esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effects of SGS Severity on Glottal Flow Dynamics, Vocal Fold Vibration, and Acoustics
#### 3.2.1 Glottal Flow Rate Waveform  
For all of the cases, self-sustained vibration was reached after the second cycle, and eight steady vibration cycles were generated. Following the method in the baseline case, the phase-averaged glottal flow rate waveform over one vibration cycle was plotted for each case and shown in Figure 8a. It is found that there are no noticeable changes (less than 1%) in the peak glottal flow rate up to 75% area reduction, which is similar to the results in [PERSON] and [PERSON] [18]. Beyond 75% area reduction, the flow rate drops rapidly with the area reduction. It is also interesting to notice that, when the area reduction is beyond 75%, the glottal flow rate waveform shows a delayed glottic closure. Quantitatively, the glottic closure occurs at the phase of 0.71 T, 0.71 T, 0.72 T, and 0.76 T, corresponding to 75%, 90%, 96%, and 99% area reduction, respectively. With the phase of the peak flow rate remaining unchanged in all of the cases, the delayed glottic closure means a prolonged duration of flow deceleration. As a result, the skewness quotient decreases much quicker when the area reduction is beyond 90% (Figure 8b). The combined effect of the reduced peak flow rate and prolonged flow deceleration time also results in corresponding decreases of MFDR (Figure 8c). A lower MFDR leads to reduced sound intensity. Therefore, our results indicate that both the flow rate and sound intensity start to show noticeable reduction when the area reduction is above 75% and quicker reduction when the area reduction is above 90%; below 75% area reduction, they are nearly not affected. Interestingly, the 75% threshold corresponds well to the clinical observation that area reduction below 70% is categorized into Grade I and Grade II SGS, for which no treatment is normally needed.  
Figure 7: (**a**) The phase-averaged sound pressure waveform at a distance of 5 cm outside of the mouth in the baseline case. (**b**) Frequency spectrum of the sound pressure.
## 3 Results and Discussion
### Thermal Reflection Characteristic
#### 3.2.3 The Change in Heat Flux Reflected for 3 Consecutive Days  
As illustrated in Figure 12, there is minimal variation in the heat flux reflection by the three walls over three consecutive days. According to Table 1, the heat flux reflection ratio of the earth wall remains consistent over time, whereas the ratios for the block and brick walls show some variation, suggesting that the coating on the earth wall achieves a stable insulation effect more rapidly.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Block Wall** & **Brick Wall** & **Earth Wall** \\ \hline The first day & 0.22 & 0.18 & 0.22 \\ The second day & 0.26 & 0.22 & 0.21 \\ The third day & 0.26 & 0.26 & 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Heat flux reflectance ratio of three walls for three consecutive days after one coat of paint (\(\theta\) is 10\({}^{\circ}\)).  
Figure 11: Three walls after one coat of paint and wall reflection heat flux comparison of indoor reflective heater (\(\theta\) is 10\({}^{\circ}\)\(\sim\)60\({}^{\circ}\), step size is 10\({}^{\circ}\)).  
Figure 12: (**a**) Heat flux reflected by the heat source for 3 consecutive days by the block wall with one coat of paint. (**b**) Heat flux reflected by the brick wall with one coat of paint for 3 consecutive days by the heat source. (**c**) Heat flux reflected by the earth wall with one coat of paint for 3 consecutive days by the heat source (\(\theta\) is 10\({}^{\circ}\)).
# Entropy Optimization in MHD Nanofluid Flow over an Exponential Stretching Sheet
## 7 Conclusions  
In this study, the physical influences of certain parameters on nanofluid flow and heat-mass transfer past an exponentially stretching sheet, and entropy generation, have been analyzed. From this study, we may conclude as follows:  
1. The MD-BSQLM converges rapidly with a high degree of accuracy. The accuracy may be enhanced by increasing the number of collocation points.
2. Increases in thermophoresis and thermal radiation parameters lead to increases in both the skin friction and mass transfer coefficients.
3. An increase in thermophoresis, Brownian motion, or thermal radiation parameters leads to a decrease in the rate of heat transfer.
4. The entropy generation rate can be minimized through the temperature difference parameter and temperature Grashof number.  
Figure 8: The influences of (**a**) the temperature difference parameter, and (**b**) temperature Grashof number on the entropy generation profiles.  
Figure 7: The influences of (**a**) the local Reynolds and (**b**) Brinkman numbers on the entropy generation profiles.  
**Author Contributions:** Formal analysis, M.A.; Methodology, H.M.; Resources, Z.M.; Supervision, P.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Data and Methodology
### Methodology
#### 3.3.2 Global Models  
Ordinary Least Squares  
In the OLS model, regression parameters are estimated using the least-squares method. OLS assumes that the relationship between independent variables and the dependent variable is uniform across the study area, and the regression coefficients represent the average effect across the entire region [60], which can be denoted in Equation (5):  
\[y=\beta_{0}+X\beta+\varepsilon \tag{5}\]  
where \(y\) is the vector of the dependent variable, \(X\) is a matrix of independent variables, \(\beta_{0}\) represents the intercept of the regression model, \(\beta\) presents the vector of the regression coefficients, and \(\varepsilon\) is the vector of random errors.  
However, since OLS does not account for the influence of neighboring regions, when spatial autocorrelation is present in the data, OLS may lead to inaccurate estimates. Therefore, in addition to the OLS model, this study applies SEM and SLM to account for spatial dependencies and improve the accuracy of the estimates.  
Spatial Error Model  
SEM assumes that error terms exhibit spatial dependence, meaning that the error term of one observation can influence the error terms of neighboring observations [28]. To reflect this, SEM introduces spatially autocorrelated error terms into the regression model, which helps to capture the spatial correlations between the errors, thus improving the estimation precision. The basic form of SEM can be expressed in Equation (6):  
\[y=\beta_{0}+X\beta+\mu,\ \mu=\lambda W\mu+\varepsilon \tag{6}\]  
where \(\mu\) is the vector of spatially correlated error terms, referring to those similar urban environmental characteristics hidden between adjacent neighborhoods, in which \(\lambda\) is the spatial error coefficient and \(W\) is the spatial weight matrix obtained based on queen contiguity.  
In contrast to SEM, SLM assumes that the impact of independent variables on an observation has a lag effect. Specifically, the independent variables at a given observation not only influence that observation's dependent variable but may also affect neighboring observations. To model this, SLM introduces a spatially lagged dependent variable, linking the dependent variable to neighboring areas. This method effectively captures spatial dependencies, which can be denoted as in Equation (7):  
\[y=\beta_{0}+\rho Wy+X\beta+\epsilon \tag{7}\]  
where \(\rho\) is the spatial lag coefficient. This formulation allows the model to capture the effect of spatial autocorrelation by directly including the influence of neighboring values of the dependent variable on each observation, with the coefficient \(\rho\) quantifying the strength and direction of this spatial dependence.
## 2 Numerical Model Validation  
### Numerical Model  
The numerical models in this study were built based on a geosynthetic reinforced segmental retaining wall that was part of the SR-18 highway in Seattle, Washington [10]. [PERSON] et al. [41] also simulated this wall using the finite-difference program FLAC. Figure 2 shows the numerical model of the SR-18 wall that was built using FLAC. The total wall height was 6.4 m, including a backfill height of 6.1 m and a pavement thickness of 0.3 m. The height of the embedment soil at the wall toe was 0.8 m. The vertical facing column consisted of concrete blocks with 460 mm in length, 300 mm in with, and 200 mm in height. The reinforcement layers had a uniform vertical spacing of 0.6 m and a uniform length of 7.9 m. The spacing between the lowest reinforcement layer and the foundation surface was 0.2 m. A high-density polyethylene uniaxial geogrid (Tensor UXK1100) was used as the reinforcement layer. The backfill was a well-graded, silty, gravelly sand with a maximum particle size of 38 mm and was compacted to reach a 95% maximum dry density (corresponding to a density of 2200 kg/m\({}^{3}\)) to construct the wall. The foundation soil consisted of dense, silty, sandy gravel.  
The wall was constructed in layers. The final grading was completed at 4060 h after the beginning of construction. At this time, the wall height was 6.1 m. Pavement construction started at 7200 h and ended at 10,100 h. The final height of the wall was 6.4 m at 10,100 h. The time of 4060 h was considered as the effective end time of construction because, after that, construction progressed very slowly. The different parameters of the wall were also recorded at this time. Hence, the numerical modeling in this paper was conducted for the effective construction stage.  
Figure 2: Numerical model of SR-18 wall.  
In this numerical modeling, the modeling procedures reported by [PERSON] and [PERSON] [17] were used for backfilling in layers of the reinforced soil wall. The procedures were as follows: (1) generating the grids of the facing block and the backfill soil behind the block, and subsequently adding the related interfaces; (2) cycling the model with a temporary vertical load of 8 kPa on the backfill surface to equilibrium followed by unloading; (3) generating the structural elements to simulate the reinforcements at a specific height of the wall; and (4) repeating the first three steps until the wall model reached the desired height. The left and right boundaries of the numerical model were fixed in the horizontal direction. The bottom boundary of the numerical model was fixed horizontally and vertically.
# A Viscoelastic-Plastic Creep Model for Initial Damaged Coal Sample Affected by Loading Rate
###### Abstract
[9] developed a moisture-dependent creep constitutive model for coal-rock composites, with its predictive capability rigorously validated through multiscale numerical simulations under complex hydro-mechanical coupling conditions. Yang et al. [10] systematically investigated the time-dependent deformation characteristics of highly friable and soft coal-rock masses under varying moisture content conditions, revealing the critical occurrence of secondary creep stages under specific hydro-mechanical coupling thresholds. Tarifard et al. [11] conducted a comprehensive evaluation of the long-term effects of creep deformation and groundwater interaction on tunnel lining loads in weak rock masses, revealing the critical coupling between rock mass creep behavior and hydrogeological conditions that dominate structural degradation mechanisms. Liu et al. [12] developed an anisotropic time-dependent deformation and damage constitutive model for rocks under true triaxial compression, thereby providing enhanced mechanistic insights into the long-term stability evolution of engineering structures in complex stress regimes. Yin et al. [13] conducted short-term uniaxial creep experiments on sandstone-coal composite samples, revealing the influence of sandstone on the creep behavior of coal. Zhang and Wang et al. [14] developed a nonlinear damage creep model for rocks under cyclic loading and unloading. This model explains how rock behaves differently in transient, steady-state, and accelerated creep stages, providing theoretical support for predicting rock deformation. Huang et al. [15] experimentally investigated the creep behavior of coal with initial damage and elucidated the influence of strain history on the deformation characteristics of initially damaged coal specimens. King [16] conducted creep tests on shale under different temperatures and axial stress levels, obtaining creep curves of shale at various temperatures and stress levels. Y. Fujii [17] performed triaxial creep tests on granite and sandstone, acquiring axial creep, circumferential creep, and volumetric creep curves. B. Ladanyi [18] considered the rheological effects of rock and indicated through monitoring the stress and deformation of the initial lining in underground chambers that the calculation results of the viscoelastic model were lower than actual values. Ito H [19,20] carried out decade-long bending creep tests on granite and -limestone via creep experiments. Pomeroy [21] studied the creep characteristics of coal at room temperature in the laboratory. Danesh [22] investigated the creep characteristics of coal and their impacts on permeability, analyzing the permeability characteristics of two gases under different confining pressures and pore pressures. Extensive research has been conducted by scholars both domestically and internationally on creep models of coal-rock masses.
# Detecting the Speed Change Intention from EEG Signals:
## 2 Materials and Methods
### Analysis IMUs  
To create a predictive model that could detect the brain patterns associated with the intention of changing the walking speed, the registered EEG signal must be properly labeled according to the IMUs information (Figure 1). For the four selected IMUs, the calculation of the acceleration modulus the three axes were used: X, Y and Z. A moving mean (_movement_ in MATLAB) filter was applied to the four signals with a processing size equal to the IMUs sampling period and a three order Savitzky-Golay filter. The average of each signal was suppressed and the four signals were averaged. In order to label the speed changes, all peaks of the processed signal (_findpeaks_ MATLAB function) were detected after the first six steps to avoid the first slope when the subject starts walking (Figure 3). The difference in amplitude between each point and the previous point was also computed. The first point of the maximum difference between peaks (see red points in Figure 3) was selected as the change instant. This value was resampled to the frequency sample of the EEG.  
Figure 2: On the left of the image, the protocol times for training trials of experiments (**a**) E1 (without exoskeleton) and (**b**) E2 (with passive exoskeleton) are shown. Without assistance or any kind of feedback, the user brain signal is recorded. In test training trials (right side of the image), the exoskeleton is controlled in closed-loop active mode, changing the speed of the exoskeleton depending on the real-time decoding of the subject’s intention. This second protocol allows to validate the BMI obtaining the assessing metrics of its effectiveness executing the mental task in an unplanned way.  
It was also detected when the first walking step was performed in each repetition, following a segmentation method [20].  
To characterize the acceleration direction, the following rule was applied: _If the maximum value of the repetition is twice the average of the first two seconds after the first step, it is labeled as Increase. If not, the repetition is labeled as Decrease._
## 3 Methodology
### Preprocessing  
As computation time is crucial in medical diagnosis applications, the original image is resized from 4050 \(\times\) 2050 pixels to 640 \(\times\) 640 pixels to achieve a low-cost process. After resizing the image, the input RGB image is converted to a 2D intensity image of its green component in order to reduce processing complexity and achieve effective nuclei extraction. As shown in Figure 5, we investigated the corresponding image's individual R, G, and B components from the original RGB image. It is reasonable to infer that cell nuclei on the green channel are more distinguishable from other components due to higher contrast, thereby motivating us to use the green component of CPE images in further processes. The quality of the image is further improved using histogram stretching [19], and contrast limited adaptive histogram equalization (CLAHE) [20; 21] on the green components. The visual before and after results and the preprocessing step are shown in Figure 6.
## 3 Results
### Seasonal Variations  
The variability in multiyear mean MODIS, BESS, and VPM GPP in China exhibited a clear seasonal pattern for 8-day periods (Figure 4). The changing trends throughout the year were quite consistent, despite different magnitudes among these products. Specifically, during the wintertime, GPP was quite low because of lack of active photosynthetic activity for the deciduous forests and grasslands in most areas of China. However, GPP started to rise rapidly with vegetation growth in the springtime, reached peak values in mid-to-late July of the year, and then gradually fell with leaf withering in autumn. From the end of a plant growing season to the next growth cycle in early spring, almost no difference existed between the MODIS and BESS GPP products, whereas the VPM GPP estimates were a bit lower. However, during the summertime, both BESS and VPM GPP products were apparently higher than MODIS GPP. This can be explained by the algorithms of both BESS and VPM models considering the differences of C3/C4 plants, while the MODIS model neglected this point. This resulted in MODIS overestimates for small values and underestimates for high values. Thus, treating photosynthesis pathways of C3/C4 vegetation separately is crucial for the accurate estimates of regional GPP. Meanwhile, the scatterplots in Figure 5 showed that compared with VPM GPP, both MODIS and BESS GPP have strong correlations. Particularly, a strong linear relationship was found between BESS GPP and VPM GPP, with R\({}^{2}\) and RMSE attaining to 0.997 and 0.35 g C m\({}^{-2}\) d\({}^{-1}\). Therefore, this study indicated that by comparison with MODIS GPP, the BESS product provided a more reliable GPP estimate of terrestrial ecosystems over 8-day periods.  
Figure 3: Comparison of multiyear mean MODIS and BESS GPP over the eight climate zones in China from 2001 to 2015. Error bars represent \(\pm 1\) standard error. The asterisks mean significantly different for GPP values in 2001–2015 (\(p<0.05\)).
## 2 Materials and Methods
### Blood Culture, In Vitro Blood LPS Stimulation and Effects of Caffeine and 5-CQA  
Whole heparin blood samples (n = 10) obtained by venepuncture were subsequently diluted with a 1:3 ratio with culture media (RPMI-1640 Medium, Sigma, St. Louis, MO, USA). The diluted whole blood was then pipetted into the wells of a sterile 12-well plate (Sarstedt, Numbrecht, Germany). To each well, 50 \(\upmu\)L of either medium (control sample), pharmaceutical grade caffeine powder (1,3,7-trimethylxanthine, dissolved in medium to yield final concentrations in a culture of 5, 10, 15, 30, and 50 \(\upmu\)M) or 5-CQA (dissolved in medium to yield final concentrations in a culture of 5, 20, 200 nM, and 2 and 20 \(\upmu\)M) were added. For each experiment, caffeine and 5-CQA, a different set of controls was applied. Following a 1 h incubation at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) atmosphere, a total of 50 \(\upmu\)L of LPS (Escherichia coli serotype 055:B5; Sigma, St. Louis, MO, USA; final concentration 10 ng/mL dissolved in culture media) or the same volume of culture media (spontaneous production) were added to each well. Culture plates were then incubated for a further 24 h at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) atmosphere. Immediately following incubation, the samples were then centrifuged at 1000\(\times\)\(g\) for 10 min to obtain the supernatants. The supernatant aliquots were then stored at \(-\)70 \({}^{\circ}\)C until measurements were performed.
# Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning
## 1 Introduction
This algorithm is tailored to meet the requirements of current network identification tasks and not only identifies known traffic but also distinguishes unknown traffic in real time with high identification accuracy. The main contributions of this paper are as follows:  
* We propose an intelligent feature processing method that uses a multiple-channel parallel neural network to extract temporal and spatial features from raw network traffic data, combined with feature fusion based on the mRMR algorithm, which achieves a good balance between accuracy and time consumption in traffic detection. * We present a clustering approach based on the density ratio to distinguish between known and unknown traffic features and construct new classes for unknown traffic. The method can dynamically expand the clustering results by adding new data, thus improving efficiency and handling noise. * We improve an incremental learning multi-class SVM classifier that autonomously learns based on the features of known traffic and detected unknown traffic, without the need to train the classifier from scratch each time. * We establish an incremental learning model for unknown traffic identification and validate its performance on the public datasets ISCX-VPN-Tor, NSL-KDD, and a self-coself-collected dataset SelfDataset. The remainder of this paper is structured as follows: The study background and related studies are given in Section 2, with an emphasis on the commonly used techniques for identifying unknown network activity. Our suggested framework for an incremental unknown traffic detection model is detailed in Section 3. The setup and results of the experiment are described and discussed in Section 4. Finally, the entire paper is summarized in Section 5.