esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Morphology and Chemical Composition
This research found that all sample powders are mesoporous, with pore diameters averaging 11 nm, and the specific surface area of un-doped, 2.87 Ag-doped, 5.75 Ag-doped, 2.87 Ce-doped, 5.75 Ce-doped, and 2.87 Ag/2.87 Ce co-doped (mol%) MBG was 230.68 \(\pm\) 10.61, 217.78 \(\pm\) 13.07, 186.01 \(\pm\) 7.57, 170.12 \(\pm\) 19.99, 57.39 \(\pm\) 1.49, and 191.93 \(\pm\) 0.54 m\({}^{2}\)/g, respectively, as well as the specimen's pore volume (cc/g), which was 0.7227 \(\pm\) 0.018, 0.6144 \(\pm\) 0.052, 0.5274 \(\pm\) 0.026, 0.4822 \(\pm\) 0.018, 0.2927 \(\pm\) 0.090, and 0.6225 \(\pm\) 0.006, as shown Table 2. Moreover, the MBG powders at a higher concentration have a lower specific surface area (for instance,  
Figure 4: Particle size distributions of (**a**) un-doped MBG, (**b**) 2.87 mol% Ag-doped, (**c**) 5.75 mol% Ag-doped, (**d**) 2.87 mol% Ce-doped, (**e**) 5.75 mol% Ce-doped, and (**f**) 2.87 mol% Ag/2.87 mol% Ce co-doped MBG. 230.68 m\({}^{2}\)/g for the case of an un-doped MBG, but 57.39 m\({}^{2}\)/g for the 5.75 mol% Ce-doped MBG case) and pore volume (for instance, 0.7227 cc/g for the case of an un-doped MBG, but 0.2927 cc/g for 5.75 mol% Ce-doped cases). The N\({}_{2}\) sorption isotherms of obtained samples (Figure 6) are type IV characteristic mesoporous materials, and the H1 hysteresis loop indicated the narrow pore-size distribution. Figure 5: TEM images of (**a**) un-doped MBG, (**b**) 2.87 mol% Ag-doped, (**c**) 5.75 mol% Ag-doped, (**d**) 2.87 mol% Ce-doped, (**e**) 5.75 mol% Ce-doped, and (**f**) 2.87 mol% Ag/2.87 mol% Ce co-doped MBG. Figure 6: The N2 adsorption–desorption isotherms of (**a**) un-doped MBG, (**b**) 2.87 mol% Ag-doped, (**c**) 5.75 mol% Ag-doped, (**d**) 2.87 mol% Ce-doped, (**e**) 5.75 mol% Ce-doped, and (**f**) 2.87 mol% Ag/2.87 mol% Ce-doped MBG. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Type** & **Specific Surface** & **Total Volume** & **Pore Radius** \\  & **Area (m\({}^{2}\) g\({}^{-1}\))** & **(cm\({}^{3}\) g\({}^{-1}\))** & **(nm)** \\ \hline Un-doped MBG & 230.68 \(\pm\) 10.61 & 0.7227 \(\pm\) 0.018 & 5.09 \(\pm\) 2.12 \\
2.87 mol\% Ag-doped MBG & 217.78 \(\pm\) 13.07 & 0.6144 \(\pm\) 0.052 & 4.67 \(\pm\) 1.50 \\
5.75 mol\% Ag-doped MBG & 186.01 \(\pm\) 7.57 & 0.5274 \(\pm\) 0.026 & 4.82 \(\pm\) 1.73 \\
2.87 mol\% Ce-doped MBG & 170.12 \(\pm\) 19.99 & 0.4822 \(\pm\) 0.018 & 5.45 \(\pm\) 1.69 \\
5.75 mol\% Ce-doped MBG & 57.39 \(\pm\) 1.49 & 0.2927 \(\pm\) 0.090 & 8.16 \(\pm\) 0.06 \\
2.87 mol\% Ag/2.87 mol\% & 191.93 \(\pm\) 0.54 & 0.6225 \(\pm\) 0.006 & 6.38 \(\pm\) 0.10 \\ Ce co-doped MBG & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specific surface area, total pore volume, and pore radius of un-doped, 2.87 mol% Ag-doped, 5.75 mol% Ag-doped, 2.87 mol% Ce-doped, 5.75 mol% Ce-doped, and 2.87 mol% Ag/2.87 mol% Ce-doped MBG.
# Depth-Optimized Quantum Circuit of Gauss-Jordan Elimination
## 2 Preliminaries
### Grover's Algorithm  
The Grover algorithm is a quantum search algorithm that reduces complexity compared to classical computers by a square root. Exhaustive search on quantum computers has a complexity of \(O(2^{n})\). On the other hand, quantum search using the Grover algorithm reduces the complexity to \(\sqrt{2^{n}}\). The process of [PERSON]'s search is summarized as follows:  
At first, the database of search targets is prepared in a quantum superposition state using Hadamard (\(H\)) gates. Applying \(n\) Hadamard gates to \(n\) qubits yields the following superposition state:  
\[H^{\otimes n}|0\rangle^{\otimes n}(|\psi\rangle)=\Big{(}\frac{|0\rangle+|1 \rangle}{\sqrt{2}}\Big{)}=\frac{1}{2^{n/2}}\sum_{x=0}^{2^{n}-1}|x\rangle \tag{1}\]  
The primary element, the Grover oracle, includes the quantum circuit used for querying. The Grover oracle answers the solution by inverting the sign of the solution state of the query as follows:  
\[f(x)=\begin{cases}1\text{ if }Query(\psi)=Solution\\ 0\text{ if }Query(\psi)\
eq Solution\end{cases} \tag{2}\]  
\[U_{f}(|\psi\rangle|-\rangle)=\frac{1}{2^{n/2}}\sum_{x=0}^{2^{n}-1}(-1)^{f(x)} |x\rangle|-\rangle \tag{3}\]  
The amplitude of the solution provided by the Grover oracle is enhanced using the diffusion operator. The Grover algorithm repeatedly applies the oracle and the diffusion operator \(\sqrt{2^{n}}\) times to sufficiently increase the amplitude of the solution. Finally, it recovers the solution with a high amplitude (probability) by measuring the qubits.
## 2 Materials and Methods
### Green Space Classification and Extraction System (U-net-SVM)
#### 2.3.4 Accuracy Verification  
To assess the validity of the extraction outcomes produced by the classification system (U-net-SVM) developed in this study, a comparative analysis was conducted between these results and those obtained solely through the SVM methodology. The prediction outcomes for urban green space derived from both approaches, along with the corresponding ground truth data, were compared by using a confusion matrix. Overall, 262,518 pixels were randomly selected from 100 sample areas representing true values of shrub, grassland, and non-green space. The metrics of user's accuracy (UA), producer's Accuracy (PA), overall accuracy (OA), and kappa coefficient were computed through the application of a confusion matrix. Table 1 presents the calculation equations.
# PPG-Based Systolic Blood Pressure Estimation Method Using PLS and Level-Crossing Feature
## 2 Materials and Methods
### Feature Extraction  
In order to reliably obtain features from the SDPPG waveform, we propose the use of LCFs extracted from the SDPPG waveforms, and we apply these features in PLS regression to estimate the SBP. By assuming that the maximum amplitude of the waveform is 100% and by drawing contour lines at arbitrary intervals in the waveform, we can obtain two types of LCFs: (1) the number of crossings between each contour line and a waveform and (2) the total length of each contour line lying within the waveform. The procedure used to extract the features is described as follows.  
1. Record the PPG signal and compute the SDPPG.
2. In the negative direction of time from the point of a wave, detect the zero crossing point first as the endpoints of each SDPPG. Segment the SDPPG waveform corresponding to each pulse by those endpoints, and standardize the waveform on the basis of the positive maximum value of each pulse (i.e., a wave height).
3. Draw contour lines at arbitrary intervals on the standardized SDPPG waveform with a maximum positive value of 1. Extract the crossings (LCF 1) and the total length of the contour line (LCF 2) from all of the contour lines drawn on the waveform.  
Figure 4 shows a representative example of the LCFs extracted from the standardized SDPPG waveform. In our method, it is possible to extract features reliably from an SDPPG waveform with missing peaks because the LCFs are extracted from the contour lines rather than the peaks. When there are no significant changes in the upper part of the SDPPG waveform, the LCFs will have similar trends. Therefore, there is a strong correlation between the LCFs.  
On the basis of a previous study related to the SDPPG [16], we extracted the LCFs from the contour lines drawn at 5% intervals from an amplitude of \(-100\%\) to \(100\%\). We deleted the features that have the same value for all subjects. The total number of LCFs after data processing was 76. The LCFs were extracted for each pulse in the SDPPG waveform for a subject, and the mean values of these features were determined on the basis of the number of pulses recorded for the subject. These mean values were taken as the LCFs for the subject. The SBP was estimated using these mean values of the LCFs. Similarly, mean values were used for the HRFs.
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 4 Catalytic Applications of Mesoporous Silica Materials
### Organic Synthesis
#### 4.2.10. Cycloaddition Reaction  
One of the most efficient synthetic tools for the convenient preparation of triazole derivatives in a regioselective manner is the click reaction [127]. Azole scaffolds are utilized in many biological applications, including the treatment of tumors [128; 129], allergies [130], HIV [131], and microbial diseases [132; 133; 134; 135; 136; 137]. Copper species catalysts are some of the most important catalysts in modern organic transformations [138; 139].  
[PERSON] et al. reported cyclization reactions through a copper-based catalyst, i.e., Cu@\(\beta\)CD (\(\beta\)-cyclodextrintrin) PEG-meso-GO NPs [140]. The hydrophilic and waterproof characteristics of NPs are provided by PEG. In addition, \(\beta\)-CD can be used as an efficient ligand for the Cu catalyst and help the phase transfer into the aqueous phase [141; 142; 143; 144]. Copper species play critical roles in the cycloaddition reaction needed to synthesize 1,2,3-triazole and the click and oxidation reactions necessary for synthesizing benzimidazole derivatives (Scheme 2a,b, respectively). The activity and efficiency of Cu@\(\beta\)CD-PEG-meso-GO NPs as a catalyst was estimated for the click reaction and C-N bond oxidation. It was shown to be a water tolerance, efficient, and recyclable catalyst [145; 146; 147; 148].  
Figure 15. Esterification reaction of oleic acid through [BsAlM][OTf]/SCF.  
The benefits of these green procedures (water was used as a solvent) were their high-yield reactions at short reaction times without laborious purification, plus the catalyst was easily recoverable and water dispersible. These NPs exhibited good reusability without significant decreases in activity.  
Additionally, the tetrazole scaffold is one of the most important heterocycles and has wide-ranging applications in hormonal, antibacterial, and anti-inflammatory areas [149; 150]. A one-pot, three-component green synthesis of tetrazole cycloadducts was carried out through a Cu-Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-PVA NP catalyst by [PERSON] et al. [151]. The heterogeneous magnetic performance of this NP was studied in the N-arylation of amines with aryl halides in high-yield syntheses of 1H-tetrazoles without external ligands. The advantages of this green procedure were the low amount of catalyst used, ease of separation, good reusability, and excellent yields of the final products [151].
## 3 Methodology
### Grounded Theory  
Grounded theory is a qualitative research method based on empirical data, aimed at deriving new concepts and ideas from the data. It focuses on exploring logical relationships rather than verifying existing hypotheses [39]. The grounded theory method is a qualitative research approach suitable for theory construction, enhancing the contextual and practical relevance of research. Its methodological guidelines help to avoid limitations from procedural constraints and preset assumptions, allowing data collection and conclusions to remain free from empirical restrictions [40]. This method derives theory by gradually analyzing and summarizing empirical data, following a bottom-up, exploratory approach that results in more natural and objective concepts and categories.  
The steps of grounded theory mainly include defining the research question, identifying data sources, data collection and organization, open coding, axial coding, selective coding, initial theory construction, and testing for theoretical saturation [41; 42].  
Step 1: It is essential to clarify the core research theme to allow flexible exploration during the study.  
Step 2: Identify data sources, including interviews, case studies, and other diverse information sources, to ensure data richness.  
Step 3: Proceed with data collection and organization to lay the foundation for subsequent analysis.  
Step 4: Based on this, conduct open coding to break down data into various concepts and categories, discovering new themes and patterns.  
Step 5: This is followed by axial coding, where relationships among categories are analyzed to form a higher-level framework.  
Step 6: Selective coding then focuses on the core category, linking the main concepts to the core category and refining the theoretical framework.  
Step 7: Afterward, an initial theory is constructed by deriving a model or framework that explains the data phenomena based on coding results.  
Step 8: Theoretical saturation testing is conducted, with continuous data collection and analysis until no new concepts or relationships emerge, indicating that the theory has reached saturation.  
In this study, the research question was first clarified to determine the core focus, centering on key factors influencing college students' emergency cognitive ability. Data were primarily collected through typical incident cases and on-site interviews. Using grounded theory's steps of open coding, axial coding, and selective coding, interview content, event types, and causes in accident cases are analyzed and factors extracted, ultimately constructing an index system of factors affecting college students' emergency cognitive ability.
# Optimization Strategies in Quantum Machine Learning: A Performance Analysis
## 2 Literature Review and Background  
### Variational Quantum Algorithms (VQAs)  
Variational quantum algorithms (VQAs) have emerged as a leading paradigm for achieving a quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) devices. These algorithms have demonstrated versatility across a wide range of scientific and computational problems, including solving linear systems of equations, simulating quantum dynamics, and determining the ground states of molecules.  
VQAs operate within a hybrid quantum-classical framework, leveraging the complementary strengths of quantum and classical computing. The core structure of VQAs involves encoding data into a parameterized cost function, which is then evaluated using a quantum system, such as a quantum simulator or hardware. Subsequently, a classical optimizer is employed to iteratively refine the parameters of the quantum circuit, aiming to minimize the cost function [11; 12; 13]. This iterative optimization process enables VQAs to effectively address complex problems while mitigating the impact of noise inherent in NISQ devices. Figure 1 illustrates the general architecture of a VQA that operates through the collaboration of a quantum simulator and a classical computer system. The algorithm takes classical data as its input, processed by a parameterized quantum circuit that encodes the quantum model. Following this computation, the resultant predictions are transferred to the classical component, where a cost function evaluates the performance and iteratively adjusts the model parameters. These optimized parameters are subsequently relayed back to the quantum circuit, forming a recursive feedback loop to minimize the cost metric and enhance model accuracy.  
This process, aided by parity measurements--which play a crucial role in quantum machine learning (QML) by transforming quantum state information into classical prediction outcomes--effectively bridges the gap between quantum computation and classical interpretation. This hybrid architecture enables efficient optimization by leveraging quantum computational resources for state space exploration while retaining classical systems for scalable parameter tuning and cost analyses.
# Mechanical Behavior of Steel Fiber-Reinforced Lightweight Concrete Exposed to High Temperatures
## 2 Experimental Study
### Mix Proportioning and Preparation of Test Specimens  
Considering the high porosity of LWAs, a large amount of water will be absorbed in the mixing process. Therefore, the crushed shale ceramics, sintered fly ash ceramics, and shale ceramics stand were presoaked for 1 h to achieve a saturated dry surface state for all the LWAs. The total amount of water absorbed by LWAs was calculated, and the effective water binder ratios of SLWC and ALWC were kept at 0.36 and 0.28, respectively. The mixture proportions are listed in Table 4. On this basis, the remaining mixing water, silica fume, fly ash, and cement were mixed for 2 min. Then, the steel fiber was gradually dispersed in the mixture, and the volume fractions of each steel fiber of the concrete mixes in this research were set as 1%. Finally, according to ASTM C143 [30], a superplasticizer (water-reducing admixture) was used for the LWC and SFLWC mixes to achieve workability with slump values of 5-12 cm. After the final mixing for another 5 min to achieve uniformity, steel molds were filled with fresh concrete and vibrated for 60 s. Two kinds of samples were prepared: 100 \(\times\) 100 \(\times\) 300 mm prism specimens were employed for axial compressive strength tests and stress-strain curves and 100 \(\times\) 100 \(\times\) 100 mm cubic specimens were employed for the UPV and splitting tensile tests. A total of fifteen prisms and fifteen cubes were cast for each mixture proportion, in which three prisms and three cubes were prepared at each temperature. After curing for 24 h, concrete specimens were demolded and stored in the standard curing room for another 28 days at a temperature of 20 \(\pm\) 2 \({}^{\circ}\)C and a relative humidity of 95%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Concrete Type** & **Water** & **Cement** & **Silica Fume** & **Fly** & **Coarse** & **Fine** & **Super** & **Steel** \\  & & & **Ash** & **Aggregate** & **Aggregate** & **Plasticizer** & **Fiber** \\ \hline NWC & 187 & 390 & - & - & 1154 & 663 & - & - \\ ALWC & 154 & 450 & 20 & 80 & 489 & 476 & 8.0 & - \\ CF-AL & 154 & 450 & 20 & 80 & 489 & 476 & 8.4 & 78 \\ HF-AL & 154 & 450 & 20 & 80 & 489 & 476 & 8.7 & 78 \\ SLWC & 167 & 390 & 14 & 60 & 439 & 821 & 5.4 & - \\ CF-SL & 167 & 390 & 14 & 60 & 439 & 821 & 5.8 & 78 \\ HF-SL & 167 & 390 & 14 & 60 & 439 & 821 & 6.1 & 78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mixing proportions of the different mixtures (kg/m\({}^{3}\)).  
Figure 1: Lightweight aggregates (LWAs) and steel fibers used in the experiment: (**a**) shale ceramics sand; (**b**) crushed shale ceramics; (**c**) sintered fly ash ceramics; (**d**) crimped shape steel fiber; (**e**) hooked end steel fiber.
# A Watermarking Method for 3D Printing Based on Menger Curvature and K-Mean Clustering
###### Abstract  
Nowadays, 3D printing is widely used in many areas of life. This leads to 3D printing models often being used illegally without any payment to the original providers. Therefore, providers need a solution to identify and protect the copyright of 3D printing. This paper presents a novel watermarking method for the copyright protection of 3D printing based on the Menger facet curvature and K-mean clustering. The facets of the 3D printing model are classified into groups based on the value of Menger curvature and the K-mean clustering, and the mean Menger curvature of each group will then be computed for embedding the watermark data. The watermark data are embedded into the groups of facets by changing the mean Menger curvature of each group according to the bit of watermark data. In each group, we select a facet that has the Menger curvature closest to the changed mean Menger curvature, and we then transform the vertices of the selected facet according to the changed Menger curvature for the watermarked 3D printing model generation. Watermark data are extracted from 3D-printed objects, which are printed from the watermarked 3D printing models by the 3D printer. Experimental results after embedding the watermark verified that the proposed method is invisible and robust to geometric attacks such as rotation, scaling and translation. In experiments with an XYZ Printing Pro 3D printer and 3D scanner, the accuracy and performance of the proposed method was higher than the two previous methods in the 3D printing watermarking domain. The proposed method provides a better solution for the copyright protection of 3D printing.  
3D printing copyright protection; 3D printing security; 3D printing data; Menger curvature; K-mean clustering Article
# A Multi-Strategy Improved Honey Badger Algorithm for Engineering Design Problems
## 3 Experiments
### Friedman Test  
In an in-depth analysis of the comprehensive performance of the 12 algorithms, the Friedman test is a powerful statistical tool to rank the performance of these algorithms [44]. In this way, we can not only obtain a relative ranking of the algorithms' performance but also identify the algorithms that excel in a particular benchmark test. With [PERSON]'s test, we can observe the performance of the MIHBA against the other 11 algorithms on 23 benchmark functions, and these detailed ranking data can be found in Table 7.  
Based on the data in Table 7, we can clearly observe that the MIHBA excels among all the algorithms involved in the comparison. Among them, MIHBA has a rank-count of 41.5 and an ave-rank of 2.7667. This result indicates that MIHBA tops the list in terms of comprehensive performance among the 23 benchmarking functions involved. The results of the statistical analysis of [PERSON]'s test further corroborate the superiority of the MIHBA over other algorithms in optimization problems.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Function** & **NO** & **PSO** & **GA** & **DBO** & **HBA** & **HBA1** \\ \hline \(f_{1}\) & 7 & 11 & 12 & 10 & 8 & 9 \\ \(f_{2}\) & 7 & 11 & 12 & 10 & 8 & 9 \\ \(f_{3}\) & 7 & 11 & 12 & 10 & 9 & 8 \\ \(f_{4}\) & 7 & 11 & 12 & 9 & 8 & 10 \\ \(f_{7}\) & 10 & 12 & 11 & 9 & 8 & 7 \\ \(f_{8}\) & 12 & 10 & 11 & 5 & 7 & 4 \\ \(f_{9}\) & 5 & 11 & 12 & 10 & 5 & 5 \\ \(f_{10}\) & 5 & 11 & 12 & 5 & 10 & 5 \\ \(f_{11}\) & 5 & 11 & 12 & 10 & 5 & 5 \\ \(f_{15}\) & 12 & 2 & 11 & 1 & 8 & 4 \\ \(f_{17}\) & 11 & 5.5 & 12 & 5.5 & 5.5 & 5.5 \\ \(f_{18}\) & 12 & 3.5 & 8 & 3.5 & 9 & 3.5 \\ \(f_{19}\) & 12 & 1.5 & 11 & 7 & 5 & 6 \\ \(f_{21}\) & 11 & 9 & 12 & 10 & 8 & 2.5 \\ \(f_{22}\) & 11 & 6 & 12 & 9 & 7 & 2 \\ \hline Rank-Count & 134 & 126.5 & 172 & 114 & 110.5 & 85.5 \\ Ave-Rank & 8.9333 & 8.4333 & 11.4667 & 7.6000 & 7.3667 & 5.7000 \\ Overall–Rank & 11 & 10 & 12 & 9 & 8 & 5.5 \\ \hline Function & HBA2 & HBA3 & HBA12 & HBA13 & HBA23 & MIHBA \\ \hline \(f_{1}\) & 5 & 4 & 6 & 3 & 1.5 & 1.5 \\ \(f_{2}\) & 5 & 3 & 6 & 4 & 2 & 1 \\ \(f_{3}\) & 3 & 6 & 5 & 4 & 1.5 & 1.5 \\ \(f_{4}\) & 5 & 3 & 4 & 6 & 2 & 1 \\ \(f_{7}\) & 2 & 3 & 6 & 5 & 4 & 1 \\ \(f_{8}\) & 8 & 6 & 2 & 3 & 9 & 1 \\ \(f_{9}\) & 5 & 5 & 5 & 5 & 5 & 5 \\ \(f_{10}\) & 5 & 5 & 5 & 5 & 5 & 5 \\ \(f_{11}\) & 5 & 5 & 5 & 5 & 5 & 5 \\ \(f_{15}\) & 9 & 7 & 5 & 6 & 10 & 3 \\ \(f_{17}\) & 5.5 & 5.5 & 5.5 & 5.5 & 5.5 \\ \(f_{18}\) & 11 & 7 & 3.5 & 3.5 & 10 & 3.5 \\ \(f_{19}\) & 9 & 8 & 1.5 & 4 & 10 & 3 \\ \(f_{21}\) & 5 & 6 & 2.5 & 2.5 & 7 & 2.5 \\ \(f_{22}\) & 10 & 5 & 2 & 4 & 8 & 2 \\ \hline Rank-Count & 92.5 & 78.5 & 64 & 65.5 & 85.5 & 41.5 \\ Ave-Rank & 6.1667 & 5.2333 & 4.2667 & 4.3667 & 5.7000 & 2.7667 \\ Overall-Rank & 7 & 4 & 2 & 3 & 5.5 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: [PERSON] test results for 12 algorithms.
# Iterative Decoding of LDPC-Based Product Codes and FPGA-Based Performance Evaluation
## 2 Residual Error Characteristics of Multiple Consecutive LDPC Codes  
In this section, the construction method of product codes using very high rate outer codes in [23] is reviewed. Furthermore, the residual error characteristics of LDPC codes are illustrated to show the feasibility of constructing product codes.