esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### System Suitability and Method Validation of the Develop HPLC Analytical Method  
The stability of the HPLC system for the qualitative and quantitative analysis of marker analytes was assessed by determining the _k\({}^{\prime}\)_, \(x\), \(N\), _Rs_, and _Tf_ parameters, which were found to be 3.94-9.91, 1.14-1.89, 551122-1122666, 6.94-40.42, and 1.07-1.13, respectively, as shown in Table S4. These values are suitable for both qualitative and quantitative analysis of the marker analytes. In the developed assay, as shown in Table 1, the coefficient of determination (_r\({}^{2}\)_) value representing the linearity for quantification of the marker analytes prepared at different concentration levels was 0.9999-1.0000, showing excellent linearity; LOD and LOQ were also calculated as 0.08-0.46 \(\upmu\)g/mL and 0.24-1.41 \(\upmu\)g/mL, respectively. Recovery tests, which were performed by adding three different concentrations (low, medium, and high) to the DGJT sample for accuracy evaluation, confirmed that all four marker analytes showed a good recovery of 97.01-100.88% (Table 2). The RSD (%) values for the retention time and peak area were measured to be less than 1.0% in repeatability tests performed six times with a standard solution of analytes (Table S5), and the RSD (%)  
Figure 1: Representative HPLC chromatograms of (**A**) a standard solution and (**B**) 70% methanol extract of Daegunjoongtang (DGJT) samples, at 203 and 255 nm. Hyperoside (**1**), quercitrin (**2**), ginseng Rg1 (**3**), and 6-gingerol (**4**).  
value indicating intraday (for one day) and interday (on three consecutive days) precisions also showed excellent precision values of less than 1.0% (Table 3). The above results show that the HPLC analysis method developed in this study is suitable for both qualitative and quantitative analysis of the markers hyperoside, querctrin, ginseng Rg1, and 6-gingerol in DGJT.
## 3 Proposed Model
### Stage 1: Feature Selection Using GA Algorithm
Here, an FF is implemented that adds these purposes of FS, as shown in Equation (4):  
\[ \begin{array}{l}Fitness(X)=\alpha\cdot E(X)+\beta\;*\;(1-\frac{|R|}{|N|}) \end{array}  \tag{4}\]  
In the equation, \(|R|\) represents the selected feature count, and \(|N|\) denotes the total number of new features in the database. Fitness(\(X\)) stands for the fitness value of \(X\), while \(E(X)\) indicates the classifier errors when using the optimal features from subset \(X\). The terms \(\alpha\) and \(\beta\) represent the classifier errors and reduction ratio, where \(\alpha\) belongs to the range \([0,1]\) and \(\beta\) equals \((1-\alpha)\). The author proposed an HDL with GA because GA's ability to optimize the feature set is crucial in reducing the dimensionality of high-dimensional IoT data, which improves both the accuracy and efficiency of the subsequent DL model. The combination of GA with DL elements such as RNNs and attention mechanisms ensures that the most relevant features are selected, allowing the HDL model to focus on identifying complex patterns in the data more effectively. This hybrid approach leverages the strengths of both evolutionary algorithms and DL to enhance the detection of botnet attacks in IoT environments.
# The XMUSPEECH System for Accented English Automatic Speech Recognition
## 3 Experimental Results
### Results of Different Countries' Accents  
In addition, we further tested the performance of our system on accents of different countries. As shown in Table 5, our system had the lowest WER on the British accent, but it also had a relatively low WER on Japanese and Korean accents.
## 5 Conclusions  
The PEDF system is currently undergoing the early stages of scalable demonstration, highlighting the need for optimized configuration. This research proposes an optimization and evaluation framework for the PEDF system using a dual-layer model for planning and operating, encompassing essential principles, methods and evaluation indexes. Validated through two case studies, this framework offers the following insights:  
* The dual-layer configuration optimization model facilitates flexible design of system topology and the configuration of related elements. It supports the economic, low-carbon, and reliable operation of PEDF systems, accommodating various building applications and configuration objectives.
* The findings from this framework and case studies indicate that PEDF systems can effectively serve diverse building types, including office and commercial spaces. They offer potential benefits such as renewable energy utilization, energy conservation, and emission reduction, improving interaction with the grid and the conversion of building loads to DC. This framework can be applied to various scenarios where PEDF systems are relevant.
* Moreover, this scalability of this framework opens opportunities for future research to investigate expanded control strategies and operational conditions for the PEDF system. Developing related configuration optimization software will further enhance the system's capabilities, providing a broad landscape for future research and optimization efforts related to the PEDF system.  
Conceptualization, J.K. and T.L.; methodology, T.L. and J.K.; software, T.L., P.Y. and H.W.; validation, T.L., P.Y. and H.W.; formal analysis, P.Y. and X.H.; investigation, H.W. and X.H.; resources, J.K.; data curation, T.L., P.Y. and H.W.; writing--original draft preparation, T.L.; writing--review and editing, T.L., W.L. and J.K.; visualization, T.L., W.L. and J.K.; supervision, W.L. and J.K.; project administration, W.L. and J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2021 YFB1600200); the Guizhou Science and Technology Plan Project (Guizhou Science Support [2023] General 409) and the Key R&D Program of Shaanxi Province (2023-YBSF-285).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors have no competing interests to declare that are relevant to the content of this article.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} PEDF & Photovoltaic, energy storage, direct current and flexibility \\ PV & Photovoltaic \\ ES & Energy storage \\ ROI & Return on investment \\ LOCE & Levelized cost of energy \\ CCER & Carbon emission reduction \\ RSC & PV self-consumption rate \\ RSS & PV self-sufficiency rate \\ LPSP & Loss of power supply probability \\ \end{tabular}
## 2 Methodology
### Unit Commitment and Dispatch Model Formulation
#### 2.2.1 System Inertia Constraints Formulation  
As introduced in Section 2.1, future electrical grid systems will comprise of a minimum synchronous inertia part and a non-synchronous inertia part. To account for both system requirements in the unit dispatch model, two constraints are created. First, the minimum synchronous inertia constraint as expressed in Inequation (7) in which \(J^{min\_sys\_sync}\) represents the minimum system inherent inertia while the right part of the Inequation the overall moment of inertia of all synchronously connected generators.  
\[J^{min\_sys\_sync}\leq\sum_{g}x_{g}^{source\_inertia}\cdot y_{g}^{sync} \tag{7}\]  
Second, the overall minimum system inertia requirement is depicted by Inequation (8). The minimum system inertia requirement is depicted via the variable \(J^{min\_sys}\). The right side of Inequation (8) shows the overall accumulated inertia of all connected generators providing either an inherent inertial response (\(J_{g}^{sync}\)) or an non-inherent inertial response \(J_{g}^{SI}\), i.e., SI.  
\[J^{min\_sys}\leq\sum_{g}x_{g}^{source\_inertia}\cdot(J_{g}^{sync}+J_{g}^{SI}) \tag{8}\]
# The Fast Detection and Identification Algorithm of Optical Fiber Intrusion Signals
## 3 The Implementation of Recognition Algorithm and the Experimental Analysis
### Experiment Analysis of Actual Data
#### 3.2.4 The Performance Comparison of Fast Algorithm with Other Methods  
As shown in Table 5, the false alarm rate of single-channel detection algorithm proposed in this article is 2.6% lower than Cell Average Constant False Alarm Rate (CA-CFAR).  
The recognition performance of each method is shown in Table 6. The recognition rate of the methods used in this paper is relatively high, and the average recognition rate is 93.51%.  
The real-time performance of each method is shown in Table 7 that is better than other strategies with the time consumption of 0.659 s. It can be seen that the method adopted in this paper not only guarantees the recognition rate, but also is superior to other algorithm in time efficiency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Recognition Rate**} \\ \cline{2-5}  & **Picking** & **Mechanical** & **Walking** & **Average** \\ \hline This strategy & 97.3\% & 86.6\% & 96.7\% & 93.5\% \\ Literature [3] & 93.5\% & 84.3\% & 91.8\% & 89.9\% \\ Literature [5] & 86.4\% & 80.0\% & 84.1\% & 83.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of Recognition performance of each cognitive strategy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Method**} & **Processing Time/s** & **Realization Method** \\ \hline This strategy & 0.6590 & Embedded platform \\ Literature [3] & 0.9867 & Embedded platform \\ Literature [7] & 0.8923 & Embedded platform \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of Recognition performance of each cognitive strategy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Method**} & **False Alarm Rate** & **Realization Method** \\ \hline single-channel detection algorithm & 5.2\% & Embedded platform \\ CA-CFAR & 7.8\% & Embedded platform \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of detection performance of single-channel detection algorithm with CA-CFAR.  
Figure 22: Picking signal recognized as walking signal. (**a**) Time domain; (**b**) Frequency domain;
## 3 System Model and Problem Formulation
### ILP Model
#### 3.4.3 Packet Loss Rate Constraint  
This consideration is limited to the packet loss rate on the links, excluding VNF processing. It is required that the selected path for IoT-SR\({}_{i}\) satisfy the constraint on the end-to-end packet loss rate:  
\[\prod_{m,n\in F_{i}}\prod_{uv\in\mathcal{L}}\xi_{i,uv}^{loss}x_{i,uv}^{mm} \leq R_{i}^{loss} \tag{7}\]  
where \(\xi_{i,uv}^{loss}\) is the packet loss rate of IoT-SR\({}_{i}\).  
As the equation presented above is non-linear, we can linearize it by taking a logarithm on both sides.  
\[\sum_{m,n\in F_{i}}\sum_{uv\in\mathcal{L}}log(\xi_{i,uv}^{loss}x_{i,uv}^{mm} )\leq log(R_{i}^{loss}) \tag{8}\]
# A 4.1 GHz-9.2 GHz Programmable Frequency Divider for Ka Band PLL Frequency Synthesizer
## 1 Introduction  
Due to the requirement of modern high-rate communication, ultra-wide-band transceivers are of demand. To generate a high quality carrier signal for modulating and demodulating in the transmitters and receivers is a challenge in the mmW integrated circuit design [1]. Phase-locked-loop (PLL) is always used to generate a carrier signal. It can multiply a low reference frequency by a ratio to obtain a high frequency signal [2; 3; 4]. The usage of PLL is more and more important in mmW circuit design like 5G communication system and radar, as the quality of the signal is important in modulating and demodulating in the transmitter and receiver. A divider is an important part in the PLL system, it divides the high-frequency signal from the output of the voltage-controlled oscillator (VCO) to the reference frequency [5].  
Two types of dividers are used in the frequency synthesizer, prescaler and multi-modulus-dividers (MMD). The division ratio of the prescaler is a constant and it has a high operating frequency with a high power consumption [6]. MMD's operating frequency is lower compared with the prescaler, but the division ratio of MMD can be adjusted by digital control. As the VCO in the PLL operates at the highest frequency in the PLL, the output signal of VCO will be sent to several prescaler at first [7], and the output low frequency signal from the prescaler is divided to the reference frequency by the MMD [8]. A high operating frequency and low power consumption pulse swallow MMD is presented in this paper. The improvement of low operating frequency of such MMD is a challenge [9; 10; 11; 12; 13]. The operating frequency of N/N + 1 prescaler and counters is the restriction of the operating frequency. In recent years, high operating N/N + 1 prescaler is reported in the papers. However, high operating counter is still a challenge and it restricts the operating frequency range of MMD.  
In this design, besides a high operating frequency N/N + 1 prescaler, high operating frequency TSPC DFFs are used in the programmer counter, which improves the upper limited frequency of the counters and the whole system, with less power consumption. In the following section, a high operating frequency MMD is presented. Section 2 introduce the circuit design of MMD. Section 3 presents the measured result. And the conclusion is in the Section 4.
## 4 Security Analysis of the Proposed Protocol
### Heuristic Analysis
#### 4.2.9 Privileged Insider Attack  
In order to prevent the adversary (even corrupted \(GWN\)) from using privileged insider attacks and extracting the identity information of legitimate users during the registration phase, \(U_{i}\) only submits \(A_{0}\ (A_{0}=h(ID_{i}\oplus PW_{i}\parallel r)\ \text{mod}\ n_{0})\), which encapsulates \(ID_{i}\) relative to \(GWN\), rather than bare string \(ID_{i}\), and the adversary cannot obtain the real \(ID_{i}\).
# A Review on Styrene Substitutes in Thermosets and Their Composites
## 1 Introduction
The typical monomers include vinyl derivatives, (methyl)acrylate derivatives, and others. Among vinyl monomers, the most common one is divinyl benzene (DVB), which has a profound application in UV curing coatings. \(N\)-vinyl-2-pyrrolidone (NVP), _tri_(ethylene glycol)divinyl ether (TDE), and dimethyl itaconate (DI) are also investigated as styrene alternatives. (Methyl)acrylates such as trimethylolpropane triacrylate (TMPTA), trimethylolpropane trimethacrylate (TMPTMA), and methyl methacrylate (MAA) have been investigated for styrene replacements; meanwhile, some RDs such as hydroxyethyl methacrylate (HEMA) and hydroxypropyl acrylate (HPA) have raised more concern. The obtained resins and composites present for responding to the attempts on using typical monomers, which will be further exhibited and compared in this review. Novel monomers synthesized from renewable materials are extremely attractive. According to its chemical composition, the biobased monomers can be divided into the following categories: (1) oxygen-rich monomers, such as carboxylic acid and furan; (2) hydrocarbon-rich monomers, such as vegetable oil, terpen, and rosin acid; (3) hydrocarbon monomers, such as biovinyl, biopropylene, bioisopropylene, and biobutylene [21]. They attract wide attention from research and industrial circles for their inherent advantages including low cost, easy availability, renewability, and biodegradability. The Pacific Northwest National Laboratory (PNNL) and the National Renewable Energy Laboratory (NREL) of USA concluded 12 kinds of potential renewable building blocks [22]. [PERSON] et al. further commented on the top 10 biobased products from biorefinery that were affirmed by the Department of Energy, USA [23]. However, these biobased monomers are unable to perform free-radical polymerization and hence many strategies were developed for introducing unsaturated sites to synthesize crosslinkable monomers. The commonly adopted routes for bestowing them unsaturated groups are classified and discussed detailly in this review. The simple comparison on material performance between products will be given as well. The purpose of this review, therefore, is to provide an up-to-date overview of the field by highlighting and discussing the latest advances in RDs for styrene-free thermosets and their composites, covering both traditionally crosslinkable monomers and newly developed monomers from renewable feedstocks.
# Compact Quad-Port MIMO Antenna with Ultra-Wideband and High Isolation
## 3 Quad-Port UWB MIMO Antenna Design  
To meet the channel capacity requirements in modern wireless communications, a compact low-coupling four-cell UWB MIMO antenna is designed in this section. Only four UWB antennas are required as fundamental units, and the effectiveness of the decoupling is achieved by using polarization diversity and introducing a decoupling gap in the metallic ground while keeping the overall dimensionality unchanged. Isolation can be reflected in transmission and correlation coefficients. To further validate the effectiveness of this decoupling combination, we performed a sample and test. The test curves are in general in good agreement with the simulations, and the slight deviations are mainly due to manual manipulation, within the error tolerance.
# Acceleration of a Production-Level Unstructured Grid Finite Volume CFD Code on GPU
## 3 Parallelization on GPUs
### LU-SGS Algorithm Parallelization  
#### 3.2.1 Cell Colouring  
As mentioned in Section 2.4, the updated solution appears on both the left-hand and right-hand sides when solving the linear systems. If executing line 4 of Algorithm 2 in parallel, the ordering of computation is not one way (the data race occurs for \(\Delta W\) of the right), and the solution is unreproducible. Referring to the face colouring technique used in parallel flux calculation, this paper introduced the cell colouring technique [25] to solve this problem. As shown in Figure 3, transform the unstructured grid topology into an undirected graph. The vertices in the graph represent each grid cell, and the edges represent the adjacency relationship between two cells sharing the same face. All the computational cells are painted with multiple colours so the colours of neighbour cells are different from one another. Then traverse all the colours forward and backward respectively, each time performing the elimination in parallel within the same colour.
## 3 Results
### Impact of Spatial Resolution  
A pixel comparison analysis was carried out to assess how image spatial resolution affects the relationship between Gi* z-score and image derivatives. This experiment was carried out only on NDVI, given that it outperformed the other spectral indices (see the previous section). NDVI values were resampled using the nearest neighbour technique from 10 m (native resolution) to 20 m, 30 m, 50 m, 100 m, 250 m, 500 m and 1000 m (Table 4). The strongest model (r\({}^{2}\) = 0.87) between Gi* z-score and NDVI was found to be at 100 m resolution. The strongest model at 100 m resolution was statistically stronger compared to the other spatial resolutions tested. This finding is supported by Figure 5 in which the drainage lines become undetectable at resolutions lower than 250 m. The weakest models (r\({}^{2}\) < 0.2) were produced for the largest pixel sizes at 500 m and 1000 m (Table 4).  
\begin{table}
\begin{tabular}{c  
Figure 4: Regressions with untransformed data showing the relationship between the Gi* z-score from the optimized hotspot analysis and the following vegetation and soil moisture index scores: (**a**) ARVI, (**b**) BSI, (**c**) GCI, (**d**) GNDVI, (**e**) MSAVI2, (**f**) MSI, (**g**) NDMI, (**h**) NDVI.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Spatial Resolution (Area)** & **Regression Equation** & **r2** & \(p\) & **F1199** \\ \hline
10 m \(\times\) 10 m (100 m\({}^{2}\)) & y = \(-\)1869.5x\({}^{2}\) + 567.62x \(-\) 23.703 & 0.8187 & \(1.48\times 10^{-53}\) & 847.00 \\
20 m \(\times\) 20 m (400 m\({}^{2}\)) & y = \(-\)1870.6x\({}^{2}\) + 569.13x \(-\) 23.769 & 0.8324 & \(1.84\times 10^{-76}\) & 922.68 \\
30 m \(\times\) 30 m (900 m\({}^{2}\)) & y = \(-\)2288.5x\({}^{2}\) + 626.32x \(-\) 25.7 & 0.8212 & \(2.98\times 10^{-73}\) & 842.19 \\
50 m \(\times\) 50 m (2500 m\({}^{2}\)) & y = \(-\)2472.1x\({}^{2}\) + 643.45x \(-\) 26.0 & 0.8214 & \(1.34\times 10^{-73}\) & 850.63 \\
**100 m \(\times\) 100 m (10,000 m\({}^{2}\))** & **y = \(\mathbf{3235x^{2}-54.33x-6.2795}\)** & **0.8684** & **2.73 ×10\({}^{-86}\)** & **1210.24** \\
250 m \(\times\) 250 m (62,500 m\({}^{2}\)) & y = \(-\)9350.8x\({}^{2}\) + 1523x \(-\) 53.774 & 0.5977 & \(1.45\times 10^{-38}\) & 267.03 \\
500 m \(\times\) 500 m (250,000 m\({}^{2}\)) & y = \(-\)5 \(\times\) 10\({}^{+0.6}\)x\({}^{2}\) + 582,888x \(-\) 18,363 & 0.1809 & 0.00015 & 14.94 \\
1000 m \(\times\) 1000 m (1,000,000 m\({}^{2}\)) & y = 17.778x + 3.7778 & \(-\)2 \(\times\) 10\({}^{-16}\) & n/a & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Regression and test statistics of linear and quadratic regression analyses fitted to the Gi\({}^{\star}\) z-score from the optimized hotspot analysis and NDVI scores.  
Figure 5: NDVI image pixel comparison at (**a**) 10 m, (**b**) 20 m, (**c**) 30 m, (**d**) 50 m, (**e**) 100 m, (**f**) 250 m, (**g**) 500 m and (**h**) 1000 m of the grazing stocking rate trial.
## 2 Literature Review
Another study investigated the ability of the ANN method for modeling sophisticated nonlinear SSL in the Himalayan area [25]. The research not only presented an understanding of the sedimentation process, but it also studied the impact of other hydrological variables on SSL. A three-layer back propagation model was used to estimate SSL. Rainfall, water discharge, and sediment discharge were model inputs [4]. To investigate the ability of the model, error was computed by benchmarking the observed and simulated data. It was found that the model could present accurate outputs in the prediction of SSL. In general, the literature review showed that soft computing models are widely used for SSL prediction. However, soft computing models can be improved by combining them with optimization algorithms. In the current study, two new optimization algorithms are used to improve the ANFIS and MFNN models. Bat algorithm (BA) and weed algorithm (WA) are widely used in a number of different issues, including water resource management, water quality, hydraulic structure design, mathematical problems, power generation, hydrological simulations, and flood control [26; 27]. Thesealgorithms are widely used because of their fast convergence, simple computation process, good balance between exploration and exploitation abilities, high flexibility, accurate results, and the high capability of local and global searches. To our knowledge, the hybrid ANFIS-BA, MFNN-BA, ANFIS-WA, and MFNN-WA models have not been previously used for SSL prediction. The current study not only predicts monthly SSL using improved ANFIS and MFNN models, but it also presents comprehensive information about the uncertainty of the prediction models. In addition, a comprehensive evaluation is carried out to study the advantages and disadvantages of the applied soft computing models.