esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Method
### Finite Element Analysis
The nodes highlighted in green belonging to the anvil are master while the ones, highlighted in green, belonging to the tooth are slave. In red are highlighted the nodes in which the stress history has been studied. in STBF. In the contact, a friction coefficient of 0.05 have been set. However, the effect of friction has a negligible effect on the tooth root bending fatigue. With respect to the MG simulations, the gears have been positioned fixing their axes of rotation at a theoretical center distance. The teeth were positioned half an angular step out of phase (since both wheels have the same number of teeth) in order to ensure meshing. The motion has been assigned to the driving gear (the top gear in Figure 6) and the resistant torque to the driven one (the bottom gear in Figure 6) accordingly. In MG simulations, multiple teeth can engage simultaneously as in the real tests and, therefore, the stiffness of the entire meshing arc was taken into account in MG simulations [26]. With respect to the STBF simulations, the symmetry was set as fixed constrain and a pulsating force varying sinusoidally with \(R=0.1\) was applied to the anvil as in the real tests. The maximum force in STBF and the torque applied in MG simulations have been set in order to lead to the same \(\sigma_{F}\) according to the standard [16]. Moreover, force or torque should lead to \(\sigma_{F}\sigma_{Film}\) for simulating the conditions where the crack nucleates and propagate leading to the fracture at approximately \(10^{6}\) cycles [63]. In the present study, the material considered is the 39 NiCrMo3. This typical gear steel presents \(\sigma_{Film}=281\) MPa [18], a bending fatigue limit \(\sigma_{f}=367\) MPa [66; 71], a torsional fatigue limit \(\tau_{f}=265\) MPa [66; 71], and an ultimate tensile strength \(\sigma_{R}=856\) MPa [66; 71]. For each simulation, the \(\overline{\sigma}(t)\) of all the positions within the tooth root region of the loaded tooth have been extracted (red area in Figures 5 and 6). These points are the ones in which the crack has higher probability to nucleate. Since the performed simulation are 2D, \(\tau_{xz}(t)\); \(\tau_{zx}(t)\); \(\tau_{yz}(t)\); \(\tau_{zy}(t)\) are equal to zero due to the plane stress condition.
## 2 Lateral Stiffness of an Aerostatic Slide
Eight steel blocks are used to apply the lateral forces, as shown in \[k_{\text{s}}=\lim_{d\to 0}\frac{2}{\frac{h_{3\text{R}}-h_{3\text{L}}}{f(h_{3 \text{L}})-f(h_{3\text{R}})}+\lambda} \tag{10}\]2 The gravity of the steel blocks is converted into lateral force by two pulleys. The mass of each steel block is 7.5 kg. Four lateral forces can be applied by using two, four, six, and eight steel blocks, which are 147 N, 294 N, 441 N, and 588 N, respectively. A capacitive displacement sensor is used to measure the lateral displacements. To decrease the influence of the solid deformation, the measuring position is in the middle of the upper surface of the slide. For each external lateral force, the measurement is repeated five times. The lateral displacements under the four lateral forces are shown in \[k_{\text{s}}=\lim_{d\to 0}\frac{2}{\frac{h_{3\text{R}}-h_{3\text{L}}}{f(h_{3 \text{L}})-f(h_{3\text{R}})}+\lambda} \tag{10}\]3  
Figure 7 shows the lateral displacements under the four lateral forces, which are measured and calculated based on the proposed theory. The measured lateral displacements are fitted by a proportional function. The fitting line is shown as the red line in \[k_{\text{s}}=\lim_{d\to 0}\frac{2}{\frac{h_{3\text{R}}-h_{3\text{L}}}{f(h_{3 \text{L}})-f(h_{3\text{R}})}+\lambda} \tag{10}\]4 \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Lateral Force** & **1** & **2** & **3** & **4** & **5** & **Average Value** \\ \hline
147 N & 0.373 & 0.366 & 0.375 & 0.374 & 0.368 & 0.3712 \\
294 N & 0.713 & 0.715 & 0.711 & 0.706 & 0.695 & 0.7080 \\
441 N & 1.079 & 1.066 & 1.067 & 1.069 & 1.091 & 1.0744 \\
588 N & 1.444 & 1.421 & 1.431 & 1.422 & 1.402 & 1.4240 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Lateral displacements under different external lateral forces (\(\mu\)m). Figure 5: Lateral supporting forces under different film thicknesses. Figure 6: Tests for the lateral stiffness of the aerostatic slide. The slope of the fitting line is 0.00246. Hence, the measured lateral stiffness is 406.5 N / \(\upmu\)m, which approaches the calculation. The error between them is 18.7%.
# Numerical Investigation of the Relationship between
## 1 Introduction
In this paper, we aimed to address bypass graft failures by minimizing hemodynamic disturbances through the investigation and optimization of anastomosis angles for ridged spiral flow bypass grafts. We hypothesized that due to the significant influence of the junction angle in local flow fields within the anastomosis region, it can largely affect blood flow, and optimizing it could provide a synergistic effect with the spiral flow in stabilizing local hemodynamics. Steady-state CFD analysis was used to determine which angle could optimize the hemodynamics in a ridged graft design. Lower anastomosis angles were found to create more favorable flow conditions through the minimization of the pressure drop and areas affected by recirculation and abnormally high WSS. Finally, transient CFD analysis was performed to confirm that the spiral-flow-inducing graft design is more beneficial than the conventional graft design even at lower anastomosis angles. To our knowledge, this is the first study to look into the relationship between the anastomosis angle and spiral flow in the context of ridged bypass graft design. The findings of the study may be taken into consideration in the design of bypass grafts to prevent their failure due to hemodynamic disturbances associated with previous designs. This may prove beneficial to people at risk or suffering from atherosclerosis.
# Assessing Crisis Management Tools for Sustainability of Industrial Safety
## 5 ALOHA Software, Strengths, Limitations, and Need for Improvements
### Strengths of ALOHA Software  
ALOHA has proven effective in short-term incident modelling, particularly for scenarios where rapid response is critical. Its user-friendly interface enables emergency responders to quickly input environmental variables and chemical properties to predict gas dispersion and the extent of hazard zones. In the case study of [PERSON] et al. (2024), ALOHA was used to model ammonia leakage at the Duyen Hai 2 Thermal Power Plant, confirming its ability to handle varying meteorological conditions while identifying critical zones for human and environmental safety [15; 17; 18].
## 6 Results and Discussion
### Cost Function Analysis on the Proposed Model  
Comparison of the proposed ABC-ROA-based privacy preservation system convergence rate to existing meta-heuristic algorithms with various datasets is shown in Figure 8. Compared to HHO, EF-HHO, BCO, and ROA, the performance of the ABC-ROA-based privacy preservation system, the cost function is highly improved by 2.98%, 3.07%, 3.56%, and 4.12%, respectively, in dataset 3 at iteration value of 15. If the iteration increases, then the cost function of the designed ABC-ROA method gets decreased. Hence, the graph  
Figure 6: Effectiveness analysis of the developed blockchain-based privacy preservation system to (**a**) Euclidean distance, (**b**) Pearson correlation and (**c**) Spearman correlation.  
Figure 7: Effectiveness analysis of the designed blockchain-based privacy preservation model in terms of (**a**) Pearson correlation and (**b**) Spearman correlation.  
analysis shows better performance in the recommended method. The existing EF-HHO algorithm achieves second-best performance. As a result, the developed ABC-ROA-based privacy preservation model using blockchain technology performs more effectively than other algorithms.
# An Approach to Trustworthy Article Ranking by NLP and Multi-Layered Analysis and Optimization
###### Abstract  
The rapid growth of scientific publications, coupled with rising retraction rates, has intensified the challenge of identifying trustworthy academic articles. To address this issue, we propose a three-layer ranking system that integrates natural language processing and machine learning techniques for relevance and trust assessment. First, we apply BERT-based embeddings to semantically match user queries with article content. Second, a Random Forest classifier is used to eliminate potentially problematic articles, leveraging features such as citation count, Altmetric score, and journal impact factor. Third, a custom ranking function combines relevance and trust indicators to score and sort the remaining articles. Evaluation using 16,052 articles from Retraction Watch and Web of Science datasets shows that our classifier achieves 90% accuracy and 97% recall for retracted articles. Citations emerged as the most influential trust signal (53.26%), followed by Altmetric and impact factors. This multi-layered approach offers a transparent and efficient alternative to conventional ranking algorithms, which can help researchers discover not only relevant but also reliable literature. Our system is adaptable to various domains and represents a promising tool for improving literature search and evaluation in the open science environment.  
trustworthiness ranking; similarity computation; recommendation system; multi-layered factor analysis; open data [table]capposition=top  
match content to queries, then applies a machine learning filter to screen out problematic papers and finally ranks results using both relevance and trustworthiness signals. We have collected abundant datasets to test the proposed approach and achieved positive outputs. A complete pipeline merging semantic search with quality assessment was built and thoroughly tested. Experiments revealed which factors most strongly signal article trustworthiness, with citation behavior proving more indicative than journal reputation. The development of a ranking method successfully balances finding relevant and reliable papers. This approach was validated using a substantial dataset containing both retracted and non-retracted articles across multiple disciplines.  
Our research responds to a genuine need in science: helping researchers more efficiently find trustworthy publications relevant to their work. The following sections detail how we gathered our data, built our three-layer approach, and measured its performance, and what our results mean for improving how scientists find and evaluate research literature.
# Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
## 3 Proposed Artificial Neural Network (ANN) Model
### Network Training  
Before analyzing new input data, a network must be trained. There are a number of methods for training neural networks, but the back propagation algorithm is the most flexible and efficient. This is the most effective way for multilayer neural networks to learn. In addition, the extensive application of back propagation algorithms results from their efficiency in resolving prediction issues. Three layers are always included in feed-forward back propagation neural networks (BPNNs): input, hidden, and output. Thus, the feed-forward back propagation neural network consists of input, hidden, and output layers (BPNN). Each layer contains several neurons, the fundamental processing units, and each neuron is connected to the next layer through weights. The neurons in the input layer transmit their output to the neurons in the hidden layer. The number of input and output neurons corresponds to the input and output variables.  
Training a network involves sending data from the input layer to the hidden layers, and then to the output layer (forward pass). The output is then compared to the recorded values (the \"true\" output). To change connection weights, the network adjusts the difference or error, and each neuron biases input-output training pairs. This procedure is repeated for each training pair in the dataset until the network error converges to a function; typically,  
Figure 6: The architecture of a back propagation neural network (BPNN), with its three layers (i.e., input layer, hidden layer, and output layer).  
the root-mean-square error (RMSE), or sum of the squared errors, is used to determine the convergence (SSE).
### Problem Statement  
Data dissemination in VANETs should be performed efficiently in terms of reachability and network congestion. For that, a suitable data dissemination algorithm that selects appropriate forwarding vehicles to reduce the number of redundant packets should be employed. The ratio between the relative Euclidean distances between two vehicles with respect to the vehicle's communication range is envisioned as the most suitable parameter to select forwarding vehicles in probabilistic data dissemination approaches. However, obtaining the actual vehicle's communication range in real scenarios is unlikely to be effectively achieved due to the varying and dynamic behavior of the wireless medium. Therefore, it is necessary to find alternative metrics that are able to estimate the relative distance between vehicles and substitute the Euclidean distance in well-known data dissemination schemes like p-persistence approach [18].
## 2 Materials and Methods
### Methods
#### 2.3.6 Enhanced GPP Estimation Model  
This study evaluated three machine learning models (RF, ANN, XGB) alongside two linear regression models (LASSO and RIDGE) for estimating forest ecosystem GPP. The comparative analysis aimed to assess the potential and advantages of machine learning approaches through the integration of multi-source data and advanced model optimization algorithms. The specific steps are outlined in Figure 3: (1) Select eight variables, including vegetation indices (NDVI, EVI, LAI), radiation data (PAR, NR, SR), and meteorological data (Temp, VPD), as the baseline feature set for model development. (2) Standardize all data to generate the required input for the model and split the dataset into training (2003-2008, -75%) and testing sets (2009-2010, -25%). (3) Optimize the hyperparameters for three ML models (RF, ANN, XGB) and two linear regression models (LASSO, RIDGE) using the RGSA developed in this study. (4) Address multicollinearity, noting significant correlations (e.g., EVI with NDVI and SR, NR, PAR, correlations > 0.88, see Figure 4). Use variable combinations in Figure 5 for further optimization. Temp and VPD, representing temperature and moisture, are not split. (5) Evaluate post-optimization of ML models with site data to select the best model and optimal variable combination. (6) Apply the selected model and feature combination for spatial inversion analysis of forest GPP in the relevant province (2009-2015) and compare results with other GPP products (GOSIF, GLASS, FLUXCOM).  
Figure 4: Correlation among multiple variables.  
Figure 3: Technical roadmap. The workflow integrates vegetation indices (NDVI: Normalized Difference Vegetation Index, EVI: Enhanced Vegetation Index), biophysical parameters (LAI: Leaf Area Index, PAR: Photosynthetically Active Radiation), radiation measurements (NR: Net Radiation, SR: Solar Radiation), and environmental variables (Temp: Temperature, VPD: Vapor Pressure Deficit) as features. Data was split chronologically (75%/25%) between training and test sets. Machine learning (RF, ANN, XGBoost) and linear models (LASSO: Least Absolute Shrinkage and Selection Operator, RIDGE: Ridge Regression) were implemented with optimization procedures (RGSA: Random Grid Search Algorithm) for GPP estimation.
# Principles and Characteristics of Different EDM Processes in Machining Tool and Die Steels
## EDM Process Details  
### Working Principle of EDM  
The EDM process was invented in the 1940s [23]. The process uses thermoelectric energy to erode workpieces due to rapidly recurring electrical discharges (sparks) between the non-contact electrode and workpiece [43; 44; 45; 46]. The discharge current flows from between the electrode and workpiece through the small distance between them, called a \"gap\", which is filled with dielectric fluid. Dielectric fluid also simultaneously flushes the gap continuously during the machining and carries away debris (particle erosion produced as a result of machining) and restores the sparking condition in the gap. A proper gap is carefully selected to a value such that it generates surge current by breaking down the dielectric film under a set voltage. There are no cutting forces used between the electrode and the workpiece as there is no contact between them. This condition eliminates vibration and problems arising due to machining forces [47; 48].  
Figure 1: The processes of electric discharge machining (EDM) and their process parameters and performance measures.
# Numerical Simulation of Anchor Pullout and Shear Tests Using a Regularized Damage Model
## 4 Simulation of Tests with Complete Anchoring System
### Simulation of Anchoring System Under Shear  
As the main loadings that an anchorage system must support are tension and shear, a shear test is now studied. An experimental comparison is made on the tests described in [2]. The parameters for concrete and steel are the same as those used in the tensile anchorage tests (Table 1). The links between the different components are also similar; the tests were conducted with three embedment lengths: 75 mm, 100 mm, and 310 mm. The experimental loading conditions are depicted in Figure 17a. It was observed that the plate could rotate during the experiment. To be as representative as possible of the experimental conditions, the loading is applied over a two-centimeter band at the end of the plate with a vertical offset of 5 cm (Figure 17b).  
The numerical results effectively represent the failure mode. For an embedment length of 75 mm, failure occurs through a concrete cone [2], and this cone is well represented by the damage model (Figure 18). Similarly, the maximum resistance between the simulation and the experiment is comparable (Table 6).  
Figure 16: Reinforcement influence.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Embedded Length (mm)** & **80** & **120** & **130** & **310** \\ \hline Group effect & 30\% & 28\% & 32\% & \textless{}1\% \\ \hline Failure mode & Concrete & Concrete & Concrete & Rod \\ \hline \end{tabular}
\end{table}
Table 5: Group effect.  
Figure 15: Comparison of a simulation with and without reinforcement.  
For longer embedment lengths (100 mm and 310 mm), failure occurs at the stud due to steel shear, both numerically and experimentally (Figure 19). The maximum resistances from the tests are very close to the simulations (Table 6).  
These simulations confirm the capability of the proposed numerical methodology to represent a shear loading, both at the global and at the local scales.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Experimental** & **Simulation** & **Experimental** & **Simulation** & **Experimental** & **Simulation** \\ \hline h\({}_{\text{ef}}\) (mm) & 75 & 75 & 100 & 100 & 310 & 310 \\ \hline Max strength & 365 & 387 & 482 & 495 & 535 & 498 \\ \hline Gap & 6\% & & 3\% & & 7\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of tests and simulations for a shear anchorage.  
Figure 19: Shear deformation (**a**) and damage (**b**), h\({}_{\text{ef}}\) = 310 mm.  
Figure 17: Experimental (**a**) [2] and numerical (**b**) loading condition.  
Figure 18: Shear failure of concrete, h\({}_{\text{ef}}\) = 75 mm.
## Experiments
### Experimental Setup  
For the experimented systems, the acoustic feature involves 19-dimensional MFCCs and the log-energy extracted every 10 ms, using a 20 ms Hamming window via the SPro library [27]. Together with the delta (first derivative) and delta-delta (second derivative) of the 19-dimensional MFCCs and the log-energy, the frame-level acoustic feature used in our experiments was given by a 60-dimensional vector.  
We trained the UBM containing 32 mixture components in a gender- and age-independent manner, using all the speech utterances in the TIMIT dataset. Training the UBM, total variability matrix, and the i-vector extraction was done by using the MSR Identity Toolbox via MATLAB [28]. The encoders and decoders of the VAEs were configured to have a single hidden layer with 4096 rectified linear unit (ReLU) nodes, and the dimensionality of the latent variables was set to be 200. The implementation of the VAEs was done using Tensorflow [29] and trained using the AdaGrad optimization technique [30]. Additionally, dropout [31] with a fraction of 0.8 and L2 regularization with a weight of 0.01 were applied for training all the VAEs, and the Baum-Welch statistics extracted from the entire TIMIT dataset were used as training data. A total of 100 samples were used for the reparameterization shown in (15).  
For all the extracted utterance-level features, linear discriminant analysis (LDA) [1] was applied for feature compensation, and the dimensionality was finally reduced to 200. PLDA [32] was used for speaker verification, and the speaker subspace dimension was set to be 200.  
Four performance measures were evaluated in our experiments: classification error (Class. err.), EER, minimum NIST SRE 2008 DCF (DCF08), and minimum NIST SRE 2010 DCF (DCF10). The classification error was measured while performing a speaker identification task where each trial utterance was compared with all the enrolled speakers via PLDA, and the enrolled speaker with the highest score was chosen as the identified speaker. Then, the ratio of the number of wrongly classified trial samples to the total number of trial samples represented the classification error. The EER and minimum DCFs are widely used measures for speaker verification, where the EER indicates the error when the false positive rate (FPR) and the false negative rate (FNR) are the same [1], and the minimum DCFs represent the decision cost obtained with different weights to FPR and FNR. The parameters for measuring DCF08 and DCF10 were chosen according to the weights given by the NIST SRE 2008 [33] and the NIST SRE 2010 [34] protocols, respectively.
## 2 Materials and Methods  
### Synthesis  
#### 2.1.1 Fabrication of Ag/TiO2/p-Si Photocathode and SiO2 Patterned Ag/TiO2/p-Si Photocathode  
A _p_-Si wafer (0.01-0.1 \(\Omega\)-m, \(<\)100\(>\) plane, DASOMRMS Co., Ltd., Korea) was cleaned in acetone, isopropyl alcohol, and deionized (DI) water, using ultrasonication. The water was purified by a water purification system (Human Power, Human Co, Ltd., 183 K\(\Omega\)-m, Korea) for DI water. The cleaned _p_-Si wafer was soaked for 1 min in 5% hydrofluoric acid (HF) to etch the native SiO2 layers. Then, a 5 nm thick TiO2 thin film was deposited using an electron-beam evaporator (KVE-E2004L, KOVA, Korea). Subsequent heat treatment was conducted for TiO2/_p_-Si substrates in a tube furnace at 350 \({}^{\circ}\)C for 30 min under N2. For SiO2 patterned Ag/TiO2/_p_-Si photocathode, after deposition of TiO2 thin film, a patterned 500 nm-thick SiO2 was deposited using the electron-beam evaporator, using a patterning mask (Supplementary Figure S1a). The patterning mask has square holes of 500 \(\upmu\)m in length, and their total area is 50% of the total area of the mask. Before the electrodeposition, a Cu wire was connected to the scratched backside of the TiO2/_p_-Si substrate using an In-Ga alloy (Sigma Aldrich) and silver paste. Then, except for the active area of 1 \(\times\) 1 cm, the whole surface of the TiO2/_p_-Si surface was covered with an adhesive Kapton tape to prevent contact with the electrolyte.  
The solution for the deposition of Ag is prepared from 5 mM AgNO3, 50 mM NaNO3, which were dissolved in DI water. The Ag catalysts were electrodeposited on the TiO2/_p_-Si electrodes in a standard three-electrode system with a Pt mesh as a counter electrode and Ag/AgCl (sat. KCl) as a reference electrode. The deposition of Ag was performed by applying \(-\)0.1 V vs. Ag/AgCl on the working electrode for 15 min. Then, the electrodeposited samples were rinsed with DI water.