esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 3 Cooperative Control of Variable-Sweep Wing UAV via MADDPG
### Structure and Training Process of the MADDPG-VSC Algorithm
#### 3.3.2 Training Process
These setups provide varying trajectory complexities to enhance the model's adaptability to environments significantly different from the training conditions. In addition to convex obstacles, future simulations will include randomly generated non-convex obstacles to better assess the algorithm's robustness and adaptability in dynamic environments. Figure 7 illustrates the detailed layout of this simulation environment, including the positions of the UAVs, the targets, and the radars, thereby highlighting the complex dynamics and challenges encountered during the simulation process. The MADDPG-VSC algorithm is implemented in Python using the TensorFlow 1.9 deep learning framework. The computational platform comprises an Intel E5-2630v4 @ 2.20 GHz CPU and 16 GB of memory, ensuring sufficient resources for efficient training and execution. Training is conducted in a closed-loop manner, integrating both exploratory and high-quality experiences to enhance learning efficiency. Environmental data combined with the UAVs' state information guide the determination of control variables according to the evolving control policy. This policy is continuously refined through feedback from the environment, enabling progressive improvement in the UAVs' performance over time. The simulation environment and parameter settings establish a robust foundation for evaluating the MADDPG-VSC algorithm's capabilities in regards to both obstacle avoidance and cooperative formation control tasks. Table 4 outlines the key hyperparameters used during training, ensuring clarity regarding the configuration and methodological considerations. This setup ensures a comprehensive and realistic assessment of the algorithm's performance. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Max Episodes & 3000 \\ Maximum Number of Steps T & 200 \\ Discount Factor \(\gamma\) & 0.2 \\ Critic Learning Rate \(\eta_{1}\) & 0.002 \\ Size of Buffer U & 5000 \\ Batch Size & 32 \\ Actor Learning Rate \(\eta_{2}\) & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Configurations of hyperparameters. Figure 7: Schematic representation of the simulation environment. (**a**) Terrain schematic; (**b**) simple scenario; (**c**) complex scenario.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 5 Fair Editions of Ensemble Models
### FAEM: Fairness-Aware Ensemble Model  
The authors of [28] propose the Fairness-Aware Ensemble Model (FAEM), which comprises two main components: Hybrid Sampling-Based Bias Alleviation and Two-Layer Stacking-Based Fairness-Aware Ensemble Learning. The first component is a pre-processing technique designed to balance both class labels and sensitive attributes by employing cross-validation-based under-sampling and sensitive attribute-based over-sampling using the Adaptive Synthetic Sampling (ADASYN) algorithm. The second component, two-layer stacking-based fairness-aware ensemble learning, consists of two stages. In the first layer, multiple base classifiers, including XGBoost, Random Forest, LightGBM, Gradient Boosting Decision Tree (GBDT), and AdaBoost, are trained to generate prediction probabilities, with high-confidence majority-class predictions adjusted to enhance fairness. In the second layer, Logistic Regression serves as a meta-classifier, aggregating the adjusted predictions to produce the final output. This two-stage approach ensures a balance between accuracy and fairness, effectively mitigating bias while preserving model performance, as demonstrated through experiments on benchmark datasets such as German Credit, Adult, Bank, and Compas.
## 2 Related Work
### The Failure Models  
The establishment of failure models is also a crucial aspect of fault diagnosis. Additionally, methods for modeling, such as using mathematical models to simulate fault models for motor failure detection [20] and employing physics-based back electromotive force (EMF) estimation for motor fault detection [21], are also analyzed. Additionally, state observers can generate specific vector residuals by decomposing current estimation errors, which are used for model-based detection strategies of stator inter-turn short circuits in induction motors [22]. Since the observer includes an adaptive scheme for rotor speed estimation, the proposed approach allows for online monitoring by measuring only the stator voltage and current. Aside from the aforementioned methods, parameterized models can also be used to simulate healthy and faulty SPMSM conditions to study the effects of stator ITSCF [23]. Failure models can also be studied under non-stationary conditions, including load or speed variations. A dynamic model of a faulted Surface-Mounted Permanent Magnet Synchronous Motor (SPMSM) was derived using a deformed flux model [24].  
Reference [25] proposes the incorporation of salient pole modeling into the IPMSM model and develops an ITSCF model for IPMSM with series and parallel winding connections. This model is validated through finite element method simulations and experimental measurements. Reference [26] introduces modeling and detection methods for intermittent ITSCF in the stator end windings of PMSM. During the steady-state operation, fault-related information is contained in the stator current and reference voltage. The effectiveness of this method is confirmed through fault detection using wavelet transform, with simulation and experimental results validating the approach. References [27; 28; 29; 30; 31; 32] focus on the application of rapidly developing machine learning techniques for diagnosing faults in motors, including the analysis of harmonics, voltage, and current signals.  
Through the literature review, it is evident that wavelet transform can analyze signals to produce multiple time-frequency representations. Extracting features from fundamental information such as voltage and current has gradually become one of the mainstream methods for fault analysis, and it can help avoid issues related to motor signal measurement. Due to the high computational load of continuous wavelet transform and the fact that most data are now digitized, discrete wavelet transform is more suitable for motor fault diagnosis design compared to continuous wavelet transform. This study will apply discrete wavelet transform as the analytical method. In summary, motor fault diagnosis is a key technology for ensuring the stable operation of industrial automation systems. Through efficient data collection and processing, along with precise model development and evaluation, timely fault warning and effective preventive maintenance can be achieved, thereby enhancing the overall system performance and safety.
## 7 Results
### Accuracy  
The performance test for finding the effectiveness of the vacuum leak detection method proposed in this paper was conducted as follows. In total, 500 consecutive training runs were followed by test runs with and without vacuum leaks (295 and 44 runs, respectively). For performance comparison, the performance index was obtained using the IRC, IRC (no update), and ARIMA. The ARIMA model is one of the widely used statistical methods for predicting time series data. ARIMA, short for \"Auto Regressive Integrated Moving Average\", is actually a class of models that \"explain\" a given time series based on its own past values--that is, its own lags and the lagged forecast errors--meaning that an equation can be used to forecast future values [44]. The ARIMA model was adopted for performance comparison because the methods proposed in this study had similar considerations for past data and forecast errors. The best condition result for each parameter was extracted and used from the final results sequence. The best gain of the IRC method was \(\beta\) = 0.5 and \(\gamma\) = 0.6. For the IRC that did not use the update, the gains were set to \(\beta\) = 1 and \(\gamma\) = 1. The ARIMA model used best order (1,1,0). The test results are shown in Table 4. The F1 score is the harmonic mean of the precision and recall, where it reaches its best value at 1 (perfect precision and recall) and worst value at 0. Therefore, the F1 score is close to 1 when both the recall and precision are high. The accuracy of the IRC model was 0.97 and the F1 score was 0.92. In the performance comparison experiment, the IRC method had the best performance. In the case of the IRC model without using the update, 100% of vacuum leaks were detected but the vacuum leak was misdiagnosed as leaks 29 times as opposed to 4 times, and the equipment had to be checked 25 more times than when the update was used. On the other hand, the proposed model misdiagnosed three out of all vacuum leaks as normal. The ARIMA model test results showed that the vacuum leak detection was excellent, but there were 108 type 1 errors, which judged normal as a vacuum leak, showing a low accuracy of 66%. The root cause of the large error count in the ARIMA model can be attributed to the non-periodicity of the data gathered in the facility.
# Experimental Analysis of Pore and Permeability Characteristics of Coal by Low-Field NMR
## 2 Coal Samples and Experiments  
### Sample Description  
Coal samples were collected from Jincheng, Luan, and Yulin mining areas of China. The block raw coals were sampled in mining face and heading face. The coal cores were drilled along the bedding or vertical direction to obtain a cylindrical coal sample with a diameter of 25 mm and a length of about 50 mm, which was used for the NMR scanning and permeability test (Figure 1). After the above tests, the cores were crushed, sieved, and made into 3-6 mm, larger than 80-mesh samples, respectively, for the MIP test and proximate analysis.  
The vitrinite reflectance measurement was performed using a Zeiss Axioskop-40 polarizing microscope (Zeiss, Oberkochen, Germany), according to the Chinese standard of GB/T 6948-2008 [17]. The proximate analysis was performed using the Automatic Proxima5E-MAG6700 (Changsha Kaiyuan Instruments Co., Ltd., Changsha, China), according to the Chinese standard of GB/T212-2008 [18]. The test results and the physical properties of the coal samples are shown in Table 1.  
In Table 1, CZ-1, CZ-2, and CZ-4 are the bedding coal samples from different layers in the No. 3 coal seam of the Chengzhuang (CZ) coal mine; CZ-3 is a vertical coal sample from the same layer as CZ-2. WY-1 and WY-2 are the bedding and vertical coal samples from the same layer of the Wuyang (WY) coal mine. YJL-1 and YJL-2 are the bedding and vertical coal samples from the same layer of the Yujialiang (YJL) coal mine. The maximum vitrinite reflectance of the samples is between 0.66% and 3.10%. The three kinds of coal samples of CZ, WY, and YJL belong to anthracite, lean coal, and long flame coal, respectively. The macrolithotype of the CZ and WY coal samples are mainly bright coal, followed by dull coal with vitrain strip. The macrolithotype of the YJL coal samples are mainly semi-dull coal, followed by semi-bright coal and dull coal and little vitrain strip.
## 2 Proposed Model: QPE Analytical Approximation Model
### Vector in the Line-of-Sight Method  
In the analytical approximation model presented in this paper, the attitude errors of the spaceborne SAR satellite are not taken into consideration. The true value of the attitude of the platform is used in both the analytical approach and the following numerical verification.  
With this assumption, the vector in the line-of-sight (VLS) method can be used to calculate the state vectors of the point of interest. The relative range from the SAR antenna phase center to the point of interest can be derived from the time delay of the echo signal. For spaceborne SAR geometry, the echo signal enters the receiver after several pulse repetition intervals (PRIs) until the moment of transmission, and then it is demodulated and sampled by the receiver. In each range profile of the sampled data, each sampled time delay can be determined by the index number of its range gate. The signal transmission delay from the antenna phase center to the sampling ADCs in the receiver can be accurately measured in the state-of-the-art spaceborne SAR system. \(t_{delay}\) represents the delayed time of the echo of the point-of-interest signal from its transmission time, and \(c\) represents the velocity of light, whose relative range is described by  
\[\rho=\frac{ct_{delay}}{2} \tag{1}\]  
A unit line-of-sight vector \(\hat{\mathbf{u}}_{u,ECI}\) in the ECI system can be created; it is perpendicular to the aperture of the SAR antenna and points to the surface of the Earth. The position vector \(\mathbf{P}_{tar,ECI}\) and velocity vector \(\mathbf{V}_{tar,ECI}\) of the point on the surface of the Earth at which the antenna points can be shown in stripmap SAR geometry as  
\[\mathbf{P}_{tar,ECI}=\mathbf{P}_{sat,ECI}+\rho\cdot\hat{\mathbf{u}}_{a,ECI}= \begin{bmatrix}P_{tar,ECI,x}\\ P_{tar,ECI,y}\\ P_{tar,ECI,z}\end{bmatrix} \tag{2}\]  
\[\mathbf{V}_{tar,ECI}=\begin{bmatrix}-\omega_{e}\cdot P_{tar,ECI,y}\\ \omega_{e}\cdot P_{tar,ECI,x}\\ 0\end{bmatrix} \tag{3}\]  
in which \(\omega_{e}\) is the angular speed of the Earth's rotation.  
Because there are errors in the satellite's position and velocity measurements from the onboard GNSS-based orbit determination system, the calculated \(\mathbf{P}_{tar,ECI}\) and \(\mathbf{V}_{tar,ECI}\) will also contain errors at the same time. The relative range \(\rho\) remains unaffected because errors exist in measurements while the real geometry remains unchanged, which means that the time delay remains equal to the accuracy value.
## 3 Results and Discussions
### Adsorption Selectivity and Recycling Performance Study  
In consideration of the complexity and heterogeneity of the real aquatic environment, ICP-AES was adopted to check the adsorption selectivity of PVKAP versus U(VI) in a mixed solution containing Zr,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**adsorbate** & \(\mathbf{K_{1}\text{ (min}^{-1})}\) & \
\begin{tabular}{c} **Pseudo-First-Order** \\ \(q_{\text{e}}\) **(mg g\({}^{-1}\))** \\ \end{tabular} & \(\mathbf{R^{2}}\) & \(\mathbf{K_{2}\text{ (g mg min}^{-1})}\) &
\begin{tabular}{c} **Pseudo-Second-Order** \\ \(q_{\text{e}}\) **(mg g\({}^{-1}\))** \\ \end{tabular} & \(\mathbf{R^{2}}\) \\ \hline PVK & 0.07 & 13.6 & 0.985 & \(7.92\times 10^{-3}\) & 14.6 & 0.996 \\ PVKAP & 0.11 & 587.8 & 0.980 & \(2.72\times 10^{-4}\) & 632.9 & 0.994 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for pseudo-first-order and pseudo-second-order models.  
Figure 6: Influence of pH and ionic strength on the capturing U (VI) by PVKAP, \(C_{\text{U(VI)initial}}\) = 36.0 mg L\({}^{-1}\), \(T\) = 293 K.  
Cu, Eu, Co, Pb, Cr, U, Hg, Cd, La, Sr, Th, and Cs (\(C_{0}\) = 0.5 mmol L\({}^{-1}\) for all these cations). As shown in Figure 7, PVKAP has significantly enhanced preferential affinity for uranyl compared to PVK, indicating that it is more capable of selectively enriching U(VI) from multi-component systems. According to Equation (3), the sorption selectivity (\(S_{\text{U}}\)) of PVKAP to U(VI) is almost 70.36%, while PVK has almost no adsorption selectivity in the same condition. So, the excellent adsorption selectivity of PVKAP is mainly attributed to the excellent complexing ability between P = O group of PVKAP and U(VI). In addition, PVKAP shows a selective adsorption sequence: U(VI) \(>\) Eu(III) \(\approx\) La(III) \(\approx\) Th(IV) \(>\) other metal ions, which can be ascribed to the intrinsic properties of different metal ions and their affinity with PVKAP [61; 62]. Moreover, the reusability of the adsorbent is an important performance of practical applications. Figure 8 shows the five recycling studies of PVK and PVKAP. The removal rate of U(VI) by PVK is reduced from 90% to 75%, while the removal rate of U(VI) by PVKAP is maintained above 96% (99% to 96%). Thus, it was shown that the PVKAP has largely improved recyclability.
# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 1 Introduction
proposed a higher-density wavelet packet neighborhood coefficient denoising algorithm, and experiments showed that the algorithm can significantly improve the signal-to-noise ratio [22]. [PERSON] et al. proposed a fault detection method based on SNR [23]. [PERSON] et al. used the improved CNN LeNet-5 model to detect railway anomalies. Ref. [24] [PERSON] et al. trained a classifier based on two indicators of experimental data and confirmed the reliable performance of ADD [25]. [PERSON] et al. present a method for evaluating the surface texture of machine parts using wavelet transform of three selected indices. This paper proposes a fault type classification and identification model based on wavelet denoising and machine learning. There are a lot of interference factors in the operating environment of machinery, which makes it ineffective to use traditional detection methods to judge the fault location and type of fault of the machinery, and even misjudgment of the fault location and type may occur. Therefore, how to realize intelligent fault diagnosis in an environment full of a large number of interference factors is an urgent problem to be solved. We use two methods of wavelet soft threshold denoising and wavelet packet analysis to denoise the original fault signal data. Eliminate the interference factors in the environment, check the correctness of the denoising results, and select the denoising method with better effect on the basis of quantitative indicators. Based on the wavelet denoising algorithm, five of the most widely used machine learning models are applied: K-means clustering, quadratic support vector machine (SVM), random forest (RF), decision tree (DT) and AdaBoost algorithm. These machine learning algorithms are used for binary classification of bearing faults and to compare their results. By designing the fault classification evaluation prediction criteria, the following conclusions are drawn. If unsupervised machine learning methods are used, K-means clustering can achieve good classification results; if supervised machine learning methods are used, decision tree classification can achieve the best results. In addition, the focus of our future work is as follows: First, increase the types of rolling bearing faults, narrow the characteristic differences of fault signals, and improve the adaptability and flexibility of the model. Second, more types of faults and larger datasets are considered to improve the generalization ability of the model. Finally, the wavelet denoising algorithm can be further optimized by selecting different wavelet bases, decomposition levels and threshold functions according to different types of signals. Additionally, machine learning models can further improve their performance by tuning parameters, selecting features, or combining multiple models. In this paper, it is of great significance to intelligently diagnose the faults of instruments and equipment.
# BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups
## 3 BLE Signal Analysis
### Wall Gateways BLE Signal Segments
The width of each \"violin\" represents the density of data points across different signal values, with wider sections indicating higher concentrations of signal strengths at specific values. The central line within each violin denotes the median signal strength, while the range of signal values is shown from the minimum to maximum. This visualization allows for easy comparison of signal distribution and variability across different body positions, highlighting differences in signal strength performance at each location relative to the wall-mounted gateways. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Position** & **Segments Length Range** & **Segment Quantity** & **Avg Quality STD** \\ \hline \hline \multirow{8}{*}{Shoulder} & 0–10\% & 11 & 1.8816 \\ \cline{2-4}  & 10–20\% & 33 & 2.3153 \\ \cline{2-4}  & 20–30\% & 20 & 2.7733 \\ \cline{2-4}  & 30–40\% & 29 & 2.85 \\ \cline{2-4}  & 40–50\% & 34 & 2.8345 \\ \cline{2-4}  & 50–60\% & 42 & 3.2635 \\ \cline{2-4}  & 60–70\% & 30 & 2.9359 \\ \cline{2-4}  & 70–80\% & 24 & 2.97 \\ \cline{2-4}  & 80–90\% & 28 & 3.1915 \\ \cline{2-4}  & 90–100\% & 16 & 3.0038 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantity of BLE signal segments and RSSI standard deviation across different segment length ranges from wall-mounted gateways. Figure 6: Distribution of BLE signal segments received from the wall GWs and their quality across different body positions. The left subfigure shows the quantity of signal segments within various length ranges, while the right subfigure illustrates the standard deviation of signal quality within each group of signal segments.
# Pairwise Likelihood Estimation of the 2 PL Model with Locally Dependent Item Responses
## 1 Introduction
### IRT Models with Correlated Item Residuals for Modeling of Local Dependence  
We now present an IRT model with correlated item responses that accommodate local dependence. Assume that, marginally, a 2 PL model holds for items \(X_{i}\) (\(i=1,\ldots,I\)). It is assumed that there is an underlying latent item response \(X_{i}^{*}\) for item \(X_{i}\) such that  
\[X_{i}=\mathbf{1}(X_{i}^{*}>0), \tag{11}\]  
where \(\mathbf{1}\) denotes the indicator function. Specifically, \(X_{i}\) takes a value of \(1\) if \(X_{i}^{*}\) exceeds zero. Let the item residual \(\varepsilon_{i}\) follow the logistic distribution. Then, we have  
\[\mathsf{P}(\varepsilon_{i}<y)=\Psi(y)\text{ for all }y\in\mathbb{R}. \tag{12}\]  
We define the latent item response \(X_{i}^{*}\) as  
\[X_{i}^{*}=a_{i}\theta-b_{i}-\varepsilon_{i}. \tag{13}\]  
Then, we obtain, using (11), (13), and (12),  
\[\mathsf{P}(X_{i}=1)=\mathsf{P}(X_{i}^{*}>0)=\mathsf{P}(\varepsilon_{i}<a_{i} \theta-b_{i})=\Psi(a_{i}\theta-b_{i}). \tag{14}\]  
Hence, \(X_{i}\) marginally follows the 2 PL model. Local dependence can be modeled by letting the logistically distributed item residuals \(\varepsilon=(\varepsilon_{1},\ldots,\varepsilon_{I})\) be dependent. In principle, any dependence structure with a fixed marginal distribution to the logistic one can be assumed. In fact, this leads to copula models [32] of correlated item residuals [21; 33].