esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Method
### Mesh Sensitivity Study and Validation  
To reduce computational cost and guarantee precision, we conducted a mesh sensitivity study at the incident wind velocity of 6.7 m/s. By refining the mesh on the surfaces of the model and the far field, three different meshes were obtained, including coarse, medium and fine meshes with the cell number of 2.86, 3.79 and 6.97 million, respectively. The results of the average velocity in the Venturi tube for different grids are shown in Figure 6. When the mesh number exceeded 3.79 million, the results remained basically unchanged. At the same time, it can be seen that the calculation results of this paper are in good agreement with those of the references [20,23], and the maximum relative error is only 2.5%. Furthermore, the velocity variations on a line, which is at the Venturi section, is plotted in Figure 7. As the figure shows, the medium mesh can be selected as the best mesh resolution because further increasing the cell count has no considerable changes in the numerical results and it only increases the computational cost.  
Figure 5: Computational domain and boundary conditions.  
In order to evaluate the convergence of the mesh sensitivity study, the grid convergence index (GCI) [27] is used. The GCI can be determined by  
\[\text{GCI}=\frac{\delta}{r^{e}-1} \tag{7}\]  
where  
\[\delta=\left|\frac{\varphi_{fine}-\varphi_{medium}}{\varphi_{fine}}\right| \tag{8}\]  
\[r=\left(\frac{N_{fine}}{N_{medium}}\right)^{1/3} \tag{9}\]  
\[e=\frac{\ln\left[\frac{\varphi_{medium}-\varphi_{coarse}}{\varphi_{fine}- \varphi_{medium}}\right]}{\ln(r)} \tag{10}\]  
where \(\varphi\) and \(N\) are the average flow velocity in the Venturi tube and the grid number, respectively. The GCI values for this study are given in Table 1. The values of the GCI are less than 0.4% for medium and fine meshes, which confirms that the numerical results are independent of the grid. Therefore, the mesh with 3.79 million grid cells was selected as the computational mesh for the subsequent simulation.  
Figure 6: Grid study and validation of the numerical results.  
Figure 7: Velocity variations on a line at the Venturi section for three mesh resolutions.
## 3 Results and Analysis
### Influence of Hydration on Mechanical Properties of Lamellar Shale
As shown in Figure 8, with the increase in the hydration time, the elastic modulus of the lamellar shale sample gradually decreases, and the rate of decrease gradually reduces too. For example, for the sample with \(\beta\) = 30\({}^{\circ}\), the elastic modulus of the sample after 24-h hydration is 11.9% lower than that of the dry sample, and the elastic modulus of the sample after 48-h hydration is 5.1% lower than that of the sample after 24-h hydration. In addition, the elastic modulus of the samples with different \(\beta\) is affected differently by hydration. For example, after 48-h hydration, the elastic modulus of the sample with \(\beta\) = 30\({}^{\circ}\) decreases by 16.3% compared with that of the dry sample, while the elastic modulus of the samples with \(\beta\) = 0\({}^{\circ}\) and 90\({}^{\circ}\) decreases by 13.5% and 11.7%, respectively. It appears that the most significant changes in the elastic modulus are affected by hydration for the sample for which \(\beta\) is closer to 30\({}^{\circ}\). These findings are similar to those obtained for bedding shale [32]. This may be due to the high clay minerals content in the lamellar shale oil reservoir of the Ordos Basin and the existence of a large amount of berry pyrite wrapped in clay minerals [38]. Therefore, the mechanical properties are significantly reduced after hydration. In particular, the sample with \(\beta\) = 30\({}^{\circ}\) is more likely to be destroyed at the lamina plane, and the elastic modulus of it decreases more significantly than that for the samples with other \(\beta\). Figure 8: Elastic modulus of the lamellar shale samples under confining pressure of 15 MPa changed with hydration time. Figure 7: Compressive strength (deviatoric stress) of the lamellar shale samples under confining pressure of 15 MPa changed with hydration time.
## 2 Materials and Methods
### Numerical Details  
_Grid_  
The semi-implicit method for pressure-linked equations (SIMPLE) algorithm [17] is used for the pressure-velocity coupling. The convection terms of the momentum and radiation equations are discretized using a second order upwind (SOU) scheme [20], while central differences are used for the diffusion terms. For the convection terms of turbulence model and energy equations, the quadratic upstream interpolation for convective kinematics (QUICK) [21] and monotonic upstream-centered scheme for conversation laws (MUSCL) [22] are used, respectively, while for the temporal discretization, a second order scheme is used. The convergence criterion has been set to \(10^{-5}\) for the continuity, momentum, and turbulence equations, to \(10^{-8}\) for the energy equation, and to \(10^{-6}\) for the radiation equations. The radiation transport equations for the DO model are solved for four wavelength bands \(\lambda\) = 0.1-0.4-0.76-1.4-1000 \(\upmu\)m. The computational domain is discretized using 19,372 cells, while care has been taken in order for the dimensionless wall distance y+ of the first node to take values between 30 and 40. The time step has been set to 60 s. The simulations' executions begin from at sunrise and finish at the sunset of every examined day, and the solution initialization is obtained by a steady-state solution field.
## 2 Confocal Ellipsoidal Densification Model (CEDM)  
### Description of CEDM  
In this study, the improvement range of soil foundation under DC was considered to be approximately ellipsoidal, and its vertical section was described using a confocal ellipsoidal densification model (CEDM), as shown in Figure 1. The proposed CEDM was established on three basic assumptions, as follows:  
**Assumption 1**: _The improvement range of soil foundation induced by DC is divided into two ellipsoidal zones: a heavy compacted zone (HCZ) and a weak compacted zone (WCZ). Considering that the densification zones around the tamping point typically exhibit smooth, continuous transitions in soil properties and behaviors, HCZ and WCZ ellipses are assumed to share a common focus with  
Figure 1: Confocal ellipsoidal densification model of improvement range induced by DC.  
reference to cavity expansion theory. This allows for convenient coordinate transformations in polar coordinates, facilitating closed-form or semi-analytical solutions for densification patterns._  
**Assumption 2**.: _After tamping, the soil in HCZ is assumed to reach its maximum dry density or unit weight (\(\gamma_{\max}\)) due to the high energy concentration and intense particle rearrangement caused by DC. As the distance from the tamping point increases, the compaction effect gradually decreases due to energy dissipation, extending to the outer boundary of WCZ with initial dry density or unit weight (\(\gamma_{0}\)). Considering that a linear function allows for simplified integration in volume-average calculations, the soil dry density or unit weight in WCZ is assumed to decrease linearly from \(\gamma_{\max}\) at the HCZ edge (e.g., point \(P_{\rm H}^{i}\)) to the initial dry density \(\gamma_{0}\) at the WCZ edge (e.g., point \(P_{\rm W}^{i}\)), as shown in Figure 1. In addition, the extended line of point \(P_{\rm W}^{i}\) and point \(P_{\rm W}^{i}\) is assumed to pass through the common focus of the HCZ and WCZ ellipses._  
**Assumption 3**.: _The soil in the range of HCZ and WCZ before and after DC satisfies the mass balance equation, which considers changes in soil dry density in different compacted zones._
## 2 Materials and Methods  
### Initial Considerations  
Performing uniaxial tensile tests on long and slender biological tissues presents challenges, as traditional clamping techniques often lead to premature failure due to excessive strain in the clamping region. To mitigate this issue, a new clamping system was developed (Figure 1). This capstan clamping mechanism features a contoured apparatus that reduces the required clamping force by wrapping the specimen at the clamping site along a curved surface. This gradual distribution of tensile force helps minimize stress in the clamping region, reducing the likelihood of premature failure.  
In order to compare traditional and capstan clamps, their respective force relationships are first analyzed theoretically by considering the Euler-Eytelwein equation. For a mathematical comparison of the normal forces necessary for clamping, it is assumed that the tensile forces \(\mathrm{F}_{Z}\) are equal for both clamping setups. In a traditional clamp, the tensile force \(\mathrm{F}_{Z}\) results from friction during clamping and is given by:  
\[\mathrm{F}_{Z}=\upmu\cdot\mathrm{F}_{\mathrm{NT}} \tag{1}\]  
where \(\upmu\) is the coefficient of friction and \(\mathrm{F}_{\mathrm{NT}}\) is the normal force at a traditional clamp applied during clamping.  
In the case of a capstan clamp, the force redirection around a curved surface follows the Euler-Eytelwein equation:  
\[\mathrm{F}_{Z}=\mathrm{F}_{\mathrm{C}}\cdot\mathrm{e}^{\upmu\cdot\varphi} \tag{2}\]  
where \(\varphi\) is the wrap angle (in radians) and \(\mathrm{F}_{\mathrm{C}}\) is a reduced force by rope friction, in this estimation named capstan force.  
The capstan force \(\mathrm{F}_{\mathrm{C}}\) can be expressed by normal force \(\mathrm{F}_{\mathrm{NC}}\) and the friction coefficient:  
Figure 1: Comparison of traditional and capstan clamping setups. This schematic highlights the respective forces involved within each configuration.  
\[\mathrm{F_{C}}=\upmu\cdot\mathrm{F_{NC}}. \tag{3}\]  
Through combination of Equations (2) and (3), the transmitted force of a capstan clamp can be expressed as follows:  
\[\mathrm{F_{Z}}=\upmu\cdot\mathrm{F_{NC}}\cdot\mathrm{e^{\upmu\cdot\upvarphi}} \tag{4}\]  
Rearranging for the normal force \(\mathrm{F_{NC}}\) in the capstan setup yields:  
\[\mathrm{F_{NC}}=\frac{\mathrm{F_{Z}}\cdot\mathrm{e^{-\upmu\cdot\upvarphi}}}{\upmu} \tag{5}\]  
Finally, both clamping methods can be compared using the ratio of their normal forces from Equations (1) and (5):  
\[\frac{\mathrm{F_{NC}}}{\mathrm{F_{NT}}}=\mathrm{e^{-\upmu\upvarphi}} \tag{6}\]  
This ratio estimates the reduction in normal force by the capstan clamp compared to traditional clamps. Using a curvature of 180\({}^{\circ}\) (which means \(\varphi=\uppi\) and typical friction coefficients \(\upmu\) from the literature for 3D-printed materials like ABS (\(\upmu\) = 0.05) [22], PLA (\(\upmu\) = 0.15) [23], and PETG (\(\upmu\) = 0.44) [24]), clamping force reductions to different extents can be achieved, e.g., 15% for ABS, 38% for PLA, and 75% for PETG.
## 2 Materials and Methods
### Phenological Modeling Based on Vegetation Indices from Sentinel-2 Images  
The extraction of phenological metrics from vegetation index time-series data was conducted using the phenofit package [55] in R v4.5.0 [56], which integrated curve fitting, smoothing, and quality assessment of seasonal dynamics. This analysis was performed at the individual yield sample point level, with each sample processed independently to account for spatial variability in crop growth patterns. For each vegetation index, a separate phenological curve was fitted to ensure comprehensive characterization of crop development stages. The Beck logistic model was selected as the primary curve-fitting algorithm due to its demonstrated effectiveness in smoothing noise inherent in remote sensing data while accurately capturing critical phenological transitions [57]. The curve-fitting process involved optimizing model parameters to best represent the observed VI temporal patterns while maintaining biological validity [58]. Curve fit diagnostics were performed using the coefficient of determination, the Nash-Sutcliffe model efficiency coefficient, and the observed and simulated coefficients of variation, which were used for the weighting of vegetation indices during the calculation of transition dates [59].  
Seven key phenological transition dates were derived from the fitted curves, following established protocols in vegetation phenology research: start of season (SOS), greenup, maturity, peak of season (POS), senescence, dormancy and end of season (EOS). Each transition point was defined based on specific characteristics of the fitted logistic curve and its ecological significance in crop growth cycles, as explained in [26]. For each derived phenological metric, both the day of year (DOY) and corresponding vegetation index value were retained as covariates for yield potential prediction. This dual representation allowed for the investigation of both temporal (growth stage timing) and physiological (vegetation status at key stages) influences on crop productivity.
# A Predictive Model Using Long Short-Time Memory (LSTM) Technique for Power System Voltage Stability
## 1 Introduction
Over the years, LSTM has become a popular choice for addressing various real-world problems due to its efficiency in handling both linear and non-linear data patterns. Figure 1: Architecture of modern electric power systems. This article proposes the long short-term memory (LSTM) system to forecast the VS of a 33-bus power network. The LSTM technique was evaluated by comparing its performance in forecasting VS with other methods, including support vector machine (SVM), Naive Bayes (NB), and convolutional neural network (CNN). The major contributions of this paper are as follows:  
1. [label=.] 2. Designing a model that can predict the electrical grid voltage stability by combining deep learning and machine learning techniques. The proposed LSTM technique for VS assessment outperforms conventional models that rely on shallow machine learning methods, such as SVM, NB and CNN, in terms of accuracy and response time in simulations run on the IEEE 33-bus scheme. 3. Validating that the LSTM-based voltage stability evaluation method is more accurate and has a faster response time compared to conventional techniques based on traditional machine learning, such as artificial neural networks (ANNs), CNN, SVM, and decision trees (DTs), as demonstrated by the simulation outcomes of the IEEE 33-bus scheme. 4. Enhancing the performance of each model through hyper-tuning of the parameters. The remaining parts of this research are organized as follows: Section 2 describes the methods used to resolve the VS assessment issue in the electrical power system. Section 3 presents the analysis and discussion of the findings. Section 4 concludes with a summary of the findings and proposals for future research.
# Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique
## 2 Materials and Methods
### KPCA-PSO-ELM Combination Modeling Process  
This paper selects KPCA for reducing the feature dimensionality. It utilizes neural networks to solve nonlinear problems. PSO is applied to optimize the ELM calculation for predicting the external corrosion rate of buried pipelines. The detailed steps of the KPCA-PSO-ELM algorithm are presented in Figure 4.  
Figure 4: Flowchart of the KPCA-PSO-ELM model.  
Step 1: Pretreat the raw data and introduce it into the KPCA algorithm for data dimensionality reduction.  
Step 2: Conduct random division of the training and test sets in proportion to the data normalization process.  
Step 3: Construct the ELM prediction model network structure.  
Step 4: Set the particle population and the number of particles.  
Step 5: Limit the maximum number of iterations, and preset learning factors \(c_{1}\) and \(c_{2}\).  
Step 6: Calculate the fitness value of the particles.  
Step 7: Determine the number of runs corresponding to when the required error is reached.  
Stop running when the optimal accuracy is reached, and go to Step 9 or return to Step 8.  
Continue running until the requirement is met.  
Step 8: Update the particle velocity and position to execute the iteration.  
Step 9: Bring the globally optimal weights and thresholds into the ELM prediction model for training.  
Step 10: Predict the corrosion rate test set.
# Direct Spread Spectrum Technology for Data Hiding in Audio +
###### Abstract  
Direct spread spectrum technology is traditionally used in radio communication systems with multiple access, for example, in CDMA standards, in global satellite navigation systems, in Wi-Fi network wireless protocols, etc. It ensures high security and reliability of information transfer. In addition, spread spectrum technology provides the transmitted signals with a noise-like appearance, thus hiding the semantic content of the messages. We researched this technology for other implementations. The purpose of our study was to investigate new technologies for hiding data in multimedia files. In particular, we investigated the direct spread spectrum in the context of the development of methods for hiding data in audio containers. We considered various spreading sequences (chip codes) and also explored their use for hiding information in audio files. We conducted experimental studies and estimated the bit error rate (BER) in the recovered data. The article also evaluates the distortion of an audio container by the peak signal-to-noise ratio (PSNR). The results of our research enable us to find out which method of forming chip codes gives a lower BER with equal PSNR. We provide recommendations on the formation of spreading sequences to reliably and safely hide informational messages in audio files.  
spread spectrum technology; data hiding; steganography; chip codes; audio Article
# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 4 Experiment Setup
### Data Generation and Model Training Parameters  
In the data generation process, we considered a scenario where WLAN signals generated by STAs in a time-triggered WLAN environment are received by AP under interference from three types of CTI signals: Bluetooth, ZigBee, and microwave oven signals. The frequency band is 2.4 GHz, and the sampling rate is 20 MHz. The waveforms of the WLAN signals were generated using the MATLAB WLAN Toolbox, while the waveforms of Bluetooth and ZigBee signals were generated using the MATLAB Bluetooth Toolbox and Communication Toolbox, respectively. The microwave oven waveform was generated by referring to [37]. Each waveform was sampled according to various parameter settings supported by the standards, as described in Table 3. The diversity in the generation of WLAN signal's standards adds to the difficulty of CTI signal identification.  
Depending on each wireless technology's access pattern, the CTI waveforms interfere with the WLAN waveforms, and the WLAN waveforms with CTI waveforms are collected over a duration of \(T_{\text{scan}}\) to generate baseband raw signal samples. \(T_{\text{scan}}\) was set to 1 ms. We collected I/Q samples with SNRs ranging from 0 dB to 20 dB. The sliding window size of the STFT was set to 256. The STFT was converted into three image sizes, 32 \(\times\) 32, 64 \(\times\) 64, and 128 \(\times\) 128. A total of 500,000 data samples were generated and divided into training, testing, and validation sets in a ratio of 80%, 10%, and 10%, respectively.  
For training CTINet, we employed various depth multipliers (\(\alpha\)) of 1.0, 0.75, 0.5, and 0.25 to adjust the network's capacity and complexity. The models were trained using the Adam optimizer with a learning rate of 0.001, for 100 epochs, and a batch size of 64.
# Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series
## 1 Introduction
To our knowledge, there are no products available that map paddy rice area in mainland China using MODIS data at 250-m resolution and a concise classification method. Therefore, the objectives of this study are to:  
* develop a method to classify paddy rice areas in the entirety of mainland China based on MODIS-derived time series using a one-class classifier;
* create maps of paddy rice areas in mainland China for the years 2002, 2005, 2010 and 2014 and validate their accuracy with reference datasets;
* analyze changes in China's paddy rice area from 2002-2014.
## 4 Materials and Methods
### Volatile Component Analysis  
Volatile Compounds from _L. cubeba_ Fruits:  
Three biological replicates from the fruit of _L. cubeba_ plants (3.0 g) were ground into a uniform powder under liquid nitrogen with a mortar and pestle. The 0.5 g powder was placed in a 20-mL solid-phase microextraction (SPME) vial containing 50-mL aliquot of freshly prepared ethyl decanoate (802180, Merck, Darmstadt, Germany) solution as an internal standard, then sealed and placed in 4 \({}^{\circ}\)C until used [39]. Analytical procedures were performed on an Agilent 8890-5977B (Agilent Technologies, La Jolla, CA, USA) GC-MS system and comprised three sequential phases--HS-SPME (Headspace Solid-Phase Microextraction) pre-treatment involving 1 min of equilibration at 50 \({}^{\circ}\)C via the CTC system; 50 min of combined extraction/adsorption; and desorption for 5 min at 250 \({}^{\circ}\)C in the GC-MS injection port.  
Gas chromatography (GC) conditions: DB-5 MS (60 m \(\times\) 0.25 mm ID \(\times\) 0.25 \(\upmu\)m, Agilent) chromatographic column was used, with the injector temperature set to 50 \({}^{\circ}\)C for 2 min, ramped up to 80 \({}^{\circ}\)C at 3\({}^{\circ}\)C/min (2 min hold), increased at 5 \({}^{\circ}\)C/min to 180 \({}^{\circ}\)C (1 min hold), followed by a 10\({}^{\circ}\)C/min rise to 230 \({}^{\circ}\)C (5 min hold), then heated at 20 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C (3 min hold), and finally elevated at 40 \({}^{\circ}\)C/min to 280 \({}^{\circ}\)C (5 min hold). The carrier gas was high-purity helium (99.999%), and the column flow rate was 1.5 mL/min.  
Mass spectrometer (MS) conditions: Ionization mode: EI, electron energy 70 eV, ion source temperature 230 \({}^{\circ}\)C, interface temperature 250 \({}^{\circ}\)C, acquisition mode: full scan; mass scan range 50-600 \(m/z\).  
Qualitative analysis of compounds: Volatile compounds were identified by comparing the mass spectra with the published standard substance retaining index as well as information from the National Institute of Standards and Technology (NIST). The specific methods are as follows: The original data acquired by GC-MS were first deconvoluted using Masshunter Agilent Analysis (Agilent) to obtain the Retention time (RT), Compound name, CAS number, Matching factor, Structural formula, and Component area. The single peak was then filtered, and only the peak area data of a single group or all groups with a null value no >50% were retained. Internal standards were used to normalize data processing. Each chromatographic peak area represented the relative content of the corresponding substance. Finally, the integral data of all chromatographic peak areas were exported for statistical analysis. The compounds were determined by computer matching and the retention time of each compound was reported in the literature. For compound quantitative determination, an internal standard normalization method was employed, with compound contents calculated as ethyl decanoate equivalents based on the ratio of individual peak areas to the internal standard peak area.