esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Strength Mismatch Constraint Ahead of a Growing Crack  
In this subsection, a so-called CTOD-\(Q\)-\(M\) formulation was proposed to describe the near-tip stress field in the presence of both geometry and mismatch constraints, in which the \(Q\) parameter describes the geometry constraint and the \(M\) value is used to rank the mismatch effect on the crack tip constraint.  
The finite element analysis results have shown that the strength mismatch applied to models after a crack will induce an additional crack tip constraint. The mismatch constraint parameter \(M\) of SENT and pipe versus CTOD is calculated by Equation (6) and respectively plotted in Figure 12. The mismatch constraint \(M\) transforms from positive to negative with the increase of mismatch ratio  
Figure 11: Comparisons of resistance curves for SENB, SENT and pipe, (**a**) \(M_{y}\) = 0.8; (**b**) \(M_{y}\) = 1.0; and, (**c**) \(M_{y}\) = 1.3.  
Figure 10: Stress triaxiality distributions ahead of the current crack tip for pipes at different crack propagation, \(a\)/t = 0.2, (**a**) \(\Delta a\) = 0.1 mm; (**b**) \(\Delta a\) = 0.5 mm; and, (**c**) \(\Delta a\) = 1.0 mm.  
for the mismatched specimens with the same crack growth, unlike the geometry-induced crack tip constraint \(Q\)-parameter that is usually negative and decreases with the plastic deformation, which can further explain the effect of strength mismatch on ductile fracture behavior. Additionally, with crack growth, the value of \(M\) varies in a similar range, which indicates the effect of the strength mismatch on resistance curves is not quite associated with the amount of crack extension. At the same mismatched ratios (e.g., under-match and over-match), the mismatch constraint parameter \(M\) for pipes are comparatively lower than that of the corresponding SENT specimen.
# Further Advances in Atrial Fibrillation Research: A Metabolomic Perspective
## 2 Application of Metabolomics in Atrial Fibrillation
### Metabolomic Study of Atrial Fibrillation in an Animal Model  
Additionally, for the identification in human samples of specific markers for an accurate and effective diagnosis of AF, animal models have been used to improve our understanding of the pathology. For example, a big animal model of ventricular tachycardia was developed by [PERSON] et al. [6], to study AF caused by heart failure in dogs. In this study, metabolic changes in cardiomyocytes of left atrium were analyzed. They observed several metabolites changes that suggested an increase in metabolomic stress along with poor energetic management and a shift from glycolysis to ketoacid metabolism. This study focused on metabolomics of heart tissue affected by AF, without directly addressing the association between circulating metabolites and the risk of developing the disease [6; 10]. They used Nuclear Magnetic Resonance (NMR) and found important time-dependent alterations in protein levels involved in cardiac oxidative injury, including metabolic enzymes, heat shock proteins, and antioxidant and contractile filament proteins. The metabolomic analysis they carried out showed consistent results with these disturbances. This study was the first one using an animal model to analyze changes to protein and metabolite expression and point out the importance of these alterations to the development of AF [6].  
A nonsustained atrial pacing sheep model was developed by [PERSON] et al. [19] to study energy metabolism remodeling during persistent AF. Metabolomic analysis found an up-regulation of glucose metabolism and a down-regulation of lipid metabolic pathways.
## 3 Results
### Study on Permeability of Steel Slag-Cement-Bentonite Barrier Material
#### 3.3.2 Effect of Bentonite Dosage on the Permeability of Barrier Materials  
Figure 8 illustrates the permeability coefficients of cement bentonite structures maintained for 28d with different cement substitutions [24; 25; 26]. In this study, the bentonite dosage was 46%, 50%, and 54%. From the figure, it can be found that no matter the cement replacer, the permeability coefficient of the cement bentonite-based barrier material decreases with the increase in bentonite dosage, indicating that the addition of bentonite has the ability to reduce the permeability of the barrier wall.  
Figure 7: Change in permeability coefficient of steel slag–cement–bentonite material with replacement rate of steel slag under different bentonite dosages.
# Experimental & FEM Analysis of Orthodontic Mini-Inplant Design on Primary Stability
###### Abstract  
The main objective of this research is to establish a connection between orthodontic mini-implant design, pull-out force and primary stability by comparing two commercial mini-implants or temporary anchorage devices, Tomas(r)-pin and Perfect Anchor. Mini-implant geometric analysis and quantification of bone characteristics are performed, whereupon experimental in vitro pull-out test is conducted. With the use of the CATIA (Computer Aided Three-dimensional Interactive Application) CAD (Computer Aided Design)/CAM (Computer Aided Manufacturing)/CAE (Computer Aided Engineering) system, 3D (Three-dimensional) geometric models of mini-implants and bone segments are created. Afterwards, those same models are imported into Abaqus software, where finite element models are generated with a special focus on material properties, boundary conditions and interactions. FEM (Finite Element Method) analysis is used to simulate the pull-out test. Then, the results of the structural analysis are compared with the experimental results. The FEM analysis results contain information about maximum stresses on implant-bone system caused due to the pull-out force. It is determined that the core diameter of a screw thread and conicity are the main factors of the mini-implant design that have a direct impact on primary stability. Additionally, stresses generated on the Tomas(r)-pin model are lower than stresses on Perfect Anchor, even though Tomas(r)-pin endures greater pull-out forces, the implant system with implemented Tomas(r)-pin still represents a more stressed system due to the uniform distribution of stresses with bigger values.  
mini-implants; primary stability; pull-out test; Abaqus; FEM analysis Article
# New Possibilities for Constructing Heuristic Solutions to Problems of Electromagnetic Diffraction
## 1 Introduction
### Reference Problems for Constructing Heuristic Solutions  
When solving practical problems, both for semi-infinite objects and for objects of finite size, 1D, 2D and 3D reference problems are used.  
Types of diffraction reference problems are the following:  
* Linear integral (3D): This is a solution in the PO approximation to the diffraction problem on a plane perfectly conducting scatterer. In this case, the far zone condition (FZC) is fulfilled both for the source and for the observation point. The integral over the area of the scatterer is reduced to an integral over its contour [13; 14; 15]. If the FZC is not fulfilled, the technique described in [16] can be applied.
* Vertex waves (3D): This is a solution to the diffraction problem for a plane angular sector. The analytical solution is described in [17], the heuristic ones are described in [18; 19; 20; 21].  
* Solutions to the problem of diffraction by perfectly conducting semi-infinite edges of different profiles (2D) [22; 23; 24; 25; 26; 27; 28].
* Reflection and transmission coefficients (1D) R and T for an unbounded plane.
* Solutions to the problems of diffraction by semi-infinite objects with imperfect boundary conditions (2D, 2.5D--oblique incidence) [29; 30; 31; 32; 33; 34]: For such structures, heuristic solutions are constructed, despite the presence of more rigorous solutions (both analytical and numerical).  
Reference problems can be applied in the form of strict analytical formulas, heuristic formulas, engineering formulas (which may not have physical meaning), within the framework of hybrid approaches (combining elements of numerical and heuristic methods) or in the form of databases (pre-calculated or experimental) [3].
# Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review
## 2 Working Principle of CW Doppler Radar
### Theory
However, researchers have found that, for CW Doppler radar, there is a strict relationship between carrier frequency and the displacement of the target movement [62]. Among all the carrier frequencies, 2.4 GHz and 5.8 GHz are probably the most popular ones likely due to the vast availability of low-cost RF/microwave components and integrated circuits (ICs) in the ISM bands. Studies have shown that both 2.4 GHz and 5.8 GHz Doppler radar are able to successfully detect human vital signs of respiration and heartbeat [33; 34]. To optimize the detection of heartbeat that has small displacement of around one millimeter or even less, researchers have proposed higher frequency radars such as 24 GHz [62], millimeter-wave 60 GHz/96 GHz [53; 63], and even 228 GHz [64]. Although higher frequencies are subject to higher path loss, they are beneficial in high modulation sensitivity and could the optimal choice for measuring small displacement in short range.
## 4 Overview of GNSSs
### Galileo
#### 4.2.2 GALILEO Space Segment  
The basic configuration of the Galileo constellation is defined as a 24/3/1 Walker constellation: 24 nominal satellites in medium Earth orbit are arranged in three orbital planes, with their ascending nodes evenly spaced at 120-degree intervals, inclined at 56 degrees to the equator. Each orbital plane contains eight satellites evenly spaced in the plane, at 45-degree latitude intervals. The angular offset between satellites in two adjacent planes is 15 degrees. The constellation is supplemented by spare satellites, which can be moved to any nominal slot in any plane of the orbit as needed for maintenance or service evolution [76; 83].
## 4 Receiver Implementation Approaches
### Scalar Tracking Receiver  
Scalar tracking is based on a decentralized technique in signal tracking and navigation solution computation [14]. In this approach, each satellite is separately tracked in an independent channel, as shown in Figure 2. Therefore, there is no exchange information between the tracking loops or between the navigation and tracking modules. The tracking parameters of each channel are used to estimate the measurements of the tracked satellite. These measurements are then fed to the navigation processor; in turn, the navigation processor calculates the positioning information. This approach has some robustness as a faulty channel will not affect another channel. Moreover, it is simple and efficiently implemented [15]. However, it does not exploit the inherent coupling between the dynamics in each channel and the dynamics of the receiver [14].  
Figure 1: Software-defined receiver (SDR) architecture.
## 3 Results  
The proposed methodological approach is validated through its application to three RC currently operating in an industrial environment, corresponding to assembly, machining, and screwing processes. Each cell is treated as an independent case study, allowing for the assessment of the robustness, flexibility, and generalization capability of the applied evolutionary algorithms.  
For each case, a comparison is made between the original configuration of the RC and the optimized layout generated by the proposed algorithm, considering two main performance metrics:  
* Occupied area of the RC, measured as the minimum rectangular area that encloses all the stations;  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pheromone** & **Heuristic** & **Pheromone** & **Initial** & **Ants (m)** & **Actual** & **Best Time (s)** \\
**Levels (\(\alpha\))** & **Influence (\(\beta\))** & **Evaporation (p)** & **Pheromone \(\tau_{0}\)** & **Interaction** & **Best Time (s)** \\ \hline
1.5 & 2.0 & 0.3 & 1.5 & 6 & 75 & 11.28 \\
1.5 & 2.0 & 0.2 & 1.5 & 6 & 79 & 12.16 \\
2.0 & 1.5 & 0.3 & 2.0 & 6 & 83 & 12.54 \\
1.5 & 2.0 & 0.3 & 1.0 & 6 & 87 & 12.77 \\
2.0 & 1.5 & 0.3 & 1.0 & 6 & 9* Total cycle time, which refers to the time required to complete a full sequence of operations between stations.  
Additionally, the performance of the optimized trajectories was compared to the estimations generated by Mitsubishi RT TOOLBOX3 PRO, a widely adopted industrial software for robot motion planning. This comparison served to benchmark the proposed model against a validated commercial reference, confirming that the algorithmic solutions align closely with those obtained using industry-standard tools. The consistency in cycle times and trajectory feasibility further supports the practical relevance of the optimization framework.
## 3 Results and Discussion
### Assessment of Cutting Noise
However, all the input parameters are said to be significant and have relevant effects on cutting noise. Among all input parameters, the depth of cut was attributed to the highest impact (33%), succeeded by flow rate (28.16%), feed (23.89%), and cutting speed (14.73%).
# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 3 Materials and Methods
### Classifier Models
#### 3.4.2 Recurrent Neural Networks (RNNs)  
Recurrent Neural Networks trust the principle that sequential information processing is primarily based on the Elman network [44]. An RNN recursively applies the previously computed results into a computation for every instance in an input sequence. Figure 5 shows a simple RNN structure [11; 43]  
The capacity for memorization of the previous results is the main difference or advantage of an RNN [11]. So, it is convenient for various NLP tasks like sentiment analysis, speech recognition, etc. In practice, these simple RNNs suffer from a vanishing gradient problem, which complicates the learning and tuning parameters of the preceding layers in the network [11].  
This problem has led to the development of various RNN derivative models like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU).
## 3 Results and Discussion
### Stress Relaxation Characteristics of Machine-Harvested Seed Cotton
#### 3.2.1 Effect of Moisture Content on Stress Relaxation Characteristics  
As shown in Figure 4a, as the moisture content increases, a smaller compression force is required when the seed cotton is compressed to the same density [23; 32]. The main reason for this phenomenon is that the cotton fiber, cottonseeds, cotton and other components of the water absorption are slightly different, the higher the moisture content of is, the smaller the elastic modulus is would be [25; 42]. With the increase of seed cotton moisture content, it is easier for seed cotton to be densely compressed, and it requires less compression force. As shown in Figure 4b,c, the minimum value of \(T_{1}\) appeared at 18% with increasing moisture content, and \(E_{e}\) and \(\varepsilon\) gradually decreased with moisture content increasing. This is due to the fact that the increasing moisture content disrupts the hydrogen bonds in the cell wall, shielding the interaction forces between macromolecules, and increasing intermolecular flexibility [41], thus making it easier to move, requiring less compression force, and the faster the stress relaxation rate, the smaller the residual stress ratio. It shows that seed cotton relaxes better at a high moisture content.
# On the Robustness of No-Feedback Interdependent Networks
## 2 Models
### Failure Mode and Evaluation Indicator of Robustness  
In order to analyze the influence of subnetworks (e.g., power grid and communication network) reliability on the robustness of interdependent systems, many studies have considered the case of failure nodes in only one subnetwork, which is insufficient to fully reveal the failure characteristics of interdependent networks. Therefore, according to the location of the initial failure nodes, we defined two failure modes, i.e., unilateral failure and bilateral failure, to analyze the robustness in this paper. Denote \(N_{A}^{f}\), \(N_{B}^{f}\) as the number of initial failure nodes in subnetworks A and B, respectively, then the two failure modes can be described as follows [45].  
Unilateral failure (UF): a fraction of \(p_{uf}\) failure nodes are randomly distributed in only one subnetwork, for example, subnetwork A.  
\[p_{uf}=\frac{N_{A}^{f}}{N_{A}},\ \ \ \ 0\leq p_{uf}\leq 1 \tag{1}\]  
Bilateral failure (BF): a fraction of \(p_{bf}\) failure nodes are randomly distributed in the whole interdependent networks [16; 43].  
\[p_{bf}=\frac{N_{A}^{f}+N_{B}^{f}}{N_{A}+N_{B}},\ \ \ \ \ 0\leq p_{bf}\leq 1 \tag{2}\]  
Based on Reference [1], we assumed that the initial random failure led to a cascade of failures, and that only nodes belonging to the giant component of subnetworks remained functional. The cascading failure ended when the size of the giant component no longer changed. At this point, there are two common indicators to measure the robustness. The first one is the size of the giant component after cascading failure, which is denoted as \(S\). Specifically, this indicator under UF is the giant component size of subnetwork A, which is remarked as \(S_{A}\) hereinafter. When it comes to BF, it means the giant component size of the whole interdependent networks, and is denoted as \(S_{AB}\). The second indicator is a collapse threshold, denoted as \(f_{c}\), representing the minimum size of initial failure that causes the interdependent networks to complete collapse. The collapse threshold under UF is defined as \(f_{c}^{A}=\min(p_{uf})\), where \(p_{uf}\) is restricted by \(S_{A}(p_{uf})=0\). Similarly, the collapse threshold under BF is defined as \(f_{c}^{AB}=\min(p_{bf})\), where \(p_{bf}\) subjects to \(S_{AB}(p_{bf})=0\).
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 4 Methodological Approaches in Crash Research
### Advanced Bayesian and Spatial Methods  
The field progressed toward more sophisticated approaches that could better handle the complex spatial and temporal dependencies inherent in crash data. [PERSON] and [PERSON] [35] employed Full Bayes (FB) hierarchical modeling frameworks, incorporating spatial and temporal effects for county-level crash analysis. In subsequent methodological work [36], they demonstrated the importance of spatial correlation structures in road crash-frequency models, showing that first-order adjacency structures improve model fit and reduce bias in parameter estimates. Their Bayesian multivariate Poisson lognormal modeling approach [37] shows methodological improvements in precision estimation across severity levels, though requiring extensive data for effective calibration.  
[PERSON] et al. [43] advanced the field with bivariate negative-binomial spatial conditional autoregressive models for joint analysis of crashes and violations, enabling the identification of high-risk areas while accounting for spatial relationships. [PERSON] and [PERSON] [42] investigated spatial heterogeneity using random-parameter negative binomial and semi-parametricgeographically weighted Poisson regression methodological frameworks, demonstrating that geographically weighted approaches better capture spatial heterogeneity and crash data correlation for regional crash modeling.  
[PERSON] and [PERSON] [38] proposed integrated modeling approaches under the Multinomial Generalized Poisson architecture for the simultaneous analysis of crash frequency and severity. [PERSON] et al. [39] extended this methodological framework with spatial Multinomial Generalized Poisson models, demonstrating superior performance of spatial modeling approaches, particularly the spatial exogenous-EMGP model for capturing spatial dependencies in crash data. [PERSON] and [PERSON] [40] developed accident modification factors using cross-sectional data, particularly effective for large roadway systems, with their work on curve radius AMFs showing higher crash risks on curves.
# Numerical Simulation-Based Study on the Arching Effect in Subsequent Backfill
## 3 Numerical Simulation
### Study on the Influence of Interface Friction Angle on Stress Distribution Patterns  
As shown in Figure 10, this section analyzes the evolution of vertical stress (\(\sigma\)v) and horizontal stress (\(\sigma\)h) along the central axis of the backfill when the ratio of the interface friction angle to the backfill's internal friction angle varies between 0.4 and 1.0. Numerical simulation results reveal that when the interface friction angle is smaller than the backfill's internal friction angle (\(\delta<\varphi\)), both vertical and horizontal stresses in the backfill gradually decrease with increasing ratio.  
By comparing the working conditions with or without the embedding of the contact unit, it is found that when \(\delta<\varphi\), the stress state of the filling body is mainly affected by \(\delta\). When \(\delta=\varphi\), the stress distribution result tends to be consistent with the working conditions without the addition of the contact surface unit. It further indicates that in the stope, in the stope numerical simulation, if the actual friction angle between the surrounding rock and the filling body is less than the internal friction angle of the filling body, a contact surface element needs to be added to the model to accurately reflect the interface mechanical behavior. Otherwise, due to the absence of the contact surface effect, the arching effect during the simulation process will be overestimated, resulting in the stress calculation results of the filling body being significantly lower than the actual value, thereby affecting the reliability of the simulation results.  
In-depth analysis indicates that the introduction of the contact element significantly alters the stress response mechanism of the filling body. Under the non-contact unit working condition, the stress distribution is mainly regulated by the arching effect formed by the friction angle (35.7\({}^{\circ}\)) within the filling body. After introducing the contact element (with an interface friction angle of 25\({}^{\circ}\)), the stress control rights are transferred to the contact interface. Since the friction angle at the interface is significantly lower than that inside the  
Figure 10: Comparative analysis of the influence of interface element friction angle on stress.  
filling body, the arched effect in the contact element model is significantly weakened, and the stress value increases accordingly. This phenomenon clearly reveals the dominant role of the mechanical parameters of the contact interface in stress regulation: When there are contact elements, the interface strength parameters will take priority over the parameters of the filling body itself in determining the stress distribution characteristics. This discovery provides an important basis for understanding the interaction mechanism between the filling body and the surrounding rock, indicating that in engineering practice, the accurate determination and reasonable design of contact interface parameters must be emphasized.