esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Modeling and Simulating Si-SC Structures
### Modeling and Simulation of Si-SC with SLARC
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Structure**} & \multirow{2}{*}{**Type**} & \multicolumn{6}{c}{**Wavelength (nm)**} \\ \cline{3-6}  & & **350** & **450** & **550** & **650** & **750** \\ \hline \multirow{2}{*}{Si-SC (no ARC)} & measured & 68.98 & 44.18 & 37.63 & 34.93 & 33.43 \\ \cline{2-6}  & simulated & 51.98 & 45.77 & 41.50 & 38.45 & 35.86 \\ \hline \multirow{2}{*}{Si-SC with SiO\({}_{2}\) SLARC} & measured & 68.95 & 24.67 & 10.85 & 9.15 & 11.24 \\ \cline{2-6}  & simulated & 46.62 & 22.52 & 13.52 & 13.85 & 16.27 \\ \hline \multirow{2}{*}{Si-SC with Si\({}_{3}\)N\({}_{4}\) SLARC} & measured & 60.20 & 39.76 & 16.77 & 3.64 & 0.16 \\ \cline{2-6}  & simulated & 47.54 & 41.61 & 16.98 & 2.58 & 0.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measured and simulated reflectivity of Si-SC structures with and without SLARC of SiO\({}_{2}\) (90 nm) and Si\({}_{3}\)N\({}_{4}\) (90 nm). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{} & \multicolumn{3}{c}{**IQE (\%)**} & \multicolumn{3}{c}{**EQE (\%)**} \\ \hline
**Structure** & **350** & **550** & **750** & **350** & **550** & **750** \\ \hline Si-SC (no ARC) & 90.54 & 96.71 & 99.36 & 43.46 & 56.57 & 63.49 \\ \hline Si-SC with SiO\({}_{2}\) SLARC & 95.93 & 96.69 & 99.47 & 46.65 & 82.19 & 87.13 \\ \hline Si-SC with Si\({}_{3}\)N\({}_{4}\) SLARC & 90.55 & 96.60 & 99.25 & 41.81 & 93.14 & 94.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: IQE and EQE of Si-SC structures with and without SLARCs of SiO\({}_{2}\) (101 nm) and Si\({}_{3}\)N\({}_{4}\) (73 nm), at selected wavelengths: 350, 550, and 750 nm.
# Use Electroencephalogram Entropy as an Indicator to Detect Stress-Induced Sleep Alteration
## 4 Discussion  
### Footshock Reduces REM Sleep Duration  
The present study demonstrated that an acute stressor not only altered the duration of sleep stages, but also affected the EEG features during the subsequent sleep-wake states.  
Figure 5: Comparison of the phase entropy after the footshock stimuli. (**A**) Abrupt and edge waveforms result in a low entropy whereas smooth sine-wave-like waveforms cause uncertainty of occurrence rate and results in a high entropy. (**B**) Distribution of phase entropy obtained from NREM sleep. Solid line and light gray shadow are the values of means \(\pm\) SEMs for every 15 min of light period obtained from the control group. Dashed line and dark gray shadow are results acquired from the footshock group. (**C**) Statistical significant test for the phase entropy. White bar is the value of mean \(\pm\) SEM for 15-min bins across the total of 12-h light period obtained from the control group and gray bar represents the phase entropy acquired from the footshock group. (**D**,**E**) represented the data of REM sleep. (**F**,**G**) demonstrated the results for wakefulness. *** Indicates the difference reaches statistical significance of \(p<\) 0.01.  
We showed that the random and inescapable footshock stimuli reduced NREM sleep in the first hour and it changed the quantity of distinct vigilance states during a quarter hour; that is, REM sleep was decreased and wakefulness was increased. Further investigating the values, we found that the increased wakefulness mostly contributed to the changes in the composition of sleep-wake states. The wakefulness duration increased 0.84 min and REM sleep duration decreased approximately 0.5 min in a unit of 15 min. This finding is similar to other report which used the footh shock to create a rodent model of stress-induced sleep disorder [29]. We postulated that our protocol employing the footh shock stimuli partially simulates the stress-induced sleep problems in humans, such as acute stress disorder (ASD) or post-traumatic stress disorder (PTSD). Hyper-arousal is one of the main symptoms of ASD and PTSD [14]. Although most of the clinical studies report a reduction in NREM sleep in patients with PTSD, the alterations in REM sleep are still inconclusive [30]. A meta-analysis report states that the percentage of REM sleep decreases in PTSD patients with age below 30-year-old and affects less in patients older than 30 [30]. Even though the inescapable footshock is not specific as the PTSD model in rodents [3], our data suggest the hypothesis of decreased REM sleep in PTSD patients.
## 2 Materials and Methods
### Machine Learning Methods
#### 2.3.1 K-Nearest Neighbor (KNN)  
The KNN algorithm is a common classification method in data mining and statistics. Given its simplicity and outstanding classification performance [43], this model has drawn wide attention from various fields, such as in hydrological modeling [44] and remote-sensing image classification [45]. KNN is nonparametric because it does not assume data distribution [46; 47], making the model easy to build in our study. In KNN, the Euclidean distance between the test sample and all the training samples is frequently used to obtain the nearest neighbors of the test data. In accordance with the distance calculation result, the labels of the test samples can be assigned using the majority rule on the basis of the labels of the selected nearest neighbors. K = 5 is proven to be the optimal value in the current study for all the considered models.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Scenario** & \multicolumn{3}{c}{**Input Data**} & & & \\ \hline  & **R\({}_{\text{n}}\)** & **T\({}_{\text{mean}}\)** & **W\({}_{\text{s}}\)** & **RH** & **LAI** & **S\({}_{\text{m}}\)** \\ \hline Scenario 1 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Scenario 2 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & \(\surd\) \\ Scenario 3 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & \\ Scenario 4 & \(\surd\) & \(\surd\) & \(\surd\) & & & \\ Scenario 5 & \(\surd\) & & \(\surd\) & \(\surd\) & & \\ Scenario 6 & & \(\surd\) & \(\surd\) & \(\surd\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The input scenarios of variables for different machine learning models.  
Figure 2: Flowchart of the data processing and model building in this study.
## 2 Biological Signal Extraction and Processing  
### EMG and EEG Signal Abstraction  
Measurement of EMG signals is derived from the biceps and triceps of the right and left arms. The output of the muscle may vary from user to user, and the output may degrade and change depending on the activity of the muscle and the muscle fatigue. When muscle conduction occurs, neurons are activated in the brain, and the movement of each electron causes vibrations with a specific individual frequency, which is shown through the EEG. In this study, we gather the activity of neurons according to the activation of these brains and muscles to determine if there is any basis for generating specific motion signals. The channels of EEG are measured from the center of the brain with a patch attached, as shown in Figure 2a. The EMG is measured by attaching two electrodes to each of the two channels, the biceps and the triceps, as shown in Figure 2b. In the case of an EMG signal, the type depends on the subject's state, so the definition and conditions of muscle activity in the experimental environment design must be maintained accurately.  
Figure 2: Measurement points for EEG (electroencephalogram) and EMG. (**a**) EEG pole measurement point; (**b**) EMG pole measurement point.
## 6 Comparative Results and Analysis
### Contours of Flow-Field under Alternating Stall  
#### 6.3.1 Pressure Coefficient \(C_{p}\)  
Based on the alternating stall pattern and the differences among different impellers, the flow-field was also compared by plotting relevance parameters in contours. Firstly, the pressure distributions in the impellers were important. The dimensionless pressure coefficient, \(C_{p}\), was used:  
\[C_{p}=\frac{p-p_{\infty}}{pgH}-\frac{V_{\infty}^{2}}{2 gH} \tag{15}\]  
where \(p\) is pressure, \(p_{\infty}\) is the reference pressure on the impeller inlet boundary, and \(V_{\infty}\) is the reference velocity on the impeller inlet boundary.  
Figure 11 shows the contour of the pressure coefficient, \(C_{p}\), on the spanwise 50% surface. Caused by the backflow region near leading-edge, the wide low-pressure region occurred at the inlet of B passages in the blunt and sharp impellers. The pressure value near the leading-edge in the A passages was at a relative high level, because all of the fluid smoothly flowed into A passages. Differently, the low-pressure region near the leading-edge is much narrower in the ellipse and round impellers. However, local pressure-drop can be found on the suction side of every blade's leading-edge. It means that leading-edge flow separation also happened in the A passages in the ellipse and round impellers. This is why backflow \"vortex\" also exists in the A passages.  
Figure 11: Contour of the pressure coefficient, \(C_{p}\): (**a**) blunt, (**b**) sharp, (**c**) round, and (**d**) ellipse.
# Impact of Atmospheric Pressure Microwave Plasma Treatment on Quality of Selected Spices
## 3 Results and Discussion
### Microorganism Growth  
Plasma activity on black pepper seeds for 15 s caused a reduction of the initial _Aspergillus niger_ number by about two logarithmic cycles compared to the control (Figure 6A). The prolongation of the exposure time over 45 s in all spices (black pepper seeds, juniper and allspices berries) resulted in complete elimination of _A. niger_ mold number (Figure 6B,C). In the case of the Gram-positive _Bacillus subtilis_ bacteria, there was no large decrease in the number of microorganisms in each time variant. Probably, unfavorable conditions could have affected the formation of the sporulating form of _B. subtilis_, which allowed the bacteria to survive extremely unfavorable conditions.  
Figure 6: The results of microbiological analysis of untreated and plasma-treated inoculated (**A**) black pepper seeds, (**B**) juniper berries and (**C**) allspices berries. The same capital or small letters indicate homogenous groups (\(\alpha\) = 0.05) for bacteria and molds growth results, respectively. \(n\) = 3.  
Data provided by [PERSON] et al. [38] showed that the use of plasma resulted in a reduction in the number of _Escherichia coli_ microorganisms relative to a control by approximately 4.4 logs for 30 min. Operation of microwave plasma for microorganisms is likely to cause digestion of the wall and membrane assembly of microorganisms because of both the large amount of highly reactive molecules in the plasma and the high temperature. As a consequence, this affects leakage into the extracellular environment of cell organelles. In addition, the formation of volatile compounds may have an impact on these phenomena, as a result of a slow combustion process using oxygen atoms or plasma radicals [39]. The work presented by [PERSON] et al. [40] showed that the number of _E. coli_ bacteria in lettuce was reduced by 3.6 log cfu/cm\({}^{2}\) by treatment with an atmospheric stream of cold plasma. Treatment of red chicory leaves contaminated with _L. monocytogenes_ with a cold plasma stream (air, 15 kV, 30 min) reduced the number of microorganisms by approximately 2.2 log cfu/cm\({}^{2}\)[41]. In a similar experiment [PERSON] et al. [42] showed that plasma duration (60 s, air, 70 kV) reduced the number of _Salmonella typhimurium_ on tomatoes from 6.3 log cfu/sample to undetectable levels. In studies presented by [PERSON] et al. [29], the action of plasma (30 min) reduced the number of _Bacillus subtilis_ and _Bacillus atrophaeus_ spores by 2.4 and 2.8 logs, respectively. In addition, [PERSON] et al. [43] showed a reduction in colony-forming units for _p.fluorescens_ of 4 log steps, and a reduction of 3 log steps in _Listeria monocytogenes_ suspension cultures. In summary, the action of plasma inhibits the growth of various groups of microorganisms. In the current research, the plasma treatment led to the increase of temperature as presented in Figure 5. This elevation of temperature, in addition to other previously presented reasons, contributed to the microorganisms' population decrease.
## 4 Results and Discussion
### Model Assessment  
Next, this paper comprehensive compared the application of the optimal structure of the five proposed machine learning algorithms in advance rate prediction to determine the most effective algorithm. The evaluation method still adopted the indicators mentioned in Section 3.4. The values of the performance indices for the proposed MLR, KNN, SVR, CART, and BPNN models were calculated, as presented in Figure 9 and Table 5. The results were divided into a training set and a test set, since the quality of a model was judged by its effect on the test set to a greater extent. Only the data obtained from the test set was used in Figure 9.  
Figure 8: BPNN model training process of Strategy A. (**a**) the \(MSE\) values in total process. (**b**) the \(MSE\) values in 50–500 epochs.  
Figure 9: Evaluation result: (**a**) five models’ \(MAE\) and \(RMSE\) values in Strategy A; (**b**) five models’ \(MAE\) and \(RMSE\) values in Strategy B; (**c**) five models’ \(MAE\) and \(RMSE\) values in Strategy C; (**d**) five models’ \(MAE\) and \(RMSE\) values in Strategy D; (**e**) five models’ \(MAE\) and \(RMSE\) values in Strategy E; (**f**) BPNN models’ \(R^{2}\) values in each strategy.  
It can be seen from the Figure 9a that the errors of regression prediction implemented by different algorithms were different. Both _MAE_ and _RMSE_ showed significant advantages of BPNN. Figure 9b-e shows Strategy B, Strategy C, Strategy D, and Strategy E performance of each model, we can also conclude that BPNN had achieved the smallest error fitting prediction. For the other four models, there were also slight differences for different strategies. The second-best prediction model for Strategy B, C, and D was the KNN algorithm, while that for Strategy A and E was CART and SVR respectively. It can be seen that each algorithm would have its own field and can achieve good results. At the same time, it can be also seen that--among the five strategies--the model error obtained by MLR was the largest, which also showed that the advance rate of the shield machine was not a simple linear relationship with the shield parameters and geological parameters, but a more complex nonlinear relationship, so it was difficult to achieve an ideal fitting effect by using linear regression.
# Numerical Simulation of Interaction between Plasma and Azithromycin Based on Molecular Dynamics
## 5 Conclusions  
In the present study, a molecular dynamics simulation and reactive forcefields were used to investigate the mechanisms of nonthermal plasma particle degradation of azithromycin molecules. The simulation results indicated that the C-H and C-C bonds in the azithromycin molecules were firstly broken by the active groups in the nonthermal plasma, after which additional C=C and C=O bonds were formed. As a result, the azithromycin molecules were decomposed into mainly H\({}_{2}\)O, CO\({}_{2}\), and other organic small molecules.  
The ability of degrading azithromycin molecule for different types of active particles, namely, oxygen atom (O), ozone (O3), and hydroxyl radical (-OH), were also investigated in the present study. It was found that the oxygen atoms had the strongest ability to decompose the azithromycin molecule, with 38.61% of the C-H bonds broken, as compared with other oxygenated species. The findings from this computational simulation could provide theoretical support and guidance for subsequent practical experiments.  
It was discovered during the study that the position where plasma particles interact with azithromycin could affect the chemical activities. Therefore, the effects of the reaction position of the azithromycin molecule should be investigated in further studies. In addition, the effects of other air-plasma species, such as active nitrogenous particles, on the degradation of antibiotics should also be studied.  
Validation, M.Z.; Formal analysis, Y.W.; Resources, S.H.; Writing--original draft, Y.Z.; Writing--review & editing, S.Q. and J.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Neuroimaging of Pediatric Cerebellum in Inherited Neurodegenerative Diseases
## 1 Introduction
### Ataxias with Oculomotor Apraxia (AOAs)  
The AOAs include AOA1 (early-onset cerebellar ataxia with oculomotor apraxia type 1, ocular motor apraxia, peripheral neuropathy, and hypoalbuminemia), which is most frequent in Portugal and Japan, and AOA2 (ataxia with ocular apraxia type 2, ataxia with ocular apraxia and increased alpha-fetoprotein levels) [41]. The onset of AOAs is mostly in childhood and early teens. AOA1 and AOA2 are associated with mutations in the aprataxin gene and the senataxin gene, respectively. In AOA1, cerebellar ataxia with atrophy of the cerebellum on MRI and sensorimotor axonal neuropathy is present in all patients, whereas oculomotor apraxia occurs in about 85% of cases [42]. A typically choreic movement disorder, which is evident in the early stages and declines over time, is present in 50% of cases.
## 2 Perforation Experiment
### Experiment of Penetrating Steel Target  
The experimental apparatus is depicted in Figure 1, comprising primarily of shaped charge, metal sheet, blast tube, and steel target. Among them, the primary structure of the shaped charge (with a body diameter of 5.2 cm) comprises a shell case, explosive, and powder liner. A metal sheet with a thickness of 0.25 cm is utilized to simulate the wall of the perforating gun (the blind hole region), while a blast height cylinder is employed to regulate the distance (blast height) between the shaped charge and the steel target. In this experiment, the blast height remains fixed at 7 cm. The steel target consists of steel 45 and takes on a cylindrical shape with dimensions measuring 10 cm in diameter and 50 cm in height.  
At the onset of the experiment, detonation of the explosive is initiated through a detonating cord. After the explosion, rapid oxidation of the powder liner within the shaped charge occurs under high temperature and pressure generated by the explosive detonation, resulting in the formation of a metal jet possessing substantial impact force. Once formed, this jet effectively perforates both a metal sheet and subsequently penetrates through the  
Figure 1: Experimental device of penetrating steel target: (**a**) schematic diagram; (**b**) physical drawings.  
steel target; ultimately, a perforating channel with a certain depth and aperture is formed on said target.  
Based on statistical principles and economic considerations, we repeated seven sets of targeted charge shooting experiments with the above devices. During the implementation of the experiment, we strictly controlled the experimental environment (such as temperature, humidity, etc.) and ensured that the materials (shaped charge, sheet metal, and steel target) came from the same batch to reduce the variability of the experimental results. Key parameters (such as blast height, powder liner, sheet metal thickness, etc.) were consistent in all experiments to ensure standardization of the experimental process. In addition, we calibrated the detonating equipment to ensure the accuracy of the measurements. Upon completion of each experiment, a metal probe was inserted into the perforation channel to measure the maximum depth reached, referred to as the perforation depth. Additionally, the diameter of the steel target opening was measured and recorded as the perforation aperture. The experimental results are presented in Figure 2. The perforation depth and aperture of the seven groups exhibited relatively stable characteristics. The depth ranged from 28.90 cm to 31.10 cm, averaging 30.04 cm and displaying a standard deviation of 0.80 cm. The size of the apertures varied between 1.05 cm and 1.20 cm, with an average value of 1.13 cm and a standard deviation of 0.05 cm. The statistical results indicate a low degree of data dispersion, which can provide reliable support for subsequent numerical simulations of perforation and the inversion of constitutive parameters for the powder liner.
## 3 Theoretical Analysis and Simulation Model of Temperature Error
### Overall Model  
The temperature error model in near space includes the measured temperature values of the temperature sensors and the Joule heat, radiant heat, and aerodynamic heat error values caused by external disturbances. To obtain the real temperature value of the atmosphere, it is necessary to eliminate the temperature error value caused by disturbance factors from the measured temperature value of the temperature sensor. Therefore, we constructed a temperature error model that can express the calculated value of the temperature sensor as  
\[T_{0}=\frac{T_{1}+T_{2}}{2} \tag{14}\]  
where \(T_{1}\) and \(T_{2}\) are the measured data of two temperature sensors.  
According to the definition, the near space temperature \(T\) is calculated as shown in Equations (15) and (16):  
\[T=T_{0}-\frac{Q_{A}-Q_{B}-Q_{1}-Q_{2}}{cm}-T_{c} \tag{15}\]  
\[T=\frac{T_{1}+T_{2}}{2}-\frac{\frac{\upsilon^{2}\iota_{\textbf{k}}}{R}+A_{3} \big{\{}\sigma\varepsilon(T_{m}^{4}-T_{n}^{4})+I_{s}(1-\varepsilon)\big{\}}-A_ {1}\lambda_{1}\Delta T_{1}-A_{2}\lambda_{2}\Delta T_{2}}{cm}-0.5\times\rho \times V^{3}\times A_{s}c_{P} \tag{16}\]
## 2 Materials and Methods
### OLR, Removal Efficiencies, and BioH\({}_{2}\) Yield  
The organic loading rate (OLR), expressed as TS, VS, or COD, was calculated by Equation (4):  
\[OLR_{i,in}=\frac{c_{i,influent}}{V_{r}}\times F_{influent} \tag{4}\]  
where \(OLR_{i,in}\) is the OLR of the parameter \(i\) (TS, VS, or COD) applied to the bioreactor (g TS.L\({}^{-1}\).d\({}^{-1}\) or g VS.L\({}^{-1}\).d\({}^{-1}\) or g O\({}_{2}\).L\({}^{-1}\).d\({}^{-1}\)), \(c_{i,influent}\) is the concentration of the parameter \(i\) (TS, VS, or COD) (g TS.L\({}^{-1}\), g VS.L\({}^{-1}\), or g O\({}_{2}\).L\({}^{-1}\)) in the influent, \(F_{influent}\) is the daily volume of the influent (L.d\({}^{-1}\)), and \(V_{r}\) is the bioreactor working volume (L).  
The removal efficiencies of organic matter in the bioreactor, expressed as TS, VS, tCOD, sCOD, N, and P, were calculated according to Equation (5):  
\[\eta_{i,removal}=\frac{c_{i,influent}-c_{i,effluent}}{c_{i,influent}}\times 100 \tag{5}\]  
where \(\eta_{i,removal}\) is the removal efficiencies of the parameter \(i\) (TS, VS, COD, N, or P) (%), \(c_{i,influent}\) is the concentration of the parameter \(i\) (TS, VS, COD, N, or P) (g TS.L\({}^{-1}\), g VS.L\({}^{-1}\), g O\({}_{2}\).L\({}^{-1}\), mg N.L\({}^{-1}\), or mg P.L\({}^{-1}\)) in the influent, and \(c_{i,effluent}\) is the concentration of parameter \(i\) (TS, VS, COD, N, or P) (g TS.L\({}^{-1}\), g VS.L\({}^{-1}\), g O\({}_{2}\).L\({}^{-1}\), mg N.L\({}^{-1}\), or mg P.L\({}^{-1}\)) in the effluent of the bioreactor. The 100 factor allows expressing the removal efficiency as a percentage.  
The BioH\({}_{2}\) yield was calculated through Equation (6):  
\[\eta_{bioH_{2}}=\frac{F_{bioH_{2}}}{c_{VS,influent}\times F_{influent}}\times 1000 \tag{6}\]  
where \(\eta_{bioH_{2}}\) is the \(bioH_{2}\) yield expressed as a function of the OLR for VS in the influent (L \(H_{2}\).kg\({}^{-1}\) VS\({}_{\text{influent}}\)), \(F_{bioH_{2}}\) is the bioH\({}_{2}\) production rate (L \(H_{2}\).d\({}^{-1}\)), \(c_{VS,influent}\) is the VSconcentration in the influent (g VS.L\({}^{-1}\)), and \(F_{influent}\) is the daily volume of the influent (L.d\({}^{-1}\)). The 1000 factor allows the conversion of g into kg VS.