esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Statistical Analysis
### Results--ANOVA for Mean Comparisons
#### 4.2.4 ANOVA 433-9 cu vs. 433-9 cr  
The basic statistics of the 433-6 cu and 433-6 cr samples are presented in Table 15. The comparison of means, the comparison of ranges of means +/ - standard deviations, and the comparison of means +/ - 1.96 times standard deviations for these two samples are presented in Figure 18.  
Results of [PERSON]'s test, i.e., test statistics \(W\) and \(p\) values are presented in Table 16.  
The normality of both samples is compared through the Shapiro-Wilk test.  
\(p=0.661979>\alpha=0.05\)  
\(p=0.628698>\alpha=0.05\)  
So, the homogeneity of the variances of these two samples can be tested with the Levene test. The results of that test are presented in Table 17.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Soil Mixture** & **Number of Crush Tests** & **Value \(W\) (Shapiro–Wilk Test)** & \(p\) **Value** \\ \hline
433-9 cu & 5 & 0.939440 & 0.661979 \\ \hline
433-9 cr & 3 & 0.962677 & 0.628698 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Shapiro–Wilk’s test results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Samples** & **Levene’s** & \multicolumn{2}{c}{_df_} & \multicolumn{2}{c}{_p_ **Value**} \\  & _F(1,df)_ & \multicolumn{2}{c}{(Degree of Freedom)} & \multicolumn{2}{c}{_p_ **Value**} \\ \hline
433-9 cu vs. 433-9 cr & 0.015156 & 6 & 0.906041 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Levene’s test—results.  
Figure 18: Comparison of means and standard deviations for 433-9 cu and 433-9 cr samples.  
As \(p=0.906041\) is higher than \(\alpha=0.05\) the homogeneity of variances, this is confirmed. Then, the significance of the difference between means can be verified with an ANOVA. To conduct that, the set 433-9 cu is supplemented by two results that are equal to the mean of the results of the real three crush tests made (to make an equal number of specimens in each sample, i.e., 5). The results of the ANOVA for 433-9 cu and 433-9 cr samples are presented in Table 18.  
Instead of comparing the value of statistics \(F\) (presented in Table 18) to \(F\) critical (read from the Fisher table, dependent on confidence level \(\alpha\), and \(df_{\mathit{futw}}\), and \(df_{\mathit{tot}}\)), Statistica software gives a \(p\) value. As \(p=0.180723\) is higher than the assumed confidence level \(\alpha=0.05\) it can be stated that there is no reason to reject the hypothesis \(H_{0}\) and the means of these two samples are equal.
# Prediction of Air Pollution Concentration Based on mRMR and Echo State Network
## 1 Introduction
[19] applied the convergent cross-mapping method to analyze the causal relationship between meteorological factors and PM\({}_{2.5}\) concentration in Beijing-Tianjin-Hebei region, and obtained quantitative causality analysis results. Therefore, for complex air pollution problems, we also need to choose the appropriate correlation or causality analysis method to analyze the impact variables, thus achieving high-precision detection and forecasting. At present, air pollution has become one of the major environmental problems. However, the causes of air pollution are very complicated. Taking the PM\({}_{2.5}\) concentration as an example, its concentration is not only affected by the air pollutants such as NO\({}_{2}\), CO, O\({}_{3}\), and SO\({}_{2}\), but also by meteorological variables such as temperature, pressure, humidity, wind speed and wind direction. How to choose effective information from a variety of variables for prediction is an important research topic. However, in the previous studies, the correlation between influencing factors and PM\({}_{2.5}\) was not considered in the establishment of most predictive models. In order to solve the above problem, this paper considers using the minimum redundancy maximum relevance (mRMR) [20] feature selection method to select the appropriate input variables, which can select the most relevant information and reduce redundant information. Furthermore, considering the chaotic characteristics of variables, we use phase space reconstruction to extract evolutionary information of relevant variables. At last, the new input variables are transferred to the supplemental leaky integrator echo state network (SLI-ESN) for prediction. The improved model not only enhances the feature extraction and memory ability of the reservoir for multivariate time series, but also improves the influence of historical evolution state on the current state. In practical applications, accurate prediction is conducive to monitoring air quality and making reasonable and scientific decision-making on air pollution prevention. In order to verify the validity of the proposed method, we select the dataset of air quality index and meteorological time series to predict PM\({}_{2.5}\) concentration in Beijing, China. The rest of this paper are arranged as follows: Section 2 describes the preliminary knowledge of this paper, including feature selection method and echo state network; Section 3 introduces in detail the four aspects of the feature selection method, phase space reconstruction, PM\({}_{2.5}\) prediction model and algorithm flow; In Section 4, we analyzed the experimental results of the time series prediction of PM\({}_{2.5}\) concentration in Beijing, China. Section 5 gives conclusions and illustrates the challenges that will still be faced in the future.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**HTM Thickness (nm)** & **VOC (V)** & **JSc (mA/cm\({}^{2}\))** & **FF (\%)** & **Eff (\%)** & **R\({}_{\mathrm{S}}\) (f)** & **Pmax (mW)** \\ \hline
100 & 0.96 & 20.4 & 67.4 & 13.2 & 16.9 & 1.32 \\
200 & 1.02 & 20.9 & 66.6 & 14.2 & 15.8 & 1.42 \\
300 & 1.00 & 20.9 & 65.6 & 13.7 & 16.2 & 1.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristic parameters of the novel solar cells at the various HTM thicknesses using a 600 \({}^{\circ}\)C thermal annealing temperature. Figure 8: Compositional dependence of Raman spectra of CuIn\({}_{1-\mathrm{X}}\)Ga\({}_{\mathrm{x}}\)Se\({}_{2}\) hole-transporting material layer that was grown on a bi-layer Mo/FTO glass substrate. measured EQE curves with an entire major spectral feature, which reproduced in both wavelength and amplitude. Notably, the EQE results with ultra-thin CuIn\({}_{1-x}\)Ga\({}_{x}\)Se\({}_{2}\) HTM exhibited the maximum EQE of ~78% for the 600 \({}^{\circ}\)C-annealed sample, observed between 500 nm and 600 nm. It was attributed to the combined effects of Fabry-Perot resonances and efficient coupling into the waveguide modes of the absorber layer [67; 68]. Planar CuIn\({}_{1-x}\)Ga\({}_{x}\)Se\({}_{2}\) solar cells contained translational invariant and an electric field distribution by resonances.
## 2 Materials and Methods
### Excitation Source Identification and Theoretical Analysis
#### 2.2.2 Cutting Header Excitation Analysis  
The cutting header is a crucial front-end component of the harvester, responsible for crop cutting and initial conveying. Its vibration is primarily generated by the reciprocating motion of the cutter, the rotation of the reel, and the chain drive of the conveyor trough. The cutter consists of two parts, a vertical cutter and a horizontal cutter. Its operating principle involves a chain drive that transmits power to an eccentric wheel. A long connecting rod then transfers this power to a crank mechanism, ultimately driving the reciprocating motion of the cutter. A physical diagram and a kinematic model of the cutter mechanism are shown in Figures 3 and 4, respectively.  
Figure 3: Physical diagram of the cutter mechanism.  
During harvester operation, to ensure harvesting efficiency and quality, the cutting speed of the reciprocating cutter is generally required to be between 1 and 2 m/s. The crank speed can be calculated using Equation (2).  
\[n_{q}=\frac{60v_{c}}{2\pi r_{d}} \tag{2}\]  
In the above equation, \(n_{q}\) represents the crank speed of the cutter mechanism (in r/min), \(v_{c}\) is the cutting speed of the cutter (in m/s), and \(r_{d}\) is the crank length (\(r_{d}\) = 125 mm).  
Based on this calculation, the crank speed ranges from 76.39 to 152.79 r/min. As the reciprocating motion of the cutter generates significant dynamic forces, the vibration frequency of the rotating component can be determined by the Equation\(f_{q}=n_{q}/60\). The theoretical vibration frequency range for the reciprocating motion of the cutter is thus calculated to be 1.27-2.55 Hz.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 5 Future Roadmap: The Promise of AI
### Neural Networks in Image Processing  
Convolutional Neural Networks (CNNs) see much use in the realm of image processing. Many complex image problems are solved using CNNs, including facade parsing (determining distinct faces of buildings) from street-level images [211], crowd stability calculations (count and density) from video surveillance [212], Hyperspectral Image (HSI) classification [213, 214], Polarimetric Synthetic Aperture Radar (PoISAR) image classification [215], medical image diagnostics [216], and tree species identification from aerial images [217].  
Another effective use for image processing is to convert data into images so that existing image processing techniques can be used to solve other problems. For example, a technique proposed by [PERSON] et al. [218] converts EGG data into images to convert the problem of EGG emotion reading to the problem of image recognition. In another proposal by [PERSON] et al. [219], graph classification using CNNs was mixed with image segmentation to yield better structural information.  
CNNs also present several shortcomings when used with image processing. A common issue is the complexity of the networks, which leads to high computational costs and infeasibility for real-time applications [214, 215]. The lack of a labeled training table can also be a common issue and leads to over-fitting and loss of detail [213]. To overcome these issues, [PERSON] and [PERSON] [216] proposed an algorithm that uses a deepening then pruning approach to shrink the size of a network while maintaining its effeteness. Another proposal by [PERSON] et al. [214] trims the input to the network to use integer values instead of floating point values, which greatly speeds up the computation time.
## 3 Classical Routing Protocols
### Modification of LEACH Based on CH Selection
#### 3.2.9 Mod-Leach  
Another modification of LEACH is proposed in [PERSON] et al. (2018), which segregates the intra and inter-cluster communication by allocating two different signal amplifications. Since intra-cluster communication deals with the transfer of information between nodes, a low amplification signal is allocated. On the other hand, inter-cluster communication is the transfer between CHs, and a high amplification signal is chosen.  
The protocol was further modified as EMOD-LEACH by [PERSON] et al. in [PERSON] et al. (2018) using dual transmitting energy levels. EMOD-LEACH is a reactive protocol that uses two threshold values: a soft and a hard threshold. The hard threshold is the absolute sensed value, and the soft threshold is the minor change in the sensed value attribute. If the energy of a CH falls below the threshold value, then only that CH gets replaced in the next round. As a result, the network longevity increases due to optimal data transmission. Both MOD-LEACH and EMOD-LEACH perform better in terms of power consumption and network lifetime. However, synchronization is a significant issue in these protocols due to different signal amplifications.
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 2 Related Work
### Current Detection and Prevention Methods
#### 2.3.2 Gaps in Ransomware Prediction and Detection
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Author** & **Problem** & **Solution** & **Method** & **Tools** & **Empirical** & **Limitation** \\ \hline  & The detection of ransomware & The behavioral patterns & Support vector & & & This approach also uses historical \\  & based on past attack data is not & extracted from the dynamic & machines (SVM) were & & & behavior to predict future ones. \\  & suitable to detect novel, zero-day & analysis of ransomware & used to build the & & ScikitLear, and & & This is not suitable for evasive \\  & attacks, which are common & during the execution time & prediction model & Pandas & & ransomware that uses obfuscation \\  & nowadays. & were used to train a & & based on the & & and polymorphism to change its \\  & & & prediction model. & behavioral data. & & behavior from time to time. \\ \hline  & Advanced malware can obfuscate & A regression model to & The dataset is created & & & \\  & much of its traces through many & predict advanced malware & & by executing & & \\  & mechanisms, such as & & & real-world malware & & \\  & metamorphic engines. Therefore, & significant features extracted & & & & \\  & the detection of such malware has & from a dataset of malware & & & & \\  & become a significant challenge for & & runtime data. & & & \\  & malware analysis mechanisms. & & & & \\  & & & & & \\ \hline  & Sophisticated Android malware & & & & & \\  & families often implement & & & & \\  & techniques aimed at avoiding & & & & & \\  & detection. Split-personality & Exploiting sandbox detecting & & heuristic approach, & & \\  & malware, for example, behaves & heuristic prediction to & & was used based on & & \\  & benignly when it detects that it is & predict and automatically & & API calls collected & & \\  & running on an analysis & & generate byteoude patches. & during the execution & & \\  & environment such as a malware & & & time of the malware. & & \\  & sandbox, and maliciously when & & & & \\  & running on a real user’s device. & & & & & \\ \hline  & & & & & \\  & Zero-day malware attacks are & & & & & \\  & challenging due to the & & existing malware signatures & & & \\  & polymorphic nature of the & & derived from the static & & \\  & malware. & analysis of malware & & & \\  & & payloads. & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Studies related to ransomware behavior prediction.
## 3 Results
### Angular and SSD Dependence  
The angular dependence of the CZT detector was determined by normalizing the measured signal intensities to the signal recorded at a gantry angle of 0\({}^{\circ}\). Figure 5a depicts the results obtained at 6 MV. Because the solid water phantom has a hexahedral shape, the signal intensities measured by the CZT detector at each gantry angle were divided by those measured by the ionization chamber to correct for depth dependence occurring during the angular dependence measurements. Figure 5b represents the changes in the signal intensities obtained from the CZT detector when varying the SSD from 80 to 120 cm. At an SSD of 100 cm, the normalized signal intensity obtained from the CZT detector is higher than that output from the ionization chamber, which reveals decreasing signal intensities with increasing SSD.  
Figure 4: Energy dependence of the signal intensities obtained from the CZT cell at various irradiated photon beam energies.  
Figure 5: (**a**) Angular and (**b**) SSD dependence of the signal intensities output obtained from the CZT cell and ionization chamber under 6 MV beam irradiation. Data are normalized to the signal intensity recorded at a gantry angle of 0\({}^{\circ}\) and an SSD of 100 cm.
## 3 Results
### Yield Estimation Model
\begin{table}
\begin{tabular}{c c} \hline
**Growth Stage** & **Model Expression** \\ \hline Single-growth stage & Y = 29882.803 \(\times\) NDVI(\({}_{(740,768)}\) (Booting)\(-3.017\)\(\times\) FDI1\(-78.851\)\(\times\) FDI2-7.921 \(\times\) FDI4 + 2331.725 \\ Two-growth stage & Y = 24173.141 \(\times\) NDVI(\({}_{(740,768)}\) (Booting) + 33292.36 \(\times\) DVI(\({}_{(752,864)}\) (Filling) + 24.836 \(\times\) FRI1 + 43897.073 \(\times\) FRI4 + 15.488 \(\times\) FDI5\(-32.097\) \\ Three-growth stage & Y = 30193.832 \(\times\) NDVI(\({}_{(740,768)}\) (Booting) - 15773.406 \(\times\) RVI(\({}_{(748,832)}\) (Heading) + 73832.326 \(\times\) DVI(\({}_{(752,864)}\) (Filling) + 41129.09 \(\times\) FRI4\(-5.211\)\(\times\) FDI4 + 16010.301 \\ \hline \end{tabular}
\end{table} 
Table 5: The expressions for the optimization model with single, two and three growth stages involved. The estimation accuracy of models using vegetation indices alone was improved when more growth stages were involved. With the introduction of florescence spectral indices in these models, estimation accuracy was further improved, especially for the single-growth stage model and two-growth stage model, which showed the most obvious improvements. The obvious increase in estimation accuracy for these two models was partly because of their relatively low accuracy, making them have a greater potential for improvement, while the accuracy of the three-growth stage model was already high and the room for optimization was limited. It could be concluded that spectral information at the flowering stage was very useful in improving the performance of yield estimation models using vegetation indices alone. Taking florets as interference information, weakening the efficiency of spectral information was actually a waste of useful information at the flowering stage.
## 4 Results
### Temporal and Spatial Changes
#### 4.1.2 NPP  
The NPP refers to the remainder after deducting the autotrophic respiration from the total organic dry matter produced by the photosynthesis of green plants per unit time and unit area. As a key variable for characterizing vegetation vitality, the NPP is the basis of the energy and material circulation of both the organisms and the ecosystem and is related to the ecosystem's ability to fix carbon. The TEC model provided by the National Meteorological Center was used to calculate and synthesize the monthly and annual NPP and thus to analyze the temporal variation trend and spatial distribution characteristics of the NPP in the Kokyar Project Area.  
The NPP in the Kokyar Project Area presented the distribution characteristics of \"high in the south and low in the north\" (Figure 3a), which is consistent with the spatial distribution characteristics of the FVC (Figure 2a). Additionally, from 2000 to 2021, the annual NPP in the Kokyar Project Area showed a gradual upward trend (Figure 3b), with an average annual increase of about 1.34 gC/m\({}^{2}\). In addition, the \"greening\" in the north of the Kokyar Project Area was obvious (Figure 3a). In 2013, the average NPP in the whole region was the highest, 108.95 gC/m\({}^{2}\). The lowest NPP was in 2009 (27.65 gC/m\({}^{2}\)). In 2021,  
Figure 2: Spatiotemporal changes in FVC in Kokyar Project Area from 2000 to 2021. (**a**) Spatial distribution map of FVC in Kokyar Project Area from 2000 to 2021. (**b**) Time-series changes in FVC in the Kokyar Project Area from 2000 to 2021.  
the average NPP in the whole region was 62.20 gC/m\({}^{2}\), showing an increase of 90.39% compared with that in 2000 (32.67 gC/m\({}^{2}\)).
# Boron Nitride Nanotubes for Curcumin Delivery as an Anticancer Drug: A DFT Investigation
## 4 Conclusions  
In our work, we studied the BNNT interactions between the armchair (5,5) BNNT and the curcumin molecule in both the inner and outer positions of the nanotube with the DFT method. The results show that the drug molecule studied can adsorb both in the outer and inner positions of (5,5) BNNT, but has better adsorption in the inner position. It has been observed that the HOMO values increased marginally after adsorption, while the LUMO value in these systems decreased considerably in the curcumin-BNNT structures, thus reducing the energy gap (Eg). This result further indicates an increase in the electrical conductivity of these systems. As a result, it was found that the curcumin drug can be physically adsorbed by the walls of the studied BNNT, and that the BNNT can reliably perform as a carrier for curcumin drug molecules in drug delivery to eventually improve the bioavailability of the drug studied.  
Conceptualization, A.Y. and E.B.S.; methodology, A.Y.; software, A.Y.; validation, A.Y., E.B.S. and V.A.A.; formal analysis, A.Y.; investigation, S.N.; resources, E.B.S.; data curation, S.N.; writing--original draft preparation, S.N.; writing--review and editing, A.Y., E.B.S. and V.A.A.; visualization, A.Y.; supervision, A.Y.; project administration, E.B.S.; funding acquisition, A.Y. and V.A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable for studies not involving humans or animals.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
All data for this work are provided in the text.  
A.Y. acknowledges financial support from BANGA-Africa, University of Ghana, and the Carnegie cooperation of New York, USA, for the team grants. The authors are grateful to the Centre for High-Performance Computing (CHPC), Cape Town, South Africa, for computer time on the Lengau cluster.  
The authors declare no conflict of interest.  
Figure 3: DOS for a (5,5) BNNT-curcumin complex; (**a**) inside position, and (**b**) outside position.
# Nanomaterials in Bone Regeneration
## 4 Nanoparticles
### Platinum Nanoparticles  
Platinum is a heavy (density 21.45 g/cm\({}^{3}\)) and chemically extremely resistant noble metal; it is one of the rarest precious metals in the world. The most used are platinum nanoparticles (Pt-NPs) with sizes in the range of 10-50 nm. They have excellent properties such as biocompatibility, biodegradability, osteoconductivity, and high stability. It is known that a large number of nanoparticles have different biomedical applications. They are used in the detection of cancer cells, reduction of cellular oxidative stress, and treatment of Parkinson's disease; they have bacteriotoxic effects and cytotoxic effects on cancer cells [112]. Pt-NPs have anti-oxidant and anti-inflammatory properties, which protect the cells from induced bone loss by diminishing the formation of osteoclasts (osteoclastogenesis) [113].  
[PERSON] et al. compared the properties of calcium phosphate scaffolds loaded with Pt-NPs prepared by heat-sintering and calcium phosphate scaffolds free of Pt-NPs. The results of this study showed better morphology and degradation rate of calcium phosphate scaffold with Pt-NPs than the calcium phosphate scaffold free of Pt-NPs. The adhesion and proliferation of human osteoblast cells on Pt-NPs-loaded scaffolds were superior [114].  
A recently investigated new scaffold was composed of nanohydroxyapatite combined with chitosan (CS) in the matrix of poly-L-lactic acid (PLA) nanofibers and enriched with Pt-NPs, Au-NPs, and TiO\({}_{2}\)-NPs. Better biomineralization, cell adhesion, proliferation, and differentiation of MG-63 cells was observed in scaffolds enriched by Pt-NPs and Au-NPs compared to TiO\({}_{2}\)-NPs [115].