esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of MW-HPCO\({}_{2}\) Extraction Parameters on Pectin Yield  
The pectin yield obtained in MW-HPCO\({}_{2}\) extraction from GP varied between 2.70% and 28% (Table 1). The lowest pectin yield was obtained as 2.7% at 100 \({}^{\circ}\)C for 2 min and a 15 mL/g liquid/solid ratio. The highest yield was obtained as 28% at extraction conditions of 150 \({}^{\circ}\)C, 5 min, and 10 mL/g. In MW-HPCO\({}_{2}\), under these conditions, pectin yield was higher than previously reported pectin yield (20.93%) by [PERSON] and [PERSON] [32], who performed the microwave-assisted pectin extraction with grapefruit peel. The yield obtained by the MW-HPCO\({}_{2}\) system from GP was also higher than that reported by [PERSON] et al. [6] for GP in acid (16.24% to 21.28%), and alkali (17.93%, 24.52%) extractions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**R\({}^{2}\) Value** & **Response Pectin Yield (Y1)** \\ \hline R\({}^{2}\) & 0.9860 \\ Adjusted R\({}^{2}\) & 0.9721 \\ Predicted R\({}^{2}\) & 0.8963 \\ Adeq. Precision & 24.686 \\ Lack of fit & 0.2086 \\ \hline \hline \end{tabular}
\end{table}
Table 2: R\({}^{2}\) values of model given by response surface methodology for dependent variables.  
The 3D response surface plots clearly show relationships between dependent and independent factors. According to Figure 1A, the extracted pectin yield increased as the extraction time and temperature increased. It can be seen that pectin yield decreased at temperatures above 147 \({}^{\circ}\)C and with increasing hydrolysis time. Figure 1B shows the interaction of liquid/solid ratio and temperature. While the yield increased with increasing temperature, the liquid/solid ratio did not have a significant effect on the yield. Similarly, in Figure 1C, the yield increased with the increasing hydrolysis time; however, the liquid/solid ratio did not have a significant contribution to the yield.
## 2 Materials and Methods
### Cell Viability  
HaCaT cells were cultured in 60 mm petri dishes, and when the concentration required for the experiment was reached, the old medium was discarded and replaced with new medium. After NTP treatment, the cells were incubated for 24 h. The medium was discarded again, and 1.5 mL of preconfigured 3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT, Sigma-Aldrich, St. Louis, MO, USA, 298-93-1) working solution was added to each dish. After further incubation in the incubator for 4 h, an equal volume of dimethyl sulfoxide (DMSO, Sangon Biotechnology, Shanghai, China, 67-68-5) was added and shaken for 10 min to completely dissolve the blue-purple crystals in living cells. The culture solution was then transferred to a 96-well plate and the absorbance of each well was measured with a microplate reader at 492 nm. Cell viability was calculated from a standard curve. To investigate the effect of ROS on the viability of HaCaT cells, N-acetyl-L-cysteine (NAC, Beyotime, Shanghai, China, 50077), a scavenger of ROS, was used to verify the results. HaCaT cells were pretreated with 100 uL NAC (10 mM) for 1 h before NTP treatment and their cell viability was measured again.  
In addition, in order to explore whether short-term NTP treatment (15 s) promoted the viability of HaCaT cells through the PI3K/AKT/mTOR pathway, we added PI3K inhibitor LY294002 (Beyotime, Shanghai, China, S1737) to conduct an MTT experiment; the working concentration was 25 uM, and cells were divided into four groups: the control group (without NTP treatment and LY294002 pretreatment), the NTP treatment group (NTP treatment 15 s), the inhibitor group (LY294002 pretreatment only), and the inhibitor pretreatment group (LY294002 pretreatment before NTP treatment). The MTT experimental method was the same as before.
# Estimating the Thermal Conductivity of Unsaturated Sand
## 3 Model Development
### Model Validation  
It is therefore necessary to obtain dependable experimental data to better evaluate the thermal conductivity model. Thus, the experimental data comply with several important criteria: (1) the experimental results are measured on soil samples with the transient method and shown to be accurate and reproducible; (2) cohesionless soil is a collection of objects with a detailed description of the grain size distribution, such as the contents of clay, silt, and sand; (3) the detailed properties of specimens are reported, including grain size distribution, quartz mineral content, texture of sand, saturation degree \(S_{r}\), porosity ratio \(n\), and particle density \(\rho_{s}\); (4) the ambient temperature of the experiment is within the range from 20-30 \({}^{\circ}\)C; (5) each selected experimental dataset should contain a wide range of \(S_{r}\) from dryness to saturation. Table 1 in Appendix A shows that a total of 41 sands were collected from 15 sources published in the literature.  
For each referenced sand, at least four measurements of thermal conductivity were made with the variation of \(S_{r}\) including dry and saturated. Thus, the 41 sands from the literature were able to fulfil the condition of building an independent MPTC model for each sand. The framework of building the MPTC model is shown in Figure 6, and the predicted values of thermal conductivity for each referenced sand calculated by the independent MPTC model were obtained, which corresponded to \(S_{r}\) in the test. From the above findings, it was concluded that for constructing \(f(S_{r})\), as least three independent sets of thermal conductivity experiments of the sample are performed under dry conditions and two different degrees of saturation conditions. Figure 7 shows the comparison of the calculated thermal conductivity values of sand calculated by the MPTC model with measurements of the 41 sands from the literature, as presented in Table 1 of Appendix A. Figure 7 shows that most of the thermal conductivities calculated using the MPTC model were in good agreement with the experimental data. Of the errors, 86% were less than 10% of the calculated values, which verified the accuracy and effectiveness of the proposed model.  
Figure 5: Parameters of \(f(S_{r})\) are determined by nonlinear regression analysis.
# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 4 Multi-Type Anomaly Detection in Civil Aviation  
In this section, we provide a detailed description of our proposed **M**ulti-type **A**nomaly **D**etection method based on **X**GBoost enhanced with **F**eature engineering and hyper **P**aramenter optimization techniques, called MAD-XFP.  
MAD-XFP mainly overcomes the shortcomings of the XGBoost model that make it not suitable for the inherent characteristics of QAR data by incorporating feature engineering and parameter optimization. By doing this, our method can identify multi-type anomalies in one operation routine. Specifically, MAD-XFP first performs feature engineering on each input sequence to enrich the representation of data features. Then, automated parameter optimization is utilized to set optimized values for parameters of the model. Finally, a decision tree is constructed using XGBoost to obtain the final prediction output. Figure 4 shows the general framework of MAD-XFP. The operation of the method will be described in detail below.  
Figure 4: Overview of MAD-XFP.  
Figure 3: Distribution of samples using data balance techniques.
# GS-AGC: An Adaptive Glare Suppression Algorithm Based on Regional Brightness Perception
## 1 Introduction
The disadvantages of deep learning-based high-dynamic-range imaging methods and unsupervised methods include the need for large training samples and high computation requirements, making them impractical for embedded devices. Thus, this paper introduces a GS-AGC algorithm that operates on a hardware platform utilizing a field programmable gate array (FPGA). After conducting a theoretical analysis and FPGA hardware verification, the proposed algorithm emerged as an easily implementable solution under resource constraints, and at the same time, it meets the real-time processing requirement of glare suppression at a framerate of 52 f/s. The algorithm effectively adjusted low-light images while simultaneously reducing glare, thereby enhancing their visual quality. The primary contributions of this paper are as follows:  
* Luminance region delineation for single and multiple light sources was carried out using a region luminance perception algorithm, which helped guide the subsequent image processing. * The GS-AGC algorithm, based on region brightness perception, was proposed to effectively suppress glare from single and multiple light sources in complex real-life scenarios. This algorithm achieved a balanced brightness of low-light images and provided valuable reference ideas and inspiration for related research fields. * Building upon this foundation, the GS-AGC algorithm was accelerated with hardware, and corresponding modules for algorithm acceleration were designed. The implementation was carried out using a field programmable gate array (FPGA). The description of the study consists mainly of:  
Section 2 explores some related work. In Section 3, a thorough overview of the GS-AGC algorithm and its FPGA implementation is provided, including theoretical details. Then, our algorithm is compared to traditional and deep-learning algorithms using publicly available datasets in Section 4, evaluating implementation speed and energy consumption across both CPUs and FPGAs and presenting experimental results and analysis. Finally, in Section 5, the primary research in this paper is summarized, and potential avenues for future investigation are suggested.
## 2 Materials and Methods
### Dataset Partitioning and Setup  
The initial stage of the methodology involves data preparation, tailored to the nature of the available data. For this experiment, we consider two scenarios. On one side, we assume that an original complete dataset containing all relevant features is accessible. This complete dataset is divided into a reference dataset and an incomplete dataset, simulating missing data for iterative generation purposes. On the other side, if two different datasets are available, one can be considered the reference dataset and the other the incomplete dataset. However, different initial setups can be suitable as long as the initial incomplete and reference datasets are available.  
Considering an original and complete dataset partitioned in two datasets--incomplete and reference databases--it is important to maintain a balanced representation of the original classes and feature values, especially in the reference dataset. The cluster validation step relies on the cluster distribution of the UMAP space. An imbalanced distribution may restrict the diversity of synthetic data generation, resulting in clusters that lack samples and thus do not allow for a reliable assignment of synthetic samples. Ensuring an even distribution across the reference dataset helps create a more robust synthetic dataset that accurately reflects the diversity of the original data.
## 3 Effect of Joint Geometry on Peel Stress
### Effect of Number of Adherent Plies on the Peel Stress  
All the joint parameters are kept constant while the number of plies is varied. Table 3 lists the influence of the number of piles on the peel stress.  
According to Table 3, maximum peel stress falls by increasing the number of layers. Obviously, the strength of the joint is improved with a rise in the number of plies with fibers aligned to a constant force since more layers will endure the stress. The change rate is not linear and reduced on a larger number of layers. Table 4 illustrates the percentage of stress changes with adding more layers, and these changes are calculated relative to the previous arrangement. The data which are presented in the first row are compared with peel stress related to the [0\({}_{5}\)] arrangement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Arrangement of**} & **Percentage of** & **Percentage of** & **Peel Stress Changes** \\  & **Changes in the** & **Changes in** & **According to Adding** \\  & **Number of Layers** & **Peel Stress** & **Each Layer** \\ \hline
[0\({}_{10}\)] & \(100\%\) & \(-89.79\%\) & \(-17.96\%\) \\
[0\({}_{15}\)] & \(50\%\) & \(-65.61\%\) & \(-13.12\%\) \\
[0\({}_{20}\)] & \(33\%\) & \(-48.74\%\) & \(-9.75\%\) \\
[0\({}_{25}\)] & \(25\%\) & \(-38.1\%\) & \(-7.62\%\) \\
[0\({}_{30}\)] & \(20\%\) & \(-31.12\%\) & \(-6.22\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variation in maximum peel stress by increasing the number of layers.  
Figure 8: Changes in maximum peel stress due to alteration of fiber angle.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Arrangement of Adherent No. 1**} & **Maximum Peel Stress Magnitude (KPa)** \\ \hline
[0\({}_{5}\)] & \(1188.9\) \\
[0\({}_{10}\)] & \(121.35\) \\
[0\({}_{15}\)] & \(41.731\) \\
[0\({}_{20}\)] & \(21.390\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Variation in maximum peel stress due to changes in the number of layers.  
According to the data mentioned in Table 4, the improvement rate of the strength of the joint will rise slightly in larger layers.
# Scan Matching by Cross-Correlation and Differential Evolution
## 3 Background
### Differential Evolution
The elementary DE operations can be defined using the following equations [55]: the random initialization of the \(i\)th vector of length \(N\) is given by  
\[\vec{x}_{i}[j]=\text{rand}(b_{j}^{\text{L}},b_{j}^{\text{U}}),\ \ j\in\{1,\ldots,N\}, \tag{9}\]  
where \(b_{j}^{\text{L}}\) is the lower bound of the \(j\)-th parameter, \(b_{j}^{\text{U}}\) is the upper bound of the \(j\)-th parameter and \(\text{rand}(a,b)\) returns a random number from the range \([a,b]\). The basic differential mutation is defined as  
\[\vec{v}_{i}=\vec{v}_{r1}+F(\vec{v}_{r2}-\vec{v}_{r3}), \tag{10}\]  
where \(F\) is the scaling factor and \(\vec{v}_{r1}\), \(\vec{v}_{r2}\) and \(\vec{v}_{r3}\) are three distinct vectors selected randomly from the population. The vector \(\vec{v}_{r1}\) is the base vector, \(\vec{v}_{r2}\) and \(\vec{v}_{r3}\) are the difference vectors and \(\vec{v}_{i}\) is the trial vector. The uniform (binomial) crossover that combines the target vector, \(\vec{x}_{i}\), with the trial vector, \(\vec{v}_{i}\), is given by  
\[v_{i}[j]= \begin{cases}v_{i}[j]&\text{if }(\text{rand}(0,1)<C)\text{ or }j=j_{\text{rand}}\\ x_{i}[j],&\text{otherwise}\end{cases}  \tag{11}\]  
for each \(j\in\{1,\ldots,N\}\). The index \(j_{\text{rand}}\) is selected at random as \(j_{\text{rand}}=\text{rand}(1,N)\). The operator swaps the parameters in \(\vec{v}_{i}\) with the parameters in \(\vec{x}_{i}\) with probability equal to \(1-C\). The outline of the classic DE according to [55] is summarized in Algorithm 1. ```
Randomly initialize the population, \(P\), composed of \(M\) vectors by Equation (9);  For each vector in \(P\), evaluate the objective function and rank the vectors according to their fitness; whileTermination criteria not metdo  Let \(G\) = index of current generation; for\(i\in\{1,\ldots,M\}\)do  Mutation: Create trial vector \(\mathbf{v}^{i}\) according to Equation (10);  Validation: Check the range of all coordinates in \(\mathbf{v}^{i}\) and adjust them so that \(\mathbf{v}^{i}\) is a feasible problem solution;  Crossover: Randomly chose a parameter \(j_{\text{rand}}\) in \(\mathbf{v}^{i}\) and modify the trial vector according to Equation (11);  Evaluation: Compute the fitness of the trial vector. ; ifthe trial vector, \(\mathbf{v}^{i}\), represent a better solution than the target vector, \(\mathbf{x}^{i}\), then  add \(\mathbf{v}^{i}\) to \(P^{G+1}\) else  add \(\mathbf{x}^{i}\) to \(P^{G+1}\)  end if  end while
```  
**Algorithm 1**A summary of the basic DE.
## 2 Materials and Methods
### Characterization of MSCs
#### 2.2.4 MSC Immunomodulation Capacity  
The in vitro immunomodulation capacity of MSC and S-MSC was evaluated on Phytohemagglutinin (PHA)-activated peripheral blood mononuclear cells (PBMCs), as previously described [12]. PBMCs (100,000 PBMCs/well) were cultured in the presence or absence of PHA (4 mg/mL, Sigma-Aldrich). These two conditions were tested at different MSCs:PBMCs ratios (1:2, 1:20, and 1:200). After 48 h incubation at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\), cultures were pulsed with \({}^{3}\)H-thymidine (1\(\mathrm{\SIUnitSymbolMicro}\)/well, Perkin Elmer, Waltham, MA, USA) and harvested after 18 h. \({}^{3}\)H-thymidine incorporation was measured by gamma counter (PerkinElmer, Hopkinton, MA, USA). Experiments were performed in triplicate, and results were expressed as a stimulation index (SI = cpm stimulated/cpm unstimulated).
## 3 Materials and Methods
### Characterization of Silica Particles  
#### 3.5.1 FTIR Spectroscopy  
Surface functionality of bare and mPEG-grafted silica particles was characterized using a Fourier transform infrared spectrometer with an attenuated total reflectance attachment (Shimadzu, Japan). The FTIR spectra were recorded at a resolution of 4 cm\({}^{-1}\) over 64 scans in the range of 4000-400 cm\({}^{-1}\).
## 7 Conclusions  
The dynamic models of the fully flexible base-link-joint space robot and satellite were established by using the Lagrange equation and the Newton-Euler method, which provided the theoretical basis for the study of the capture process. With the help of the geometric position relationship and force transfer relationship between the contact points of the robot and the satellite, the impact effect on the collision was calculated, and the dynamic model of the system after capturing was established. Using the singular perturbation method, the system was decomposed into a slow subsystem (containing a base, joint rigid motion, and flexible link vibration) and a fast subsystem (containing a flexible base and joint vibration). A repetitive learning sliding mode controller based on the virtual force for the slow subsystem and an optimal controller for the fast subsystem were proposed. The simulation results show that the proposed control strategy can suppress the vibration of the disturbed system base attitude within 0.5 mm, the vibration of both joints within 0.05 rad, the first mode coordinates of both links within 0.2 mm, and the second mode coordinates of the two links within 0.05 and 0.02 mm, respectively. It can not only effectively suppress the elastic vibration of the base, joint and links of the system, but it can also realize the stable tracking of the desired signal through only one control input within 10 s.  
Conceptualization, X.F.; methodology, X.F. and H.A.; software, X.F. and L.C.; investigation, X.F. and H.A.; writing--original draft preparation, X.F.; writing--review and editing, X.F. and H.A.; supervision, H.A. and L.C.; funding acquisition, H.A. and L.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 51741502, 11372073), Science and Technology Project of the Education Department of Jiangxi Province (Grant No. GJJ200864), Jiangxi University of Science and Technology PhD Research Initiation Fund (Grant No. 205200100514).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Ablation Study on Different Data Obfuscation Methods
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **FPS** \\ \hline Gradient-based Obfuscation (Medical) & 0.69 & 0.64 & 0.67 & 32 \\ Pure Gaussian Noise (Medical) & 0.82 & 0.76 & 0.79 & 48 \\ Proposed Method (Medical) & 0.93 & 0.89 & 0.91 & 57 \\ \hline Gradient-based Obfuscation (Financial) & 0.71 & 0.67 & 0.69 & 41 \\ Pure Gaussian Noise (Financial) & 0.85 & 0.80 & 0.83 & 49 \\ Proposed Method (Financial) & 0.95 & 0.91 & 0.93 & 54 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ablation study results. Figure 5: Visualization of different data obfuscation methods: (**a**) obfuscation method based on gradient information; (**b,c**) two methods based on Gaussian noise; (**d**) the proposed method. computational complexity, further improving throughput. This multi-module collaborative optimization design demonstrates strong adaptability and advantages across both task scenarios, providing a robust theoretical and practical framework for privacy preservation and efficient data analysis.
# Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller +
###### Abstract  
Concomitant improvements in wireless communication and sensor technologies have increased capabilities of wearable biosensors. These improvements have not transferred to wireless prosthesis/orthosis controllers, in part due to strict latency and power consumption requirements. We used a Bluetooth Low Energy 5.3 (BLE) network to study the influence of the connection interval (10-100 ms) and event length (2500-7500 us), ranges appropriate for real-time myoelectric prosthesis/orthosis control on the maximum network size, power consumption, and latency. The number of connections increased from 4 to 12 as the connection interval increased from 10 to 50 ms (event length of 2500 us). For connection intervals \(\leq\)50 ms, the number of connections reduced by \(\geq\)50% with the increasing event length. At a connection interval of 100 ms, little change was observed in the number of connections vs. event length. Across event lengths, increasing the connection interval from 10 to 100 ms decreased the average power consumed by approximately 16%. Latency measurements showed that an average of one connection interval (maximum of just over two) elapses between the application of the signal at the peripheral node ADC input and its detection on the central node. Overall, reducing the latency using shorter connection intervals reduces the maximum number of connections and increases power consumption.  
BLE biosensor network; latency; orthosis; power consumption; prosthesis Article  
Evaluation of BLE Star Network for Wireless Prosthesis/Orthosis Controller +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
[PERSON], 1, 10455. [[https://doi.org/10.3390/APP142210455](https://doi.org/10.3390/APP142210455)]([https://doi.org/10.3390/APP142210455](https://doi.org/10.3390/APP142210455))