esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Wireless information transmission systems are increasingly being used to control the vibrations of rotating components [24]. Information on the use of acoustic emission signals (AE) for monitoring various technological processes is presented much more modestly in the literature. It can be assumed that the situation is determined by the more complex nature of the occurrence of AE signals and the difficulties of their analysis. At the same time, it was established that AE signals accompany not only technological processes of blade processing but are also observed during phase transformations accompanied by changes in the crystal lattice of the material [27; 28; 29; 30; 31], during crystallization and melting of the substance, and during the formation of particles of a new phase in supersaturated solutions [32; 33; 34; 35]. Directly related to the cutting process are works on studying of AE signals during the plastic deformation of materials, accompanied by the development of defects in their structures [36; 37; 38]. Monitoring the condition of tools operating with relatively low power consumption is possible by monitoring the vibrations accompanying the cutting process. Vibrations can be controlled using accelerometers and microphones installed at a sufficient distance from the cutting area. Changes in the cutting ability of tools find a broad response when changing many parameters of AE signals accompanying cutting. The main disadvantage of diagnostic signals, including AE signals, is that their parameters can change not only due to changes in the state of the cutting tool but also when other cutting conditions change, which include processing modes. If such dependencies have high gradients, then it becomes necessary to train the diagnostic system, which causes specific difficulties, especially in a single production, or to search for parameters that are less dependent on processing modes [6]. To reduce the dependence of AE parameters on the variety of modes and the impacts of other factors, work was carried out where the condition of the diagnostic object was assessed using AE parameters taken in different frequency ranges [39; 40; 41; 42; 43; 44]. Works in multiparametric diagnostics of the state of the cutting process using the analysis of AE signals in different frequency ranges also began to appear more often [6; 45; 46; 47; 48; 49; 50]. The danger of using AE signal parameters in different frequency ranges is that such parameters may be closely related. In this case, the use of multiparametric diagnostics will not provide additional information and will not justify the complication of the processing algorithm [6; 46]. The purpose of this work is to study the physical causes that differentially affect the parameters of the AE signal in different frequency ranges and the possibilities of using several signal parameters to monitor various metalworking processes.
## 4 Results and Discussion
### Atomization Experiment Results
#### 4.1.2 Effect of Air Pressure on Atomization  
The effect of water volume on atomization was tested for under the air pressure of 0.35 MPa, as shown in Figure 10.  
Figure 10: Variation of atomization parameters with water volume.  
Figure 9: Variation of atomization parameters with air pressure.  
As shown in Figure 10, when the pressure is constant, the number of droplets will increase with the increase of water volume. Especially when the water volume is less than 30 L/h, the number of droplets increases significantly. When the amount of water is more than 30 L/h, the increasing trend of droplet number is very small, not even increasing with the increase of water volume. The proportion of fog droplets below 50 \(\upmu\)m increases first and then decreases with the increase of water volume under the condition of constant air pressure.  
1. The number of fog droplets and the proportion of fog droplets below 50 \(\upmu\)m increase with the increase of water content in a certain range. Therefore, increasing the amount of atomized water is conducive to improving the atomization effect.
2. When the water content is more than 30 L/h, the droplet number increases very slowly, but the droplet size increases. Therefore, when the amount of water reaches a certain value, the atomization effect will begin to decline if the amount of water continues to increase.  
In order to improve atomization effect, minimize water consumption and air pressure, and avoid the difficulty of equipment maintenance caused by high pressure and large water volume; the influential factors of air pressure and water volume should be considered comprehensively. Additionally, economical and reasonable air supply pressure and water supply quantity should be determined. The results show that the optimum operating conditions of the ultrasonic nebulizer are: air pressure 0.35 MPa, water volume 30 L/h.
# Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine
## 5 Computational Results  
The proposed HOUM algorithm was run using both a linear SVM (HOUM-L) and a radial SVM (HOUM-R). In the present study, KNN, RF, SVM, and ANN classifiers were used. In the first stage of our research, we balanced our datasets using the developed techniques HOUM-R and HOUM-L. Additionally, the well-known SMOTE and hybrid resampling methods such as SMOTE-ENN and SMOT-Tomek were utilized to compare the proposed approach. These balanced datasets, along with an imbalanced version referred to as the \"Original Dataset,\" were analyzed across stated classification algorithms. In the second stage, we examined the contribution of the proposed method to the literature by comparison with similar studies such as W-SIMO and RusAda.  
In the application, the \"R i386 4.0.3\({}^{\prime\prime}\) version of the R statistical software development and data analysis program was used [43; 44]. The packages and parameter values used are summarized in Table 3.  
In the present study, sample reduction and augmentation operations were only applied to the training data. The test data underwent no processing. In the hybrid method's implementation, the \"e1071\" package was used for the SVM function during the sample reduction phase, deriving data for both radial and linear kernels. The decision variable values (decision.values) were determined via the SVM, and instances before the first quarter of the majority data class's decision values were removed from the dataset. Then, the volume of minority-class data was augmented using the SLS function from the \"smotefamily\" package, employing the safe-level SMOTE method.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 3 Swarm Robotics Design and Simulation
### Swarm Model Design
#### 3.3.2 Ant Model  
Starting from a basic framework, the ant model introduces role specialization, drawing inspiration from the division of labor observed in leafcutter ants [33; 34]. In this model, specific tasks are assigned to two distinct groups of robots: the blue picker robots, analogous to leafcutter ants, focus on ore collection, while the red loader robots, similar to porter ants, are responsible for transporting the ore to a central \"nest\". This role specialization enhances efficiency in ore transportation by reducing the distance traveled, thereby decreasing transportation time. The model is particularly effective in environments where ore is concentrated in specific areas, such as in vein mining or block saving. The ant model operates under a decentralized control system, allowing robots to independently execute their designated roles while adhering to a structured finite state machine (FSM) that governs role-specific states and transitions, such as \"picking\" for pickers and \"transporting\" for loaders [35]. This FSM-driven approach ensures that each group of robots efficiently performs its specialized tasks, as illustrated in Figure 6.  
Figure 5: Finite-state machine for the baseline model.
## 4 Conclusions  
This paper focused on the nonlinear aeroelastic characteristics of a small aspect ratio supersonic all-movable fin with single or multiple freeplay nonlinearities. The conclusions that can be drawn from the numerical results can be summarized as follows.  
1. The DF method has shown good applicability to the analysis and solution of the LCO characteristics of the system with single or multiple freeplay nonlinearities. In this paper, a discrete scanning method based on the DF method was established to solve the problem of amplitude alignment, and this can avoid missing solutions and give consideration to efficiency.
2. Systems with a single freeplay nonlinearity exhibit two different types of LCO. A system with LCO-i characteristics has stable LCO motions below the linear flutter boundary, while a system with LCO-ii characteristics only has unstable LCO motions above the linear flutter boundary, and the divergence boundary is not lower than the linear flutter boundary. From a flutter suppression perspective, LCO-ii is more beneficial.
3. Systems with bending and torsion freeplay nonlinearities exhibit complex dynamical behaviors, such as LCO and quasi-periodic and chaotic motions. The initial conditions have a great influence on the system response. The introduction of harmonic initial conditions will effectively improve the efficiency of using time-domain integration to verify frequency-domain LCO solutions, especially for those with a small attraction domain.
4. The support stiffness at the root of the fin and freeplay size ratio have impressive effects on the LCO characteristics. The ratio of bending stiffness to torsional stiffness determines the LCO characteristics of a fin with a single freeplay nonlinearity. For a fin with multiple freeplay nonlinearities, when the difference between the two freeplay sizes is large, its nonlinear aeroelastic characteristics are mainly determined by the freeplay with the larger value.  
The small aspect ratio fin in this paper is treated as a rigid fin because its elastic modes have higher frequencies and have little influence on the flutter characteristics of the fin. However, for the flexible fin with a high aspect ratio, the elastic modes cannot be ignored. Further research is needed to investigate the influence of the elastic modes. In addition, damping is not involved in this paper, and the suppression effect of damping on the vibrations of the fin with multiple freeplay nonlinearities needs further study.  
Conceptualization, Z.W. and L.B.; methodology, Z.W. and L.B.; software, L.B.; validation, Z.W. and L.B.; formal analysis, L.B.; writing--original draft preparation, L.B.; writing--review and editing, Z.W.; supervision, C.Y.; project administration, C.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Biform Game Approach to Strategy Optimization of Autonomous Vehicle Lane Changes on Highway Ramps
## 4 Example Analysis
### Numerical Analysis
#### 4.4.1 Parametric Analysis
Figures 2 and 3 show the changing trends in the optimal stable speeds of three vehicles when adjusting the acceleration probability of the front vehicle, the deceleration probability of the rear vehicle, and the forced lane-changing probability of the target lane-changing vehicle. For example, when the acceleration probability of the front vehicle and the deceleration probability of the rear vehicle were determined, the forced lane-changing probability of the lane-changing vehicle had a linearly increasing relationship with the optimal stable speeds of the three vehicles, and the speed change rate of the target lane-changing vehicle was greater. This reflects the model's search for optimal solutions under different parameter settings, indicating  
Figure 2: Optimal stable speed change trend when adjusting the probability of different speed changes of three vehicles. (**a**) When the forced lane-changing probability of the target vehicle is fixed, the speeds of the three vehicles show an upward trend; (**b**) when the deceleration probability of the rear vehicle is fixed, the speeds of the three vehicles remain unchanged; (**c**) when the acceleration probability of the front vehicle is fixed, the speeds of the three vehicles show an upward trend. that the model can effectively analyze vehicle speed distribution and provide strategic support for achieving the optimal equilibrium speed for the vehicles. 2. Verification of model effectiveness: These results are consistent with the theoretical expectations of the biform game model, further verifying the effectiveness of the model. In actual traffic scenarios, the driving speeds of vehicles are affected by multiple factors. The model, through the analysis of these parameters, accurately reflects the influence rules of various factors on the optimal stable speed. For example, the optimal stable speeds of the three vehicles had nothing to do with the deceleration probability of the rear vehicle, but did have a linearly increasing relationship with the acceleration probability of the front vehicle and the forced lane-changing probability of the target vehicle, and the forced lane-changing probability had a greater impact. This is consistent with the driving logic of vehicles in actual traffic to pass through intersections quickly, indicating that the model can reasonably simulate and predict the speed changes of vehicles during the lane-changing process and provide a reliable basis for the lane-changing strategies of autonomous vehicles. Figure 3: Trends in optimal stable speed changes of three vehicles vs. probability of speed changes of front and rear vehicles (Red is the ahead car speed, green is the lane-changing car speed, and blue is the rear car speed).
# A Precise Modeling Method for PMSM Servo System of Spacecraft
## 2 PMSM Servo System  
The structure of the PMSM servo system for spacecraft is shown in Figure 1. PMSM drives the ball screw to rotate through gear transmission, thereby controlling the deflection angle of the wing. In gear meshing, ball screw and mechanical transmission connections, there are many nonlinear factors such as elasticity, backlash, and friction, which come from gear clearance, meshing stiffness, torsional and axial elasticity of ball screw, wing damping, etc. The existence of these nonlinear factors seriously affects the performance of the PMSM servo system and should be fully considered in precise modeling.  
The structural diagram of the whole servo system is shown in Figure 2. The vector control is carried out considering the friction model and the two-mass elastic system with backlash.  
The servo system of the spacecraft uses PMSM as the executive motor, and its dynamic equation is as follows:  
\[\left\{\begin{array}{l}\frac{dx}{dt}=\mathbf{A}\mathbf{x}+\mathbf{B}u\\ y=\mathbf{C}\mathbf{x}\end{array}\right\} \tag{1}\]  
where \(\mathbf{x}=\begin{bmatrix}\omega_{\text{m}}\\ T_{\text{L}}\end{bmatrix}\), \(\omega_{m}\) is the mechanical angular speed of the motor, \(T_{L}\) is the load torque of the motor; \(\mathbf{A}=\begin{bmatrix}-\frac{B_{\text{m}}}{I_{\text{m}}}&\frac{-1}{I_{\text{m }}}\\ 0&0\end{bmatrix}\), \(B_{m}\) is the viscous friction coefficient, \(J_{m}\) is the rotational inertia of the motor; \(\mathbf{B}=\begin{bmatrix}\frac{1}{I_{\text{m}}}\\ 0\end{bmatrix}\); \(\mathbf{C}=\) ; \(u=T_{e}\), \(T_{e}\) is the electromagnetic torque of the motor; \(y=\omega_{\text{m}}\).  
Figure 1: The structure of PMSM servo system for spacecraft.  
Figure 2: The structural diagram of the whole servo system.
## 5 Evaluation
### Results
#### 5.4.1 Results for RQ1
DeepFM shows a mean of 9.87% and a margin of 3.62, which represents an improvement over the CNN, although the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Project** & **Mean** & **Margin** \\ \hline \multirow{4}{*}{CNN} & JUnit & 15.83 & 4.94 \\  & LC-problems & 6.44 & 5.64 \\  & Concurrency & 12.47 & 4.39 \\  & [PERSON] & 30.45 & 6.34 \\ \hline \multirow{4}{*}{FM} & JUnit & 7.82 & 15.32 \\  & LC-problems & 0.87 & 5.24 \\  & Concurrency & 13.24 & 8.76 \\  & [PERSON] & 28.74 & 6.89 \\ \hline \multirow{4}{*}{DeepFM} & JUnit & 13.53 & 10.58 \\  & LC-problems & 1.77 & 3.56 \\  & Concurrency & 9.87 & 3.62 \\  & [PERSON] & 23.67 & 8.46 \\ \hline \multirow{4}{*}{DeepPerf} & JUnit & 6.03 & 2.26 \\  & LC-problems & 0.32 & 0.10 \\  & Concurrency & 8.99 & 2.07 \\  & [PERSON] & 25.21 & 7.29 \\ \hline \multirow{4}{*}{HINNPerf} & JUnit & 5.96 & 2.00 \\  & LC-problems & 0.35 & 0.10 \\  & Concurrency & 8.99 & 2.06 \\  & [PERSON] & 22.78 & 10.94 \\ \hline \multirow{4}{*}{MovePerf} & JUnit & 5.41 & 0.03 \\  & LC-problems & 0.28 & 0.01 \\ \cline{1-1}  & Concurrency & 4.92 & 0.02 \\ \cline{1-1}  & [PERSON] & 20.16 & 8.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction results for each model on the datasets. prediction MRE remains. HINNPerf has a mean of 8.99% and a smaller margin of 2.06; while the margin is smaller, the model's prediction MRE is still significant, resulting in lower prediction accuracy in this project. By contrast, MovePerf performs exceptionally well, with a mean of 4.92% and a margin of 0.02, demonstrating minimal prediction MREs and high stability. Therefore, on datasets with a moderate number of samples and minimal label variation, MovePerf outperforms HINNPerf, reducing the mean by 4.07% compared to HINNPerf. For Kevin, the CNN achieves a 30.45% mean and 6.34 margin, indicating that the prediction results are highly unreliable, with large prediction MREs. In contrast, DeepFM shows a mean of 23.67% and a margin value of 8.46, demonstrating a significant improvement over the CNN, although its prediction accuracy remains relatively low. HINNPerf achieves a mean of 22.78% and a margin value of 10.94, showing a further improvement compared to DeepFM. Our MovePerf model excels, with a mean of 20.16% and a margin value of 8.53, further validating the effectiveness of our approach. In datasets with a larger number of samples and significant label variation, the prediction accuracy of MovePerf slightly decreases. However, compared to HINNPerf, the prediction MRE is still reduced by 2.62%. In summary, MovePerf outperforms the CNN, DeepFM, and HINNPerf across multiple test projects, particularly demonstrating higher accuracy and stability on complex and feature-rich datasets.
# Attention-Based Hypergraph Neural Network: A Personalized Recommendation
## 4 Experiments
### Evaluation Metrics  
To objectively evaluate the performance of various algorithms, the performance 2.metrics used include NDCG (Normalized Discounted Cumulative Gain), Recall, and F1-SCORE. The higher the values of these metrics, the better the model's performance. They are defined as shown in Equations (17)-(19).  
The NDCG formula is as follows:  
\[DCG=\sum_{i=1}^{K}\frac{rel_{i}}{\log_{2}(i+1)} \tag{17}\]  
\[IDCG=\sum_{i=1}^{K}\frac{rel_{i}^{ideal}}{\log_{2}(i+1)} \tag{18}\]  
\[NDCG=\frac{DCG}{IDCG} \tag{19}\]  
In Equation (17), DCG (Discounted Cumulative Gain) represents the unnormalized cumulative gain, and IDCG (Ideal DCG) represents the maximum possible DCG value under the ideal ranking. The \(rel_{i}\) in the Equation represents the relevance score of the item at the i-th position in the recommendation list, and the _reli\({}_{i}^{ideal}\)_ represents the relevance score of the item at the i-th position in the ideal ranking.  
The Recall formula is as follows:  
\[Recall=\frac{TP}{FN+TP} \tag{20}\]  
F1-SCORE formula is as follows:  
\[F1\text{-}SCORE=\frac{2*Precision*Recall}{Precision+Recall} \tag{21}\]  
In Equation (20), TP, TN, FN, and FP represent true positives, true negatives, false negatives, and false positives, respectively, in the recommendation results.  
From Figures 3-5, it can be observed that the impact of the learning rate on NDCG, F1-Score, and Recall shows a trend of first increasing and then decreasing, with the learning rate of around 0.01 yielding the best results. The NDCG, F1-Score, and Recall metrics gradually improve as the length of the recommendation list increases, indicating that with a longer recommendation list, more relevant courses can be recommended.  
From Figures 6-8, it can be observed that the NDCG, F1-Score, and Recall metrics exhibit the best performance for the recommendation sequence when the embedding dimension D is 128. This may be because, at this dimension, the model can effectively capture the characteristics of the data. As D increases further, all three metrics, NDCG, F1-Score, and Recall, begin to decline, indicating that the model starts to overfit when the embedding dimension D exceeds 128 dimensions.  
Figure 3: Learning rate experiment (k = 5).  
Figure 4: Learning rate experiment (k = 10).  
Figure 5: Learning rate experiment (k = 15).  
Figure 6: Embedding dimension D experiment (k = 5).  
Figure 7: Embedding dimension D experiment (k = 10).  
From Figures 9-11, it can be observed that when the Batch-size is 128, the model performs best in terms of the NDCG, F1-Score, and Recall evaluation metrics. When the Batch-size exceeds 128, the performance of these metrics begins to decline. This may be because, at this Batch-size, the model can effectively perform gradient updates and convergence.  
Figure 8: Embedding dimension D experiment (k = 15).  
Figure 9: Batch-size experiment (k = 5).
# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 2 Methodology
### Metaheuristics
#### 2.3.4. Flower Pollination Algorithm  
The FPA was inspired by the flower pollination process [45]. The general rules for this algorithm are stated in the following four steps:  
1. A biotic process is global pollination and obeys Levy flights.
2. On the other hand, an abiotic process is local pollination.
3. Pollinators are the probabilities of reproduction.
4. Probability switches between local and global pollination.  
As a part of the global pollination process, insects and other pollinators transport flower pollen across vast distances. Therefore, the strongest individuals are the ones to be pollinated and produce offspring, denoted as \(g_{*}\). The global pollination plus flower constancy can be modeled as shown in Equation (6):  
\[x_{i}^{t+1}=x_{i}^{t}+L\big{(}x_{i}^{t}-g_{*}\big{)} \tag{6}\]  
where \(i\) denotes the pollen or solution vector \(x_{i}\) at iteration \(t\), \(g_{*}\) refers to the current best solution, and \(L\) is a step size representing the pollination strength. The pollination strength, represented by the parameter \(L\), is analogous to a step size. A Levy flight is a useful tool for simulating insect flight since it can cover large distances in discrete increments, like those that insects can take. The traits of pollinators are based on the Levy flight probability distribution. The Levy can be approximately modeled as shown in Equation (7):  
\[L\ \sim\ \frac{\lambda\Gamma(\lambda)\sin\left(\frac{\pi\lambda}{2}\right)}{ \pi}.\frac{1}{S^{1+\lambda}}(S\gg S_{0}>0) \tag{7}\]  
The local pollination plus flower constancy can be modeled as shown in Equation (8):  
\[x_{i}^{t+1}=x_{i}^{t}+\rho\Big{(}x_{j}^{t}-x_{k}^{t}\Big{)} \tag{8}\]  
Equation (8) represents the local pollination. The pseudocode for FPA is described in Algorithm 5.  
```
Objective function \(f(x),\ x=(x_{1},\ x_{2},\ldots,x_{d})\) Initialize population Find the best solution \(g_{*}\) in the initial population Define a switch probability \(p\in[0,\ 1]\) FOR \(t=1:\ max\ generation\) FOR \(i=1:\ n\) IF \(rand<p\) Draw \(d\)-dimensional step vector \(L\) Global phase Equation (6) ELSE Draw \(\rho\) in Equation (8) from a uniform distribution [0, 1] Local phase Equation (8) END Assess new solutions If new solutions > old solutions, update population END current best solution \(g_{*}\) END
```  
**Algorithm 5**Flower pollination algorithm