esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis
## 4 Methodology of the Novel Deepfake Detection Framework
### Proposed Framework  
The goal of the new framework is to identify common features and artifacts from different GANs to improve detection capabilities, and to use the most effective methods to classify images as real or fake. Thus, the detection process consists of two important steps: Data Preparation and Classification. Our framework applies all data preparation and classification techniques to images or datasets, yielding results from all combinations. When a new image or dataset is provided, the detection pipeline processes all combinations alongside genuine images to ensure reliable results.  
* **Data Preparation**: Before assessing whether a raw image is fake, it must go through preprocessing. This step includes extracting features from the image and scaling them to ensure that each feature holds equal significance in the analysis. Various methods can be used for feature extraction; in this study, we will employ a combination of Fourier transform, wavelet transform, and Histogram of Oriented Gradients (HOG) 1D transform, alongside pairwise Red, Green, and Blue (RGB) color correlations.
* **Binary Classification**: After features are usefully extracted, a classifier algorithm is employed to categorize the image either as real or fake [23].  
In our experiment, we utilized several classifiers, including Gaussian Mixture Model, XGBoost, Random Neural Network, Support Vector Machines (SVM), Logistic Regression, K Nearest Neighbours, Gradient Boosting, Naive Bayes, AdaBoost, and Extra Trees. Each model contributes to the creation of a robust deepfake detection pipeline. The combination of different feature extraction methods (Fourier transform, wavelet transform, and HOG transform, along with color differences) with the advanced classifiers mentioned above ensures that both subtle and obvious manipulations introduced by deepfake generation are detected accurately. Figure 3 illustrates how the detection pipeline works.
## 3 Methodology
### Computational Analysis
#### 3.2.2 Modeling and Boundary Conditions  
This study analyzed the internal flow field of shed-type CSS and determined the results using the flow visualization method. Computational analysis is described below. Ansys Fluent 2021 R2, a CFD S/W is Ansys Fluent 2021 R2, was used to analyze the internal flow field. The modeling photos of CSS and the boundary conditions for computational analysis are shown in Figure 2 and Table 1, respectively. The building is 66 m high and 125 m wide, and the central wall is 28 m high. In the Figure below, The green line indicates the coal pile, the blue arrows show the window inlet and the monitor louver is red. The inclined angle of the coal pile is 40 degrees, assuming a coal pile with 100% coal storage. The diameter of the coal particles is assumed to be 0.01 m, [20] considering the characteristics of the inclined surface of the coal pile. Therefore, the coal pile was set to a porosity of 0.2. The initial boundary condition was set in such a way that air flows in at a speed of 2 m/s from the louvers and windows on the first floor of the CSS. The temperature of the air was 20 \({}^{\circ}\)C. The results were output under steady-state conditions, and the turbulence model employed the standard k-\(\varepsilon\) model commonly used in the industry.  
\begin{table}
\begin{tabular}{c c c} \hline
**Conditions** & **Value** & **Remark** \\ \hline Coal storage & 100\% & \\ Angle of repose & 40\({}^{\circ}\) & Porous zone \\ Coal diameter & 0.01 m & Laminar flow zone \\ Porosity & 0.2 & \\ Inlet velocity & 2.0 m/s & 1 st floor \\ Initial temperature & 20 \({}^{\circ}\)C & Louver and window \\ Turbulent model & Standard k-\(\varepsilon\) model & \\ \hline \end{tabular}
\end{table}
Table 1: Boundary conditions for computational analysis.
# GPU-Enabled Serverless Workflows for Efficient Multimedia Processing
## 3 Architecture of the Serverless Processing Platform
### Integration of SCAR with AWS Batch  
Introducing support for AWS Batch in SCAR required extending the framework in order to create and configure the Batch resources employed depending on the execution modes selected. The development used the AWS SDK for Python (Boto3) [26]. Furthermore, it also required extending the _faas-supervisor_ (FaaS Supervisor: [[https://github.com/grycap/faas-supervisor](https://github.com/grycap/faas-supervisor)]([https://github.com/grycap/faas-supervisor](https://github.com/grycap/faas-supervisor)) (accessed on 26 November 2020)), an open-source library to manage the execution of user scripts and containers in AWS Lambda and also in charge of managing the input and output of data on the Amazon S3 storage back-end. This was redesigned to delegate jobs to AWS Batch. To do this, the FaaS Supervisor, which runs in the AWS Lambda runtime as a Layer, is able to identify the execution mode specified in the function definition. When it must delegate the execution to AWS Batch, it will submit a new job to the function's job queue based on the job definition previously created by the SCAR client, embedding the event in an environment variable. Thus, three execution modes are now supported depending on the user's preference regarding where the job will be executed:  
* _lambda_: The jobs are ran as user-defined container images on AWS Lambda. The FaaS Supervisor employs _udocker_[27] to pull the Docker image from a container registry and execute it inside the AWS Lambda runtime.
* _batch_: AWS Lambda acts as a gateway for events but function invocations are translated into AWS Batch jobs. The event description is passed down to the job as an environment variable, allowing the FaaS Supervisor, which runs on the EC2 instance, to parse it to perform data stage in/out. When functions are defined in this mode, the SCAR client is responsible for creating the required AWS Batch components (compute environment, job queue and job definition).
* _lambda-batch_: Functions are first executed on AWS Lambda. If the execution fails or the function timeout has almost been reached, the job is automatically delegated to AWS Batch. This mode allows using AWS Lambda to effectively scale upon a large burst of short jobs while ensuring that more demanding jobs will be eventually processed whenever the AWS Lambda limits are exceeded.  
The FaaS Supervisor runs on the AWS Batch jobs as a binary that is downloaded during the startup of each EC2 instance belonging to the ECS cluster created by AWS Batch. To do this, the SCAR client automatically creates a launch template containing the download commands in the _cloud-init_[28] user data. Then, the path containing the FaaS Supervisor is mounted automatically as a volume on the job containers.
## 4 Finite Element Numerical Simulation
### Concrete Damage Model Theory
#### 4.1.3 Yield Criterion and Law of Flow  
For the smeared cracking model, the yield surface equation is based on classical elastoplastic theory and is expressed as follows:  
\[F=\sqrt{3J_{2}}+3\kappa I-\sqrt{3}\tau_{c} \tag{3}\]  
where the \(\tau_{c}\) hardening parameter can be calculated from the uniaxial stress-strain relationship for concrete.  
The flow law for the concrete dispersion cracking model is the ordinary associative flow law, and the plastic potential function is the same as the yield surface equation.  
For the plastic damage model, the yield surface function is:  
\[F=\frac{1}{1-\alpha}\left[\sqrt{3J_{2}}+\alpha I_{1}+\beta\langle\sigma_{\text{ max}}\rangle-\gamma\langle-\sigma_{\text{max}}\rangle\right]-\sigma_{c0} \tag{4}\]  
where \(I_{1}\) and \(J_{2}\) are the first invariant of the stress tensor and the second invariant of the partial stress tensor, respectively, and the remaining parameters are calculated as follows:  
\[\alpha=\frac{\frac{\sigma_{\text{0}}}{\sigma_{\text{0}}}-1}{\frac{2\sigma_{ \text{0}}}{\sigma_{\text{0}}}-1} \tag{5}\]  
\[\beta=\frac{\sigma_{c0}}{\sigma_{\text{0}}}(1-\alpha)-(1+\alpha) \tag{6}\]  
\[\gamma=\frac{3(1-K_{\text{c}})}{2K_{\text{c}}-1} \tag{7}\]  
where \(\sigma_{b0}\) is the biaxial compressive strength of the concrete; \(\sigma_{c0}\) is the uniaxial compressive strength of the concrete; \(\sigma_{t0}\) is the biaxial tensile strength of the concrete; and K\({}_{\text{c}}\) is the parameter controlling the shape of the projection of the concrete yield surface on the offset plane.  
The flow law for the plastic damage model uses the uncorrelated flow law with a plastic potential function of:  
\[G=\sqrt{\left(\lambda\sigma_{\text{0}}\tan\phi\right)^{2}+1.5\rho^{2}}+\sqrt{3 }\xi\tan\phi \tag{8}\]  
where \(\rho=(2J_{2})^{0.5}\); \(\rho\) is the expansion angle of the concrete yield surface in the strengthening process, taking a value range of \(37^{\circ}\) to \(42^{\circ}\); \(\lambda\) is the eccentricity of the concrete plastic potential function, which can be taken as 0.1.  
For the seismic finite element analysis of the new assembled beam-column joints in this paper, the concrete constitutive model was based on the plastic damage model. The experimental parameters were calculated concerning the test specimens of the same concrete batch.
## 2 Problem Formulation and Model
### Sensor Model  
In this paper, each UAV is equipped with a moving target indicator radar (MTIR) to localize the position of the target. In the MTIR, the sensor measurement \(\mathbf{z}_{i}(k)\) is composed of the relative range, \(\widetilde{r}_{i}(k)\), and azimuth, \(\widetilde{\theta}_{i}(k)\), of the target with respect to the position of the airborne UAV. Using the target position \(\mathbf{x}_{i}(k)\) and the UAV position \(\mathbf{x}_{i}(k)\), the sensor measurement is defined via:  
\[\mathbf{z}_{i}(k) =\left[\begin{array}{c}\widetilde{r}_{i}(k)\\ \widetilde{\theta}_{i}(k)\end{array}\right]=\mathbf{h}_{i}(k,\mathbf{x}_{i}(k),\mathbf{x }_{\mathrm{t}}(k))+\mathbf{v}_{i}(k) \tag{6}\] \[=\left[\begin{array}{c}\sqrt{\left(x_{\mathrm{t}}(k)-x_{i}(k) \right)^{2}+\left(y_{\mathrm{t}}(k)-y_{i}(k)\right)^{2}}\\ \arctan\frac{y_{\mathrm{t}}(k)-y_{\mathrm{t}}(k)}{x_{\mathrm{t}}(k)-x_{ \mathrm{t}}(k)}\end{array}\right]+\mathbf{v}_{i}(k)\]  
In Equation (6), \(\mathbf{v}_{i}(k)\)-\(N(0,\,\mathbf{R}_{i}(k))\) is the measurement noise, and its covariance matrix \(\mathbf{R}_{i}(k)=\mathrm{diag}(\delta_{r}{}^{2}(k),\,\delta_{\theta}{}^{2}(k))\); \(\delta_{r}(k)\) and \(\delta_{\theta}(k)\) are the standard deviations of range and azimuth, respectively. Let the matrix \(\mathbf{H}_{i}(k)\)-be the Jacobian of the nonlinear function \(\mathbf{h}_{i}(k,\,\mathbf{x}_{i}(k),\,\mathbf{x}_{\mathrm{t}}(k))\) with respect to the target state \(\mathbf{x}_{\mathrm{t}}(k)\):  
\[\mathbf{H}_{i}(k)=\left[\begin{array}{cccc}\cos\theta_{i}(k)&0&\sin\theta_{i}(k) &0\\ -\frac{\sin\theta_{i}(k)}{r_{i}(k)}&0&\frac{\cos\theta_{i}(k)}{r_{i}(k)}&0\end{array}\right] \tag{7}\]Thus, Equation (6) can be rewritten in terms of the following linear equation:  
\[\mathbf{z}_{i}(k)=\mathbf{H}_{i}(k)\cdot\mathbf{x}_{i}(k)+\mathbf{v}_{i}(k) \tag{8}\]
# Distribution and Deposition of Cylindrical Nanoparticles in a Turbulent Pipe Flow
## 2 Models and Equations
### Volume Fraction \(\Phi\) of Cylindrical Nanoparticles  
In the past research, the particle volume fraction \(\Phi\) in Equation (2) is usually assumed to be a constant and in a uniform distribution. In fact the distribution of \(\Phi\) is non-uniform because of particle convection and diffusion. Therefore, in order to obtain the volume fraction \(\Phi\) of cylindrical nanoparticles in Equation (2), the equation of the mean number density \(\overline{n}\) for cylindrical nanoparticles should be solved. Considering the effect of convection and diffusion, the mean equation of mean number density \(\overline{n}\) for cylindrical nanoparticles is [23]:  
\[\frac{\partial\overline{n}(v)}{\partial t}+\overline{u}_{j}\frac{\partial \overline{n}(v)}{\partial x_{j}}-\frac{\partial}{\partial x_{j}}[(D_{tB}+v_{ t})\frac{\partial\overline{n}(v)}{\partial x_{j}}]=0 \tag{10}\]  
where \(v\) is the volume of particle; \(v_{t}=0.09\,\,k^{2}/\varepsilon\) is the turbulent diffusion coefficient and \(D_{tB}\) is the Brownian translational diffusion coefficient [21]:  
\[D_{tB}=\frac{k_{b}T}{\sqrt{\big{[}2\pi\mu L_{p}/(\ln\lambda-0.207+\frac{0.980} {\lambda}-\frac{0.133}{\lambda^{2}})\big{]}^{2}+\big{[}4\pi\mu L_{p}/(\ln \lambda+0.839+\frac{0.185}{\lambda}+\frac{0.233}{\lambda^{2}})\big{]}^{2}}} \tag{11}\]  
\(\overline{n}(v)\) can be transformed into the particle number \(N\) and volume \(V\):  
\[\int_{0}^{\infty}\overline{n}(v)\mathrm{d}v=N,\ \int_{0}^{\infty}v\overline{n}(v) \mathrm{d}v=V \tag{12}\]  
Then \(\Phi\) can be calculated based on \(V\).
# Seismic Reflection Analysis of AETA Electromagnetic Signals
## 2 AETA System  
Acoustic and electromagnetics to artificial intelligence (AETA) is a system designed by the IMS Laboratory of Peking University for earthquake monitoring and prediction. It consists of an underground signal acquisition probe, a ground data processing terminal, a monitoring cloud platform, and a data analysis system, as shown in Figure 1. The underground part contains electromagnetic and geoacoustic sensor probes, which are utilized to collect electromagnetic and geoacoustic signals in real time. First, the collected data are transmitted to the terminal with the function of data processing on the ground. Processed data are then received by the monitoring cloud platform through the network for further analysis by the researchers [14].  
This article focuses on the anomaly analysis of an impending earthquake based on AETA electromagnetic data. The design of the AETA electromagnetic sensor is similar to that of the search-coil magnetometer instrument (IMSC) on the French DEMETER and that on the Chinese electromagnetic satellite, based on [PERSON]'s electromagnetic theory. The induced electromotive force obtained from the magnetic field changing in the vertical direction is inputted into the front-end signal processing circuit for amplification, and is then filtered and processed via digital-to-analog conversion (DAC). The processed signal passes through the back-end acquisition circuit, which has a strong processing speed, STM32F407 as the control unit, and a W5300 high-speed Ethernet communication interface chip as the data transmission module to meet the needs of real-time detection. The parameters of the electromagnetic sensor are as follows: the electromagnetic probe monitors 0.1 Hz\(\sim\)10 kHz, covering very low frequency and ultra-low frequency electromagnetic bands with a wide dynamic range of 0.1-1000 nT. The device has an 18-bit resolution, a sensitivity of >20 mV/nT@ 0.1 Hz-10 kHz, and a noise level of 0.1-0.2 pT/Hz@ (10 Hz-1 kHz). It has a low frequency sampling rate of 500 Hz and a full frequency sampling rate of 30 kHz [15].  
So far, the IMS laboratory has installed more than 200 AETA devices nationwide, and 150 of them are in the regions of Sichuan and Yunnan (22\({}^{\circ}\) N-34\({}^{\circ}\) N, 98\({}^{\circ}\) E-107\({}^{\circ}\) E). The reason for this is that this area has high incidence of earthquakes, and more than 200 earthquakes with a magnitude of 3.5 or higher have occurred there since 2017, which requires further investigation. The distribution of all stations in Sichuan and Yunnan is shown in Figure 2.  
Figure 1: Block diagram of AETA system.  
Figure 2: AETA stations located in Sichuan–Yunnan area.
## 4 Calculating Time-Varying Mesh Stiffness  
### Model Establishment and Stiffness Analysis  
The time-varying mesh stiffness model in this paper is obtained through FEA by establishing time-varying mesh stiffness models with different roughness and importing the model into the analysis software. The solution is obtained in three steps. Unlike the ideal smooth surfaces, the engagement between rough surfaces involves multipoint contact among the asperities. The average deformation of all asperities constitutes the contact deformation of the rough surface. Figure 6 represents the surface conditions of gears at different roughness levels.  
The roughness reflected in the relationship between fractal dimension and roughness corresponds to the contour arithmetic mean deviation \(Ra\). The fractal dimensions corresponding to roughness levels of 0.8, 0.4, and 0.2 are 1.55, 1.60, and 1.65, respectively.  
Figure 6: Tooth surface conditions based on different fractal dimensions of W-M functions.  
The expression for the single-tooth meshing stiffness is generally the following:  
\[k_{\text{n}}=\frac{F_{\text{n}}}{u_{\text{n}}} \tag{37}\]  
where \(F_{\text{n}}\) is the normal contact force acting on the tooth surface, and \(u_{\text{n}}\) is the deformation during single-tooth meshing.  
The normal contact force on the spiral bevel gear tooth profile surface includes tangential, radial, and axial components. Analytical calculations for this are hard to get. Therefore, FEA is taken to obtain its numerical solution directly in this paper.  
The comprehensive elastic deformation of the gear tooth should include contact elastic deformation due to the local Hertz contact (\(u_{h}\)), displacement of the gear tooth contact position due to gear bending (\(u_{b}\)), and the influence of deformations from bearings, shafts, and supporting structures on the contact point position (\(u_{f}\)). However, considering the deformations of bearings, shafts, and supporting structures would make the problem more complex. Therefore, \(u_{h}\) and \(u_{b}\) are considered as the total tooth surface deformation, and the calculation formula is expressed as follows:  
\[u_{n}=\sum_{i=1}^{2}u_{Hi}+\sum_{i=1}^{2}u_{bi} \tag{38}\]  
When spiral bevel gears undergo multitooth meshing, there is a coupling relationship between the individual tooth pairs. Therefore, the overall contact stiffness of spiral bevel gears is expressed as the following:  
\[k_{m}=\sum_{i=1}^{p}k_{ni} \tag{39}\]  
where \(p\) is the number of teeth simultaneously in contact.
# Candidate Waveforms for ARoF in Beyond 5G
## 3 Candidate Modulation Formats for MmWave Over UDWDM-PONs
### Multi-Band Carrierless Amplitude and Phase Modulation (Multi-CAP)  
Carrierless amplitude and phase modulation (CAP) is a multilevel and multidimensional modulation seen as a particular implementation of single carrier QAM using filters with orthogonal response. This absence of a carrier leads to less expensive and simpler transceivers compared to single carrier QAM, although increasing features in terms of spectral efficiency and performance [30; 31].  
As a representative feature, CAP is characterized by a low PAPR and simple implementation. However, CAP is proven to be very sensitive to frequency-selective channels and, to overcome this impairment, it requires a very complex equalizer and, consequently, suffers from inefficient MIMO integration. In this respect, a variant of CAP dividing the signal into different sub-bands, multi-CAP,is proposed [30], where signal power and modulation order can be adapted to the concrete channel condition associated to each sub-band.
# Joint Location-Allocation Model for Multi-Level Maintenance Service Network in Agriculture
## 5 Case Study
### Sensitivity Analysis of Selected Parameters  
In this section, sensitivity analysis is conducted to evaluate the effect of several parameters on the final results, which include the following: (a) the number of service stations, \(l\); (b) the maximal service distance for service stations, \(D1_{max}\).  
First, this paper conducts a sensitivity analysis of the number of service stations, \(l\). Several examples are conducted to find the minimum value of \(l\), and the computational results show that there is no optimal solution when the value of \(l\) is less than 9 due to the restrictions on the number and the maximal service distance of the service vehicles. After determining the lower bounded of \(l\), sensitivity analysis of the number of service stations \(l\) is conducted after increasing the value of \(l\) from 9. Computational results with different  
Figure 5: The location–allocation results in multi-level service network.  
values of \(l\) are shown in Figure 6. Total service costs experience a downward trend as \(l\) increases from 9 to 35, and the reduction becomes smaller with the increase in the value of \(l\). For example, total service costs reduce by 12.31% when \(l\) changes from 9 to 10, whereas total service costs reduce by 3.37% when \(l\) increases from 34 to 35. Thus, in this case, the number of service stations may be set as 20 to 30 from a comprehensive perspective.  
This paper then changes the value of \(D1_{\text{max}}\) to examine the effect of maximal service distance of service stations on total service costs. Multi-level facility location-allocation problems without the constraints of maximal service distance are first obtained, and the total service costs are 5.678 \(\times\) CNY \(10^{5}\) with the maximal service distance of 237 km. After that, the maximal service distance for service stations is reduced from 240 km by 10 km each time, and optimal results are obtained using the proposed solution algorithm, which is shown in Figure 7. The optimal results show that the total service costs increase with the decrease in the value of \(D_{\text{max}}\), and there is no optimal solution until the maximal service distance \(D_{\text{max}}\) is less than 120 km. Thus, manufacturers usually choose a suitable maximum service distance to ensure that they can obtain a small service cost while improving customer satisfaction. Therefore, the maximal service distance may be set as 130 km to 160 km from a comprehensive perspective.  
Figure 6: Computational results with different values of \(l\).  
Figure 7: Computational results with different values of \(D_{\text{max}}\).