esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Non-destructive testing and evaluation is the process of assessing the structural integrity of a material or component without causing any physical damage to the test object [1]. Eddy current testing is an effective method to detect fatigue cracks and corrosion in conductive materials because it is cheap and can monitor subsurface defects or defects under insulating coatings without touching the surface of a specimen [2; 3].  
Many studies investigated the effect of different parameters on eddy current signal. Material permeability and strength of magnetic induction are influenced by the type of material [4]. Thus, more secondary electromagnetic waves are produced in ferrous metals than in non-ferrous metals. Therefore, permeability significantly influences the eddy current defect signal. Crack orientation strongly influences the output of the eddy current probe. Cracks must interrupt the surface eddy current flow to be detected. Defects parallel to the current path will not cause any significant interruption and may not be detected [5; 6; 7; 8]. The response of the pickup coil or receiver coil to an eddy current depends on the conductivity and permeability of the test material and the frequency selected [9].  
Artificial intelligence was used in many types of research in eddy current testing. In [10] [PERSON] proposed a neural network algorithm for fast classification of the aluminum plate defects, such as cracks and holes. Moreover, the discrete wavelet transform allied with an artificial neural network (ANN) was used in [11] to estimate the depth of defect based on eddy current testing ECT data. [PERSON] and [PERSON] introduced a fuzzy neural approach to localize holes in conducting plates. However, the limitation is that fuzzy inference systems are effective with a few number of inputs [12]. In 2016 an ANN with the statistical technique of principal component analysis PCA was applied to steam generator data in simultaneous measurement by using PEC. Combining PCA and ANN improves the sensitivity of pulsed eddy current PEC [13].  
The eddy current testing was investigated and, statistically, the optimized condition was achieved through response surface methodology (RSM) using central composite design. An adaptive neuro-fuzzy inference system (ANFIS) model was utilized to compare with RSM. The materials of the artificial defect block are mild steel, brass, and copper with dimensions of 420 \(\times\) 30 \(\times\) 10 mm. The depth of the defect is between 1 and 2.5 mm from the surface of the artificial defect block, whereas the width of a defect is located between 0.2 and 1 mm. The ANFIS model is used to predict the future behavior of processes, as well as the evaluation of the statistical significance of the effects of the process effects on the desired response.
## 2 Background Knowledge
### Graph Convolutional Networks and Graph Classification  
Inspired by the theories related to the graph convolution in graph signal processing, a set of graph neural networks (GNNs) based on graph convolution operations have been developed in recent years. Earlier methods based on spectral domain need to process the whole graph simultaneously and have high time complexity in matrix decomposition. Thus, recently, spatial graph convolution has been proposed and widely used. For the input/output graph signal matrix \(X/X^{\prime}\), the graph convolution operation can be expressed as Equation (2), where \(\theta\) are trainable filters, \(U\) is the orthogonal matrix composed of eigenvectors of the Laplacian matrix of the graph, and \(\sigma(\cdot)\) represents nonlinear activation function.  
\[X^{\prime}=\sigma\left(Udiag(\theta)U^{T}X\right)=\sigma(\theta X) \tag{2}\]  
Considering that the effective information in real graphs is usually contained in the low frequency band, a local filtering method was proposed [15]. It used the Chebyshev polynomial to approximate the graph filter coefficients and simplify the learnable parameters. The graph convolution layer is defined in Equation (3), where \(\widetilde{L}_{sym}\) is symmetric normalized laplacian matrix. The calculation of \(\widetilde{L}_{sym}X\) is equivalent to the first-order aggregation of the eigenvectors of neighboring nodes. Additionally, the parameterized weight matrix \(W\) is introduced to enhance the network fitting capability. Stacking multiple graph convolution layers can achieve the filtering ability of frequency response function in high order polynomial form.  
\[X^{\prime}=\sigma\left(\widetilde{L}_{sym}XW\right) \tag{3}\]  
Specifically, tasks related to the graph data can be divided into node level, edge level and graph level. As an important graph level task, graph classification aims to learn a model from the graph to the corresponding label with consideration for the attributes of each node and the overall structure information of the graph. In graph classification tasks, the graph readout operation is usually adopted to generate the whole graph representation after the multi-layer message passing and state updating of each node. The standard approach based on the global pooling operator is to generate embedding for all nodes using a simple aggregation. However, common global pooling methods treat all the nodes equally, and thus are more suitable for handling small-scale graph data. Recently, many hierarchical pooling operators [16; 17; 18] have been developed that are suitable for more complex graph structures by compressing the information gradually to obtain the whole graph representation.
## 2 Materials and Methods
### Mathematical Models  
Within this section, two mathematical models are described. First, it is a computationally efficient 1D model employing the Riccati equation. This model is used for the optimization. Second, it is a detailed and accurate 2D model employing the linearized Navier-Stokes equations (LNSEs) and the finite element method (FEM). This model is used for the validation of the numerical results of the 1D model.  
All the numerical calculations are conducted in the frequency domain, so that all the acoustic variables \(q(\mathbf{r},t)\), where \(\mathbf{r}\) is the position vector and \(t\) is the time, are represented by their complex amplitudes, denoted with hats, introduced as \(q(\mathbf{r},t)~{}=~{}\Re[\hat{q}(\mathbf{r})\exp\mathrm{i}\omega t]\), where \(\omega\) is the angular frequency and \(\mathrm{i}~{}=~{}\sqrt{-1}\) is the imaginary unit.
# Performance Analysis of a Color-Code-Based Optical Camera Communication System
## 2 Proposed OCC System Architecture  
We designed the VLC system using a Redmi Note Pro 9 mobile phone as the receiver, an AsusX540U laptop computer as the transmitter, and a 21.5-inch AsusVP228 HE monitor connected to this computer via a high-definition multimedia interface (HDMI) cable. Cross-platform application development software was used on the laptop used as a transmitter. The phone with the Android 10 operating system has 4 rear cameras and 1 front camera, and the main camera, with a 64 MP, f/1.9, Sony IMX686 sensor, was used in the tests. The phone has an 8-core CPU (2 \(\times\) 2.3 GHz Kryo 465 Gold + 6 \(\times\) 1.8 GHz Kryo 465 Silver) and Adreno 618 GPU and the chipset is Qualcomm Snapdragon 720 G. The Asusx540U model laptop has a 2.50 GHz Intel Core i5-7200U dual-core processor. The resolution of the 15.6-inch screen is 1366 \(\times\) 768 pixels. The 21.5-inch Asus VP228 HE monitor has Full HD (1920 \(\times\) 1080 pixels) image quality and is of the TN (Twisted Nematic) type. The brightness is 250 cd/m\({}^{2}\) and response time is 1 ms.  
The general view of the designed communication system is shown in Figure 1.  
The cross-platform application was written on an application development platform that allows a single software application to run on operating systems such as IOS, Android, Windows, Linux, MacOS, Raspberry Pi, etc. Although many software languages are supported for cross-platform use, for this study, the Tkinter graphical interface library, which comes with the Python language, was preferred.  
The Tkinter graphical interface library has features that allow it to work with the Pydroid compiler on mobile devices and is compatible with the open source OpenCV. OpenCV provides flexible usage in image processing applications. It is also frequently used in deep learning applications. Therefore, the Pydroid compiler was installed on the cell phone used as a receiver. Thanks to the Pydroid compiler, the data sent from the transmitter are received from the phone's camera and converted into useful data with image processing techniques on the receiving phone. Thus, there was no need to connect a computer to the phone.  
The panel type of the ASUS monitor is a liquid crystal display (LCD). Adjustments such as brightness and saturation can be made on the transmitter side. However, these parameters can also be adjusted at the receiver side with image processing. A serial monitor was created with Canvas to monitor the data packets. Figure 2 shows the block diagram of the system.
###### Abstract  
An efficient data-driven prediction strategy for multi-antenna frequency-selective channels must operate based on a small number of pilot symbols. This paper proposes novel channel-prediction algorithms that address this goal by integrating transfer and meta-learning with a reduced-rank parametrization of the channel. The proposed methods optimize linear predictors by utilizing data from previous frames, which are generally characterized by distinct propagation characteristics, in order to enable fast training on the time slots of the current frame. The proposed predictors rely on a novel long short-term decomposition (LSTD) of the linear prediction model that leverages the disaggregation of the channel into long-term space-time signatures and fading amplitudes. We first develop predictors for single-antenna frequency-flat channels based on transfer/meta-learned quadratic regularization. Then, we introduce transfer and meta-learning algorithms for LSTD-based prediction models that build on equilibrium propagation (EP) and alternating least squares (ALS). Numerical results under the 3 GPP 5G standard channel model demonstrate the impact of transfer and meta-learning on reducing the number of pilots for channel prediction, as well as the merits of the proposed LSTD parametrization.  
channel prediction; meta-learning; multi-antenna frequency-selectivity; equilibrium propagation +
Footnote †: journal: 2022, 24, 1363. [[https://doi.org/10.3390/e24101363](https://doi.org/10.3390/e24101363)]([https://doi.org/10.3390/e24101363](https://doi.org/10.3390/e24101363))
# Research on Rollover Stability and an Anti-Rollover Warning System for the Vibroseis Truck
## 2 Rollover Stability Analysis for Vibroseis Truck
### Analysis of the Rollover Stability Mechanism of the Vibroseis Truck
#### 2.2.2 Flat-Road Steering Stability  
In Figure 4, the vibroseis truck is shown during the steering process on a flat road. Th centrifugal force is \(F\), the critical steering speed is \(u_{1}\), the steering angle is \(\beta\), the steering radius is \(R\), the distance between the front axle and the hinged joint is \(K\), the wheelbase is \(L\), the ground support force on the left side is \(N_{1}\), and the ground support force on the right side is \(N_{2}\). According to the principle of force balance, the formula derivation of flat-road steering stability is  
\[N_{2}B-\frac{1}{2}MgB+FH=0 \tag{4}\]  
\[R=\frac{K\cos\beta+L-K}{\sin\beta}+\frac{B}{2} \tag{5}\]  
\[F=M\frac{u_{1}^{2}}{R} \tag{6}\]  
So, we can obtain the critical steering speed \(u_{1}\):  
\[u_{1}=\sqrt{\frac{gBR}{2H}} \tag{7}\]  
Figure 3: Relationship between critical rollover angle, tread, and centroid height for the vibroseis truck during slope straight driving.  
As shown in Figure 5, with the increase of the steering angle \(\beta\), the critical steering speed \(u_{1}\) tends to decrease and then to gradually stabilize. The critical steering speed is 10.5 km/h while the steering angle reaches 30\({}^{\circ}\). The side-friction force provided by the ground cannot reach the centripetal force needed for the steering when the actual steering speed is beyond the critical speed, and this will cause a rollover accident.
## 2 Related Work  
### Development of Acoustic Features  
Mel-frequency cepstral coefficients (MFCCs) are the most widely used perceptual incentive features. Their improved forms [29], such as log frequency power coefficients [30] and linear prediction cepstral coefficients [31], have been used in SER. In addition to these basic features, researchers have constructed many new acoustic features for SER. In reference [32], inspired by the AM-FM modulation model, the features of modulation spectra and modulation frequency were extracted and combined with cepstral features. In reference [33], three features were selected: the MFCC feature of the typical human auditory model, the feature of Zero Crossings with Maximal Teager Energy Operator (ZCMT) of the uncompensated human auditory model, and the feature of Glottal Compensation to Zero Crossings with Maximal Teager Energy Operator (GCZCMT) feature. In reference to [34], a new set of harmonic features was proposed. Reference [35] proposed weighted spectral features based on Local Hu moments (HuWSF), which is based on Local Hu moments. However, these features do not reflect the relationship between the frequency domain and time domain at the same time, and therefore many researchers have turned their attention to spectrogram.
# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
## 3 Materials and Methods
### Integration with Inertial Odometry  
As illustrated in Figure 8, the proposed SCP-VO framework can be seamlessly integrated with inertial measurements to form a complete SCP-VIO system, which further improves the accuracy and robustness of motion estimation. The pose information derived from both the IMU and panoramic images is jointly processed within a tightly coupled motion bundle adjustment module, following conventional visual-inertial optimization techniques as described in [30].  
Figure 8: The structure of the integrated SCP-VIO.  
The final pose estimation is obtained by jointly minimizing the photometric error from visual tracking and the preintegrated inertial residuals. This enables the SCP-VIO system to fully exploit the complementary characteristics of panoramic vision and inertial sensing. It is worth noting that most representative visual odometry systems for panoramic images are typically designed in conjunction with IMU fusion. Therefore, incorporating IMU measurements in our framework not only enhances accuracy but also ensures fair and meaningful comparisons with state-of-the-art methods under similar conditions, as demonstrated in the experimental section.
## 3 A Beam-Hopping-Based Global Beam Scheduling and User Resource Allocation Algorithm
### Algorithm A: Beam-Footprint Partitioning Algorithm  
In this section, we propose an ILP-based beam-footprint partitioning algorithm. The Algorithm A predefines the set of beams that can be scheduled for the entire BH cycle of the LEO system, based on the spatial distribution of LEO users located outside the GEO protection zones. The objective is to maximize the number of users served per beam, ensuring that each beam is utilized as fully as possible.
## 4 Simulation Results and Discussions  
In this section, the performance of the proposed BC-UAC method is evaluated with simulated and measured underwater channels.  
The Pearson correlation coefficient (PCC) is used to measure the concealment of the biomimetic modulated signal. The BER is used to measure the reliability of the proposed BC-UAC method.  
The PCC is widely used to compare the similarity between two data sets [35]. Let the sets of the TFC sampling points of the original whistle signal before modulation and after modulation be \(\left\{f^{(1)}_{\text{whistle}^{\prime}}f^{(2)}_{\text{whistle}^{\prime}}, \cdots,f^{(I)}_{\text{whistle}}\right\}\) and \(\left\{f^{\prime(1)}_{\text{whistle}^{\prime}}f^{\prime(2)}_{\text{whistle}^{ \prime}},\cdots,f^{\prime(I)}_{\text{whistle}}\right\}\), where \(I\)is the number of sampling points in the set. The PCC of \(\left\{f^{(1)}_{\text{whistle}^{\prime}}f^{(2)}_{\text{whistle}^{\prime}},\cdots,f^ {(I)}_{\text{whistle}}\right\}\) and \(\left\{f^{\prime(1)}_{\text{whistle}^{\prime}},f^{\prime(2)}_{\text{whistle} },\cdots,f^{\prime(I)}_{\text{whistle}}\right\}\) can be calculated as  
\[\text{PCC}=\frac{\sum\limits_{i=1}^{I}\left(f^{(i)}_{\text{whistle}}-\overline{ f}_{\text{whistle}}\right)\left(f^{\prime(i)}_{\text{whistle}}-\overline{f}^{ \prime}_{\text{whistle}}\right)}{\sqrt{\sum\limits_{i=1}^{I}\left(f^{(i)}_{ \text{whistle}}-\overline{f}_{\text{whistle}}\right)^{2}\sum\limits_{i=1}^{I }\left(f^{\prime}_{\text{whistle}}-\overline{f}^{\prime}_{\text{whistle}} \right)^{2}}} \tag{16}\]  
where \(\overline{f}_{\text{whistle}}\) and \(\overline{f}^{\prime}_{\text{whistle}}\) are the average frequencies of the original whistle signal at each sampling time before and after modulation, respectively. When the value of the PCC approaches 1, it indicates high similarity between the bionic modulated signal and the original whistle signal. The larger the value of the PCC, the better the bionics of the modulated signal and the stronger the concealment.
## 4 Results and Discussion  
### Results of In Situ Stress Measurement  
A total of eight hydraulic fracturing in situ stress measurement experiments and two impression experiments were conducted in Borehole No. 1. In Borehole No. 2, seven hydraulic fracturing in situ stress measurement experiments and two impression experiments were performed. Additionally, five hydraulic fracturing in situ stress measurement experiments and two impression experiments were executed in Borehole No. 3. Due to the close proximity of the three test boreholes at Nanshanku, their results were analyzed collectively. The calculations indicate that within the depth range of 34.20 m to 430.90 m, the maximum horizontal principal stress ranges from 2.33 MPa to 16.60 MPa, while the minimum horizontal principal stress varies between 1.86 MPa and 10.79 MPa. The impression results, as illustrated in Table 1, show that the determined directions of the maximum horizontal principal stress are NE42\({}^{\circ}\), NE54\({}^{\circ}\), NE32\({}^{\circ}\), NE21\({}^{\circ}\), NE33\({}^{\circ}\), and NE31\({}^{\circ}\), suggesting that the predominant direction of the maximum horizontal principal stress in the vicinity of the measurement boreholes is the NNE direction.  
A total of eight hydraulic fracturing in situ stress measurement experiments and two impression experiments were conducted in the Xiaogangou measurement point. The results indicate that at burial depths ranging from 182.50 m to 595.10 m, the maximum horizontal principal stress varies between 9.37 MPa and 17.20 MPa, while the minimum horizontal principal stress ranges from 4.92 MPa to 10.62 MPa. The impression results, as presented in Table 1, demonstrate that the determined orientations of the maximum horizontal principal stress are NE20\({}^{\circ}\) and NE30\({}^{\circ}\). This suggests that the predominant orientation of the maximum horizontal principal stress near the measurement well is directed towards the NNE direction.  
In August 2005, [PERSON] et al. [26] conducted seven hydraulic fracturing in situ stress measurement experiments and five impression measurement experiments in the Xidatan borehole. The findings from the borehole suggest that the burial depth varies between 44.00 m and 176.00 m, the maximum horizontal principal stress ranges from 2.32 MPa to 11.07 MPa, while the minimum horizontal principal stress varies between 2.30 MPa and 7.00 MPa. The impression test results are presented in Table 1. The identified orientations of the maximum horizontal principal stress were NE21\({}^{\circ}\), NE29\({}^{\circ}\), NE43\({}^{\circ}\), NE34\({}^{\circ}\), and NE42\({}^{\circ}\), suggesting that the predominant orientation of the maximum horizontal principal stress in the vicinity of the measurement borehole is northeast.
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 5 Numerical Experimental Analysis
### Comparative Analysis with Other Intention Recognition Methods
CNN and LSTM also demonstrate certain advantages during training, but they still have limitations in deep feature extraction and handling complex missions. GAN requires further optimization of the training strategy to improve its stability and efficiency. Overall, the experimental results strongly validate the effectiveness and superiority of the proposed ResCNN model. Compared to traditional CNN, LSTM, and GAN models, ResCNN exhibits enhanced generalization ability and stability in classification missions, providing a more efficient solution for practical applications. Future research could further enhance model performance by integrating attention mechanisms or other advanced techniques.
###### Abstract  
To keep its business reliable, Google is concerned to ensure the quality of apps on the store. One crucial aspect concerning quality is security. Security is achieved through Google Play protect and anti-malware solutions. However, they are not totally efficient since they rely on application features and application execution threads. Google provides additional elements to enable consumers to collectively evaluate applications providing their experiences via reviews or showing their satisfaction through rating. The latter is more informal and hides details of rating whereas the former is textually expressive but requires further processing to understand opinions behind it. Literature lacks approaches which mine reviews through sentiment analysis to extract useful information to improve the security aspects of provided applications. This work goes in this direction and in a fine-grained way, investigates in terms of confidentiality, integrity, availability, and authentication (CIAA). While assuming that reviews are reliable and not fake, the proposed approach determines review polarities based on CIAA-related keywords. We rely on the popular classifier [PERSON] to classify reviews into positive, negative, and neutral sentiment. We then provide an aggregation model to fusion different polarities to obtain application global and CIAA reputations. Quantitative experiments have been conducted on 13 applications including e-banking, live messaging and anti-malware apps with a total of 1050 security-related reviews and 7,835,322 functionality-related reviews. Results show that 23% of applications (03 apps) have a reputation greater than 0.5 with an accent on integrity, authentication, and availability, while the remaining 77% has a polarity under 0.5. Developers should make a lot of effort in security while developing codes and that more efforts should be made to improve confidentiality reputation. Results also show that applications with good functionality-related reputation generally offer a bad security-related reputation. This situation means that even if the number of security reviews is low, it does not mean that the security aspect is not a consumer preoccupation. Unlike, developers put much more time to test whether applications work without errors even if they include possible security vulnerabilities. A quantitative comparison against well-known rating systems reveals the effectiveness and robustness of CIAA-RepDroid to repute apps in terms of security. CIAA-RepDroid can be associated with existing rating solutions to recommend developers exact CIAA aspects to improve within source codes.  
reputation; android; application; sentiment analysis; reviews; security service; NLP; Google Play; polarity +
Footnote †: journal: _Future Internet_**2020**, _12_, 145; doi:10.3390/612090145