esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
The collected data were analyzed with the use of SPSS/WIN 20.0. The normality assumption of dependent variables was examined through the Kolmogorov-Smirnov (K-S) test, histograms, skewness, and kurtosis. As a result, the score for computational thinking right after the intervention and the score for core competencies in the 12 th week of the intervention failed to meet the normality assumption (D range of K-S test: 0.15, \(p<0.05\); 0.15, \(p<0.05\)), but other variables satisfied the normality assumption (D range of K-S test: 0.07-0.13, \(p>0.05\)). The results indicated that the normality assumption was not met for the scores for computational thinking immediately after intervention and core competencies 12 weeks after intervention (K-S test's D range: 0.15, \(p<0.05\); 0.15, \(p<0.05\)). However, other variables satisfied the normality assumption (K-S test's D range: 0.07 to 0.13, \(p>0.05\)). The skewness values for the three variables ranged from \(-0.82\) to 0.06, and kurtosis values ranged from \(-0.31\) to 1.23, all within the \(-2\) to +2 range. Since most variables met the normality assumption, parametric statistics were used to analyze the study results. General characteristics of the participants were analyzed with the uses of a real number and percentage, and the test of homogeneity was performed through the Chi-square test and independent \(t\)-test. To verify the effects of the international training program for enhancing intelligent capabilities on computational thinking, artificial intelligence competency, and core competencies the experimental and control groups have, a repeated measures analysis of variance was conducted. In cases where the sphericity assumption was not met, the Greenhouse-Geisser epsilon correction was applied. Group comparisons at each point of time for changes in dependent variables were analyzed with the use of an independent samples \(t\)-test.
# Modeling and Control of an Articulated Multibody Aircraft
## 3 Control System Design
### System Description  
The nonlinear equations of motion have been derived in Sections 2.6 and 2.7, which can be represented as a function of states \(\mathbf{x}\) and inputs \(\mathbf{u}\) in the form,  
\[\dot{\mathbf{x}} =f(\mathbf{x},\mathbf{u}) \tag{53}\] \[\mathbf{y} =g(\mathbf{x},\mathbf{u})\]  
where \(\mathbf{x}\in\mathbb{R}^{n}\) is state of the system, \(\mathbf{u}\in\mathbb{R}^{m}\) and \(\mathbf{y}\in\mathbb{R}^{p}\) is the output of the system.  
Linearizing the nonlinear model developed around a steady-state condition (trimmed flight) produces a linear system of the form  
\[\dot{\mathbf{x}} =\mathbf{A}\mathbf{x}+\mathbf{B}\mathbf{u} \tag{54}\] \[\mathbf{y} =\mathbf{C}\mathbf{x}+\mathbf{D}\mathbf{u}\]  
where matrix \(\mathbf{A}\in\mathbb{R}^{n\times n}\) is the system matrix, \(\mathbf{B}\in\mathbb{R}^{n\times m}\) is the input matrix, \(\mathbf{C}\in\mathbb{R}^{l\times n}\) is the output matrix, \(\mathbf{D}\in\mathbb{R}^{l\times m}\) is the feedthrough matrix, and \((\mathbf{A}\),\(\mathbf{B})\) can be stabilized. It is assumed that all the states \(\mathbf{x}\) are measurable.  
In designing linear control systems, controllability and observability of the system should be investigated. Controllability determines the possibility of achieving the desired response of the system states \(\mathbf{x}\) using the input \(\mathbf{u}\). To check the controllability (\(\mathcal{C}\)) of a system, the matrix given in Equation (55) is used. If the matrix is full rank, the system is controllable [96].  
\[\mathcal{C}=[\mathbf{B},\mathbf{A}\mathbf{B},\mathbf{A}^{2}\mathbf{B},\cdots, \mathbf{A}^{n-1}\mathbf{B}] \tag{55}\]  
Observability (\(\mathcal{O}\)), on the other hand, is to know the possibility of determining all the states from the output and input signals. The system is observable if the matrix in Equation (56) is full rank [96].  
\[\mathcal{O}=\left[\begin{array}{c}\mathbf{C}\\ \mathbf{C}\mathbf{A}\\ \vdots\\ \mathbf{C}\mathbf{A}^{n-1}\end{array}\right] \tag{56}\]
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 3 Engineering Background and Database Construction
### Introduction to the Background of the Project  
In July 2020, Kumamoto Prefecture was hit by a historically rare persistent rainstorm. The disaster was triggered by the stagnation of the Ume rain front, with cumulative rainfall exceeding 800 mm in 72 h, locally reaching the extreme rainfall standard of \"once in decades\" as defined by the Japan Meteorological Agency (JMA). The heavy rainfall triggered large-scale landslides and flooding, resulting in more than 50 deaths and 20 missing people, as well as the destruction of more than 2000 houses and severe damage to infrastructure (e.g., roads and bridges). Kumamoto Prefecture is located in the central part of Kyushu, with a predominantly mountainous and hilly topography, and complex geologic conditions, which have been the cause of many landslides in the past, due to both typhoons and torrential rainfalls. The large amount of multidimensional data on topography, meteorology, soil, and human activities from this disaster provides a valuable resource for landslide disaster prediction and research. Based on these data, this study processes the data and constructs a comprehensive sample database to provide rich input for model training.
## 2 Methodology
### Principal Component Regression (PCR)  
Principal component regression (PCR) is a method that combines the advantages of a principal component analysis with linear regression. It is a powerful tool for analyzing high-dimensional data when the number of observations is smaller than the number of predictor variables. PCR works by constructing a small set of principal components and then using them as predictors in a regression model.  
The mathematical formula for PCA is:  
\[\mathrm{Y}=\mathrm{XB}+\varepsilon \tag{3}\]  
where \(\mathrm{Y}\) is the response variable, \(\mathrm{X}\) is the observed predictor matrix, \(\mathrm{B}\) is the matrix of regression coefficients, and \(\varepsilon\) is the vector of residual errors.  
The solution of multiple linear regression is:  
\[\hat{\mathrm{B}}=(\mathrm{X}^{\prime}\mathrm{X})^{-1}\mathrm{X}^{\prime} \mathrm{Y} \tag{4}\]  
In PCR, the collinearity that exists in the predictor variables can be eliminated by extracting a group of orthogonal predictors through the application of PCA on \(\mathrm{X}\) and then performing regression on \(\mathrm{Y}\) using a subset of the resulting components of \(\mathrm{X}\).  
\[\mathrm{X}=\mathrm{USV}^{\prime}+\varepsilon \tag{5}\]  
Figure 1: Flowchart of loosely-coupled static aeroelastic analysis.  
\[\text{U}=\text{XV} \tag{6}\]  
where U is the matrix of scores, S is the diagonal matrix of singular values, and V is the matrix of loadings.  
The multiple linear regression can be written as the following:  
\[\text{Y}=\text{UB}+\varepsilon \tag{7}\]  
The solution of regression can be written as the following:  
\[\hat{B}=(\text{U}^{\prime}\text{U})^{-1}\text{U}^{\prime}\text{Y} \tag{8}\]
## 3 Materials and Methods
### Isolation and Analysis of Fatty Acids in Leaves and Rat Liver  
The Folch method was used to extract lipids from _Ilex_ leaves and rat liver samples [46]. Samples of crushed leaves and freeze-dried liver in the amounts of 0.5 and 0.1 g, respectively, were poured over 10 mL of the solvent mixture chloroform:methanol (in ratio 4:1) and heptadecanoic acid as an internal standard. Samples were allowed to stand for 1 h under a cover at 25 \({}^{\circ}\)C (the operation was performed twice), and then the organic phase was filtered and transferred to a round-bottom flask to evaporate the solvents to dryness under reduced pressure on a rotary evaporator (Heidolph-VAP Core). The contents of the flask were hydrolyzed using 2 mL KOH/MeOH, and the entire flask was heated on a heating panel at 70 \({}^{\circ}\)C for 5 min. Then, 1.5 mL of 14% (\(V/V\)) BF\({}_{3}\)/MeOH was added as a catalyst for the fatty acid esterification reaction. After the extract was cooled, 1 mL NaCl and 4 mL hexane were added to the flask for phase separation. The mixture was shaken for 5 min by hand, and then the hexane fraction was collected. The organic fraction that already contained methyl acid esters was dried on anhydrous magnesium sulphate, filtered on a silica celite filter and transferred to round bottom flasks to concentrate the sample (to 1 mL) on a rotary evaporator. The thus-prepared sample was then transferred to a GC-MS vial and stored at -18 \({}^{\circ}\)C until GC-MS analysis.  
Separation of fatty acid methyl esters isolated from _Ilex_ leaves and rat liver was achieved on a Zebron ZB-FAME capillary column (60 m, 0.20 mm, 0.20 \(\upmu\)m; Phenomenex, Torrance, CA, USA). The GC-MS apparatus was operated under the conditions listed below: scanning was set from 40 to 400 \(m/z\) in electron ionization (EI) at 70 \({}^{\circ}\)C, the carrier gas type was helium with a flow rate of 0.98 mL min\({}^{-1}\) at ratio 1:20, the temperature program was as follows: 80 \({}^{\circ}\)C for 2 min; rate of 3.0 \({}^{\circ}\)C \(\times\) min\({}^{-1}\) from 80 \({}^{\circ}\)C to 180 \({}^{\circ}\)C; rate of 8 \({}^{\circ}\)C \(\times\) min\({}^{-1}\) from 180 \({}^{\circ}\)C to 240 \({}^{\circ}\)C for 4 min. The injector was maintained at 280 \({}^{\circ}\)C.
## 4 Experiment Results and Comparison
### Results and Discussion
#### 4.5.1 Overall Accuracy Evaluation  
Figure 6 shows the overall accuracy of the proposed approach and the baseline algorithms on both NSL-KDD and ISCX data sets. As shown, the results demonstrate that the proposed approach SAL outperforms all baseline algorithms. Its overall accuracy scores are approximately (98.7%) for the NSL-KDD data set and (99.9%) for the ISCX data set, which are higher than kNNVWC and PGM, which record overall accuracies of (26.6%) and (71.9%) for the NSL-KDD data set and (91.7%) and (85.6%) for the ISCX data set, respectively.  
We applied the [PERSON] test [PERSON] (1986) followed by the Nemenyi posthoc test [PERSON] (1986) to further investigate the overall accuracy of semi-supervised algorithms on both datasets. The former investigates the proposed approach as well as baseline algorithms across \(n\) data sets by ranking each model separately on each data set, so that rank 1 is assigned to the algorithm with the highest performance, rank 2 is assigned to the second highest, and so on. In the event of a tie, the average rank of all algorithms across all datasets is computed. In the latter test, the efficiency of the semi-supervised models is compared pairwise.  
Figure 7 shows Friedman-Nemenyi test of the overall accuracy results with (\(\alpha=0.05\)) on both datasets. The results indicate that the overall accuracy of SAL is scored the highest value (with rank 1) compared to the PGM and KNNVWC which have ranks 2 and 3, respectively.  
Figure 6: Overall accuracy comparison of semi-supervised algorithms on both datasets.
## 1 Introduction
Finally, we tested our proposed method on a near-infrared image dataset that combines the advantages of both infrared and visible images by preserving more details, edges, and texture features from the visible light images while retaining the benefits of the infrared images.
# Preparation and Modification of Biochar Materials and their Application in Soil Remediation
## 2 Preparation and Modification of Biochar
### Modification of Biochar
#### 2.2.2 Chemical Reduction  
Chemical reduction is also known as alkali modification method. The reducing agent was used to reduce functional groups on the surface of biochar, so as to improve its non-polarity. Meanwhile, chemical modification also can improve porosity and specific surface area of biochar. Finally, adsorption capacity of biochar for pollutants is enhanced, especially for non-polar adsorbates. The commonly used reducing agents are NaOH [62], KOH [63], NH\({}_{4}\)OH [64], etc. [65]. Different reducing agents have different modification effects. In order to determine suitable modified biochar for improving adsorption capacity of volatile organic compounds (VOCs), [PERSON] et al. [64] used NH\({}_{4}\)OH, NaOH, HNO\({}_{3}\), H\({}_{2}\)SO\({}_{4}\), and H\({}_{3}\)PO\({}_{4}\) to carry out chemical treatment on coconut shell-based carbon. The results showed that, compared with the poor adsorption capacity of acid-treated carbon, high adsorption capacity was obtained for alkali-treated carbon. The reason is that surface area and pore volume increased and total oxygen containing function groups were diminished when treated by alkalis, while acid treatment was the opposite. [PERSON] et al. [66] found that the process of biochar activation by KOH and NaOH is different. Atomic species, K, formed in situ during KOH activation intercalates between the layers of the carbon crystallite, while there is hardly any evidence for the intercalation of Na with carbon.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Histogram Analysis
#### 3.2.3 Irregular Deviation  
The irregular deviation (\(D_{irregular}\)) is another metric used in image encryption to evaluate the quality of an algorithm by measuring the difference between the histograms of a plaintext image and its cipher [51]. Unlike the maximum deviation metric, irregular deviation also considers the difference between neighboring pixels. A cipher image with a lower value of \(D_{irregular}\) indicates better encryption algorithm quality. The irregular deviation can be mathematically expressed as follows:  
\[D_{irregular}=\sum_{i=0}^{255}\Bigl{[}|d_{i}-D_{arg}|\Bigr{]}, \tag{13}\]  
where  
\[D_{arg}=\frac{1}{256}\sum_{i=0}^{255}d_{i}, \tag{14}\]  
where \(d_{i}\) represents the histogram value at index \(i\) (from Equation (11)), and \(D_{arg}\) denotes the average value of the pixels that are deviated at every deviation value.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Size** & **128\(\times\)128** & **256\(\times\)256** & **512\(\times\)512** & **1024\(\times\)1024** \\ \hline Upper limit & 8192 & 32,768 & 262,144 & 524,288 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Upper limit of maximum deviation for different-sized images.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Size** & **128\(\times\)128** & **256\(\times\)256** & **512\(\times\)512** & **1024\(\times\)1024** \\ \hline Upper limit & 4,177,920 & 16,711,680 & 66,846,720 & 267,386,880 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Upper limit of Chi-square \(\chi^{2}\) for different-sized images.  
From Equation (13), the upper limits of \(D_{irregular}\) can be calculated. If we assume that both images are completely different from each other, \(d\) will have one element with value \(H\times W\), and the rest will be zeros. By substitution \(d\) in Equation (13), the upper limit can be calculated as follows:  
\[\begin{split} D_{irregular}&=\bigg{|}H\times W-( \frac{H\times W}{256})\bigg{|}+(255\times\bigg{|}0-\frac{H\times W}{256}\bigg{|} )\\ &=\bigg{|}H\times W-(\frac{H\times W}{256})\bigg{|}+(\frac{255 \times H\times W}{256}),\end{split} \tag{15}\]  
The lower limit of \(D_{irregular}\) is 0 for any size image. Table 5 utilizes Equation (15) to compute the upper limit for different-sized images.
## 2 INS/GNSS Concepts
### INS/GNSS Integration  
In INS/GNSS integration, the nominal trajectory is often unknown in advance. Therefore, the current best estimate of the actual trajectory is used as the nominal trajectory. When Kalman filtering is applied to a system that has been linearized around this estimate, it is known as the EKF [36; 32; 10]. INS/GNSS integration can be performed in two modes: direct (state model) and indirect (error state model). In direct integration, the Kalman filter directly estimates states such as position, velocity, and attitude. In indirect integration, the Kalman filter is used to estimate the errors of the state vector of the inertial navigation algorithm, and these errors are subsequently corrected. Additionally, two types of error feedback mechanisms are employed: open-loop and closed-loop. In the open-loop configuration, corrections to position, velocity, and attitude are applied externally to the INS, with the estimated errors subtracted from the INS solution at each iteration. In contrast, the closed-loop configuration feeds the EKF error estimates back into the system, continuously correcting the INS solution. This feedback keeps the INS errors small, ensuring that the linearity assumption required for the EKF technique is maintained throughout the process [32].  
Furthermore, there are different levels of coupling in INS/GNSS integration. Loosely coupled integration processes the GNSS and INS data separately before combining them. This method is simple and efficient, but less robust in environments with degraded GNSS signals. Tightly coupled integration directly incorporates GNSS measurements into the INS navigation equations, providing greater accuracy and robustness when dealing with weak or intermittent GNSS signals [37]. Ultra-tightly coupled integration goes further, directly integrating raw GNSS signal measurements into the navigation filter, resulting in greater resilience to degraded GNSS environments and improved performance in high-dynamic scenarios [38]. The integration of INS and GNSS in this work is based on a loosely coupled approach with a closed-loop configuration, as the trajectory is not known in advance.
## 3 Simulations on the Positioning System and Results  
In this section, the results of the tests and simulations conducted on the Cartesian robot are reported with the aim of investigating the ability of the positioning system to reproduce the project trajectory. This trajectory was implemented by using the Matlab code and was transformed into a G-code by means of an algorithm properly developed for this  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Data** \\ \hline Number of positions in x & 3 \\ Number of positions in y & 2 \\ Number of total positions & 6 \\ Acquisition height & 10/40/90 mm \\ Collimator admittance angle & 0\({}^{\circ}\) \\ Stop time in each position & 0.2 s \\ Total simulation time & 118.08 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the project trajectory.  
Figure 8: Robot diagram.  
Figure 7: Project trajectory: x position of the end-effector over time.  
purpose. The G-code program allows controlling both the translation along the three axes and the rotation of the collimator to allow multi-angle acquisitions.  
Therefore, the control board was programmed to execute the implemented G-code to trace the desired trajectory.  
The rotation of the end-effector, simulating the movement of the collimator around the proper axis designed in the handling support, is controlled by the robot board and actuated by a stepper motor that allows changing and setting the inclination angle.  
To simulate the phases of the spectral data acquisition during the movement of the collimator along the project trajectory, a controlled LED is positioned on the end-effector, which turns on in correspondence to the time periods when the spectrometers are programmed to acquire images.  
Simulations on the positioning system were performed by executing the programmed code moving the Cartesian robot along the project trajectory and registering the differences between the input trajectory and the real path followed by the end-effector.  
The results of the simulations are reported in Figure 9, where the simulated trajectories are represented and superimposed on the project ones. The results are presented in terms of the variation of the three coordinates x, y, and z over time.  
From these results, one can observe that the simulated and project trajectories show minimal differences and are superimposable.  
This means that the test rig is able to reproduce the defined and programmed acquisition trajectories, and therefore, it results in being optimized and conformal to perform  
Figure 9: Input and simulated trajectories of the end-effector: (**a**) x direction; (**b**) y direction; (**c**) z direction.  
Vis-NIR spectral analyses. With these results, the positioning system of the test rig can be considered qualified.
# Process of Formaldehyde and Volatile Organic Compounds' Removal from Waste Gases
## 3 Off-Gas Purification Process
### Energy Production  
Currently, the steam produced is not used for electricity generation, so a computer simulation of electricity production by involving a steam turbine in the waste gas treatment process was done (Figure 2).  
Two basic kinds of steam turbines are available. One is a pulse design, in which a rotor turns as a result of the steam force on the blades. The second is a reaction design, in which the rotor produces its rotational force from steam when leaving the blades.  
The primary type of steam turbine is a condensing steam turbine, which is used for high-power applications. The steam is discharged directly into one or more condensers that maintain vacuum conditions. Condensing steam turbine processes produce the highest mechanical power and efficiency; however, the output power of condensing steam turbines is sensitive to ambient temperature. Condensing steam turbines are expensive, large, and complex.  
The second type is a back-pressure steam turbine, which is suitable for mechanical drive applications. The term back-pressure refers to turbines that release steam at a pressure higher than atmospheric pressure. Low pressures are often used in heating systems, while high pressures are often used in the supply of steam to industrial processes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**N** & **(A)** & **kg/hr** & **Weight\%** & **N** & **(B)** & **kg/hr** & **Weight\%** \\ \hline
1 & N\({}_{2}\) & 13,492 & 89.4667 & 1 & N\({}_{2}\) & 13,492 & 89.94667 \\
2 & O\({}_{2}\) & 916 & 6.10667 & 2 & O\({}_{2}\) & 594 & 3.96000 \\
3 & CO & 221 & 1.47333 & 3 & CO & 0.284 & 0.00189 \\
4 & Dimethyl ether & 82 & 0.54667 & 4 & Dimethyl ether & 0.583 & 0.00389 \\
5 & Formaldehyde & 3.5 & 0.02333 & 5 & Formaldehyde & 0.033 & 0.00022 \\
6 & Methanol & 15.5 & 0.10333 & 6 & Methanol & 0.10 & 0.00067 \\
7 & Water & 270 & 1.80000 & 7 & Water & 400 & 2.66667 \\
8 & CO\({}_{2}\) & 0 & 0\% & 8 & CO\({}_{2}\) & 513 & 3.42000 \\  & & 15,000 & 100 & & & 15,000 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The composition of waste off-gases (**A**) before the purification process and (**B**) after the catalytic process of purification.  
Figure 2: Computer simulation of waste gas treatment in the oxide catalyst formaldehyde production process, utilizing heat to produce electricity.  
The third type is an extraction steam turbine, which has one or more openings in the casing for extracting a portion of the steam at a given intermediate pressure, which can be used for process purposes.