esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles +
## 3 Methodology
### Deep Reinforcement Learning Structure
```
Input: mini batch size B, pre-train step \(t_{p}\), training episode length \(N_{s}\), learning rate \(\alpha\), greedy \(\epsilon\), discount factor \(\gamma\), target network update rate \(\tau\), target network update frequency K Initialize primary network \(Q_{\theta}\), target network \(Q_{\theta^{-}}\), replay memory D with capacity M Loop for each episode: Initialize simulator environment Initialize time step \(t=0\) Observe current state \(S_{t}\) while time step \(t<N\): With probability \(\epsilon\) select action \(A_{t}\) randomly otherwise select \(A_{t}\longleftarrow argmaxQ_{\theta}(S_{t},a)\) Execute action then observe next state \(S_{t+1}\) and reward \(R_{t}\) Store \((S_{t},A_{t},R_{t},S_{t+1})\) in replay memory D \(S_{t}\longleftarrow S_{t+1}\) if current step \(t>\) pre-training step \(t_{p}\): Sample a minibatch of B experience tuples \((S_{t},A_{t},R_{t},S_{t+1})\) from D Compute target Q values for each experience: \(Q^{*}(S_{t},A_{t})\approx R_{t}+\gamma Q_{\theta^{-}}(S_{t+1},argmax_{\omega} Q_{\theta}(S_{t+1},a^{\prime}))\) Perform a gradient descent step with loss: \(\|Q^{*}(S_{t},A_{t})-Q_{\theta}(S_{t},A_{t})\|^{2}\) Update target network \(\theta^{-}\) every K steps: \(\theta^{-}\longleftarrow\tau\theta+(1-\tau)\theta^{-}\) \(t\longleftarrow t+1\)
```  
**Algorithm 1**Pseudo-code for training algorithm of DRL-based agent  
A better approach is to use average vehicle speed as the reward signal. This allows the agent to improve the overall traffic mobility and reduce average travel delay, while also promoting fuel efficiency, which is mainly related to the vehicle speed and idle time. Therefore, we use the average vehicle speed \(\bar{V}\) of the entire intersection, calculated at the end of each control cycle, as the reward signal \(R\), which is defined below:  
\[R=\bar{V}=\frac{1}{T}\sum_{t=1}^{T}\frac{1}{N}\sum_{i=1}^{N}v_{i}, \tag{1}\]  
where \(v_{i}\) is the velocity of vehicle \(i\), \(T\) is the length of the current signal cycle, \(N\) is the total number of vehicles in the control zone, and \(t\) is the time-step of the simulation.
## 3 Experiments and Analysis  
### Experimental Setup  
The indoor experimental setup is shown in Figure 3. The motor's output shaft is connected to the sensor's rotor, while the stator is fixed to a supporting platform. The motor's rotation simulates the rotation of a downhole power drill. The motor speed is controlled by a controller, allowing precise control from 0 to 1000 rpm. The sensor's output signal is processed sequentially by a data acquisition card and an electrometer before being input to a computer. LabVIEW (version 13.0)-based data processing software installed on the computer allows for real-time display and storage of the sensor's output data. Experiments were conducted to test the sensor's rotational speed measurement, angular measurement, power generation, and environmental adaptability functions, as detailed below.  
Figure 2: Schematic diagrams illustrating the sensor operating principle. (**a**) Schematic diagram of the rotational speed measurement principle and (**b**) schematic diagram of the angular measurement principle.
## 3 Results and Discussion
### Surface Free Energy  
As mentioned in the Introduction section, polar groups in plastic are responsible for chemical bonds in laser-joined metal and plastic hybrids. The low-pressure plasma treatment is a process that introduces these groups on the surface of plastics. Measurements of the wetting angles and the resulting surface free energy provide information about the performance of the plasma treatment. In Figure 16, two water droplets are shown on the surface of polypropylene samples. In Figure 16a, the polypropylene is untreated, while in Figure 16b, the sample was treated in an O\({}_{2}\) plasma for 180 s.  
The wetting angle of the untreated water droplet is over 90\({}^{\circ}\), while the droplet on the O\({}_{2}\) plasma-treated sample was reduced to an angle under 10\({}^{\circ}\), indicating a significant increase in the polar component of the surface free energy. The measured contact angles were used to calculate the surface free energy of both plastics in interaction with every plasma gas, as shown in Figure 17.  
Figure 16: Image of a water droplet on a polypropylene sample at a magnification of \(\times\)50 at the following treatment steps: (**a**) untreated and (**b**) treated with O\({}_{2}\) plasma for 180 s.  
Figure 15: BSE analysis of a sandblasted AISI 304 sample shown in Figure 14 (**a**) at a magnification of \(\times\)200; (**b**) at a magnification of \(\times\)1000 with marked areas of Al\({}_{2}\)O\({}_{3}\) particles.  
In an untreated state, the polar component of polypropylene is near zero. Comparing the polar component to the measurement of polyamide 6.6, the lack of polar groups in the resulting surface free energy can be seen. At every investigated time for all plasma gases, polyamide 6.6 has higher surface free energy values than polypropylene. At 10 s, the dispersive and polar components rise for both plastics. The polar component of polypropylene increases significantly in comparison to the untreated state, with water plasma showing the highest rise at 10 s compared to the other gases. At 180 s, the measured surface free energy peaks at all three plasma gases and for both polymers while dropping slightly for polypropylene at 600 s. The polyamide 6.6 samples maintain a high value at 180 s and 600 s treatment times. It is also worth mentioning that the trend of the surface free energy is similar for all used plasma gases. Due to the maximum surface free energy at 180 s, the treatment time was chosen to identify the influence on the lap shear strength.
## 5 Discussion
### Partial Dependent Plot (PDP) Analysis  
PDPs provide a useful tool for analyzing the relationship between input variables and the FV predictions of the ML and the HML models. PDPs can help to identify potential interactions between input and dependent variables and can guide future analysis [43; 44]. Figure 14 displays the 3D PDP interrelationships between the two input features involved in the predictions of the FV for the trained RBFNN-GO model.  
Figure 14 reveals that as the FD values increase the MFV values exert a substantial but highly variable impact on the FV predictions. As the MFV value initially increases from a minimum value of 40 sec/quart, the FV values decline but then progressively rise and fall as the MFV values increase further. The extent of this change in the MFV influence is itself influenced by variations in the FD values. The minimum FV values are generated by the low FD values coupled with the MFV in the range of 40 to 60 sec/quart. Conversely, the maximum FV values are associated with the low values of MFV coupled with the high FD values. The non-linearity of the inter-relationships of the FD and MFV variables (Figure 14) highlight the need for sophisticated ML/HML models to capture their subtleties for accurate semi-real-time prediction purposes.  
Figure 13: Visualizing the effect of the two input features on the FV predictions with SHAP values for the RBFNN-GO model applied to the training subset: (**a**) SHAP detailed feature impact plot and (**b**) SHAP summary plot of the feature importance.
# Fault Diagnosis of Planetary Gear Train Crack Based on DC-DRSN
## 3 Fault Diagnosis Method Based on DC-DRSN and Verification
### Method Validation
#### 3.2.2 Experimental Results and Discussion
Comparison of DC-DRSN with other deep learning models  
The proposed method, DC-DRSN, is compared with DRSN, VGG-16, GoogleNet, and Alexnet-18, where 4 dB noise is added to the original vibration signal, and the time-frequency image is input into each model to test the fault diagnosis performance of each model. The results are shown in Table 5. With the increase in model complexity, the diagnostic effect of DRSN is better than that of VGG-16, GoogleNet, and Alexnet-18. However, STFT-DCDRSN allows the network to process image and one-dimensional signal data separately, which allows each channel to focus on extracting its own features. So STFT-DCDRSN has the best diagnostic effect, with a diagnostic accuracy of 99.56%, which is 0.49% better than DRSN.
# SBNNR: Small-Size Bat-Optimized KNN Regression
## 3 Preliminaries  
### BatAlgorithm  
The bat algorithm (BA) is a metaheuristic optimization technique inspired by the echolocation behavior of bats [31]. Bats navigate and hunt by emitting sound waves, with the echoes bouncing back from objects aiding in prey location. The bat algorithm emulates this echolocation process to tackle intricate optimization problems. By replicating how bats modulate their pulse rates and loudness in response to prey proximity, the algorithm adeptly harmonizes exploration and exploitation within the optimization domain. This flexibility allows the BA (Algorithm 1) to be a versatile solution for resolving diverse optimization obstacles [32].  
```
1:Input: objective function \(f(x)\) and search space of optimization problem
2: Initialize bat population (positions \(x_{i}\), velocities \(v_{i}\), and pulse frequencies \(f_{i}\))
3: Set maximum iterations \(T\), pulse rates \(r_{i}\), and loudness values \(A_{i}\)
4:for\(t=1\) to \(T\)do
5:for each bat \(b_{i}\)do
6: Update bat's position and velocity using Equations (1) and (2)
7: Generate a random number \(rand\) uniformly in \([0,1]\)
8:if\(rand>r_{i}\)then
9: Select a solution from the list of best solutions
10: Produce a solution in the neighborhood of the best solution
11:endif
12:if\(rand<A_{i}\) and \(f(x_{i})<f(GlobalBest)\)then
13: Accept the new solution
14: Increase \(r_{i}\) and decrease \(A_{i}\)
15:endif
16:endfor
17:endfor
18: Update GlobalBest by sorting bats
```  
**Algorithm 1** Bat Optimization Algorithm (BA)  
To solve a specific optimization task, the initial phase involves setting up the bat frequency \(f_{i}\), velocity \(v_{i}\), and position \(x_{i}\), in addition to the loudness \(A_{i}\) and pulse rates \(r_{i}\). Subsequently, the bat's position and velocity undergo iterative updates based on Equations (1) and (2) until the predetermined threshold of iterations is reached.  
\[f_{i}=f_{min}+(f_{min}-f_{max})\,\beta \tag{1}\]  
\[x_{j}^{i}(t)=x_{j}^{i}(t-1)+v_{j}^{i}(t) \tag{2}\]  
Here, \(f_{max}\) signifies the maximum frequency, \(f_{min}\) represents the minimum frequency, and \(\beta\) signifies a randomly selected value in the range of 0 to 1. The j-th dimension of a bat pertains to the velocity component (\(V\)) and position component (\(X\)) of the position (\(x_{i}\)) and velocity (\(v_{i}\)). Equation (3) is employed to integrate the notion of random walks, whereas Equations (4) and (5) govern the updates for loudness and pulse rates.  
\[x_{new}=x_{old}+\varepsilon A(t) \tag{3}\] \[A_{i}(t+1)=\alpha A_{i}(t)\] (4) \[r_{i}(t+1)=r_{i}(0)\big{[}1-e^{-\gamma t}\big{]} \tag{5}\]  
The mean loudness at a given time (\(t\)) is denoted by \(A(t)\), while the pulse emission rate at \(t=0\) is influenced by \(e\in[0,1]\), governing the direction and intensity of the random walk, as also indicated by \(r\in[0,1]\). The loudness at \(t=0\) is determined through a stochastic selection process at the algorithm's initiation [33].
## 3 Results and Discussion
### Image Segmentation Results  
#### 3.3.1 Ablation Experiments  
To evaluate the potential enhancement in model performance brought by Adaptive Bagging and pseudo-label techniques in image segmentation tasks, this study employed ablation experiments. Ablation experiments are a systematic analysis method that involves the gradual removal or replacement of the various components of the model to quantitatively assess their specific impact on the final performance. The datasets used were Synthetic20000, train420, valid90, and test90. This approach allows for a precise analysis of the performance of each component on the test90 dataset and subsequently assesses their contributions to the overall model performance. Specifically, this experiment aims to verify the contributions of the Adaptive Bagging and pseudo-label strategies in segmentation tasks. By comparing performance changes with the removal or application of different strategies, we can gain a deeper understanding of their effectiveness. The experimental results, as shown in Table 3, clearly demonstrate the impact of these strategies on model performance.  
Figure 7: (a) Real 2D CT grayscale image; (b) 2D CT grayscale image generated by StyleGAN2-ADA.  
The initial baseline U-Net model, without any additional strategy optimization, maintained an IoU metric of around 0.96. The porosity difference reached 0.0231, reflecting the model's deficiency in identifying clay-filled areas within the images, leading to the over-segmentation of the pore regions and resulting in deviations in porosity calculations. Introducing pseudo-label significantly enhanced the model's segmentation performance: IoU surged to 0.9796, Recall increased to 0.9680, and PA rose to 0.9724. These substantial improvements indicate a qualitative leap in segmentation accuracy, markedly reducing the porosity difference to 0.0156. Simultaneously, the improvement in Local Consistency indicates a significant enhancement in the quality of the segmented images, effectively reducing noise. Further application of Adaptive Bagging optimized the model performance even more. Through iterative selection, ensemble learning continually enhanced model performance. Ultimately, combining pseudo-label and Adaptive Bagging strategies, the model achieved scores above 0.99 in key metrics such as IoU, Recall, PA, and Local Consistency, with PA reaching 0.9993, indicating a high overlap between the predicted and true segmentation regions. The porosity difference further decreased to 0.0035, demonstrating the model's exceptional performance and accuracy in image segmentation tasks.
## 5 Discussion and Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & [3] & [6] & [7] & [35] & This work \\ \hline Technology & 0.5 \(\mu\)m & 0.35 \(\mu\)m & 0.5 \(\mu\)m & 0.13 \(\mu\)m & 0.35 \(\mu\)m \\ Power source & inductive link & battery & battery & NA & battery \\ Number of channels & 100 & 128 & 32 & 64 & 64 (16 avail.) \\ Overall gain & 60 dB & 57–60 dB & 68–78 dB & 54–60 dB & 65–83 dB \\ Input noise & 5.1 \(\mu\)V & 4.9 \(\mu\)V & 9.3 \(\mu\)V & 6.5 \(\mu\)V & 3.05 \(\mu\)V \\ TX frequency & 433 MHz & 4 GHz & 915 MHz & 915 MHz & 400 MHz \\ Modulation & 2 FSK & IR-UWB & 2 FSK (PWM) & FSK-OOK & 2 MC-FSK \\ Data type & spike detection & raw data & raw data & raw data & AP waveform \\ Data rate & \(330\) kbit/s & \(90\) Mbit/s & \(640\) kbaud/s & \(1.5\) Mbit/s & \(1.25\) Mbit/s \\ Bandwidth & \(0.8\) MHz & \(1\) GHz & \(38\) MHz & \(3\) MHz & \(3\) MHz \\ TX range & \(13\) cm & i\(1\) m & 1m & \(1\) m & \(30\) m \\ Power per channel & \(135\)\(\mu\)W/ch & \(47\)\(\mu\)W/ch & \(220\)\(\mu\)W/ch & \(80\)\(\mu\)W/ch & \(269\)\(\mu\)W/ch \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of wireless neural recording ICs.
# A Binaural MFCC-CNN Sound Quality Model of High-Speed Train
## 5 Conclusions  
In this paper, a binaural sound quality model for HSTs was developed using CNN techniques. In order to obtain sufficient training samples, a HST on-track test was carried out to collect the interior noise data. To train the sound quality model and verify its accuracy, the collected noise was subjectively evaluated using a five-scale semantic differential method. The conclusions are summarized as follows:  
1. The binaural MFCC-CNN sound quality model has a similar architecture to the hearing system. It better represents the nonlinearity of human hearing and makes the model highly accurate. The model achieved an accuracy of 96.2%. Thus, the proposed model was feasible for the HST sound quality evaluation, and the CNN method was appropriate.
2. The sound signals were converted into MFCC features as input parameters for the CNN, so both time domain and frequency domain information can be considered simultaneously. The proposed model considered the influence of the time-varying characteristics of the sound, which led to better performance than the traditional neural network model.
3. Two input channels were applied to simulate binaural hearing. The time and the level difference between the two ears were important factors affecting the HST subjective evaluation, and such differences also affected the accuracy of the sound quality model, especially at a low annoyance level. Hence, the monaural MFCC-CNN sound quality model had lower accuracy than the binaural model.
4. The MFCC features extracted from the sound signal helped capture the main characteristics of HST noise at high annoyance levels and reduced the input data dimensions. It provided frequency domain information that facilitates the distinction of various noise samples, so the proposed model outperformed the times series sound quality model.  
Overall, the results show that the proposed model can be used to evaluate and optimize the HST interior sound quality, reducing man labor and material resources. To improve the proposed model, it will be important that future research investigates the sound quality of HSTs under different operating conditions (for example, different speeds) as well as the impact of transient noise.  
This article was prepared through the collective efforts of all the authors. Conceptualization, and methodology, software, and original draft preparation, P.R.; funding acquisition, Y.Q. and X.Z.; resources, and supervision, Y.Q. and Z.H.; validation, X.Z. and P.R.; writing--review and editing, X.Z., P.R., Y.Q. and Z.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China grant number 51975515 and 51705454.  
Not applicable.  
The authors declare no conflict of interest.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 2 Related Works
### Summary of Existing Methods  
Overall, the existing multispectral detection methods demonstrate considerable progress in enhancing feature fusion strategies and leveraging cross-modal information. Traditional convolution-based models benefit from strong spatial locality and mature design paradigms but often struggle with global context modeling, limiting their adaptability in complex environments. Attention-based methods, especially those combining channel and spatial mechanisms, improve cross-modal feature selection and robustness by emphasizing informative regions. However, these methods frequently introduce substantial computational overhead, posing challenges for real-time or resource-constrained deployment.  
Transformer-based approaches excel in capturing global dependencies and inter-modal correlations, offering a powerful framework for modeling complex semantic relationships. Nevertheless, their effectiveness often depends on extensive annotated data and training time, which can be difficult to obtain in some practical settings. Additionally, current  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline \multirow{2}{*}{**VATT**[26]} & Convolution-free Transformer & Requires extensive computational \\  & enables joint learning across & resources, lacks fine-grained \\  & multiple modalities & feature localization \\ \hline \multirow{2}{*}{**EM-Trans**[27]} & Dual-band decomposition and & \\  & fusion modules improve RGB-D & Limited adaptability to tasks \\  & saliency detection & beyond saliency detection \\ \hline \multirow{2}{*}{**ETFormer**[28]} & Single encoder processes & \\  & RGB-D-T tri-modal data, & Requires large-scale pretraining, \\  & benefiting from multimodal & complex optimization strategies \\  & attention fusion & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of Transformer-based multimodal learning methods.  
Transformer variants tend to lack efficient architectural designs that are well suited for lightweight applications.  
In summary, the current approaches achieve varying degrees of success in balancing detection accuracy, computational complexity, and generalization capability. Future work should focus on designing lightweight yet effective fusion architectures that reduce redundancy, improve robustness, and better adapt to diverse imaging conditions across modalities.
# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
###### Abstract  
Flight Operations Quality Assurance (FOQA) is an internationally recognized solution to ensure the safety of civil aircraft flights based on Quick Access Recorder (QAR) data. The traditional approach to anomaly detection in civil aviation is to detect the over-limit values of monitoring parameters for each monitoring event based on the standards issued by civil aviation authorities. Usually, for each anomaly detection operation routine, this only works for one monitoring event. Furthermore, the causal analyses for the detected anomaly events are based on the relevant worker's expertise. In order to improve the efficiency of FOQA, this paper proposes an automated anomaly detection and causal analysis method called MAD-XFP. Due to the unique industry characteristics of QAR data and the requirements of FOQA, feature engineering and hyper-parameter optimization techniques are utilized to enhance the machine learning model. The proposed method can monitor multiple events in one routine and provide a causal analysis. In the causal analysis process, the Shapley additive interpretation method is applied to produce analysis report for detected anomalies. Experimental evaluations are conducted on real civil aviation datasets. The experimental results show that the proposed method can efficiently and automatically detect different abnormal events with high precision in the approach phase and produce preliminary causal analysis.  
anomaly detection; QAR data; civil aviation; causal analysis Article
## 5 Conclusions  
In this study, the authors proposed a biomimicry idea based on a spider and its web to enhance the damage detection performance of glass fiber reinforced epoxy composite plates subjected to debonding damage. The first experiment was performed by using two composite plates of size 210 mm \(\times\) 210 mm \(\times\) 0.6 mm bonded to each other using an epoxy adhesive where debonding was given to 8 different areas (A1 to A8). Here, all the RMSD values were below 0.5 with the average value of 0.39 when all 8 areas were averaged. Such low RMSD values can cause false alarms when detecting damage as the EMI technique is a sensitive technique where the impedance signatures can vary with a small change in the environmental temperature. Thus, to overcome this problem, the proposed idea was tested against three composite structures with different top layer thicknesses of 0.6 mm, 0.4 mm and 0.2 mm.  
By analyzing the results from the three tests, the RMSD values were higher with the proposed idea with the highest value of 3.34 subjected to debonding on A1 with the 0.6 mm thickness top composite plate. Furthermore, the averaged values for all three tests (0_6, 0_4 and 0_2 from Figure 7) were 1.19, 1.25 and 1.55 which indicates that the EMI technique  
Figure 7: Calculated RMSD values for all tests on A1 to A8.  
performs better with a thinner structure. Although one could say that the differences between RMSD values are quite large between areas such as A1 and A6, the shape of the PZT transducer and the length of the wire can affect the outcome. For this reason, more research will be conducted in detail in a future study.  
[PERSON] was responsible for conceptualization and methodology of the work; [PERSON]-S.K. carried out investigation, conducted experiments and participated in writing the manuscript by analyzing data. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Research Program funded by the SeoulTech (Seoul National University of Science and Technology).  
Not applicable.  
The authors declare no conflict of interest.