esa-sceva/satcom-chunk-collection huggingface dataset:
# Transformer Fault Diagnosis Method Based on Incomplete Data and TPE-XGBoost
## 2 Theory  
### Random Forest Regression Method  
The random forest regression (RFR) method is an integrated regression algorithm based on the classification and regression tree (CART) model [19]. It is well suited to problems of numerical prediction. The process of constructing an RFR model is shown in Figure 1. The CART is an algorithm that recursively constructs a binary tree, dividing the current sample set into two subsets at each node except the leaf [20]. Suppose \(\mathbf{D}\) is a subset of samples, and X and Y are the input samples and output variables, respectively, then \(\mathbf{D}=\{(x_{1},\,y_{1}),\,\ldots,(x_{i},\,y_{i}),\,\ldots,\,(x_{N},\,y_{N})\}\). The application steps of the CART algorithm are as follows.  
1. Construct a root node containing a subset of all samples.
2. Iterate over all features and, for feature \(j\), divide the sample set by the intersection point \(s\). First, the values of all features \(j\) in the sample subset are ranked from smallest to largest, and \(s\) is the average of the two adjacent values after feature ranking. Optimal sample segmentation is the minimization of the objective squared error. By solving for the minimum of Equation (1), the optimal feature and optimal cut-off point of the segmented sample set is obtained.  
\[\underbrace{min}_{j,s}\left[\underbrace{min}_{c_{1}}\sum_{x_{i}\in\mathbf{R}_{1}( j)s}\left(y_{i}-c_{i}\right)^{2}+\underbrace{min}_{c_{2}}\sum_{x_{i}\in\mathbf{R}_{2}( j,s)}\left(y_{i}-c_{i}\right)^{2}\right] \tag{1}\]  
where \(\mathbf{R}_{1}\) and \(\mathbf{R}_{2}\) are the two subspace units divided according to the cut-off point \(s\).  
\[\mathbf{R}_{1}(j,s)=\left\{x_{i}\Big{|}x_{i}^{(j)}\leq s\right\}\]  
\[\mathbf{R}_{2}(j,s)=\left\{x_{i}\Big{|}x_{i}^{(j)}>s\right\}\]  
where \(i=1,\,2,\,\ldots,\,N\), and \(j=1,\,2,\,\ldots,\,f,\,c_{1},\,c_{2}\) are the mean values of the output variable \(\mathbf{Y}\) on subspace cells \(\mathbf{R}_{1}\) and \(\mathbf{R}_{2}\) respectively.  
Figure 1: The structure diagram of RFR model.  
3. Continue to repeat step 1 and step 2 for the 2 subunits until the entire decision tree is grown and all samples in the subset are assigned to the leaf nodes. The prediction result corresponds to \(y_{i}\) is [21] \[\hat{y}_{i}=\frac{1}{K}\sum_{k=1}^{K}\hat{T}_{k}(x_{i})\] where \(K\) is the number of decision tree, and \(\hat{T}_{k}(x_{i})\) denotes the estimation results produced by the \(k\)-th tree. The RFR model uses bagging sampling to randomly select samples in a put-back fashion. Then \(q\) variables (\(q<m\), \(m\) is the number of features) are randomly selected at each node and used as candidates for splitting the node to construct a single decision tree. The above steps are repeated to generate a large number of regression decision trees. The final prediction result of the model is the average of the prediction results of several CART models.
# The Energy Efficiency of Heterogeneous Cellular Networks Based on the Poisson Hole Process
## 6 Conclusions  
This paper studies the performance of two-tier HetNets and improves the EE of HetNets by optimizing the PBS transmit power. First, the coverage probability and average achievable rate of the PPP-PHP HetNets are derived by the method based on the MISR, and the expression of the EE of HetNets is obtained according to the definition of EE. Then, an optimization algorithm, the quadratic interpolation method, is proposed to maximize the EE of HetNets by setting the appropriate PBS transmit power. Finally, the theoretical derivation and optimization algorithm are simulated and analyzed. The simulation results show that the transmit power of the PBS has a certain impact on the EE of HetNets, and the system EE can be effectively improved by optimizing the PBS transmit power. In addition, the PPP-PHP network is superior to the PPP-PPP network in both the coverage probability and EE. This paper only considers optimizing the PBS transmit power to improve the energy efficiency. Future work can improve the energy efficiency of the HetNets by jointly optimizing the transmit power of the PBS and the density of the PBS.  
Software, L.X.; Writing--original draft, Y.C.; Writing--review and editing, S.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China No. 61871241, Nantong Science and Technology Project, No. JC2021129, and Natural Science and Technology Project of Jiangsu College of Engineering and Technology (GYKY/2022/7).  
**Data Availability Statement:** Not applicable, the study did not report any data.  
Figure 5: Energy efficiency \(\eta_{EE}\) versus SIR \(\theta\) under different \(\alpha\).  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Running Time** & **Iterates Times** \\ \hline The golden section method [43] & 20.37 s & 32 \\ The quadratic interpolation algorithm & 10.88 s & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of algorithm performance.
# Authenticity at Risk: Key Factors in the Generation and Detection of Audio Deepfakes +
## 4 Methodology
### Generation of Deepfakes  
In our study, audio sample generation was performed using the StarGANv2-VC [13], an unsupervised speech conversion (VC) model based on Generative Adversarial Networks (GANs), designed to perform speech conversions between multiple speakers without requiring parallel pairs of samples. One of its most outstanding features is its ability to transform the speech identity of one speaker into that of another while keeping the linguistic content intact. Despite being trained only on a limited dataset of 20 English speakers, the model is able to generalize to a variety of speech conversion tasks, such as one-to-many conversion, cross-lingual conversion, and voice style modification, such as _falsetto_ or emotional voices.  
The main components used for model inference are shown in Figure 3 adapted from [13]. To generate deepfakes, we have used the provided Python 3.12.3 inference notebook. The model is available for training at [[https://github.com/yl4579/StarGANv2](https://github.com/yl4579/StarGANv2)]([https://github.com/yl4579/StarGANv2](https://github.com/yl4579/StarGANv2)) -VC/tree/main (accessed on 6 November 2024).  
* Generator (G): an encoder-decoder architecture that receives a mel-spectrogram as input and transforms it into one that reflects the vocal style and fundamental frequency (F0) of the target speaker. The style is obtained through a style encoder, while the fundamental frequency (F0) is derived through a network specialized in its detection.
* Style encoder (S) and mapping network (M): The style encoder extracts the stylistic features from a reference spectrogram, allowing conversion between different vocal styles.
* F0 extraction network: In order to preserve tonal consistency in the converted voice, the model uses a pre-trained F0 detection (JDC) network [37], which extracts the fundamental frequency, important to avoid distortions in the generated voice and ensures the pitch remains true to the target speaker.  
An innovative feature of this model is the _perceptual loss_, which incorporates an automatic speech recognition (ASR) network and an F0 extraction system [37]. In addition, StarGANv2-VC is fully convolutional and, when integrated with an efficient vocoder such  
Figure 3: StarGANv2-VC framework (adapted from [13]). \(X_{\text{src}}\) is the source input, \(X_{\text{ref}}\) is the reference input that contains the style information, and \(\hat{X}\) represents the converted mel-spectrogram. \(h_{\text{x}}\), \(F_{\text{conv}}\) and \(s\) denote the latent feature of the source, the \(FO\) feature from convolutional layers of the source, and the style code of the reference in the target domain, respectively.  
as Parallel WaveGAN [14], is capable of real-time speech conversions. The advantage of this system is that it achieves the conversion without the dependence on textual labels.  
The source and reference audios to generate the deepfakes were extracted from _WE-LIVE_ and VCTK [38] datasets, respectively, as will be detailed in Section 5.
## 2 System Model
### Features Extraction  
Increases in the dimensionality of input observations to an SVM greatly affect the performance of the classifier. To reduce the dimensionality, input to the SVM is given in the form of features extracted from the input observation that reflect the characteristics of the observation. Feature extraction and feature selection have significant importance in machine learning-based classification and regression  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Kernel Function** & \(\boldsymbol{K(\mathbf{x},\mathbf{y})}\) \\ \hline Linear Function & \(\mathbf{x}^{T}\mathbf{y}+c\) \\ Polynomial Function & \((\mathbf{x}^{T}\mathbf{y}+1)^{p}\) \\ Radial-Basis Function & \(\exp\left(-\frac{\left\|\mathbf{x}-\mathbf{y}\right\|^{2}}{2\sigma^{2}}\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kernel functions for arbitrary input vectors \(\mathbf{x}\) and \(\mathbf{y}\).  
problems. This improves performance of the classifier in terms of computational cost, accuracy, and delay time to solve the problem. In this work, the three statistical features extracted from the input sensed signal are given in Table 3. The first feature \(X_{1}\) is obtained by taking power 10 of the mean value of sensed signal and then multiplying by 10. The constant value 10 in power and multiplication terms are used such that the small difference in the mean values of different classes is magnified, resulting in better classification accuracy. A variation in this constant value can affect the performance of classifier, however satisfactory results are obtained with the given value. The second feature \(X_{2}\) is simply the mean of the squared elements of the received signal. The third feature \(X_{3}\) is obtained by taking the root of the cubic sum of the first \(q\) elements of the received signal arranged in descending order. To the best of the authors' knowledge, this work is the first to adopt this feature.
###### Abstract  
In this work, a deep Gaussian process (DGP) based framework is proposed to improve the accuracy of predicting flight trajectory in air traffic research, which is further applied to implement a probabilistic conflict detection algorithm. The Gaussian distribution is applied to serve as the probabilistic representation for illustrating the transition patterns of the flight trajectory, based on which a stochastic process is generated to build the temporal correlations among flight positions, i.e., Gaussian process (GP). Furthermore, to deal with the flight maneuverability of performing controller's instructions, a hierarchical neural network architecture is proposed to improve the modeling representation for nonlinear features. Thanks to the intrinsic mechanism of the GP regression, the DGP model has the ability of predicting both the deterministic nominal flight trajectory (NFT) and its confidence interval (CI), denoting by the mean and standard deviation of the prediction sequence, respectively. The CI subjects to a Gaussian distribution, which lays the data foundation of the probabilistic conflict detection. Experimental results on real data show that the proposed trajectory prediction approach achieves higher prediction accuracy compared to other baselines. Moreover, the conflict detection approach is also validated by a obtaining lower false alarm and more prewarming time.  
Keywords:confidence interval; conflict detection; deep Gaussian process; nominal flight trajectory; neural network; trajectory prediction +
Footnote †: journal:
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
## 4 Discussion and Analysis  
Imperceptibility and resilience against attacks are essential in designing watermarking methods. In this section, the protective mechanisms offered by the proposed watermarking method are presented and analyzed. In the past decades, many watermarking algorithms had been developed to protect intellectual properties in AM. To our knowledge, these methods are dedicated for protecting either polygonal models or physical products. On the other hand, our watermarking system is designed to enhance the securities of geometric models, G-code programs, and printed parts. Thus, the application scope of our approach is wider. In addition, the computational spaces and fundamental methodology of our method are different from those of the conventional approaches. These issues should be outlined and compared. In this research, we developed new techniques to watermark voxel models. The potential usage of these algorithms also deserves discussion.
# A Versatile 100 Hz Laser System with Few-Cycle and TeraWatt Pulses for Applications
## 5 The TW Laser
### Compressor, and the Characteristics of the Output Beam  
The pulses are sent to a classical, Treacy-type two-grating compressor [4] (Figure 1) through a 1:2 and a 1:3 beam expander, and the last beam stabilization unit (Figure 9). In the compressor, we use reflective gratings blazed to an angle of 28.7\({}^{\circ}\) with a groove density of 1200 mm\({}^{-1}\). The angle of incidence to the gratings is 24.3\({}^{\circ}\), so that the total transmission is close to 70%. The 42.5 cm distance between the gratings was optimized to compensate for the dispersion in the TW branch, with the help of the AOPDF.  
Upon the alignment of the compressor, first, the angular dispersion was checked with the use of an imaging spectrograph [24]. We managed to reduce the residual angular dispersion to a value lower than 0.15 \(\upmu\)rad/nm in both directions. The duration of the compressed pulses was measured at the compressor chamber, picking up 1% of the compressed beam. The dispersion of the output window was compensated with a pair of chirped mirrors, resulting in a pulse duration of around 26.0 fs. This, combined with the 50 m pulse energy, results in a peak power of almost 2 TW.  
The beam leaving the compressor has a size of 8.04 mm \(\times\) 5.79 mm (FWHM) with a homogeneous profile (Figure 10a). The far-field, recorded in the focal plane of a f = 2000 mm achromatic lens, has a satisfactory Strehl ratio of 0.5 (Figure 10b).  
Figure 9: Schematics for the beamline between the power amplifier and the Treacy-type pulse compressor, including the contour of the vacuum chamber.  
For applications, it is essential that the laser should run in a stable manner over several hours. Hence, we measured the energy, spectral, and pointing stabilities for an hour. As can be seen, the energy stability of the system substantially exceeds that of the pump laser (Figure 11a), reaching a value of 0.84% (RMS). The stability of the spectrum over the operation period (Figure 11b) allows for a pulse duration stability of 0.96%. The pointing stability of the beam is \(\pm\)2 \(\upmu\)rad, which corresponds to 25% of the diffraction-limited focal size.
###### Abstract  
Transfer learning (TL) utilizes knowledge from the source domain (SD) to enhance the classification rate in the target domain (TD). It has been widely used to address the challenge of sessional and inter-subject variations in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). However, utilizing knowledge from a combination of both related and non-related sources can significantly deteriorate the classification performance across individual target domains, resulting in a negative transfer (NT). Hence, NT becomes one of the most significant challenges for transfer learning algorithms. Notably, domain selection techniques have been developed to address the challenge of NT emerging from the transfer of knowledge from non-related sources. However, existing domain selection approaches iterate through domains and remove a single low-beneficial domain at a time, which can massively affect the classification performance in each iteration since SDs respond differently to other sources. In this paper, we compare domain selection techniques for a multi-source manifold feature transfer learning (MMFT) framework to address the challenge of NT and then evaluate the effect of beneficial and non-beneficial sources on TL performance. To evaluate the effect of low-beneficial and high beneficial sources on TL performance, some commonly used domain selection methods are compared, namely, domain transferability estimation (DTE), rank of domain (ROD), label similarity analysis, and enhanced multi-class MMFT (EMC-MMFT), using the same multi-class cross-session and cross-subject classification problems. The experimental results demonstrate the superiority of the EMC-MMFT algorithm in terms of minimizing the effect of NT. The highest classification accuracy (CA) of 100% is achieved when optimal combinations of high beneficial sources are selected for two-class SSMVEP problems, while the highest CAs of 98% and 87% are achieved for three- and four-class SSMVEP problems, respectively. The highest CA of 98% is achieved for two-class MI classification problems, while the highest CAs of 90% and 71.5% are obtained for three- and four-class MI problems, respectively.  
brain-computer interface (BCI); electroencephalogram (EEG); transfer learning (TL); negative transfer (NT); multi-source manifold feature transfer learning (MMFT); enhanced multi-class MMFT (EMC-MMFT) +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Selection of Input Features for the Deep Learning Approach  
In the KF Particle Finder, various cuts are applied to determine whether reconstructed particles align with the assumptions of the underlying particle model. If a particle is statistically unlikely to match the model, it is excluded. However, since particles are typically assigned to multiple model hypotheses initially, this exclusion does not necessarily remove the particle entirely from the analysis.  
In this work, ten input features, five of which are \(\chi^{2}\) values used for cuts, are used for the neural-network-based approach. One of them is \(\chi^{2}_{prim}\), which determines whether a particle's track is classified as a primary or secondary track. This distinction is particularly important for daughter tracks, as they should be secondary tracks originating from a particle decay (here of \(\Lambda\)) and not pointing directly to the primary vertex. Pairs of secondary tracks often overlap in time and space, suggesting they may have originated from a common mother particle. To identify such mother particle candidates, \(\chi^{2}_{geo}\), evaluates the quality of the intersection point of the daughters' tracks, indicating a likely point of decay. The third \(\chi^{2}\) value, \(\chi^{2}_{topo}\), defined as the sum of \(\chi^{2}_{geo}\) and \(\chi^{2}_{prim}\), captures the overall topology of the particle. In addition to these \(\chi^{2}\) values for the \(\Lambda\) particle, other features are used as input for the classification model. These include the decay length normalized by its error, the particle mass, transverse momentum (momentum perpendicular to the beam axis), and the transverse momentum and \(\chi^{2}_{prim}\) values of the mother particle candidate's daughter particles. The complete list of input features is shown in Table 1.  
To support the subsequent application of the deep learning model, it is crucial to avoid prematurely discarding particles that may be misclassified as background due to not meeting the cut criteria. Therefore, the thresholds for \(\chi^{2}_{prim}\), \(\chi^{2}_{geo}\), and \(l/\Delta l\) were relaxed to retain more particles. In the KF Particle Finder, the threshold value of \(\chi^{2}_{prim}\) is, unlike the other two threshold values that are directly hard-coded, calculated by a function that converts a given probability of incorrectly identifying a primary track as a secondary track into a corresponding threshold value. Here, the probability was relaxed from 0.0001 to 0.1. All resulting cut thresholds are shown in Table 2. This provides the network with greater room for decision-making, ensuring the cuts do not preemptively reject particles that the model might classify as signal.
# A Study of Correction to the Point Cloud Distortion Based on MEMS LiDAR System
## 1 Introduction  
In the field of active optical imaging, a target was exposed and the backscattered light was detected to extract the depth and spatial information for 3D image formation. Light detection and ranging (LiDAR) systems are one of the most important sensors for 3D optical imaging technology [1; 2], which has shown enormous potential in different applications including target recognition, ground surface estimation, robotics, and driving on autonomous vehicles [3; 4; 5; 6; 7; 8]. Therefore, in order to obtain high-quality target information, it is necessary to select appropriate scanning components to improve the imaging performance of LiDAR, which includes, e.g., a galvanometer scanner [9] or Risley prisms [10]. With the rapid development of laser technology, photoelectric control technology, and image processing technology, 3D imaging LiDAR is tending towards miniaturization, high resolution, high-speed, low cost, and so on. The traditional mechanical scanners (such as vlp-16 [11], LMS511 and Lux-4L/8L) are the earliest developed and the most mature technologies, and have been applied to autonomous driving concept vehicles such as Google and Baidu or industrial applications [12; 13; 14]. The core technology is to place the linear array laser and linear array detector on the speed-regulating motor, which can rotate at a high speed in one dimension to achieve panoramic imaging. Generally, the volume of mechanical scanners is relatively large and bulky, they need rather high-power consumption (from 6 W in vlp-16 to 22 W in Alpha prime), and it is placed on the roof of the car, so the accuracy is very unstable; Solid-state laser 3D image sensor, the core technology is by waveguide or liquid crystal optical phased array device, through the electronic control technology to guide the laser pulse in any direction. However, the optical phased arraytechnology is essentially based on the diffraction effect, optical transmittance is the bottleneck of its further development. The core technology of the hybrid solid-state laser 3D image sensor is to replace the traditional one-dimensional mechanical rotation device with a micro-electro-mechanical system (MEMS), which is placed inside the panoramic optical system to solve the problem of solid-state. The MEMS mirror has been widely used in the LiDAR system due to its advantage including small size, fast scanning speed, low power consumption, low price, and strong anti-interference. The MEMS scanning LiDAR has become the main application direction of today's LiDAR imaging system [15; 16]. However, in the actual detection process, the obtained point cloud has obvious distortion, which seriously affects the application performance of the MEMS LiDAR, reduces the coverage of the detected target, and causes the loss of target information. Hence, aiming at point cloud distortion phenomenon, we have analyzed the reason for distortion initially, proposed and experimentally demonstrated a de-distortion method based on an improved coordinate correction.