esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Prediction Model of Blasting Vibration Velocity in the Dahuangshan Mine
## 4 Result and Discussion
### Predictive Analysis of Nodal Vibration Velocity  
By building a numerical model of high and steep slopes and comparing and analyzing the variation of blasting vibration velocity at the center positions of different platforms, the following rules are found: the Figure 7 shows the blasting vibration velocity changes at the center positions of the platforms 51, 66, 81, 96, and 111 within the initial 10 ms, when the blastholes are detonated at the same time. The vibration velocity of the 111 platform exceeds the safe vibration velocity specified in the blasting safety regulations [28]. From the 96 platform to the 111 platform, the blasting vibration velocity decays rapidly. The vibration velocity of the 51, 66, and 81 platforms does not change significantly, and the peak particle velocity is less than 1 cm/s.  
The 111 platform is close to the explosion source and is greatly affected by the shock wave, so the response fluctuates violently in the vibration velocity. With the increase in the explosion center distance, the vibration parameters change gradually. According to this, the safety monitoring of the 111 platform should be strengthened, and active protection should be carried out.  
Figure 6: Relative results histogram of the prediction results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Q(Kg)**} & \multirow{2}{*}{**R(m)**} & \multirow{2}{*}{**H(m)**} & \multirow{2}{*}{**Measured Value**} & \multirow{2}{*}{**Predicted Value**} & \multirow{2}{*}{**Measured Value**} & \multirow{2}{*}{**Predicted Value**} \\  & & & & & & & \\ \hline
1 & 9975 & 38 & 30 & 8.390 & 8.231 & 3.882 & 3.877 \\
2 & 5745 & 683 & 121 & 0.051 & 0.057 & 0.013 & 0.012 \\
3 & 7401 & 47 & 15 & 5.830 & 5.800 & 1.566 & 1.563 \\
4 & 9545 & 55 & 15 & 2.283 & 2.369 & 0.941 & 0.946 \\
5 & 9812 & 523 & 81 & 0.798 & 0.806 & 0.063 & 0.064 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test sample data and comparison of forecast results.  
The Figure 8 is a line chart of the peak blasting vibration velocity of each platform height with different delay times. Compared with simultaneous blasting, delayed blasting has a significant impact on the peak particle velocity of the 66-111 platform. The difference in elevation is 45 m. The peak blasting vibration of the delay times of 130 ms and 80 ms is not significantly reduced, and, among them, the peak particle velocity of the 111 platform has the largest decrease. The peak particle velocity of the 80 ms delayed detonation is reduced by 17% compared to the 130 ms delayed detonation on the 111 platform. The 51 platform is located in the middle, and its peak particle velocity has been significantly strengthened. The blasting vibration velocity below the 51 platform has an amplification effect, and the peak particle velocity has slightly rebounded.
## Appendix A Implementation Details
### Analysis of Parameter Settings  
**Appendix A.2.1**.: Setting batch_size as 16  
Setting a smaller value for 'batch_size' can yield the following advantages:  
* Reduced memory consumption: A smaller batch_size leads to decreased memory usage since fewer data samples need to be stored per batch. This enables a larger number of batches to fit within the available memory, facilitating efficient training and inference processes.
* Improved model stability: A smaller batch_size enhances the stability of the model by introducing greater randomness in the samples within each batch. This randomization can help mitigate the risk of overfitting, resulting in a more robust and generalizable model.
* Improved tuning effectiveness: A smaller batch_size allows for faster observation of the model's training progress. This expedited feedback loop enables quicker adjustments and fine-tuning of hyperparameters.  
Furthermore, the model's performance was compared for various batch_sizes and the corresponding quantitative results are displayed in Figure A1. It should be noted that ED\({}^{2}\)IF\({}^{2}\)-Net-L was not trained with a batch_size of 32 due to memory limitations. From the figure, it can be observed that both ED\({}^{2}\)IF\({}^{2}\)-Net-T and ED\({}^{2}\)IF\({}^{2}\)-Net-L achieve the best performance when the batch_size is set to 16. However, it is worth noting that these models also exhibit the highest memory utilization among the tested batch_sizes.  
**Appendix A.2.2**.: Setting Learning Rate as \(5\times 10^{-5}\)  
The learning rate should be carefully selected in conjunction with the batch_size to achieve optimal performance. ED\({}^{2}\)IF\({}^{2}\)-Net is trained using different learning rates for a batch_size of 16. We consider three main learning rates: \(1\times 10^{-4}\), \(5\times 10^{-5}\), and \(1\times 10^{-5}\), and compare the performance of the models trained with these different learning rates. Figure A2 illustrates the performance of ED\({}^{2}\)IF\({}^{2}\)-Net-T and ED\({}^{2}\)IF\({}^{2}\)-Net-L under different learning rates. It can be observed that the optimal model performance is achieved with a learning rate of \(5\times 10^{-5}\) and a batch_size of 16.
## 4 Discussion
### Limitations of the Study  
This study was the first study that explored the effect of whole-body vibration training on the physical capability, ADL, and sleep quality of older people receiving long-term care in care facilities. Therefore, this study featured innovation and relevance. Nevertheless, this study had several research limitations. First, this study is a quasi-experimental study and adopts single-group pretest-posttest design. This design enabled the research to be conducted in a natural situation and few variables need to be controlled; however, no control group was used to compare different interventions, which was a disadvantage of this design. Meanwhile, because this research was conducted in a quasi-experimental design, the external validity had limited explanatory power to make inferences. Second, since this study measured multiple outcomes, it needed to be aware of the type-I error \"priorities these\" in terms were necessary. Third, because of manpower, resource, and time limitations, the sample size was small; however, the test power was sufficient to detect differences between different conditions. Finally, WHO strongly recommends an RCT should be registered. Although this was a quasi-experimental research, it is necessary to be registered for future research.
# Full-Reference Image Quality Assessment Based on Multi-Channel Visual Information Fusion
## 2 Proposed Method
### Contrast Similarity Image Calculation  
Contrast reflects the brightness variation in images and is a significant visual attribute of image quality. There are many methods for calculating image contrast [16], among which RMS contrast is suitable for capturing natural stimuli with low computational complexity, and performs high consistency with the subjective contrast of natural images [10]. Therefore, this paper adopts RMS contrast as the second feature of the image, and its global contrast expression is:  
\[LC_{i}(x)=\sqrt{\frac{1}{H-1}{\sum\limits_{j=1}^{H}{{{(x_{j}-\mu_{x})}^{2}}}}} \tag{6}\]  
where \(\mu_{x}=\frac{1}{H}\sum\limits_{j=1}^{H}{{{x_{j}}}}\) is the average intensity of the image.  
Figure 1: Comparison of performance between MLG and traditional LoG filtering; (**a**,**b**) are original images; (**c**,**d**) are distorted images, with subjective scores of 7.2 and 4.8286, respectively; (**e**,**f**) are MLG similarity images, with objective scores of 0.008526 and 0.008884, respectively; (**g**,**h**) are traditional LoG filtering similarity images, with objective scores of 0.015842 and 0.01499, respectively.  
Based on Equation (6), the global contrast similarity images of the original image\(f_{1}\) and the distorted image\(f_{2}\) can be calculated, and the expression is:  
\[SIM_{LC}(x)=\frac{2 LC_{1}(x)\cdot LC_{2}(x)+T_{2}}{LC_{1}{}^{2}(x)+LC_{2}{}^{2}(x )+T_{2}} \tag{7}\]  
where \(T_{2}\) is a constant for increasing the stability of the equation.
## 3 Results
### Behavioral Metrics  
#### 3.2.1 Number of Checks  
In Figure 4A, the total number of checks is shown across the conditions and at different risk levels. No significant effects of the condition (\(F=0.096,p=0.758\)) and the risk (\(F=1.330,p=0.254\)) were found. This indicates that the participants did not check the model's decision more often, even when the low-accuracy objects were communicated to them, nor when the risk varied. Figure 4B demonstrates the difference in the number of checks between the trials with low-accuracy target objects and high-accuracy trials. The respective ANOVA results are summarized in Table 1. There was a significant effect of the condition (\(F=35.373,p<0.001\)), indicating a clear shift of the interventions in the \"revealed\" condition toward the objects that were communicated to be predicted by the model with low accuracy. A post hoc Tukey test showed a significant difference between the checks for both low risk (\(p_{adj}<0.01\)) and high risk (\(p_{adj}<0.001\)). Note that the total number of trials where the target object was drawn from the set of low accuracy scenarios was significantly less than that of high accuracy as discussed in Section 2.3. Nonetheless, the total number of checks for the low-accuracy objects is clearly larger in the \"revealed\" condition. There was no significant effect of the risk (\(F=0.266,p=0.608\)) nor the interaction between the condition and risk (\(F=0.516,p=0.476\)).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Factor** & **DF** & **Sum Sq** & **Mean Sq** & \(F\)**-Value** & \(p\)**-Value** \\ \hline Risk & 1 & 16.4 & 16.4 & 0.266 & 0.608 \\ Condition & 1 & 217.3 & 217.3 & 35.373 & 2.82 \(\times\) 10\({}^{-7}\) \\ Risk-Condition & 1 & 31.7 & 31.7 & 0.516 & 0.476 \\ Residuals & 49 & 3007.7 & 61.4 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The summary table for ANOVA results for the number of checks. DF is degrees of freedom, Sum Sq contains values for the sum of squares, Mean Sq are mean squares, and \(F\)- and \(p\)-values are respective ANOVA statistics values.  
Figure 4: The number of trials where participants chose to check the model’s prediction. (**A**) The total number of checks; (**B**) the difference between the checks of the objects drawn from the low-accuracy set and the ones from the high-accuracy set. A positive value corresponds to a larger amount of checks for the objects of low accuracy. The data are visualized using bar plots and violin plots with superimposed individual data for each participant. In violin plots, the thick gray bar in the center represents the interquartile range. The thin gray line represents the rest of the distribution except the outliers. The error bars in the bar plots indicate confidence intervals of 95% computed using bootstrapping. The indicators of significant differences obtained from the ANOVA analysis are **:**: \(p<0.005\), ***: \(p<0.001\). Not significant differences are not indicated.
# Oral Health and Cognitive Decline: A Systematic Review of the Periodontitis-Alzheimer's Connection
## 4 Discussion
### Therapy: Periodontal Interventions and Cognitive Protection  
Therapeutic research highlights the potential of periodontal care to mitigate AD progression. [PERSON] et al. (2024) [220] conducted a 24-month feasibility study on individuals with mild dementia, demonstrating that personalized periodontal treatment improved oral health and stabilized cognitive decline. [PERSON] et al. (2023) [229] corroborated these findings, showing that reducing periodontal inflammation mitigated Alzheimer's-related brain atrophy and slowed cognitive deterioration.  
Several studies have highlighted early intervention as a key therapeutic strategy. [PERSON] et al. (2017) [222] noted that improved management of periodontal disease could delay the onset of dementia. [PERSON] et al. (2020) [235] demonstrated that severe periodontitis with significant tooth loss independently increased the risk of developing AD, vascular dementia, and mixed dementia. These findings suggest that maintaining periodontal health may reduce systemic inflammatory burden and protect against cognitive decline [258].  
Therapeutic mechanisms are also supported by preclinical research. [PERSON] et al. (2019) [238] explored the use of small-molecule gingipain inhibitors, which reduced bacterial load, blocked A\(\beta\) production, and mitigated neurodegeneration in animal models. This approach highlights the potential of pharmacological interventions targeting periodontal pathogens [259; 260; 261; 262].  
Nutritional and metabolic factors play a crucial role in therapy. [PERSON] et al. (2021) [236] found that pro-inflammatory diets and vitamin D deficiency exacerbated the relationship between periodontitis and cognitive dysfunction. These findings emphasize the need for dietary modifications and supplementation to support periodontal and cognitive health. [PERSON] et al. (2024) [223] highlighted the importance of metabolic pathways, suggesting that interventions targeting dysregulated metabolites in GCF could offer additional therapeutic benefits [263].  
Intervention studies also address systemic inflammation as a therapeutic target. [PERSON] et al. (2022) [232] and [PERSON] et al. (2024) [225] emphasized the role of inflammatory cytokines and infection burden in exacerbating cognitive decline. Reducing systemic inflammation through periodontal care, pharmacological agents, or lifestyle modifications may, therefore, provide protective effects against neurodegeneration.  
The integration of dental, neurological, and nutritional care is crucial for effective therapy. [PERSON] et al. (2021) [226] demonstrated that periodontal treatment had a favorable impact on brain atrophy markers in a quasi-experimental study. Similarly, [PERSON] et al. (2024) [231] emphasized the importance of addressing both localized and systemic inflammatory processes to protect brain structure and function.
## 4 Possible Methods for Computing Probability of Failure for Inspection Planning
### Kernel Density Estimation  
KDE, a nonparametric method to estimate the PDF of a random variable, is useful when the data cannot be represented using a specific distribution function [49; 50; 51; 52]. KDE has been adapted as an extrapolation approach to efficiently solve probabilistic problems [53]. Based on KDE, the PDF \(f_{X}(x)\) and CDF \(F_{X}(x)\) of a random variable (\(X\)) are expressed as [49; 54]:  
\[f_{X}(x)=\frac{1}{N_{s}b}\sum_{i=1}^{N_{s}}K\bigg{(}\frac{x-x_{i}}{b}\bigg{)} \tag{7a}\] \[F_{X}(x)=\frac{1}{N_{s}}\sum_{i=1}^{N_{s}}G\bigg{(}\frac{x-x_{i}}{b}\bigg{)} \tag{7b}\]  
where \(b\) is the bandwidth, \(x_{i}\) is a random sample, and \(K(\cdot)\) is the kernel smoothing function. \(G(x)\) is expressed as:  
\[G(x)=\int_{-\infty}^{x}K(y)dy \tag{8}\]  
The smoothness of the kernel PDF is affected by the bandwidth (\(b\)) and kernel smoothing function (\(K(\cdot)\)). A kernel smoothing function (\(K(\cdot)\)) must satisfy the following three conditions: (a) \(K(\cdot)\) is symmetric, (b) \(\int K(x)dx=1\), and (c) \(\lim\limits_{x\to\infty}K(x)=\lim\limits_{x\to\infty}K(x)=1\)Uniform, Gaussian (i.e., normal), triangular, and Epanechnikov functions can be used as kernel smoothing functions (\(K(\cdot)\)). The Gaussian-based kernel smoothing function (\(K(\cdot)\)) is the most widely used function for KDE [55]. When a Gaussian kernel function is adopted for \(K(\cdot)\), the optimal bandwidth (\(b\)) required to minimize the mean integrated square error is selected as \(b=\{4/(3N_{s})\}^{0.2}\cdot\sigma\), where \(\sigma\) is the standard deviation of the random samples [56]. The reflection method, which is the boundary correction method for KDE, can be used when bounded random samples are considered. Accordingly, the PDF \(f_{X}(x)\) (Equation (7a)) and CDF \(F_{X}(x)\) (see Equation (7b)) were modified as [54]:  
\[f_{X}(x)=\frac{1}{N_{s}b}\sum_{i=1}^{N_{s}}\left[K\bigg{(}\frac{x+x_{i}-2L_{l}}{ b}\bigg{)}+K\bigg{(}\frac{x-x_{i}}{b}\bigg{)}+K\bigg{(}\frac{x+x_{i}-2L_{u}}{b} \bigg{)}\right]\text{ for }L_{l}\ \leq\ x\ \leq\ L_{u} \tag{9a}\] \[F_{X}(x)=\ \ \frac{1}{N_{s}}\sum_{i=1}^{N_{s}}\left[G\left(\frac{x+x_{i}-2L_{ l}}{b}\right)+G\left(\frac{x-x_{i}}{b}\right)+G\left(\frac{x+x_{i}-2L_{u}}{b} \right)\right]\] \[-\frac{1}{N_{s}}\sum_{i=1}^{N_{s}}\left[G\left(\frac{x_{i}-L_{ l}}{b}\right)+G\left(\frac{L_{l}-x_{i}}{b}\right)+G\left(\frac{L_{l}+x_{i}-2L_{ u}}{b}\right)\right] \tag{9b}\]  
where \(L_{l}\) and \(L_{u}\) are the lower and upper limits of a random sample, respectively. To improve the approximation at the tail of the KDE, an adaptive KDE was investigated by [57, 58, 52, 53] among others, in which an adaptive bandwidth is used in different regions of the sample space instead of a fixed bandwidth.
## 2 Materials and Methods  
### Materials  
All purchased chemicals were used without further purification. Turpentine (\(\alpha\)-pinene \(\geq\) 92% by GC) was purchased from Junsei Chemical Co., Ltd. (Tokyo, Japan). Stannic chloride molten salt hydrates (SnCl\({}_{4}\).5H\({}_{2}\)O, 98%) and n-hexane (95%) were obtained via Samchun Pure Chemicals Co., Ltd. (Seoul, Korea).  
General Procedure for Catalytic Conversion of \(\alpha\)-pinene by Stannic Chloride Molten Salt Hydrates  
We set reaction time as 1 h and considered the effect of the reaction temperature (60, 70, 80, 90, 100, and 110 \({}^{\circ}\)C) or set reaction temperature as 70 \({}^{\circ}\)C and considered the effect of reaction time (1, 3, and 5 h) because, upon the reaction temperature crossed, the melting point of stannic chloride molten salt hydrates (SnCl\({}_{4}\).5H\({}_{2}\)O), \(\alpha\)-pinene, quickly disappeared from the reaction mixture. In a typical experiment, 10 g of turpentine and 1 g of SnCl\({}_{4}\).5H\({}_{2}\)O was added to 50-mL double-neckround flask equipped with a magnetic Teflon coated stirrer, a thermometer, and a reflux condenser. After being purged with nitrogen gas, the reactor was then loaded on a preheated aluminum heating block and stirred smoothly for 1 h. The temperature of a reaction mixture was reached at the desired temperature (\(\pm\)3 \({}^{\circ}\)C) in less than 5 min. Upon completion of the reaction, the reactor was then removed from the heating block and immediately cooled to room temperature with a water bath. After cooling, the crude reaction mixture was diluted in 100 mL of n-hexane and filtered over a elite pad to remove metal compounds. The solvent was removed under reduced pressure. The purified product was analyzed by Agilent Technologies 7890B gas chromatograph equipped with a DB-5 ms column (30 m x 250 \(\upmu\)m, 0.25 \(\upmu\)m thickness), a 5977A mass spectrometer detector, and a flame ionization detector (Agilent-Technologies, Seoul, Korea). The yield of products was calculated based on the below equation on the basis of the internal standard method (tridecane).  
\[\text{Yield of product }i\left(\%\right)\ =\frac{\text{Weight of product }i\text{ after reaction}}{\text{Initial weight of }\alpha-\text{pinene}}\times 100 \tag{1}\]
# Phase Calibrated Ring Oscillator PUF Design and Application
## 3 RO PUF Improvement and Implementation on FPGAs
### RO PUF Implementation on an FPGA
To eliminate the effect of unexpected delay, we adjust the unsatisfied wire delay by changing paths manually in the Vivado implementation user interface, which is saved in the constraints. A key metric of any PUF is its reliability. In order to evaluate the stability, we have implemented three RO PUFs on the FPGA, which are based on Figure 3. We generated 100,000 random challenges and applied the challenge twice to each PUF. The two responses for each PUF were compared, and the Hamming distance between the two responses were recorded. Ideally, there should be no difference between the two responses because the challenge was the same and the Hamming distance should be zero. Figure 8 shows the Hamming distance distribution for the three PUFs. Without any error  
Figure 6: RO PUF manual placement overview. Figure 7: RO PUF code generation. correction or fault tolerance methodology, there are about 10 bit errors in each 128-bit response. It is clear that a 7.81% bit error rate is unsatisfactory for authentication. Without very strong ECC, we will have a high false negative rate. To address the instability of PUFs, we will introduce a phase calibration process in the next section.
## 3 Materials and Methods
### SPME-GC-MS of Volatile Compounds in Orange Juice  
The volatile compounds were analyzed by a 7890A gas chromatograph with SPME and a 5973 mass selective detector (MSD) (Agilent Technologies). Samples were conducted using DB-Wax analytical fused silica capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.25 um; Agilent, Santa Clara, CA) and a DB-5 fused-silica capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.25 um, Agilent). Conditions for GC-MS analysis were as follows: the carrier gas was helium at a constant flow rate of 1 mL/min. The oven temperature was held at 50 \({}^{\circ}\)C, ramped at a rate of 10 \({}^{\circ}\)C/min to 100 \({}^{\circ}\)C for 5 min, and then ramped to 140 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min for 10 min, finally it reached 230 \({}^{\circ}\)C at a rate of 2 \({}^{\circ}\)C/min for 5 min. The desorption time was 5 min.  
The MSD was used for chemical identification. Its electron ionization energy was 70 eV. The ion source temperature was set at 230 \({}^{\circ}\)C. The compounds were identified by matching retention time of authentic standards, retention indices (RIs), and mass spectra in the NIST 11 database. The RIs of unknown compounds were determined by alkanes C4-C30.