esa-sceva/satcom-chunk-collection huggingface dataset:
# Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach
## 3 Robust ENF Estimation Techniques
### Parametric Spectral Estimation Techniques  
The spectrum estimates using parametric methods result in a higher-quality spectrum. It requires less data compared to that of non-parametric methods. However, it is essential that the model parameters be estimated appropriately; otherwise, the estimated modelcould give wrong or misleading estimates. Among the parametric methods, in this paper, we adopted the Multiple Signal Classification (MUSIC) technique based on the subspace analysis of the signal and noise model [57].  
MUSIC is a subspace-based frequency estimation model depending on the eigen-analysis of the observed discrete time signal data. For this algorithm, let the discrete time signal \(v(n)\) of finite length \(L\) with \(K\) sinusoidal components be represented as  
\[v(n)=\sum_{k=1}^{K}A_{k}e^{jna_{k}}+w(n)\]  
where \(A_{k}=|A_{k}|e^{\phi_{k}}\) is the complex magnitude of the \(K\)th harmonic signal component with \(\phi_{k}\) being unknown and assumed to be unknown and uniformly distributed in \([-\pi,\pi]\) and \(w(n)\) is the noise.  
For a data sequence of length \(L=N+M-1\), the auto-correlation matrix \(R_{v}\) of size \(M\times M\) is computed. \(M\) is the dimension spanned by \(v(n)\), and \(K\) is the signal subspace, while the \(N\)-point observed signal,  
\[\tilde{K_{v}}=\frac{1}{N}\mathbf{V}^{H}\mathbf{V}\]  
where \((.)^{H}\) is a Hermitian operator:  
\[\mathbf{V}=\begin{pmatrix}\mathbf{v}^{T}(0)\\ \mathbf{v}^{T}(1)\\ \ldots\\ \mathbf{v}^{T}(N-1)\end{pmatrix}^{T}=\begin{pmatrix}v(0)&v(1)&v(2)&\ldots&v(M -1)\\ v(1)&v(2)&v(3)&\ldots&v(M)\\ \ldots&\ldots&\ldots&\ldots\\ v(N-1)&v(N)&v(N+1)&\ldots&v(N+M-2)\end{pmatrix}_{N\times M}\]  
With eigen-analysis on \(\tilde{K_{v}}\), the eigen vectors corresponding to the \(K\) signal subspace \((U_{s})\) are \(q_{1},q_{2},\ldots,q_{K}\) and the remaining \(q_{K+1},q_{K+2},\ldots,q_{M}\) span the noise subspace \((U_{n})\). Assuming a signal eigenvector \(e\), then it must be orthogonal to the noise subspace eigenvectors \(e\perp q_{i}\) for \(\{q_{i}\}_{i=K+1}^{M}\), where  
\[e(\omega_{l})=[1,e^{i\omega_{l}},e^{j2\omega_{l}},\ldots,e^{i(M-1)\omega_{l}} ]^{T},l=1,2,\ldots,K\]  
The MUSIC algorithm defines a squared norm function:  
\[d^{2}=\|U_{n}^{H}e\|^{2}=\sum_{i=K+1}^{M}|e^{H}q_{i}|^{2}\]  
If the \(e\) vector belongs to the signal subspace, then \(d^{2}=0\) due to the orthogonality condition. The reciprocal of the squared norm will result in sharp peaks at desired signal frequencies.  
\[P_{MUSIC}(e^{j\omega})=\frac{1}{\sum_{i=K+1}^{M}|e^{H}q_{i}|^{2}} \tag{1}\]
## 1 Introduction
This two-stage DC/DC converter topology enjoys the following advantages:  
(1) It makes full use of the parasitic capacitance and inductance that participate in the resonant switching, without adding additional components, in order to achieve soft switching and thereby increase efficiency. (2) The power switches of the push-pull converter and the diodes of the second side of the rectifier can be operated to provide zero current and zero voltage both at the time of switching off and switching on, resulting in increased efficiency and reduced voltage and current limitations. (3) Dual interleaved drivers are used in the buck circuit, so the magnetic reset process does not need to be considered for duty cycles that exceed 50%, and the design of the drive  
Figure 1: The two different topologies of a two-stage converter: (**a**) The buck and push–pull topology, and (**b**) the boost and push–pull topology. transformer is more compact than that of a single-drive method, resulting in a significantly broader range of applications. (4) This topology produces less EMI noise due to the shapes of the current and voltage waveforms, which contain very low magnitudes of fundamental switching frequency interference. (5) The cost is lower as a result of the low-current/voltage limitation (also a result of point 1), and it is simple to implement overload limitation without causing component damage. (6) The control scheme can be simpler than full-bridge or other topologies if the switches in both the buck and push-pull operate on the same frequency. This paper begins with Section 2, which examines operational modes. Section 3 introduces the key parameter utilized in the topology design. Section 4 presents the experimental results from the laboratory prototype. Section 5 compares buck and push-pull converters with other topologies and discusses potential future research enhancements. Section 6 concludes the paper.
# Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation
## 2 Materials and Methods
### Monte Carlo Simulation
#### 2.5.2 Autoregressive Integrated Moving Average (ARIMA)
Given such a scenario, it is crucial for the agricultural sector to be aware and anticipate exchange rate fluctuations and seek alternatives to mitigate the impact of changes in the dollar exchange rate on production costs. The lowest variances and standard deviations were found for the prices of diesel fuel, dollar exchange rate, soybean seed, and corn seed. This indicates that the values of these inputs and the exchange rate did not deviate significantly from the mean during the study period, indicating lower cost risks here. Indeed, the coefficient of variation results indicated that the prices of the following variables: diesel fuel, glyphosate herbicide, dolomitic limestone corrective, NPK 05-25-25 fertilizer, corn bushel, soybean bushel, soybean seed, potassium chloride fertilizer, and urea fertilizer exhibited a high dispersion. On the other hand, the prices of tiamethoxam lambda-cyhalothrin insecticide, dollar exchange rate, corn seed, and casual labor had a moderate dispersion, while the prices of trifloxystrobin tebuconazole fungicide and tractor operator labor were classified as having low dispersion. The highest coefficient of variation was found for the glyphosate herbicide. The literature suggests that in addition to the external factors, internal factors such as market demand, production costs, distribution, and sales of each company, as well as government intervention (such as taxation on imported products), influence the costs and consequently the price formation of this herbicide [41]. The lowest coefficient of variation was found in the price of \"tractor driver\". Although it showed low dispersion over the study period, costs related to tractor drivers' such as wages have shown great relevance in the total production costs of corn and soybeans [42; 43]. A descriptive analysis of the variables used in this study was made using data collected on their prices (in USD). No variable exhibited symmetry in the analyzed prices during the period from 2018 to 2022. Positive skewness distributions were found for diesel oil, fungicide trifloxystrobin tebuconazole, herbicide glyphosate, insecticide thiamethoxam lambda-cyhalothrin, dolomitic limestone corrective, fertilizer NPK 05-25-25, soybean bag price, corn bag price, soybean seed, tractor operator labor, daily laborer labor, potassium chloride fertilizer, and urea fertilizer. This means that the prices of these inputs remained above the average for a significant part of the analyzed period, which can be explained by the factors discussed previously. The US dollar and corn seed were the only variables in which prices exhibited
## 3 Results and Discussions
### Impacts of Mixed Liquid Suspended Solids (MLSS) on the ROUR  
MLSS had buffering capacities for the inhibitory effects of toxic substances, as the ROUR increased with the MLSS contents for both PTEs and phenol (Figure 4). When the MLSS content was 5000 mg/L, the ROUR reached 99.0 \(\pm\) 5.5% and 79.8 \(\pm\) 21.5% for Zn\({}^{2+}\) and Ni\({}^{2+}\), respectively. Phenol had the same trend and the ROUR increased to 66.6 \(\pm\) 12.7% at 5000 mg/L of MLSS. Accordingly, the ROUR-reciprocal was positively correlated with MLSS-reciprocal, as expressed in Equation (4).  
\[\text{ROUR}^{-1}\propto[\text{MLSS}]^{-1} \tag{4}\]  
As the carrier of microbes in the activated sludge, MLSS content represents the amount of microbial biomass [66]. At higher MLSS or biomass, microbes tend to form aggregates and improve their resistance to toxic substances by decreasing the surface area of single microbial cells directly exposure to toxins [67]. In addition, microbes are capable of excreting extracellular polymeric substances (EPS), which are involved in the detoxification process [68, 69]. Accordingly, higher MLSS can improve the resistance of the activated sludge to the shock load from toxins in industrial wastewater, designated as the buffering effect.  
Figure 3: Dose-effects of Zn\({}^{2+}\) (**a**), Ni\({}^{2+}\) (**b**), and phenol (**c**) on the relative oxygen uptake rate (ROUR).
# Wheeled Permanent Magnet Climbing Robot for Weld Defect Detection on Hydraulic Steel Gates
## 2 Mobile Robot System  
### Robot Control System Setup  
The overall design of the climbing robot control system is illustrated in Figure 1 and is divided into two main components: the robot body and the control cabinet. The robot body consists of several drive modules and additional input/output (I/O) devices. To enhance the stability of the robot and load-bearing capacity during climbing operations, a six-wheel independent drive configuration is employed. Among these, the two drive wheels located at the center of the robot are equipped with independent suspension systems to improve obstacle negotiation capabilities. The robot body is powered by the control cabinet, which also issues control commands to the robot.  
The control cabinet is mainly composed of three parts: the motion controller, the main control PC, and the user interface device. Depending on operational requirements, the user interface can be configured with an industrial touchscreen or a control handle. Unlike smaller robots that typically rely on battery power, this robot is powered via cables, thereby significantly increasing its load-carrying capacity. To minimize the wiring complexity between each servo drive module and the controller, as shown in Figure 2, and to improve system scalability, the climbing robot control system adopts EtherCAT bus architecture. The motion controller acts as the master of the bus system, while the servo drives and other I/O modules function as slaves, communicating with the master via EtherCAT cables.  
The slave stations, such as servo drive modules, are housed within the robot body. The wiring between the climbing robot body and the control cabinet is simplified to just a single Category 5e Ethernet cable and a set of DC power cables. If additional axes or functionalities need to be integrated, new slave stations can be connected to the existing  
Figure 1: Robot control system setup.  
Figure 2: Servo drive module.  
network by linking them to the last slave station via a network cable and configuring the relevant parameters. This design greatly facilitates the system's functional expansion.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## Materials
### Remote Sensing Data
#### 2.3.2 Sentinel-2 Optical Data  
Currently, S2 offers fine spectral and spatial resolution optical images with 13 bands ranging from 10 m to 60 m spatial resolution with 5 days revisit time. Seventeen free optical images were obtained over our study site during the period between August 2017 and December 2018 at a frequency of one image per month. The S2 images were downloaded from the Theia (French land data center) website ([[https://www.theia-land.fr/](https://www.theia-land.fr/)]([https://www.theia-land.fr/](https://www.theia-land.fr/))). The Theia website provides ortho-rectified S2 images at level-2A corrected for atmospheric effect. However, starting from April 2018, Theia website started providing a monthly synthesis of cloud free images (level-3A). These images were used for the period between April 2018 and December 2018.
# Cycle Consistent Generative Motion Artifact Correction in Coronary Computed Tomography Angiography
## 3 Methodology
### Extracting Corresponding Coronary Patches from 4D CT  
The 4D CT data is captured using retrospective gating with a dual-source CT scanner. The data is systematically reconstructed at 10% intervals across the heartbeat cycle, highlighting phases at 40% and 70% for their minimal motion disturbance in the coronary artery, while other phases are often plagued by significant motion artifacts. We base this process on that of [PERSON] et al. [14].  
Focusing on the highly motion-sensitive midsection of the right coronary artery (RCA), an experienced radiologist manually annotates this region on each temporally sampled 3D CT volume. This annotation, done using the commercial coronary analysis software (QAngioCT, Medis Medical Imaging Systems, Leiden, the Netherlands), spans from the first right ventricle branch to the acute marginal branch.  
Adhering to a process parallel to our prior studies, we discretize the mid-RCA centerline at each phase \(\phi\) into a set of ordered 3D coordinates, forming a linear approximation between these points. The total length of the mid-RCA centerline is calculated by summing the distances between adjacent point pairs. For extracting patches along the centerline, we first establish corresponding points aligned with the same anatomical landmarks across different phases. A fixed number of equidistant points are sampled along the centerline, with interpolation to precisely determine their 3D coordinates. Planar patches, centered at these points, have their normals defined by the tangent to the centerline at each respective point. Figure 3 illustrates this process, highlighting the alignment of corresponding points across various temporal phases of the 3D CT volumes.  
The corresponding patches are then extracted by sampling the voxel intensities within the 3D CT volume on a grid, centered and aligned at each equidistant point along the mid-RCA. This approach ensures consistency in the extracted patches across different phases, which is crucial for effective motion artifact correction in subsequent steps.  
Figure 2: Workflow of the proposed method. Step 1, generate synthetic motion-corrected patch (SynGT) using style transfer method. Step 2, train the cycle consistency adversarial network(CycleGAN) using SynGT. Detailed descriptions of steps 1 and 2 are given in Sections 3.2 and 3.3, respectively.
# Changes in Collagen across Pork Tenderloin during Marination with Rosehip Nanocapsules
## 3 Results
### Histological Test and Collagen Quantification
Acid marination also accelerates the proteolysis reaction caused by cathepsins, initiates the weakening of muscle structure due to meat swelling, and causes an increase in the conversion of collagen to gelatin at low pH levels during cooking [8; 9; 44]. Early studies demonstrated that collagen's solubility is not affected by the temperature and time of conditioning [5]. Later, it was reported that during refrigerated storage, endogenous enzymes in muscle, such as collagenase, could promote collagen degradation or transform insoluble collagen into soluble collagen and reduce the total collagen content. Soluble collagen can be dissolved in water and come out of the muscle as the water-holding capacity of the muscle decreases, causing a decrease in the soluble collagen content [36]. These changes in collagen with thermal properties can be attributed to peptide bond hydrolysis and the low breakage of covalent cross-links in collagen [4; 7]. Figure 6 shows the collagen area percentage quantified transversally. When the NC-RH were applied, the highest percentage of collagen was obtained (28.63 \(\pm\) 4.6% area of collagen) at pH 6.5 and 25 \({}^{\circ}\)C. However, this increment could have the opposite effect on tenderness, making the meat harder. At pH 4 and 4 \({}^{\circ}\)C, there was an 11.47 \(\pm\) 2.2% area of collagen, representing a better quantity of collagen. It was demonstrated that the transverse breaking stress of meat, which reflects connective tissue strength, decreases in pH, and cannot be explained only by swelling. This suggests the significant weakening of connective tissues at low pH levels [7].
# Fractional Order Processing of Satellite Images
## 2 Definitions and Formulations
### Edge Detection Operators
\begin{bmatrix}0&1&0\\ 1&-4&1\\ 0&1&0\end{bmatrix} \quad \begin{bmatrix}1&1&1\\ 1&-8&1\\ 1&1&1\end{bmatrix} \quad \begin{bmatrix}-1&2&-1\\ 2&-4&2\\ -1&2&-1\end{bmatrix} \] (10) To tackle the sensitivity of second order derivatives to noise, in the LoG the image is Gaussian smoothed before applying the Laplacian filter reducing high frequency noise. The smoothing filter can also be convolved first with the Laplacian kernel and only then is the result convolved with the input image. In the fractional case [14],  
\[\left[ \begin{array}{cccccc}0&\cdots&0&(-1)^{k}C^{\alpha}_{K-1}&0\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ 0&\cdots&0&\left(\alpha-\alpha^{2}\right)/2&0\\ (-1)^{k}C^{\alpha}_{K-1}&\cdots&\left(\alpha-\alpha^{2}\right)/2&2\alpha&-1\\ 0&\cdots&0&-1&0\end{array} \right] \tag{11}\]  
For color images [25], a pixel is considered as part of an edge if zero-crossings are found in any of the color channels. Using this definition, an algorithm that detects edges in color images was developed. The algorithm convolves each channel of the input color image with the LoG mask in (11). Then, a search for zero-crossings in the results for each channel is performed. If any zero-crossings are found, the corresponding pixels are flagged as part of an edge. * **CRONE. ** The CRONE operator [10] uses the coefficients of (1): \[a_{k}=(-1)^{k}\left( \begin{array}{c}n\\ k\end{array} \right)=(-1)^{k}\,\frac{n(n-1)\cdots(n-k+1)}{k! }\] (12) In order to detect edges on images, the formulated detector can be used in two dimensions with mask \[\left[ \begin{array}{cccccc}+a_{m}&\cdots&+a_{1}&0&-a_{1}&\cdots&a_{m}\end{array} \right]\] (13) and its transpose. Color is handled as for the Canny operator. * **Fractional Derivative Operator. ** Fractional mask (9) from the Roberts operator was used in this paper by itself as a fractional edge detector. For color images, the Jacobian is reduced to a vector \[\mathbf{J}=\left( \begin{array}{c}R\\ G\\ B\end{array} \right)\] (14) and the magnitude of the gradients is given by \[\|\
abla f\|=\sqrt{R^{2}+G^{2}+B^{2}}\] (15)
# Analytical Investigation of Viscoelastic Stagnation-Point Flows with Regard to Their Singularity
## 3 Analytical Solutions of a Wall-Free Stagnation-Point Flow and Their Singularities
### Case \(k_{2}=0\)
#### 3.1.3 Case \(k_{1}+1=0\)  
Analogous to the last two subsections, the solutions in this case are distinguished for the following cases.  
\[\text{(i)}\quad\text{If }W_{1}=\frac{1}{2}\text{:}\] \[T_{11} =x^{-2}y^{2}F^{\prime\prime}-4x^{-3}yF^{\prime}+6x^{-4}F+6C_{1}x^{- 4}+C_{2}y^{4}-\frac{k_{3}}{2}+1,\] \[T_{22} =2(k_{3}+2)\ln y+F^{\prime\prime}+C_{3}\ln(xy)+C_{4}, \tag{49}\] \[T_{12} =x^{-1}yF^{\prime\prime}-x^{-2}F^{\prime}.\]  
where the logarithmic singularity caused by \(\ln y\) in \(T_{22}\) could only be avoided with the extra restriction \(k_{3}+2=0\). However, for the Oldroyd model there is \(k_{3}=2(1-\beta)\) at \(W_{1}=\frac{1}{2}\) with \(<0\beta<1\) and for the Maxwell model \(k_{3}=0\), so that logarithmic singularity is non-removable. The similar conclusion was also drawn by [11] for a Maxwell fluid.  
\[\text{(ii)}\quad\text{For }W_{1}\
eq\frac{1}{2}\text{:}\] \[T_{11} =C_{1}x^{-\frac{2}{W_{1}}}+C_{2}y^{\frac{2}{W_{1}}}-\frac{k_{3}}{ 2}+1,\] \[T_{22} =\frac{k_{3}+2}{W_{1}}\ln y+C_{3}\ln(xy)+C_{4}, \tag{50}\] \[T_{12} =0.\]  
Similar to the last cases, the singularity in \(T_{11}\) and \(T_{22}\) can only be avoided at \(k_{3}=-2\) with \(C_{i}=0\) (\(i=1,2,3\)). This again corresponds to a uniform stress field and thus may be physically disputable.
## 2 Materials and Methods
### Model Configuration and Simulation Setup
#### 2.2.3 Model Calibration  
In order to localize the model, we corrected some ecophysiological and allometric parameters of the model (Table 2). In the ED-2.2 model, specific leaf area (SLA) is a more sensitive parameter for plant functionality [72; 73], and we modified the SLA of PFT2, PFT4, and PFT7 to replace the default parameters of the model according to the existing studies from [PERSON] et al. (2009) [74] and [PERSON] et al. (2022) [65], in which the parameters used are from the TRY global plant functional trait databases [75; 76]. According to the existing research [65], the allometric growth equation of PFT7 was modified. We also modified the DBH-height allometric equation (i.e., a modified Weibull function, Equation (1)) of PFT2 and PFT4 in the ED-2.2 model [48] by using forest inventory data (Table 1):  
\[\mathrm{Height}=\mathrm{H}_{\mathrm{ref}}\cdot\left(1-\exp\left(-\mathrm{b1 Ht }\cdot\mathrm{DBHb^{2 Ht}}\right)\right) \tag{1}\]  
where Height is tree height (m); DBH is diameter at breast height (cm); H\({}_{\mathrm{ref}}\) was reference height [61.7, m]; b1 Ht [m] was allometry intercept; b2 Ht [cm\({}^{-1}\)] was allometry slope.  
After calibration, the RMSE and MAE of the corrected DBH-height allometry for PFT2 decreased by 34.7% and 57.4% compared with the default allometry, respectively. The RMSE and MAE of the corrected DBH-height allometry for PFT4 decreased by 35.8% and 58.8% compared with the default allometry, respectively (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Plant Functional Type** & **SLA [m\({}^{2}\) kgC\({}^{-1}\)]** & **b1 Ht [m]** & **b2 Ht [cm\({}^{-1}\)]** \\ \hline PFT2 & 24.5 & 0.026 & 0.715 \\ PFT4 & 36.6 & 0.026 & 0.717 \\ PFT7 & 7.4 & 51.037 & \(-\)0.014 \\ \hline \hline \end{tabular}  
* SLA: specific leaf area; b1 Ht: height allometric intercept; b2 Ht: height allometric slope.  
\end{table}
Table 2: Ecophysiological and allometric parameters for the plant functional types.