esa-sceva/satcom-chunk-collection huggingface dataset:
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 4 Experimental Results and Analysis  
The experiments were conducted on Visual Studio2021 and MATLAB2016b platforms, using the H.264/AVC test model JM8.6 for video encoding and decoding, and programming in the C-language. The specific settings of the experimental parameters are shown in Table 1. In this paper, Hall.qcif, Mobile.qcif, Foreman.qcif, News.qcif, Stefan.cif and Bus.cif are selected as the test videos, among which, Stefan.cif is 90 frames, Bus.cif is 150 frames and all other videos are 300 frames.
# Clutter-Masked Waveform Design for LPI/LPD Radarcom Signal Encoding
###### Abstract
The only LPI/LPD-capable radarcom approaches noted in that work were RF tag-based methods, e.g., [4]. The idea of using machine learning (ML) for clutter identification has also been investigated in recent years. In [15], the Ozturk algorithm was tested alongside the sparse recovery method of batch orthogonal matching pursuit (BOMP) algorithm. These methods were tested on Log-Normal and Weibull distributions. BOMP outperformed the Ozturk method with an accuracy of between 74-95% compared to the Ozturk's 22-42% accuracy. Samples ranged from 300 to 2800, while dictionary sizes ranged from 500-2500. A non-supervised ML method was used in [16] to distinguish between two sets of 100 random vectors (each with 100 elements) from two different Spherically Invariant Random Vectors (SIRV). It was able to do so with 86% accuracy. In our work, we will show that verifying a heuristic estimate of the distribution from experimental data is a computationally efficient way to determine the clutter mode when compared to ML techniques. The purpose of this work is to introduce a method of clutter model construction, based on real experimental measurements of radar returns from which noise contribution is removed--then, apply the results to the construction of clutter-masked transmissions, which are used for radar sensing and data communication purposes simultaneously. These transmissions are deemed secure in the sense that an unauthorized receiver will likely interpret these intercepts as local clutter returns and will not attempt decoding them for their information content. The rest of the paper is organized as follows: we briefly review the RSE method of radarcom signal design in Section 2; then, we introduce our approach to clutter and noise deconvolution from real radar data in Section 3; we present our results of clutter modeling in Section 4; Section 5 discusses results of performance evaluation of the created clutter-masked radarcom signals with regards to: (a) interceptor classification of these signals as clutter returns; (b) communication efficiency; and (c) synthetic aperture radar (SAR) image reconstruction with backprojection. Finally, we offer concluding remarks and suggestions for future work in Section 6.
## 1 Introduction
A classical approach based on minimization of a proposed cost function has been explored by [PERSON] and [PERSON] [29] while, more recently, ant colony optimization method [30] and spectral parameter estimation approach have been also investigated [31]. However, it must be highlighted that these references generally assess the goodness of the model behavior towards ideal and virtual materials acknowledging the potential of the mathematical formulation under study to describe a vast frequency range or the validation is obtained towards very limited experimental data of the material viscoelastic response. For this reason, to assess a generalized model and its robust parameter identification procedure to reproduce the experimental data of dynamic tests (in a wide range of frequency values), with a unique set of parameters, the aim of this work is to discover the optimum model formulation that can describe the real viscoelastic behavior with a minimum number of parameters. To this purpose, three completely different polymers have been considered to compare the response of two chosen models: the Generalized Maxwell model (GM) and the relative fractional derivative model (FDGM). For the both models, the parameters' optimum sets have been identified adopting a pole-zero formulation introduced by [PERSON] et al. in [32], limited to the case of the GM model. Furthermore, once the best model formulation has been discovered, a further study on the minimum amount of experimental data and their distribution within the frequency domain, necessary to correctly reproduce the materials' viscoelastic behavior in a wide frequency range with an acceptable accuracy, has been carried out. The paper is organized as follows: in Section 2, the experimental data, employed as an experimental reference dataset for the study, are presented; in Section 3, the rheological models adopted are presented starting from their definition in the time domain and their re-definition in the frequency domain to obtain the relative pole-zero formulation; in Section 4, the parameters' identification procedure developed to achieve the fitting of the experimental data is presented; in Section 5, the model comparison in terms of the dynamic response and the ability to reproduce the viscoelastic behavior of the compounds under analysis are illustrated; in Section 6, a specific study to understand how the minimum number of experimental points should be acquired and distributed to identify the model parameter set in a robust way is reported.
# Optimal Maintenance Schedule for a Wind Power Turbine with Aging Components
## 7 Case Studies
### Sensitivity Analysis 2  
One of the most crucial parameters of our model is \(m\), the monthly value depreciation of a genetic component. In this section, we consider the one-component model taking the rotor component as an example. We study how the optimal time to perform the next PM increases as \(m=m^{1}\) becomes larger. All other parameters are fixed at their primary values.  
Recall the formula for the time-average maintenance cost in the one-component setting:  
\[q_{t}=\frac{(1-e^{-\theta t^{\beta}})g+e^{-\theta t^{\beta}}(h+mt)}{\sum_{k=1}^ {t}(e^{-\theta(k-1)^{\beta}}-e^{-\theta k^{\beta}})k+e^{-\theta t^{\beta}}t},\]  
where \(t\) is the planning time for the next PM. Considered as a function of \(m\), the average cost \(q_{t}\) is a linear function as depicted on the right panel of Figure 4 for different values of the planning time \(t\).  
The left panel of Figure 4 reveals an interesting phenomenon: there exists a critical value \(m_{0}\) of the parameter \(m\), beyond which the optimal PM plan is to never perform a PM activity. Indeed, at the value \(m_{0}=1.22\), we observe a jump from optimal planning time \(t=80\) up to \(t=T\), which is equivalent to \(t=\infty\) for no planned PM.  
The sudden jump on the left panel graph is explained on the right panel by comparing \(q_{t}\) for different values of \(t\). The five straight lines compare \(q_{70},q_{80},q_{90},q_{100},q_{\infty}\) with respect to different values of the parameter \(m\). For \(m=1.02\), the minimal cost among the five options is given by \(q_{70}\). For \(m=1.15\), the minimal cost is given by \(q_{80}\). For \(m=1.25\), the minimal cost is given by \(q_{\infty}\). It is also clear that starting the horizontal line for all \(m>1.22\), corresponding to \(t=\infty\), always gives the minimum cost.  
Figure 3: Plots of \(B_{a}^{4}\) for different combinations of parameters \((g,h_{0},h^{4},m^{4})\).
# Part-Based Obstacle Detection Using a Multiple Output Neural Network
## 4 Training the Multi-Output CNN  
Multi-task learning requires special datasets and databases for training. For the proposed solution, we have used three well-known datasets: CityScapes [35], the Berkeley Deep Drive (BDD) [36], and Mapillary [37]. All the images were processed in order to have the same size and aspect ratio; we also filtered the images to select only those that feature a large number of road pixels. After this selection, we processed the number of images used for training, as follows: 2759 images were taken from BDD, 2975 images from CityScapes, and 17,109 images from Mapillary. These databases contain relevant  
Figure 14: The vote-map features of the vanishing point: the left side features (**first image**), the right side features (**center**), and the multiplication result of the first two images (**right**).  
information for semantic segmentation and the bounding boxes of the obstacle objects. By using this data, we could extract the four obstacle quarters. The input images were split into four quarters, using the available bounding boxes of the objects. The next step consisted of masking each quarter with the semantic segmentation maps in order to generate the top-left, top-right, bottom-left, and bottom-right binary images of the individual object instances. The process is presented in Figure 15.  
The prepared images from the datasets were also augmented during training. We used the techniques of random intensity and saturation adjustments in the HSV color space, as well as random image scaling and translation.  
The semantic segmentation module used binary cross-entropy and the Sorensen-Dice [38] loss function, which is a modified version of the intersect over union loss. The loss function for the obstacle detection was the same. The vanishing point module also used the same loss function as the semantic segmentation; generating the training data is described in Section 3.6 During the training process, each loss function can be configured with a different weight. We used the same weight for all three loss functions, having experimented with various settings.  
Training the neural network was performed for a total of 500 epochs; it was executed on a system equipped with two Nvidia 1080 Ti GPUs that featured a total of 22 GB of memory. The patience parameter was set to 50 epochs; therefore, if the loss function did not improve for 50 epochs, the training process was stopped early. With this hardware, training one epoch took around 300 \(s\), which means that a fully working multi-output CNN model can be obtained in less than 10 h (with early stopping). For our system, the ResNet-50 encoder was initialized with the weights from ResNet-50 and trained on ImageNet, which helped speed up the training process. We also tried freezing some layers during training, but this did not improve the final results.
# A Review of the Development and Future Challenges of Case-Based Reasoning
## 4 Application Fields of CBR
### Diagnosis  
Diagnostic tasks usually include technical fault diagnosis [97, 98] in electronics, engineering and other fields and medical health case diagnosis [39, 99, 100], which can be regarded as a variant of classification tasks. Many AI methods can simply diagnose problems. However, CBR is different from these methods. The main advantage of using CBR to achieve the diagnosis task is not only to determine the diagnosis itself but also to provide a reference solution through CBR. The specific process is shown in Figure 9.  
With the development of technology, the function and structure of modern mechanical equipment or electronic systems have become increasingly complex, which makes the fault mechanism staggered and changeable. In addition, fault diagnoses of complex systems have become very difficult. Therefore, the fault diagnosis method based on CBR is mostly combined with other methods to improve the diagnosis efficiency. [PERSON] et al. [98] developed an aero-engine fault system based on CBR. They designed a tree structure based on a semantic graph to quantify the similarity between semantic attributes and defined the relationship between fault components and fault modes to accurately match them.  
Because the decision-making of the human medical health process itself is highly dependent on historical experience therapy and literature [39], medical pathology diagnosis based on CBR has always been a popular research topic in the medical field. [PERSON] et al. [99] established a fuzzy CBR application system on the JColibri platform to diagnose diabetes. They used symbolic learning to build a fuzzy decision tree on the Fispro platform, extract fuzzy rules, and import them into the JColibri platform to establish a fuzzy case base. The case index of the diabetes surveillance plan was selected by the retrieval method based on the fuzzy reasoning mechanism. This method not only optimizes the time but also reduces the complexity of the similarity calculation between individuals.
## 4 Case Studies: Part I: Simulation Analysis
### Performance Comparison with Advanced PQD Classification Methods
#### 4.4.2 Performance Comparison with Advanced PQD Classification Algorithms
In addition, due to the rapidity of data conversion and the lightweight design of the model structure, the total time for converting and classifying a signal sample is only 23.32 ms, fully meeting the real-time performance requirements of real-world PQ monitoring data analysis.
# Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet
## 5 Results
### Palm Vein Segmentation  
To analyze and evaluate the proposed segmentation network's performance, the IoU coefficient and the dice similarity coefficient are calculated. Aside from the baseline U-Net model, residual blocks and an attention gate module are also separately used as different training configurations of the U-Net architecture to be evaluated. As shown in Figure 9, the segmentation result of the U-Net model (c) contains several incorrect predictions. In contrast, our proposed model (d) results in better segmentation performance while suppressing irrelevant areas. Table 1 displays the results of the comparison between the state-of-the-art methods and our proposed method for palm vein segmentation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **IoU Coef.** & **Dice Coef.** \\ \hline U-Net & 95.59 & 97.75 \\ Attention U-Net & 95.83 & 97.87 \\ Residual U-Net & 96.16 & 98.04 \\ Proposed method & 96.24 & 98.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with other state-of-the-art models for palm vein segmentation.  
Figure 9: Palm vein segmentation results: (**a**) Histogram equalized original palm images; (**b**) Ground-truth label images; (**c**) Segmentation results from original U-Net model; and (**d**) Segmentation results from proposed attention-gated residual U-Net model.  
To address the significance of our segmentation model for palm vein recognition and verification, the ECA-ResNet model is additionally trained with the original grayscale ROI images without connecting the segmentation model. As can be seen in Table 2, both the recognition and verification results became lower than the proposed approach as the network suffer learned enough discriminative features from low-resolution and unclear palm vein ROI images.
# Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics
## 3 Distributed Kernel Extreme Learning Machines
### Likelihood Probability Fusion  
In the regression problem, for the input characteristics \(\mathbf{x}\), the output of \(l\) subnetworks can be represented as \(\{\mu_{i},\sigma_{i}\}_{i}^{l}\), where \(\mu_{i}\) is the regression value of \(i_{th}\) subnetwork and \(\sigma_{i}\) is the standard deviation of the training process. Assuming that the predicted value of regression obeys gaussian distribution, the probability density function of each sub-learning machine regression value can be expressed as  
\[p_{i}\left(t|\mathbf{x}\right)=\frac{1}{\sqrt{2\pi}\sigma_{i}}\exp\left(-\frac{ \left(t-\mu_{i}\right)^{2}}{2\sigma_{i}^{2}}\right) \tag{13}\]  
Thus, the probability density function of the comprehensive output of the learning unit is proportional to the product of the densification density output of the sub-learning machine  
\[p\left(t|\mathbf{x}\right)\propto p_{1}\left(t|\mathbf{x}\right)p_{2}\left(t|\mathbf{x} \right)\cdots p_{l}\left(t|\mathbf{x}\right) \tag{14}\]  
Figure 2: Distributed framework of proposed distributed kernel extreme learning machines (DKELMs) algorithm.  
If we take the natural logarithm of the above expression and convert the product into a sum, the following relation can be obtained  
\[\ln(p)\propto\ln(p_{1})+\ln(p_{2})+\cdots+\ln(p_{l}) \tag{15}\]  
By substituting the probability density of the sub-learning machine, the comprehensive output probability density can be transformed into the form of quadratic function  
\[\ln\left(p\left(t|\mathbf{x}\right)\right)\propto-\sum_{1}^{l}\frac{\left(t-\mu_{ i}\right)^{2}}{2\sigma_{i}^{2}} \tag{16}\]  
According to the principle of maximum likelihood probability, the maximum value of probability density is taken as the predicted value of regression  
\[\hat{t}=\operatorname*{arg\,min}_{y\in R}\sum_{1}^{l}\frac{\left(t-\mu_{i} \right)^{2}}{2\sigma_{i}^{2}} \tag{17}\]
# Integrated Double-Sided Random Microlens Array Used for Laser Beam Homogenization
## 2 Structure Design of D-rMLA  
### Principle of Beam Homogenization  
The principle of laser beam homogenization based on D-rMLA is shown in Figure 1a. The collimated laser beam is incident on the front surface of the D-rMLA. Then the laser beam is divided into several sub-beams by the sub-lens in the D-rMLA. After that it is modulated by the structure of the back surface of the D-rMLA and emitted to the target surface. The distance between the back side of the D-rMLA and the target surface is Z. Due to the randomness of the aperture size, radius of curvature, and arrangement of each sub-lens of the D-rMLA, the interference conditions between the sub-beams are disturbed to improve the uniformity of the homogenized laser spot.  
Figure 1: (**a**) Principle of laser beam homogenization; (**b**) a series of beam propagation paths.  
The beam propagation is shown in Figure 1b, and the difference of the transmission optical path of the sub-beams can be analyzed. The optical path calculation method is expressed by Equation (1).  
\[\mathrm{I_{i}}=\mathrm{n_{1}}(\mathrm{s_{i1}}+\mathrm{s_{i2}})+\mathrm{n_{2}} \mathrm{s_{i3}} \tag{1}\]  
where \(\mathrm{n_{1}}\) and \(\mathrm{n_{2}}\) are the refractive index of the different transmission mediums, and \(\mathrm{s_{i1}}\), \(\mathrm{s_{i2}}\) and \(\mathrm{s_{i3}}\) the transmission distance of one arbitrarily light path. Assume the refractive index is \(\mathrm{n_{1}}\) when the laser beam transmits in the air, and the refractive index is \(\mathrm{n_{2}}\) when the laser beam transmits in the glass medium. Because of the different structure parameters of different sub-lens, the different sub-beams transmit through different sub-lens units at the same location (such as the optical axis) will have different optical paths. Therefore, the different optical path differences \(\Delta\mathrm{n_{n}}\) will be generated between the sub-beams after the beam passing through the D-rMLA. The optical path difference can be expressed as Equation (2).  
\[\Delta\Phi_{\mathrm{n}}=\frac{2\pi}{\lambda}\Delta\mathrm{I_{n}} \tag{2}\]  
where \(\Delta\Phi_{\mathrm{n}}\) is the phase difference, and \(\lambda\) the wavelength of the incident laser beam. Due to the different \(\Delta\mathrm{I_{n}}\), As the different \(\Delta\Phi_{\mathrm{n}}\) will exist between the sub-beams after they are emitted through the D-rMLA. The conditions for coherent light are the same frequency, the parallel direction of vibration and the same phase or constant phase difference. The conditions for the coherence of the incident beam are disturbed due to the random phase difference. Thus, a spot with uniform energy distribution on the target surface can be obtained.
## 4 Discussion
Because of their behavior between solid and liquid materials, surface coatings are suitable for a variety of applications [36]. Moreover, to target creams and gels, active compounds are added to base vehicles at a minimum concentration of 1%, which is considered the minimum amount to produce a significant effect and is mandatory for labeling [37; 38]. The analyzed base creams and gels seem to exhibit a pseudo-plastic-like behavior, and they do not appear to be altered by the addition of either the bulk form or nanoparticles. However, the addition of nanoparticles seems to reduce the viscosity and change the spreadability of the baseline values. The study of the rheological properties of creams and gels after the inclusion of these compounds is useful for their better exploitation. Yu is a measure of the stress that must be applied to obtain significant flow alterations in a cream or gel. It significantly affects the ability of creams or gels to be squeezed from a tube (i.e., pourability) and their spreadability on the skin [39; 40]. Samples with high Yv values resist squeezing and spreading. Under our experimental conditions, the eudernic base creams (C1) with and without 1% NAG or 1% nNAG had the lowest apparent viscosities at 100 RPM. Moreover, the inclusion of compounds greatly decreased the Yv with respect to the baseline. The lipophilic base cream (C3) had the lowest viscosity, at 100 RPM, whereas the inclusion of NAG increased its value. On the other hand, nNAG inclusion reduced the viscosity compared to the base cream. The inclusion of nNAG reduced the Yv much more than NAG. Thus, we can conclude that lipophilic cream containing nNAG has a superior spreadable property. A hydrophilic gel base with Carbopol 940 (G3) has a viscosity that depends on the Carbopol concentration. We used a commercial gel with a viscosity at 100 RPM of 8176 mPa\(\cdot\)s [41]. To measure the viscosity after the inclusion of 1% NAG, a lower RPM speed, from 100 to 60, was needed. Under these conditions, an increased viscosity was observed. The inclusion of 1% nNAG, although to a lower extent, had the same effect as 1% NAG. However, both compounds increased the Yv, suggesting a decrease in the pourability. Lipophilic gel base (G1) exhibited the lowest apparent viscosity at 100 RPM, and it had the highest viscosity among the studied nonsolid vehicles. The inclusion of 1% NAG and 1% nNAG slightly reduced the apparent maximum viscosity. Moreover, the inclusion of 1% nNAG decreased the Yv, resulting in lower spreadability than G1 and G1 plus 1% NAG. Overall, the inclusion of 1% NAG and 1% nNAG in creams and gels affects the viscosity and the Yv by modifying the spreadability and pourability of the vehicles, making them suitable for different types of topical applications.
## 1 Introduction
### Organisation  
The remainder of this paper is organised as follows. Section 2 presents the vehicle state estimation, including vehicle dynamics model, state estimator design and EKF-based state estimation. MPC-based longitudinal and lateral tracking control are shown in Section 3, including reference path generation, dimension reduction-based errors calculation, MPC and online active set algorithm. Simulation results and discussions are shown in Section 4. Conclusions, limitations and future works are given in Section 5.