esa-sceva/satcom-chunk-collection huggingface dataset:
# A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis
## 4 Experimental Verification and Analysis
### Dataset Description  
#### 4.1.1 CWRU Dataset  
The evaluation was conducted on the CWRU dataset [30], where the bearing experimental system at CWRU is depicted in Figure 3. Single-point defects of specified diameters were introduced on the drive end and fan end bearings (6205-2 RSJEM SKF deep groove ball bearings, SKF, Gothenburg, Sweden) and the rolling elements.  
In this study, we focused primarily on defects 7, 14, and 21 milli-inch in diameter, as their vibration signals are relatively small compared to defects of larger diameters. Therefore, our experiments included four bearing conditions: Healthy (H), Ball Fault (BF), Outer Race Fault (OF), and Inner Race Fault (IF). Data collection was conducted under four load conditions (0, 1, 2, 3 hp), corresponding to motor speeds of 1797, 1772, 1750, and 1730 revolutions per minute (r/min). This study designed a total of 10 fault categories, detailed in Table 3.  
For each fault class, 500 samples were randomly selected from the original signal data. Each sample consisted of amplitude signals containing 2048 data points. In industrial scenarios, vibration signals often exhibit different characteristic distributions due to varying operating conditions and environments. This variability can degrade the performance of models trained in a supervised manner on signals collected under different operating conditions. Therefore, unsupervised domain adaptation models are highly useful and necessary in different settings.  
As shown in Table 4, labels A to F represent different operating conditions, defining data from the drive end as the source domain data and data from the fan end as the target domain data. Domain adaptation tasks are denoted as A\(\rightarrow\)D, B\(\rightarrow\)E, C\(\rightarrow\)F, D\(\rightarrow\)A, E\(\rightarrow\)B, and F\(\rightarrow\)C. Taking experiment A\(\rightarrow\)D as an example, this means training the model under condition A (source domain, drive end, 1 hp load) and transferring it to condition D (target domain, drive end, 2 hp load).  
Figure 3: The bearing experimental system of CWRU [30].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Labels** & **Fault Type** & **Fault Size** \\ \hline
0 & Healthy & Non \\
1 & IF & 7 mils \\
2 & IF & 14 mils \\
3 & IF & 21 mils \\
4 & BF & 7 mils \\
5 & BF & 14 mils \\
6 & BF & 21 mils \\
7 & OF & 7 mils \\
8 & OF & 14 mils \\
9 & OF & 21 mils \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification of fault types in CWRU dataset.
# Zero Drift Infrared Radiation Thermometer Using Chopper Stabilised Pre-Amplifier
## 3 Results and Discussion  
### Comparison of the Capabilities of the IRTs to Maintain 'Zero' and Steady-State Output Voltages  
The capability of the IRTs to maintain steady-state output was determined based upon the variability of output voltage measurements recorded under two separate measurement conditions, as shown in Figure 2. These measurements were performed with (a) the IRTs blanked with a cover at ambient temperature and (b) the IRTs sighted on the blackbody source radiating at an arbitrary steady-state temperature of 700 \({}^{\circ}\)C.  
The variances for the zero-drift IRT can be seen to be smaller than the variances incurred by the precision IRT; these variances were calculated to be \(8.6\times 10^{-4}\) (mV)\({}^{2}\) and \(19.1\times 10^{-4}\) (mV)\({}^{2}\), respectively, therefore representing over 50% reduction in voltage noise. It was also evident by inspection of Figure 2a that the zero-drift IRT maintained the nominal 'zero' output voltage closer to zero millivolts than the precision IRT. The mean output voltages were +0.024 mV and \(-0.10\) mV, respectively, therefore representing a 75% reduction in offset voltage. The precision IRT incurred a larger magnitude drift from zero-than the zero-drift IRT; the drift and cycling incurred by the precision op-amp IRT mostly occurred within the range \(-0.05\) mV to \(-0.10\) mV during the blanking tests. In comparison, the zero-drift IRT incurred variations mostly between 0.00 mV and +0.05 mV. Both IRTs demonstrated some larger excursions that occurred randomly during the tests, although the precision IRT incurred more excursions than the zero-drift IRT. It is believed that these excursions were caused by external interference sources, suggesting that the zero-drift IRT was more robust against EMC related pickup. Both IRTs demonstrated larger magnitude changes in the output voltage than the noise incurred around the mean values. These had similar magnitudes and occurred concurrently during the steady-state 700 \({}^{\circ}\)C set-point temperature tests, as illustrated by inspection of Figure 2b. The similarity of magnitude and occurrence of these output voltage changes for both IRTs implied that the cause was common. The upwards and downwards trend observed in the output voltage of  
Figure 2: IRT output voltage comparison with (**a**) IRTs blanked by the cover at ambient temperature and (**b**) IRTs sighted upon blackbody source radiating at 700 \({}^{\circ}\)C.  
both IRTs is believed to be due to the red-shift in the cut-off wavelength of the photodiodes. This shift occurs due to drift in the ambient temperature, which is identical for each IRT.
# Dual-Path Beat Tracking: Combining Temporal Convolutional Networks and Transformers in Parallel
## 4 Methodology  
The proposed method closely follows the pipeline outlined in Section 2, with specific adaptations. A total of 7 datasets were used for model training and evaluation, and all audio samples were resampled to a standard sampling rate of 44,100 Hz. As the initial input representation, a single log-magnitude spectrogram was created with a window size of 4096 samples and a hop size of 1024 samples. A diagram representing the model architecture is presented in Figure 2.  
The proposed model is designed with four distinct blocks.  
1. **Convolutional block** Rather than using the log Mel spectrogram directly, a convolutional frontend is applied to learn a compact, intermediate representation. Three sequential convolutional layers are used, with filter counts increased from 32 to 64 and finally to 128. Each convolution is followed by max-pooling, which preserves the temporal resolution of the input while gradually reducing the frequency dimension down to a single channel. Dropout with a rate of 0.2 is applied after each convolution to prevent overfitting. This gradual reduction in channels is inspired by SOTA methods. To prepare the data for the Transformer and TCN blocks, 128 frequency bins are retained throughout, aligning with the typical embedding dimension (d_model) used in Transformer architectures. Diagram representing convolutional block is presented in Figure 3.  
2. **TCN block** Dilated 1D convolutions are employed in the TCN to process a 128-dimensional feature vector derived from the log Mel spectrogram. The TCN's objective is to output an activation vector indicating beat positions in the sequence. As outlined in [13], the TCN is designed to be highly configurable, with parameters such as the number of filters, kernel size, number of layers, dilation rates, and stacking options adjusted to fit model needs. Eleven TCN layers are used, with dilations increasing exponentially from \(2^{0}\) to \(2^{11}\). This setup allows the TCN to capture strong local dependencies with fewer parameters, providing valuable inductive biases. These qualities make the TCN a good complement to the Transformer, which is applied to capture a more global context. Even though the dilation rates can be customized, previous work on beat tracking using TCN has noted that increasing the temporal context of the TCN by either using larger kernel sizes or adding more layers (with exponentially increasing dilation rates) did not improve any of the tasks under investigation [6]. Diagram representing TCN block is presented in Figure 4.  
Figure 3: Convolutional block.  
Figure 2: Overview of the proposed dual-path model.
# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 3 Physical Basis of Multi-Sensor Precipitation Estimation
### Weighted-Average-Based Method  
[PERSON] and [PERSON] [127] proposed a maximum likelihood estimation method based on the weighted average approach to combine diverse observations. The weighting coefficients in this procedure are inversely proportional to the squares of individual random errors, which are determined by comparing the errors with gauge observations and subjective assumptions. This methodology assumes that (1) each type of observation error Pi is random, unbiased, and normally distributed and (2) the observation errors \(\sigma_{\text{i}}\) are independent across different types of observations. The maximum likelihood estimate for C is defined by the following equations:  
\[C=\sum W_{i}P_{i} \tag{10}\]  
\[W_{i}=\frac{\sigma_{i}^{-2}}{\sum\sigma_{i}^{-2}} \tag{11}\]  
[PERSON] and [PERSON] [127] linearly merged the estimates from infrared (IR) data (GPI), microwave scattering-based data (Grody), microwave emission-based data (Chang), and numerical model predictions (ECMWF). Theoretically, a linear combination of these estimates results in smaller random errors compared to individual estimates; however, the biases present in the individual datasets persist in the combined analysis. To address these limitations, the authors developed a stepwise strategy to reduce both the random errors in the individual sources and the bias in the combined analysis. This methodology was successfully applied to create the CPC Merged Analysis of Precipitation (CMAP) [128], which integrates gauge observations, satellite-derived estimates, and National Center for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis data. [PERSON] et al. [116] employed a weighted average method to combine AGPI estimates with rain gauge analysis and Numerical Weather Prediction (NWP) model precipitation data. Similar methods have also been utilized in other GPCP precipitation products [117; 118; 119].
## 3 Optimization-Based Strategies
### Global Information-Driven Optimization
#### 3.2.1 Pontryagin's Minimum Principle (PMP)
On the other hand, the challenges of solving the Hamiltonian function and acquiring the driving cycle in advance result in difficulty in the real-time application of the PMP-based strategy; therefore, it is necessary to develop approximate PMP to enhance computational efficiency and reduce the computational burden to implement online control.
## 3 Key Aspects for Practical Implementation of DL-Based NOMA
### Resource Allocation
Through Q-learning, they were able to successfully implement both power allocation and user pairing with reduced computational complexity. In [47], the authors introduced an optimal power allocation technique with a given sub-channel assignment through a closed-form approach. Considering this, a traditional deep reinforcement learning (DRL) algorithm named Deep Q-Network (DQN) algorithm is used to investigate the optimal user pairing scheme. The DQN algorithm provides better performance of the feature extraction ability and higher learning efficiency than conventional reinforcement learning (RL) schemes.
## 4 Experimental Setup  
In this paper, to demonstrate the ability of SA-MPCMOEA to solve expensive constrained multi-objective problems, two test suites, CF and LIRCMOP, are used to compare SA-MPCMOEA with the current state-of-the-art algorithms. The number of decision variables is set to 10 for all test instances. It is assumed that the evaluation of their objectives and constraints is expensive. The performance of the algorithms is evaluated using IGD metrics  
\[\text{IGD}(S_{f},S^{*})=\frac{1}{|S^{*}|}\sum_{x\in S^{*}}\text{dis}(x,S_{f}) \tag{16}\]  
where \(\text{dis}(x,S_{f})\) denotes the minimum Euclidean distance from \(x\) to \(S_{f}\) and \(|S^{*}|\) denotes the number of points in \(S^{*}\). A total of 10,000 reference points are used to compute the IGD values. Smaller IGD values indicate better convergence and diversity of the algorithm's non-dominated set.  
The hypervolume (HV) is a metric used to measure the volume of the objective space covered by a solution set relative to a reference point. A larger HV value indicates that the solution set obtained by the algorithm covers a wider range of the true Pareto front, demonstrating better solution quality and algorithm efficiency. The HV calculation method is shown in the following equation:  
\[HV(A)=\lambda\left(\bigcup_{i=1}^{|S|}v_{i}\right) \tag{17}\]  
where \(\lambda\) denotes the Lebesgue measure, \(S\) represents the non-dominated solution set, and \(v_{i}\) represents the hypervolume formed by the reference point and the non-dominated solutions. In this paper, we adopt the nadir point method for reference point selection, which uses the maximum value of all objective functions as the reference point. The advantage of the HVindicator lies in its ability to be calculated without prior knowledge of the true Pareto front of test problems, making it particularly suitable for real-world applications.  
To ensure the fairness of the experiments, the parameter settings for all compared algorithms are configured as follows:  
1. All algorithms are independently executed 30 times.
2. The maximum number of real function evaluations for each algorithm is set to 1000.
3. The population size is uniformly set to 100 across all test problems.
## 3 Results
For instance, S6 applies LSTM for temporal pattern analysis, while CNNs extract spatial features. This combination strengthens real-time threat detection and improves incident response in IoT security infrastructures. Beyond conventional deep learning models, Deep Reinforcement Learning (DRL) and adversarial learning techniques have been integrated to enhance cybersecurity defenses dynamically. Notably, S5, S7, S18, S22, S43, and S44 incorporate DRL-based approaches that optimize real-time decision-making to counteract cyberattacks adaptively. For example, S18 and S22 implement DRL to improve network intrusion detection systems (NIDSs) and firewall protection for IoT access points, while S44 explores adversarial attacks against Automated Traffic Control Systems (ATCSs), evaluating their resilience against security breaches. Meanwhile, alternative deep learning methodologies such as YOLO + ResNet-50 (S13) and SSD with MobileNet (S29) have been leveraged for real-time security event detection. These models, primarily used in surveillance and object recognition, provide enhanced precision in detecting threats in urban and IoT-based monitoring environments. Such vision-based AI models contribute to cyber-physical security, integrating image analysis with anomaly detection to prevent cyber and physical threats in smart infrastructures. Finally, genetic algorithms (GAs) have been integrated into cybersecurity frameworks to enhance feature selection, optimize hyperparameters, and improve intrusion detection efficiency. Notably, S14, S23, S24, and S25 incorporate GA-based approaches that refine deep learning models by reducing dimensionality and enhancing classification accuracy in IoT security applications. For example, S14, S24, and S25 utilize GAs to optimize feature selection in intrusion detection systems (IDSs), significantly improving attack classification
# Transverse Wave Propagation in a Thin Isotropic Plate Part I
## 2 Materials and Methods
### Analytical Solution of Geometric Models of Thin Isotropic Plates
#### 2.2.1 [PERSON]'s Model  
[PERSON]'s model is the simplest model of a thin plate. In this model, only displacements and their corresponding tangent rotations and inertial effects are considered. * Displacements \(w\), \(u\), \(v\) in axis \(z\), \(x\), \(y\), respectively, are defined: \[w=w(x,y,t)=\frac{16}{abc}\frac{F_{0}}{\rho\hbar}\sum\limits_{n=1}^{\infty} \sum\limits_{n=1}^{\infty}\frac{J_{1}(\gamma_{mn}c)\sin\,a_{n}x_{F}\,\cos\, \beta_{n}y_{F}}{\omega_{mn}^{2}}\sin\,\alpha_{n}x\,\sin\,\beta_{m}y\,\sin^{2} \frac{\omega_{mn}}{2}t\] \[\omega_{mn}=\left(\alpha_{n}^{2}+\beta_{m}^{2}\right)\sqrt{\frac{ D}{\rho\,\hbar}}=\left(\alpha_{n}^{2}+\beta_{m}^{2}\right)\frac{c\,\hbar}{ \sqrt{12}}\ D=\frac{E\,\hbar^{3}}{12\,\left(1-\,\mu^{2}\right)}\] (30) \[\gamma_{mn}=\sqrt{\alpha_{n}^{2}+\beta_{m}^{2}}\,\alpha_{n}{=}\,n \,\frac{\pi}{a}\,\beta_{m}{=}\,m\,\frac{\pi}{b}\] \[u=-z\varphi_{x}{=}-\frac{16z}{abc}\frac{F_{0}}{\rho\hbar}\sum \limits_{m=1}^{\infty}\sum\limits_{n=1}^{\infty}\frac{J_{1}(\gamma_{mn}c)}{ \gamma_{mn}}\frac{a_{n}}{\omega_{mn}^{2}}\sin\,\alpha_{n}x_{F}\,\sin\,\beta_{m }y_{F}\,\cos\,\alpha_{n}x\,\sin\,\beta_{m}y\,\sin^{2}\frac{\omega_{mn}}{2}t.\] (31) \[v=-z\varphi_{y}{=}-\frac{16z}{abc}\frac{F_{0}}{\rho\hbar}\sum \limits_{m=1}^{\infty}\sum\limits_{n=1}^{\infty}\frac{J_{1}(\gamma_{mn}c)}{ \gamma_{mn}}\frac{\beta_{m}}{\omega_{mn}^{2}}\sin\,\alpha_{n}x_{F}\,\sin\, \beta_{m}y_{F}\,\sin\,\alpha_{n}x\,\cos\,\beta_{m}y\,\sin^{2}\frac{\omega_{mn} }{2}t\] (32)  
Figure 5: Plate excitation load variants. \(x_{F}k\), \(y_{F}k\)—coordinates of the circular load, \(x_{F}\), \(y_{F}\)—coordinates of single force loading, \(x_{F0}\), \(y_{F0}\)—coordinates of the rectangular load, \(a\), \(b\), \(h\)—plate dimensions, \(p(x,y,t)\)—circular load, \(F(t)\)—single force loading, \(p(x,y)\)\(T_{F}(t)\)—rectangular load, \(a_{F0}\), \(y_{F0}\)—dimensions of rectangular load, \(c\)—radius of circular load.
## 3 Results and Discussion
### Flow around the NACA0021 Airfoil
#### 3.3.2 U-Net Model Training and Testing  
In this section, we present the results obtained using the U-Net model. As previously explained, this model helps us to avoid the instability of the classical FFNN approach. In our initial approach, we attempted to find the correlation of the correction field with some local flow variables. This correlation might not exist, leading to instability problems during the numerical transient, even if we started from the converged SA original model. By using the U-Net model, we leverage the converged results of the original SA model to find the correct turbulent viscosity field, which we then impose as the solution. We allow the other fields to evolve to find a new steady solution while the eddy viscosity field remains frozen as predicted by the U-Net model. Below, we show the results for \(\alpha=8^{\circ}\) and \(\alpha=16^{\circ}\).  
From Figure 25, the results are in agreement with those obtained using field inversion, as shown in Figure 19. Despite some differences in the turbulent viscosity field at \(\alpha=16^{\circ}\), we can clearly see that the separation in the velocity field is closer to the field inversion results than to the original SA model.  
To demonstrate the improvement gained with the U-Net model, Figure 26 compares the results in terms of CL and CD among the experimental data, the original model, the field inversion augmented model, and the U-Net model. Additionally, we tested the model for angles of attack not included in the training process: \(\alpha=9^{\circ},11^{\circ},13^{\circ},15^{\circ},17^{\circ}\) and \(18^{\circ}\). We did not observe significant improvements in the pre-stall region, but we did observe substantial improvements in the post-stall region, even for the untrained angles of attack. Thus, we can conclude that the new augmented model has good predictive capabilities even in the presence of transition and separation.  
Figure 25: Results of the U-Net-augmented Spalart–Allmaras model for the NACA0021 test case. (**a**) Velocity magnitude and (**b**) turbulent viscosity at \(\alpha=8^{\circ}\). (**c**) Velocity magnitude and (**d**) turbulent viscosity at \(\alpha=16^{\circ}\).
## 5 Experiments
### Running Time  
This study conducts runtime experiments on sandstorm images at three different resolutions, as shown in Table 3. The experimental results represent the average runtime from ten runs, with the hardware specifications and the experimental platform indicated. From the table, it is evident that, as the resolution increases, the runtime of the proposed algorithm increases approximately linearly. The atmospheric light and transmission estimation modules rely on local patches of size \(15\times 15\) for computation. As a result, the algorithm's computational complexity is approximately \(O(MN)\), where \(M\) and \(N\) represent the image width and height, respectively. While this block-based computation improves the accuracy of the parameters, it also significantly increases the computational complexity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & \(e\uparrow\) & \(\bar{r}\uparrow\) & **NIQE \(\downarrow\)** & **NPQI \(\downarrow\)** &
\begin{tabular}{c} **SNP-** \\ **NIQE \(\downarrow\)** \\ \end{tabular} \\ \hline FBE [3] & 3.1179 & 2.2255 & 3.2991 & 9.1606 & 5.5158 \\ HRDCP [11] & 2.8110 & 4.5513 & 3.6905 & 9.4413 & 5.7885 \\ NGT [4] & 0.7043 & 1.9480 & 3.3056 & 9.2859 & 5.5565 \\ ROP [12] & 3.4139 & 2.0363 & 3.3206 & 9.5956 & 5.1726 \\ SCB [5] & 3.5480 & 2.2997 & 3.2501 & 9.1667 & 5.1128 \\ ROP+ [13] & 4.2756 & 3.5223 & 3.2979 & 8.6822 & 5.3899 \\ CVC [14] & 2.6295 & 1.7033 & 3.4279 & 10.4143 & 5.3632 \\ DedustGAN [10] & 5.1937 & 3.7031 & 3.5251 & 8.7102 & 4.7425 \\ Ours & 5.2528 & 3.0270 & 3.2285 & 8.5987 & 5.0383 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average metrics of 500 sandstorm images. The best, second-best and third results are highlighted in red, green and blue, respectively.
###### Abstract  
As air pollution becomes progressively more serious, accurate identification of urban air pollution characteristics and associated pollutant transport mechanisms helps to effectively control and alleviate air pollution. This paper investigates the pollution characteristics, transport pathways, and potential sources of PM\({}_{2.5}\) in Weifang based on PM\({}_{2.5}\) monitoring data from 2015 to 2016 using three methods: Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), the potential source contribution function (PSCF), and concentration weighted trajectory (CWT). The results show the following: (1) Air pollution in Weifang was severe from 2015 to 2016, and the annual average PM\({}_{2.5}\) concentration was more than twice the national air quality second-level standard (35 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)). (2) Seasonal transport pathways of PM\({}_{2.5}\) vary significantly: in winter, spring and autumn, airflow from the northwest and north directions accounts for a large proportion; in contrast, in summer, warm-humid airflows from the ocean in the southeastern direction dominate with scattered characteristics. (3) The PSCF and CWT results share generally similar characteristics in the seasonal distributions of source areas, which demonstrate the credibility and accuracy of the analysis results. (4) More attention should be paid to short-distance transport from the surrounding areas of Weifang, and a joint pollution prevention and control mechanism is critical for controlling regional pollution.  
PM\({}_{2.5}\); spatiotemporal characteristics; back-trajectory clustering; potential source contribution; concentration weighted trajectory +
Footnote †: journal: Applied Physics Letters B
# Two-Level-Composite-Hashing Facilitating Highly Efficient Anonymous IoT and D2D Authentication
## 5 Security Analysis and Performance Evaluation
### The Performance Evaluation
In our schemes, even though the server or any devices need to initialize and update the pseudonym vectors (which require \(2w1\)\(T_{h}\) initially), the amortized cost per session is only a few number of hashing. From Table 4, we can see that the level-2 IoT D2S authentications of [12] demand the least computational complexity; but we should note that the schemes use the fixed pseudonyms, and a single compromised device can impersonate any other group members in the D2S group authentication. [PERSON] et al. 's direct D2D group authentication [22] demands lots of intensive computations like exponentiations, encryptions, digital signatures, and especially the pairing-based IDPE [26]. The number of pairing computations of the pair-based IDPE also depends on the number of the items in the identity string [26].