esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of the Failure Mechanism of Soft Rock Mining Roadways Based on Limit Analysis Theory
## 2 Analysis of the Deformation and Failure of a Soft Rock Mining Roadway  
### Limit Analysis Theory  
#### 2.1.1 Upper Bound Limit Analysis Method  
The upper bound limit analysis method is based on the principle of virtual work and the variational extremum theorem. It analyzes the problem from the perspective of the failure mode and energy dissipation of the loaded system and does not pay attention to whether the stress distribution of the system satisfies the static equilibrium condition. Its biggest feature is the establishment of a correct failure mode for the loaded system and a corresponding velocity field before solving the upper bound solution. The failure mode is mainly composed of a rigid slider and a velocity discontinuity line, where the rigid slider is separated by the velocity discontinuity line. In the plastic flow area, the velocity of the particle changes in size and direction along a line, which is called the velocity discontinuity line. It can also be considered as the boundary between the rigid zone (elastic zone) and the plastic zone. When failure occurs, the entire rigid slider motion system needs to meet the plastic flow and velocity compatibility conditions required by limit analysis theory and should also meet the strain-velocity compatibility conditions and velocity boundary conditions. According to the principle of virtual work, the power of the slider in the velocity discontinuity line by the external force is equal to the internal energy dissipation rate on the velocity discontinuity line. Its expression is  
\[\int v\sigma_{\bar{\mathrm{ij}}}\epsilon_{\bar{\mathrm{ij}}}\mathrm{d}V= \int_{S}T_{\mathrm{i}}v_{\mathrm{i}}\mathrm{d}s+\int_{V}X_{\mathrm{i}}v_{ \mathrm{i}}\mathrm{d}V \tag{1}\]  
In this formula, \(\sigma_{\bar{\mathrm{ij}}}\) is the stress tensor in the velocity field; \(\epsilon_{\bar{\mathrm{ij}}}\) is the strain rate in the velocity field; \(T_{\mathrm{i}}\) is the surface force acting on boundary \(S\); \(X_{\mathrm{i}}\) is the body force vector acting in region \(V\); and \(v_{\mathrm{i}}\) is the velocity vector on the velocity discontinuity line.
## 2 The Pliant Probability Distribution Family
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distribution** & **Parameters and Domain of the Pliant** & **Approximated CDF** \\  & **Probability** & \\ \hline Weibull & \(\alpha>0\), \(\beta>0\), \(\gamma=1\), \(m>0\), \(x\in(0,m)\) & \(F_{W}(x;\alpha,\beta)=\left\{\begin{array}{ll}0&\text{if }x\leq 0\\ 1-e^{-ax^{\beta}}&\text{if }x\geq 0\end{array}\right.\) \\ Exponential & \(\alpha>0\), \(\beta=1\), \(m>0\), \(x\in(0,m)\) & \(F_{exp}(x;\alpha)=\left\{\begin{array}{ll}0&\text{if }x\leq 0\\ 1-e^{-ax}&\text{if }x\geq 0\end{array}\right.\) \\ Logistic & \(\alpha>0\), \(\beta=1\), \(\gamma=-1\), \(m>0\), \(x\in(-m,m)\) & \(F_{L}(x;\alpha)=\dfrac{1}{1+e^{-ax}}\) \\ Standard Normal & \(\alpha=2\sqrt{2/\pi}\), \(\gamma=-1\), \(m>0\), \(x\in(-m,m)\) & \(\Phi(x)=\dfrac{1}{\sqrt{2\pi}}\int_{-\infty}^{x}e^{(-t^{2}/2)}dt\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some approximations by the Pliant CDF. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\alpha\) & \(\beta\) & \(\gamma\) & \(m\) & \(d\) & Figure 3 & **Approximation Distribution** \\ \hline
1.13 & 2.14 & 1 & 3.97 & 0.261553 & (a) & Weibull \\
7.87 & 4.68 & 1 & 3.51 & 0.145400 & (b) & Weibull \\
1.16 & 1 & 1 & 2.32 & 0.327034 & (c) & Exponential \\
0.76 & 1 & 1 & 1.12 & 0.269252 & (d) & Exponential \\
5.76 & 1 & \(-1\) & 1.12 & 0.218460 & (e) & Logistic \\
0.23 & 1 & \(-1\) & 2.58 & 0.472546 & (f) & Logistic \\ \(2\sqrt{2/\pi}\) & 1 & \(-1\) & 2.71 & 0.359576 & (g) & Standard Normal \\ \(2\sqrt{2/\pi}\) & 1 & \(-1\) & 0.98 & 0.355597 & (h) & Standard Normal \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Some examples for Pliant probability function \(F_{p}(x;\alpha,\beta,\gamma,m)\).
# Simplified Fly Tower Modeling for Preliminary Acoustic Predictions in Opera Houses
## 2 Methodology
### Simplified Fly Tower Modeling Applicable Conditions  
The proposed simplified fly tower modeling aims to reduce the problems of the theater design phase. Giving fixed stage conditions to be used to check the acoustic design development of the hall of a new opera house, it optimizes the sound necessities according to the architectural requests, ensuring the low sensitivity of the acoustic field by changing variable stage set elements during the commissioning and show activities phase. It can also be used to study the acoustic field of an existing theater if measurements on-site are not available, or if the survey and calibration are conducted under conditions with house or fire curtains closed.  
The suggested model is appropriate only if the theater plan design rules relating to proportions between hall dimensions, proscenium opening, and stage floor have been respected; this means the sightlines on-stage work according to the theater's shape, volume, and building construction period [32], which define the positions of the vertical and lateral wings, and consequently the free stage space to set the opera, satisfying the set designers' expectations.  
In the case of a fan shape, for example, a proscenium theater with frontal seats, lateral sightlines normally should be within a preferred \"view angle\" of 30\({}^{\circ}\). This view angle is measured from the perpendicular at the end of the proscenium opening. Floor and balcony slopes should be designed so that the seated audience will have an unobstructed view of the entire performing area, performers, and scenery [32] (Figure 5). If these conditions are not respected, the simplified fly tower modeling no longer represents the free fly tower space for the opera set.  
In the case of the lateral stages or deep lateral wings of the fly tower, to ensure the usability of the simplified model, heavy curtains on tracks, characterized by high absorption coefficients, should be installed to separate the lateral areas from the main central stage.  
In multipurpose auditoriums, the proscenium line may vary from full width for ballet and opera to smaller widths for drama and music performances by soloists or small ensembles. If the opera space is defined, the simplified proposed model can be applied in the prediction acoustic analysis for this configuration. In future research, specific instructions could be provided on how to design acoustic adaptation schemes in conjunction with spa  
Figure 5: Preferred 30\({}^{\circ}\) sightline limit for fan-shaped theaters.  
tial functional conversion and the use of new acoustic materials such as metamaterials [49] can be developed.
## 5 Conclusions  
In this article, a planar, bow-tie antenna comprising three distinct bow-tie geometries superposed in an overlapped fashion is presented for wideband applications. The antenna has an area of 85.6 mm \(\times\) 82.2 mm (0.35\(\lambda\)\(\times\) 0.33\(\lambda\)) and a \(kr\) of 1.10, calculated at the lowest frequency in the \(-10\) dB bandwidth. Based on the concept of the multi-resonances principle, the superposition of three different bow ties paired with circular top loading results in an enhanced \(-10\) dB bandwidth of 80.3% (1.23-2.88 GHz) from the measurement. The antenna maintains a steady bi-directional radiation pattern and a realized gain of 1.5-3.8 dBi over its entire bandwidth. The antenna is fabricated and measured for verification. The proposed antenna design does not require any ground plane and since its design is two dimensional, it can be printed on a thin film for potential use as a flexible antenna for conformal applications, wearable antennas and smart textiles, drones, and energy harvesting. Its wide bandwidth capabilities allow for high data transmission rates, and the simultaneous transmission of multiple signals at different frequencies with no interference, supporting multiple functions (for example, localization, communication, etc.) with a single antenna.  
Data curation, L.S.; writing--original draft preparation, L.S.; writing--review and editing, S.L.; supervision, S.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 6 Micromegas Readout of TPCs
### Micromegas Readout TPCs for Long Baseline Neutrino Experiments
This scheme provides three major improvements with respect to the standard Micromegas: (i) the spreading of the charge over a larger number of pads provides more complete information about the track position, which greatly improves the spatial resolution with a lower number of individual channels; (ii) the resistive layer provides a way to quench and decrease the spark energy, which enables the design of a much more compact Front-End Electronic readout without dedicated discrete spark protection; and (iii) the grounded mesh provides a better uniformity of the electric field near the module edges, minimizes the track distortions, and allows, if needed, the operation of the ERAM detectors at a different gas gain. The final RC configuration of the T2K/HA-TPC ERAM detector is still under final definition (see a pre-series detector on Figure 14b) as a trade-off between the charge spreading and the effective gain of the detector. The measured performances of the firstnon-optimized prototypes are given in [72] and already confirmed the improvement in the spatial resolution, 300 \(\upmu\)m with respect to the 600 \(\upmu\)m obtained with the standard Micromegas configuration.
# Path Tracking Control Strategy Based on Adaptive MPC for Intelligent Vehicles
## 1 Introduction
While enhancing dynamic adaptability, the method suffers from MPC's computational demands and fuzzy rules' empirical limitations in complex scenarios. [PERSON] et al. [31] enhanced APF with the safety ellipse theory and velocity potential field for dynamic obstacles, combined with 3-DOF MPC for tracking. While improving path efficiency and dynamic avoidance, manual ellipse parameter tuning limits adaptability, and real-time performance suffers in highly dynamic scenarios. To enhance the tracking control performance and driving stability of the MPC controller under complex curve conditions, a condition-adaptive MPC controller for intelligent vehicles is proposed in this paper. The proposed MPC algorithm differs significantly from the traditional MPC algorithm in several aspects. Firstly, in terms of model establishment, the traditional MPC may employ a relatively simplified vehicle model. In contrast, this paper develops a three-degree-of-freedom vehicle dynamics model that accounts for longitudinal, lateral and yaw motions of the vehicle. This comprehensive model better reflects the actual motion state of the vehicle, providing a more accurate basis for path tracking control. Secondly, regarding constraints, the traditional MPC often employs fixed constraints. In this study, the stability boundaries for the yaw rate and lateral velocity are derived, and adaptive constraints are established. This means that constraints can be adjusted according to the vehicle's actual driving conditions rather than remaining static. This adaptability allows the controller to better handle various complex scenarios. Thirdly, the parameter determination differs. The traditional MPC typically uses pre-set time domain parameters. In this work, based on the traditional MPC theory, the optimal time domain parameters under 100 typical conditions are obtained using a genetic algorithm. Subsequently, the feedback from vehicle speed and road curvature is introduced into the MPC controller. This real-time matching of control parameters enables the controller to adjust the strategies based on current vehicle speed and road curvature, achieving adaptive path tracking control. This represents a notable improvement over the traditional MPC, which may struggle to adapt well to changes in driving conditions.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 3 Proposed Scheme
### Hiding Module
#### 3.1.4 Decimal-Binary Conversion  
Hereafter, each value of \(S\) is normalised so that the peak-to-peak range is the same as the original host signal (i.e., [\(-\)1 1] V). The result of this normalisation corresponds to the stego signal, in the time domain.  
Due to the low modification percentage of the original signal, the stego signal is perceptually equal to the original host signal. It must keep the content of the host signal with the least possible distortion (that is, with a low decrease of the signal quality or with a low SNR (Signal-to-Noise Ratio) value). Distortion is strictly related to the \(BH\) parameter. The lower the \(BH\) value, the higher the distortion. In the results section, the relation Distortion versus \(BH\) will be analysed.
## 3 Results and Discussion
### Crack Evolution Process
#### 3.1.3 Analysis of Crack Evolution  
In order to directly reflect the process of crack generation and expansion of the cement mortar specimen under load, the stages of 3600 N and 5000 N with obvious cracks were selected for analysis. Image analysis software was used to obtain the voxel number of the crack, as shown in Figure 7. As can be seen from the figure, with the increase in the load, the number of cracks in different voxel ranges increases, especially in the range of 0-50 voxel. The number of cracks in this range under different loads is significantly higher than that in other voxel ranges, indicating that there are many microcracks in the specimen. When the load increased to 5000 N, there were no more large cracks but many small cracks.  
Figure 5: Three-dimensional crack distributions under different loads.  
XY plane image slices with cracks under 3600 N and 5000 N were compared. Fifteen cracks were selected from the image slice under 5000 N to measure the average crack width, and the same measurement was performed at the corresponding position on the image slice under 3600 N (Figure 8a,b). The crack width at positions 1-15 under different loads is presented in Figure 8c. It is noticeable that a few cracks were distributed inside the mortar under 3600 N, and they mainly appeared at positions 4, 8, 12, and 13. When the load increased to 5000 N, cracks appeared at all selected positions. Crack widths under 5000 N were higher than those under 3600 N, except for position eight, indicating that new cracks were generated, and original cracks continued to expand with the increase in the load. The crack width at position eight under 5000 N was lower than that under 3600 N because the formation of new cracks around position eight compressed the existing cracks and produced a closure effect [28].  
Figure 6: Two-dimensional crack distributions in the central section (XY plane) of cement mortar at different loading stages. (**a**) F = 0 N; (**b**) F = 2000 N; (**c**) F = 2500 N; (**d**) F=3000N; (**e**) F = 3600 N; (**f**) F = 5000 N.  
Figure 7: Crack volume count distribution of sample.
## 4 Discussion
### Isolation and Identification Methods  
Culture-based methods favor the growth of microorganisms best fitting with the laboratory conditions used (namely, culture media, temperatures, and incubation times). In this study, we found that the most frequent experimental settings were favorable to fast-growing, highly-sporulating fungi, with the use of culture media rich in easily accessible carbon sources, alongside short incubation times and optimal growth temperatures favoring their sporulation. Otherwise, lower growth temperatures (\(\leq\)20 \({}^{\circ}\)C), wide temperature ranges, different isolation media, and a longer incubation time could enlarge the detectable culturable fraction.  
Since the early 2000s, molecular phylogenetic methods have highlighted the limitations of morphological identification, allowing us to gain a better understanding of the kingdom of Fungi [132]. Nowadays, the identification by barcode regions sequencing is a common practice. Even if the nuclear ITS region has been recognized as a fungal barcode, its discriminating power changes within the taxonomical groups, and other/more barcode regions are often necessary to have a reliable identification [133]. This is the case of the identification of species within large groups, as _Fusarium_, _Penicillium_, _Aspergillus_, and _Cladosporium_ genera, where cryptic species can be detected only by sequencing multiple molecular markers [134].  
In detail, _Fusarium_ species determination has been best made with the combined phylogeny of protein coding genes such as elongation factor (TEF1), RNA polymerase (RPB2) and the partial \(\beta\)-tubulin (BT2) gene [134]. To discriminate between _Penicillium_ and _Aspergillus_ species, \(\beta\)-tubulin (BT2) and calmodulin (cmdA) genes have been proposed as secondary barcodes, respectively [135; 136]. While the most phylogenetic informative markers for _Cladosporium_ were TEF1 and actin gene (actA), ITS sequences being identical for species of the same complex [137; 138].  
The correct identification of strains is required in order to provide restorers more information about strains' ecology and degradative potential. In this light, standardized identification protocols should be implemented.  
High throughput sequencing methods have recently been applied to cultural heritage purposes. These methods represent a powerful tool to define the whole fungal diversity present but not necessarily to deepen the mechanisms and the main actors of the deterioration phenomena [139]. The combination of culture-based and molecular methods should be used for a better understanding of deterioration processes. Indeed, pure cultured microorganisms represent the key to uncover settlers' physiological and ecological traits, as well as representing a resource for many in silico applications and barcoded identifications [123; 124].
# A Graph Neural Network Node Classification Application Model with Enhanced Node Association
## 5 Experiment and Result Analysis
### Analysis of the Over-Fitting Phenomenon  
When classifying small samples with graph neural networks, overfitting is likely to occur due to the comparatively small number of samples in the training set. The principal manifestation is that the training set has a very high accuracy rate in the classification results, while the validation set has a very low validation result. This will impact the final accuracy of the experiment, so the Dropout layer and the early stop algorithm are added to the graph convolutional model in the experimental phase of this paper to immediately stop the graph neural network classification experiment when there is a clear overfitting phenomenon, and the result of the last iteration is used to complete the node classification. Table 5 displays the classification results derived by the ENode-GAT model by dividing the dataset using various scales.  
When experimenting with limited samples of graph neural networks, the division of arbitrary dataset proportions does not significantly affect the accuracy of the training set or the validation set, as shown in the table above. The various scaled divisions and respective stop rounds demonstrate that the introduction of the early stop algorithm plays a significant role in terminating the experiment prior to the occurrence of the overfitting phenomenon, thereby ensuring the accuracy of the experimental classification results. In addition, the results in the table above demonstrate that this method can effectively prevent the occurrence of overfitting and that graph neural networks have the unique ability to classify tiny sample nodes.
###### Abstract  
Recent advancements in sensor technology have resulted in the collection of massive amounts of measured data from the structures that are being monitored. However, these data include inherent measurement errors that often cause the assessment of quantitative damage to be ill-conditioned. Attempts to incorporate a probabilistic method into a model have provided promising solutions to this problem by considering the uncertainties as random variables, mostly modeled with Gaussian probability distribution. However, the success of probabilistic methods is limited due the lack of adequate information required to obtain an unbiased probabilistic distribution of uncertainties. Moreover, the probabilistic surrogate models involve complicated and expensive computations, especially when generating output data. In this study, a non-probabilistic surrogate model based on wavelet weighted least squares support vector machine (WWLS-SVM) is proposed to address the problem of uncertainty in vibration-based damage detection. The input data for WWLS-SVM consists of selected wavelet packet decomposition (WPD) features of the structural response signals, and the output is the Young's modulus of structural elements. This method calculates the changes in the lower and upper boundaries of Young's modulus based on an interval analysis method. Considering the uncertainties in the input parameters, the surrogate model is used to predict this interval-bound output. The proposed approach is applied to detect simulated damage in the four-story benchmark structure of the IASC-ASCE SHM group. The results show that the performance of the proposed method is superior to that of the direct finite element model in the uncertainty-based damage detection of structures and requires less computational effort.  
surrogate models; uncertainties; non-probabilistic; interval analysis; wavelet packet decomposition (WPD); data analytics +
Footnote †: journal: Applied Sciences  
Article