esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Candidate Regions Detection to Be Oil Spills  
Once the noise has been eliminated, we employ a method based on a Fourier transform and the concept of residual spectral [37]. This method obtains a saliency map that simplifies the representation of the image without noise (Figure 5). The aim is to identify the pixels that provide a greater level of appearance information. The saliency technique is, therefore, used to locate objects from a non-homogeneous and non-uniform background as a previous step to the segmentation of regions method. The location of regions using segmentation methods based on color or grayscale without carrying out preprocessing beforehand does not provide good results when the brightness values of the pixels in the background change. The saliency map, therefore, provides a pre-processing step with which to perform a meaningful segmentation process that detects the pixels representing the oil spills in the image. In order to compute the saliency map, it is first necessary to calculate the spectral residue of the input image (Figure 5c).  
The spectral residue is denoted as \(R(f)\), where \(f=DFT(I(x,y))\) is the Discrete Fourier Transform of the image \(I(x,y)\). It can also be expressed in polar coordinates in terms of two real functions, amplitude \(\mathrm{A}(f)\) and phase \(\mathrm{P}(f)\), where \(\mathrm{A}(f)=\sqrt{Re\{DFT^{2}(I(x,y))\}+Im\{DFT^{2}(I(x,y))\}}\) is a matrix related to the intensity values of the pixels and \(\mathrm{P}(f)\) is another matrix with information related to the pixel positions. The spectral residue can, therefore, be denoted using \(R(f)\) and is calculated as:  
\[R(f)=(\log(\mathrm{A}(f))-H_{m}\cdot\log(\mathrm{A}(f))) \tag{5}\]  
where \(H_{m}\) is a mean filter that we have chosen with a 3 \(\times\) 3 sized mask. The map saliency of an image \(S(I(x,y))\) is later calculated using the inverse of the spectral residue generated from the Discrete Fourier Transform (DFT) of image \(I(x,y)\) as follows:  
\[S(I(x,y))=DFT^{-1}(e^{R(f)+P(f)})^{2} \tag{6}\]The result of applying the saliency map technique is the generation of several candidate oil-spill regions in the image. Each of the regions detected represents a slick in the SLAR image which could be considered as an oil spill. After obtaining the saliency map, we apply an adaptive thresholding method. We can use any of the methods discussed in [38] to reduce the non-uniformity of intensity in the saliency regions. In particular, Figure 5d shows the result of a binarization using [PERSON]'s method. We consider a neighborhood window for our seed pixel and then we use as threshold the intensity mean of windows. After, we compare the threshold with the rest of pixels which are not black taking into consideration of standard deviation as tolerance value.
# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 3 Proposed Method
In contrast to [27], which employs density functions to determine DBSCAN parameters, GraphDBSCAN utilizes a more efficient KNN-based approach that does not require the estimation of multiple hyperparameters, thereby reducing computational overhead. Additionally, while previous approaches rely on fixed adjacency matrices, GraphDBSCAN integrates proximity-based feature representations to capture more granular community structures, particularly in large-scale, heterogeneous networks. The experimental results demonstrate that GraphDBSCAN consistently outperforms baseline methods, particularly in datasets with complex noise distributions and diverse community structures. The adaptive outlier detection process ensures that the final clustering step is applied to high-quality, denoised data, leading to more meaningful and interpretable community partitions. By combining spatial proximity feature extraction, autoencoder-driven dimensionality reduction, adaptive DBSCAN clustering, and an improved NG algorithm, GraphDBSCAN delivers a comprehensive, scalable, and robust community detection solution. In summary, GraphDBSCAN presents a unified and adaptive framework that addresses key limitations in community detection, including manual parameter selection, unstable edge removal, and computational inefficiencies. The proposed method ensures superior clustering accuracy and robustness, making it a promising alternative for real-world network analysis applications. The following section provides an in-depth analysis of the experimental evaluation, validating the performance of GraphDBSCAN across multiple datasets.
## 4 Analysis Example and Methods
### Numerical Solution and Experimental Methods
#### 4.2.3 Flow Pulsation Excitation  
The calculation and verification are carried out based on a seven pistons swash plate type variable axial piston pump; the instantaneous flow of piston pump could be expressed as:  
\[\left\{\begin{array}{ll}q_{sh}=AR\omega\tan\gamma\frac{\cos(\alpha/2-\varphi )}{2\sin(\alpha/2)}&(0\leq\varphi\leq\alpha)\\ q_{sh}=AR\omega\tan\gamma\frac{\cos(3\alpha/2-\varphi)}{2\sin(\alpha/2)}&( \alpha\leq\varphi\leq 2\alpha)\end{array}\right. \tag{46}\]  
where \(2\alpha\) is the central angle between two adjacent plungers, \(2\alpha=2\pi/z=2\pi/7\).  
The rotational speed of the motor in the pumping plant is 1000 r/min, and the system flow rates are 6.82 L/min and 27.3 L/min, respectively; that is, the fluid velocities in the pipe are 2 m/s and 8 m/s. The flow pulsation of the axial piston pump is shown in Figures 5 and 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Work Conditions**} & **Fluid Pressure (MPa)** & **Flow Velocity (m/s)** & **Flow Rate (L/min)** & **Reynolds Number** & **Flow Form** \\ \hline
1 & 5 & 2 & 6.82 & 967.01 & Laminar \\
2 & 5 & 8 & 27.3 & 3868.02 & Turbulence \\
3 & 10 & 2 & 6.82 & 967.01 & Laminar \\
4 & 10 & 8 & 27.3 & 3868.02 & Turbulence \\ \hline \end{tabular}
\end{table}
Table 2: Four system work conditions.  
Figure 5: Flow pulsation curves of axial piston pump (The flow velocity is 2 m/s). **(a)** Pulsation curve in time domain; **(b)** Pulsation curve in frequency domain.  
According to the discussion about the excitation matrix model in Section 2.2, the first four-order harmonics of flow pulsation are used for calculating and studying. The first four-order spectrum characteristics of flow pulsation are listed in Table 3.  
According to \(\omega=2\pi f\) and \(V=Q/A_{f}\), the parameters of flow velocity can be calculated, as shown in Table 4.  
The right end of the pipe is an opening valve and no external excitation. According to Equation (19), excitation vectors at both ends of the pipe can be expressed as:  
\[\left\{\begin{array}{cccccccc}\mathbf{Q}_{0}=\left[\begin{array}{cccccccc} V_{e}&0&0&0&0&0&0\\ \mathbf{Q}_{L}=\left[\begin{array}{cccccccc}0&0&0&0&0&0&0\\ 0&0&0&0&0&0\end{array}\right]^{\mathrm{T}}\end{array}\right. \tag{47}\]
# Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark
Secondly, the results of the proposed methodology applied to a considerable number of real and synthetic datasets using the Apache Spark MLlib, a machine learning library optimized for distributed systems which provides among others the SVD functionality, are discussed. Several classification models such as decision trees, random forest, and logistic regression, have been investigated and their performance in terms of precision, recall and \(F1\) metric, as the dataset size varies, has been recorded. As a secondary objective, the specifics of the Spark system, along with the PySpark and the SparkQL modules developed in order to perform the two-step classification, are outlined. The principal motivation behind this article is to show the potential of preprocessing in a typical data science pipeline. Additionally, the distributed nature of Apache Spark makes the preprocessing step considerably easier, thus gaining valuable insight into the attribute space with comparatively low complexity. The remainder of this work is structured as follows. Section 2 briefly overviews the relevant scientific literature and various cloud computing methodologies. Section 3 presents the classification algorithms available by MLlib and used in the proposed approach along with the proposed methodology. Section 4 discusses the training steps, the analysis cases as well as the two datasets used. Moreover, Section 5 describes the evaluation experiments conducted and comments on the results. Finally, Section 6 recapitulates the main conclusions and draws directions for future work. Matrices are represented by boldface uppercase and vectors by boldface lowercase, whereas ordinary lowercase are reserved for scalars.
# Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains
## 2 Study Area and Data
### Data
#### 2.2.3 Downscaling Predictors  
The elevation data in this study were obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer GDEM V2 30 m TIFF digital elevation model (DEM) products downloaded from the Geospatial Data Cloud website of the Computer Network Information Center, Chinese Academy of Sciences. Their spatial resolution is up to 1 arcsecond horizontally and 20 m vertically [55]. Slope and aspect data were derived from the DEM.  
The MODerate-resolution Imaging Spectroradiometer (MODIS) Version 6 products LST, ET, VI, and LCT were obtained from the Land Processes Distributed Active Archive Center of NASA. The LST products, MYD11A1, provided daily surface temperature and emissivity with a 1 km resolution. ET was from the evapotranspiration/latent heat flux product MOD16A2, synthesized from 8 days with a 500 m resolution. The VI product MOD13A2 was synthesized for 16 days with a 1 km resolution. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were used. The LCT annual product, MCD12Q1, had a resolution of 500 m. The International Geosphere-Biosphere Programme was used in this study, including 17 types. The variable types, sources, spatiotemporal resolutions, and data units for this study are listed in Table 1.
## 2 System Definition
### System Specifications
The rate of false alerts can be kept low through several techniques: performing the sensing close to the event source, using selective sensing methods less susceptible to interference from unrelated events and using data fusion from multiple sensor types [74]. Maintenance may be needed to periodically clean the nodes to restore their nominal sensing or energy harvesting levels or to replace exhausted batteries. This can be reduced by using maintenance-free transducers and low-energy long-life sensor nodes. Nodes can fail for various reasons, including mechanical stress due to daily temperature variations or extreme climatic conditions. Adequate node hardware design and ruggedization should minimize the effects of long-term environmental exposure. * Deployment cost, which can significantly increase when the monitored area is difficult to access or if deployment errors are discovered later, during network exploitation, requiring additional field expeditions to correct them. Deployment cost depends also on other factors, such as the complexity and time needed for the deployment of the network nodes, the level of specific knowledge expected from the deployment personnel and node size and weight (if they are manually transported in field). Most platform costs can be reduced through optimizations of the design of its components with a holistic view of the full system, as will be shown in Section 3.
## 3 Results and Discussion
### Optimization of the Device by Using OAD
From the ANOVA table, the extent of the impact of the variables on extraction efficiency followed this order: the two-variable interaction A \(\times\) B > variable A > variable C. The effects of the two-variable interaction B \(\times\) C, variable B, and the two-variable interaction A \(\times\) C on extraction efficiency were not significant. Therefore, according to the results from the statistical analysis, we suggest that the better conditions were with (1) the fiber parallel and (2) down to the fan, and (3) the airstream providing positive pressure to the flower and (4) the fan being near the flower. For the method validation, under the optimum condition, the calculated MDL value of the model compound was 0.14 ng on the column, which is equal to 1.41 ppm for the injection. Moreover, the relative standard deviations (RSD) of intra-day and inter-day precisions were 1.21% and 3.05%, respectively. Therefore, the results demonstrated that the performed method comply with the acceptance range. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{6}{c}{**Level**} \\ \cline{2-9}  & \multicolumn{3}{c}{**Low (\(-1\))**} & \multicolumn{3}{c}{**High (\(1\))**} \\ \hline The location of fiber to fan (A) & \multicolumn{3}{c}{Up} & \multicolumn{3}{c}{Down} \\ \hline The airstream (B) & \multicolumn{3}{c}{Positive pressure} & \multicolumn{3}{c}{Negative pressure} \\ \hline The distance of fan from flower (C) & \multicolumn{3}{c}{Low} & \multicolumn{3}{c}{High} \\ \hline Run & A & B & A*B & C & A*C & B*C & – & \(\overline{Y}_{I}\) (\(n\,=\,3\)) \\ \hline \(1\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) & 899,329 \\ \(2\) & \(-1\) & \(-1\) & \(-1\) & \(1\) & \(1\) & 1 & 723,738 \\ \(3\) & \(-1\) & \(1\) & \(1\) & \(-1\) & \(1\) & 1 & 2,147,733 \\ \(4\) & \(-1\) & \(1\) & \(1\) & \(-1\) & \(1\) & \(-1\) & 1,816,269 \\ \(5\) & \(1\) & \(-1\) & \(1\) & \(-1\) & \(1\) & \(-1\) & 1 & 3,103,363 \\ \(6\) & \(1\) & \(-1\) & \(1\) & \(1\) & \(-1\) & \(1\) & 2,416,830 \\ \(7\) & \(1\) & \(1\) & \(-1\) & \(1\) & \(-1\) & \(1\) & 4,411,773 \\ \(8\) & \(1\) & \(1\) & \(-1\) & \(1\) & \(-1\) & \(1\) & 1,585,760 \\ K1 & 5,587,070 & 7,143,260 & 4,620,600 & 7,562,198 & 7,049,652 & 7,404,721 & 6,544,201 \\ K2 & 8,517,725 & 6,961,535 & 9,484,195 & 6,542,597 & 7,055,143 & 6,700,074 & 7,560,593 \\ Range & 2,930,655 & 181,725 & 4,863,596 & 1,019,600 & 5490 & 704,646 & 1,016,392 \\ \hline \hline \end{tabular}  
* K1: the sum of the experiments of the factor in low levels. K2: the sum of the experiments of the factor in high levels. \(\overline{Y}_{I}\) : the average of three repeated experiments.  
\end{table} 
Table 2: The parameters of the second part and levels of the OAD method.
# Detecting Deepfake Voice Using Explainable Deep Learning Techniques
## 4 Methods
### Feature Extraction  
Through the Fourier transform, a complex signal can be sorted into multiple spectra at various frequencies [14]. Such a converted form is used for audio or video analyses. For audio analysis, the amplitude of each frequency of the audio wave is analyzed using a certain window size. The series of spectra are converted into a visualized form: a spectrogram. Using a spectrogram for the analysis, characteristics, such as general waveform and voiced-unvoiced-silenced frames, can be visually inspected by humans.  
Generally, short-term Fourier transform is used to convert a audio signal into a two-dimensional function of time and frequency. The representation can be shown as follows:  
\[\text{STFT}(\tau,\omega)=\int_{-\infty}^{\infty}x(t)w(t-\tau)\exp(-iwt)dt \tag{1}\]  
where \(x(t)\) is the audio signal and \(w(\tau)\) is the window function. For the window function, the Hann window is used [22].  
Specifically, for human voice analysis, a mel spectrogram is used. Humans do not recognize audio in a linear scale; instead, they focus on low frequencies. Considering this characteristics of human perception, voice analyses traditionally use a mel-filterbank to convert a spectrogram into mel-scale. In this study, visual and audio interpretation was provided using spectrogram-based attribution scores in both visual and audio formats.
RS protocols have been analyzed from classical adaptive diversity combining techniques (e.g., a diversity combiner adds diversified branches until the cumulative output is over a threshold [23; 24]). This study also uses RS protocol to propose a new RS strategy enabling for green and secure IoT network. In particular, a friendly jammer is promising as a PLS strategy to improve data safety by adopting jamming signals to poison illegitimate device [26; 27; 28; 29; 30; 31; 32]. In [30], friendly jammer strategies were employed to enhance the secrecy metric to maximize the secrecy outage probability (SOP) for an eavesdropper. In [31], the OP, of the legitimate devices and the intercept probability (IP) of the eavesdropper was obtained to investigate the impact of the jamming signal. The authors proposed an algorithm to pair source and destination usingmatching theory with a particular jammer [32]. In contrast, this study adopts the SWIPT protocol. There are two major SWIPT techniques, i.e., time switching (TS) and power slitting (PS). In [34], the authors investigated the wireless-powered cooperative networks with TS and PS protocols. Moreover, [PERSON] et al. [16] investigated the impact of PA factors on the NOMA network with SWIPT. By different scope, this paper investigates the impact of the PS factor on the system performance of IoT networks. Inspired by the above-mentioned studies, this paper employed the cooperation of a coupled IoT relay, on one hand, to improve OP performance and PLS performance for a green IoT network. To reach these aims, some work was undertaken, which are also the contributions of this paper, such as:  
1. This study designed a green-and-cooperative IoT wireless network, where IoT relays and IoT devices are powered by solar and communicate using RF. 2. To prolong IoT network lifetime, this study adopted SWIPT for EH at coupled relays by applying PS protocol. In particular, the study optimized OP performance of legitimate IoT devices by PS factor optimization in the first-half transmission block time period. 3. In the second-half transmission block time period, the EH at the rest IoT relay intercepts the confidential information being exchanged between legitimate IoT devices. For clarity, the study proposed a selected IoT relay for forwarding signal to legitimate IoT devices using EH while another IoT relay for transmitting jamming signals to illegitimate device using EH as well. In this way, the study reached IP performance at an illegitimate device tending to one. The rest of the present paper is structured as follows: Section 2 sketches and describes the design of IoT wireless communication models, and follows this by making formulations. Section 3 presents an analysis based on the proposed IoT model and presented the algorithms using for RS strategy and OP investigations. Section 4 presents the analysis and Monte Carlo simulation results. Section 5 concludes the achieved results of the study.
## 5 Results and Discussion
### Ablation Study
#### 5.3.1 Ablation 1  
We removed the data augmentation method to get the model (w\(\backslash\)o A), which is only trained using the original dataset. Then, we eliminated the feature fusion strategy to obtain the model (w\(\backslash\)o F), which concatenates the features of the three modalities and performs classification. The experimental results are presented in Table 6. The lack of a data augmentation method leads to a drop in the F1-score (Dropped from 79.0% to 74.8%), proving that our data augmentation method is effective. No feature fusion strategy affects  
\begin{table}
\begin{tabular}{c c|c c c c c c c} \hline \hline \multicolumn{2}{c|}{**Model**} & \multicolumn{1}{c|}{**Modality**} & \multicolumn{1}{c|}{**T**} & \multicolumn{1}{c|}{**A**} & \multicolumn{1}{c}{**V/\(V^{*}\)**} & \multicolumn{1}{c}{**T + A**} & \multicolumn{1}{c}{**T + V/\(V^{*}\)**} & \multicolumn{1}{c}{**A + V/\(V^{*}\)**} \\ \hline \multirow{3}{*}{P} & [PERSON] et al. [9] (V) & 65.1 & 65.9 & 65.2 & 66.6 & 69.6 & 68.1 & 72.5 \\  & SCE [30] (\(V^{*}\)) & 53.4 & 68.0 & 71.4 & 60.4 & 70.4 & 73.2 & 72.3 \\  & Ours (V) & 73.9 & 65.2 & 74.6 & 69.0 & 77.7 & 72.5 & **79.0** \\ \hline \multirow{3}{*}{R} & [PERSON] et al. [9] (V) & 64.6 & 64.6 & 65.2 & 66.2 & 69.6 & 68.1 & 72.5 \\  & SCE [30] (\(V^{*}\)) & 53.4 & 67.2 & 71.2 & 60.5 & 70.3 & 72.0 & 72.3 \\  & Ours (V) & 73.9 & 65.2 & 74.6 & 68.8 & 77.5 & 72.5 & **79.0** \\ \hline \multirow{3}{*}{F1} & [PERSON] et al. [9] (V) & 64.6 & 64.6 & 65.2 & 66.2 & 69.6 & 68.1 & 72.5 \\  & SCE [30] (\(V^{*}\)) & 53.4 & 66.5 & 71.2 & 60.4 & 70.4 & 71.9 & 72.3 \\ \cline{1-1}  & Ours (V) & 73.9 & 65.2 & 74.6 & 68.8 & 77.5 & 72.5 & **79.0** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results on the multimodal sarcasm detection augmented dataset. The best results are shown in bold. P means precision, R means recall, F1 means F1-score, SCE means sequential context encoder, T means text, A means voice, V means face image, and \(V^{*}\) means the whole image.  
the performance of the model (a drop from 79.0% to 74.2%), proving that our feature fusion strategy is more necessary than simply concatenating the features of the three modalities.
# Effects of Velocity and Permeability on Tracer Dispersion in Porous Media
## 4 Conclusions
Using the method of Laplace transform allows the mathematical model for tracer flow at the pore scale to be expressed as follows:  
\[\overline{C}(s)=L[C(t^{\prime})] \tag{15}\]  
\[D\frac{d^{2}\overline{C}}{dx^{2}}-u\frac{d\overline{C}}{dx}-s\overline{C}=0 \tag{16}\]  
\[\overline{C}(0)=\frac{C_{0}}{s} \tag{17}\]  
\[\overline{C}(\infty)=0 \tag{18}\]  
where \(s\) is the Laplace variable. Equation (16) is an ordinary differential equation (ODE) with a general solution as follows:  
\[\overline{C}=c_{1}e^{\lambda_{1}x}+c_{2}e^{\lambda_{2}x} \tag{19}\]  
where \(\lambda_{1}=\frac{u+\sqrt{u^{2}+4D}}{2D}\) and \(\lambda_{2}=\frac{u-\sqrt{u^{2}+4D}}{2D}\). Considering the initial and boundary conditions (Equations (17) and (18)) allows the analytical solution to be obtained in Laplace space. \[\frac{\overline{C}}{C_{0}}=e^{\frac{u\overline{u}}{2D}}\frac{1}{s}e^{-a_{1} \sqrt{b_{1}^{2}+s}} \tag{20}\]  
where \(a_{1}=\frac{x}{\sqrt{D}}\) and \(b_{1}=\sqrt{\frac{u^{2}}{4D}}\). Through an inverse Laplace transform, the analytical solution in the time domain may be expressed as follows:  
\[\frac{C}{C_{0}}=e^{\frac{u\overline{u}}{2D}}L^{-1}\left[\frac{1}{s}e^{-a_{1} \sqrt{b_{1}^{2}+s}}\right]=\frac{1}{2}erfc\left(\frac{x-ut^{\prime}}{2\sqrt{ Dt^{\prime}}}\right)+\frac{1}{2}e^{\frac{u\overline{u}}{D}}erfc\left(\frac{x+ut^{ \prime}}{2\sqrt{ Dt^{\prime}}}\right) \tag{21}\]