esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Function Genes
### Metabolism-Related Genes  
A considerable number of genes involved in metabolite biosynthesis were isolated and identified from tea plants. The _CsF3H_ gene was cloned from tea plants, and transgenic _Arabidopsis_ plants overexpressing _CsF3H_ genes significantly increased the flavonoid content in the seeds [184]. [PERSON] et al. (2018) reported the _UGT73A17_ gene was involved in the biosynthesis of flavonoid glucosides [194], and _CsGSTF1_ encoding phi (F) class glutathione transferase positively regulated the anthocyanins biosynthesis [195]. Previous studies revealed that MYB family genes were associated with flavonoid biosynthesis [196]. CsMYBB4a negatively regulates the biosynthesis of phenylpropanol and anthocyanins [196], while CsMYBB6A and CsMYBB75 positively regulate anthocyanins biosynthesis, respectively [195; 197]. [PERSON] et al. (2018) also reported that the overexpression of CsMYBB5b in transgenic tobacco significantly increased the content of catechchin monomers and polymers in transgenic tobacco lines, compared with WT [198]. Moreover, MYB interacting with bHLH and WD40 form ternary WBM complexes in tea plants, and these regulated flavan-3-ols biosynthesis [199]. A total of three _CsLAR_a-c genes encoding leucoanthocyanin reductase and transcription factor CsHB1 significantly increased the catechchin content [200; 201]. In addition, an _Arabidopsis_ mutant (td84-2) that overexpressed the _CsANS_ gene significantly increased the epicatechin content in seeds [186]. [PERSON] et al. (2021) reported that CsDOF regulates glutamine metabolism in tea plants [202]. Although many metabolic genes were identified in tea plants, the functions of most genes were still unclear. Thus, the functions of most genes should be investigated in model plants using the genetically modified method.  
Tea aroma is one of the most critical aspects of tea quality. Beta-primeverosidase from tea plants specifically hydrolyze aroma precursors of beta-primeverosides to produce numerous aromatic compounds [203]. The _CsSAMT_ gene, encoding salicylic acid carboxyl methyltransferase, catalyzes SA to generate MeSA with floral aromas [204]. Theanine also plays an important role in tea flavor and quality [205; 206]. A comparative genomics analysis suggests that the glutamine synthase (GS) family of genes differentiates into theanine synthase (TS) genes [207]. [PERSON] and [PERSON] (1964) purified and identified TS for the first time [208]. Overall, two genes (_TS1_, DD410896 and _TS2_, DD410895) were amplified from the tea plant cDNA library. Further analysis showed that the sequence similarity between _TS1_ and _GS3_ is as high as 99%, and the sequence similarity between _TS2_ and _GS1_ is as high as 97%. [PERSON] et al. (2017) revealed that the glutamine:2-oxoglutarate aminotransferase (_CsGOGAT_) gene played important roles in regulating theanine content in the postharvest leaves of tea plants treated under different temperatures and shade conditions [140]. So far, there are few studies on aroma-related genes. In the future, mining aroma-related genes should be intensified using the omic analysis.
## 3 Results and Discussion
### Results of 2D CFD Modeling of the Liquid-Gas Ejector
#### 3.2.2 The Impact of Absorber's Concentration on the Amount of CO\({}_{2}\) Removal  
Figure 17 shows the impacts of absorber's concentration on the amount of CO\({}_{2}\) removal. The mentioned figure has been extracted for a specific liquid mass flow rate (2.85 \(\times\) 10\({}^{-4}\) m\({}^{3}\)/s) with an outlet pressure about 2000 Pag. As can be seen, by increasing the absorber's concentration from 10% to 30%, the amount of removed carbon dioxide increases from 68% to 95%. This improvement is due to an increment in the concentration of carbon dioxide in the intersection area.
## 4 Acquisition and Analysis of VMF Experimental Data
### Experimental Scheme  
Combining two VMF datasets and different interpolation methods, the calculation schemes 1-4 are composed as follows: the first scheme uses VMF1 data and the bilinear interpolation method (VMF1 + BIL); the second scheme uses VMF1 data and inverse distance weighted interpolation (VMF1 + IDW); the third scheme uses VMF3 data and bilinear interpolation (VMF3 + BIL); and the fourth scheme uses VMF3 data and inverse distance weighted interpolation (VMF3 + IDW). The experimental results are shown in Figures 3 and 4.  
As shown in Figure 3, the ZTD at the stations obtained using four experimental schemes is greater than the true value, except for the TCMS and HKSL stations. It can be seen from Figures 3 and 4 that except for URUM and BJFS, the interpolation accuracy obtained by the four schemes at each station is less than 40 mm, BIL is slightly better than IDW, and ZTD obtained by VMF3 grid is slightly higher than VMF1. The interpolation accuracy is lower when using VMF1 to interpolate the ZTD at BJFS, and its average root mean square error is 81.785 mm. Similarly, the interpolation accuracy is lower when using VMF3 and VMF1 to interpolate the ZTD at URUM, and its average root mean square error is 96.47 mm.  
Figure 3: ZTD average deviation under the four solution schemes.  
Figure 2: Station distribution map.  
In order to verify the relationship between the tropospheric solution accuracy and the elevation of the four grid points around the station, the elevation difference of the four points around each station is calculated. Information on the stations is shown in Table 1. The calculation formula for elevation difference is as follows [25]:  
\[\Delta h=\sqrt{\sum_{i=1}^{4}(h_{i}-h)^{2}/4} \tag{13}\]  
where \(h\) is the elevation of the site and \(h_{i}\) is the elevation of the four grid points (unit: m). The height difference between the IGS reference station and the surrounding four grid points can be calculated using Equation (13), as shown in Figure 5.
## 4 Simulation Results and Discussion
### Functional Verification
Consequently, our proposed system possesses unique advantages in dealing with relatively complex attacks within WDM optical networks, which addresses critical gaps and advances the state of the art in this field. Figure 8: Simulation results of recognizing 4-bit bit-flip BPSK targets in the input data using the reconfigurable system at frequencies of (**a**) 192.8 THz and (**b**) 193.4 THz.
# Improving Juvenile Age Estimation Based on Facial Landmark Points and Gravity Moment
## 2 Related Work
### Age Estimation  
The idea of the label distribution method is to present ages as distributed labels that cover a certain number, with each number describing the extent to which each label defines an instance of the face. Consider an image \(f\); label distribution is represented as a vector \(d\) that describes the age \(g\), such that the descriptions are real numbers presented as \(d_{g,f}\in[0;1]\). The label distribution is expected to satisfy two conditions:  
1. The description degree of \(f\) should have the highest value.
2. Description degree of the neighboring ages should decrease while going away from \(f\).  
Inspired by the label distribution method, [PERSON] et al. [44] used an ensemble of classifiers to distinguish children and adults. Setting the child threshold at 21 years, they proceeded to estimate the age using an aging function. To mitigate dataset constraints, [PERSON] et al. [42] proposed label distribution by learning chronological age and adjacent ages for each image. Their proposal included the image itself in the distribution and assumed the representation to be consistent with the entropy condition. Additionally, [PERSON] et al. [43] combined the distribution learning of age labels with age prediction. Their approach considered context relationships by using different face samples to find the correlation of the cross-age. The method was proposed for problems that consider a preconception-less label distribution learning or for learning sample-specific, context-conscious label distribution properties capable of solving multiple tasks, including age distribution and age prediction. The label distribution method can be extended to address specific age estimation problems if the ordinal relationship is adequately exploited to represent the aging features.  
From the related works, we made the following observations:  
1. Aging features come in two major kinds--global and local features, corresponding to face shape-related and skin texture-related features.  
Shape-related features are more useful for juvenile age estimation, whereas skin texture (wrinkle) better serves the adult age estimation proposals. However, both of them can be exploited to address specific age estimation objectives.
3. Age prediction methods can be categorized as classification and regression, but considering the implicit ordinal relationship of faces within a similar aging subspace could advance the age estimation task.
4. Although some publicly available datasets are commonly used for age estimation, in general, there is a lack of datasets for juvenile age estimation.
## 1 Introduction
Consequently, it facilitates and minimizes the workload of doctors in clinical centers by allowing continuous remote patient monitoring. Since the biosensors are low-cost, compared to the expensive and secure equipment in the hospital, a much larger population can be covered by the BASN monitoring system than the traditional medical care system. Thus, it frees patients from intensive personal care [13]. However, the wide applications of BASNs in the medical setting are seriously hindered by concerns such as reliable data transmission and the short life span of the embedded batteries of the sensor units. Though wireless communication in BASN is limited to an area of several meters wide, the channel conditions on the human body are highly unstable and unpredictable, which imposes great challenges for reliable data transmission [14; 15]. Therefore, an effective data transmission strategy needs to be developed to enhance data reliability with reduced power consumption of the biosensor nodes as much as possible in the network. Therefore, in this study, a novel, multiobjective, lion mating optimization inspired routing protocol for BASN-based IoT health monitoring applications is presented. In the rest of the paper, Section 2 discusses the existing routing protocols in BASNs. In Section 3, we briefly describe the research challenges and research motivations. In Section 4, we discuss our proposed SARP routing protocol. In Section 5, we discuss the channel and energy consumption models used in the simulation studies. In Section 6, we provide simulation results and analyze the performance of SARP against existing routing protocols in BASNs. Finally, in Section 7, we conclude the paper with future works.
## Appendix A
\begin{array}{l}\chi u=\sum\limits_{n=0}^{N-1}\Psi_{l}^{n}\chi P_{n}(\chi)= \sum\limits_{n=0}^{N-2}y_{01}{}^{n}_{l}P_{n}(\chi)\\ \chi^{2}u=\sum\limits_{n=0}^{N-1}\Psi_{l}^{n}\chi^{2}P_{n}(\chi)=\sum\limits_{n =0}^{N-2}y_{02}{}^{n}_{l}P_{n}(\chi)\\ \frac{\partial^{2}u}{\partial\chi^{2}}=\sum\limits_{n=0}^{N-1}\Psi_{l}^{n} \frac{\partial^{2}P_{n}(\chi)}{\partial\chi^{2}}=\sum\limits_{n=0}^{N-3}y_{20} {}^{n}_{l}P_{n}(\chi)\\ \frac{\partial u}{\partial\chi}=\sum\limits_{n=0}^{N-1}\Psi_{l}^{n}\frac{ \partial\rho_{n}(\chi)}{\partial\chi}=\sum\limits_{n=0}^{N-2}y_{10}{}^{n}_{l}P _{n}(\chi)\\ \chi^{2}\frac{\partial^{2}u}{\partial\chi^{2}}=\sum\limits_{n=0}^{N-1}\Psi_{l} ^{n}\chi^{2}\frac{\partial^{2}P_{n}(\chi)}{\partial\chi^{2}}=\sum\limits_{n=0 }^{N-3}y_{22}{}^{n}_{l}P_{n}(\chi)\\ \chi\frac{\partial^{2}u}{\partial\chi^{2}}=\sum\limits_{n=0}^{N-1}\Psi_{l}^{n} \chi\frac{\partial^{2}P_{n}(\chi)}{\partial\chi^{2}}=\sum\limits_{n=0}^{N-3} y_{21}{}^{n}_{l}P_{n}(\chi)\\ \chi^{2}\frac{\partial u}{\partial\chi}=\sum\limits_{n=0}^{N-1}\Psi_{l}^{n} \chi^{2}\frac{\partial P_{n}(\chi)}{\partial\chi}=\sum\limits_{n=0}^{N-2}y_{ 12}{}^{n}_{l}P_{n}(\chi)\\ \chi\frac{\partial u}{\partial\chi}=\sum\limits_{n=0}^{N-1}\Psi_{l}^{n}\chi^{ 2}\frac{\partial P_{n}(\chi)}{\partial\chi}=\sum\limits_{n=0}^{N-2}y_{11}{}^{ n}_{l}P_{n}(\chi)\\ \end{array} \] (A10)  
The coefficients in Equation (A10) after simplification are:  
\[ \begin{array}{l}y_{10}{}^{m}_{l}=(2m+1)\sum\limits_{n=m+1,m+3}^{N-1}\Psi_{l }^{n}\\ y_{12}{}^{m}_{l}=m(m-1)\Psi_{l}^{m-1}{}_{l}{}_{+}(\frac{(m+1)^{2}}{2m+3}{}^{n} _{l}{})\Psi_{l}^{m+1}{}_{+}(2m+1)\sum\limits_{n=m+2,m+4}^{N-1}\Psi_{l}^{n}\\ y_{11}{}^{m}_{l}=m\Psi_{l}^{m}{}_{+}(2m+1)\sum\limits_{n=m+2,m+4}^{N-1}\Psi_{l }^{n}\\ y_{02}{}^{m}_{l}=\frac{(m+1)(m+2)}{(2m+1)(2m+5)}\Psi_{l}^{m-2}{}_{+}2\frac{2m ^{2}+2m-1}{(2m-1)(m+3)}\Psi_{l}^{m}{}_{+}+\frac{m(m-1)}{(2m-1)(2m-3)}\Psi_{l}^{ m+2}\\ y_{01}{}^{m}_{l}=\frac{m}{2m-1}\Psi_{l}^{m-1}{}_{-}1{}_{+}^{+}\frac{m+1}{2m+3} \Psi_{l}^{m+1}\\ y_{20}{}^{m}_{l}=(\frac{2m+1}{2})\sum\limits_{n=m+2,m+4}^{N-1}(n(n+1)-m(m+1)) \Psi_{l}^{n}\\ y_{22}{}^{m}_{l}=m(m-1)\Psi_{l}^{m}{}_{+}(\frac{2m+1}{2})\sum\limits_{n=m+2,m+4 }^{N-1}(n(n+1)-m(m+1)-4)\Psi_{l}^{n}\\ y_{21}{}^{m}_{l}=(\frac{2m+1}{2})\sum\limits_{n=m+1,m+3}^{N-1}(n(n+1)-m(m+1)-2) \Psi_{l}^{n}\\ \end{array} \] (A11)  
Equation (7) is derived after simplifying the remaining terms using Equations (A10) and (A11).
# The Future of Cybersecurity in the Age of Quantum Computers
## 5 Quantum Supremacy and the Q-Day Threat  
There is a race among companies to achieve supremacy in quantum technology because it will drastically reduce computing time from years to hours or even minutes. The power of quantum computing will be a boon for data-intensive industries such as pharmaceuticals, artificial intelligence, industrial design, logistics, and national security [31]. However, it raises serious threats to the cybersecurity of classical computing devices. Theoretically, all cryptography algorithms are vulnerable to quantum attacks. Q-Day is when quantum computers will break the Internet [4]. Given the ubiquity of cryptographic schemes in our everyday online activities, this could be catastrophic. Computer scientists are predicting how quantum computers can cause havoc with encryption, resulting in a global catastrophe [32].  
With Google's declaration of quantum supremacy in 2019, claiming that they solved a problem in three minutes that would take a classical computer over a thousand years, quantum computing is no longer a hypothetical idea [33]. Nevertheless, the millions of qubits of computing power required to break modern cryptography remains a challenging goal. Practical quantum computers with millions of qubit capacity will be able to break nearly all modern public-key cryptographic systems. Before quantum computers arrive with sufficient qubits, we must be ready with quantum-safe cryptographic algorithms, tools, techniques, and deployment strategies to protect the classical ICT infrastructure.
## 3 Results and Discussion
### PCA and Mussel Expert System (MES) Data Integration  
The output of the PCA performed on biomarker data measured in adult mussels exposed to MP, CHR-MP, 0.1 mg/L and 0.73 ng/L CHR is shown in Figure 5.  
The first two axes accounted for the 83.5% of the total variance among samples/endpoints. Data from control mussels tended to cluster together in the PC1 < 0/PC2 < 0 domain. Data from MP and CHR-MP scored for PC1 > 0/PC2 < 0. Data from 0.1 mg/L CHR were scaled at PC1 > 0/PC2 > 0, while those from 0.73 ng/L CHR at PC1 < 0/PC2 > 0. Vectors for CAT activity (in both gills and digestive gland) and GST activity measured in gills show coordinates in the PC1 < 0/PC2 < 0 domain; the GST activity in digestive gland was scaled at PC1 < 0/PC2 > 0; AChE, NL and LF clustered in the PC1 > 0/PC2 < 0 domain, while LMS was the only variable clustering at PC1 > 0/PC2 > 0. The spatial distribution of  
Figure 5: Biplot showing the output of the Principal Component Analysis (PCA) on biomarker data obtained in adult mussels exposed to polystyrene microplastics (MP), chrysene-loaded microplastics (CHR-MP), 0.1 mg/L and 0.73 ng/L of freely dissolved CHR.  
data from treatments and variables identifies three main clusters. Data from control and 0.73 ng/L CHR tended to cluster together in line with the substantial lack of differences between biomarker levels measured at these conditions. A second cluster (PC1 > 0/PC2 < 0) includes data collected in organisms exposed to MP and CHR-MP; this association seems to be mainly explained by AChE, NL and LF data, although similar levels between these two conditions were also observed for most selected endpoints. Finally, data from mussels exposed to 0.1 mg/L CHR tended to cluster differently from the other treatments, in line with the generally higher impact induced on most selected endpoints, notably on LMS. The relationships identified by the PCA output seem to be confirmed by the MES data integration (Table 2). Given its sensitivity and known responsiveness to stress factors [56], the LMS was selected as a guide parameter to perform the data integration. The system did not identify health alterations in control and at 0.73 ng/L CHR (HSI = A); the exposure to MP and CHR-MP resulted in the onset of a low stress condition (HSI = B) while a moderate stress level (HSI = C) was identified for mussels exposed to 0.1 mg/L CHR.
## 5 Conclusions  
In this work, we compare AC signal analysis methods in terms of processing speed, memory consumption, amplitude, and phase accuracy for embedded solutions. First, we compare the computational complexity of all algorithms on a system without hardware acceleration. The memory consumption of each algorithm is then assessed, along with the expected accuracy in amplitude and phase determination. For validation, a test scenario is used, including four arbitrary sine waves. The AC signal analysis methods were first implemented in a Windows PC reference system using MATLAB and Visual C++. The results are compared with a native C program running on the STM32 and Teensy 3.6, with less hardware acceleration. Then a vectorized, hardware-accelerated CMSIS DSP program is implemented in STM32. It is shown that for native solutions, the Goertzel filter provides an all-rounder solution. In contrast, the FFT provides an all-rounder solution for the CMSIS DSP-based solution, given complete signals or barycenter correction technique for incomplete signals. Although, the phase accuracy lacks behind DTFT. However, when precision is desired in the expenses of execution time, linear least-squares sine fitting is the best solution, especially for prematurely truncated signals.  
Z.H. has made the concept, A.Y.K., Z.H. and O.K. proposed the methodology, A.Y.K. realized the implementation and validation, A.Y.K. carried out the formal analysis, A.Y.K. has prepared the original draft, Z.H. and O.K. have taken care of reviewing and editing. All authors have read and agreed to the published version of the manuscript.  
The publication of this article was funded by Chemnitz University of Technology.  
The authors declare no conflict of interest.
Remote Sens._**2010**, \(2\), 1142-1156; doi:10.3390/rs2041142  
_Remote Sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Review_  
**Ten Years of SeaWinds on QuikSCAT for Snow Applications**  
**[PERSON]**  
Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Gusshausstrasse 27-29, Vienna, Austria; Tel.: +43-1-588-011-2221; Fax: +43-1-588-011-2290;  
E-Mail: [EMAIL_ADDRESS]  
_Received: 2 March 2010; in revised form: 24 March 2010 / Accepted: 30 March 2010 / Published: 16 April 2010_  
**Abstract:** The scatterometer SeaWinds on QuikSCAT provided regular measurements at Ku-band from 1999 to 2009. Although it was designed for ocean applications, it has been frequently used for the assessment of seasonal snowmelt patterns aside from other terrestrial applications such as ice cap monitoring, phenology and urban mapping. This paper discusses general data characteristics of SeaWinds and reviews relevant change detection algorithms. Depending on the complexity of the method, parameters such as long-term noise and multiple event analyses were incorporated. Temporal averaging is a commonly accepted preprocessing step with consideration of diurnal, multi-day or seasonal averages.  
**Keywords:** scatterometer; Ku-band; snow; circumpolar
## 5 Discussion
### Impact  
The revolutionary aspect of the OSWS is its democratization of weather data through low-cost sensors, facilitating accessible and affordable weather monitoring. By enabling real-time data sharing, the OSWS fosters timely decisions, which is crucial for sectors sensitive to weather fluctuations. Apart from real-time weather monitoring, the broader vision behind the OSWS extends to its application across diverse sectors like agriculture and urban development. Its educational utility, offering a tangible tool for learners, further underscores its multifaceted applicability.  
The OSWS, with its potential for regional weather analysis and integration from multiple units, lays the groundwork for enhanced predictive capabilities, with notable relevance in sectors relying heavily on accurate weather forecasts.
## 2 Method
### Multi-band spectral subtraction method
The band specific over-subtraction factor \(\alpha_{i}\) is a function of the segmental noisy signal to noise ratio \(\mathrm{SNR_{i}}\) of the \(i\)th frequency band which is calculated as:  
\[SNR_{i}(dB)=10\log_{10}\frac{\sum\limits_{k=h_{i}}^{e_{i}}\left|Y_{i}(\omega) \right|^{2}}{\sum\limits_{k=h_{i}}^{e_{i}}\left|\hat{D}_{i}(\omega)\right|^{2}} \tag{5}\]  
According to the \(\mathrm{SNR_{i}}\) value calculated in Eq. (5) and shown in Figure 1, also consistent with [PERSON] _et al._[16] and [PERSON] _et al._[17], the over-subtraction factor \(\alpha_{i}\) is calculated as:  
\[\alpha_{i}=\left\{ \begin{aligned} & 5&& SNR_{i}<5\\ & 4-\frac{3}{20}(SNR_{i})&&-5\leq SNR_{i}\leq 20\\ & 1&& SNR_{i}>20\end{aligned} \right. \tag{6}\]  
The use of this over-subtraction factor \(\alpha_{i}\) can provides a degree of control over the noise subtraction level in each band. Another factor \(\delta_{i}\), which is shown in Equation (4) can be used to provide an additional degree of control within each band, since most of the speech energy is present in the lower frequencies, smaller \(\delta_{i}\) values were used for the low frequency bands in order to minimize speech distortion. The values of \(\delta_{i}\) were empirically determined and set to:  
\[\delta_{i}=\left\{ \begin{aligned} & 1&& 60 Hz\leq f_{i}\leq 300 Hz\\ & 1.3&& 0.3 KHz<f_{i}\leq 1 KHz\\ & 1.6&& 1 KHz<f_{i}\leq 2 KHz\\ & 1.8&& 2 kHz<f_{i}\leq 3 kHz\\ & 1.3&& 3 kHz<f_{i}\leq 5 kHz\end{aligned} \right. \tag{7}\]  
Both factors, \(\alpha_{i}\) and \(\delta_{i}\) can be adjusted for each band for different speech conditions to get better speech quality.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 1 Introduction
Compared to prior surveys, especially the two surveys [7; 9] which are of vast scope, this survey is narrow in scope, with the focus on congestion control and load balancing. However, this survey reviews several publications which are not reviewed in [7] or [9] and aims to analyze research efforts in the literature of congestion control in more detail. The rest of the paper is organized as follows. Section 2 provides an overview of RPL. Section 3 discusses classification and Section 4 summarizes each of the congestion control schemes for RPL. Section 5 discusses research challenges. In Section 6, we draw conclusions.
# Stable Rules Definition for Fuzzy TS Speed Controller Implemented for BLDC Motor
## 4 Hardware Construction and Tests of the Stable Fuzzy Speed Controller
### Encoder  
A speed control loop requires the provision of continuous feedback about the actual speed value. In the constructed BLDC motor controller, this was realized by a simple, low-cost incremental encoder. Its resolution seemed to be high enough to provide proper speed information. It produced 400 pulses per revolution for one phase. Considering the fact that the STM32 was equipped with a dedicated timer with an encoder mode, it was possible to increase the above-mentioned value to 1600 pulses per revolution by using signals from two phases at once and setting the trigger detection at both the rising and falling edge.