esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Verification
### Impact of Grid Impedance  
Figure 18 shows the simulation results of the VSC-HVDC system with a virtual impedance control strategy under a different grid impedance. The voltage control parameters are \(k_{pole}=15\), \(k_{ioxc}=100\), \(k_{i}=10\), and the transmission power is set as \(-500\) MW. At 2 s, the grid impedance is switched from 1 mH to 5 mH. The blue line in Figure 18 indicates the simulation results when the virtual impedance parameter is \(L_{eq}=0.02\)\(\Omega\). The DC voltage in the blue line begins to oscillate, and the system loses stability. The red line in Figure 18 indicates the simulation results when virtual impedance parameters are \(L_{eq}=0.02\)\(\Omega\) and \(R_{eq}=2\)\(\Omega\). The DC voltage in the red line stays stable, and the system remains stable. The simulation results are consistent with the theoretical analysis in Section 4.6.  
Figure 17: DC-link voltage and active power of the VSC–HVDC system under the proposed control strategy and traditional virtual impedance.  
Figure 18: DC-link voltage and active power of the VSC–HVDC system under different grid impedance.
## Abstract  
In recent years, medical data have vastly increased due to the continuous generation of digital data. The different forms of medical data, such as reports, textual, numerical, monitoring, and laboratory data generate the so-called medical big data. This paper aims to find the best algorithm which predicts new medical data with high accuracy, since good prediction accuracy is essential in medical fields. To achieve the study's goal, the best accuracy algorithm and least processing time algorithm are defined through an experiment and comparison of seven different algorithms, including Naive bayes, linear model, regression, decision tree, random forest, gradient boosted tree, and J48. The conducted experiments have allowed the prediction of new medical big data that reach the algorithm with the best accuracy and processing time. Here, we find that the best accuracy classification algorithm is the random forest with accuracy values of 97.58%, 83.59%, and 90% for heart disease, M-health, and diabetes datasets, respectively. The Naive bayes has the lowest processing time with values of 0.078, 7.683, and 22.374 s for heart disease, M-health, and diabetes datasets, respectively. In addition, the best result of the experiment is obtained by the combination of the CFS feature selection algorithm with the Random Forest classification algorithm. The results of applying RF with the combination of CFS on the heart disease dataset are as follows: Accuracy of 90%, precision of 83.3%, sensitivity of 100, and consuming time of 3 s. Moreover, the results of applying this combination on the M-health dataset are as follows: Accuracy of 83.59%, precision of 74.3%, sensitivity of 93.1, and consuming time of 13.481 s. Furthermore, the results on the diabetes dataset are as follows: Accuracy of 97.58%, precision of 86.39%, sensitivity of 97.14, and consuming time of 56.508 s.  
m 2022 2021 2022 2022 2021 2022 2021 2022 2021 2022 2021 2021 2022 2021 2021 2022 2  
more frequently used, overcome these challenges by extracting knowledge and reaching new patterns, rather than simply accessing information [6]. In the medical field, the choice of an effective machine learning algorithm is a critical issue, since each algorithm has an impact on the accuracy of the result. No one algorithm works best for all issues, due to the fact that each algorithm has its own characteristics. The most commonly used machine learning algorithms in mechanical engineering can be separated into the following classes: Regression, estimation, classification, and clustering. Specifically, the regression or classification algorithms operate for a significant prediction [7]. The application of big data analytics in healthcare is important for several reasons [8]:  
* Continuous detection of the patient's health and state. In the case of an unusual event, an alarm is sent to the patient's doctor for an early intervention.
* Early detection of disease.
* Prediction of new disease.  
Different formats of big data are shown in Figure 1. Big data is composed of four diverse data formats, including structured, semi-structured, quasi-structured, and unstructured data formats.
## 3 Marine Environment Monitoring Network Model
### UWSN Energy Consumption Model  
Since the rate v of the underwater acoustic channel is 1500 m/s [22; 23; 24], the transmission delay, T(K), of K-bit packets under v is as follows:  
\[\text{T}(K)=\frac{K}{v} \tag{10}\]  
According to the underwater channel attenuation formula in Section 3.1 and the distance formula in Section 3.2, the energy consumption of sending K-bit data from node j far away from node i, d m can be expressed as follows:  
\[\text{E}_{i,j}^{\text{sent}}\left(\text{k},\text{d}\right)=\text{P}\times T(K )\times\text{ A}(\text{ d},\text{f}) \tag{11}\]where, P is the received power of the node if the ordinary nodes that are active can be represented as ON. Then, the total amount of data received via CH node is as follows:  
\[K_{\text{total}}^{\text{rec}}=K_{CH}+\sum_{j=1}^{K}K_{ON_{i,j}} \tag{12}\]  
where \(K_{ON_{i,j}}\) represents the data packet received via the ON node, and \(K_{CH}\) represents the amount of data that the CH node itself can perceive. If the transducer of the CH node is set to \(R_{x}\) mode, then the energy consumption of the CH node when receiving data is the same; therefore, the energy consumption of node CH to receive K-bit data can be expressed as follows:  
\[E_{\text{rec}}=K_{\text{total}}^{\text{rec}}e_{\text{rec}} \tag{13}\]  
where \(e_{rec}\) represents the energy consumption of receiving data per unit bit. Similarly, the energy consumed via data fusion when node i acts as CH is as follows:  
\[E_{\text{integ}}=K_{\text{total}}^{\text{rec}}e_{\text{integ}} \tag{14}\]  
where \(e_{integ}\) indicates the energy consumption per bit of data fusion.
# Influence of Acoustic Streams on the Efficiency of Ultrasonic Particle Agglomeration
## 3 Results
### Determining the Optimum Gas Flow Rate
Figure 8: Dependences of particle capture efficiency on gas flow rate (**A**) and the dependence of the particle size at the outlet of the agglomeration chamber on the flow rate (**B**): 1—flat radiator; 2—step radiator; 3—without ultrasonic influence. With a further increase in gas flow (above 6.2 m\({}^{3}\)/h), in the presence of ultrasonic influence, the capture efficiency begins to decrease. This is due to the fact that reducing the size of the formed agglomerates (due to a further reduction in the residence time of particles in the agglomeration chamber) becomes a determining factor affecting efficiency. When the flow rate exceeds 8-10 m\({}^{3}\)/h, the decrease in efficiency increases. This can be explained by the additional influence of the cyclone itself, the effectiveness of which is reduced due to increased turbulization of the flow, which leads to mixing of the flow in the cyclone, reducing stratification  
This indicates that the efficiency of particle agglomeration has a greater influence on the capture efficiency than establishing the optimum gas flow rate. Thus, the efficiency of particle agglomeration under the impact of ultrasonic vibrations depends on the gas flow rate (on duration of ultrasonic exposure). It is obvious that the longer the period of ultrasonic exposure, the higher the efficiency of agglomeration. In addition, analysis of the relationships presented in Figure 8B showed that there is a nonlinearity in agglomeration efficiency versus flow rate. In the case of using a stepped radiator, nonlinearity is associated with an increase in the probability of collision of large particles held at the nodes of a standing wave with newly arrived particles. An increase in flow speed leads to the separation of large particles from the nodal areas and their entrainment into the cyclone. When using a flat radiator, nonlinearity is associated both with the factor described above and with a decrease in the intensification of agglomeration due to acoustic flows. In this case, the movement of particles between nodal regions by acoustic flows is reduced. As a result, with increasing gas flow, the agglomeration efficiency curves for both types of radiators approach each other. Further studies were conducted with an optimum flow rate of 6.2 m\({}^{3}\)/h.
## 4 Classification of Routing Protocols
### QoS-Aware Routing Protocols
#### 4.1.3 New QoS and Geographic Routing (LOCALMOR)  
A distributed QoS aware module-based protocol is proposed in [25], to help the system to meet different QoS requirements based on the nature of data (energy efficiency, reliability and latency). The proposed mechanism divides the patient's data into: Regular Traffic, Reliability-Sensitive Traffic,Delay-Sensitive Traffic, and Critical Traffic. The coordinator, which they called a body sensor more collects the raw data from the bio-medical sensor nodes and after required data processing, and data aggregation, sends it to the sink node(s). Each (fixed) sink node may cover more than one patient (fixed and/or mobile). The proposed protocol has two kinds of sink nodes for every patient: Primary Sink and Secondary Sink and each sink receive a separate copy of each message.  
In their scheme, they use four different modules: power efficiency module, reliability-sensitive module, delay-sensitive module, and neighbor manager module. The power efficiency module is responsible for the regular traffic data packets and may be used by other modules to optimize the data-related metrics. Power efficiency can be achieved by considering both the transmission power and residual energy using Min-Max Approach discussed in [34]. The reliability drawn data packets use the reliability-sensitive module to achieve the required reliability by sending a copy of each data packet to both the primary and secondary sinks. The delay-sensitive module is used to route the latency sensitive data packets by using Pocket Velocity Approach given in [35]. The neighbor manager module is responsible to send/receive the Hello packets and update neighbors' information. The system architecture of the proposed protocol is shown in Figure 4.
# Design of Zoom Optical System from Visible to NIR-II for Vivo Fluorescence Imaging Device
## 2 Design Strategy
### Elimination of Wide Band Chromatic Aberration
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Material** & \multicolumn{2}{c}{**Refractive Index**} & \multicolumn{4}{c}{**Chromatic Dispersion**} \\ \hline  & n\({}_{0.55\,\mathrm{\SIUnitSymbolMicro m}}\) & n\({}_{0.85\,\mathrm{\SIUnitSymbolMicro m}}\) & n\({}_{1.35\,\mathrm{\SIUnitSymbolMicro m}}\) & C\({}_{0.4-0.7\,\mathrm{\SIUnitSymbolMicro m}}\) & C\({}_{0.7-1.0\,\mathrm{\SIUnitSymbolMicro m}}\) & C\({}_{1.0-1.7\,\mathrm{\SIUnitSymbolMicro m}}\) & C\({}_{0.4-1.7\,\mathrm{\SIUnitSymbolMicro m}}\) \\ N-BK7 & 1.51852 & 1.50986 & 1.50322 & 3.42\% & 1.08\% & 1.70\% & 3.00\% \\ N-PK52A & 1.49830 & 1.49184 & 1.48750 & 2.70\% & 0.81\% & 1.05\% & 2.20\% \\ N-SF1 & 1.72308 & 1.69889 & 1.68900 & 7.70\% & 1.85\% & 2.50\% & 12.30\% \\ N-LAF2 & 1.74753 & 1.73074 & 1.72146 & 5.05\% & 1.32\% & 1.32\% & 3.57\% \\ N-LAF34 & 1.77582 & 1.75965 & 1.74921 & 14.50\% & 1.28\% & 1.63\% & 3.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of five optical glasses in 0.4–1.7 \(\mathrm{\SIUnitSymbolMicro m}\). materials of the weaker inter-band dispersion ability or material combinations to ensure that the chromatic aberration introduced by the zoom group is as small as possible.
## 4 Excavation Design Based on SSR and Stress Unloading Effect
### Excavation Procedure Design Based on the SSR  
Based on SRR, the coupling effect of in situ stress, surrounding rock structure and rock strength is fully considered. On the basis of the traditional design, the selection trend and value range of the excavation procedure and design parameters can be suggested qualitatively and quantitatively. (1) Excavation procedure selection and H/S of top arch zone. Figure 20 presents the excavation procedure distribution and the value suggestion of the key parameter. For excavation procedures, the MDBS and MDBS(P) methods are distributed in various ranges of SSR, mainly in the range of SSR \(<\) 7.0. The BSMP method is mainly focused on medium \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification of** & **Maximum Principal Stress** & **Strength–Stress Ratio** \\
**In Situ Stress** & \(\sigma_{\text{max}}\) (MPa) & **(SSR)** \\ \hline Extremely high & \(\sigma_{\text{max}}\geq\) 40 & \(<\)2 \\ high & 20 \(\leq\sigma_{\text{max}}\) \(<\) 40 & 2–4 \\ Medium & 10 \(\leq\sigma_{\text{max}}\) \(<\) 20 & 4-7 \\ Low & \(\sigma_{\text{max}}\) \(<\) 10 & \(>\)7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: In situ stress classification of rock mass. strength-stress ratio (4.0 < SSR < 7.0), as shown in Figure 20a. Thus, the methods of MDBS and MDBS(P) are more suitable under the low strength-stress ratio. Figure 20b shows the relationship between the excavation height (H), excavation span (S), H/S and SSR. With the increasement of SSR, the excavation height (H) and span (S) show a certain trend of slight reduction while H/S is relatively dispersive around 0.38. (2) Protective cover thickness for the rock anchor beam zone. Figures 21 and 22 show the excavation procedure distribution trend in the rock anchor beam zone and the value suggestion of the protective cover thickness. For excavation procedures in Figure 21, the MEP and MEP(P) methods are basically applicable in the entire range of SSR. To control the vibration effect, the MEP(P) method becomes a new trend. In Figure 22, SSR is divided into three intervals. The protective cover thickness is between 1.0 m and 6.0 m entirely. In addition, when SSR is 2-4, the protective cover thickness is concentrated around 3.5-6.0 m. When SSR is high (>7.0), the protective cover thickness is about 1.0-5.0 m.  
(3) Stratification height and protective cover thickness at the high side wall zone. Powerhouses are excavated in layers in most projects, as listed in Figure 1. The stratification height determines the rock volume of each excavation, which means the charge quantity and free surface of each excavation. In general, as the stratification height increases, the vibration, deformation and damage caused by blasting excavation increases as pointed in many research reports of field monitoring and numerical simulation. Therefore, stratification height is vital for excavation design and compared through statistical analysis here.
###### Abstract  
The Korean Peninsula, characterized by low-to-moderate seismicity, faces a shortage of strong ground motion records, posing challenges for the seismic safety assessment of critical infrastructures. Given the rarity of large-magnitude earthquakes, generating a variety of earthquakes with rational values of Peak Ground Acceleration (PGA) is essential for robust seismic fragility and risk analysis. To address this, a new stochastic approach is proposed to simulate artificial earthquakes at multiple source-to-site distances and derive the probability distribution of PGA based on recorded data from a single seismic event. Two key source parameters, seismic moment and corner frequency, are treated as random variables with a negative correlation, reflecting their uncertainties and dependence on source-to-site distance. The Monte Carlo simulation with copula sampling of the key source parameters generates Fourier spectra for artificial earthquakes, which are transformed into the time domain to yield PGA distributions at various distances. A comparison with recorded data shows that the proposed method effectively simulates ground motion intensities, with no statistically significant differences between the simulated results and recorded data (\(p>0.05\)). The present method of determining PGA distributions provides a robust framework to enhance seismic risk analysis for the safety assessment of structures.  
stochastic approach; peak ground acceleration; monte carlo simulation; copula sampling; seismic safety assessment; artificial earthquake generation; response spectrum +
Footnote †: journal: Applied sciences  
1
Footnote 1: [[https://doi.org/10.3390/appl42110031](https://doi.org/10.3390/appl42110031)]([https://doi.org/10.3390/appl42110031](https://doi.org/10.3390/appl42110031))
## 2 Experiments
### ISO 230 Test with Proportional Error  
To check whether the proportional error can be found from the ISO 230-2 and -6 tests, we performed ISO 230-2 and -6 tests for \(y\)-axis and \(xyz\) diagonal line. The testing results before we gave a proportional error are shown in Figure 6a. In Figure 6, the vertical axis represents the deviation value in unit of mm and the horizontal axis represents measurement length in unit of mm. Slop of \(\delta y\) is about \(-0.003\%\) after curve fitting. After we gave a proportional error of \(100.01\%\) for \(y\)-axis, slop of \(\delta y\) is increasing to \(+0.007\%\), as shown in Figure 6b. To compare Figure 6a,b we can see that the amount of the slop deviation exactly equals to the proportional error that is we giving, and the shape of \(\delta xyz\) is also changed. The testing results also show the proportional error could be compensated by multiplying a constant.  
Figure 6: Experiment results for \(x\)-axis and \(xyz\) diagonal line test. (**a**) Original test result; (**b**) test result after proportional error given.
# Correcting Ski Resort Trajectories Extracted from Video
## 5 Future Work  
In the future, we plan to integrate artificial intelligence (AI) into the trajectory correction process. Specifically, employing neural networks to analyze skier trajectories offers several advantages over traditional methods, including enhanced accuracy, adaptability to diverse environmental conditions, and the capability for real-time data processing.  
Data Requirements and Preprocessing: The implementation of neural networks would require large-scale, high-quality datasets consisting of trajectory data, environmental parameters (e.g., slope gradients and snow conditions), and potential collision events. These data would need to be preprocessed to ensure consistency, such as normalizing input variables and addressing missing or noisy data.  
Model Training and Validation: The neural network training process would involve defining appropriate architectures (e.g., convolutional neural networks for spatiotemporal data) and optimizing the hyperparameters using training datasets. Validation would be conducted with unseen test data to assess the model performance and generalizability.  
Benefits of AI Integration: Compared with traditional methods, AI-powered systems can dynamically adapt to diverse terrains and skier behaviors, making them particularly valuable for real-time applications. Additionally, AI can identify complex patterns in large datasets, leading to more precise risk assessments and improved collision-prevention systems.  
These advancements would enable the development of intelligent safety management systems at ski resorts, leveraging AI for dynamic risk monitoring and adaptive safety interventions.  
By the way, we plan to collaborate with ski resort operators to obtain ground truth data using GPS-based systems or motion capture technologies. This will allow us to validate the corrected trajectories more rigorously and further enhance the robustness of the proposed methodology.  
Author Contributions: Conceptualization, B.Z. and A.S.; software, B.Z.; formal analysis, B.Z.; investigation, B.Z.; data curation, B.Z.; writing--original draft preparation, B.Z.; writing--review and editing, A.S.; visualization, B.Z.; supervision, A.S. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the third-party.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors thank [PERSON] for providing the video footage of the skiing resorts from Austria. [PERSON] thanks the financial support from the China Scholarship Council (Grant No. 202006150010).  
The authors declare no conflicts of interest.
## 9 Commercial Availability  
The power switches used in the LLC resonant converter should support some key requirements including: low conduction loss originating from on-state resistance, fast operation, which means zero turn-on and turn-off time, less gate-driving power consumption originating from parasitic capacitors, small packaging, and low price [64; 122].  
The total loss resulting from a power switch can be classified into driving loss, conduction loss, and switching loss [25]. A GaN HEMT like GS55504B has all three mentioned advantages over its Si MOSFET counterpart, like IPx65R110 CFD, when used in a high frequency LLC resonant converter.  
Compared to Si-based Field Effect Transistor (FET), GaN HEMT has lower output capacitance and reduced deadtime for ZVS; also, circulating currents allow higher magnetizing inductance to reduce the magnetic fringing losses [114]. As well, its much lower gate input charge results in the reduced gate driver loss.  
Material properties in three power switches from different types are listed in Table 1. \(E_{g}\), \(E_{BR}\), \(V_{s}\), and \(\mu\) are the bandgap energy, critical electric field for the breakdown in crystal, saturated drift velocity and mobility of electrons, respectively.  
Also, three power switches from GaN, Si and SiC with similar voltage and current rating available in the market are compared in the results presented in Table 2.
# Evaluation of the Occurrence of Phthalates in Plastic Materials Used in Food Packaging
## 3 Results and Discussion
### Optimization of HS-SPME Procedure  
To obtain the feasibility of the proposed analytical method, using the HS-SPME/GC-MS technique, a factorial design based on fiber coating, extraction time, and extraction temperature was optimized. The optimal conditions were chosen based on extraction efficiency measured by the GC-MS response (GC peak area) and reproducibility (the lowest % RSD). Figure 1 shows that the best extraction efficiency for DIBP, BBP, and DOP was achieved using the PDMS/DVB fiber at 80 \({}^{\circ}\)C, whereas for BDE, this was obtained using PDMS at 80 \({}^{\circ}\)C. Moreover, for DIBP, no significant difference was observed in terms of extraction efficiency between PDMS and the PDMS/DVB fiber at 80 \({}^{\circ}\)C. Therefore, the PDMS/DVB fiber was chosen for the HS-SPME extraction procedure since it provided a better extraction efficiency, with the exception of the BDE target, and better reproducibility than PDMS. Moreover, the PDMS/DVB fiber was reported to be the most suitable fiber for phthalates analysis in several environmental waters and food-related products [12; 20].  
It is well known that SPME is an equilibrium-based extraction procedure and therefore a time-dependent process. Nonetheless, the extraction time can be shortened with the increase in the extraction temperature, since high temperatures improve the diffusion kinetics of the target analytes [21]. In the current study, temperatures higher than 80 \({}^{\circ}\)C were not considered to avoid the reduction in fiber sensitivity for phthalates due to overloading the fiber with H\({}_{2}\)O molecules as well as to avoid possible degradations [20]. In addition, as microextraction is an endothermic process, high temperatures hamper the process. The extraction efficiency of HS-SPME increased for all phthalates with the increasing temperature. DOP is the only phthalate that does not show this remarkable increase, since a slight increase was observed between 70 and 80 \({}^{\circ}\)C; thus, 80 \({}^{\circ}\)C was selected as the extraction temperature. It has been reported that 80 \({}^{\circ}\)C is an optimal temperature to extract phthalates from environmental waters [22]. Regarding the extraction time, BBP, DOP, and BDE reached their equilibrium after 30 min, independently of the extraction temperature. For DIBP, the equilibrium was reached at 45 min. Thus, the optimal extraction conditions selected to determine phthalates in food packages were 30 min at 80 \({}^{\circ}\)C using a PDMS/DVB fiber coating.