esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method of Features' Extraction  
### Features' Definition  
In order to extract the teaching data for neural classifiers, the analyzed signal fragment was divided into \(\mathbf{x}\) blocks with \(N\) length of samples, where:  
\[\mathbf{x}=\{x_{1},\dots,x_{n},\dots,x_{N}\}. \tag{1}\]  
For each \(\mathbf{x}\) signal block, a set of statistical parameters typically used in the analysis of AE signals was identified [36; 37], which was further used as input data for the neural network. The following parameters were used: signal energy (\(e\)), band power (\(p_{BP}\)), signal upper envelope (\(env\)), skewness (\(skew\)), and kurtosis (\(kurt\)).  
The signal energy is identified based on the following relationship:  
\[e=\sum\limits_{n=1}^{N}x_{n}^{2} \tag{2}\]  
Skewness, as the probability distribution asymmetry measure, is identified in the following way:  
\[skew=\frac{\overline{x}-med(\mathbf{x})}{\sqrt{\sigma}} \tag{3}\]  
where \(\overline{x}\) is the mean value of samples, med (\(\mathbf{x}\)) the block median, and \(\sigma\) the sample block variance.  
Kurtosis, which is the measure of the results' concentration around the mean, amounts to:  
\[kurt=\frac{\mu_{4}}{\sigma^{4}}-3 \tag{4}\]  
where \(\mu_{4}\) is the fourth central moment of samples in the block and \(\sigma\) the sample block variance. A teaching data vector \(\mathbf{d}\) was identified for each \(\mathbf{x}\) signal sample block, where:  
\[\mathbf{d}=\left\{e,p_{BP},env,skew,kurt\right\} \tag{5}\]  
In order to obtain training information \(\mathbf{y}\), which belongs to the set {0,1}, the recorded signal was blasted using the wavelet decomposition with the fifth-order Daubechies wave. Then, using the method of exceeding the trigger threshold, which was set at 20 mV for the presented problem, the information value y was allocated for each training set: 0 for no acoustic emission and 1 for the acoustic emission event (these are the red areas in Figure 2). Figure 2 presents one signal fragment with the sampling frequency of 1 MS/s selected for further studies. The red areas mark acoustic emission events determined on the basis of threshold crossing.  
Figure 2: Fragment of the recorded signal where dielectric degradation occurred.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 1 Introduction
### Main Research Contributions  
To address this challenge, we propose a lightweight TL network that effectively tackles the issue of model retraining in varying underwater environments. By leveraging TL, the OFDM network can adapt to new conditions with minimal computational cost, significantly improving performance while reducing the need for large training datasets and long training times. This makes TL a promising approach to enhance the robustness, adaptability, and efficiency of UWA OFDM communication systems in real-world deployments. The main contributions of this paper are:  
* We propose the first TL-based pre-trained model for UWA communication, trained on five distinct watermark channels to enable effective generalization across varying underwater environments.
* Development of a novel TL-based model that specifically addresses the channel mismatch problem in UWA systems and can efficiently adapt to new underwater conditions.
* We validate the robustness of the proposed model by conducting real-world experiments in Qingdao Lake, which show that our proposed TL-based OFDM receiver can generalize well to new environments, addressing challenges of model retraining and computational complexity.  
* Compare our TL-based OFDM receiver against traditional channel-estimation methods in multiple aspects, including improved BER and adaptability to fluctuating channel conditions. In the rest of paper, Section 2 covers traditional and deep learning approaches to UWA OFDM communication. Section 3 gives a detailed explanation of the proposed technique, and Section 4 discusses the experimental setup. Section 5 provides an explanation of the results from simulation and real experiments. Finally, Section 6 concludes the study.
# MILP-Based Approach for High-Altitude Region Pavement Maintenance Decision Optimization
###### Abstract  
Affected by climatic factors (e.g., low temperature and intense ultraviolet radiation), high-altitude regions experience numerous pavement diseases, which compromise driving safety and negatively impact user travel experience. Timely planning and execution of pavement maintenance are particularly critical. In this paper, considering the characteristics of pavement maintenance in high-altitude regions (e.g., volatility of traffic volume, seasonality of maintenance timing, and fragility of the ecological environment), we aim to derive optimal monthly maintenance plans. We develop a multi-objective nonlinear optimization model that comprehensively accounts for minimizing maintenance costs, affected traffic volume and carbon emissions, and maximizing pavement maintenance effectiveness. Utilizing linearization methods, the model is reconstructed into a typical mixed-integer linear programming (MILP) model, enabling it to be solved directly using conventional solvers. We consider five types of decision strategies to reflect the preferences of different decision-makers. Given the uncertainty of maintenance costs, we also utilize the robust optimization method based on the acceptable objective variation range (AOVR) to construct a robust optimization model and discuss the characteristics of optimistic, robust, and pessimistic solutions. The results suggest that different decision strategies show differences in the indicators of maintenance costs, affected traffic volume, carbon emissions, and pavement performance. When multiple decision objectives are comprehensively considered, the indicators are between the maximum and minimum values, which can effectively balance the decision needs of maintenance effectiveness, maintenance timing, and environmental protection. The number of maintenance workers, the requirement of the minimum pavement condition index (PCI), and the annual budget influence the maintenance planning. The obtained robust solution can somewhat overcome the conservative nature of the pessimistic solution. The method proposed in this paper helps address the complexities of pavement maintenance decisions in high-altitude regions and provides guidance for pavement maintenance decisions in such areas.  
pavement maintenance; high-altitude regions; mixed-integer linear programming; model reconstruction; robust optimization 2024 14 7670 (0) 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Methodology
### Basic Feature Processing
#### 2.3.3 Intersection Coordinate Calibration  
The accuracy of intersection coordinates directly affects the precision of vehicle path identification. In practical application, it was found that the original intersection coordinate data deviated from the actual convergence centers of trajectory points. Without calibration, it would be impossible to correctly identify the sequence of intersections passed by vehicles. The data shows that multiple intersections exhibit varying degrees of coordinate deviation, with intersection 8 deviating by tens of meters, and other intersections showing deviations ranging from several meters to tens of meters. Such deviations may result from inconsistent coordinate systems or data entry errors.  
Intersection coordinate calibration employs a semi-automated method based on trajectory data: first, road network visualization is performed using MATLAB's plotRoad() function to intuitively display deviations between intersection positions and trajectory convergence points; then, based on trajectory density distribution, intersection coordinates are manually adjusted to trajectory convergence centers; finally, the calibration effect is verifiedthrough re-visualization. Figure 4 shows the post-calibration results, demonstrating that all intersections are accurately positioned at trajectory convergence centers, providing a foundation for subsequent path identification. In Figure 4, the asterisk (*) symbols indicate the calibrated intersection center positions after coordinate adjustment.
# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
## 3 Materials and Methods
### Analysis and Filtering of Training Datasets  
One of the main steps to achieve a successfully supervised classification is to have a good training dataset [44]. For that, we analyzed the POST and DELTA datasets in order to remove those pixels that could eventually be misclassified by the expert or showed low separability values. We evaluated the mean; median; standard deviation; 1, 2 and 3 quartiles; and minimum and maximum values for the burned and unburned categories, and eliminated the atypical found values. Regarding separability, several indexes or distances can be applied to image classification [45]. However, for burned-area classification, the separability index (SI) [46] (equation (5)) has been broadly used on different sensors and ecosystems [5; 9; 47] to select spectral bands and indices that provide a higher discriminating capacity (SI \(>=\) 0.75) to the classification process.  
\[SI=\frac{\left|\mu_{q}-\mu_{nq}\right|}{\delta_{q}+\delta_{nq}} \tag{5}\]  
where:  
\(\mu_{q}\): mean of the burned category.  
\(\mu_{nq}\): mean of the unburned category.  
\(\delta_{q}\): standard deviation of the burned category.  
\(\delta_{nq}\): standard deviation of the unburned category.  
Training datasets were randomly balanced in order to obtain the same number of pixels for the burned and unburned categories. This allows for the the proper training of the supervised classifiers [22].
A Multi-Level Non-Uniform Spatial Sampling Method for Accuracy Assessment of Remote Sensing Image Classification Results  
[PERSON]  
1 College of Information Science, Shanghai Ocean University, Shanghai 201306, China;  
1  
[PERSON] [PERSON]  
1 College of Information Science, Shanghai Ocean University, Shanghai 201306, China;  
1  
[PERSON]  
1 College of Information Science, Shanghai Ocean University, Shanghai 201306, China;  
1  
estimator to estimate the population mean in stratified random sampling [10]. Using stratified sampling method, [PERSON] et al. reported agreement statistics between map and reference labels for national land cover database (NLCD) 2011, which includes land cover for ca. 2001, ca. 2006, and ca. 2011 [4]. [PERSON] et al. developed a reasonable spatial sampling plan for crop area estimation based on the SPOT-5 image, combined with the selection of stratified standards and the size of sampling units [11]. However, these sampling methods are more suitable for the accuracy assessment of products with independent items. By contrast, remote sensing images have dependent feature information and are characterized by spatial correlation and spatial heterogeneity. Recently, several sampling methods were proposed for the accuracy assessment of spatial images. [PERSON] et al. used the spatial stratified sampling method for the accuracy assessment of regional surface coverage [12]. [PERSON] et al. proposed a \"sandwich\" sampling model for the accuracy assessment of small-cultivated land areas [13]. [PERSON] proposed a unified framework for crop area estimation based on a confusion matrix [14]. [PERSON] et al. calculated the species richness and uniformity of each sample grid using a diversity index (Shannon-Weaver (SW)) [15]. These spatial sampling methods only consider the feature of spatial correlation and ignore spatial heterogeneity. Spatial heterogeneity is also an important determinant in accuracy assessment. Spatial heterogeneity within pixels biases the non-linear estimation of classification variables from remote sensing images.  
In this study, a multi-level non-uniform spatial sampling method (MNSS) was proposed for the accuracy assessment of classification results of remote sensing images, which was deduced by calculating spatial correlation and heterogeneity.
# Repair Performance of Self-Healing Microcapsule/Epoxy Resin Insulating Composite to Physical Damage
## 4 Conclusions  
In this paper, the preparation method and self-healing performance of the microcapsule/epoxy resin insulating composite were explored exploitively and creatively. Moreover, the repair performance to physical damage and the change rule of electrical insulation performance are emphatically studied. The application advantage of self-healing insulating composite is proved by linking the micro-changes with the macro-properties. The main conclusions are as follows:  
1. For the mechanical damage, the microcapsule/epoxy resin composite exhibit excellent self-healing ability. The repair efficiency of scratch width is 70%-100%, and the repair rate is relatively fast. Moreover, the core material of the microcapsule has great fluidity and reactivity. In addition, the repair process is mainly affected by the action of the capillary and molecular diffusion model on the defect surface.
2. For the electrical damage, the repair effect of composite to the electrical tree is obvious. The microcapsule can not only attract the electrical tree and inhibit its propagation, but also repair the tubules of electrical tree effectively. It has good application value in reducing the harm of electrical damage to insulating material.
3. Physical damage can reduce the insulation strength of material, thus threatening the stability of the power system. The composite can not only fill the structural defect caused by electrical damage (such as an electrical tree) and mechanical damage (such as a scratch), but also homogenize the local high electric field in the defect area, restoring its insulation strength. The restoration effect of electrical insulation performance is mainly affected by the reaction effect of repairing agent and the interface characteristic between the repair product and the matrix material.  
In addition, the microcapsules are uniformly dispersed in epoxy resin insulating material with great morphology. It is conducive to repairing the damaged parts effectively on the premise of ensuring the basic properties of the material (such as thermal and mechanical properties).  
Conceptualization, Y.W. and Y.L.; Methodology, Y.L.; Validation, Y.W., Y.L., and Z.Z.; Data curation, Y.Z., Z.Z., and H.Z.; Formal analysis, Y.W. and Y.L.; Writing--Original draft preparation, Y.L.; Writing--Review and editing, Y.W.; Visualization, Y.L. and Y.W.; Project administration, Y.W.  
**Funding:** This research was funded by National Natural Science Foundation of China (51777018), Fundamental Research Funds for the Central Universities (2019 CDXYDQ0010) and the Science and Technology Project of SGCC under Grant (SGTYHT/14-JS-188).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Combined-Treated Samples
This could also be observed at 600 MPa, but when lower pressure (300 MPa) was applied, the difference between the order of the treatments was much more important (Figure 1b). While the pressure level had a significant (\(p<0.001\)) effect on the texture parameter (hardness), the effect of the order of the treatments was not detected. A higher pressure level led to firmer samples. Similar findings have been published on the combined treatment of beef [49]. The hardness values of the 300 MPa combined-treated samples were not higher than the single sous-vide-treated values. The hardness or firmness of meat is a critical parameter not only in gastronomy but also from a dietetical point of view. [PERSON] et al. [58] also found a relationship between water retention and texture parameters. Their aim was to find a processing method that kept protein structural changes and weight loss at a minimum level and added water to ground pork to facilitate swallowing. Regarding the microbiological stability of the samples, all the combined-treated samples were stable with very low or below detection limit (DL) log\({}_{10}\) CFU/g values (the detection limit (DL) was 0.7 log\({}_{10}\) CFU/g (aerobic and anaerobic TVC) and 1.7 log\({}_{10}\) CFU/g (\(L\)_monocytogenes_ TVC)). In the challenge test, \(L\)_monocytogenes_ TVC was below the detection limit in all combined-treated samples on day 0, but single sous-vide at 60 \({}^{\circ}\)C and single HHP treatment at 600 MPa were alone sufficient to reduce the bacterial count by at least 5 log. In the combined-treated meat samples, _Listeria_ was not detected at the end of the storage period neither at 2 \({}^{\circ}\)C nor at 8 \({}^{\circ}\)C. The aerobic viable count increased during storage from just above the detection limit (0.7 log cfu/g) up to 3 log CFU/g at 2 \({}^{\circ}\)C and up to 6 log CFU/g at 8 \({}^{\circ}\)C. The anaerobic viable count was under the detection limit on day 0, and while in creased to log 2-3 values (at 8 \({}^{\circ}\)C) is still considered stable. Significant statistical differences were not measured, but we would like to draw attention to the effect of the treatment order on the stored samples. The effect of pressure levels was observed at both temperatures. As expected, the increase in the pressure level resulted in more stable products.
## 2 Related Works
### Attention Mechanism  
The attention mechanism emerged in natural language process tasks [25] and shows outstanding performance [26]. As the attention mechanism proved to be highly effective, it began to be widely used in various vision tasks [27; 28]. In computer vision, the attention mechanism imitates human perception [29; 30; 31], which amplifies important information while suppressing irrelevant details for inference. The early attention mechanism combinesnormalized feature maps that represent saliency maps [31] or uses reinforcement learning with tiny patches so that the model can predict using only some parts of patches, like human perception [29].  
Residual channel attention [30] computes an attention map from input by downsampling and upsampling (soft mask). And the model multiplies it to a feature map obtained from the residual convolution branch. Inspired by residual channel attention, several attention mechanisms are developed. Spatial attention [32] focuses on emphasizing important features based on their spatial locations. By leveraging the spatial relationships within the input feature map, this mechanism induces the model to focus on specific regions, enabling the processing of key features selectively. Channel attention [28; 32] prioritizes the relationships between feature map channels, enhancing the importance of significant channels while reducing the influence of less relevant ones. This approach helps the model learn more meaningful feature representations.  
Triplet attention [33] is designed to capture both spatial and channel dependencies while maintaining computational efficiency. It independently applies attention mechanisms along two of the three axes--height, width, and channel--and then integrates these to produce a more refined feature representation. Non-local attention [27] considers relationships among all elements within the input data, learning how each element interacts with every other element. This mechanism is inspired by self-attention [26], which has achieved significant success in natural language processing, particularly through transformer models, and has recently been adapted for computer vision tasks, demonstrating strong performance.  
Many studies [24; 34; 35; 36; 37; 38; 39; 40] are using attention mechanisms in their traffic prediction models, but there is currently no consensus on which type of attention is most effective or where it should be placed within the network architecture. For example, some studies use self-attention, but they differ in how they handle spatiotemporal data [24; 34; 36] and external data [37] or embedded features [35; 40]. Other studies use squeeze and excitation block [39] channel attention and spatial attention [38]. In this work, we explore various attention modules and evaluate their effectiveness when applied to different positions in ADNet architecture. Since different attention mechanisms compute attention weights based on distinct criteria, we conduct experiments utilizing various attention modules to analyze their effectiveness in network traffic prediction.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### Working Principle of Accelerometer  
The basic accelerometer comprises a proof mass (or seismic mass) suspended by a spring. When the device experiences acceleration, the spring is compressed and exerts aforce on the mass to counteract that acceleration [1; 2]. Figure 1 presents the basic working principle of an accelerometer. To simplify the calculation, we consider only an accelerometer experiencing a force in the x direction.  
A spring constant characterizes the spring, denoted \(k\), representing the force that varies linearly with the amount of compression, and by knowing the mass \(m\) and spring constant, the amount of compression represents a measure of the perturbed force. There is a dumping involved, characterized by the \(\beta\) dumping coefficient. Dumping is necessary to explain why the oscillations will be amortized in time. To obtain the acceleration \(a\), we measure \(x\), and we use the following equations:  
\[m\cdot\ddot{x}+2\cdot\zeta\cdot\omega_{n}\cdot\dot{x}+\omega_{n}^{2}\cdot x=-a \tag{1}\]  
The natural frequency \(\omega_{n}\) and the dumping ratio \(\zeta\) determine the behavior of this dynamic system. Equations (2) and (3) describe their expressions.  
\[\omega_{n}=\sqrt{k/m} \tag{2}\]  
\[\zeta=\beta/\sqrt{4\cdot m\cdot k} \tag{3}\]  
Using those parameters, the equation of the movement becomes as follows:  
\[\ddot{x}+2\cdot\zeta\cdot\omega_{n}\cdot x+\omega_{n}^{2}\cdot x=-a \tag{4}\]  
The solution of this equation represents the transient response, which depends on the initial conditions, and a steady-state response, which is independent of initial conditions. For a fast enough system, the transient response can be ignored. The steady-state equation can be written as follows:  
\[m\cdot a=k\cdot x \tag{5}\]  
An upper boundary of the bandwidth of an open-loop accelerometer is its natural frequency \(\omega_{n}\). It can be seen that the bandwidth of an accelerometer sensing element has to be traded off with its sensitivity. Sensitivity is defined by Equation (5).  
\[S=\frac{x}{a}=\frac{m}{k}=\frac{1}{\omega_{n}^{2}} \tag{6}\]  
The Laplace transform of Equation (4) can describe the dynamic behavior:  
\[\frac{x(s)}{a(s)}=\frac{1}{s^{2}+\frac{\beta}{m}\cdot s+\frac{k}{m}} \tag{7}\]  
Figure 1: The basic working principle of an accelerometer.  
The damping coefficient \(\beta\) is crucial for an accelerometer's dynamic performance. For maximum bandwidth, the proof mass should be critically damped, a fact that is not easy to achieve technologically.