esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in the Biosynthesis and Molecular Evolution of Steroidal Saponins in Plants
## 5 Distribution of Steroidal Saponins in Plants
### Eudicots  
In eudicots, steroidal saponins were identified mainly from a few plants, such as the Fabales, Ranunculales, and Solanales. Among them, Fabaceae plants only contained a small number of steroidal saponins in the seeds of fenugreek [71; 73]. A small number of steroidal saponins was identified in two plants (_Helleborus niger_ and _H. orientalis_) in the Ranunculaceae, including one polyhydroxy saponin: polyhydroxy helelebosaponins [118; 119]. In Solanaceae plants, steroidal saponins are mainly distributed in Solanum plants. For example, three furostanol saponins, four spirostand saponins, two cholestane-type saponins, and one spirostand saponin were identified in _Solanum abutilides_, _S. anguivi_, and _S. melongena_[120; 121; 122; 123] (Supplementary Table S1).  
In addition, more than 17 steroidal saponins, including Terrestrial C-T, were identified in the Zygophyllales plant _Tribulus terrestris_[124; 125].  
It can be seen from the above research results that steroidal saponins are mainly distributed in the early diverged lineages of monocots, including the Asparaceae (_Asparagus_), Dioscoreaceae (_Dioscorea_), Liliaceae (_Lilium_), Melanthiaceae (_Paris_ and _Trillium_), and Smilacaceae (_Smilax_). There are also a few eudicots, mainly distributed in Solanaceae plants, that produce these compounds (Supplementary Table S1). Furthermore, [PERSON] et al. evaluated the evolution and distribution pattern of steroidal saponins in _Dioscorea_ plants through ancestral states analysis [20]. Their results showed that steroidal saponins were distributed intensively in _Dioscorea_ sect. _Stenophora_ species. With the evolution of _Dioscorea_ species, the frequency of steroidal saponins in plants is decreasing, with only small numbers in the more evolved clades, such as New world I, New world II, sect. _Opsophyllon_, and Malagasy clade [20; 126]. A large number of ancestral nodes of steroidal saponins have been evaluated in the _Dioscorea_ sect. _Stenophora_ branches, indicating that steroidal saponins are an ancestral trait in _Dioscorea_ species, mainly distributed in the early differentiated lineages of _Dioscorea_, and are phylogenetically conserved [120].  
However, the mechanism of the distribution and accumulation of steroidal saponins in a few species of the above families and genera and how the distribution differences evolved are still unknown. The answers to these questions are of great significance for the systematic understanding of the distribution and evolution pattern of steroidal saponins in plants.
# Key Factors for a Successful Telemedicine Solution for Cardiovascular Diseases: A Systematic Review
## 3 Results
### Outcomes  
Most of the blood pressure studies involved (8/9, 88.9%) showed a positive effect of telemonitoring intervention on patients' BP control ratios. Concerning quality of life, it must be remembered that the studies do not use a unique metric to measure it, so it is impossible to compare the various trials objectively. However, four out of nine studies (44.4%) monitoring QoL show improvements compared to the control group due to the telemedicine intervention. Most studies (5/7, 71.43%) examining patient adherence indicated a positive effect due to telemonitoring, highlighting its role in improving adherence to treatment regimens. Furthermore, six out of seven studies (85.71%) focusing on self-care demonstrate increases in this variable due to the telemedicine intervention with the control group. Finally, studies concerning hospitalization showed, for the biggest part (3/4, 75%), a positive impact of telemedicine on these outcomes when compared to the control patients.
# Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
## 5 Analysis of Eviction Set Construction Algorithms
### Evaluation Framework  
#### 5.1.1 Simulation Framework Overview  
Modern micro-architectures include many optimization features, such as hardware prefetching, TLBs, buses, etc. These optimizations allow programs to run faster and more effectively. However, they introduce noise into the attacker's observations, making the construction of eviction sets less reliable. In this work, a noise-free cache model was adopted to analyze the complexity of systematically finding eviction sets. This cache framework abstracted the implementation details of the hardware and simulated only the behavior of the memory hierarchy. The cache model was written in Python and had the following features:  
* Constant access latency: The latency to access a cache block was the same regardless of its location (set, way or slice);
* Ideal cache hit/miss state: Without latency, a given process could accurately determine the state of a cache block (hit or miss). This eliminated errors due to the access latency measurements on the actual processors. For each memory access, our framework returned the address state (i.e., present or not) in all cache levels of the memory hierarchy and indicated whether it was a miss or a hit;* Replacement policies: The cache framework supported two replacement policies: the least recently used (LRU) policy and the random policy. The LRU policy maintained a list containing the order in which the cache ways were accessed. When a cache set needed to be filled, the least used cache way was chosen. As its name suggests, the random policy randomly selected a way from the target set;
* No translation lookaside buffer (TLB) noise: Accessing a large candidate set could trigger false positive errors when the TLB entries were mistakenly removed from the TLB [56]. To ignore this effect, we did not model the memory management unit (MMU) or the TLB component;
* No cache prefetching: The hardware prefetchers were used to improve performance by predicting the next memory addresses that would be accessed. However, they introduced noise into the attacker's observations by accessing unnecessary memory addresses [57]. Thus, the framework did not support model prefetching;
* Address randomization of caches: Address randomization could be enabled at any cache level. The framework used a keyed cryptographic function to distribute the addresses among all cache sets (see Section 6.1). It also supported a configurable _remapping interval_ that changed the address to set mapping after a certain number of memory accesses.  
The cache framework also implemented _performance counters_ that could be used to measure the various side effects of the simulated applications. For example, they could provide information about the number of accesses, misses, hits, evictions and more. The performance counters were implemented in each cache level to evaluate and analyze the behavior of the eviction set construction algorithms. The performance counters of each level could be reset or loaded at any time.
# Energy Efficiency of D2D Multi-User Cooperation
## 4 Simulation Results
Furthermore, Figure 5 also shows that for the case of \(L=1024\) and \(L=512\), the energy efficiency almost keeps stable when \(K\) increases. For the case of \(L=216\), when the number of cluster D2D users \(K\) is smaller than a certain value, energy efficiency rises very slowly with the increase of \(K\), but also decreases quickly when \(K\) is larger than a certain value. This is because the amount of consumed energy for relaying excessive short data packets with constant overhead to many D2D users is excessive. From the above analysis, it is obvious that energy efficiency can reach a peak value at certain \(K\) or \(L\). Hence, it is possible to achieve high energy efficiency in different communication environments where multiple D2D users cooperate by coordinating the number of cooperative users \(K\), the length of shared data \(L\), and the number of date packets \(n\).
## 4 Conclusions  
With the rising cost of fossil fuels and additional environmental concerns, the importance of latent heat thermal energy storage (LTES) for space heating and cooling is increasing. Accordingly, phase change materials (PCMs) have been used in various fields. Among the PCMs, fatty acid ester PCMs are relatively economical and paraffin PCMs have higher latent heat capacities than other PCMs. So we prepared coconut oil + _n_-hexadecane mixed shape-stabilized PCMs (Mixed SSPCMs) using exfoliated graphite nanoplatelets (xGnP) through a vacuum impregnation method to enhance their thermal performance with supplementation of drawbacks of PCMs such as the leakage in liquid state and low thermal conductivity. When preparing the Mixed SSPCMs, we impregnated the coconut oil and _n_-hexadecane at different proportions, namely 70:30, 50:50, 30:70 wt %. We analyzed the microstructure, chemical stability, thermal properties, thermal stability, and thermal conductivity of the samples using SEM, FT-IR, DSC, TGA, and TCi. Through the SEM analysis, we confirmed that both of the PCMs were well impregnated into the porous structure of the xGnP. The FT-IR graphs show all peaks of the bonding of both of the PCMs. So, we confirmed that there was no chemical interaction in the all Mixed SSPCMs. From the DSC analysis, we confirmed that the latent heat capacity of the coconut oil + _n_-hexadecane Mixed SSPCMs was higher than the coconut oil/xGnP and _n_-hexadecane/xGnP SSPCMs containing single PCM. The values of latent heat during melting were 89.06, 104.30, and 124.50 J/g while those of SSPCMs containing single PCM were 82.34 and 96.40 J/g. The greater the amount of _n_-hexadecane, the greater the latent heat capacity becomes. Also, we analyzed the decomposition temperatures and the total decomposition rates of Mixed SSPCMs through TGA analysis. As a result, we confirmed that the coconut oil and _n_-hexadecane were impregnated into xGnP, and the Mixed SSPCMs have high thermal durability. The thermal conductivities of the Mixed SSPCMs analyzed by TCi were over 284% higher than those of pure coconut oil _n_-hexadecane. Consequently, we expect the Mixed SSPCMs to be useful in applications in various fields due to their high thermal performances. For example, considering their phase change temperatures, they can be applied to improve energy loss in building design and operation in countries with a contained, microthermal climate.  
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No.NRF-2014R1A2A1A11053829).  
[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] designed and conducted the experiment. [PERSON] designed the project. All authors contributed to the analysis and conclusion.  
The authors declare no conflict of interest.  
Figure 6: Thermal conductivity of Mixed SSPCMs.
## 3 Bispectrum Analysis of EEG Signals for an Emotion Recognition System
### The Developed Methodology for Emotion Classification System  
Generally, the automatic emotion recognition process can be carried out using classification algorithms through a mathematical-based machine learning method or through a biological-basedANN method. Using both learning mechanisms of the classification algorithms, the researchers' main focus is finding the best techniques for the feature extraction subsystem in order to minimize classification error and improve the recognition rate.  
The developed methodology for our automatic emotion recognition system is illustrated in Figure 1. At the first stage, human emotions are elicited through a set of emotion stimuli, usually by pictures, sounds or videos. Then, the EEG input signals are captured by an EEG device with several electrodes using the appropriate sampling frequency. In this research, the EEG input signals were provided by the DEAP database, and these signals were stimulated by a set of music videos (clips). The EEG input signals from this database were already preprocessed [3] through certain steps, such as reducing interference noise during the EEG recording, including power line frequency noise filtering and the blink artefact removal.  
To retrieve any meaningful information hidden in the EEG signal, the original time domain signals are processed using some signal processing techniques, and the features are then calculated and extracted. Because the higher spectrum analysis is theoretically better than the power spectrum model, we processed our signals using bispectrum analysis, and to reduce the bispectrum matrix size, we also applied a 3D filtering method. The features are extracted by calculating several relevant pieces of information from the filtered bispectrum matrix, such as the entropy, the moment and the mean. The best features are then investigated further to determine the highest recognition rate.  
As the feature vectors produced by bispectrum analysis are usually larger than that of the power spectrum analysis, the higher computation cost of using bispectrum analysis could not be avoided. To reduce the size of the feature vectors, and in turn reduce the computation cost, a principal component analysis (PCA) technique that searches the best principal components (i.e., the coefficient matrix according to the number of the Eigen values desired) is utilized. We also sought to determine whether reduction in the number of channels used could contribute to reducing the computation cost without decreasing the recognition rate.  
In the classification stage, the BPNN classifier was chosen as the benchmark classifier among other ANN classifiers due to its capacity to guarantee the convergence of the minimum error using a gradient descent training method. We then compared the performance of the BPNN with another ANN classifier, namely the PNN, that uses Bayesian probability in the classification process.
# A Method for Estimating Source Depth Based on the Adjacent Mode Group Acoustic Pressure Field
## 3 Simulation
### The Influence of the Markov Chain Length on the Simulated Annealing Algorithm  
In the simulated annealing algorithm, in addition to the temperature coefficient, the length of the Markov chain affects the source depth estimation. The length of the  
Figure 11: Comparison of source depth estimation under different temperature coefficients: (**a**) the temperature coefficients is 92%; (**b**) the temperature coefficients is 94%; (**c**) the temperature coefficients is 96%; (**d**) the temperature coefficients is 98%.  
Markov chain is the internal cycle process at each temperature (the state sequence passed in the search process). The longer the Markov chain, the more sufficient the algorithm searches and the greater the possibility of obtaining the global optimal solution. Therefore, parameters other than the length of the Markov chain were controlled. The lengths were set to 500, 1000, 1500, and 2000, respectively, with a temperature coefficient of 90%, and a simulated annealing analysis was conducted. The estimation results of the source depth are shown in Figure 12.  
It can be seen from Figure 12 that with an increase in the Markov chain length, the source depth estimation results obtained by simulated annealing gradually became consistent with the real source depth. The average error rates corresponding to different Markov chain lengths were \(\Delta_{500}=3.95\%\), \(\Delta_{1000}=2.19\%\), \(\Delta_{1500}=1.54\%\) and \(\Delta_{2000}=1.41\%\); the longer the Markov chain, the better the depth estimation results. However, the time complexity of the algorithm was O(\(n^{2}\)) when the Markov chain length was taken as the starting point, so as the Markov chain length increased, the method's execution efficiency was reduced.
## Appendix F Expression for the Intrinsic Linewidth  
The laser intrinsic linewidth can be found from Equation (25). Defining:  
\[\beta_{Ag}\equiv\frac{\tau_{\rm e}^{-1}\zeta_{s}}{a_{g}G_{0}S_{0}}\qquad; \qquad\delta_{Ag}\equiv\frac{\zeta_{s}}{G_{0}}\qquad;\qquad\Delta f_{0}=\frac {R_{\rm sp}}{4\pi S_{0}}, \tag{116}\]  
where, following [3]:  
\[\delta_{Ag}\simeq 0\simeq\beta_{Ag}, \tag{117}\]  
as \(\delta_{Ag}<10^{-2}\), which accounts for shot noise in the generation and recombination of minority carriers, and \(\beta_{Ag}\) is inversely proportional to the laser power which above threshold becomes negligible. Starting from Equation (115) and setting \(\Omega=0\) as required by Equation (25):  
\[4\pi\Delta f =\Lambda_{0}/\Delta_{0}\] \[\stackrel{{\eqref{eq:A43c}}}{{=}}\frac{\big{(} \zeta_{s}\alpha_{\rm H}a_{g}\big{)}^{2}\bigg{[}R_{\rm sp}S_{0}\bigg{(}1+\frac {(G_{0})^{2}}{\zeta_{s}^{2}}+\frac{2G_{0}}{\zeta_{s}}\bigg{)}+N\tau_{\rm sp}^{ -1}\bigg{]}+\big{(}\tau_{\rm e}^{-1}\zeta_{s}+a_{g}G_{0}S_{0}\big{)}^{2}\frac {R_{\rm sp}}{S_{0}}}{\Big{(}a_{g}G_{0}S_{0}\mathsf{F}_{3}+\mathsf{K}_{c}\zeta _{s}\tau_{\rm e}^{-1}\Big{)}^{2}}\] \[\Leftrightarrow\frac{\Delta f}{\Delta f_{0}} =\frac{\Big{(}\frac{\alpha_{\rm H}}{C_{0}}\Big{)}^{2}\bigg{(} \zeta_{s}^{2}+G_{0}^{2}+2G_{0}\zeta_{s}+\frac{\zeta_{s}^{2}N\tau_{\rm sp}^{-1 }}{R_{\rm sp}S_{0}}\bigg{)}+\Big{(}\beta_{Ag}+1\Big{)}^{2}}{\Big{(}\mathsf{K }_{c}\beta_{Ag}+\mathsf{F}_{3}\Big{)}^{2}}\] \[=\frac{\Big{(}\beta_{Ag}+1\Big{)}^{2}+\alpha_{\rm H}^{2}\Big{[}1+2 \delta_{Ag}+\delta_{Ag}^{2}\bigg{(}1+\frac{N\tau_{\rm sp}^{-1}}{R_{\rm sp}S_{0 }}\bigg{)}\bigg{]}}{\Big{(}\mathsf{K}_{c}\beta_{Ag}+\mathsf{F}_{3}\Big{)}^{2}}\]  
Figure 13: Numerical solutions for \(C=1\) under condition (12) for a \(\pm 20\%\) variation of conditions (8) and (11). Double feedback case shown in blue, single feedback case shown in orange. Column **(A)** shows the lasing frequency shift results. Column **(B)** shows the threshold gain shift. Column **(C)** shows the intrinsic linewidth variations.
# GCN-Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting
## 3 Experimental Preparation and Framework
### Parameter Selection  
As shown in Table 2, we conducted experiments using three different sets of data configurations for prediction. These configurations included setting the seq_len to 96 and label_len to 48, setting seq_len to 192 and label_len to 96, and setting seq_len to 144 and label_len to 72. Among these configurations, it was observed that the best predictive performance was achieved when the seq_len was set to 96 and label_len was set to 48.  
The input features for the encoder and decoder layers were represented by enc_in and dec_in, respectively. The c_out variable represented the final prediction output. When evaluating different batch sizes, namely 32, 64, and 96, it was found that a batch size of 32 yielded the most optimal results in terms of prediction accuracy.  
In order to ensure the reliability of the experiment, we chose to set a relatively large value for the train_epochs parameter. However, since there is an early stopping mechanism in place, not all 1000 epochs will be executed. To strike a balance between experiment  
Figure 4: The result of the Pearson’s correlation.  
reliability and time efficiency, we decided to run only 50 training epochs. This approach allowed us to obtain reliable results without sacrificing too much time.
# Non-Hertzian Elastohydrodynamic Contact Stress Calculation of High-Speed Ball Screws
## 4 Results Analysis and Discussion
### Effect of the Elastohydrodynamic Contact Stress under Different Screw Speeds  
The central elastohydrodynamic contact stresses of the BSs under different helix angles and different screw speeds are shown in Figure 12. As shown in Figure 12a, the non-Hertzian solution is smaller than the Hertzian solution of the central elastohydrodynamic contact stress at contact point \(A\). The central elastohydrodynamic contact stress decreases with the increase in the helix angle and the screw speed, respectively. As shown in Figure 12b, the non-Hertzian solution is greater than the Hertzian solution of the central elastohydrodynamic contact stress at contact point \(B\). The central elastohydrodynamic contact stress increases as the helix angle increases, and decreases as the screw speed increases.  
Figure 11: Comparison of the elastohydrodynamic characteristics between the Hertzian solution and non-Hertzian solution. (**a**) Central elastohydrodynamic contact stress of point \(A\). (**b**) Central elastohydrodynamic contact stress of point \(B\). (**c**) Central elastohydrodynamic film thickness of point \(A\). (**d**) Central elastohydrodynamic film thickness of point \(B\).  
The wear depth and wear rate of the BSs are mainly influenced by the asperity contact stress. The asperity contact stresses in the elliptical contact region of the BSs under different helix angles and different screw speeds are shown in Figure 13. The law of the asperity contact stress of points \(A\) and \(B\) is the same as that of the elastohydrodynamic contact stress of points \(A\) and \(B\). Under the helix angle of 21.71\({}^{\circ}\), the calculation accuracies of asperity contact stress for the non-Hertzian solution are 2.93% and 3.01% more accurate than that of the Hertzian solution at the screw-raceway contact region and the nut-raceway contact region, respectively.
# Preventive Maintenance Decision-Making Optimization Method for Airport Runway Composite Pavements
## 2 Methods
### Long-Term Maintenance Decision-Making Optimization Method
#### 2.3.1 Stage Variable  
The maintenance cost of composite pavements in airports is not supposed to exceed the limit of the annual maintenance budget. Thus, an optimization model based on DP should divide the maintenance period into several stages at intervals of 1 year chronologically. The stage variable is defined as \(k\). As for a long-term preventive maintenance plan for \(K\) years, \(k\) is 0, 1, 2, \(\ldots\), \(K\).
# Recursive Least Squares for Near-Lossless Hyperspectral Data Compression
###### Abstract
[PERSON] proposes an approximate lossless image compression scheme that combines wavelets and CALIC [26]. It uses CALIC to compress the residual image between the wavelet approximation and the original image. [PERSON] employed CCSDS-IDC for the lossy phase, followed by bit-plane encoding (BPE) coding. However, the scheme does not obtain the optimal lossy bit rate. [PERSON] combines JPEG000, as a lossy layer, with a near-lossless layer consisting of BPE and arithmetic coding [27]. This scheme uses computationally expensive iterative methods to determine the optimal lossy bitrate and obtain a competitive coding performance. Due to the characteristics of the prediction-based compression scheme, it gradually replaces the transform-based algorithm in the on-board compression platform [28]. In this paper, we focus on the prediction-based quantization technique, which aims to enhance the performance of the near-lossless compression scheme by fully exploiting the spatial-spectral redundancy of hyperspectral images. The proposed method combines recursive least squares (RLS) with an in-loop quantizer and, subsequently, encodes the quantization residuals using an entropy encoder. Ultimately, a competitive compression ratio is produced while guaranteeing the quality of the reconstructed image. The rest of the paper is organized as follows: Section 2 provides a detailed description of the near-lossless feedback loop's compression framework with the RLS prediction. Section 3 presents the experimental dataset and comparative results of this method with an analysis and discussion. Finally, Section 4 concludes this paper.