esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, we modeled the bristles in a brush seal as a porous medium and conducted 2D CFD simulations. When the porosity is set based solely on static geometrical values, the leakage flow rate tends to be underestimated. To address this, we adjusted the porosity value to achieve accurate prediction with respect to experimental data. Additionally, we developed and validated an equation that predicts the blow-down effect for a brush seal with a clearance, without the need for FSI analysis. The key conclusions from this study are as follows:  
The leakage flow rate of a brush seal in the contact state could be accurately predicted within a pressure ratio range of 1.5-6 by correcting the porosity based on the geometry and pressure ratio. By implementing the flow resistance derived from the corrected porosity, the leakage flow rate can be predicted with more than twice the accuracy (in terms of R2) compared with standard 2D CFD simulations.  
For a brush seal with a clearance in the blow-down state, the leakage flow rate could be predicted accurately using the developed porosity correction coefficient. Observations from the 2D CFD flow field indicated that in the presence of a clearance, the pressure drop occurs more rapidly, and changes in both the axial and radial velocities are more pronounced. The maximum turbulence kinetic energy is observed near the clearance.  
In the case of a brush seal with a clearance, even without accounting for bristle bending, the leakage flow rate could be predicted with appropriate accuracy through 2D CFD simulations by considering the blow-down clearance. When the pressure ratio ranged between 1.5 and 4.0, the relative error between the leakage flow rates measured for the Turner et al. geometry [27] and those calculated using the proposed formula remained below 5%.  
The main advantage of this formula lies in the accuracy of the predictions, with the relative error being less than 5% even without the use of FSI analysis. The porosity correction factor and blow-down calculation formula developed in this study represent significant advancements in brush seal design. Compared with previous research [23], the use of 2D CFD simulations in this study represents a more efficient and cost-effective approach for predicting leakage flow rates. However, the limitations of 2D simulations must also be considered as they cannot fully capture the 3D effects and complex FSIs involved in real-world applications.  
[PERSON] carried out the simulations, analyzed the CFD data, and wrote the paper. [PERSON] supervised the research, analyzed the CFD data, and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Agency for Defense Development Grant funded by the Korean Government (UD220004 JD).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.
## 2 Numerical Simulation Methodology  
### Geometric Model and Grid Generation  
#### 2.1.1 Geometric Model  
Based on the actual dimensions and layout of the exhaust fume system of a canteen kitchen in Tianjin, the geometric model of the representative CCKEF system, including kitchen stoves, lamps, personnel, exhaust hood, air duct and terminal electrostatic purifier together with make-up air was established in this paper using CFD software.  
The case kitchen was a large cooking room, with length, width and height of 8.1, 8.6 and 3.0 m, respectively. The kitchen was located in the interior of the building, without exterior doors or windows, and was connected to a dining area and a preparation area through two doors (door 1: 1.4 m \(\times\) 2.0 m; door 2: 1.4 m \(\times\) 3.0 m). The cooktop was 5.7 m long, 1.2 m wide and 0.74 m above the ground, and it consisted of four cauldron stoves (S1 to S4 from left to right) and one stockpot (S5) located in a line against the wall. All burners used natural gas, consuming five cubic meters per hour. The cauldron stove had a rectangular combustion exhaust vent 0.35 m \(\times\) 0.12 m in size. During cooking, there were often three chefs standing about 0.10 m in front of the cooktop. The kitchen was equipped with six 130 W lamps and a 3.6 m \(\times\) 0.74 m \(\times\) 0.8 m shelf in the middle of the kitchen. The refrigerators and washing basins were located far from the working area, so they had little effect on the indoor flow field and were not considered when modeling. The exhaust fume system was composed of a wall-mounted canopy hood, terminal two-zone static purifier, rectangular duct, centrifugal fan and mechanical make-up air. The dimension of the canopy hood was 5.74 m in length, 1.2 m in width and 0.5 m in height, and it was located against the wall on three sides. The vertical distance between the front lower edge of the hood and the cooktop was 1.06 m. There were three suction vents 0.46 m \(\times\) 0.24 m in size connecting the hood and air duct. The size of the outlet was 1.03 m \(\times\) 1.0 m. The air diffusers (1.2 m \(\times\) 1.2 m) of the mechanical make-up air system were located on the ceiling, 1.2 m from the edge of the exhaust hood.  
The structures of the stoves, personnel and electrostatic purifier were relatively complex, so they were reasonably simplified when modeling. Cauldron stoves and stockpots were simplified to cylinders with diameters of 850 mm and 350 mm and a height of 60 mm. Three cooks were simplified to rectangular block models (1.7 m height, 18% head ratio, 40% upper body proportion, 42% lower body proportion) [11]. The geometric model of stoves and personnel is shown in Figure 1a. The model of the electrostatic fume purifier was simplified, as shown in Figure 1b. The diameter of the wire plate was 10 mm, with 45 mm between plates all around. The length of the collecting plate in the collection region was about 650 mm, with 45 mm between plates on both sides. The geometric model of the CCKEF system is displayed in Figure 1c.
## 2 Materials and Methods
### Seed Treatment with Cold Plasma (CP) and Electromagnetic Field (EMF)  
The equipment and the conditions used for seed treatment have been described earlier [32; 36] in more detail. Seeds were treated with radiofrequency (RF) EMF using RF generator frequency 5.28 MHz. The EMF strength components at the center of the induction coil, where the packages with seeds were placed, were following: root-mean-square value of magnetic component \(H\)-590 A/m (\(B\approx\) 0.74 mT); the electric component \(E-12.7\) kV/m; amplitude values \(H^{\star}=\sqrt{2H}\) and \(E^{\star}=\sqrt{2E}\) were 835 A/m (\(B\approx\) 1 mT) and 17.96 kV/m, respectively. The treatment was performed for 10 and 15 min (these treatments are further abbreviated as EMF10 and EMF15, respectively) at atmospheric pressure, room temperature (20-22 \({}^{\circ}\)C) and 35-50% relative humidity.  
Seed treatment with CP was performed in a planar geometry plasma reactor. The capacitively coupled RF discharge operated at 5.28 MHz in air at a pressure of 200 Pa. The RF voltage from an RF generator was applied to the upper electrode by a commutator, and the input power density of 0.025 W/cm\({}^{3}\) was applied in all experiments. An open sterile Petri dish with evenly dispersed seeds was placed on a grounded electrode before pumping air from the chamber. A pressure of 200 Pa was achieved in the chamber by pumping air from it for approximately 7 min before plasma ignition. Separate experiments were performed to test the effect of vacuum treatment (for 7 min) on seed germination, as an additional control for CP effects. The results showed that exposure to a vacuum (without plasma discharge) does not have an effect on the germination of buckwheat seeds. CP treatments lasted for 5 or 7 min (these treatments are abbreviated as CP5 and CP7, respectively). Control, CP- and EMF-treated seeds were stored at room temperature (19-22 \({}^{\circ}\)C) until further investigation. All experiments were performed in at least three replicates as indicated in the legends.
## 3 Results and Discussion
### Drying Effect Depending on Enzyme Dosage
The equation for calculating the DEI is as follows (Equation (6)), and the DEI was plotted, as shown in Figure 2.  
\[\text{DEI (\%)}=100-\text{(Digestibility of OD pulp/Digestibility of ND pulp) * 100} \tag{6}\]  
The highest DEI, which represents the biggest drying effect, increased as enzyme dosage increased and were revealed at the earlier stages of hydrolysis (Figure 2). The highest DEIs were 22.3 (4 FPU), 24.1 (10 FPU) and 29.3 (20 FPU) and these were observed at 96, 24 and 6 h, respectively. Enzymatic hydrolysis of the OD pulp began in the non-collapsed part first, followed by hydrolysis of the collapsed part because enzyme access to the non-collapsed part was easier than access to the collapsed part [39]. Higher enzyme dosage resulted in the biggest difference in digestibility (DEI) at the earlier stage (29.3 at 6 h) because there was a considerable difference in the amount of collapsed pores in the ND and OD pulp. The ND pulp was hydrolyzed quickly due to the small amount of collapsed pores and the fast hydrolysis of the non-collapsed pores in the ND pulp by more enzymes resulted in a big difference in the DEI in 6 h. As the hydrolysis progressed, the reversible collapsed pores were converted to partial collapsed pores by rewetting in buffer solution and the cellulose conversion to sugars induced by progress in the hydrolysis provided more pore volume in the cellulose [40]. Thus, the DEI with 20 FPU decreased as hydrolysis progressed, which meant that there was less difference in enzymatic digestibility between the ND and OD pulp. Therefore, it was concluded that more enzymes caused a higher drying effect at the initial stage of enzymatic hydrolysis and the time for the highest drying effect as revealed earlier. Once the drying effect reached maximum, the drying effect was reduced as hydrolysis progressed when 10 and 20 FPU of enzymes were used. When 4 FPU of enzymes was used, however, there was little difference in the DEI as hydrolysis progressed, unlike 10 and 20 FPU of enzymes. It was considered that the slower rate of hydrolysis with less enzymes caused more inhibition due to a decrease in enzyme activity, and thus, new pores formed during hydrolysis could not dramatically enhance enzyme accessibility in 120 h. In summary, the drying effect on enzymatic hydrolysis increased as the enzyme dosage increased, and the highest DEI occurred in the earlier stages with higher enzyme dosage. As enzymatic hydrolysis with 10 and 20 FPU of enzymes progressed, the drying effect decreased gradually due to the formation of pores in the OD pulp during hydrolysis. When 4 FPU was used, however, a decrease in the DEI was not observed in 120 h because there was not enough pore formation from enzyme inhibition. Figure 2: Drying effect index depending on enzyme dosage and hydrolysis time.
# Comparing End-to-End Machine Learning Methods for Spectra Classification
## 3 Data-Driven End-to-End Neural Network Methods
### End-to-End Binned FCNN with the Automatically Capturing Weighting Factors Model  
In this model, the spectra classification task is regarded as a general two-dimensional (2D) space segmentation problem. The 2D mentioned here means that instead of inputting a single variable, the values of the two coordinates \((x,y)\) of each feature point are input into the neural network model, and the model is taught which class label this point belongs to. The model aims to automatically prominent features with high separability and suppress features with low separability. Obviously, the separability of the spectra in the overlapping regions is very low, which is indicated by the low classification accuracy and reliability. Taking this into account, we divided the spectral data into several bins of the same size and used a two-layer Fully Connected Neural Network (FCNN) with the same structure to learn representative features in each local bin. The learned features are then fed into another fully connected (FC) layer to learn the local believability weighting factors of different bins, which are used to classify any spectral time series.  
The structure of the end-to-end binned FCNN with the automatically capturing weighting factors model is shown in Figure 2, which is an extension of our previous work [60]. In this model, each FCNN sub-module consists of two fully connected layers (FCs), with each one followed by a batch normalization operation and a rectified linear unit (ReLU) [61] or sigmoid activation function [62] to speed up the training process and avoid overfitting. The input layer has 2 input neurons, corresponding to the dimensions (two coordinates) of the input spectra, the hidden layer has 150 neurons, and the output layer has 2 output neurons. Then, the output of these FCNN sub-modules is concentrated and fully connected to the final output neuron. The weighting factors learned by the last FC layer can assign different weights to different bins, thereby reducing the influence of the interval of indistinguishable features, and making the features in bins with high separability and reliability more dominant, which can better extract features and learn patterns from the input data. The reason why the sigmoid function is used instead of ReLU after the second FC layer is that we treat each FCNN sub-module as a complete classification model in each bin, so the function of the last FC layer is mainly to assign an overall classification influence weight to each bin.  
When the number of bins is 1, the model becomes a standard three-layer FCNN, also known as a multi-layer perceptron (MLP). In Section 4, we experimentally demonstrate the advantages of the bin-weighting technique in terms of classification confidence, required training epochs, and training time.
## 2 Methods  
### EWOD Device Fabrication  
Dimples are used for the purpose of producing a small effect to slow down a droplet, but it is an important effect that helps with position control. Although there are many parameters, such as curvature and depth, that optimize the dimple shape, the range of operable droplet sizes mainly depends on the size of the electrode tile, the gap between the electrodes, and the thickness of the insulating film. Hence, in order to introduce the beneficial effect of the dimple and the curved shape fabrication technique, which is difficult to achieve by conventional microfabrication in an easy-to-understand manner, we demonstrated a single condition as a representative example rather than the examination of a large amount of parameters.  
The structure of the EWOD device in this study was a laminate of an insulation layer and an electrode layer, as shown in Figure 1. The end shape of all electrodes on the device was a jagged tile, 2 mm square, and dimple structures were embossed at the center of the tiles. To facilitate the embossing, a thin paper sheet (Circuit Paper, TAKEO Co., Ltd. Tokyo, Japan) was used as the electrode layer [21; 22], instead of a solid material such as glass, epoxy resin, or acrylic resin. Thus, the sheet can be easily deformed into any shape by embossing with a low load.  
The electrodes were patterned onto the paper sheet by an ink-jet printer (PX-S160T, EPSON, Suwa, Nagano, Japan), as shown in Figure 2A. Subsequently, a 10-15 \(\upmu\)m-thick paraffin membrane was laminated as an insulation layer onto the paper sheet on which the electrode was printed (Figure 2B). Next, a 2 mm diameter sphere was pressed into the laminated sheet using a commercially available embossing device (CURIO, Silhouette America, Inc., Lindon, UT, USA) to form a dimple structure in the shape of a spherical cap (Figure 2C). The spherical-cap shape without any edges can avoid an unnecessary pinning effect of droplets. Finally, Fluorinert (FC-70, Sigma-Aldrich Co., St. Louis, MO, USA) of 2-3 \(\upmu\)L as a water repellent was poured with a pipette onto the surface of the laminated sheet with emphasis on a high lubricating ability and ease of production (Figure 2D). A liquid film of less than 5 \(\upmu\)m in thickness was formed on the surface. Although this thickness greatly affects the motion of the droplets in conventional EWODs, this effect could be ignored using our proposed method.
###### Abstract  
This study aimed to evaluate the intra- and inter-rater reliability of a well-used and a less-used IsoMed-2000 dynamometer for knee flexion and extension peak torque (PT) measurements in a concentric test in athletes. Two IsoMed-2000 isokinetic dynamometers were used, an old one that was well-used and a new one that was less-used. Thirty-seven weight-lifting athletes (12 female, 25 male) were included in this study. Both legs of each athlete were tested twice on each isokinetic dynamometer at 60\({}^{\circ}\)/s and 180\({}^{\circ}\)/s. The Intraclass Correlation Coefficients (ICC), Standard Error Measurement (SEM), Smallest Detectable Change (SDC), SDC% and 95% limits of agreement were calculated to evaluate the intra- and inter-rater reliability of a well-used and a less-used IsoMed 2000 dynamometer for knee flexion and extension peak torque in athletes. Intra-rater reliability was moderate to excellent (ICCs: 0.516-0.928) for knee muscle strength parameters, and the highest SDC and SEM values were 0.76 N/kg and 0.28 N/kg, respectively. Inter-rater reliability was moderate to excellent (ICCs: 0.519-915) for knee muscle strength parameters, and the highest SDC and SEM values were 0.81 N/kg and 0.29 N/kg, respectively. At least 89.2% of the obtained values of parameters fell within the 95% limits of agreement. Our findings demonstrate the intra and inter-rater reliability of a Well-Used and a Less-Used IsoMed 2000 Dynamometer for Knee Flexion and Extension Peak Torque in Athletes.  
mscle strength; muscle strength dynamometer; quadriceps muscle; hamstring muscles 40.0 +
Footnote †: journal: Applied Sciences
## 2 Existing Leak Detection and Localization Methods
### Methods Based on Model Estimation  
#### 2.2.1 State Observer  
The working principle of a state observer for pipeline leak detection is as follows: the state equations of pressure and flow amount are set up. Two pressure extremes are detected as inputs, proper algorithms are adopted for leak point detection and localization according to the error signal from measured and observed values. However, this method supposes that both pressure extremes are not affected by the size of the leak, so it is only suitable for the case of small leaks.
## 2 Bullet Low-Overlap Point Cloud Registration Based on Line Feature Detection
### Fitting the Registration Central Axis
A good coarse registration position of two low-overlapping point clouds on the coaxial line is obtained by matching the central axis of two point clouds. ```
0: Point cloud data \(P_{1}\) and \(P_{2}\) of two bullets
0: Pre-registered point clouds \(P_{1}\)' and \(P_{2}\)' along the same central axis
1: Compute \(y_{min}\) and \(y_{max}\) for point cloud \(P_{1}\)
2: Divide the \(y\)-axis range \([y_{min},y_{max}]\) into \(n\) layers, each with height \(\Delta y=(y_{max}-y_{min})/n\)
3:for each layer\({}_{k}\)do
4:\(\mathrm{layer}_{k}\leftarrow[y_{min}+(k-1)\times\Delta y,y_{min}+k\times\Delta y]\)
5:\((x_{i},y_{i},z_{i})\leftarrow\{(x,y,z)\,|\,y_{min}+(k-1)\times\Delta y\leq y \leq y_{min}+k\times\Delta y\ \}\)
6:endfor
7:for each layer\({}_{k}\)do
8: Randomly select four non-coplanar points: \((x_{1},y_{1},z_{1})\), \((x_{2},y_{2},z_{2})\), \((x_{3},y_{3},z_{3})\), \((x_{4},y_{4},z_{4})\)
9: Verify the four points are not coplanar
10: Solve the circumsphere equation to find the sphere center \((h,l,k)\) using SVD
11: Collect all sphere centers to form point set \(S\)
12:endfor
13: Remove invalid points from \(S\) that exceed the point cloud's \(y\)-axis range to obtain filtered point set \(S\)'
14: Centering and Principal Component Analysis (PCA)
15: Fit the central axis line of \(P_{2}\) similarly to find line points \(L\)'
16: Pre-register \(P_{1}\) and \(P_{2}\) by aligning their central axis lines \(L\) and \(L\)'
17: Output the pre-registered point clouds \(P_{1}\)' and \(P_{2}\)'
18:end
```  
**Algorithm 1** Fit the registration central axis
# The Multi-Branch Deep-Learning-Based Approach to Heart Dysfunction Classification
## Appendix C Detailed Classification Metrics
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Class** & **Branch \(\alpha\)** & **Branch \(\beta\)** & **Branch \(\gamma\)** & **Branch \(\delta\)** & **Branch \(e\)** & **Branch \(\zeta\)** \\ \hline BBB & **0.168511** & 0.150489 & 0.142449 & 0.125670 & 0.138135 & 0.161637 \\ PR & 0.288329 & 0.239487 & **0.293595** & 0.209079 & 0.226762 & 0.247999 \\ PVC1 VPB & 0.019314 & 0.023006 & **0.024045** & 0.022753 & 0.023053 & 0.018600 \\ LAD & **0.009799** & 0.008004 & 0.008996 & 0.008559 & 0.007857 & 0.008395 \\ RAD & 0.031662 & 0.021621 & 0.054467 & **0.060397** & 0.032883 & 0.029689 \\ RBBB1 CRBBB & **0.485937** & 0.453626 & 0.151398 & 0.272633 & 0.451654 & 0.431056 \\ TInv & **0.100043** & 0.097450 & 0.076004 & 0.080507 & 0.094054 & 0.094478 \\ LQT & 0.033165 & 0.038837 & 0.037169 & 0.033916 & **0.041195** & 0.031897 \\ AF & 0.137991 & 0.129667 & 0.117187 & 0.135084 & 0.132488 & **0.153919** \\ AFL & 0.031456 & 0.032690 & 0.028123 & 0.030008 & 0.032883 & **0.034437** \\ CLBBB1 LBBB & **0.017826** & 0.017134 & 0.015624 & 0.014879 & 0.016581 & 0.016242 \\ QAb & **0.068688** & 0.059787 & 0.043720 & 0.049373 & 0.061453 & 0.065315 \\ TAb & 0.064161 & **0.069054** & 0.048225 & 0.051718 & 0.064215 & 0.053443 \\ LPR & **0.032322** & 0.030697 & 0.023627 & 0.028327 & 0.030717 & 0.032171 \\ LQRSV & 0.225078 & 0.161834 & **0.300479** & 0.259752 & 0.198885 & 0.232782 \\ IAVB & **0.078050** & 0.077610 & 0.067554 & 0.068107 & 0.077128 & 0.075704 \\ PAC & 0.033016 & 0.029706 & 0.030575 & 0.030556 & **0.038625** & 0.032716 \\ PRWP & 0.052666 & **0.064037** & 0.032782 & 0.046813 & 0.048928 & 0.056393 \\ SB & 0.018717 & 0.016466 & **0.022133** & 0.020623 & 0.015999 & 0.019577 \\ Brady & 0.044600 & 0.040733 & 0.030434 & 0.030733 & **0.045010** & 0.042662 \\ NSR & 0.011988 & 0.008612 & **0.017281** & 0.017039 & 0.006470 & 0.010014 \\ STach & 0.068210 & 0.058044 & **0.089775** & 0.036488 & 0.056051 & 0.072335 \\ SA & 0.516392 & **0.539388** & 0.429084 & 0.468672 & 0.530921 & 0.500997 \\ LAnFB & 0.107838 & 0.113882 & 0.144941 & **0.148862** & 0.101003 & 0.099940 \\ NSIVCB & **0.282408** & 0.275423 & 0.224748 & 0.222806 & 0.267445 & 0.251068 \\ IRBBB & 0.061868 & 0.061248 & 0.059925 & 0.056756 & **0.063729** & 0.062275 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: F1-Score at each branch per class presented as average of five folds. Significantly better results are marked in bold.