esa-sceva/satcom-chunk-collection huggingface dataset:
# Expanded Scene Image Preprocessing Method for the Shack-Hartmann Wavefront Sensor
###### Abstract  
Due to the influence of atmospheric turbulence, the detector, and background noise, the subaperture image of an extended scene Shack-Hartmann wavefront sensor will have a low signal-to-noise ratio, which will introduce errors to the offset estimation and reduce the accuracy of the slope measurement. To solve this problem, this paper proposes a cross-correlation subaperture image preprocessing method, which uses the generalized Anscombe transform to convert the Gauss-Poisson noise into Gaussian noise and introduces residual feedback on the basis of BM3D to achieve the efficient denoising of subaperture images. The simulation results show that compared with the three commonly used denoising algorithms, the proposed method improves the relative error of the subaperture offset calculation by 51.96% and the corresponding Zernike coefficient of distorted reconstruction wavefront by 85.56%, which realizes the improvement in the detection accuracy on the basis of effectively retaining image details.  
adaptive optics; expanded scene; Shack-Hartmann wavefront sensor; denoising algorithm; generalized Anscombe transform +
Footnote †: journal: Applied Sciences
# Experimental Study on the Risk Preference Characteristics of Members in Supply Chain Emergencies
###### Abstract  
Since risk preference affects the behavior of decision makers, the study of its characteristics and impact on decision-making contributes to good planning for emergency coordination. The consistency of a member's risk preferences in the conventional risk field and emergencies of a supply chain was analyzed by applying the prospect theory and adapting the domain-specific risk-taking (DOSEPERT) scale. The influence of time pressure on the risk preferences and decision-making behaviors of members was studied in the emergency field and its sub-emergencies of a supply chain. The conclusions were drawn based on the empirical study. First, the risk preference could be measured in terms of conventional risk and emergencies. Second, the members tended to be risk averse with no time pressure, and the degree of risk aversion was weakened with time pressure, which had the greatest effect in the natural disaster event. Third, even though the change in risk preference had a consistency regarding the four types of sub-events of supply chain emergencies, it was inconsistent regarding the conventional risks and emergencies. With the evolution trend of risk preference demonstrated and the relationship between preference and time pressure revealed, this study may provide a decision-making reference for the formulation of a supply chain emergency coordination scheme.  
risk preference; supply chain; emergency; experimental study; time pressure Article
## 4 Discussion
### Repeatability of the Maximal Joint Angles and the ROM
As the variability in the execution of the task might differ between ROM and static postures, the comparison between these different studies allows no final conclusions on the influence of the marker placement or the kinematic model on the repeatability of the different approaches. From a clinical point of view, the results of this study highlight the importance of a protocol optimization in order to minimize errors. The repeatability of ROM measurement needs to be precise enough to detect a difference that is relevant in a patient's hand function and, therefore, allows an accurate judgment of a patient's improvement after a treatment or therapy. Thus, the MDD of a testing protocol should be smaller than the clinically relevant difference to be detected. In gait analysis, joint angle errors up to 5\({}^{\circ}\) have been shown to be generally accepted [26]. A corresponding value in hand kinematics is currently not available. Based on the high mobility in the hand joints and the significant restrictions to be expected due to injuries or pathologies, we consider this value to be transferable to hand motion analysis. The majority of the analyzed joints within this study revealed an error below 5\({}^{\circ}\) (SEM 1.2-6.4\({}^{\circ}\)) and the averaged coefficient of variation was less than 5% of the ROM. The protocol has, hence, been shown to be suitable for test-rest analysis in a clinical setting. Despite the bigger error in the thumb and MCP5 joints, the protocol is yet appropriate for applications where large individual improvements or deviations from the norm in those joints are to be expected. A comparison of the repeatability of the motion analysis protocol with the common measurement method involving goniometers facilitates the selection of the appropriate method for future researchers interested in the change in ROM [32]. Whether the repeatability of a method is high enough needs, however, to be put in context with the intended application and might vary with regards to different objectives.
# Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams
## 4 Equivalent Algorithm for Predicting Coal Seam Gas Content
### Determination of Residual Gas Content in Coal Seams  
The residual gas content within coal seams is intimately linked to the state of gas adsorption and the volatile matter content, both of which are primarily influenced by the extent of coal metamorphism. The determination of this residual gas content can be achieved through a combination of on-site sampling and measurement procedures, along with an assessment of the volatile matter content of the coal, as detailed in Table 3.  
The residual gas content within coal seams of identical quality and comparable volatile matter situated in the same mining area can be considered approximately constant, with the assumption that geological structure and other factors have negligible influence. This is due to the fact that adsorbed gas resides within the coal body's small pores and is thus seldom impacted by structural stress.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Volatile \%** & **\textless{}10** & **10\textless{}20** & **20\textless{}30** & **30\textless{}40** & **\textgreater{}40** \\ \hline Residual gas content & 5-15 & 2.5–7 & 2-3 & 1.2-1.5 & 1.1-1.6 \\ (m\({}^{3}\)/t) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The residual gas content (\(q_{mc}\)) in coal seams.
# The Influence of Track Irregularity in Front of the Turnout on the Dynamic Performance of Vehicles
###### Abstract
[21] analyzed the effect of the stiffness irregularities on the dynamic train-turnout interaction. [PERSON] et al. [22] studied the influence of conversion deviation on the safety and comfort of a train when passing the turnout. [PERSON] et al. [23] analyzed the influence of track-distance roughness on the dynamic wheel-rail geometry contact, and the result showed that appropriate gauge widening can improve the structure irregularity. From the previously published literature, there are many research achievements in track irregularity, turnout geometry, and its dynamic responses. The relationship between track irregularity and vehicle dynamic response on the main line has been extensively studied. However, few researchers have investigated track irregularities in front of the incoming turnout in depth, and the impact of the status of railway lines in front of the turnout is often neglected when vehicle-turnout dynamics analysis is performed. Many factors cause bad preceding track irregularity of the turnout, such as short-wavelength irregularity caused by rail welds [15], track buckling caused by inaccurate locked rail temperature or temperature difference [24], track gauge irregularities caused by partial loosening of fasteners, or partial separation between fasteners and rail, and side wear, uneven wear, and other abnormal wear of rail [25] in front of the turnout et al. In April 2020, a train was derailed in the turnout area of Jincheng Line, China, as shown in Figure 1. Due to the inaccurate locked rail temperature, there were significant lateral irregularities in front of the turnout and in the switch area, as a result, the track expanded, which led to derailment accidents. Therefore, it is of great importance and academic value to determine the law of the influence of the preceding irregularity on the vehicles passing through the turnout area, to guide on-site maintenance. In this paper, based on the theory of multibody dynamics (MBD), a rigid-flexible coupled model of high-speed vehicle-turnout-track was established. The influence of different irregularities in front of the turnout on the dynamic performance of a vehicle passing through the turnout in the direction from the switch to the crossing panel at a high speed was studied and discussed by a random sampling method. Then, a comprehensive assessment of different irregularities on dynamic indicators is conducted. Finally, conclusions and the next steps are presented.
# High-Efficiency Closed-Loop Control of a Robotic Fish via Virtual Musculoskeletal Methodology
## 3 The Virtual Musculoskeletal Methodology  
### Modeling the Muscular Skeletal System  
The mathematic model of the musculoskeletal system of the fish, shown in Figure 2, includes three parts: The muscles are actuators to produce torque. The spindles in the extrafusal muscles are mechanoreceptors to provide dynamic feedback for the closed-loop control. The central nervous system (CNS) in the spinal cord operates the dynamic motions of these muscles. The movement is driven by agonist muscles and antagonist muscles controlled by the CNS's excitation signals. The excitation signals are produced from two sources of information: (1) the cerebellum plans the control goal in joint space according to the movement task and (2) the CNS unifies this goal and the feedback signals from the spindles to send signals to the muscles. The CNS adjusts the position and speed of the swing and varies the total stiffness and viscosity over a considerable range. This adjustment realizes the compliant action to the water environment.
# Antibacterial Effects of _Paeonia lactiflora_ Extract on Oral Microcosm Biofilms
## 2 Materials and Methods
### PL Extract Preparation  
The prepared PL sample was obtained from Jirisangol (Sancheong-gun, Gyeongsangnam-do, Republic of Korea; latitude: 35.4290 \({}^{\circ}\)N, longitude: 127.8746 \({}^{\circ}\)E). PL samples were ground and extracted using 70% ethanol solution according to a method described in a previous study [29]. Subsequently, the extract was filtered through filter paper (Filter paper #2, Whatman, Maidstone, Kent, UK) and concentrated under reduced pressure using a vacuum evaporator (EYELA, Tokyo, Japan), followed by freeze drying to obtain a powdered PL extract.  
To conduct the MTT cytotoxicity test, PL extract powder was dissolved in an ethanol solution to prepare PL extracts at four concentrations (30.0, 50.0, 100.0, and 150.0 \(\upmu\)g/mL). These four extract concentrations were added to Roswell Park Memorial Institute (RPMI) 1640 medium (Gibco Laboratories, Grand Island, NY, USA) to create the experimental groups, whereas RPMI 1640 medium alone was used as the control group. According to the ISO 10993-5 MTT cytotoxicity test standard [35], 100 \(\upmu\)L of L929 cells were inoculated into each well of a 96-well cell culture plate (SPL Life Sciences, Pocheon, Republic of Korea), and the number of L929 cells per well was adjusted to \(1\times 10^{4}\). The cells were then incubated for 24 h, followed by treatment with 100 \(\upmu\)L of each PL extract at the assigned concentrations. After 24 h, the cells were washed with 100 \(\upmu\)L of phosphate-buffered saline (PBS; Welgene, Gyengsan, Republic of Korea). Subsequently, 1 mg/mL of MTT solution (3-[4,5-Dimethylthiazol-2-yl]-2,5-diphenyltetrazolzolium bromide; Sigma, Darmstadt, Germany) was added to the RPMI 1640 medium, and 50 \(\upmu\)L of this medium solution was added to each well, followed by an additional 2 h incubation period. To facilitate the reduction of MTT to formazan in viable cells, 100 \(\upmu\)L of isopropanol (Sigma, UK) was added to each well and incubated for 20 min. Finally, the absorbance was measured at 570 nm using a spectrophotometer (CM-3500d, Minolta, Tokyo, Japan), and the amount of formazan formed was calculated. Cell viability was calculated by standardizing the MTT reduction rate in the experimental groups as a percentage of that in the control group. According to ISO 10993-5 standards [35], a cell viability of 70% or higher indicates no cytotoxicity.
## 2 System Description
### Operating Mode under Study  
The three-cell buck converter has different operating modes that depend on the value of the steady-state duty cycle \(D\) of the switches \(S_{i}\). It should be noted here that \(D\) is the same for all the switches \(S_{i}\). This study was carried out with the condition \(\frac{1}{3}<D<\frac{2}{3}\). The same procedures can be followed for other ranges of \(D\). The driving signals sequence is given by \((u_{1},u_{2},u_{3})=(1,0,1),(1,0,0),(1,1,0),(0,1,0),(0,1,1),(0,0,1)\) as depicted in Figure 3. It develops a periodic orbit with six configurations per switching cycle. The sequence is shown as follows  
\[\mathcal{C}_{101}\rightarrow\mathcal{C}_{100}\rightarrow\mathcal{C}_{110} \rightarrow\mathcal{C}_{010}\rightarrow\mathcal{C}_{011}\rightarrow\mathcal{C} _{001}\rightarrow\mathcal{C}_{101}\cdots \tag{2}\]  
The current paths in each circuit configuration are highlighted in Figure 4. The matrices \(\mathbf{A}_{u_{1}u_{2}u_{3}}\), the vectors \(\mathbf{B}_{u_{1}u_{2}u_{3}}\) for each topology and a brief description of the behavior in the converter elements for each case are shown in Table 1.  
Figure 3: Driving signals for \(\frac{1}{3}<D<\frac{2}{3}\).  
Figure 4: Configurations used by the flying capacitor three-cell buck converter for steady-state duty cycle in the range \(\frac{1}{3}<D<\frac{2}{3}\). Green color indicates the current path and the red arrows stands for power flow direction.
## 3 Methodology
### Construction of Color Harmony Prediction Model
#### 3.2.2 Model Training
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MSE** & **R\({}^{2}\)** & **MAE** & **RMSE** & **MAPE** & **Adjusted R\({}^{2}\)** \\ \hline ElasticNet & 0.0757 & 0.3401 & 0.2129 & 0.2752 & 0.074 & 0.2979 \\ \hline Decision Tree & 0.0689 & 0.3995 & 0.205 & 0.2625 & 0.0703 & 0.3612 \\ \hline KNN & 0.0628 & 0.4525 & 0.1962 & 0.2506 & 0.0682 & 0.4175 \\ \hline Linear Regression & 0.0555 & 0.516 & 0.1845 & 0.2357 & 0.0633 & 0.4851 \\ \hline Bayesian Ridge & 0.0552 & 0.5188 & 0.1836 & 0.235 & 0.063 & 0.4881 \\ \hline ANN & 0.0514 & 0.552 & 0.1783 & 0.2267 & 0.0606 & 0.5233 \\ \hline Random Forest & 0.047 & 0.594 & 0.1706 & 0.216 & 0.0584 & 0.5676 \\ \hline SVM & 0.0462 & 0.5972 & 0.1685 & 0.215 & 0.0576 & 0.5714 \\ \hline XGBoost & 0.043 & 0.622 & 0.1628 & 0.2082 & 0.0558 & 0.5982 \\ \hline LightGBM & 0.0433 & 0.6224 & 0.1628 & 0.2082 & 0.0557 & 0.59 \\ \hline CatBoost & 0.0419 & 0.634 & 0.1606 & 0.2048 & 0.055 & 0.6111 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model performance. and R\({}^{2}\), demonstrating that ensemble learning can improve the model's accuracy and generalization ability to some extent. Figure 2 shows a line graph comparing the predicted values and actual values. The red line represents the predictions from the stacking model, while the blue line represents the actual values, making it easier to visually assess the predictive capability of the stacking model. The fitting performance of all models is shown in Figure 3, where the X-axis represents the true values of color harmony, and the Y-axis represents the predicted values corresponding to the proposed regression models. It demonstrates the relationship between the actual target values and the model's predicted values. Figure 4 shows a performance comparison of all 12 models, including stacking, allowing for a more intuitive observation of the performance metrics of each model. Figure 3: Fit quality plot. Figure 2: Actual vs. predicted line plot.
## 5 Results and Discussion  
### Microstructure Analysis of the SiCp/6061 Al Composites  
The typical microstructures of the SiCp/6061 Al composites with SiC volume fractions of 5%, 10%, and 15% are shown in Figure 4a-c. The distribution of the SiCp was relatively uniform, and there was no large-scale agglomeration. The contact between individual particles occurred only for the SiCp volume fractions of 10% and 15%. It is worth noting that in addition to the distribution of more broken SiC particles, there were also many micropores on the grain boundaries of the SiCp/6061 Al composites with different SiCp contents, and their diameters were mostly about 0.5 \(\upmu\)m. This was because during the forming stage of the SiCp/6061 Al composites, the gas could not be discharged between the solid aluminum powder particles, and finally the pores uniformly distributed in the grain boundaries were formed. The porosity of the SiCp/6061 Al composites was measured by an ED-300C professional digital density meter (Shanghai Quinlong Electronic Technology Co., Ltd., Shanghai, China), and they were 0.84%, 1.18%, and 2.00% with the SiCp volume fractions of 5%, 10% and 15%, respectively. The size of the SiCp particles and the grain size of the 6061 Al matrix were collected and analyzed by Image-pro plus 6.0 analysis software (Media Cybernetics Image Technology Company, Rockville, MD, USA). The particle size of the SiCp was mainly concentrated in the vicinity of 2 \(\upmu\)m and 12 \(\upmu\)m (particle size is equivalent diameter). The grain size of the 6061 Al matrix with different SiCp volume fractions was about 30 \(\upmu\)m (calculated by the linear intercept method), which is similar to the particle size of the 6061 Al powder.  
Figure 4d shows the tensile fracture morphology of the composite. As far as the matrix is concerned, there were a large number of dimples in the SiCp-free region of the 6061 Al matrix, which belonged to the typical ductile fracture characteristics. As far as the SiCp is concerned, the surface of the bare SiCp was very clean, and there was no impurity and aluminum matrix, which indicates that the bonding strength between the matrix and SiCp was lower than that of the 6061 Al and SiCp. The interface failure between the SiCp and 6061 Al was the main failure mode of the SiCp/6061 Al composites. On the other hand, the SiCp on the fracture surface was basically intact, and no cracks parallel to the tensile axis appeared. This was consistent with the experimental results of [PERSON] [34] and others. In summary, the main failure modes of the SiCp/6061 Al composites can be divided into two types: the interfacial tearing between the SiCp and the matrix and the ductile fracture of the matrix.
###### Abstract  
Understanding saturation magnetization and its behavior with particle size and temperature are essential for medical applications such magnetic hyperthermia. We report the effect of shell thickness and field cooling on the saturation magnetization and its behavior with temperature in Fe\({}_{3}\)O\({}_{4}\)/\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) core/shell nanoparticles of fixed core diameter (8 nm) and several shell thicknesses. X-ray diffraction (XRD) analysis and transmission electron microscopy (TEM, high-resolution transmission electron microscopy (HRTEM)) were used to investigate the phase and the morphology of the samples. Selected area electron diffraction (SAED) confirmed the core/shell structure and phases. Using a SQUID (San Diego, CA, USA), magnetic measurements were conducted in the temperature range of 2 to 300 K both under zero field-cooling (ZFC) and field-cooling (FC) protocols at several field-cooling values. In the ZFC state, considerable enhancement of saturation magnetization was obtained with the increase of shell thickness. After field cooling, we observed a drastic enhancement of the saturation magnetization in one sample up to 120 emu/g (50% larger than the bulk value). In both the FC and ZFC states, considerable deviations from the original Bloch's law were observed. These results are discussed and attributed to the existence of interface spin-glass clusters which are modified by the changes in the shell thickness and the field-cooling.  
nanoparticles; magnetization; shell thickness; Bloch's law +
Footnote †: journal: Applied Physics Letters
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 7 Concept of Surface Plasmon Resonance Biosensing  
In a FOPS detection process for biosensing, first, light needs to be launched through an optical fibre coated by a thin layer of noble metal. Then the evanescent wave propagates along the silica-metal interface and the maximum energy transfer from light to SPW occurs on the metal surface when its propagation constant is the same as that of SPW. The propagation constant of the SPW is extremely sensitive to the changes of the refractive index of the sample on the metal surface. Figure 8 illustrates an overview of the detection mechanism of a FOPS for antibody-antigen binding. The antibody elements initially immobilize themselves on the metal surface and then the interaction between the antibody and antigen results in the refractive index change of the samples, which can lead to a shift in the resonance wavelength of the FOPS. This significant wavelength shift caused by the binding introduces the changes in the refractive index that can be detected by a precise optical spectrometer [93].
## 2 Methodology
### Ionospheric Correction Evaluation Method  
In this section, firstly, the method to calculate SBAS ionospheric correction is presented. Secondly, the comparison method between SBAS ionospheric correction and the reference derived from dual-frequency GNSS measurements is discussed.
## 4 Maintaining Maximum Coverage and Connectivity Scheme
### Design of the MCC-ACO Algorithm
Based on the above discussion, we obtained a complete self-deployment algorithm for maintaining the maximum coverage and connectivity in underwater sensor networks based on an ant colony optimization, as shown in Algorithm 1. ```
1:Input: Position of the sink in the grid \(SinkLabel\), number of grids in the grid \(\gamma\), number of iterations \(N_{max}\), and number of mobile nodes \(c\). 2:Output: Distribution sites of all nodes and the paths of the nodes to the sink. 3:\(step\gets 0\)
4:while\(step\leq N_{\text{max}}\)do
5:\(tabu=SinkLabel\),\(k\in[1,m]\leftarrow\) initial stage; search begins at the sink by the node:
6:for\(GridNum\gets 1\)to\(\gamma\),do
7:for\(NodeNum\gets 1\)to\(c\),do
8: Calculation of the monitoring states of the nodes;
9:if\(full\)coverage,then
10: continue;
11:endif
12:\(allowed_{k}\leftarrow\) for the calculation of the candidate site of the \(NodeNum^{\text{th}}\) node;
13: Path-selection probabilities are calculated using Equations (10) and (12);
14: The node moves forward, and the tabu table is updated;
15:endfor
16:endfor
17: Global pheromone updating is carried out according to Equations (13) and (14). 18:endwhile
```  
**Algorithm 1** Detailed description of MCC-ACO.