esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 HDELC Algorithm
### High-Dimensional Feature Space Reconstruction Process
#### 3.1.1 Construction of the Feature Space Reconstruction Matrix
Such features usually contain information crucial for problem-solving and should be prioritized for retention in the feature selection process. On the contrary, weak features tend to have high information entropy, indicating that they are more evenly distributed in the dataset, with roughly the same probability of occurrence for each state, which results in the features being less capable of distinguishing between different classes or outcomes. High entropy features may contain a large amount of noisy information and have limited contribution to the prediction of the target variable. Moreover, weak features contribute less to improving the model performance and even introduce errors that increase the model complexity without improving the prediction ability. This results in a feature subspace \(X^{S}\) consisting of strong features and a feature subspace \(X^{W}\) consisting of weak ones. After that, the original feature space samples can be redefined after the average information entropy division as  
\[X=[X*F_{S},X*(I-F_{S})]=[X^{S},X^{W}] \tag{9}\]  
where \"\(*\)\" is the product of the corresponding elements of the two matrices, i.e., the Hadamard product of the two matrices. \(I\) is a unit matrix of \(I\) rows and \(n\) columns, and \(F_{S}\) denotes the eigenspace reconstruction matrix obtained after the eigenspace has been partitioned using a threshold, which is a matrix of \(l\) rows and \(n\) columns, defined as follows:  
\[F_{S}= \begin{bmatrix}\partial_{1}^{\,1}&\partial_{2}^{\,1}&\cdots&\partial_{n}^{ \,1}\\ \partial_{1}^{\,1}&\partial_{2}^{\,2}&\cdots&\partial_{n}^{\,2}\\ \cdots&\cdots&\cdots&\cdots\\ \partial_{1}^{\,1}&\partial_{2}^{\,1}&\cdots&\partial_{n}^{\,l}\end{bmatrix}  \tag{10}\]  
Of which,  
\[\partial_{i}^{\,j}= \begin{cases}1\big{(}i\in\{1,\ldots,n\},j\in\{1,\ldots,l\},x_{i}\in X^{S}\big{)}\\ 0\big{(}i\in\{1,\ldots,n\},j\in\{1,\ldots,l\},x_{i}\in X^{W}\big{)}\end{cases}  \tag{11}\]  
Based on the division of the average information entropy, the feature space reconstruction matrix is established, the significance of which is able to divide the features with strong and weak feature information, obtaining a feature space reconstruction matrix consisting of 1 and 0, representing strong and weak features, respectively. It is also possible to observe the change in the importance of the feature space more clearly and to extract the selected feature space more quickly when the model is trained for classification.
## 4 Results and Discussion  
### Results  
1.1 Workability (VC Value), Paste Margin (\(\alpha\)), Mortar Margin (\(\beta\)), Pearson Correlation Coefficient, and Compressive Strength Development  
Tables 19 and 20 show the results of the workability (using the Vebe test) of the freshly prepared CSGR mixture after wet-sieving, the paste and mortar margins calculated from Equations (A1)-(A5) in Appendix A, and the results of a series of tests on the averages of the compressive strength development for both projects at 7 days, 14 days, 28 days, 90 days, and 180 days. Table 21 shows the completed Pearson correlation and statistical significance (t-statistics and \(p\)-values) between the paste and mortar margins and the compressive strength development for both projects under study. 1.2 Graphical Representation of the Behavior of the Relationship Between Paste Margins, Mortar Margins, and Compressive Strength Development  
Figures 3 and 4 provide a graphical representation of the behaviour of the relationship or correlation between the paste margin, mortar margin, and the compressive strength development of all curing days of the CSGR material or samples for both the Lengshuibe CSGRD and Shaping I CSGRD, respectively. These graphs offer a straightforward way to select the appropriate margins or paste-to-mortar ratio for the C\({}_{180}\)20 or lower compressive design strength of Rich-Mix CSGR for CSGRD applications during the compressive strength trial tests in the laboratory. Once the paste and mortar margins are selected from the chart based on the compressive strength required and the sand ratio is also known (18-35%) [14] as well as the coarse aggregate content, the void content in the fine aggregate and coarse aggregate is then determined per cubic meter using packing density or any acceptable method. The paste and mortar can then be computed with the help of Equations (A1)-(A5) in Appendix A. This saves time, money, and energy while boosting technician confidence. Note that the value on the chart is the excess paste and mortar; the user needs to add 1 (i.e., void paste or mortar = 1) to each value to have the total paste or mortar margin as shown in the conclusion. Depending on the strength requirement for the Rich-Mix CSGR, the 180-day graphs in either project may be used for the selection of paste and mortar margins where applicable. The properties of the materials to be used in any project of CSGR can be compared to those of the materials discussed in Section 2 of this paper in conjunction with SL678-2014 [14], the technical guideline for cemented granular materials. Figure 2: (a) Laboratory compressive strength test sample preparation. (b) Testing.
# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 3 Results and Discussion
### Applications of Unsupervised Learning  
For unsupervised clustering requirements, we use different scales of Shannon entropy E5, E6, E7, E8 and seven indicators of kurtosis, margin, and center of gravity frequency as clustering indicators for clustering. Among them, indicators E5, E6, E7, and E8 are energy indicators.  
During data processing, it was observed that direct K-means clustering analysis on the original data is not effective, because K-means clustering only considers the absolute distance between each sample point and is not sensitive to data of different orders of magnitude. Therefore, it is considered to perform logarithmic transformation on the data  
Figure 4: 5-level of decomposition of original signal and denoised signal.  
Figure 3: The original signal and denoised signal of the four signals of Type B fault.  
to expand the influence of the order of magnitude on the distance, so that it meets the requirements of cluster analysis. It is found that the clustering effect is better and the clustering result is stable. At this time, the effect diagram of K-means cluster analysis is shown in Figure 5.  
Table 5 shows the evaluation results of 100 K-means clustering.  
For this result, the mean accuracy was 94%, and each item achieved 90%. The clustering result is stable, and the standard deviation of accuracy is 1.63.
## 3 Proposed Methodology  
In this section, we elaborate on the underpinnings of our proposed approach for cost-sensitive feature ranking. In most of the feature ranking techniques, a feature weightage is produced which in turn is used for the feature ranking. Afterward, a threshold value is used to filter-out undesirable features while the retained features are fed to a data classification model. One of the major challenges in this regard is to find an appropriate threshold value as shown in Figure 1a. Furthermore, it is also important to select the feature scoring function which is not biased towards any particular data characteristics, e.g., information gain tends to favor attributes that take on a large number of distinct values [41]. Filter-based feature weighting measures such as the Gini index do not account for feature interaction and hence the measure may not be comprehensive enough to capture the complementary feature interaction i.e., a set of features that may not be highly relevant but when considered collectively, they enhance the overall model's performance. In this regard, the feature ranking approach depends on the comprehensiveness of the weighting function. Therefore, in this research, we use three decision tree-based classification models, Classification and Regression Tree (CART), RF and, Gradient Boosting Trees (GBT), which evaluate both the relevance and redundancy of a feature set. It is important to note that the model-based scoring functions can be executed in parallel, therefore the running time of the ensemble is proportional to the running time of its slowest classifier. In this regard, using concurrent processes we can execute the classifiers and afterward combine their results. The main objective of the feature weightage step is to score features based on their importance as well as their interaction with other features in the dataset. Once a reliable feature score is obtained, based on their weightage, features are ranked in descending order of their importance. Subsequently, an averaging operation is performed on the obtained feature weightage and a final feature score is generated.  
In this research, we propose two approaches for combining features i.e., ensemble-1 is based on combining individual scores, whereas ensemble-2 generates three partial solutions in the form of three subsets obtained from the scoring functions. Afterward, the partial solutions are combined using the majority vote. In ensemble-1, a threshold value based on the feature-cost intersection is selected after combining multiple lists, whereas, in ensemble-2, the threshold is applied on an individual list which is obtained from the scoring function and a resultant feature subset is generated. Each feature subset is treated as a partial solution. A final solution is obtained by applying the majority vote scheme to the partial solutions. We normalized the cost values; hence, the overall cost of the entire feature set adds-up to 1. The proposed ensemble-1 is depicted in Figure 3, while Figure 4 shows ensemble-2.
# Near Infrared Illumination Optimization for Vein Detection: Hardware and Software Approaches
## 2 Methodology
### Vein Image Acquisition  
#### 2.2.1 Experiment Setup  
The single-board computer Raspberry Pi 3 was used to process and store the captured image. The regions of interest (ROI) in this research were the palm and arm areas. The distance between cameras was kept constant at 15 cm as it provides an ideal resolution [14] in the ROI and image acquisition, the experimental setup is shown in Figure 4. The ROI was illuminated with the NIR light at different wavelengths (720 nm, 760 nm, 850 nm, 900 nm, and 940 nm), two different LEDs arrangements (Square LED, Ring LED) and different square LED arrangements (\(3\times 3\), \(4\times 4\), \(5\times 5\), \(6\times 6\)). The purpose of increasing the square LED arrangement and the diffuser on the light illuminations is to achieve optimal diffusing potential and gives the best result for vein viewing [15]. Figure 5 shows the configuration of the ring LED and square LED arrangement. The infrared source and camera were placed side by side to ensure a good match lighting system in the ROI area. The infrared LEDs were powered by a power source of 9 V. The lighting from the environment was kept constant with an average luminous emittance of 27.1 cd.  
The Pi NoIR was paired with a Raspberry Pi 3 to capture the raw images. The Raspberry Pi Foundation, the developers of an affordable but competent System on a Chip-microcomputer, produced the PI NoIR, a 5 MP camera with a complete manual [16]. The Pi NoIR camera does not have an infrared filter, increasing the sensitivity to capture infrared images. Figure 6 shows the PI NoIR camera.  
Figure 4: The experimental setup of the equipment.  
Figure 5: The LEDs arrangement for square LED (**left**) and ring LED (**right**).
# Experimental Study on the Effect of Different Parameters on Rotor Blade Icing in a Cold Chamber
## 3 Experimental Results and Analysis
### Effect of LWC on Ice Shape  
The change of LWC had an obvious influence on the amount of ice. The ice shapes of cases 1 and 4 were compared, as shown in Figure 10. The result showed that with the increase of LWC, the amount of ice also increased. This was because the increase of LWC led to the increase of the mass of collected water and frozen ice at the same time. From a quantitative point of view, the amount of ice was almost proportional to the LWC under different conditions. Therefore, the most severe icing conditions with the most LWC should be considered in the design of an ice protection system to ensure the effectiveness of the system.  
Figure 8: Ice shapes under different rotation speeds: (**a**) case 4, rotation speed was 450 Revolutions Per Minute (RPM); (**b**) case 5, rotation speed was 600 RPM; (**c**) case 6, rotation speed was 750 RPM.  
Figure 9: Comparison of ice shapes under different rotation speeds.
# A Novel Method for Building Displacement Based on Multipopulation Genetic Algorithm
## 3 Introducing BDMPGA  
In this section, we introduce the proposed building displacement method based on the multipopulation genetic algorithm (BDMPGA). This approach introduces multiple populations with different control parameters for simultaneous search optimization. Each population can specify different crossover probability and mutation probability to achieve different search objectives. In this way, the defect of single parameter value in traditional GA is avoided, and the global search and local search of the algorithm are taken into account at the same time. The immigration operation is used to connect each population, which introduces the optimal individual from each population to other populations to realize the information exchange between different populations and maintain the diversity of individuals in the population. The acquisition of the optimal solution is a comprehensive result of multiple population coevolution. The manual selection operation is used to select and preserve the optimal individual of each population in the elite population during evolution. The elite population is different from other populations. The elite population does not carry out genetic operations such as selection, crossover and mutation, so as to ensure that the optimal individuals produced by each population will not be destroyed and lost. Meanwhile, this method uses the least preserving generation of the optimal individual as the termination basis, which is more reasonable than the maximum genetic generation.  
The BDMPGA can be divided into five main parts: (1) chromosomal coding based on the real coding method; (2) genetic operations include the selection operation based on roulette wheel method, crossover operation based on arithmetical crossover strategy, and mutation operation based on nonuniform mutation method; (3) immigration operation between different populations; (4) manual selection operation base on the elite retention strategy; and (5) the definition of the objective function and fitness function. The framework is shown in Figure 1, and the detailed operations are as follows.
## 1 Introduction
The advantages of expanding anaerobic digestion facilities are manifold and include bolstering the local economy, generating new job opportunities (both during construction and operation), safeguarding the environment and public health, reducing greenhouse gas emissions, and enhancing the production of renewable fuels [13; 14; 15; 16]. Implementing anaerobic digestion for waste management in small and medium-sized animal husbandry operations presents significant challenges. Moreover, the EU agro-food sector is characterized by a multitude of small and medium-sized enterprises [17]. In such scenarios, the expenses associated with collecting and transporting waste to large centralized anaerobic digestion facilities are substantial, ranging from 0.15 to 0.20 \(\ell\) tn\({}^{-1}\) km\({}^{-1}\)[18]. For instance, for a 500-kW anaerobic digestion facility processing 30,000 tn of livestock and/or agro-industrial waste annually, transportation costs could amount to as much as 120,000 \(\ell\) per year [19]. Additionally, plant operators often resort to energy crops to enhance biogas production, a practice that can potentially double operational expenditures, considering the utilization of 2000 tn of maize silage per year, with prices ranging between 40 and 60 \(\ell\) tn\({}^{-1}\)[19]. Small-size anaerobic digestion facilities designed for individual farms do not necessitate additional raw materials or the use of energy crops. Consequently, operating costs are primarily limited to electricity consumption (for mechanical equipment), labor salaries, equipment maintenance (including CHP unit, motors and pumps), the use of chemicals (e.g., for biogas desulfurization), and consulting services. These consulting services typically offer specialized expertise to optimize digester operation and may also provide chemical additives aimed at enhancing biogas production efficiency. This study aimed to assess the economic feasibility of small-size anaerobic digestion facilities equipped with combined heat and power (CHP) engines ranging from 50 to 400 kW, for treating livestock waste from individual animal husbandry enterprises. To achieve this goal, data were gathered from various livestock operations (including dairy cattle, poultry, swine, dairy sheep and goats) regarding their annual production, revenues, electricity and fuel usage, as well as waste generation. Additionally, waste samples were collected and analyzed to determine their biomethane production potential, thus estimating the revenue from renewable electricity generation. The capital and operational costs of anaerobic digestion facilities, ranging from 50 and 400 kW in capacity, were calculated using the equations developed within the \"eMT cluster\" project. The economic feasibility was further evaluated by computing the investment payback period, considering different feed-in-tariffs for electricity fed into the grid (ranging from 0.21 to 0.26 \(\epsilon\) kWh\({}^{-1}\)), as well as potential subsidies for the construction of anaerobic digestion plants.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 4 Optimizing Ground Station Systems Through Numerical Design
### The Parameters for Calculating the Capabilities of the Ground Station
#### 4.2.1 \(G/t\) Ratio of Earth Station  
The \(G/T\) ratio is a figure of merit of the antenna system that is used to specify the quality of a ground station system. It is the ratio of the antenna gain (\(G\)) to the system noise temperature (\(T\)) in decibels per kelvin (dB/K) and can be calculated with the following Equation (3), where \(G_{r}\) is the antenna gain:  
\[G/T=G_{r}-10\text{log}(T_{s}) \tag{3}\]
# Design, Synthesis, and Cell Lines Studies of Oleanolic Acid--Hydrogen Sulfide Donor Hybrids
## 2 Materials and Methods
### General Procedure for the Synthesis of Targeted Compounds
#### 2.1.4 General Procedure for the Preparation of **12a**, **12b**  
A mixture of OA (1.0 mmol) and Et3N (1.0 mmol) in anhydrous CH2 Cl2 (25 mL) was stirred at room temperature for 10 min. The reaction mixture was cooled to 0 \({}^{\circ}\)C, oxalyl chloride (5.0 mmol) in anhydrous CH2 Cl2 (2 mL) was added dropwise, and the mixture was allowed to stir at 0 \({}^{\circ}\)C for an additional 30 min. Then, the solvent was removed in vacuo to give the middle product 9, which was used in the next step without further purification. To a solution of different ethyl-amino acid (5.0 mmol) and TEA (10.0 mmol) in dry CH2 Cl2 (25 mL), the middle product 9 (1.0 mmol) in anhydrous CH2 Cl2 (2 mL) was added dropwise at 0 \({}^{\circ}\)C, and the mixture was stirred at room temperature for about 5 h. Then, the solvent was removed in vacuo, and the residue was diluted with water and extracted with EtOAc. The organic layer was washed with saturated NH4 Cl aqueous solution, brine, dried over anhydrous sodium sulfate, filtered, and evaporated under reduced pressure to obtain the middle products **10a** or **10b**, respectively. The middle product, **10a** or **10b** (1.0 mmol), was dissolved in dry THF (25 mL), and 4 N NaOH (20.0 mmol) was added into the flask. The mixture was stirred at room temperature until the starting material was totally consumed, as indicated by TLC. The reaction mixture was diluted with H2O (20 mL), neutralized with 1 N HCl, and extracted three times with CHCl3. The combined organic layers were washed with brine, dried over Na2 SO4, and concentrated under vacuum to afford the middle products **11a** or **11b**. The reaction mixture with 20 mL of water was diluted and neutralized with 1 N HCl for acid-base, the neutralized solution was extracted three times with chloroform, and the organic layer was collected. The brine was used to wash the composite organic layer, dried over anhydrous sodium sulfate, and concentrated in vacuo to obtain intermediate product **11a** or **11b**. A solution of 20 mL anhydrous dichloromethane was added to **11a** or **11b** (1.0 mmol) to dissolve it, and certain amounts of dimethyl-aminopyridine and dehydrating agent EDCI (490 mg, 2.4 mmol) were added. This mixture was stirred for 20 min at room temperature. ADT-OH 2.0 mmol was dissolved in 10 mL anhydrous dichloromethane solution and added to the reaction solution, and the reaction continued at room temperature until the end of the reaction. Then, the reaction solution was washed with water (20 mL \(\times\) 3), saturated aqueous sodium bicarbonate solution (20 mL \(\times\) 3), and saturated brine (20 mL \(\times\) 3) successively, dried over anhydrous sodium sulfate, filtered, and the solvent was distilled off under reduced pressure; after separation and purification by column chromatography, the pure products **12a** or **12b** could be obtained. _Oleanolic acid 28-[4-(5-thioxo-5H-1,2-dithiol-3-yl)phenyl] glycinate_ (**12a**). Yield: 20%. Orange solid, m.p.
# Anti-Fouling Properties of Phosphonium Ionic Liquid Coatings in the Marine Environment
## 3 Result and Discussion
### Field Testing Results
#### 3.2.4 Correlation of Anti-Microbial Activity and Marine Anti-Fouling Performance
Figure 13: The graph shows the relative fluorescence readings of trioctylphosphonium doocusate samples with respect to the control sample (where no bacterial growth was inhibited), recorded by exciting the samples at 540 nm and measuring the emission 585 nm. The sample labeled P888[a,b,c], where ’a’ represents the percentage of the polymerizing group, ’b’ represents the percentage of free ionic liquid, and ’c’ represents the percentage of copper oxide used in the formulation, was subjected to treatments with both Gram-negative and Gram-positive bacteria. The error bars represent the relative standard deviation of the samples. The samples inhibited the growth of S. epidermidis, as the relative absorbance values in the figure were smaller than that of the control sample used, whereas there was zero inhibition of _E. coli_ by the samples.