esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Polar Codes as Forward Error Correction Coding for Digital Video Broadcasting Systems
## 2 Preliminaries
### Polar Codes Construction  
Polar codes construction is an ordering process to synthetic channels according to their reliability and selecting good channels for information transmission. In this paper, Polar code is constructed using GA method for BI-AWGN channel proposed by [PERSON] [18]. For the BI-AWGN original channels with noise variance \(\sigma^{2}\), the transition probability \(W(y|x)\) can be written as:  
\[W(y|x)=\left\{\begin{array}{l}\frac{1}{\sqrt{2\pi\sigma^{2}}}\ e^{-\frac{(y- x_{0})^{2}}{2\sigma^{2}}}\,\ x=0\\ \frac{1}{\sqrt{2\pi\sigma^{2}}}\ e^{-\frac{(y+x_{0})^{2}}{2\sigma^{2}}}\,\ x=1 \end{array}\right. \tag{1}\]  
where \(x_{o}\) is the mean value for the transmitted signal \(x\), assuming that bits are modulated using binary phase-shift keying (BPSK), then the LLR of each received symbol \(y\) is given by:  
\[l(y)=ln\frac{W(y|0)}{W(y|1)}=\frac{2 yx_{0}}{\sigma^{2}} \tag{2}\]  
Taking into consideration that \(y\) is a Gaussian random variable with \(y\sim N\big{(}x_{o},\sigma^{2}\big{)}\), thus \(l(y)\) can be considered also as a Gaussian random variable with \(l(y)\sim N\big{(}\frac{2 yx_{0}^{2}}{\sigma^{2}},\frac{4 xy_{0}^{2}}{\sigma^{2}} \big{)}\). For a data block of \(N\) bits, \(N\) synthetic channels are created by polar code transformation with a different error probability for each sub-channel \(i\). GA method assumes that LLR for each sub-channel \(i\) follows a Gaussian distribution constraint in every recursion step of the code tree, in which the Expected value \(E\) is half of the variance [18]. This enables one to compute only the mean value of LLR for each synthetic channel \(l_{N}^{(i)}\), drastically reducing the complexity to obtain the reliability of each sub-channel. The recursion process is applied on the coding tree according to following calculations [18]:  
\[E\Big{[}l_{N}^{(2i-1)}\Big{]}=\phi^{-1}\Big{(}1-\Big{(}1-\phi\Big{(}E\Big{[}l_ {N/2}^{(i)}\Big{]}\Big{)}\Big{)}^{2}\Big{)} \tag{3}\]  
\[E\Big{[}l_{N}^{(2i)}\Big{]}=E\Big{[}l_{N/2}^{(i)}\Big{]} \tag{4}\]  
where the expected value of the original channel \(E\Big{[}l_{1}^{(1)}\Big{]}=\frac{2 xy_{0}^{2}}{\sigma^{2}}\), and:  
\[\phi(t)=\left\{\begin{array}{l}1-\frac{1}{\sqrt{4\pi t}}\int_{-\infty}^{ \infty}tanh\frac{y}{2}e^{-\frac{(u-t)^{2}}{u}}\ du,\ t>0\\ 1,\end{array}\right. \tag{5}\]  
\(t=0\)  
However, the exact calculation of complex integration in \(\phi(t)\) requires a high computational complexity. Therefore, an approximation function of \(\phi(t)\), denoted by \(\psi(t)\) is introduced, as follows [33]:  
\[\psi(t)=\left\{\begin{array}{l}e^{-0.4527\sigma^{2}\left(\frac{1}{2}\right) \left(\frac{10}{\pi t}\right)}\,\ t\leq 10\\ \sqrt{\frac{\pi}{t}}e^{-\frac{t}{2}}\ \Big{(}1-\frac{10}{\pi t}\Big{)}\,\ t>10\end{array}\right. \tag{6}\]  
which is often used to simplify the construction of polar codes.
## 3 Nonlinear Identification Method Based on Broad Learning System and Particle Swarm Optimization
### Construction of a Nonlinear Friction BLS Network
All such features collectively form the total feature mapping matrix, as shown in Equation (24):  
\[Z=\left[Z^{(1)},Z^{(2)},\ldots,Z^{(m)}\right]\in R^{1\times(m\cdot k)} \tag{24}\]  
The feature-mapped inputs are passed to the enhancement layer, where complex nonlinear combinations are generated through enhancement functions:  
\[H^{(j)}=\xi^{(j)}\Big{(}ZW_{h}^{(j)}+b_{h}^{(j)}\Big{)}\in R^{1\times l},j=1, 2,\ldots,n \tag{25}\]  
By concatenating all enhancement nodes and generating the output, Equation (26) is obtained:  
\[H=\left[H^{(1)},H^{(2)},\ldots,H^{(n)}\right]\in R^{1\times(n\cdot l)} \tag{26}\]  
The final layer is trained through the output layer to fit the friction torque, resulting in the regression matrix:  
\[A=[Z,H]\in R^{1\times M} \tag{27}\]  
Let the target friction torque be denoted as \(\tau_{\text{fric}}^{\text{target}}\) ; the training objective is to minimize the fitting error:  
\[min_{W}\left\|A\cdot W-\tau_{\text{fric}}^{\text{target}}\right\|_{2}^{2} \tag{28}\]The weight W is the final output weight and can be directly used for friction torque prediction. \[\dot{z}=\dot{q}-\frac{\left|\dot{q}\right|}{\sigma_{0}g(\dot{q})}z,\tau_{f}= \sigma_{0}z+\sigma_{1}\dot{z}+\sigma_{2}\dot{q} \tag{20}\]0  
To enhance the generalization ability and stability of the BLS network, a regularization loss function is introduced in the output layer training objective. Let N be the number of samples, \(\hat{\tau}_{i}\) represent the predicted friction torque of the \(i\)-th sample by the BLS network, \(\tau_{i}\) denote the corresponding ground truth, and \(\lambda\) be the regularization coefficient used to suppress overfitting. The loss function is defined as shown in Equation (30):  
\[\dot{z}=\dot{q}-\frac{\left|\dot{q}\right|}{\sigma_{0}g(\dot{q})}z,\tau_{f}= \sigma_{0}z+\sigma_{1}\dot{z}+\sigma_{2}\dot{q} \tag{20}\]1  
Compared to deep neural networks, the shallow and flexible architecture of BLS makes it particularly well suited for high-frequency vibration modeling, nonlinear friction learning, and non-uniform dynamic response modeling. In disturbed nonlinear systems, it offers improved identification accuracy and enhanced stability performance. Figure 3 shows the BLS simplified model under the non-linear system.
## 3 Dataset Description
### Annotation of Dataset with Physiological Indicators  
Our primary contribution to the dataset entails the annotation, performed at one-minute intervals, of the videos encompassing four main physiological indicators: blood pressure, heart rate, oxygen saturation, and respiratory rate. We selected the one-minute interval because, as we discuss in more detail in Section 3.3, the models used for parameter estimation make predictions at the level of seconds, frames, or minutes. Therefore, a one-minute interval was deemed the most suitable choice for synchronizing the estimations. Moreover, supplementary metrics were computed, including estimation of head pose via Euler angles (roll, pitch, and yaw), the proportion of frames exhibiting Euler angles surpassing 30 degrees relative to the total frames within a one-minute span, ratios of eye closure and mouth opening, instances of yawning, numbers of prolonged eye closures exceeding two seconds, and characteristics pertaining to breathing patterns, encompassing rhythmicity and stability.  
The computation of the aforementioned parameters was carried out by using computer vision and deep learning approaches developed by members of our laboratory, which are discussed in the subsequent subsection. These approaches were applied to each video within the OperatorEYEVP dataset, and annotations were made for the 15 parameters at each minute of the recordings. For each participant, a comprehensive CSV file was created to document all the details of the session. This file encompassed information related to the CRT and Tetris tasks, as well as eye parameters and HRV parameters provided by the dataset in addition to the parameters extracted by our models with additional features we calculated from the coordinates of the eye-tracking data. A more in-depth exploration of the CSV files, including their structure and contents, is provided later in the discussion.
# Enhancement of In-Plane Seismic Full Waveform Inversion with CPU and GPU Parallelization
## 3 Parallel Computational Model  
In terms of CPU clock speed, current generation computing has reached saturation, primarily due to the enormous increase in cost and power requirement for increasing clock frequency further from current standards [34]. Until 2004 [PERSON]'s law was in full force, with chip densities doubling every two years. It also increased clock speed as the chip dimension decreased due to Dennard scaling. According to this principle, the power needed to run transistors in a given volume remains constant regardless of the number of transistors. Because transistors do not scale with size, power density increases as transistors get smaller. Increasing clock speeds also implies a higher voltage, which increases power consumption. Since the scaling was performed assuming most of the power was dynamic, with a smaller transistor, the static power, namely leakage, began to dominate. In addition, more heat is generated with increasing clock speeds, requiring more robust cooling solutions.  
The processor shifted from a single-core to a multicore to combat the abovementioned issue. Power and frequency are fundamentally linked in multicore processors. By incorporating multiple cores, the frequency of each core can be lowered, which allows the power generally given to a single core to be distributed among multiple cores. Thus, a multicore processor produces significantly higher performance [35] and results in parallel processing. By focusing more on increasing the core count rather than the clock speed, parallel computing is one of the most optimal methods of achieving high performance from a CPU [36]. The comparison between OpenMP and CUDA is given in Table 1.  
Figure 4: Perfectly matched layers in finite difference grid (\(npml\) = Number of grids in perfectly matched layers).
## 3 Materials and Methods
### Spatio-Temporal Feature Network
#### 3.4.1 Spatio-Temporal Feature Block  
To solve the problem of water demand forecasting, the key step is to extract the temporal and spatial correlation in series data. However, due to the uncertainty in the correlation between nodes, it is difficult for CNN-based methods to handle such irregular connections. Therefore, we use a graph convolution network to extract spatial correlations. The underlying mechanism involves aggregating the features of neighboring nodes through Chebyshev polynomials to update the features of the node. To enhance the ability to capture nonlinear relationships, we apply the sigmoid function to process the output of GCN, and dynamically update the time series in the time window at each time step by using the output of the DG. The formulaic representation of this process is shown in Equation (16).  
\[\chi_{gcn}=\sigma\bigg{(}\sum_{k=0}^{K-1}\alpha_{k}T_{k}(\widetilde{L})X_{(t) }\bigg{)}\in\mathbb{R}^{B\times N\times F_{1}\times H} \tag{16}\]  
where \(\alpha_{k}\in\mathbb{R}^{F\times F_{1}}\) is the learnable weight, and \(K\) is the order of the Chebyshev polynomial.  
As for the temporal correlation analysis, the RNN-based method and CNN-based method are two widely used methods for analyzing the autocorrelation of time series. The RNN-based method can capture the time dependency but suffers from high computation and vanishing gradients. In contrast, CNN-based methods have the advantage of low computational effort and fast computation, and can effectively extract local and global features from time series. Here we choose the CNN-based method to extract temporal features from the \(\chi_{gcn}\). Specifically, we transform two-dimensional convolution into one-dimensional convolution by changing the size of the convolutional kernel, enabling us to extract temporal features. Equation (17) shows the temporal feature extraction process in the temporal dimension.  
\[\chi_{cnn}=Conv2d\big{(}permute(\chi_{gcn})\big{)}\ \in\ \mathbb{R}^{B\times F_{1} \times N\times H} \tag{17}\]To address the issue of gradient vanishing in stacking multiple STF Blocks, a residual connection is introduced at the output of each STF Block. Additionally, to further enhance the non-linear modeling capability, we use the Rectified Linear Unit (\(ReLU\)) function to process the output of STF Block to \(\chi_{stgcn}\).
## 4 Carrier Phase Accuracy Tests
### Tests and Analysis Based on Signal Simulator
This is because the simulation signal was strong, which only caused small phase error, consistent with the analysis in Figure 5. When the dynamics occur, the phase error of PLL with a bandwidth of 5 Hz is larger than that of 15 Hz. This is caused by the time delay of accelerometer data recording, whose impact increases with bandwidth narrowing. This phenomenon was analyzed in detail in our previous research [31]. The simulation results showed that the accelerometer-aided PLL could weaken the impact of dynamics on the measurement accuracy of carrier phase, which was helpful to improve GNSS accuracy under strong earthquakes.
# Advanced and Potential Methods for Extraction of Bioactive Compounds from Avocado Peel--A Review
## 4 Advanced Methods for Extraction of Bioactive Compounds from Avocado Peel
### Pressurized Liquid Extraction (PLE)  
Pressurized liquid extraction (PLE) is also well-known as accelerated solvent extraction (ASE), pressurized hot solvent extraction (PHSE), high-pressure solvent extraction (HPSE), and high-pressure high-temperature solvent extraction (HPHTSE). This method is a constant high-pressure extraction that facilitates the improvement of cell permeability, intermolecular physical interactions, and penetration of the extraction solvent or solvents, thus improving the extraction efficiency.  
PLE was first presented at the Pittcon Conference in 1995 by Dionex Corporation, and it is today a considerably more well-established technology. This approach has recently been integrated into the food field, which is a recent example. [PERSON] et al. [29] demonstrated that PLE was successfully employed to extract phenols from avocado peel dominated by phenolic acid. It was reported that the most effective extraction conditions were at 200 \({}^{\circ}\)C with a solvent containing 1:1 \(v/v\) as ethanol/water ratio.  
Subcritical water above the standard boiling temperature of 100 \({}^{\circ}\)C, also called compressed hot water (CHW), is another solvent commonly used in PLE. This method is also well-known as accelerated water extraction (AWE). This extraction procedure is considered a green, non-toxic, and environmentally friendly technique involving subcritical water as the appropriate solvent for extracting solid and semi-solid samples. The use of water in the subcritical region as a \"green\" solvent has attracted the interest of many scientists worldwide. It is a solid-liquid extraction technique in which organic or aqueous solvents are used to extract compounds from a solid or semi-solid material at high temperatures and pressures [30]. To the best of our knowledge, no study focuses on avocado peel extraction using subcritical water. In the study of [PERSON] et al. [31], subcritical water extraction was used to extract phenols from avocado fruit flesh. The extraction duration employed in this study was 10-30 min, with the subcritical water conditions studied at 18 bar, 87 mL/min, and 105-140 \({}^{\circ}\)C. This condition may also apply to extracting bioactive compounds from the avocado peel.  
PLE is regarded as a more environmentally friendly alternative to Soxhlet extraction. At the same time, the enhanced pressure keeps the solvent below its boiling point, allowing the sample to become more soluble and attain a higher diffusion rate. Solvents penetrate solid samples more efficiently at higher pressures and temperatures, thus reducing solvent usage [32]. Compared to traditional Soxhlet extraction, PLE achieved a considerable reduction in analysis time with improved recoveries for more pollutants, with no discrepancies in the data [33]. Thus, further exploration of this technique for extracting biological active compounds from avocado peel is still required.
## 4 Conclusions  
In this study, the effect of printing nozzle temperature changes on tensile strength, hardness, and dimensional values of samples fabricated from ABS material was examined. The results showed that the nozzle temperature change had a great impact on these values. For experimental studies, 36 tensile test samples were fabricated with FFF, one of the AM methods. The nozzle temperature of the 3D printer was changed between 220 \({}^{\circ}\)C and 270 \({}^{\circ}\)C in 10 \({}^{\circ}\)C increments during the fabrication process. ANOVA was applied to examine the effect of nozzle temperature change on the findings obtained as a result of the experimental study. After performing the necessary tests and measurements during the experimental process, the following conclusions were reached:  
* The mass of the specimens decreased by 55.95% with the increase in the nozzle temperature from 220 to 270 \({}^{\circ}\)C.
* The tensile strength decreased by 41.52% with the increase in the temperature of the nozzle from 220 to 270 \({}^{\circ}\)C.
* The highest tensile strength value was measured, at 24.59 MPa, at 220 \({}^{\circ}\)C.
* The hardness decreased by 14.59% with the increase in the temperature of the nozzle from 220 to 270 \({}^{\circ}\)C.
* The length of the sample generally decreased and the average length error decreased with increasing nozzle temperature.
* The average width errors decreased, as did the length, with increasing temperature.
* Among the dimensional parameters, the only parameter in which the average error amount increases with increasing temperature is thickness.
* ANOVA tables demonstrated that nozzle temperature variation is an important factor in mass, ultimate tensile strength, hardness, and all the dimensions studied.  
Methodology, O.U., I.E. and S.E.; Software, O.U.; Validation, O.U.; Formal analysis, I.E., S.E. and B.Y.; Investigation, O.U., I.E. and S.E.; Resources, I.E. and S.E.; Data curation, I.E. and B.Y.; Writing--original draft, O.U., I.E. and B.Y.; Writing--review & editing, S.E. and B.Y.; Visualization, S.E. and B.Y. All authors have read and agreed to the published version of the manuscript.  
This study was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) within the scope of the Scientist Support Program (BIDEB-1059B1922011961).  
Figure 8: Images taken from the SEM of the broken parts of the samples fabricated at different temperatures as a result of the tensile test. (**a**) ABS-220 \({}^{\circ}\)C, (**b**) ABS-240 \({}^{\circ}\)C, (**c**) ABS-270 \({}^{\circ}\)C.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data underlying the results are available as part of the article, and no additional source data are required.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Speaker Diarization: A Review of Objectives and Methods
## 5 Models to Apply for Speaker Diarization
### Neural (Machine Learning) Methods
#### 5.5.4 Recurrent Neural Networks (RNNs)  
To accommodate the uneven distribution of information in speech for both time and frequency, a mechanism that is selective for nodes is useful. CNNs exploit local data correlations, but recurrence can assist for correlations over longer time ranges. RNNs use feedback and hidden states to store information about past input. Network gates (called input, output, and forget) control the data flow among layers. Typical RNNs are _long short-term memory_ (LSTM) and _residual network_ (ResNet) [50, 51].
# Effect of Zr Modification on NH\({}_{3}\)-SCR Reaction Performance of Cu-Ce/SAPO-34 Catalysts
## 2 Materials and Methods
### Experimental Conditions and Equipment
#### 2.2.2 Characterization of the Catalyst  
The crystal phase composition of the catalyst was characterized by an X-ray diffractometer (DX-2700B, Dandong Haoyuan Instrument Co., Ltd., Dandong, China). The scanning angle was 6-70\({}^{\circ}\), and the scanning rate was 2\({}^{\circ}\)/min. Field emission scanning electron microscopy (JSM-7800F, Japan Electron Optics Laboratory Co., Ltd., Tokyo, Japan) was used to test the surface micromorphology and element distribution of the catalyst samples. A TP-5076 TPD/TPR dynamic adsorption instrument from Tianjin Xianquan was used to conduct the temperature-programmed adsorption (NH\({}_{3}\)-TPD) experiment on the catalyst to test the acidity of the catalyst. Fourier transform infrared absorption spectroscopy (IRT racer-100, Shimadzu Corporation, Kyoto, Japan) was used to determine the surface functional groups of the catalyst samples and the types of acidic sites of the catalyst samples.
## 3 Analysis
### Fresnel Transform  
For brevity of notation, we will consider only one lateral dimension, \(x\). Extension to (\(x\),\(y\)) analysis is straightforward. The diffraction field \(g(x\),\(z)\), seen at a distance, \(z\) (Figure 2a), is described by the Fresnel transform [7; 23]:  
\[g(x,z)=FR_{z}\{g(x,0)\}=\frac{\exp(i2\pi z/\lambda)\exp(-i\pi/4)}{\sqrt{ \lambda z}}g(x)*f_{k}(x,z) \tag{1}\]  
where, \(g(x)=g(x\),0) means the initial field, and \(\lambda\) stands for the wavelength in vacuum (or air), whereas \(*\) is the simple of convolution; \(g(x\),0) is also called the object. In general, this object has a complex expression. The Fresnel kernel is then [4]:  
\[f_{k}(x,z)=\exp\left(i\pi\frac{x^{2}}{\lambda z}\right) \tag{2}\]The constant phase term of propagation \(\exp(i2\pi z/\lambda)\), as well as the complex factor \(\exp(-i\pi/4)/\sqrt{\lambda z}\), are not considered, since they do not intervene in the analysis. In the Fourier plane, Equation (1) becomes [24; 25]:  
\[G(u,z)=G(u)\exp\Bigl{(}-i\pi\lambda zu^{2}\Bigr{)} \tag{3}\]  
The distribution \(G(u,z)\) is a spectrum, namely the Fourier transform (FT) of \(g(x,z)\), whereas \(G(u)\) is the FT of the object.  
The diffraction field at \(z\) is expressed by an FT [24]:  
\[g(x,z)=\exp\left(i\pi\frac{x^{2}}{\lambda z}\right)G^{\prime}\left(u^{\prime} \right)|_{u^{\prime}=x/(\lambda z)} \tag{4}\]  
\[\text{with }G^{\prime}\left(u^{\prime}\right)=FT\biggl{\{}\exp\left(i\pi \frac{u^{2}}{\lambda z}\right)g(x)\biggr{\}}\]  
Once the FT is performed, the spatial frequency \(u^{\prime}\) will be replaced by the scaled term \(x/(\lambda z)\). The two phase-terms, in relation to (4), namely \(\exp\left(i\pi\frac{x^{2}}{\lambda z}\right)\) and \(\exp\left(i\pi\frac{u^{2}}{\lambda z}\right)\), describe two divergent waves (spherical) having a radius that is equal to \(z\). To obtain an exact FT of the object, we should omit two waves (spherical) by using two identical spherical lenses. The focal length of each lens should be z (Figure 2b). The FT of the object (initial field) \(g(x)\) is realized physically by using a spherical lens. This object should be placed in its front (anterior) focal plane [7]. The FT of the object is then observed in the back (posterior) focal plane of this lens, as depicted in Figure 3b. To implement the Fresnel transform, \(\text{FR}_{\text{z}}\), of the initial field, we use two divergent spherical lenses, performing the two phase-terms in relation (4), as shown in Figure 3a. This lenses-based formalism enables handing the compound systems.  
Figure 2: Diffraction in free space, in the presence of lenses, as well as without them: (**a**) the field \(g(x,z)\) observed after diffraction at a certain distance \(z\) in free space (Fresnel transform). (**b**) By adding two lenses, diffraction results in the Fourier transform \(G(x)\) of the object \(g(x)\). The focal length of the identical lenses should be equal to the observation distance \(z\).