esa-sceva/satcom-chunk-collection huggingface dataset:
# Parametric Analysis for 3D Modeling of Consolidation-Induced Solute Transport Using OpenFOAM
###### Abstract  
Most previous investigations for consolidation-induced solute transport models have been limited to one-dimensional studies in unsaturated porous media and lack systematic parameter sensitivity analysis. This study addresses these gaps by analyzing the effects of hydraulic conductivity (\(K\)), shear modulus (\(G\)), saturation (\(S_{r}\)), Poisson's ratio (\(\
u\)), partitioning coefficient (\(K_{d}\)), and anisotropy ratio (\(\frac{K_{d}}{K_{d}}\) and \(\frac{K_{d}}{K_{d}}\)) on pore water pressure, soil deformation, and solute transport. The findings reveal that higher \(K_{d}\) values significantly hinder solute migration through enhanced adsorption and reduced vertical transport to deeper layers, while increasing anisotropy ratios primarily enhance horizontal migration, with their effects diminishing beyond a threshold. Additionally, a higher \(K\) accelerates pressure dissipation and solute movement, while a lower \(G\) increases soil deformation and speeds up solute migration. Saturation has a minor effect on solute concentration, with slight increases under higher \(S_{r}\). The Poisson ratio significantly impacts the transport of the solute, with smaller \(\
u\) accelerating and larger \(\
u\) slowing migration. These insights offer valuable theoretical support for optimizing models in unsaturated porous media.  
solute transport; consolidation; OpenFOAM; deformable media Article
## 1 Introduction
To minimize the impact, noise reduction processing is required before feature extraction. Based on this, this article proposes a bearing fault diagnosis model using an improved transfer component analysis and deep belief network (ITCA-DBN) and a method named samples rejection and pseudo label correction using K-means (SRPLC-K-means). ITCA-DBN is a segmented data processing model that requires manual feature extraction and fault diagnosis. Compared to end-to-end models, segmented data processing models require less time for feature extraction. The proposed ITCA uses LMMD instead of MMD and adds a divergence factor. LMMD solves the problem of data distribution discrepancies within the same category between different domains. The divergence factor can be used to measure the sample compactness of a category. Improving the intraclass clustering of samples and reducing the impact of noise can improve the accuracy of classification. When there are significant discrepancies in the data distribution, the accuracy of pseudo labels sharply decreases, which greatly affects the transfer ability of the model. To improve the accuracy of pseudo labels in the target domain, K-means clustering is used to correct the pseudo labels and remove the edge samples. Finally, based on multiple experimental comparisons, it was verified that the proposed method could complete bearing fault diagnoses under variable working conditions in a short time and with high accuracy. The contributions are presented as follows:  
1. The divergence factor is proposed to measure the compactness of samples within a category. Considering subdomain adaptation and divergence factors aims to improve the TCA, reducing subdomain discrepancies and increasing sample compactness;
2. A simple method named SRPLC-K-means was designed to filter the noisy samples and correct the pseudo labels. The experimental results have verified that the SRPLC-K-means method is helpful in overcoming the issue of false pseudo labels;3. The experimental results on Case Western Reserve University and underground drum motor bearing datasets have demonstrated that the proposed method can reduce the time required for a fault diagnosis and increase accuracy. The rest of this paper is presented as follows: The fundamental theories of the TCA, subdomain adaptation, and DBN are illustrated in Section 2. Section 3 introduces the ITCA and SRPLC-K-means in detail. In Section 4, the experiments are detailed, which include the accuracy, visualization, and time required for a fault diagnosis on the Case Western Reserve University case and underground drum motor bearing datasets. Finally, the overall work and future research directions are summarized in Section 5.
# Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals
## 3 Results
### Mathematical Analysis of PP Interval Series
#### 3.2.3 Nonlinear Analysis  
The introduction of the non-linear analysis of PRV provides additional information on the health status of the subjects studied. Figure 8 shows the graphs that were obtained through the Poincare plot for healthy subject and patient with arrhythmia. This graphical method presents the correlation between consecutive PP intervals and constructs an ellipse with dimensions: width (SD1) and length (SD2).  
Figure 9 shows the multifractal spectrum and behavior of the grassland Hurst exponent of the studied groups while using the MFDFA method.  
Figure 6: Power Spectral Density for healthy subject. (Five minutes of the studied series).  
Figure 7: Power Spectral Density for patient diagnosed with arrhythmia. (Five minutes of the studied series).  
Table 7 shows the parameter values of the two study groups obtained by applying nonlinear PRV analysis methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & \begin{tabular}{c} **Group1 (Arrhythmia)** \\ _N = 42_ \\ \end{tabular} & \
\begin{tabular}{c} **Group2 (Healthy)** \\ _N = 40_ \\ \end{tabular} &
\begin{tabular}{c} **\(p\) Value** \\ \end{tabular} \\ \hline SD1 [ms]-Poincaré plot & \(21\pm 6\) & \(32\pm 9.8\) & 0.0005 \\ SD2 [ms]-Poincaré plot & \(84\pm 16\) & \(101\pm 23\) & 0.004 \\ SD1/SD2-Poincaré plot & \(0.24\pm 0.11\) & \(0.31\pm 0.13\) & 0.07 \\ Alpha 1- DFA & \(1.43\pm 0.18\) & \(1.05\pm 0.23\) & 0.0001 \\ Alpha 2- DFA & \(1.02\pm 0.08\) & \(0.97\pm 0.08\) & 0.07 \\ Multifractal spectrum-MFDFA & \(0.2\pm 0.15\) & \(0.5\pm 0.22\) & 0.0001 \\ Hurst exponent-R/S plot & \(0.55\pm 0.03\) & \(0.81\pm 0.07\) & 0.0001 \\ AppEn & \(1.08\pm 0.3\) & \(1.62\pm 0.4\) & 0.0001 \\ SampEn & \(1.01\pm 0.25\) & \(1.6\pm 0.35\) & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Nonlinear methods Parameters.  
Figure 8: Poincaré plot for healthy subject and patient with arrhythmia.  
Figure 9: MultiFractal Detrended Fluctuation Analysis (MFDFA) for healthy subject and patient with arrhythmia.
# Biomethane Potential Test: Influence of Inoculum and the Digestion System
## 2 Materials and Methods
### Taxonomic Profiling and Statistical Analysis  
Taxonomic profiles of the microbial community within the analyzed inocula were determined by 16S RNA amplicon-sequencing. Microbial DNA was extracted with the FastDNA (r) SPIN Kit for Soil (MP Biomedicals, Illkirch-Graffenstaden, France) and cleaned with Genomic DNA Clean & Concentrator TM Kit (Zymo Research, Irvine, CA, USA). The 16S RDNA gene amplicon-library was constructed by using the \"16S Metagenomic Sequencing Library Preparation\" protocol (Illumina, San Diego, CA, USA) and the primer pairs Pro341F and Pro805R [29]. The sequencing was done on the Illumina MISeq platform applying the 300x bp paired-end protocol. Afterwards bioinformatics preprocessing of the sequencing data was done. Forward and reverse reads were merged with FLASH [30], primer were removed with cutadapt [31], while quality trimming of reads was done with sickle [32]. Taxonomic classification was done by using the QIIME platform [33] in combination with the SILVA 16S rDNA reference database (Release 132, 10.04.2018). Taxonomic profiles were presented as a bar chart for each inoculum. More detailed information is given in [PERSON] et al. [34].  
The statistical analysis of specific methane yield was performed by ANOVA post-hoc Tukey test at \(\alpha=0.05\) using Microsoft Excel 2016.
## 4 Discussion
### Environmental Factors Analyses of Temporal Variance of Parameter SIs  
The numerical experiment results showed that the SIs of key parameters have temporal variations on the GPP and NEE. More importantly, the mild trade-off effect was found in the parameter SIs during the growth season. An earlier study also found that sensitivities of several soil respiration-associated parameters have strong seasonal variations [29]. Therefore, temporal characteristics and the trade-off effect were essential features of parameters' SIs.  
As for GPP (Figure 3a), the SIs of \(E_{Vm}\) and \(D_{0}\) changed over the growing season. SIs of \(E_{Vm}\) increased from DOY10 to DOY150, weakened from DOY160 to DOY240, and increased from DOY160 to DOY240.  
The study found that the variation in sensitivity had a trade-off effect (Figure 4). The SIs of \(\alpha_{q}\), \(f_{ci}\), and \(E_{Vm}\) declined with DOY, whereas SIs of \(ER_{0}\) and \(Q_{10}\) increased and became more sensitive from DOY110 to DOY210. After DOY210, SIs of \(ER_{0}\) and \(Q_{10}\) declined, and SIs of \(f_{ci}\), \(E_{Vm}\), and \(\alpha_{q}\) increased.  
The dynamic evolution of the parameter SI indicated that the sensitive parameters from DOY10 to DOY340 for different outputs (GPP and NEE) were fluctuant. To further explore the causes of this phenomenon, the correlation between average air temperature(\(T_{a}\)), minimum air temperature (\(T_{min}\)), maximum air temperature (\(T_{max}\)), precipitation (\(P\)), PAR and showed SI of \(\alpha_{q}\), \(D_{0}\), \(f_{ci}\) and \(E_{Vm}\) was analyzed. The result illustrates that only \(T_{a}\) was the more important environmental factor that influences the temporal variation of parameter, as shown in Figures 6 and 7.  
Parameters SI of \(f_{ci}\) and \(ER_{0}\) did not change significantly with an increase in \(T_{\rm a}\), this was due to the two parameters that were not closely related to temperature. So, the temperature change is not the cause of variation of these two parameters in the whole growth season. Unfortunately, what causes changes of SI of \(f_{ci}\) for NEE during the growing season Figure 7b was unclear and remained to be discussed in future studies.  
Parameters SI of E\({}_{Vm}\) decreased with an increase in \(T_{\rm a}\), and the trend of \(Q_{10}\) was identical to that of \(T_{\rm a}\). SA experiment found that the effect of \(Q_{10}\) for the model output was different. However, \(Q_{10}\) was regarded as a constant in previous studies and had caught uncertainty in CO\({}_{2}\) simulation [63; 64]. Therefore, it was worth further investigate that the parameters were taken as constants and the uncertainty analysis resulting from parameters on the model output in the existing model.  
Figure 6: Correlation analyses of average air temperature (_Ta_) and variation in the sensitivity index of the main parameter from DOY10 to DOY340 when model output was GPP: (**a**) SI of \(\alpha_{\rm q}\) vs. \(T_{a}\); (**b**) SI of D\({}_{0}\) vs. \(T_{a}\); (**c**) SI of \(f_{ci}\) vs. \(T_{a}\); (**d**) SI of \(E_{Vm}\) vs. \(T_{a}\).
## 2 Oscillating Dipolar Source and Integral Expressions for the Radiation Field
### Steps Needed to Write the Integral Expression for the Electric Field  
With reference to Figure 1, consider an oscillating dipolar source with two opposite charges at \(\mathbf{x}=-\mathbf{a}\) and \(\mathbf{x}=+\mathbf{a}\), of the following form:  
\[\rho(t,\mathbf{x})=q\cos(\omega t)f(\mathbf{x});\ \ \mathbf{J}=0 \tag{15}\]  
where \(f(\mathbf{x})\) is essentially a regularized double-\(\delta\), whose support can be adapted to describe a sphere or a disk (see below, Equation (22))  
\[f(\mathbf{x})\simeq\delta^{3}(\mathbf{x}-\mathbf{a})-\delta^{3}(\mathbf{x}+ \mathbf{a}) \tag{16}\]The absence of current (\(\mathbf{J}=0\)) violates local conservation and can be described as the consequence of a \"strong-tunnelling\" process [17]. In a real source, only a small part of the total charge will oscillate without a current, so we focus our attention on the field generated by that part. In order to compute the field of the source (15) using the extended Maxwell equations we must write the potentials \(\phi\) and \(\mathbf{A}\) as double-retarded integrals like in Equations (10) and (11), and then we have \(\mathbf{E}=\mathbf{E}^{\phi}+\mathbf{E}^{A}=-\
abla\phi-k\partial_{t}\mathbf{ A}\). The integrand in Equations (10) and (11) is given by \(I=\partial_{t}\rho+\
abla\cdot\mathbf{J}\); therefore, since \(\mathbf{J}=0\), one has here  
\[I(t,\mathbf{x})=\partial_{t}\rho(t,\mathbf{x})=-q\omega\sin(\omega t)f( \mathbf{x}) \tag{17}\]  
The steps needed to obtain the contribution \(E_{i}^{\phi}\) are then the following:  
* Retardate \(t\to t-k|\mathbf{y}-\mathbf{z}|\) in \(I(t,\mathbf{z})\), divide \(I\) by \(|\mathbf{y}-\mathbf{z}|\) and integrate in \(d^{3}z\). * Differentiate with respect to \(t\) and multiply by \((-k^{2})\). * Retardate \(t\to t-k|\mathbf{x}-\mathbf{y}|\). * Multiply by \(1/|\mathbf{x}-\mathbf{y}|\) and integrate in \(d^{3}y\). * Differentiate with respect to \(x_{i}\) and multiply by \((-1)\). The steps needed to obtain the contribution \(E_{i}^{A}\) are the following:  
* Retardate \(t\to t-k|\mathbf{y}-\mathbf{z}|\) in \(I(t,\mathbf{z})\), divide \(I\) by \(|\mathbf{y}-\mathbf{z}|\) and integrate in \(d^{3}z\). * Differentiate with respect to \(y_{i}\) and multiply by \((-k)\). * Retardate \(t\to t-k|\mathbf{x}-\mathbf{y}|\). * Multiply by \(1/|\mathbf{x}-\mathbf{y}|\) and integrate in \(d^{3}y\). * Differentiate with respect to \(t\) and multiply by \((-k)\). Through these steps one arrives, after long but straightforward manipulations, at the following expression for the electric field, as a double retarded integral:  
\[E_{i}(t,\mathbf{x})=E_{i}^{\phi}(t,\mathbf{x})+E_{i}^{A}(t,\mathbf{x}) \tag{18}\]  
Figure 1: Geometrical setting for the calculation of the extra-current \(I\) (17) produced by the failure of local conservation. The charge \(q\) oscillates between points placed on the \(x_{3}\)-axis, at \(-\mathbf{a}\) and \(+\mathbf{a}\). The field is computed in the plane \(x_{1}\)-\(x_{3}\), with angle \(\theta=45^{\circ}\).
## 3 Results
### Spatial Error (SE)  
No significant effects were observed for spatial error. The main effect of condition was nonsignificant, \(F\)(1, 24) = 0.042, \(p\) = 0.839, \(\eta\)p\({}^{2}\) = 0.002, as were the effects of time, \(F\)(2, 48) = 0.576, \(p\) = 0.566, \(\eta\)p\({}^{2}\) = 0.023, and the condition \(\times\) time interaction, \(F\)(2, 48) = 0.919, \(p\) = 0.406, \(\eta\)p\({}^{2}\) = 0.037. All observed effect sizes ranged from small to medium, indicating limited practical significance. Since the repeated-measure ANOVA did not yield any statistically significant effects, no follow up t-tests were performed.  
In the rPMS condition, spatial error increased by 0.7% immediately post-stimulation (Post 0) and decreased by 6% at 30 min (Post 30), but increased by 5% at 60 min (Post 60). In the sham condition, spatial error decreased by 3.7% at Post 0, increased by 4.6% at Post 30, and rose by 3.8% at Post 60. These fluctuations were not statistically significant (Figure 3).  
Figure 3: Percentage change in spatial error (SE) from baseline at 0, 30, and 60 min post-stimulation for rPMS and sham conditions. Bars represent group means; individual participant data points are overlaid. “X” markers denote group means. No significant effects of time, condition, or their interaction were observed.  
Figure 2: Percentage change in spatiotemporal error (NRMSE) from baseline at 0, 30, and 60 min post-stimulation for rPMS and sham conditions. Bars represent group means; individual participant data are overlaid. “X” markers denote group means. No significant main effects or interactions were observed.  
Descriptive statistics for mean and standard deviation values of both NRMSE and SE across all time points are provided in Table 2. As detailed in Table 2 and Figures 2 and 3, changes in both spatiotemporal and spatial errors were generally small and followed similar trajectories in both rPMS and sham conditions. While rPMS showed modest fluctuations over time, these did not differ meaningfully from the sham condition. These trends align with the nonsignificant statistical findings, suggesting no clear time- or condition-dependent effects of a single rPMS session on motor skill performance.
## 5 Simulation Results
### Multi-Domain Coverage Planning in Static Environments
#### 5.1.2 Trajectory Generation  
The output paths of GLNS and ACO were put into the trajectory generator. Through searching in KD tree _treTree_ built of terrain map _Map_, the length of the corridor was set. Then, trajectories were generated for the GLNS and ACO paths. Output examples are illustrated in Figure 16.  
\begin{table}
\begin{tabular}{c c} \hline \hline _The Parameters_ & _Value_ \\ \hline Number of ants & 75 \\ Volatilization of pheromone trail & 0.2 \\ Influence of pheromone & 1 \\ Influence of heuristic values & 4 \\ The constant Q & 10 \\ The max times of iterations & 100 \\ Heuristic function & \(1/\text{D}\); where D is the weighted distance between two spots. \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters chosen for the ACO in this work.  
Figure 15: A comparison of the paths generated by ACO and GLNS. In this scenario, 189 spots were divided into eight groups. The HAUV began at one spot and went back to the starting spot when the mission was finished. With the conditions in the table above, the total length of the path found by ACO was 719.0682 within 1.037 s, whereas GLNS’s path was 528.578, found in 3.0140 s.
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 6 Discussion
This aligns with the findings of [PERSON] and [PERSON] (2021) [29], who emphasize the importance of efficiently managing data updates in high-frequency systems to prevent cascading failures during critical events. * Modular Inference: The separation of the framework's modules (BERT, NMC, and LSTM) enables parallel execution, optimizing resource utilization and minimizing response time. This modular approach resonates with methodologies highlighted in [PERSON] et al. (2024) [49], where machine learning architectures were adapted to handle high-frequency trading data across multiple markets effectively. The integration of these strategies into the framework addresses key concerns raised in [PERSON] et al. (2023) [48], which demonstrates how low-latency infrastructures and modular designs enhance liquidity and market stability in high-frequency environments. By incorporating these practices, the proposed framework not only improves computational efficiency but also contributes to the robustness and adaptability required for dynamic market conditions. Beyond its technical contributions, the framework's potential extends to broader economic and societal benefits, as outlined below. Although the technical strength and predictive accuracy of the proposed framework are well recognized, it is important to discuss its wider social and economic implications as well. By providing more precise predictions of market conditions, this framework has the potential to enhance efficiency in financial markets. This improvement could result in numerous economic and societal advantages:  
* Economic Efficiency: The system facilitates optimal capital distribution by dynamically assessing market conditions and aligning investment strategies accordingly. This can lead to better resource management and higher returns for both institutional investors and individual stakeholders. * Financial Inclusion: In developing markets, these tools have the potential to democratize access to sophisticated financial analytics, enabling smaller investors to compete effectively and make well-informed choices. * Education and Transparency: Serving as a valuable educational tool, this framework aids novice investors in grasping market dynamics while formulating informed strategies. Its emphasis on easy-to-understand ontological classification further enhances transparency within decision-making processes.
###### Abstract  
The sudden increase in the operating pressure of nuclear power plants (NPPs) is due to the water hammer phenomenon, which tends to produce a whipping effect that causes serious damage to the pipes and their surroundings. The mechanical response of these pipelines under the influence of such fast fluid transients can be estimated using the fluid-structure interaction (FSI) method. The computational time and expense are predominantly dependent on the number of finite elements developed in the model. Hence, an effective modeling technique with limited and efficient nodes and elements is desired to obtain the closest possible results. A coupled 1D/3D finite element modeling approach using the FSI method is proposed to determine the influence of fast transients on the mechanical pipe whipping behavior of gas pipelines in NPPs. The geometric coupled modeling approach utilizes the presence of both the 3D solid elements and the 1D beam elements sharing a local conjunction. The computational model is modelled for a pipe-to-wall impact test scenario taken from the previously conducted French Commissariat a l'Energie Atomique (CEA) pipe whip experiments. The results of displacement, stresses, and impact velocity at the 3D section featuring the elbow are compared for the change in the 3D solid length varied at the juncture of the elbow. The computed results from the Ansys FSI coupling method using the Fluent and Transient Structural modules provides fair validation with the previously conducted experimental results and correlates with the CEA pipe whip tests on pipe-to-wall impact models. Thus, the 1D/3D coupled modeling approach, which minimizes the area of the solid region by constricting it to the impact area with appropriate contact modeling at the junctures, can be considered in the future for decreasing the computational time and the creation of finite elements.  
fluid-structure interaction; pipe whip; fast transient; 1D/3D model; computational modeling