esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient QC-LDPC Decoder Architecture for 5G-NR Wireless Communication Standards Targeting FPGA
## 5 Results  
The simulation results unveil a remarkable enhancement in the decoder's ability to tackle 5G-NR applications, surpassing the performance of all previous studies [11; 12; 13; 14; 39; 43] in multiple dimensions. Not only do our results showcase a superior performance in terms of the number of iterations and BER (bit error rate) analysis, but they also outshine earlier studies regarding handling extended codeword lengths.  
Our decoder's baseline architecture (Z = 8, R = 2/3) was modeled in Simulink, converted to VHDL, and then updated to the highest codeword length in 5G-NR (Z = 384, R = 1/3) with the intent of FPGA implementation. Table 1 presents the simulation outcomes of the enhanced architecture (Z = 384) in comparison to prior works, showcasing two codeword rates of 2/3 and 1/3. The rate 1/3 (model 1) exhibits the best performance compared to 2/3 (model 2) because it offers all BG1 entries and uses the maximum number of parity bits. However, the rate of 2/3 provides fewer parity bits, reducing hardware complexity for certain applications such as live streaming.  
Figure 6: Simulink redesign of the SOLL unit for 5G-NR usage.
# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 4 Results
### Papers Descriptions
#### 4.1.2 RNN  
Five articles that used RNN technology for stock and Forex prediction are briefly described below. Table 3 shows the author, variables, dataset, and model of the papers mentioned.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference No.** & **Author** & **Dataset** & **Variables** & **Model** \\ \hline \multirow{6}{*}{[27]} & \multirow{3}{*}{Ni, L.} & EURUND, ADUUSD & \multirow{3}{*}{Open price, close price,} & \multirow{3}{*}{C-RNN} \\  & & XAUUSD, GGRP, EURIPY, & & \\  & & GHPUSD, USDDF, USDDF, USDDF, USDDF, and USDCAD & & \\  & & China Security Index: CSI300, & & \\  & & CSI200, and CSI500 & & 1. Technical features: open price, high price, low price, close price, \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Information on the papers which used the Recurrent Neural Network (RNN) model.  
[PERSON] proposed a CRNN model to predict the price of 9 kinds of Forex pairs. The results showed that the proposed model performed much better than the LSTM model and the CNN model [27].  
[PERSON] proposed a multi-task RNN model with Markov Random Fields (MRF). The multi-multilayer perceptron (MMLP) was proposed to automatically extract diversified and complementary features from individual stock price sequences, which means that there is no need for the technical indicators. Features learned by MMPL were passed to a binary MRF with a weighted lower linear envelope energy function to utilize intra-clique higher-order consistency between stocks [28].  
[PERSON] proposed an RNN-Boost model that made use of the technical indicators and sentiment features, and Latent Dirichlet allocation (LDA) features to predict the price of stocks. Its results showed that the proposed model outperformed the single-RNN model [29].  
[PERSON] proposed a Deep and Wide Neural Networks (DWNN) model, where CNN's convolution layer was added to the RNN's hidden state transfer process. The results showed that the DWNN model could reduce the prediction mean squared error by 30% compared to the general RNN model [30].  
[PERSON] proposed a novel Attention-based RNN (ARNN), where wavelet denoised input was passed to ARNN. The forecast was calculated using the Autoregressive integrated moving average (ARIMA) and the output of the ARNN model [31].
###### Abstract  
Fluctuation in Cry1 Ac endotoxin levels expressed in transgenic _Bacillus thuringiensis_ (Bt) cotton (_Gossypium hirsutum_ L) can result in a variation in efficacy throughout the growing season. Here, a green tissue-specific strong promoter of the cotton leaf curl Kokhran virus-Burewala (CLCuKoV-Bu) C1 gene is reported that can direct consistently high levels of Cry1 Ac endotoxin expression in transformed cotton plants. The objective of this study was to investigate the capacities of the CLCuKoV-BuC1 promoter to drive transcription of Cry1 Ac and stably express endotoxin in mature leaves and bolls of transgenic cotton plants, compared to the traditional CaMV35S promoter. The Cry1 Ac gene expression cassettes were constructed under the control of a bidirectional promoter and transformed into cotton 'MNK-786'. The expression of Cry1 Ac constructs was evaluated in transient and stable expression systems using _Nicoliana tabucam_ 'Rustica' and cotton plants, respectively. Accumulation of the Cry1 Ac expressed in two resultant transgenic cotton plants harboring the constructs driven by the CLCuKoV-BuC1 and CaMV35S promoter, respectively, was analyzed using a commercially available enzyme-linked immunosorbent assay. In leaves and bolls of two cotton plants shown to express CLCuKoV-BuC1-Cry1 Ac (CLCuV-Ac), the Cry1 Ac protein accumulated at 400 and 300 ng g\({}^{-1}\) per fresh tissue weight, respectively, whereas no toxin was detectable in the roots. In contrast, CaMV35S-Cry1 Ac transgenic cotton plants accumulated three times less Cry1 Ac protein than those transformed with CLCuV-Ac. Results indicate that the greatest amount of Cry1 Ac endotoxin accumulated in transgenic cotton when expression was driven by the CLCuKoV-BuC1 compared to the CaMV35S promoter. Thus, the CLCuKoV-BuC1 promoter offered more robust transgene expression in cotton plants than the traditional CaMV35S promoter. The newly validated CLCuV-Ac promoter of begomoviral origin offers an exciting alternative as a robust promoter for genetic engineering of cotton and other plants.  
Bacillus thuringiensis; Cry1 Ac Endotoxin; cotton transformation; CLCuKoV-BuC1 promoter; gene constructs +
Footnote †: journal: Applied Sciences  
Accepted: 25 October 2022  
Published: 7 November 2022
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 3 Gaussian Process Regression for Time Series
### Kernel Functions for Time Series
#### 3.2.2 The Matern Kernel  
The class of Matern kernels is a generalization of RBF kernels with an additional hyperparameter \(\
u\), which controls the smoothness of the kernel function. A Matern kernel is defined as:  
\[k_{M(s,\ell,\
u)}(t,t^{\prime})=\frac{s^{2}2^{\
u-1}}{\Gamma(\
u)}\left(\frac {\sqrt{2\
u}|t-t^{\prime}|}{\ell}\right)^{\
u}K_{\
u}\left(\frac{\sqrt{2\
u}| t-t^{\prime}|}{\ell}\right), \tag{11}\]  
where \(\Gamma(\cdot)\) is the gamma function and \(K_{\
u}(\cdot)\) is the modified Bessel function of the second kind. Larger values of \(\
u\) result in smoother approximated time series, while as \(\
u\) grows to \(\infty\), the Matern kernel becomes equivalent to the RBF kernel. We note that the cases where \(\
u=3/2\) and \(\
u=5/2\) have nice closed forms ([14], (4.17)) and are of special interest to practical applications. In this work, we use the Matern kernel for \(\
u=3/2\).
# Stability of Feature Selection in Multi-Omics Data Analysis
## 2 Materials and Methods
### Classifiers with Embedded Feature Selection
#### 2.1.1 Support Vector Machines (SVM)  
Support Vector Machines (VAT) are powerful supervised-learning models utilized for classification tasks. By incorporating L1 regularization, SVMs can effectively perform feature selection while constructing a hyperplane that maximizes the margin between different classes. In our experiments, we employed the linear version of the kernel. An observation \(x_{i}\) is classified as belonging to the positive class \(y=1\) if it is located on the positive side of the hyperplane \(H(\mathbf{w},\theta)\); otherwise, it is assigned to the negative class.  
\[\hat{y}_{i}=sgn(\mathbf{w}^{T}\mathbf{x}_{i}+\theta) \tag{2}\]  
Assuming \(y_{i}\in\{-1,1\}\), the error function can be expressed as a hinge loss function ([PERSON] and [PERSON], 2017):  
\[error_{i}=max(0,1-y_{i}(\mathbf{w}^{T}\mathbf{x}_{i}+\theta)) \tag{3}\]  
The error, or loss, is equal to 0 if the observation \(x_{i}\) lies on the correct side of the hyperplane according to its true label \(y_{i}\).
## 5 Discussion  
Based on the aforementioned research, this paper describes an in-depth analysis of various fault modes and their hazard levels for the key components of the modular assembled track structure with built-in position retention. Accordingly, the optimization suggestions are proposed to further enhance the reliability of the new prefabricated track structure as follows:  
(1) For the EPDM interlayer, considering the failure mode of severe rupture or deformation of the EPDM interlayer (MB7), during the structural design phase, it is essential to comprehensively consider multiple factors such as stress concentration caused by train loads, temperature changes, and corrosive environmental factors to improve the durability of the interlayer. Strict quality control of the interlayer products should be implemented to ensure they meet relevant quality requirements. During the construction phase, special attention should be paid to the construction quality of this part to ensure installation precision and craftsmanship quality.  
(2) For the UHPC limiting ring, considering the failure mode of crushing of the UHPC limiting ring (MB12), in the UHPC material selection phase, it is crucial to fully consider the surface resistance to mechanical impact and wear, as well as stress concentration problems caused by train loads and temperature changes, to enhance the durability of the position retention ring. The structural form and dimensions of the position retention ring should be optimized to reduce the frequency of faults. Similarly, strict quality control during the construction of this part should be implemented to ensure it meets the design requirements.  
(3) For failure modes M2 and M6, although these fault modes are common issues in almost all track structures, including the new prefabricated track structure, this paper still recommends strengthening the design optimization of these parts. If economically feasible, materials with higher strength and better durability should be preferred. Sufficient attention should also be given to the construction quality of these parts to ensure their long-term stable operation.
## 4 Results and Discussion
### 12 m Far-Field Distance  
Figure 12 shows the force coefficients error ratios, and Table 14 provides the corresponding mean errors under different AOA ranges. SA+200 formula, SST+NACA4, SST+NACA4, SST+NACA4, SST+NACA4, and SA+NACA4 afforded the best performances, which were (0.04%, 0.00%), (1.01%, 1.17%), (3.50%, 0.95%), (6.23%, 1.00%), (7.69%, 1.52%) and (9.48%, 1.26%), respectively. At the range of (0\({}^{\circ}\), 2\({}^{\circ}\)), although the \(C_{d}\) of SST+132 formula is the minimum, the \(C_{l}\) is relatively large. Considering the \(C_{d}\) and \(C_{l}\) comprehensively, the result of SST+NACA4 is optimum. At the range of (0\({}^{\circ}\), 4\({}^{\circ}\)), despite SST+200 formula obtaining the smallest \(C_{d}\), the \(C_{l}\) of SST+NACA4 is preferred. Due to the \(C_{l}\) being a magnitude greater than the \(C_{d}\), the best result is accessed by SST+NACA4. Similarly, at the range of (0\({}^{\circ}\), 6\({}^{\circ}\)), the optimal result is obtained by SST+NACA4.
## 3 Fatigue Life Prediction Modeling
### Modeling Process
#### 3.3.2 Determine the Hyperparameters of the ANN Model
It can be seen from Figure 6 that when the number of hidden layers is 1 to 3, the evaluation index value of the model gradually decreases with the increase of the number of hidden layers, which shows that as the complexity of the model increases, the prediction accuracy of the model also improves. However, when the number of hidden layers exceeds three, both the training and testing of the model exhibit a decrease in accuracy. This indicates that three hidden layers are more suitable for fatigue life prediction under this experimental condition. Figure 5: Evaluation indexes of prediction accuracy of ANN model under different iteration times. 3. Learning rate:  
An appropriate learning rate is crucial to the training effect of the model. When the rate is too large, the model parameters may repeatedly exceed the minimum value of the objective function, resulting in suboptimal parameters. When the rate is too small, the model parameters may not converge to the optimal value within a limited time, thus affecting the prediction accuracy of the model [46]. In this experiment, four different learning rate constants were used as follows: 0.01, 0.001, 0.0001, and 0.00001. In the comparison test, 500 times augmented data were used as training data, source data were used as test data, the number of training iterations was 800, the number of hidden layers was 3, and other parameters were kept constant. Figure 7 shows the trend of prediction accuracy at four different learning rates. As shown in the figure, the prediction accuracy of the model gradually increases when the learning rate is selected as 0.01, 0.001, and 0.0001 and decreases significantly when the learning rate is reduced to 0.00001. This shows that when the learning rate is set to 0.00001, the model cannot find the optimal value in time due to the too small learning rate. Therefore, when the learning rate is 0.0001, the accuracy of the model is the highest. Figure 6: Evaluation indexes of prediction accuracy of ANN model under different hidden layers. Figure 7: The trend of model prediction accuracy at different learning rates: (**a**) results of the training stage; (**b**) results of the testing stage.
# An In-Depth Review on Blockchain Simulators for IoT Environments
## 4 A Systematic Review of Blockchain Simulators in IoT
### Comparative Analysis
#### 4.3.2 Blocksim  
Blocksim is a simulator that simulates network, consensus, and incentive layers which is built using Python language [27]. In addition to its obvious benefit of being able to simulate three distinct layers, Blocksim is user-friendly and can be used for simulating large-scale networks. Blocksim achieves the three objectives of generality, extensibility, and simplicity [43]. Simply, Blocksim is capable of simulating different blockchain models and designs. It is easily manipulated by users and the data provided is easily understood and unwanted data can be hidden. However, some limitations of this simulator include the amount of data that can be stored, and the number of concurrent users allowed to access the simulations.  
In terms of IoT environments, the simulator has been used in smart grids and IoT-based health care. In both articles, Blocksim provides the simulation framework to analyze Ethereum-based architecture. Blocksim has been used to simulate gas consumptions as well as certificate generation and verification transactions within [44]. For example, Blocksim toolkit has been used to generate Table 3 for the proposed system by [PERSON] et al. The  
Figure 6: Effect of the send rate on blockchain network transaction throughput using Hyperledger Fabric. Reprinted/adapted with permission from Ref. [42]. 2020, Elsevier B.V.  
Blocksim toolkit was used to generate certificates and verification transactions at random, to help the team have a better understanding of their proposed architecture [44]. Using Table 3, they determined that the certificate generation required more gas consumption than the verification transaction for their proposed architecture. The determined cause of this is the large number of computations done in the certificate generation transaction, when compared to the verification transaction [44]. This is useful information to know about their architecture, and was all simulated using Blocksim.  
Blocksim's main task in this simulation was to provide the environment for simulating the blockchain network, deploying smart contracts, and executing tests or experiments for evaluating performance parameters, such as latency, processing time, throughput, and computation time [44]. In the smart grid simulation, Blocksim was used to measure block interval, transaction time, and uncle rate. This was done through an Ethereum network simulation that ran on Blocksim [45].
# Low-Loss Active Grid Impedance Cancellation in Grid-Connected Inverters with LCL Filter
## 3 System Control and Modelling with the Proposed Compensator
### Regulation of the LV-VSC
#### 3.1.3 DC Capacitor Voltage Regulation  
The dc capacitor voltage of the LV-VSC can be regulated by balancing the absorbed and released energy through the LV-VSC. As discussed above, the LV-VSC absorbs high-frequency components to emulate the virtual resistance \(R_{\text{v}}\) and eliminate the effects of equivalent grid impedance. As shown in Figure 8, the absorbed high-frequency power is transformed to the line-frequency component by generating a voltage signal in phase with the line-frequency component of the capacitor current so that the dc capacitor voltage is sustained. To make the dc capacitor voltage track the reference value, \(u_{\text{dc,ref}}\), precisely, the PI controller is adopted. The control bandwidth of dc capacitor voltage regulation can be chosen much lower than the line frequency and does not interact with the inverter current control [35].  
Figure 8: Operation principle of the GC-VSI and LV-VSC.
## 3 Results and Discussions
### Mechanical Properties  
The relative compressive strength of the blended cement mortar is presented in Figure 9. The relative strength of the control mortar is represented by 100%. Compressive strength of all the blended cement mortar mixes trailed the control mortar at 3 and 7 days. SF-rich ternary blended mixes were observed to outperform the CC-rich mixes at early ages. This is attributed to the fact that calcined clay at early ages behaves as an inert material with little or no reactivity [41]. The reactivity of silica fume, on the other hand, has been reported to commence within the first 12 h of hydration [49]. It is also observed that the early-age strength (3 and 7 days) for 30 CC and 30 SF were lower as compared the other mixes. This is due to the low and slow reactivity of pozzolanic materials at early ages, leading to lower strength development. Compressive strength, however, improved at 28 and 91 days, outperforming the control. At 28 days, apart from 30 CC which fell short of the control mortar by 1%, all blended mixes recorded relative strengths greater than the control. Strengths obtained by 20 CC10 SF, 10 CC20 SF, 15 CC15 SF and 30 SF were 1.3%, 16.7%, 13% and 9.1%, respectively, greater than the control. 10 CC20 SF obtained the highest relative strength at 28 days. This enhanced strength is due to the chemical interaction between Ca(OH)\({}_{2}\) from the cement hydration and glassy SiO\({}_{2}\) phases of the pozzolans to generate further cementitious compounds in the form of calcium silicate hydrates (C-S-H). The filler and lubricating effect of silica fume in cementitious matrices cause an improvement in the packing density and rheological characteristics of the resultant mortar, resulting in improved mechanical properties [50]. In addition, a lower kaolinite content of the calcined clay could be responsible for its low reactivity, even at 28 days, leading to a relatively lower strength development. All blended mixes outperformed the control mix when curing was extended to 91 days. Pozzolanic reactivity at 91 days improved significantly, even for CC-rich mortar mixes. This pattern of strength development is supported by the results obtained for the pozzolanic reactivity measurements in Section 3.3. This observation is consistent with results obtained by [PERSON] and [PERSON] [17].  
Figure 8: Relative mortar flow of blended cement mortar.
## 2 Methods  
### Numerical Conditions  
#### 2.1.1 Large Eddy Simulation  
A large eddy simulation (LES) is a spatial average of turbulent fluctuations, that is, large-scale vortices and small-scale vortices are separated using a filtering function [42]. More specifically, large-scale vortices are simulated directly, while small-scale vortices are closed using models. The theoretical basis of an LES is the existence of inertial sub-scale vorticity in turbulent flows with high Reynolds numbers. The vorticity of such scale has statistical isotropy. It transfers the energy of vortices with an energy scale to vortices with a dissipation scale. After filtering the Navier-Stokes equation with a filter function, the governing equation of the LES method for an incompressible flow can be derived:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial\rho u_{i}}{\partial x_{i}}=0 \tag{1}\]  
\[\frac{\partial(\rho\overline{u}_{i})}{\partial t}+\frac{\partial(\rho \overline{u}_{i}\overline{u}_{j})}{\partial x_{j}}=\frac{\partial}{\partial x_ {j}}\bigg{(}\mu\frac{\partial\overline{\sigma}_{ij}}{\partial x_{j}}\bigg{)}- \frac{\partial\overline{p}}{\partial x_{i}}-\frac{\partial\tau_{ij}}{\partial x _{j}} \tag{2}\]  
where \(\sigma_{ij}\) is the stress tensor and \(\tau_{ij}\) is the subgrid stress.  
In this paper, the Smagorinsky-Lilly model was used for the subgrid model. This model was first proposed by [PERSON] [43]. The eddy viscosity can be defined as follows:  
\[\mu_{t}=\rho L_{s}^{2}\big{|}\bar{s}\big{|} \tag{3}\]  
where \(L_{s}\) is the mixed length of the grid and \(\big{|}\bar{s}\big{|}\sqrt{2\bar{s}_{i}\bar{s}_{ij}}\), \(L_{s}=\min\Bigl{(}\mathrm{kd},C_{s}V^{\frac{1}{3}}\Bigr{)}\), where k is a constant, d is the nearest distance from the wall, \(C_{s}\) is the Smagorinsky constant (in this study, \(C_{s}\) = 0.1), and V is the volume of the calculation unit.
# Accurate Extraction of Cableways Based on the LS-PCA Combination Analysis Method
###### Abstract
This method consists of two stages: preprocessing cableway data and segmenting and extracting cableway data. At the preprocessing stage, the original ropeway data are filtered and the preliminary segmentation of the cableway is primarily performed through adaptive processing. During the cableway segmentation and extraction stage, the high-precision segmentation and extraction of the cableway are completed; the  
Figure 1: Schematic diagram of the distribution of cables and the surrounding environment (\(Z_{max}\) and \(Z_{min}\) representing the maximum and minimum values of the elevation). ropeway point clouds are separated from the non-ropeway point clouds and saved in separate files. In summary, our main contributions are as follows:  
* We introduce spatial geometric distribution and an entropy function. The segmented upper part is further segmented based on the principle of spatial geometric distribution and the minimization of the entropy function. * We propose a comprehensive analysis. The final cable extraction is achieved by combining the least squares method and the principal component analysis method. * We carried out experimental verification. Three groups of samples were selected as the experimental objects, and the experimental results confirmed the efficacy of the proposed method as well as its reliability.