esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
(4) The reason for the better performance of the four optimal schemes is: by changing the shapes and corresponding design parameters of the vent holes, the maximum radial stress is adjusted to be close to the maximum circumferential stress, and, accordingly, the maximum von Mises stresses at the left and right sides of the hole are adjusted to be approximately equal to those at the bottom and top sides. It could reduce the stress concentration and achieve uniform stress distribution. Figure 10: Von Mises stress distribution along the edges of the vent holes of the initial circular and four optimal schemes. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Schemes**} & **Maximum** & \multirow{2}{*}{**Difference**} & **Minimum** & \multirow{2}{*}{**Difference**} & **Mean** & **Difference** & **Range** & **Difference** \\  & **(MPa)** & & **(MPa)** & & **(MPa)** & & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline Initial circular & \multirow{2}{*}{1272.098} & \multirow{2}{*}{0} & \multirow{2}{*}{556.080} & \multirow{2}{*}{0} & \multirow{2}{*}{818.125} & \multirow{2}{*}{—} & \multirow{2}{*}{716.018} & \multirow{2}{*}{—} \\ scheme & & & & & & & & & \\ \hline Optimal circular & \multirow{2}{*}{1100.762} & \multirow{2}{*}{\(-13.469\%\)} & \multirow{2}{*}{581.258} & \multirow{2}{*}{4.528\%} & \multirow{2}{*}{847.970} & \multirow{2}{*}{3.648\%} & \multirow{2}{*}{519.505} & \multirow{2}{*}{\(-27.445\%\)} \\ scheme & & & & & & & & \\ \hline Optimal elliptical & \multirow{2}{*}{1031.590} & \multirow{2}{*}{\(-18.906\%\)} & \multirow{2}{*}{674.632} & \multirow{2}{*}{21.319\%} & \multirow{2}{*}{859.042} & \multirow{2}{*}{5.001\%} & \multirow{2}{*}{356.958} & \multirow{2}{*}{\(-50.147\%\)} \\ scheme & & & & & & & & \\ \hline Optimal four-arc & \multirow{2}{*}{1042.111} & \multirow{2}{*}{\(-18.079\%\)} & \multirow{2}{*}{683.831} & \multirow{2}{*}{22.973\%} & \multirow{2}{*}{858.025} & \multirow{2}{*}{4.877\%} & \multirow{2}{*}{358.280} & \multirow{2}{*}{\(-49.962\%\)} \\ scheme & & & & & & & & \\ \hline Optimal elliptic & \multirow{2}{*}{927.396} & \multirow{2}{*}{\(-27.097\%\)} & \multirow{2}{*}{779.550} & \multirow{2}{*}{40.187\%} & \multirow{2}{*}{858.501} & \multirow{2}{*}{4.935\%} & \multirow{2}{*}{147.846} & \multirow{2}{*}{\(-79.352\%\)} \\ scheme & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Maximum, minimum, mean, and range (difference between the maximum and minimum values) values of the von Mises stress along the edges of the vent holes of the initial circular and four optimal schemes. Figure 11: _Cont._  
In summary, SDPSO is suitable and valuable for the stress optimization of vent holes with constraints. The developed integrated design optimization method is effective and advanced for reducing the maximum von Mises stress around the vent holes. Remarkably, the adopted four-arc hole has more tremendous advantages in reducing the maximum von Mises stress, followed by the elliptical hole, the race-track hole, and the circular hole.
# Compact Ultra-Wideband Series-Feed Microstrip Antenna Arrays for IoT Communications
## 2 Materials and Methods
### Design of the Antenna Array
Nevertheless, the use of a quarter  
Figure 3: Designed microstrip antenna arrays using a log periodic structure with (a) \(n=5\) elements (b) \(n=7\) elements and (c) \(n=9\) elements. wavelength transformer is essential, and since this array has central frequency of operation of 28 GHz, it was designed for that frequency, as shown in Figure 3b. The different arrays with five, seven, and nine elements were designed following the above conditions, and their final layouts are shown in Figure 3a-c, respectively. The entire design, simulation and optimization process of the array was performed using the Computer Simulation Technology (CST) software, and the final dimensions of the three arrays are provided into Tables 1 and 2.
## 3 Results and Discussion
### Correlation Analysis between Aroma-Active Compounds and Sensory Attributes
ferentation process of black tea promotes the degradation of \(\beta\)-primeveroside [2]. However, its content was not always the richest in every type of tea, according to a previous study [41]. This difference in outcome may be the result of different extraction methods. Compound no. 199, methyl salicylate, was another important aroma-active compound found in all of the tea samples. It is a fatty acid derivative and a major contributor to the jasmine-like aroma of tea, especially in black tea, due to full fermentation [39; 42]. However, the result of this study showed that it could be considered as one of the indicators to distinguish between different types of teas. Although some aldehydes, such as no.19 hexanal, no. 78 (_E,E_)-2,4-heptadien, and no. 72 nonanal, were also formed from fatty acids, their contents were higher in green tea than in oolong tea and in black tea. This indicates that the fermentation process helps in promoting some complicated reactions, such as lipid degradation, which generates a more complex aroma profile for oolong tea and black tea. However, weak lipid degradation resulted in a fresh aroma note of green tea. The aroma-active compounds no. 265, \(\beta\)-ionone and no. 131 safranal were generated from carotenoids [2]. Obviously, carotenoid degradation needs fermentation that can give abundant woody or strong floral aroma to the tea [43]. Moreover, no. 116, 5-methylfurfural and no. 90 furfural were Maillard reaction products [34]. These two aroma compounds were particularly higher in green tea sample no. 1 and no. 10 and oolong tea sample no. 14, which are three different kinds of tea that need roasting during manufacturing [18; 44]. In theory, different aromas of different teas are attributed to the different types of tea processing [38], but according to the results of GC-IMS and GC \(\times\) GC-O-MS, the most direct factor appears to be the degree of fermentation, which was also reported in a previous study [8]. Thus, the function of the shaking process of oolong tea might be similar to that of the fermentation process, which is promoting the degradation of precursors of tea aromas, including carotenoids, lipids, glycosides, and the Maillard reaction. However, different processing methods determine the extent to which different precursor substances participate in the degradation reactions. Figure 7: The circle heatmap graph of aroma compounds with VIP >1 in all 18 tea samples.
## 3 Materials and Methods
### Drillhole Camera Video Fracture Surveying  
On the basis of the drill hole position and the drill hole core logs, six drill holes were chosen for the drill hole camera survey. We used a drill hole camera system with spatial orientation of the bottom and side camera (Figures 4 and 5). The borehole camera was transported by terrain vehicle to the drill hole location. The camera was then centered above the drill hole by a tripod, and the depth was set to zero m. Camera orientation was checked (on each drill hole) while the camera was still on the surface. After all initial checking was set up, the camera was lowered into the drill hole with a winch. Since the camera has a smaller radius than the drill hole, centering the camera was done with a tripod placed precisely above the drill hole. Camera stillness was partially established by the tripod and partially by a slow lowering of the camera to the drill hole. The camera needs to be still for acquiring precise orientation, so sometimes it was necessary to wait for the camera to stabilize. Alternatively, it is possible to use stabilizers between the camera and the drill hole wall. Still, it is dangerous in the open hole, but it can be dangerous due to the possible collapse of the drill hole wall (in the faulted or karstified intervals).  
The fracture surface is a three-dimensional plane in space with two main characteristics: dip direction and dip angle. Each fracture is measured manually in four points (D-G, in Figure 4A) by down and side cameras (Figure 4B), so fracture can be adequately defined in space. To define the orientation of the fracture, it would be enough to measure in three points. Still, because of measurement conditions (camera rotation, the angle between camera and fracture, subjectivity due to geologist experience), we take measurements in four points to better define the plane. Each fracture measurement was entered into a spreadsheet in the notebook. Measured dip angles were also checked on the drill hole  
Figure 4: (**A**) Methodology of fracture orientation calculation. (**B**) Down camera view; (**C–G**) side camera view and fracture orientation determination.  
cores for higher precision of the measurement (Figure 3). First, from the bottom camera, we defined the strike and azimuth of the fracture, which is then checked and improved by the side camera (Figure 4A,B,D,E-G). The whole surveying was recorded so later interpretation, reinterpretation, and validation could be made. The whole process is repeated for each drill hole.
## 3 Two-Step Parametrization Method
### Procedure
The parameter bounds, \(\theta_{i\text{,min}}\) and \(\theta_{i\text{,max}}\), are derivable from physical considerations and expert knowledge. First, parametrizing the thermodynamic submodel with its parameter vector \(\theta_{\text{th}}\) reduces the solution space's dimension by \(\frac{8}{25}=32\%\) compared to the global one. With the determined optimized thermodynamic parameter vector \(\theta_{\text{th,opt}}\) and a given input \(\mathbf{u}\), the state trajectories \(\mathbf{x}\) do not change anymore. Therefore, for the electrochemical submodel's parametrization, the thermodynamic submodel only needs to be solved once, and the resulting states \(\mathbf{x}\) are stored for further usage. Second, parametrizing the electrochemical submodel with its parameter vector \(\theta_{\text{el}}\) reduces the solution space's dimension by \(\frac{17}{25}=68\%\) compared to the global one. Here only the electrochemical parameters are optimized, and the thermodynamic ones are kept constant. A parameter sensitivity analysis further reduces each sub-solution space's dimension by excluding non-significant parameters, see Section 3.4. This proceeding significantly simplifies the optimization problem. The thermodynamic submodel's parametrization solves ODEs in every iteration, and the electrochemical submodel's parametrization does not, which makes the latter considerably faster.
## 2 Materials and Methods
### Data Analysis  
To ensure the quality of flux data in our study, we implemented a series of quality control measures. We excluded data affected by the CSAT3 anemometer's support structure  
Figure 1: The site location of the flux tower in the Huitong fir forest site.  
and non-forest ecosystems surrounding our Huitong fir forest site. We also eliminated records with low RSSI indicators during rainfall events. Additionally, we removed data with excessive variance in methane flux and applied a threshold for friction wind velocity to ensure reliable observations. After these stringent controls, 34.8% of the original data remained for analysis. The method for calculating GPP was adopted from [PERSON] and [PERSON] (1997) [23].  
Subsequently, we used three circadian metrics to identify and quantify the midday depression occurring between 2016 and 2022, including the daily center of mass of GPP (CGPP), the GPP peak hour (Hourpeak), and the ratio of GPP in the morning and the afternoon (Ratio M/A). This method was adopted from [PERSON] et al. (2023) [24]. If the CGPP is less than 12, it can be regarded as one of the determining conditions for the occurrence of midday depression. Hourpeak refers to the moment when the GPP value is at its maximum between 7 a.m. and 5 p.m. If the Hourpeak occurs before 12 p.m., it can be regarded as a determining condition for the occurrence of midday depression. Additionally, the Ratio M/A exceeding 1 indicates that afternoon GPP exceeds that of the morning, and vice versa. A ratio below 1 can be regarded as a determining condition for the occurrence of midday depression.  
We analyzed the impact of three climatic events on the midday depression of the fir forest, including heat, drought, and compound drought and heat events. A heat event was defined as a day when the maximum temperature exceeded the 90 th percentile of all daily maximum temperatures recorded throughout the study period. Considering the lag effect of drought on plants, a drought event was defined as a day when the cumulative precipitation over the previous seven days was less than 14 mm. Furthermore, a day was defined as a compound event if both a heat event and a drought event occurred on the same day. Subsequently, we examined the relationship between the frequency of climatic events and the occurrence of midday depression through linear regression analysis. We estimated the probability of midday depression being triggered by each climatic event by dividing the number of days with both the event and midday depression by the total days of the event.
## 3 Results
### From Disease Persistence to Elimination in a Scenario of Increasing Heterogeneity
#### 3.1.3 Mosquito Survival Patterns  
In the present model, ivermectin is responsible for a consistent reduction in mosquito life expectancy resulting in lower chances for _Plasmodium_ parasites to mature to the stage of sporozoite inside the mosquito, after feeding on a gametocyte-carrier human host. With a higher fraction of human hosts on ivermectin treatment, mosquito mortality is higher during the initial period of the mosquito life time before the necessary ten days for parasite maturation inside the mosquito gut. See Figure 5.  
Average mosquito mortality, during the 10 day parasite incubation time period as a direct result of ivermectin treatment, is influenced by the fraction of human hosts on ivermectin treatment. Mosquito mortality was consistently stable and independent of different heterogeneity levels in all simulations with identical model parameters. In Figure 5a, we show the mosquito survival distribution curve for ta 30 year time for different values of ivermectin probability and fit it with an exponential function:  
\[N=A\exp\left(\gamma t\right) \tag{8}\]  
with \(\gamma\) and \(A\), which vary linearly with \(p_{iv}\), as shown in Figure 5b,c.  
The intersection point in Figure 5a around 10 days coincides approximately with the parasite incubation time period inside the mosquito, as ivermectin-induced mosquito mortality is typically  
Figure 4: **(a)** Malaria incidence as a function of the human-to-mosquito transmission efficiency \(w_{h}\) and the ivermectin probability \(p_{iv}\), showing that isolines of malaria spreading are approximately a linear combination of both variables. Therefore, we plot in (**b**) the malaria incidence as a function of the linear combination \(w_{h}+sp_{iv}\) with \(s=-4.2\), clearly showing a gradual transition from elimination to endemic prevalence. In all cases, the PW-rule of \(20/80\) was used, corresponding to \(\theta=0.25\).  
higher during the first ten days of treatment and becomes residual thereafter. The incubation time period found in our simulation is of the same order of magnitude of the value found in other studies [37].
# Advanced Metamaterial-Integrated Dipole Array Antenna for Enhanced Gain in 5G Millimeter-Wave Bands
## 3 Antenna Results and Discussion
### Radiation Pattern  
Figure 11 illustrates the radiation pattern of the proposed metamaterial-based non-uniform dipole array antenna at different frequencies. While the 3D directivity pattern at 26 GHz, 28 GHz, and 38 GHz is presented in Figure 11a, the 2D polar radiation gain pattern is presented in Figure 11b. It can be seen from both simulated and measured radiation pattern graphs that the antenna offers an end-fire radiation characteristic. Due to the equipment losses during measurement, there is a slight mismatch between the simulated and measured results. The presented antenna yields a maximum gain of 11.21 dBi at 41 GHz, with 30.7\({}^{\circ}\) and 43.4\({}^{\circ}\) half-power-beam widths (HPBWs) at the E and H planes, respectively. The HPBW of the presented antenna at different frequencies is listed in Table 4, and the gain plot of the proposed antenna with respect to frequency is presented in the subsequent subsection, where the gain and radiation efficiency are explained. Additionally, the proposed antenna shows a side-lobe level less than \(-6\) dB within the operating frequency band, which makes the proposed antenna suitable for the MIMO configuration for 5G technology.  
Figure 10: The reflection coefficient of the metamaterial-integrated dipole array antenna.  
Figure 9: Photographs of the presented metamaterial-integrated dipole array antenna: (**a**) the fabricated prototype and (**b**) the radiation characterization measurement setup.
# Multimodal Unsupervised Speech Translation for Recognizing and Evaluating Second Language Speech
## 5 Conclusions and Future Work  
We proposed an automatic proficiency evaluation method for L2 learners in spoken English. In the proposed method, we augmented the training dataset using the VAE-based speech conversion model and trained the acoustic model (AM) with an augmented training dataset to improve the speech recognition accuracy and time-alignment performance for non-native speakers. After recognizing the speech uttered by the learner, the proposed method measured various fluency features and evaluated the proficiency. In order to compute the stress and rhythm scores even when the phonemic sequence errors occur in the learner's speech, the proposed method aligned the phonemic sequences of the spoken English sentences by using the DTW, and then computed the error-tagged stress patterns and the stress and rhythm scores. In computer experiments with the English read speech dataset, we showed that the proposed method effectively computed the error-tagged stress patterns, stress scores, and rhythm scores. Moreover, we showed that the proposed method efficiently measured proficiency scores and improved the averaged correlation between human expert raters and the proposed method for all proficiency areas compared to the method employing conventional AM trained without data augmentation.  
The proposed method can also be used for most signal processing and generation problems, such as sound conversion between instruments or generation of various images. However, the current style conversion framework has a limitation that the conversion model learns the domain-level style factors and generates the converted speech signal rather than diverse pronunciation styles of multiple speakers included in each domain. In order to learn more meaningful and diverse style factors and perform many-to-many speech conversion, we plan to address the issues of automatic speaker label estimation and expansion to each speaker-specific style encoder in the future work.  
Conceptualization, methodology, validation, formal analysis, writing--original draft preparation, and writing--review and editing, Y.K.L.; supervision and project administration, J.G.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by an Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government (21 ZS1100, Core Technology Research for Self-Improving Integrated Artificial Intelligence System), and by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean Government (MSIT) (2019-0-2019-0-00004, Development of semi-supervised learning language intelligence technology and Korean tutoring service for foreigners).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Feature Extraction  
Feature selection and extraction is a crucial concept in machine learning that has a large impact on model performance. CNN is better used for extracting useful features which is an interesting task for character recognition. To reduce the number of features and improve the classification algorithm's effectiveness, functional features have been taken from the data. The pooling operation, phase convolution operation, and ReLU (rectified linear unit) activation function are used in feature extraction, as shown in the equations below:  
\[O_{a,b}=\sum_{a}\sum_{b}f[l,k]X[a-l,b-k] \tag{7}\]  
\[F_{-}Y_{a,b}=max(0,F_{-}O_{a,b}) \tag{8}\]  
The dysgraphia patient's input data are fed via a convolutional layer, pooling, ReLU, and dropout layer for extracting meaningful features. The input is a matrix of rank 2 with \(MxN\) (rows/ columns), M and N are indexed as (a, b), where \(0\leq a\leq M,0\leq b\leq N\). The convolutional operation layer yields the final and relevant feature map values, \(F_{-}Y_{a,b}\). The activation function is employed at each layer to allow the model for solving the nonlinear problems as illustrated in Equation (8), while the dropout and max pooling method is used to decrease the computational load.  
The extracted features that are used for handwritten character recognition include the following: stroke-based (number of strokes, the direction and curvature of the strokes, and the length and width of the strokes), shape-based (height, width, and aspect ratio of the characters), texture-based (presence of loops, dots, or other patterns), structural (number and placement of components within the characters), and grid-based (position of the characters in relation to other characters).
# M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders
## 4 SER and Computational Complexity  
\((1)\) The improved MPPSK signal can be expressed as  
\[\begin{array}{l}s_{1}=\sqrt{E_{b}}(-1,1,1,\ldots,1)^{T}\in R^{M\times 1}\\ s_{2}=\sqrt{E_{b}}(1,-1,1,\ldots,1)^{T}\in R^{M\times 1}\\ \vdots\\ s_{M}=\sqrt{E_{b}}(1,1,1,\ldots,-1)^{T}\in R^{M\times 1}\\ \end{array}. \tag{8}\]  
The probability that symbol \(p\) is erroneously detected as symbol \(q\) can be given as  
\[P_{p,q}=\mathcal{Q}\Bigg{(}\sqrt{\frac{2 Nf_{s}SNR}{f_{c}}}\Bigg{)}, \tag{9}\]  
where \(Q(\cdot)\) is the Marcum Q-function. Thus, the SER of the improved MPPSK modulation is  
\[P_{e}=1-\left(1-P_{p,q}\right)^{M-1}\simeq(M-1)\mathcal{Q}\Bigg{(}\sqrt{\frac {2 Nf_{s}SNR}{f_{c}}}\Bigg{)}. \tag{10}\]  
The SER of the improved MPPSK modulation is lower than that of the existing MPPSK in [20].  
\((2)\) The computational complexity of the stacked DSAE algorithm is given by  
\[\mathcal{O}(KX_{m1}Z_{m3}C_{in}C_{out}), \tag{11}\]  
where \(C_{in}\) and \(C_{out}\) represent the input size and output size of the stacked DSAE network, respectively. \(X_{m1}Z_{m3}\) denotes the parameter dimension in the input layer. In the training stage, it is more efficient with a much shorter training time compared to the conventional decision threshold-based approach [5], the adaptive threshold integral discriminator [20], coherent processing technique [21], the PLL scheme [22], etc., especially in the unsupervised training stage.