esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Computational Fluid Dynamics Analysis after Bimaxillary Orthognathic Surgery
## 4 Discussion
The velopharynx is where airway internal pressure changes significantly. At this location, with its large pressure gradients, wall shear stress is also high. Thus, the shape of the conductive wall can be easily altered. Figure 6: An overlay of the patient’s pre-surgery and post-surgery CT images. This shows the movement of the maxillomandibular complex. The airway at T2 decreased overall and became particularly narrower at the velopharynx, orophrx, and epiglottis. This is because the hyoid bone moved after the operation. As a result, the flow rate in the orophrx and laryngopharynx increased and significant sound pressure developed. This means that the patient will have to exert more effort to obtain the same amount of air. The greatest difference in speed and pressure occurs around the epiglottis. This creates a strong vortex structure at that level. In the oropharynx, vortices form and are located on both sides of the airway. Locations where the velocity and pressure gradients are large include the velopharynx, orophrx, and epiglottis, with narrow cross-sectional areas. Wall shear stress is also observed at this location. The velopharynx, oropharynx, and epiglottis are the structures most vulnerable to morphological changes, meaning that they can easily become obstructed. This is the same result observed in patients with OSA. In the present study, we effectively simulated the airflow pattern according to airway type of the patient using CFD, and we predicted the occurrence and location of airway obstruction when applied to an individual who underwent bimaxillary orthognathic surgery. Limitations of the present study include its small sample size (\(n=1\)) and the use of CBCT performed with the patient in an upright position, which does not consider changes in airway patterns due to gravity. Furthermore, the study did not consider other factors that cause OSA, such as a high body mass index. However, we believe that the reduction in airway area according to the amount of movement of the upper and lower jaw can be quantified by performing follow-up studies with larger sample sizes. Furthermore, we believe that airway reconstruction and CFD analysis can be performed in individual patients. As such, it is possible to anticipate and prepare for changes in airway, airflow, and potential problems after surgery. We believe that the results of the present study are sufficiently significant as a preparatory step in this regard.
# EEG Emotion Classification Based on Graph Convolutional Network
## 2 Literature Review
### Machine Learning Approach  
Machine learning is a widely used approach in EEG emotion recognition. It often starts with preprocessing the raw signal and extracting hand-crafted features. Then, features are fed into a machine learning model, such as a support vector machine, K-nearest neighbor, decision tree, etc., to classify emotion states.  
Many studies have been carried out that focus on evaluating the effectiveness of different features. Ref. [17] explores power spectral density, differential asymmetry and rational asymmetry of the paired channels under multiple frequency bands. These features are processed by a support vector machine to recognize emotions. It finds that differential asymmetry is more robust to detect the brain dynamics caused by emotions. Moreover, information provided by the channels from the frontal and parietal lobe is useful to distinguish emotions. Ref. [18] conducts studies on emotion classification with different features as input. During the process, feature dimensionality reduction techniques, such as principal component analysis and linear discriminant analysis, are adopted to improve the efficiency and accuracy. The experiment results indicate that the power spectrum was identified as the most effective amongst all input features and the high frequency band tends to be more useful in emotion classification. These studies show that the choice of input features can largely affect the results.  
To compare which classifiers have the best performance, Ref. [6] utilizes statistical data, i.e., min, max, mean and standard deviation, as the input. Then, it adopts a K-nearest neighbor, regression tree, Bayesian network, support vector machine and artificial neural network for classification. The experiments show that the K-nearest neighbor and support vector machine give the best results among all the models. However, it can be challenging for the majority of machine learning methods to work well with large datasets. Ref. [19] employs discrete wavelet transform and spectral features. In the classification stage, it applies a support vector machine with the aid of a radial basis function kernel to process features from 10 channels to do the classification. Ref. [20] employs empirical mode decomposition/intrinsic mode functions and variational mode decomposition to process the raw EEG signal which is widely used in biomedical studies. These methods are used to decompose nonlinear and non-static signals and feed them into VMD to identify low and high frequencies. Then, it extracts two non-linear features: entropy and Higuchi's fractal dimension. Finally, it carries out experiments by using Naive Bayes, K-nearest neighbor, decision tree and convolutional neural networks to recognize emotions. A common observation from these studies is that the support vector machine often generates the best outcomes in emotion classification tasks.
## 4 PFAC Calibration Results
### Sentinel-3B Cal/Val  
Three Sentinel-3B passes are calibrated at the PFAC. Calibration over sea surface is performed for the ascending Passes No. 14 and No. 71 at the CRS1 and RDK1 Cal/Val sites, respectively, using the latest processing baseline for Sentinel-3B (PB 1.33). The implemented calibration regions for Sentinel-3B are shown in Figure 19. The results refer to the Operational Phase of Sentinel-3B, which is when the satellite has been on its nominal orbit. Calibration results obtained during the Sentinel-3B Tandem Phase are given separately in Section 4.3.  
Figure 19: The Sentinel-3B calibrating regions over sea surface for its Pass No. 14, No. 71 and No. 335 in west Crete, Greece. Sea-surface calibration is carried out relying upon the CRS1 and RDK1 Cal/Val sites. The CDN1 transponder Cal/Val site has been used for the calibration of Sentinel-3B pass No. 335 (descending).  
Figure 18: The latest calibration results for the descending Pass No. 278 of Sentinel-3A using the calibration methodology at the CRS1 Cal/Val site.
## 6 Computational Complexity  
Computational complexity is an important factor in numerical simulations. The IO FDTD algorithm has \(O(n)\) complexity with regard to the number of iterations (i.e., time  
Figure 5: Spatial electric-field distribution for computational domain combining vacuum and FO material. Dashed line indicates boundary between materials, with vacuum on the left and FO material on the right side of line. Simulation results for time \(t\): (**a**) \(6.880\times 10^{-14}\) s. (**b**) \(7.556\times 10^{-14}\) s. (**c**) \(1.207\times 10^{-14}\) s. (**d**) \(1.647\times 10^{-14}\) s.  
steps). In the case of FO FDTD, complexity increases to \(O(n^{2})\) because, in every iteration, all the previous electromagnetic-field values are included in the update procedure. The quadratic dependence on the number of time-steps is confirmed numerically, with the results presented in Figure 6. All the presented execution times are obtained with the use of an Intel(R) Core(TM) i7-8750H processor. The OpenMP-based parallelization is used in our code in order to distribute computations among the CPU cores.  
The execution time, depending on the size of the computational domain, is presented in Figure 7. The complexity is linear, i.e., the same as in the case of IO FDTD.  
Apart from a larger time complexity, FO FDTD also suffers from a significantly larger memory demand. As all the previous field values need to be stored, the occupied memory grows linearly with time. In the IO case, on the other hand, it remains constant.  
Higher computational and memory demands are further increased by the necessity of taking lower values of the time-step size in order to satisfy the stability condition (37). As can be seen in Figure 1, the stable \(\Delta t\) values are significantly lower than in the IO FDTD case, even for \(\alpha\) near 1.  
Figure 6: Dependence of execution time on iteration number \(N_{t}\) for \(\alpha=0.98\) and varying domain size \(N_{z}\).
## 3 Results and Discussion
### SG4 Induced Cell Cycle Arrest of HT-29 Cells  
The effect of SG4 on cell cycle profile of HT-29 cells was examined using flow cytometry-based analysis. As shown in Figure 2a,b, when HT-29 cells were treated with 200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of SG4, SG4 exhibited a higher percentage of cells in the sub-\(G_{1}\) phase (\(13.9\%\pm 0.1\%\)) as compared to the control (\(3.0\%\pm 0.1\%\)). Previous investigations suggested that DNA fragmentation as well as small fragments of DNA can be detected when cells are treated with an apoptosis-inducing agent [3; 27]. Cells that have lost DNA possess less PI fluorescence intensity, and this reduction in DNA content is represented by the so-called sub-\(G_{1}\) peak which appears to the left of the \(G_{1}\) peak. The increased sub-\(G_{1}\) population is directly proportional to the induction of DNA fragmentation, which is a manifestation of apoptosis [27]. Thus, SG4 might cause DNA fragmentation (also known as sub-\(G_{1}\) cell cycle arrest) of HT-29 cells. The retardation of cell growth can be visualized by examining the cell cycle phase distribution [3]. In Figure 2b, a significant increase (\(p<0.001\)) of the \(S\) phase population was found after treatment with SG4 (\(17.4\%\pm 0.2\%\)) as compared to that of the untreated cells (\(16.1\%\pm 0.1\%\)). Moreover, a significant increase (\(p<0.01\)) of the \(G_{2}\)/\(M\) population was also detected after treatment with SG4 (\(20.2\%\pm 0.4\%\)) as compared to that of the untreated cells (\(17.7\%\pm 0.5\%\)). These effects were accompanied by a significant decrease (\(p<0.001\)) of the \(G_{0}\)/\(G_{1}\) population after treatment with SG4 (\(48.5\%\pm 0.3\%\)) as compared to that of the untreated cells (\(63.2\%\pm 0.4\%\)). The results clearly show that the accumulation of cells in the \(S\) phase and \(G_{2}\)/\(M\) phase might account for the induction of cell cycle arrest in HT-29 cells by SG4. In summary, SG4 retarded the growth of HT-29 cells via induction of sub-\(G_{1}\), \(S\), and \(G_{2}\)/\(M\) cell cycle arrest. Previous studies have shown that the induction of cell cycle arrest is not a separate event; indeed, apoptotic cell death is usually preceded by the arrest of cell cycle [28]. Therefore, we conducted experiments involving cell cycle arrest in order to further elucidate the possible mechanisms underlying SG4-induced apoptosis.  
Figure 1: Effects of crude extracts of fucoidans (SG1-SG4) on cell viabilities of HT-29, SV-HUC-1, HEK-293, and BEAS-2B cells: **(a)** HT-29 cells were treated with varying concentrations of SG1-SG4 for 48 h, and cell viability was assessed; **(b)** the IC\({}_{50}\) values of SG1-SG4 to HT-29 cells are presented as determined for **(a)**. (c)** SV-HUC-1, HEK-293, and BEAS-2B cells were coincubated with varying concentrations of SG1-SG4 for 48 h, and cell viability was evaluated. The experiments were run in triplicate. Differences existing between columns are labeled with different letters, \(p<0.05\).
## 3 Results
### SPME Optimization
#### 3.1.4 Incubation Time  
An extended incubation time allows for volatiles to equilibrate in the headspace before sampling. This was useful in the extraction of less volatile compounds in the wine sample, such as ethyl caprylate or ethyl decanoate (Figure 4). The incubation time did not have a large effect on the extraction efficiency of more volatile compounds such as ethyl isobutyrate, isobutyl alcohol, and isoamyl alcohol, likely due to their abundance in the headspace of the sample. Incubation times of 0, 5 min, 10 min, 15 min, and 20 min were investigated to establish the equilibrium of analytes in the headspace. The incubation time of 10 min was chosen, as determined by the extracted mass of semi-volatiles.  
Figure 4: Effects of incubation time on selected volatiles extracted using HS-SPME in an Iowa Marechal Foch. Compound letters correspond to compounds found in Figure 2 (i.e., A—ethyl isobutyrate, B—isobutyl alcohol, L—ethyl palmitate). Conditions: 5 min pre-sampling desorption of 50/30 μm DVB/CAR/PDMS SPME fiber; 4 mL wine sample in a 10 mL threaded glass amber vial with PTFE/silicone septa; 0, 5 min, 10 min, 15 min, and 20 min incubation at 50 \({}^{\circ}\)C; 10 min extraction time at 50 \({}^{\circ}\)C; agitation speed 500 rpm; 5 min desorption time into GC inlet. 10 min incubation time was chosen for the method.  
Figure 3: Effects of extraction temperature on selected volatiles extracted with HS-SPME in an Iowa Marechal Foch. Compound letters correspond to compounds found in Figure 2 (i.e., A—ethyl isobutyrate, B—isobutyl alcohol, L—ethyl palmitate). Conditions: 5 min pre-sampling desorption of 50/30 μm DVB/CAR/PDMS SPME fiber; 4 mL wine sample in a 10 mL threaded glass amber vial with PTFE/silicone septa; 10 min incubation at 35, 40, 50, 60, 70, 80 \({}^{\circ}\)C; 10 min extraction time at 35, 40, 50, 60, 70, 80 \({}^{\circ}\)C; agitation speed 500 rpm; 5 min desorption time into GC inlet. 50 \({}^{\circ}\)C was chosen for the method.
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
## 2 Materials and Methods
### Phantom Geometry  
Two phantoms are used to demonstrate the effectiveness of the single simulation tool used in this study. The first homogeneous phantom consists of a circular region of interest with radius 20 mm. Within this region, two objects are placed, a circle of radius 1 mm placed approximately 4 mm to the left of center, and an ellipse with major axis 2 mm and minor axis 1 mm placed 4 mm to the right of center. The second phantom consists of an approximation to a human breast. The breast region of interest is represented by a circle of radius 50 mm. Glandular tissue rendered as a circle of radius 10 mm surrounds an elliptical tumor with major axis 7 mm and minor axis 3.5 mm. The tumor is located 32.5 mm deep from the right side of the phantom. The optical sources and acoustic detectors are placed around the region of interest in a continuous fashion.  
For creating the finite element meshes, a characteristic length of 2 mm was used for efficiency reasons, though near the absorbing objects, the size of the elements smoothly decreased in order to properly model the objects with sufficient resolution. For the homogeneous phantom mesh, the total number of nodes in the mesh was 3635 with 7268 elements. The mesh for the breast phantom had 12,166 nodes and 24,330 elements. For the time domain simulation of the acoustic propagation, a time step of 30 nanoseconds was used in a Newmark numerical integration method. The homogeneous phantom ran for 700 time steps, while the breast phantom ran for 1700 time steps. A constant speed of sound of 1.5 mm/\(\upmu\)s was used for both the forward and time-reversed simulations.  
The time estimates for the breast phantom are: 0.6 s for the optical simulation, and 1.1 s for the RF simulation. The acoustic simulation took 18.1 s in the optical source case, and 17.3 s in the RF source case. The reconstruction using time reversal took 385.8 s and 408.0 s for the optical and RF source cases, respectively. The homogeneous phantom timings are: 0.16 s and 0.315 s for the optical and RF simulations; 2.1 s and 2.0 s for the forward acoustic simulation; and the time reversal took 38.4 s and 39.4 s for the optical and RF cases, respectively. The timings were done on a Dell Precision 5820 desktop PC, on a single thread. ONELAB has the functionality to run on multiple threads, as well as a graphical processing unit (GPU), but these options were not used in measuring these time estimates.
# Self-Gated Respiratory Motion Rejection for Optoacoustic Tomography
## 2 Materials and Methods  
### Pre-Reconstruction Motion Rejection Approach  
The algorithm suggested in this work aims at motion rejection in OAT systems based on a multi-frame acquisition of time-resolved pressure signals with transducer arrays. Figure 1a schematically depicts two examples of transducer array configurations for 2D and 3D imaging, which are described in more detail in the following sections. The acquired signals are generally arranged into so-called sinograms, where every sinogram represents a single frame (Figure 1b). At a fixed transducer position, k frames (sinograms) are acquired. These frames consist of matrices with rows representing the m time-samples of each signal and columns corresponding to the n transducer elements (channels) of the array. Step 1 of the algorithm consists in rearranging the k frames of the sequence into columns of a 2D matrix containing m x n rows and k columns, which represent the entire sequence of frames at a fixed transducer position (Figure 1c). In the experiments performed, the number of frames acquired at each array position were chosen to adequately capture a complete breathing cycle. Step 2 of the algorithm consists in calculating the autocorrelation matrix of all pairs of frames (MATLAB (Mathworks Inc, Natick, USA) function 'corrcoef'). An example of the calculated correlation coefficients is displayed in Figure 1d. At a fixed transducer position, time decorrelation is expected to be chiefly affected by respiratory motion. Clustering of frames is subsequently done in Step 3 by applying the second order k-means method to the correlation coefficients matrix (Figure 1e, MATLAB function 'kmeans'). In Step 4, the k frames are then divided into two sets based on predetermined knowledge of the characteristic physiology of the animal under specific anesthesia. As a rule, motion frames are typically fewer than static frames. Notably, when scanning at multiple transducer positions, Steps 1-4 are to be repeated for each transducer position. As an example, Figure 1f displays a comparison of the 3D views of a reconstructed image from a single position of the spherical array, as obtained from the averaged selected-frames and from the averaged rejected-frames.
# Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images
## 2 Related Work  
### Fully Convolutional Neural Networks (FCNs)  
A fully convolutional neural network (FCN) [13] is a network model based on deep learning, which is widely used in image segmentation. FCNs have the following advantages:  
1. The full connection layer is replaced with a convolution layer to achieve end-to-end convolution network training.
2. To achieve pixel-level segmentation, all pixel features in the image are classified by prediction. However, for an image with a complicated visual environment, the FCNnetwork still adopts the simplest deconvolution method, which results in blurred contours and serious adhesion of the segmented image.
# Improved Defect Detection Using Adaptive Leaky NLMS Filter in Guided-Wave Testing of Pipelines
## 5 Results
### Results Comparison
#### 5.4.3 Experimental Parameters  
The results of experimental parameters are illustrated in Figure 15. The following conclusions can be drawn from these tests:  
* For defects with higher CSA error size (above 5%), lower frequencies tend to achieve higher SNR after processing where the defect signal was enhanced by at least a factor of 2. The greatest  
Figure 14: Results achieved using fixed model parameters for all frequencies where (**a**) shows the achieved SNR and (**b**) shows the amount of improvement in comparison to the general propagation routine.  
enhancement is achieved from 34 kHz and the maximum SNR is achieved from 36 kHz for these signals.
* Although higher frequencies will result in lower SNR, their enhancements are approximately the same for different sizes of the defect. Nonetheless, their final SNR is almost always lower than all other frequencies, especially in the cases of frequencies above 46 kHz.
* For 3% and 4% CSA defects, the greatest enhancement and gain are achieved using the 38 kHz frequency. Furthermore, as can be seen in Figure 15b, this frequency is affected less with regards to the CSA loss of the defect.
# Failure Assessment of Embankment Dam Elements: Case Study of the Pirot Reservoir System
## 4 Numerical Model of the Zavoj Dam
### Material Properties  
The main goal of numerical simulations conducted within this paper is to verify the proposed methodology for the analysis of the damage impact to individual parts of the dam. For that reason, the calibration of material parameters was not performed, but they were taken from the literature [38]. The adopted material parameters used in numerical simulations of filtration and stress-strain processes are shown in Table 1. The names of the materials in the Table 1 are in accordance with the material names defined in Figure 2.  
In order to analyze the impact of the loss of water resistance function of the grout curtain parts on the total flow through the curtain, numerical simulation of filtration processes with local increase of the filtration coefficient of individual regions was performed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material** & **k (m/s)** & **E (kPa)** & **v (–)** & **\(\gamma\) (kN/m3)** & **c (kPa)** & **\(\Phi\) (\({}^{\circ}\))** & **\(\Phi\) (\({}^{\circ}\))** \\ \hline Clay core & \(1\times 10^{-10}\) & \(1.42\times 10^{5}\) & 0.45 & 17.0 & 15.0 & 22 & 0 \\ Filter 1 & \(1\times 10^{-6}\) & \(6.86\times 10^{5}\) & 0.3 & 18.0 & 88.7 & 26.3 & 26.3 \\ Filter 2 & \(1\times 10^{-5}\) & \(7.13\times 10^{5}\) & 0.3 & 20.0 & 57.2 & 22.1 & 22.1 \\ Upstream rockfill & \(1\times 10^{-3}\) & \(2.16\times 10^{5}\) & 0.3 & 21.0 & 88.7 & 26.3 & 26.3 \\ Downstream rockfill & \(1\times 10^{-3}\) & \(2.16\times 10^{5}\) & 0.3 & 21.0 & 57.2 & 22.1 & 22.1 \\ Bedrock & \(1\times 10^{-7}\) & \(6.00\times 10^{6}\) & 0.2 & 26.0 & 254.4 & 48.7 & 48.7 \\ Grout curtain & \(1\times 10^{-8}\) & \(32.0\times 10^{6}\) & 0.35 & 24.0 & 7160.0 & 35.4 & 35.4 \\ Concrete & \(1\times 10^{-9}\) & \(32.0\times 10^{6}\) & 0.2 & 24.0 & 7160.0 & 35.4 & 35.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of the dam model.  
Figure 6: Discretized spillway of the Zavoj dam.  
The values of the filtration coefficients in individual regions (r1 to r6 in Figure 5) have been increased [39] thousands of times (from \(1\times 10^{-8}\) to \(1\times 10^{-2}\)).
## 3 Data Assimilation  
To enable flexibility and reasonable computational cost, we utilised a variant of the Kalman filter, namely an Ensemble Kalman Filter (EnKF). The main procedure was the recursive computation of the means and covariance matrices of the ensemble system state. Since its first introduction by [PERSON] [32], the EnKF has been widely used in different geoscience disciplines such as oceanic [33], riverine [34], and estuarine [35] studies. The full theoretical formulation for the EnKF framework is presented in Evensen [36].