANFIS can be characterised by a time complexity of \(\mathcal{O}(M\times L)\), where \(M\) is the number of fuzzy rules and \(L\) is the number of inputs. The time complexity of the backpropagation step, or training step, is more complex and dependent on the optimisation algorithm utilised, but can typically be described as \(\mathcal{O}(n)\), where \(n\) is the number of iterations required for convergence.  
Despite its computational cost, ANFIS continues to be utilised in a range of applications due to its ability to effectively learn and represent complex nonlinear relationships. The use of ANFIS in the academic and research communities is prevalent, and its study continues to be an area of active investigation in the field of machine learning and artificial intelligence.
# Design of a Novel Linear Shaped Charge and Factors Influencing its Penetration Performance
## 5 Parametric Influences on Penetration Performance
### Ratio of Small to All Apex Angle Liners  
The effect of liners with different ratios of small to all apex angle (\(n=\mathrm{d_{1}/d_{2}}\)) on penetration depth is further explored using BLSCs with 0.6 mm thick liner, COMP B as the explosive, small apex angle of 50\({}^{\circ}\), large apex angle of 100\({}^{\circ}\), and \(n=\) 0.3, 0.4, 0.5, 0.6, and 0.7, respectively. Figure 15 represents the penetration performance of BLSCs with different values of n.  
It should be noted that the mass of explosive changes as the n charges. Taking a 200 mm long BLSC as an example, the mass of explosive is 41.32 g, 40.55 g, 39.54 g, 38.31 g and 36.86 g when \(n=\) 0.3, 0.4, 0.5, 0.6, and 0.7, respectively. Figure 15 illustrates the sectional penetration cracks and the penetration depths are shown in the parentheses. Taking the energy efficiency and the penetration performance into account, a BLSC with \(n=\) 0.5 may be the best.
# A Method for Extracting Acoustic Water Surface Waves Based on Phase Compensation
## 4 Simulation and Experiment
### Experiment
Clearly, phase compensation reduces the number of discontinuous points, and second-order differential joint outlier removal reduces the spike phenomenon caused by uncompensated discontinuous points. The acoustic water surface waves using the proposed method is more concentrated and its variance is the smallest, as shown in Table 5. It is conducive to the extraction of coding information in subsequent time-frequency analysis. However, removing outliers can lead to a shorter length of data. If the sea surface situation is complex, it may cause symbol misalignment. We transmitted a 2 FSK signal with the bit information \"01010101010101\" as shown in the orange boxes in Figure 17. STFT was performed on the extracted acoustic water surface waves using Wavelet-Kalman Filtering, compensation with a threshold and the  
Figure 16: **Experiments: acoustic water surface waves using different methods. **  
Figure 15: **Experiment: phase history using different methods. **  
proposed method as shown in Figure 17. The frequency amplitudes corresponding to 100 Hz and 200 Hz at the bit center were calculated. The green box indicates the received bit results, and the red box marks the incorrect bits. The results show that phase compensation based on whether the range bin changes can reduce the number of discontinuous points, which represents a decrease in vertical stripes in the time-frequency graphs. The second-order differential joint outlier removal can reduce the spike phenomenon caused by discontinuous points. The proposed method can improve the clarity of the time-frequency graphs. It is more conducive to extracting communication coding information and reducing communication bit error rates. To assess the effects of the proposed method on communication quality, we conducted 20 repeated experiments under laboratory conditions, with each symbol lasting 0.5 s. A total of 580 significant bits of data were collected. The code '1' corresponds to a communication frequency of 200 Hz, the code '0' corresponds to a communication frequency of 100 Hz. The communication coding information corresponding to the time-frequency graphs is extracted, the number of error bits are counted, and the bit error rate of different methods are calculated. In Table 6, three methods mentioned above are compared. They are Wavelet-Kalman Filtering, phase compensation based on a threshold and High-pass Filtering, and the proposed method. From Table 6, it can be seen that the bit error rate of the proposed method is one-fourth of that of the Wavelet-Kalman Filtering method and one-third of that of the phase compensation based on a threshold. The statistical results of multiple repeated experiments indicate that the proposed method can reduce the influences of phase jumps caused by the water fluctuation and decrease the communication BER.
###### Abstract  
In order to solve the problems of low tracking accuracy of in-orbit satellites by ground stations and slow processing speed of satellite target tracking images, this paper proposes an orbital satellite regional tracking and prediction model based on graph convolutional networks (GCNs). By performing superpixel segmentation on the satellite tracking image information, we constructed an intra-frame superpixel seed graph node network, enabling the conversion of spatial optical image information into artificial-intelligence-based graph feature data. On this basis, we propose and build an in-orbit satellite region of interest prediction model, which effectively enhances the perception of in-orbit satellite feature information and can be used for in-orbit target prediction. This model, for the first time, combines intra-frame and inter-frame graph structures to improve the sensitivity of GCNs to the spatial feature information of in-orbit satellites. Finally, the model is trained and validated using real satellite target tracking image datasets, demonstrating the effectiveness of the proposed model.  
Keywords:context information; deep learning; graph convolutional networks; satellite tracking +
Footnote †: journal: Applied sciences
# Combining the Advantages of Different Processing Solutions Using a Novel Motion Processing Approach
## 2 Example Process under Consideration
### Experimental Setup  
Generally, classical linkages are combined with modern servo drives and control systems to implement such a rise-to-dwell motion. Thereby, often one-dimensional mechanisms are used to realize those motions, e.g., in [9]. On the one hand, these mechanisms have various advantages, e.g., in terms of motion accuracy, mechanical limitation of the working space, or synchronous motion [10]. Actuating them by modern servo drives offers further advantages, especially concerning the flexibility to change the motion profile quickly [11; 12]. On the other hand, using one-dimensional mechanisms has the disadvantage that only one process solution can be implemented, because the motion specification is given by the mechanism.  
In this work, different processing solutions are to be implemented with the same test rig. A two-dimensional setup is used for this purpose, shown in Figure 1. The assembly is a five-bar linkage with two degrees of freedom, driven by two servo motors. The working tool is shaped as a comb with several tines that conveys several products in one process stroke. Since the products handled in the machine are natural products with occasionally large tolerances, the spacing between the tines is to be greater than the width of the product. This setup will be used in the following examples.  
The motion profile for a given process solution is the input for the motion processing. Each motion profile has the character of a one-dimensional path of the comb. This path must be processed with a given operating speed, which is given by a master control system. The set-points for the two servo drives are derived from the geometric parameters of the assembly and the comb's motion path. In order to follow as precisely as required, the motion set-points on the drives have to be processed synchronously. Since this work is about the combinability of different solutions, kinematic aspects and synchronization will not be discussed further.
# Experimental Verification of the CFD Model of the Squeeze Film Lifting Effect
## 1 Introduction
### Applications  
One of the major advantages of levitation is the lack of contact between the inducer and the lifted object. The contactless support allows for reducing the wear due to contact and resistance due to friction in comparison to regular bearing systems. For almost three decades, researchers have studied the phenomenon of NFAL [2]. Over the years, multiple possible applications have been proposed and studied with varying configurations.  
One of the first works was published by [PERSON] et al. [2]. It shows the possibility of utilizing a vibrating plate as a means of suspending the object above itself. The researchers concluded the conditions of stable levitation. For flexural vibrations, the size of the object should be greater than half the wavelengths of the flexural vibration. Dependency between the height and the amplitude of vibration was also observed. However, the proportionality factors differed from one specimen to the other.  
[PERSON] et al. were one of the first to propose the linear guiding system utilizing the squeeze film effect [3; 4]. The system used hinges as a way of generating a deformation. In the system used, applicable frequencies were limited to 3 kHz, resulting in high noise. Improvement of the idea was later presented in [4]. The novel design worked in ultrasonic frequencies. In both cases, the float characteristics were studied to determine the possible application.  
The transportation system was also proposed by [PERSON] et al. [5]. The set-up consisted of a plate placed over right-angle beams located in a way that formed a prism-shaped rail (Figure 1). The beams acted as a guiding rail for the plate and also, while vibrating, created a traveling wave, allowing for the plate to move along the rail guides  
An alternative idea for a linear motion system was provided by [PERSON] et al. [6]. The design utilized the idea of two independent actuators controlled collectively. Actuators would create an inequality of heights between the changing planes, thus creating a deflection angle between the plane of actuators and the sample. This inequality would allow for the tangential motion of the sample.  
Another application was proposed by [PERSON] et al. [7]. In their work, a journal bearing was proposed. The work focused on finding a proper arrangement of such a bearing. The deforming part was a deformable bush allowing for frictionless rotation of the shaft. Later, operational conditions of such and a new configuration were studied [8; 9].  
A motor utilizing the NFAL was designed by [PERSON] et al. [10]. In their work, the stator annulus was deformed by three actuators. The generation of the traveling wave in the ring allowed for the rotation of the disk suspended above the ring.  
A spherical bearing proposed by [PERSON] et al. [11] was created as a frictionless suspension for the gyro. The hemispherical bowl acted as a stator, and the sphere mounted atop was suspended and successfully rotated, giving a possibility to use NFAL for suspended gyro design.
## 4 Results Analysis and Discussion
### Learning Curves  
This section compares the performance of three machine learning regression models on the dataset: Decision Tree Regressor, Gradient Boosting Regressor, and Random Forest Regressor. The learning curves for each model on both the training set and the test set are shown in Figure 9.  
As illustrated in Figure 9, when the number of training samples increases from 20,000 to 120,000, the Decision Tree Regressor achieves a training score of 0.99 on the training set, indicating that the model fits the training data very well. However, the test set score increases only slightly from 0.925 to 0.95, suggesting that while the Decision Tree Regressor can effectively fit the training data, its performance on unseen data is slightly less robust. This highlights the need for further model optimization or the adoption of more complex models to improve generalization performance. The Gradient Boosting Regressor shows a training score increase from 0.88 to 0.89 on the training set, while the test set score slightly decreases from 0.875 to 0.87. The minimal impact of increased training samples on model performance suggests that the Gradient Boosting Regressor has already achieved a good fit for the current dataset. For the Random Forest Regressor, the training score is 0.995, and the test set score increases significantly from 0.825 to 0.975 as the number of training samples grows. This indicates that the model fits t  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Parameter Name** & **Parameter Range** & **Best Parameter** \\ \hline \multirow{3}{*}{Decision Tree} & max\_depth & 1, 5, 10, 20, 50, 100 & 20 \\  & min\_samples\_split & 2, 3, 5, 10, 20, 30 & 2 \\  & min\_samples\_leaf & 1, 2, 4, 10, 20, 30 & 1 \\ \hline \multirow{4}{*}{Gradient Boosting Regression} & learning\_rate & 0.01, 0.1, 0.2, 0.3, 0.5, 1.0 & 0.1 \\  & n\_estimators & 10, 25, 50, 100, 200, 300 & 300 \\ \cline{1-1}  & max\_depth & 1,3, 5, 9,10,30 & 9 \\ \cline{1-1}  & min\_samples\_split & 2, 5, 7, 9, 15, 20 & 7 \\ \cline{1-1}  & min\_samples\_leaf & 1, 2, 5, 8, 10, 15 & 2 \\ \hline \multirow{4}{*}{Random Forest} & n\_estimators & 10, 20, 50, 100, 300, 500 & 300 \\  & max\_depth & 1, 5, 10, 35, 50, 100 & 35 \\ \cline{1-1}  & min\_samples\_split & 2, 3, 5, 10, 20, 30 & 2 \\ \cline{1-1}  & min\_samples\_leaf & 1, 3, 5, 10, 15, 20 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model Parameters.  
Figure 9: Learning Curve Diagrams: (**a**) Decision Tree Learning Curve; (**b**) Gradient Boosting Regressor Learning Curve; (**c**) Random Forest Learning Curve.  
demonstrates a strong improvement in performance on unseen data, showing excellent generalization capability. Overall, the Random Forest Regressor outperforms both the Gradient Boosting Regressor and the Decision Tree Regressor on both the training and test sets, reflecting its superior performance and stable generalization ability. The Random Forest model, by aggregating the results of multiple decision trees, is better able to capture the complex patterns and variations within the data, making it particularly effective on large-scale datasets.