esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application
### Foliar Level
#### 3.1.2 Seasonal Changes  
Thirty articles published since 1992 linked PRI with ecophysiological changes at the seasonal scale (Figure 3). PRI was most often correlated with actual photochemical efficiency \(\Delta\)Fv/Fm' (n = 57) and explained 17%-90% of its variability (Figure 3a). Median \(R^{2}\) was higher for conifers than for other species groups. PRI accounted for 0%-86% of the variability of NPQ for broadleaved and coniferous trees (Figure 3c) and was more strongly correlated with the chlorophyll/carotenoid ratio (\(R^{2}\) between 0.55 and 0.89) than with EPS (or DEPS) (Figure 3b,e). The correlations between PRI and RUE and net CO\({}_{2}\) uptake for broadleaved species were highly variable (\(R^{2}\) ranged from 0.0 to 0.84 and 0.0 to 0.92, respectively; Figure 3d,f), but median \(R^{2}\) was 0.12 and 0.19 higher, respectively, than those reported by [PERSON] et al. [20].  
PRI was significantly correlated with other pigments or pigment ratios (e.g., chlorophyll, xanthophyll-cycle pigment pools: chlorophyll (VAZ/Chl) and lutein/chlorophyll; mean \(R^{2}\) between 0.40 and 0.86; Figure 4). PRI was significantly correlated with Fv/Fm, RWC and the electron transport rate (ETR), and predawn PRI decreased with the minimum temperature and explained 55% of the variability for mango leaves [87].  
Figure 2: The mean values of coefficients of determination of the relationships between PRI and several physiological variables at the foliar level and short daily timescale. All the correlations were significant (\(p\) < 0.05). Error bars indicate standard error.  
Figure 4: The mean values of coefficients of determination of the relationships between several physiological variables and PRI at the foliar level and seasonal timescale. All the correlations were significant (\(p<0.05\)). Error bars indicate standard error.  
Figure 3: Boxplots of the coefficients of determination of the relationships between PRI and physiological variables at the foliar level and seasonal timescale: (**a**) Actual photochemical efficiency (\(\Delta\)F/Fm’); (**b**) Epoxidation or de-epoxidation state of xanthophylls (EPS or DEPS); (**c**) Non-photochemical quenching (NPQ); (**d**) Radiation-use efficiency (RUE); (**e**) Carotenoid/chlorophyll (Car/Chl) or chlorophyll/carotenoid (Chl/Car) ratio; and (**f**) Net CO\({}_{2}\) uptake. Central lines represent medians, boxes represent 50% of the data, whiskers represent minima and maxima and circles represent outliers. The numbers of correlations reported in the literature are shown in brackets.
# Image Steganography Using LSB and Hybrid Encryption Algorithms
## 5 Conclusions  
This research introduces a novel steganography approach based on the Least Significant Bit (LSB) technique, employing AES and Blowfish encryption algorithms to conceal text within an image. The proposed method addresses the limitations of traditional LSB processes by incorporating randomization in pixel selection and utilizing a mask as a password encryption key. Future advancements in encryption and compression algorithms, along with machine learning and AI integration, are expected to enhance the method's resistance against cryptanalysis and steganalysis attacks. The user interface will also be improved to enhance user friendliness and customization for different user needs. The proposed method effectively overcomes two limitations of traditional LSB processes by incorporating a secret key derived from the image and dividing the image into smaller blocks. This enhances security by using a mask as both a password encryption key and a minimum bit shift during data hiding while minimizing the visual impact of the embedded text. By splitting the image into multiple frames and eliminating sequential order, the proposed method enhances resistance to attacks and overall security. Future work aims to optimize the dimensions of the masks used in the method. The current limitation lies in the complexity of the encoding method, which will be addressed by introducing faster, lightweight encryption and decryption algorithms. A compression algorithm will also be applied to more significant messages to reduce size. Machine learning and AI techniques will evaluate the method's robustness against cryptanalysis and steganalysis attacks. The graphical user interface (GUI) will be enhanced to be more user friendly, offering additional options for controlling desired processes.  
Formal analysis, M.A.; Data curation, B.A.; Writing--original draft, R.A.; Supervision, S.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No animals/humans were used for studies that are the basis of this research.  
Not applicable.  
The authors express their gratitude to the Deanship of Research and Graduate Studies and the Faculty of Computers and Information Technology at the University of Tabuk for their invaluable support to this research.  
The authors declare no conflict of interest.
# Rules of Heliogeomagnetics Diversely Coordinating Biological Rhythms and Promoting Human Health
## 3 Results
### Changes of Biological Characteristics of BP and HR Associated with Geomagnetic Disturbances
 } & M: Declination (degree) & 3.65 & 0.19 & 3.69 (101.0\%) & 0.2 & 3.564 & 0.003 \\ \cline{2-7}  & A(24 h): Declination (degree) & 0.09 & 0.05 & 0.18 (207.0\%) & 0.1 & 4.499 & 0.0004 \\ \cline{2-7}  & \(\Phi\)(24 h): Declination (hour.min) & 2:01 & 1.55 & 1.54 & 1.47 & \(-\)0.328 & 0.747 \\ \cline{2-7}  & M: Horizontal component (nT) & 11,020.5 & 26.0252 & 10,997.3 (99.8\%) & 39.9 & \(-\)2.499 & 0.025 \\ \cline{2-7}  & A(24 h): Horizontal component (nT) & 52.3 & 61.58 & 108.4 (207.4\%) & 75.0 & 3.754 & 0.002 \\ \cline{2-7}  & \(\Phi\)(24 h): Horizontal component (hour.min) & 15:13 & 4.23 & 13.06 & 3.08 & \(-\)3.027 & 0.009 \\ \cline{2-7}  & M: Vertical component (nT) & 51,980.8 & 39.8312 & 51,984.6 & 46.4 & 0.965 & 0.35 \\ \cline{2-7}  & A(24 h): Vertical component (nT) & 17.1 & 15.1 & 33.8 (197.6\%) & 23.0 & 3.994 & 0.001 \\ \cline{2-7}  & \(\Phi\)(24 h): Vertical component (hour.min) & 12:44 & 6.26 & 18.49 & 7.44 & 3.227 & 0.006 \\ \cline{2-7}  & M: Inclination (degree) & 78.03 & 0.03 & 78.06 (100.03\%) & 0.1 & 2.5 & 0.025 \\ \cline{2-7}  & A(24 h): Inclination (degree) & 0.05 & 0.07 & 0.12 (223.3\%) & 0.1 & 3.856 & 0.002 \\ \cline{2-7}  & \(\Phi\)(24 h): Inclination (hour.min) & 12:20 & 9.44 & 10.08 & 10.13 & \(-\)2.957 & 0.001 \\ \cline{2-7}  & M: Total field intensity (nT) & 53,136.2 & 36.8631 & 53,135.4 & 42.1 & \(-\)0.241 & 0.813 \\ \cline{2-7}  & A(24 h): Total field intensity (nT) & 20.3 & 15.6 & 26.2 & 13.1 & 1.191 & 0.252 \\ \cline{2-7}  & \(\Phi\)(24 h): Total field intensity (hour.min) & 14:57 & 3.38 & 17.47 & 6.48 & 1.71 & 0.108 \\ \Second, Table 1 (bottom) compares geomagnetic characteristics between the quieter and more disturbed day of each of the individual 48 h records. The MESOR of D (declination), Inc (inclination), and H (horizontal component) were statistically significantly higher on the more disturbed than on the quieter days. The circadian amplitude of these geomagnetic variables and of the vertical component (Z) were about twice as large on disturbed compared to quiet days. The circadian acrophase of H and Inc were significantly advanced by about 2 h on disturbed days, while the circadian acrophase of V was delayed by about 6 h compared to quiet days. Next, we show the involvement of geomagnetic activity on the circadian rhythm of BP by regressing the circadian characteristics of SBP on disturbed days as a function of the MESOR of geomagnetic indices. D showed the strongest association, as illustrated in Figure 2, suggesting that magnetic fluctuations may modulate the circadian rhythm of SBP.
# Formation Mechanism of Crystal Spots in Jian Kiln Oil-Spot Glaze Revealed by Simulation Experiments
###### Abstract  
The crystalline morphology and glaze color of Jian Kiln oil-spot glaze porcelain exhibit artistic beauty, making it one of the typical representatives of iron-based crystallized black porcelain from the Song Dynasty in China. This study sampled a series of specimens from key temperature points during simulation experiments, employing rapid air quenching to preserve the high-temperature state, capturing the formation process of oil-spot glaze crystals in Jian kiln ceramics. Key samples were subjected to microscopic structure and phase analysis using scanning electron microscopy (SEM), laser Raman spectroscopy (LRS), and X-ray photoelectron spectroscopy (XPS), revealing the formation mechanism of oil-spot glaze crystals in Jian kiln ceramics. The results indicate that the bubbles generated from the decomposition of iron oxide at high temperatures facilitate the migration and enrichment of iron-rich particles towards the glaze surface, laying a crucial material foundation for the subsequent crystallization process. The high-temperature reducing atmosphere accelerates the decomposition reaction of iron oxide, altering the concentration of Fe\({}^{2+}\) in the glaze, the viscosity of the melt, and the surface tension, all of which are critical conditions that promote the formation of oil-spot glaze crystals. During the cooling phase, Fe\({}_{3}\)O\({}_{4}\) nanocrystals oxidize into \(\epsilon\)-Fe\({}_{2}\)O\({}_{3}\) crystals, with external iron sources migrating inward to support \(\epsilon\)-Fe\({}_{2}\)O\({}_{3}\) crystal growth. This process gradually leads to the formation of micrometer-scale, leaf-shaped \(\epsilon\)-Fe\({}_{2}\)O\({}_{3}\) crystals that fully occupy the crystalline spots. The coloration of crystalline spots is closely tied to the size of the crystals. Thus, by adjusting the cooling regime, it is possible to create iron-based crystallization glazes with innovative color effects. Furthermore, this study offers significant insights for understanding the crystallization mechanisms of other ancient Chinese high-temperature iron-based crystallization glazes.  
Jian kiln; oil-spot glaze; formation process; \(\epsilon\)-Fe\({}_{2}\)O\({}_{3}\); crystallization Article  
Formation Mechanism of Crystal Spots in Jian Kiln Oil-Spot Glaze Revealed by Simulation Experiments  
1  
Jian Kiln oil-spot glaze; formation process; \(\epsilon\)-Fe\({}_{2}\)O\({}_{3}\); crystallization
# Investigation of Hydrogen Flux Influence on InGaP Layer and Device Uniformity
## 3 Results and Discussion
### Influence of Total H\({}_{2}\) Flux on the Uniformity of the InGaP Single Layer
This boost not only facilitates the transport of more growth precursors across the substrate but also ensures the timely removal of byproducts, thereby curtailing side reactions that could compromise epitaxial purity and uniformity. Second, uniform distribution of growth sources across the substrate, especially between its center and edges, is crucial for uniform epitaxial growth. A higher H\({}_{2}\) flux enhances this distribution, mitigating the impact of gas-phase precursors' depletion on layer uniformity. However, an excessively high H\({}_{2}\) flux may lead to under-utilized growth precursors and alter the V/III ratio due to the differential decomposition rates of V sources, affecting the epitaxial layer's structural and electrical characteristics negatively. Therefore, optimizing the H\({}_{2}\) flux, alongside other parameters like growth temperature and V/III ratio, is fundamental for producing high-quality, uniform InGaP epitaxial wafers. This study not only lays a solid experimental foundation but also provides theoretical insights for refining the InGaP epitaxial growth process, offering valuable guidance for the fabrication of superior InGaP-based electronic devices. Future research will delve further into the interplay between H\({}_{2}\) flux and other growth parameters, aiming for a more precise and efficient epitaxial growth methodology [32; 33; 34; 35; 36; 37; 38].
## 2 Materials and Methods
### Data Sources
#### 2.2.2 Remote Sensing Products  
Albedo data were derived from the MODIS (Moderate Resolution Imaging Spectroradiometer) MCD43B3 and MCD43B2 products. The spatial resolution was 1 km, and the temporal resolution was 8 d. These products have been widely validated worldwide, and the accuracy of the products meets the needs for quality spatial-temporal analyses of surface characteristics and climate change [28; 29] (Table 2).  
Emissivity data and land surface temperature (LST) data were derived from the MODIS product MOD11A2. The spatial resolution was 1 km, and the temporal resolution was also 8 d. According to previous studies, the accuracy of the single-band emissivity data were between 0.001 and 0.005, and this met the demands of our study. The accuracy of the LST product was less than 1 \({}^{\circ}\)C in the range of \(-\)10 to 50 \({}^{\circ}\)C [30].  
Latent heat flux data were from the MODIS MOD16A2 product. The spatial resolution was 1 km, and the temporal resolution was monthly. The mean absolute error was about 0.31 to 0.33 mm.d\({}^{-1}\)[31], and the products for forest and farmland were of higher precision than those for the other ecosystems [32].  
Snow cover data from MODIS product MOD10A2 were provided by the U.S. National Snow and Ice Data Center. The spatial resolution was 500 m, and the time resolution was 8 d. This product has been extensively validated, and snow detection accuracy was over 93% [33; 34].
## 3 Results
### Sine Sweep Test Results
#### 3.3.4 Effect of Heat Treatment Time on the Damping Capacity of NiTi WMD  
The NiTi WMD fabricated with a relative density of 0.31 is heat-treated at 200 \({}^{\circ}\)C for different times of 15 min, 30 min, and 45 min and then subjected to a 1 g sine sweep test from 5 to 200 Hz. Figure 22 shows the transmissibility function of the NiTi WMDs heat-treated for different times.  
The heat treatment time influences both the natural frequency and acceleration transmissibility. The resonant peak became wider, and peak transmissibility decreased as the heat treatment time was extended from 15 min to 30 min. A narrower resonant peak with higher peak transmissibility occurs after the heat treatment time was extended to 45 min.  
Figure 21: Characterization of vibration isolation performance of NiTi WMDs heat-treated at different temperatures. (**a**) Natural frequency; (**b**) peak transmissibility; (**c**) loss factor.  
Figure 20: Effect of heat treatment temperature on the acceleration transmissibility of a vibration-isolation system subjected to the 1 g sine sweep test.  
As shown in Figure 23, the histograms of natural frequency, peak transmissibility values, and dissipation factor illustrate the effects of heat treatment time on the damping capacity of the WMD. The resonant frequency of NiTi WMDs first increases to the maximum and then decreases to a lower level when the heat treatment time increases from 15 min to 45 min, indicating that the WMDs become stiffened first and then become softer. This phenomenon stems from the competitive relationship between the heat treatment's stiffening and softening effect on the NiTi WMD. For a given heat treatment temperature, the stiffening effect caused by fixture resistance predominates in the early stage when the heat treatment time is shorter than 30 min, whereas the softening effect caused by grain growth and dislocation rearrangement predominates in the late stage when heat treatment time is longer than 30 min. Heat treatment for 30 min gives rise to an optimum combination of the stiffness of the porous structure and the damping capacity of the NiTi wire, as reflected by the maximum resonant frequency, the minimum acceleration transmissibility, and the maximum loss factor. Heat treatment times longer than 30 min may result in abnormal grain growth, which leads to a decrease in NiTi wire stiffness and a downgrade in the WMD's load capacity. Meanwhile, the transmissibility increases and loss factor decreases.  
Figure 23: Characterization of vibration isolation performance of NiTi WMDs heat-treated for different times. (**a**) Resonant frequency; (**b**) peak transmissibility; (**c**) loss factor.  
Figure 22: Effect of heat treatment time on the acceleration transmissibility of a vibration-isolation system subjected to the 1 g sine sweep test.
# Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks
## 3 CI-Based Power Allocation Schemes
### Proposed EABC-Based Power Allocation Scheme  
In order to understand the proposed enhanced version of ABC, first we revisit the optimization agents of the classic ABC algorithm, and then highlight the modifications proposed in the EABC algorithm.  
Classic ABC stands among the most competent EA-based algorithms, offering a simple implementation with only two control parameters, i.e., maximum generation number (MGN) and limit. It consists of three colonies of honey bees (the search agents) known as Employed Bees (EBs), Onlooker Bees (OBs) and Scout Bees (SBs) [32]. EBs and OBs are the local search agents, whereas SBs act as global search agents to extend the search process to a larger search space. The search process of the ABC algorithm works as follows. First, EBs generate a neighborhood solution to each initialized solution and apply a greedy search process for updating. OBs act as followers and rely on the information shared by EBs to perform selective updates of the solutions based on their probability of fitness.  
Finally, SBs add strength to the potential solutions to prevent them from getting stuck in the local best, achieving this by randomly regenerating abandoned solutions. In the proposed EABC-based solution, two modifications are suggested as described below.  
1. OBs in the classic ABC algorithm perform selective updating of solutions by comparing the probability of fitness \(\rho\) of each potential solution with a randomly generated number between 0 and 1. However, in EABC, we introduced a probability threshold \(\delta\) to make an intelligent decision about selection of solutions by not updating any solution with a low fitness probability. This not only ensures the quality of each candidate solution but also saves the unnecessary hardware burden of updating weak solutions.
2. SBs in the classic ABC algorithm randomly regenerate exhausted solutions. However, in EABC, the abandoned solutions are replaced with new ones that are randomly generated around the global best achieved so far to offer the regenerated solutions a good initial start.  
The above two modifications of the classic ABC algorithm ensure that each potential solution in the population maintains a certain minimum performance threshold and enhances the quality of search process. The pseudocode for the proposed EABC-based power allocation scheme is provided in Algorithm 1.
## 3 Results
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline \multirow{2}{*}{**Country**} & \multirow{2}{*}{**Site**} & \multicolumn{4}{c}{**Soil**} & \multicolumn{4}{c}{**Plant**} & \multirow{2}{*}{**Reference**} \\ \cline{3-3} \cline{5-10}  & & **Cu** & **Cd** & **Pb** & **Zn** & **Cu** & **Cd** & **Pb** & **Zn** \\ \hline \multirow{4}{*}{Pakistan} & Roadside & **15.24** & **3.69** & 6.08 & 32.18 & **23.45** & **2.27** & **19.45** & **103.7** & Present study \\  & Urban & 8.32 & 1.79 & 2.69 & 21.25 & 12.63 & 1.10 & 10.36 & 60.08 & Present study \\  & Rural & 3.82 & 0.90 & 1.00 & 10.26 & 4.23 & 1.46 & 3.29 & 26.15 & Present study \\ Qatar & Industrial area & **29.4** & 0.44 & 2.5 & ND & **57.3** & 0.4 & 1.6 & ND & [30] \\  & Tannery contaminated site & **36.95** & ND & **16.00** & ND & **27.64** & ND & 10.84 & ND & [48] \\ India & Control site & 11.12 & ND & 10.56 & ND & 13.95 & ND & 11.95 & ND & \\  & Riyadh & 0.12 & 0.39 & ND & ND & 0.52 & **6.53** & ND & ND & \\ Saudi Arabia & Gazan & 0.29 & 0.34 & ND & ND & 0.92 & 1.81 & ND & ND & [9] \\  & Residential areas & 2.2 & 0.2 & **14.5** & 12.4 & 18.5 & **26.4** & **21.3** & 96.5 & \\  & Roadside & 2.6 & 0.2 & **70.0** & 26.6 & 16.9 & **22.2** & **70.0** & **122.4** & \\ Egypt & Fallow lands & 12.7 & 0.3 & 6.8 & 17.0 & 13.5 & **29.6** & **47.6** & 69.5 & [29] \\  & Railways & **60.3** & **4.8** & **22.4** & **202.9** & **24.2** & **26.9** & **72.9** & 58.5 & \\  & Control & ND & 0.82 & 5.4 & ND & ND & 2.7 & 12.4 & ND & \\  & Riverbank & ND & 1.4 & **26.4** & ND & ND & 3.8 & **32.5** & ND & \\ India & Roadside & ND & 1.5 & **30.6** & ND & ND & 3.6 & **45.5** & ND & [31] \\  & Industrial & ND & 2.8 & **134** & ND & ND & **6.6** & **135.7** & ND & \\  & Residential & ND & 2.0 & **34.4** & ND & ND & **4.8** & **47** & ND & \\ Saudi Arabia & Mining area & **48** & **320** & **105** & **67** & **34.4** & ND & 15.3 & 83.3 & [49] \\ India & Thermal power plant area & 2.62 & 0.05 & **2.82** & 4.65 & ND & 0.14 & 0.88 & 4.98 & [46] \\ Saudi Arabia & Riyadh & **80** & 1.00 & **40.8** & **820** & **49.0** & **41.5** & 8.8 & **191.0** & [50] \\  & M-2 Pakistan & ND & 0.34 & 2.57 & **96.82** & ND & 0.13 & 1.24 & 46.34 & \\ Pakistan & Faisalabad-Sarghoda road & ND & 0.34 & 2.86 & **114.0** & ND & 0.22 & 2.61 & 47.68 & [47] \\  & Control & ND & 0.001 & 0.00 & 30.49 & ND & 0.00 & 0.82 & \\ Permissible level & & 8.39 & 0.31 & 8.15 & 44.19 & 10.00 & 0.10 & 5.00 & 60.00 & [4,51,52] \\ \hline \hline \end{tabular} Note: ND = not detected; bold letters show higher concentration than permissible level.  
\end{table} 
Table 7: Comparison of metal concentration (mg kg\({}^{-1}\)) in soils and plants of the present study to other studies.
## 3 Results and Discussion
### Correlation Analysis of Heavy Metal Elements and Magnetic Parameters  
In order to comprehensively analyze the correlation between the magnetic parameters and the content of heavy metals in the dustfall samples in Shanghai, SPSS 22 software was employed for multivariate statistical analysis.  
Table 4 shows the summary of Spearman coefficients of metal and magnetic parameters. \(\chi_{\rm{If}}\) is significantly positively correlated with Fe and Pb in 2019, and the correlation coefficients are 0.554 and 0.716, respectively. The ratio parameters \(\chi_{\rm{ARM}}/\chi_{\rm{If}}\), \(\chi_{\rm{ARM}}/\chi_{\rm{Id}}\) and \(\chi_{\rm{ARM}}/\)SIRM are significantly related to the correlation of each element (0.270 \(<\) R \(<\) 0.935). In the results of the correlation analysis of 2020, \(\chi_{\rm{ARM}}\) and SIRM have extremely significant correlations with Cr, Fe, Mn, Ni, and Pb (0.521 \(<\) R \(<\) 0.997). The ratio parameters \(\chi_{\rm{ARM}}/\chi_{\rm{Ir}}\), \(\chi_{\rm{ARM}}/\chi_{\rm{Id}}\), SIRM/\(\chi_{\rm{If}}\), and \(\chi_{\rm{ARM}}/\)SIRM have different correlation degrees with each heavy metal, ranging from 0.229 to 0.890. Especially, the correlation with Fe, Pb and Zn is particularly prominent (R \(>\) 0.625). These conclusions are consistent with those of the characteristic analysis of the magnetic parameters and the enrichment factor of the elements stated above.  
Combining the correlation results of the magnetic parameters and the metal contents in 2019 and 2020, it can be found that Cu, Fe, Mn, Zn had significant correlations with \(\chi_{\rm{If}}\), \(\chi_{\rm{ARM}}\), SIRM, \(\chi_{\rm{ARM}}/\chi_{\rm{If}}\), \(\chi_{\rm{ARM}}/\chi_{\rm{Id}}\), and \(\chi_{\rm{ARM}}/\)SIRM. Pollutants such as ferromagnetic minerals produced by human activities are interdependent on heavy metals, and iron-manganese oxides largely control the geochemical cycles of metal elements [6]. These conclusions are similar to previous studies [60; 61].