esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Steps of the RD_STP Method for POD  
Figure 2 shows the simplified processing scheme of the RD_STP method for the POD of LEOs from GPS observations, which is described as follows.  
Step 1, The GPS code observations of LEOs are used to estimate the positions of the LEO via the single-point positioning approach.  
Step 2, The coarse STPs of LEOs are computed from the estimated positions in Step 1 and an a priori dynamic model (usually only Earth's gravity field, e.g., EGM2008 up to degree and order 90) based on Equation (3). Moreover, when there is a data gap \(\Delta t_{gap}\), the corresponding STP will be set as \(\overset{\rightarrow}{r}(t+\Delta t_{gap})-2\overset{\rightarrow}{r}(t)+ \overset{\rightarrow}{r}(t-\Delta t_{gap})\) with an STP error according to Equation (6).  
Step 3, An a priori orbit is estimated from the code and the coarse STP pseudo-observations generated in Steps 1 and 2 based on Equations (4)-(6), which is called the STP dynamical filtering in Figure 2.  
Note that, sometimes, there are gross errors in the code observations, and the error of the estimated LEO's positions in Step 1 has an influence on computing the accelerations. Thus, the iterative processing procedure may be necessary in Step 3. In general, 1-2 iterations are enough. The accuracy of the generated a priori orbit is usually better than 1 m, which can meet the requirements for STP \"pseudo-observations\". Then, the precise LEO's  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Kinematic** & **Dynamic** & **RD** & **RD\_STP** \\ \hline GPS observations & Yes & Yes & Yes & Yes \\ Integration and integral arc length & No & Yes & Yes, & Yes, \\ Minor perturbation & & & usually 30 h [11] & 60 s or less \\ force models & No & Yes & Yes & Usually no \\ Earth’s gravity field model & No & Yes & Yes & Yes, lower-degree \\ Pseudo-stochastic/dynamical parameters & No & Yes & Yes & No \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of the precise orbit determination (POD) methods of low Earth orbiters (LEOs).  
Figure 2: Processing scheme of the reduced-dynamic second-order time-difference position (RD_STP) method for the POD of LEOs from GPS observations.  
STP pseudo-observations are integrated based on the a priori dynamic model and the a priori orbit after several iterations.  
Step 4, The GPS code and carrier observations are screened and edited (e.g., detecting cycle slips) based on the method used by [23], according to the a priori orbit and precise STPs generated in Step 3.  
Step 5, The RD_STP POD of LEOs is estimated via the least squares adjustment/Kalman filter method based on Equations (4)-(6).  
The POD solution does not need any external reference orbit for initialization, and is fully self-contained. The performance of the proposed RD_STP method will be assessed in the following section.
## 3 Results and Discussion
### Changes in Ion Species Fraction According to PM2.5 Concentration  
Figure 4 shows the changes of the proportions of major aerosol species in PM2.5 in Seoul and Baengnyeong Island from 2012 to 2019. In Seoul, the proportion of SO\({}_{4}\)\({}^{2-}\) has steadily decreased since 2012. The proportion of NO\({}_{3}\)\({}^{-}\) has steadily increased since 2013, although the proportion decreased slightly in 2015. On Baengnyeong Island, from 2012 to 2018, the proportion of SO\({}_{4}\)\({}^{2-}\) steadily decreased, and the proportion of NO\({}_{3}\)\({}^{-}\) steadily increased. This trend on Baengnyeong Island was similar to that of Seoul; however, the tendency was much clearer than in Seoul. The proportions of NH\({}_{4}\)\({}^{+}\) at both sites did not change greatly. The proportions of EC at both sites decreased, and the proportion of OC is increasing in Seoul.  
Figures 5 and 6 show the changes in the concentrations of major inorganic ions classified by the PM2.5 concentration level at both sites from 2012 to 2019, respectively. Through this analysis, it is possible to examine which ion became major species as the concentration of PM2.5 increased and which pollutants significantly contributed to the occurrence of high PM2.5 events. Furthermore, it is possible to understand the temporal changes of the major ion species contributing to high PM2.5 episodes. The PM2.5 concentration was divided into sections for every 25 \(\upmu\)g m\({}^{-3}\). In Seoul, the SO\({}_{4}\)\({}^{2-}\) concentration increased significantly as the concentration of PM2.5 increased until 2012, but after 2013, the NO\({}_{3}\)\({}^{-}\) concentration increased significantly as the concentration of PM2.5 increased. On Baengnyeong Island, the trend was similar to Seoul, but the most significant species changed in 2014. The SO\({}_{4}\)\({}^{2-}\) concentration increased as the concentration of PM2.5 increased until 2014, but after 2015, the NO\({}_{3}\)\({}^{-}\) concentration increased most as the concentration of PM2.5 increased.  
that it was slightly acidic. However, after 2014, the most abundant ion component changed to NO3\({}^{-}\) on Baengnyeong Island, and it has developed an NH4\({}^{+}\) surplus status [8]. It has been shown that the concentration of NO3\({}^{-}\) has been increasing recently instead of SO4\({}^{2-}\). Since Seoul is a metropolitan area, it may have its own regional influence, as we discussed in Section 3.2, but Baengnyeong Island, which has no major source of pollution, is thought to clearly reflect the changes of the emissions from China.
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 4 Results and Discussion
### Model Comparison
#### 4.6.1 Comparison with Recent Methods  
To validate the effectiveness of HGE-ACN, it was compared against four recent deep-learning-based models used for sign language recognition, as shown in Table 4:
# Enhanced Dual Reversible Data Hiding Using Combined Approaches
## 4 Experimental Results
The PVD histogram represents the differences between adjacent pixels in the image, playing a crucial role in assessing how the distribution of pixel values changes after data hiding. Figure 4: Four PVD histograms of the cover images and their marked images with differences between \(-20\) and \(20\). In this study, the PVD histograms of the original image and the stego image (the image with hidden data) were compared. Through histogram analysis, the impact of data hiding on pixel value differences in the image can be visually observed. When data is hidden in the image, subtle changes may occur in the shape of the histogram, which can be more pronounced in regions with small difference values. These changes indicate that some pixel values were adjusted during the data hiding process. If the histogram does not differ significantly from the original image, it indicates that data hiding has been successfully performed without causing noticeable visual changes. Conversely, if there are significant changes in the histogram, it suggests that the data hiding process may be easily detectable. The analysis of the four images presented in Figure 4 shows that the histograms of the original image and the stego image are almost identical. This indicates that data hiding was effectively performed, and the hidden data have minimal impact on the visual quality. Therefore, this experiment demonstrates the successful application of the PVD-based data hiding technique. The selection of test images was carefully made to cover a diverse range of characteristics, ensuring a fair evaluation of the proposed method. The chosen images represent different levels of texture complexity and frequency components, making them well suited for testing the robustness and visual impact of the proposed reversible data hiding technique. Moreover, as in previous studies, we focused on commonly used benchmark images rather than evaluating the entire dataset, as not all images in the USC-SIPI dataset are relevant to reversible data hiding research.
## 3 Results  
### Denoising Using Wavelet UWT  
The denoising, multiresolution decomposition and reconstruction was tested with all the orders of the Daubechies, Coiflet, Symlets and Haar wavelets, but the most suitable for highlighting the desynchronization of the sensorimotor rhythms, in the case of the acquired signals, proved to be the Haar function, following visual analysis of the waveforms. Multiresolution wavelet analysis was applied on channels C3, CP3, C4 and CP4. The sub-component signals that are represented below are the ones of interest: the fourth order detail coefficient with the frequency 8-16 Hz and the third order detail coefficient with the 16-32 Hz frequency.  
In Figure 13, the desynchronization of the sensorimotor rhythms are shown, which in our case represented a decrease in the amplitude of the signal corresponding to the motor imagery towards the signal corresponding to relaxation. For example, if the fourth order detail coefficient (corresponding to MI the left hand movement versus relaxation on the C4 channel) is chosen, then, in Figure 13, the desynchronization is observed: in the 8-9 Hz and 12-13 Hz frequency bands; at 10-11 Hz in the second graph; at 10 Hz and 12-13 Hz when the decomposition is performed with the Haar wavelet (where there is a very good increase in the amplitude of the signal corresponding to relaxation compared to the corresponding signal imagery movement).  
The wavelet transform favors the detection of sensorimotor rhythms that can be used for a brain-computer interface used by people with neuromotor deficiencies but with the state of the central nervous system and the visual analyzer in good condition. With the help of multiresolution decomposition and reconstruction on four levels, the signal can be analyzed in the frequency bands 8-16 Hz and 16-32 Hz, which is very useful because the sensorimotor rhythms have frequencies in these intervals.  
In this method, the feature matrix is formed using the multiresolution decomposition performed only with the Haar wavelet. The classification is made for the three channels C3, CP3 and P3, corresponding to the motor imagery of the right hand and C4, CP4 and P4, corresponding to the motor imagery of the left hand.
# Quality Assessment of Banana Ripening Stages by Combining Analytical Methods and Image Analysis
## 3 Results and Discussion
### Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) Spectra Interpretation
The intensities of 1153-1149 cm\({}^{-1}\), 1000-995 cm\({}^{-1}\), and 928 cm\({}^{-1}\), which were associated with the polysaccharides of the starch granules (amylose and amylopectin), displayed a dramatic (\(p<0.05\)) decrease from day 7 until day 9, and then decreased (\(p<0.05\)) progressively or fluctuated (\(p>0.05\)), until the storage ended. Moreover, no absorption was detected at 1076 cm\({}^{-1}\) and 865-858 cm\({}^{-1}\), which were associated with the presence of starch after day 9 of storage. The intensities of 1055-1045 cm\({}^{-1}\), which were related to the presence of sucrose and fructose, presented a significant gradual (\(p<0.05\)) increase from day 7 until day 21. Obviously, during the banana ripening process, starch was gradually decomposing, leading to the accumulation of soluble sugars, such as sucrose and fructose. In accordance with these results, it has been reported that the starch content in banana flesh showed a rapid decrease as ripening progressed, until becoming undetectable [2], which has been associated with the starch decomposition through a complex mechanism [27; 59]. The intensities at 1647-1632 cm\({}^{-1}\), which were related to the moisture content, varied insignificantly until day 11; then, at day 14, they presented a significant increase before stabilizing until the final day of storage, in accordance with the moisture content (%) results and the run-length non-uniformity (the non-uniformity of the structures in the image) values. The starch conversion into sugars increased the osmotic pressure of the flesh and favored the water transfer from the peel to the flesh [28]. The intensities at 2932 cm\({}^{-1}\), which indicated the C(sp\({}^{3}\))-H stretching vibrations, exhibited a slight (\(p<0.05\)) decrease at day 7, decreased drastically at day 9, and then stabilized (\(p>0.05\)) until the day 17 to a minimum value. Possible reasons for this drastic decrease in the peak intensity could be related to the starch conversion into sugars and/or the dramatic acidity reduction at day 9 of the ripening process (Figure 5).
## 5 Conclusions and Future Work  
In conclusion, the study elucidates the capacity of a CFRP diamond wheel hub to counteract vibration frequencies and enhance surface fineness in WC workpiece materials compared to a steel hub. This performance enhancement is attributed to the CFRP hub's unique material properties, encompassing a lower density, a higher rigidity, a lower thermal expansion coefficient, and superior damping when juxtaposed with the steel grinding wheel hub. The implications are profound, with potential applications in high-speed grinding to bolster both efficiency and surface quality for WC material. Future research avenues are suggested, including using materials with enhanced hardness and brittleness to validate the CFRP hub's reliability when grinding challenging materials. The ultimate goal is to input experimental parameters into a comprehensive database systematically, optimizing machining databases for notoriously hard, brittle, and challenging-to-cut materials, thus furnishing the grinding industry with valuable references. Thermal generation and cooling efficiency can also be investigated further to refine high-speed grinding technologies. Moreover, this study underscores the multidimensional nature of the grinding process, combining material science, engineering dynamics, and operational considerations to yield precision and efficiency enhancements.  
Investigation, C.-Y.C.; Resources, Y.-X.L.; Data curation, K.-J.C.; Writing--review & editing, Y.-T.L.; Project administration, K.-J.C. and M.-Y.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by KINIK COMPANY, MOST 111-2218-E-167-001 and MOST 110-2221-E-167-016-MY3.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Weight** & **Damping** & \
\begin{tabular}{c} **Rz/Ra** \\ **at the Peripheral Speed of 20 m/s** \\ \end{tabular} &
\begin{tabular}{c} **Rz/Ra** \\ **at the Peripheral Speed of 80 m/s** \\ \end{tabular} \\ \hline CFRP hub & Lighter & Lower & 10.2 & 11.29 \\ Steel hub & Heavy & Higher & 10.8 & 15.03 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of the characteristics of the CFRP hub and steel hub.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare that this study received funding from KINIK COMPANY. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
## 3 Results and Discussion
### Emissions Characteristics
#### 3.2.2 Unregulated Gaseous Emissions-VCCs  
The results of VOCs analyzed with GC/MS for each test fuel are shown as the VOCs detected for each test condition in Tables 8 and 9. At an engine load of 0 Nm, 10 types were detected for PD0, 11 types for PD10, 10 types for PD30, and 14 types for PD100. At 40 Nm, 10 types were observed for PD0, 10 types for PD30, and 12 types for PD100. Higher blend ratios of palm oil biodiesel result in more complex combustion reactions within the cylinder. Most of the VOCs emitted are alkanes and aromatics. Alkanes include nonane, octane, decane, tetradecane, and undecane. One alkene was detected, 1-butene. Ethyl alcohol and are common VOCs that were detected in all conditions. The emission compositions of tetrahydrofuran and benzene were highest in all conditions, accounting for about 60% or more. Tetrahydrofuran is an ether (R-O-R\({}^{\prime}\)), a highly volatile, colorless liquid with four carbon atoms and one oxygen atom with a pentagon ring structure [64]. Tetrahydrofuran is known as a toxic substance and can cause symptoms of nausea, headache, and central nervous suppression when inhaled. It can also irritate the skin and affect white blood cell reduction and livers and kidneys with chronic effects. It is also a highly probable cause of cancer [64]. The emission of tetrahydrofuran increased due to the increase in biodiesel content, but research on this is lacking. Benzene, toluene, ethylbenzene, and xylene (BTEX), all known toxic aromatic VOCs, were detected in all test conditions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Engine Load** & **Test** & **NOx** & **Smoke Opacity** & **HC** & **CO** \\ \hline
**(Nm)** & **Fuel** & **(ppm)** & **(\%)** & **(ppm)** & **(ppm)** \\ \hline
0 & PD0 & 258 & 3.1 & 54 & 493 \\  & PD10 & 255 & 2.4 & 54 & 475 \\  & PD30 & 247 & 1.5 & 43 & 439 \\  & PD100 & 251 & 1.2 & 40 & 492 \\ \hline
40 & PD0 & 835 & 4.8 & 62 & 241 \\  & PD10 & 825 & 4.4 & 61 & 270 \\  & PD30 & 786 & 3.0 & 36 & 315 \\  & PD100 & 812 & 1.8 & 25 & 245 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Regulated gaseous emissions under test conditions.
# Research on the Pavement Performance of Slag/Fly Ash-Based Geopolymer-Stabilized Macadam
## 4 Conclusions  
In this paper, the effects of curing ages and dosage of slag-based geopolymer on mechanical properties, freezing resistance, and dry shrinkage are carried out on geopolymer-stabilized macadam. A detailed analysis is performed for the results of mesoscopic test including XRD, FITR, and SEM. The main conclusions are as follows:  
1. Under the skeleton-density grading, when the ratio of slag-based geopolymer, fly ash and macadam is 4:16:80, the unconfined compressive strength of slag/fly-ash-based geopolymer-stabilized macadam reached 8.76 MPa after 7 d of curing. It further reached 15.4 MPa after 28 d of curing. Therefore, it can satisfy the compression strength requirements of expressway and first-class highway base (JTG/T F20-2015).
2. Through data fitting, it is found that the compressive strength, elastic modulus, and tensile strength of 28 d geopolymer-stabilized macadam are all in a linear relationship with the dosage of geopolymer and increase linearly with increased dosage.
3. The freeze-thaw resistance of slag/fly-ash-based geopolymer-stabilized macadam is better than that of cement/fly-ash-based stabilized macadam. With increased dosage of geopolymer, the strength loss after freezing and thawing decreases from 14.2% to 2.5% when the dosage increases from 2% to 5%. In addition, the dry shrinkage performance of slag/fly-ash-based geopolymer-stabilized macadam is also better than that of cement/fly-ash-based stabilized macadam. The dry shrinkage strain of slag/fly-ash-based geopolymer-stabilized macadam with 3% content is 231.2 \(\mu\)e, which is slightly smaller than that of cement/fly-ash-based stabilized macadam (261.3 \(\mu\)e).
4. The reaction of geopolymer cementitious material itself generates stable products with higher strength. After addition of fly ash, a large amount of unreacted Ca(OH)\({}_{2}\) is consumed, the hydration and polymerization reaction is improved, and the bonding between slag/fly-ash-based geopolymer and aggregates is satisfactory.  
Conceptualization, Y.H. and J.Y.; methodology, J.Y.; software, X.N.; validation, X.N., Z.W. and J.L.; formal analysis, Z.W.; investigation, J.L.; resources, Z.W.; data curation, J.L.; writing--original draft preparation, X.N.; writing--review and editing, Y.H.; visualization, X.N.; supervision, Y.H.; project administration, Y.H.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Project of Science and Technology of Henan Transportation Department, grant number 2018G11 and grant number 2022-5-1. At the same time, this research was also funded by the Key Science and Technology Project of Henan Province, grant number 212102310937.  
Not applicable.  
Details on all data supporting the reported results can be obtained in Tables 1-6 and Figures 1-8 in this original manuscript.  
The authors declare no conflict of interest.
## 3 Related Work
### Vulnerability and Exploitability Assessment Studies  
Assessing vulnerability risk solely through exploit code availability reveals only the ease of potential exploitation by attackers. Therefore, we conducted a study to estimate the likelihood of an attacker utilizing the developed exploit code in a real-world setting. In this study, we conducted a correlation analysis between publicly available vulnerabilities and exploited data sources to determine the likelihood of real-world exploitation. We categorized in-the-wild exploits and corresponding threats, organizing the data over a specific time frame for training a prediction model.  
[PERSON] et al. [18] proposed a novel learning feature called \"Expected Exploitability (EE)\", which factors in time-varying exploitability based on various exploit data. This feature also considers methods for addressing label bias and noise to enhance the exploitability evaluation accuracy. [PERSON] et al. [19] proposed the use of machine learning to predict the likelihood of exploitation based on past attack patterns, using data collected from Twitter, the dark web, and blogs, such as Pastebin. In another approach, [PERSON] et al. [20] proposed an exploit prediction scoring system that collects publicly available vulnerabilities and exploit data. This system performs a linkage analysis between each data point, extracts features to calculate exploitability based on the correlation coefficient between them, and calculates the probability of exploitation within a 30-day timeframe, generating a score between 0 and 1. In contrast to CVSS, which generates a severity score based on the static features of vulnerability, this score can capture the dynamic risk of a vulnerability by considering factors, such as the attacker's skill level and actual exploitation incidents.
## 3 Results and Discussion
### Tensile Strength
When it comes to AC-13 under UA conditions, tensile strength decreases by an average decline rate of 18% with every increase of 10 \({}^{\circ}\)C when temperature is in the range of from 10 \({}^{\circ}\)C to 30 \({}^{\circ}\)C; however, a sharp drop in tensile strength by 50% appears when temperature goes up from 30 \({}^{\circ}\)C to 40 \({}^{\circ}\)C. Similarly, for SMA-13 under UA, STOA, LTOA5d and LTOA8d conditions, tensile strength steadily decreases with an average decline rate of 36% (UA and STOA), 22% (LTOA5d and LTOA8d) with every increase of 10 \({}^{\circ}\)C. When it comes to SMA-13 under LTOA2d conditions, volatility occurs in the tensile strength descent trend by 29% (from 10 \({}^{\circ}\)C to 20 \({}^{\circ}\)C), 7% (from 20 \({}^{\circ}\)C to 30 \({}^{\circ}\)C) and 31% (from 30 \({}^{\circ}\)C to 40 \({}^{\circ}\)C). Additionally, it is easy to see that the tensile strength of AC-13 is higher than that of SMA-13 when the aging degree and test temperature remain the same and the value of the gap can be up to 2.8 times as high (UA, 30 \({}^{\circ}\)C). This may be attributed to the following causes: the higher admixture of AC-13 aggregate renders a thicker asphalt mortar film, which has a correspondingly stronger ability to grip the aggregate than SMA-13. In addition, the higher distribution uniformity of air voids in AC-13 means that it performs better regarding the overall structural uniformity of the mixture, thus endowing AC-13 with a higher tensile strength at the macroscopic level [35; 36]. Furthermore, the gap between the tensile strength of AC-13 and SMA-13 is narrowed by the elevation of aging degree and test temperature.