esa-sceva/satcom-chunk-collection huggingface dataset:
# Authenticity at Risk: Key Factors in the Generation and Detection of Audio Deepfakes +
## 5 Experimental Settings
### Detection of Deepfakes  
For _HABLA_, 103 real and 111 synthetic audio samples were used, while for _WE-LIVE_ 176 real versus 1280 synthetic audio samples were available. Due to this imbalance in the _WELIVE_, dataset, it was decided to split the dataset into eight separate experiments. This technique ensures that each subset of data, both training and test, retains the same proportion of real and synthetic audios as in the original set. The entire dataset was divided into eight parts, as eight synthetic versions were generated from each real audio. In each experiment, both real and synthetic audio were included in the splits. In the first experiment, one of these eight parts was used as the test set and the remaining seven as the training set. In the second experiment, another subset was selected for test and the remaining ones were used for training, repeating this process until all eight experiments were completed. In each experiment, the proportion of real and synthetic audio was maintained, ensuring that each subset had an equal representation of both types of audio, which allowed for a robust and balanced evaluation of the model. On the other hand, for the _HABLA dataset_, a partition was performed in which 80% of the data was allocated to the training set and the remaining 20% was used for the test set. The rest of the process is common for both datasets.  
First, the audios were pre-processed to remove silent segments using a silence removal process based on VAD in order to analyze the voiced part of the audio only. Then, in order to compactly and robustly represent the acoustic characteristics of each audio, 20 MFCC coefficients were extracted, condensing the most important information from the audio into a more manageable and efficient format for the model. These 20 coefficients were averaged over time to obtain a fixed-size feature vector. Finally, a labeled dataset was created.  
Since our goal was to understand the influence of the acoustic context, vanilla implementations from scikit-learn [53] of the following methods were employed: Support Vector Machines (SVM) with linear kernel, Random Forest, K-Nearest Neighbours (KNN) and Naive Bayes. For each classifier, a five-fold cross-validation strategy was applied. In the case of SVM the optimal values of the C parameter were calculated for each of the partitions using GridSearchCV from Scikit-learn [53]. For Random Forest, 100 estimators were used, while for KNN, 5 neighbours were selected. For Naive Bayes, the GaussianNB classifier was used. The evaluation metric used was scikit-learn F1-score, with both mean and standard deviation calculated for each classifier.
## 2 Materials and Methods
### Maximum Value Composite (MVC)  
To minimize interference from atmospheric conditions, clouds, and solar zenith angle, this study employed the internationally recognized Maximum Value Composite (MVC) method [24], selecting the maximum NDVI value within each month as the monthly NDVI value. Additionally, the annual maximum NDVI values were calculated by the mean values of all the monthly NDVI data in a year.
###### Abstract  
The Shendong mining area is the largest coal production base in western China. Due to long-term mining activities, the ecological environment quality (\(EEQ\)) of the Shendong mining area has undergone significant changes. Investigating the evolution of \(EEQ\) during the process of mineral resource exploitation is of great importance for the sustainable development of the mining area. However, current research lacks a quantitative assessment of the contributions of climate change and human activities to the spatiotemporal variations in \(EEQ\) in the Shendong mining area. In this study, the Remote Sensing Ecological Index (\(RSEI\)) was used as an \(EEQ\) evaluation metric. The Theil-Sen slope estimation and Mann-Kendall test were applied to analyze the spatiotemporal changes of \(EEQ\) from 1990 to 2023. Additionally, the partial derivative method was used to investigate the response characteristics of \(EEQ\) to climatic factors and human activities and to quantify the relative contributions of these two driving factors. The results indicate that, over the past 34 years, the overall \(EEQ\) in the study area has shown an improving trend. Compared to 1990, the proportions of areas with good-grade and excellent-grade \(EEQ\) in 2023 increased by 28% and 23.78%, respectively. Additionally, in the second phase (2011-2023), the average \(RSEI\) time series value significantly increased compared to the first phase (1990-2010). Among the climatic factors, annual precipitation had the greatest impact on \(EEQ\), with an average contribution rate of 0.085. The conversion of unused land to forestland significantly improved the \(EEQ\), with the area showing a very significant increase in \(RSEI\), accounting for 82.30%. The areas in the mining region showing very significant, significant, and slight increases in \(RSEI\) were smaller than the overall study area. In conclusion, the overall \(EEQ\) in the study area has shown an improving trend, with climate change being the dominant factor in 71.52% of the areas where \(RSEI\) increased, while human activities were the dominant factor in 26.89% of the areas where \(RSEI\) decreased.  
ecological environment quality; remote sensing ecological index; coal mining; land-use type transitions; Shendong mining area +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Study Design  
To capture intraoral images of the upper and lower jaws, as well as the right and left bite, an intraoral scan was conducted using the Trios color (3 Shape, Copenhagen, Denmark, 2014) imaging system. The digital models obtained from the scan were utilized to design the retainer using 3 Shape design software (Splint studio software, 3 Shape, Copenhagen, Denmark, 2023), as shown in Figure 1. For the fabrication of the retainer, the 3D printing technique was employed. Biocompatible resin OrthoClear (Nextdent, Soesterberg, The Netherlands) was chosen as the material for the retainer, as depicted in Figure 2. This resin is specifically formulated to be compatible with oral tissues, ensuring a safe and comfortable fit for the patient [18]. The retainers were made using the Nextdent 5100 3D printer, which is of the same brand (Nextdent, Soesterberg, The Netherlands).  
To evaluate the occlusion, the T-Scan Novus system (Tekscan, Norwood, MA, USA, 2018) was used. The pressure-sensitive sensor of the T-Scan Novus system was placed between the dental arches, with a marker positioned between the upper central incisors.  
Figure 1: Digital design of the retainer.  
Figure 2: Retainer in the patient’s mouth.  
The patient was seated upright, and the sensor was positioned horizontally. The patient was instructed to close their teeth in tight contact. Three consecutive measurements were taken, with the first serving as a training trial to ensure proper performance by the patient and the subsequent two measurements used for analysis. The arithmetic averages of the two consecutive measurements were calculated for each patient.  
To enable a precise comparison between the intraoral images obtained from the Trios color scanner and the T-Scan system, the intraoral images were imported into the T-Scan system prior to analysis. The licensed software version 10.0.40 (T-Scan 10) was used for data analysis [T-Scan(tm) v10 Software, Tekscan, Norwood, MA, USA, 2018]. The software facilitated the division of the dental arches into quadrants, automatically calculating the percentage force distribution within each quadrant. The study focused on the distribution of maximum intercuspidation by quadrants.
## Appendix A Intervention Analysis Results
### Region 2 (Vic) Results  
According to the BinSeg segmentation and HMM analysis results in Sections 4.4 and 4.5, respectively, three different change-points were detected for the Total Ozone Column for Region 2, days 49, 74 and 105 (Figure A3). It can be seen that the fitted model (Figure A2) was able to capture the behavior of the series quite successfully with small residuals. In summary, in this model all the following components involving mean ozone level (intercept) and ozone's lagged dependence of 1 day were highly significant. The negative trend lag1 effects at the first and second intervention points at 49 and 74 days, were significant but the negative effect at the third change-point at 105 days was not significant. Aerosol levels lagging by 1 day were also shown to be insignificant.  
Based on the highest Adjusted R-squared = 0.82, and lowest MASE = 0.83 and AIC = 2588.96, Model R2 was optimal, with the resultant coefficients (Table A2) and the fitted model (Figure A4) which was able to capture the general behavior of the series. In the optimal model, the following components involving mean ozone level (intercept) and ozone's lagged dependence of 1 day were highly significant, and the negative trends  
With reference to Figure A5, the beginning of the ozone series is identified as being in State 3 (highest mean ozone intercept parameter = 7676.67 DU, and lowest mean aerosol = 2.645) until day 68 (7 November 2019), which corresponds to the day of the change-point for the aerosol data (Figure 17). This is also the same day as the first change-point detected using Binary Segmentation.  
Between day 68 (7 November 2019) and 80 (19 November 2019), ozone levels vary between States 3 and 4 (Figure A5) with State 4 having the highest aerosol intercept mean of 70.5 (State 4: ozone(DU) = 7086.40).  
From day 80 (19 November 2019), ozone levels then alternate between State 1 (mean ozone, aerosol = (6895.04, 16.48)) and State 4 (mean ozone, aerosol) = (7086.40, 70.05)) where they remain until day 140 (18 January 2020), which is 10 days after the last change-point detected for aerosol by Binary Segmentation.  
From the transmission probabilities in Table A4 the ozone level in this period primarily remains in State 1 with a probability of 0.903. Note that during this period State 1 can transition to State 4 with a probability of 0.076, and State 4 can transition to State 1 with a probability of 0.22. After day 141 (19 January 2020) ozone levels were identified as State 2 (mean ozone, aerosol) = (6450.22, 15.35).
## 3 Results and Discussion
### Crack Propagation Life Analysis
#### 3.4.5 Sensitivity Analysis of \(\mathrm{a_{0}}\) and \(\mathrm{a_{c}}\)  
A sensitivity analysis of initial crack size \(a_{0}\) and critical crack size \(a_{c}\) was conducted, as shown in Figure 17. When the detectable crack size increases to 1.0 mm, the crack growth life under three operating cases decreases by 2.10 \(\times\) 10\({}^{5}\), 4.38 \(\times\) 10\({}^{5}\), and 1.77 \(\times\) 10\({}^{5}\) cycles, respectively, although the initial crack size increases by only 0.15 mm, 0.40 mm, and 0.06 mm. Since initial crack size has a significant influence on the fatigue crack propagation life, it is necessary to ensure that crack detection technology can meet the accuracy requirement of 0.5 mm to ensure the early detection of an initial crack and to avoid sudden fracture.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Operating** & \multirow{2}{*}{\(C\) (mm/cycle)} & \multirow{2}{*}{\(m\)} & \(\Delta\sigma\) & \(a_{0}\) & \(a_{c,upper}\) & \(a_{c,lower}\) & \(N_{upper}\) & \(N_{lower}\) & \(N\) \\
**Case** & & & **(MPa)** & **(mm)** & **(mm)** & **(mm)** & **(cycle)** & **(cycle)** & **(year)** \\ \hline I & \(3.99\times 10^{-8}\) & 2.472 & 40.21 & 0.85 & 238.1 & 408.8 & \(4.00\times 10^{6}\) & \(4.18\times 10^{6}\) & 1.5/5.8 \\ II & \(3.99\times 10^{-8}\) & 2.472 & 47.76 & 0.60 & 238.1 & 408.8 & \(2.92\times 10^{6}\) & \(3.03\times 10^{6}\) & 1.1/4.2 \\ III & \(7.18\times 10^{-9}\) & 2.818 & 47.76 & 0.94 & 238.1 & 408.8 & \(6.23\times 10^{6}\) & \(6.38\times 10^{6}\) & 2.3/9.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Crack propagation life of anchorage structure under different operating cases.  
Conversely, a sensitivity analysis of \(a_{c}\) in Figure 17b shows that, even if the critical crack size is increased by 1.43 m to seven times the original value, the crack propagation life under three operating cases increases by only 5.30 \(\times\) 10\({}^{5}\), 3.46 \(\times\) 10\({}^{5}\), and 3.97 \(\times\) 10\({}^{5}\) cycles, respectively. Therefore, for assessing fatigue crack growth life, critical crack size or fracture toughness has little effect.
# Correlation Analysis and Its Application on an Asymmetry
## 2 Modeling Process
### Modeling of the Bearing Force  
The simplest way to deal with the bearing element is representing it by a spring-damper element. Then the boundary condition of the corresponding node can be decided. However, the nonlinear factors introduced by the bearing force are neglected. To further consider the nonlinear effects, a nonlinear bearing force model is introduced. In this work, a sliding bearing model is adopted. Reynolds' equation is the basic equation for oil film force analysis.  
\[\frac{1}{R^{2}}\cdot\frac{\partial}{\partial\zeta}(\frac{h^{3}}{12\eta}\cdot \frac{\partial p}{\partial\zeta})+\frac{\partial}{\partial z}(\frac{h^{3}}{12 \eta}\cdot\frac{\partial p}{\partial z})=\frac{1}{2}\Omega\cdot\frac{ \partial h}{\partial\zeta}+\frac{\partial h}{\partial t} \tag{4}\]  
where \(R\) is the radius of the journal; \(\zeta\) is the clockwise angle from vertical direction; \(h\) is the oil film thickness; \(\eta\) is the lubricating oil viscosity and \(p\) is the oil film pressure. \(z\) indicates axial-direction which is neglected in this study and \(t\) for time.  
[PERSON]'s modified oil film force model [30] is applied, and based on which the Equation (4) can be solved analytically. The oil film forces are then obtained with help from [PERSON]'s simplification.  
\[\left\{\begin{array}{c}f_{x}\\ f_{y}\end{array}\right\}=\frac{[(x-2y^{\prime})^{2}+(y+2x^{\prime})^{2}]^{1/2 }}{1-x^{2}-y^{2}}\cdot\left\{\begin{array}{c}3 xV(x,y,\alpha)-\sin\alpha G(x,y,\alpha)-2\cos\alpha S(x,y,\alpha)\\ 3 yV(x,y,\alpha)+\cos\alpha G(x,y,\alpha)-2\sin\alpha S(x,y,\alpha)\end{array}\right\} \tag{5}\]  
where  
\[\begin{array}{c}V(x,y,\alpha)=\frac{2+(y\cos\alpha-x\sin\alpha)G(x,y,\alpha) }{1-x^{2}-y^{2}}\\ S(x,y,\alpha)=\frac{x\cos\alpha+y\sin\alpha}{1-(x\cos\alpha+y\sin\alpha)^{2}} \\ G(x,y,\alpha)=\frac{2}{(1-x^{2}-y^{2})^{1/2}}[\frac{\alpha}{2}+\arctan\frac {y\cos\alpha-x\sin\alpha}{(1-x^{2}-y^{2})^{1/2}}]\\ \alpha=\arctan\frac{y+2x^{\prime}}{x-y^{2}}-\frac{\alpha}{2}sgn(\frac{y+2x^{ \prime}}{x-y^{2}})-\frac{\alpha}{2}sgn(y+2x^{\prime})\end{array}\]  
where subscript \(x\) indicates the vertical component of the oil film force in the radial direction and \(y\) the horizontal component of the oil film force in radial direction. \(x,y,x^{\prime}\) and \(y^{\prime}\) are the general displacements and velocities of corresponding directions.
## 2 Materials and Methods
### Expert-Trajectory-Based Features for AL-IRL for Robotic Manipulation
#### 2.2.1 Expert-Trajectory-Based Feature Space Definition  
The goal is to identify which zones in the state space are most valuable for the expert in order to construct a feature space that focuses on these zones and their surroundings, addressing large continuous spaces.  
To achieve this, using K-means [39], we first cluster the given expert trajectories to obtain \(C\) centers. Next, we compute the mean minimum radius \(r_{m}\) between these centers. We then define the number of layers \(L\), which correspond to the number of distinct zones around the \(C\) centers. The first layer will bound each center at a distance of \(r_{m}\) with a width of \(2r_{m}\), and the subsequent layers will maintain the same dimensions.  
We then generate random points within the bounds of the layers and sample \(K\) points for each \(C\) in each layer. This process yields the total of the feature centers, given by \(F=C+C\cdot K\cdot L\).  
These clustered points form the feature centers \(\varsigma\), which represent the feature space where the expert information is captured by the centers \(C\), while the \(L\cdot K\cdot C\) points represent the surrounding expert information.  
Figure 2 illustrates the process of generating the centers for the feature space, and Figure 3 depicts an example of a feature space based on expert trajectories in a 2D state space, with \(C=2\), \(K=2\), and \(L=2\).  
Once we have the centers, the association between a state and a feature vector is computed using the Gaussian RBF. Given the expert trajectories \(\mathcal{T}_{E}\) and the feature centers \(\varsigma\) with the mean radius \(r_{m}\), we define the distance-based function for feature definition as follows:  
\[\phi(s|\mathcal{T}_{E},\varsigma)=\varphi(s|\mathcal{T}_{E},\varsigma)=\exp \left(-\frac{\|s-\varsigma\|_{2}^{2}}{2r_{m}^{2}}\right) \tag{12}\]
###### Abstract  
Forecasting energy demand is critical to ensure the steady operation of the power system. However, present approaches to estimating power load are still unsatisfactory in terms of accuracy, precision, and efficiency. In this paper, we propose a novel method, named ELFNet, for estimating short-term electricity consumption, based on the deep convolutional neural network model with a double-attention mechanism. The Gramian Angular Field method is utilized to convert electrical load time series into 2D image data for input into the proposed model. The prediction accuracy is greatly improved through the use of a convolutional neural network to extract the intrinsic characteristics from the input data, along with channel attention and spatial attention modules, to enhance the crucial features and suppress the irrelevant ones. The present ELFNet method is compared to several classic deep learning networks across different prediction horizons using publicly available data on real power demands from the Belgian grid firm Elia. The results show that the suggested approach is competitive and effective for short-term power load forecasting.  
electricity load forecasting; deep convolutional neural network; channel attention; spatial attention; Gramian angular field Article
## 1 Introduction
The HJC model was proposed for the computation of concrete subjected to high strain, a high strain rate and high stress, where a set of parameters for normal concrete was given. The RHT model was proposed based on the JH model to make up for the shortcomings of the HJC model, where the third deviatoric stress invariants (J3) are ignored. However, the application of the RHT model is largely restricted owing to the need to determine its 34 parameters. The HJC model is an excellent constitutive model and reflects both a material's viscous and elastic-plastic characteristics. Scholars have used the HJC model to simulate and study the dynamic mechanical properties of rock materials [27; 28], but they have not proposed a set of test methods for determining the parameters of specific rocks. More importantly, the default parameters recommended by the model or the corresponding parameters of concrete of a certain strength grade have been adopted. Therefore, more efforts should be made to propose a set of test methods for determining the parameters of the HJC model for specific rocks. Although scholars have conducted extensive research on the dynamic mechanical properties of rock materials, there are few studies on the cumulative damage and regularity of the failure pattern evolution of sandstone under impact loads. Moreover, the research on the dynamic constitutive model of rock materials lags behind that of the static constitutive model, and the establishment of a dynamic constitutive model is far less abundant than that of rocks under static loads. In the study presented in this paper, the dynamic mechanical properties, damage evolution law and failure pattern of sandstone under the effect of stress waves are studied by using the SHPB test system. On this basis, a set of methods for systematically determining the sandstone parameters of the HJC model is proposed. Finally, the correctness of the parameter values is verified by simulating the SHPB test. The research results can provide theoretical support for the stability evaluation and seismic performance evaluation of the surrounding rock in tunnel excavation, mining and other engineering operations.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 3 System Model
### Sparse Jamming Estimation
#### 3.4.1 Greedy Sparse Jamming Estimation Algorithm
We limited the number of maximum iterations by \(K\), where the cardinality of the support set is known as \(K\). At the same time, we set a lower bound on \(\|\mathbf{\rho}\|\) by \(\delta\). If the prior knowledge is inexact for determining a proper termination condition, the GSJE might not be able to estimate the exact jamming. The assumption that the receiver has exact prior information a priori might be unrealistic in practice as the jammer and the receiver are adversarial to each other. These effects are of interest in the discussion of our simulation results (refer to Section 6.1). ```
Input: H, G, y, \(\delta\), \(K\) Initialize: \(\mathbf{\Lambda}\leftarrow\text{svd}(\mathbf{\mathrm{G}}^{H}\mathbf{\mathrm{H}}^{H})\), \(\mathbf{\rho}\leftarrow\mathbf{\mathrm{A}}\mathbf{\mathrm{y}}\), \(S\leftarrow\{\}\)
1:do:
2: Find the maximum correlated column index, \(k\leftarrow\underset{k\setminus\{\}}{\text{argmax}}<\mathbf{\mathrm{a}}_{k}\), \(\mathbf{\rho}>\)
3: Update the support set by storing the index in the support set, \(\mathbb{S}\ \leftarrow\mathbb{S}\cup\{k\}\)
4: Estimate the corresponding coefficient subvector by \(\hat{\mathbf{\mathrm{e}}}_{\mathbb{S}}\leftarrow(\mathbf{\mathrm{A}}_{\mathbb{S}}^{H} \mathbf{\mathrm{A}}_{\mathbb{S}})^{-1}\mathbf{\mathrm{A}}_{\mathbb{S}}^{H}\mathbf{ \mathrm{A}}\mathbf{\mathrm{y}}\)
5: Update residual by subtracting the contribution of current support set, \(\mathbf{\rho}\ \leftarrow\mathbf{\rho}-\mathbf{\mathrm{A}}\hat{\mathbf{\mathrm{e}}}\)
6:while\(|\mathbb{S}|\leq K\) and \(||\mathbf{\rho}||\geq\delta\)return:\(\hat{\mathbf{\mathrm{e}}}\)
```  
**Algorithm 1** Greedy sparse jamming estimation (GSJE) algorithm
# Motion Deblurring for Single-Pixel Spatial Frequency Domain Imaging
## 4 Discussion
A recent work proposed to binarize the grayscale patterns by the Floyd-Steinberg dithering method and demonstrated this strategy to enable high-speed FSI on a DMD-based imaging system [36]. Although this method is targeted to solve the motion blurring for single-pixel SFDI, it has other application areas. The modulation strategy is not only used in SFDI, but also in other imaging modalities, such as structured illumination microscopy and structured light profilometry [37; 38]. The realization of these imaging modalities based on SPI technique is receiving wide attention. This method is expected to provide an idea for solving the motionblur in these studies. In addition, our method is also applicable to ordinary scenarios without modulation illumination. In comparison with the previously presented motion deblurring schemes, our proposed method requires no measurement of multiple undersampled images for motion estimation or transforming of the illumination patterns for image reconstruction. It enables faster motion estimation and image reconstruction, and therefore has better real-time imaging capability.