esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Dependent Variables  
In this study, both task performance and subjective measures were collected. As task performance measures, task completion time measured as the delay between the displaying of the task on the screen and the clicking of the \"Next\" button, and task accuracy were assessed. The task completion time for each sentence was measured and the average values were computed per session. The task accuracy was calculated by the number of correct sentences per total sentences in a session. All task performance measures were compared with the measures of reference tasks and converted to the standardized performance ratios based on the non-automated condition to minimize individual difference, using the following equation:  
\[\textit{Standardized Performance Ratio}=\frac{\textit{Performance measure in automated tasks}}{\textit{Performance measure in non -- automated tasks}} \tag{1}\]  
Furthermore, the trust levels and subjective workload measure by NASA TLX questionnaire were evaluated as subjective operator performance parameters to understand the participants' perception of Routine/Failed automation at different automation levels. The NASA TLX questionnaire is a multidimensional subjective workload-rating method, which is composed of six subscales that reflect behavioral and subjective workload responses driven by the perception of task demands. In NASA TLX, the subjective workload is defined as the \"cost incurred by human operators to achieve a specific level of performance [22].\" This perceived subjective workload is evaluated as the integration of subjective responses and behaviors. These behaviors and subjective responses are guided by the perceptions of task demand, which can be quantified in terms of magnitude and importance. Table 1 showed the description of the questions for NASA TLX sub-scales and perceived trust.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Subscale** & **Description** \\ \hline Mental Demand & How much mental and perceptual activity was required? Was the task easy or demanding, simple or complex? \\ \hline Frustration & How irritated, stressed, and annoyed versus content, relaxed, and complacent did you feel during the task? \\ \hline Temporal Demand & How much time pressure did you feel due to the pace at which the tasks or task elements occurred? Was the pace slow or rapid? \\ \hline Perceived Performance & How successful were you in performing the task? How satisfied were you with your performance? \\ \hline Effort & How hard did you have to work (mentally and physically) to accomplish your level of performance? \\ \hline Perceived Trust * & How trustworthy and helpful was the automation (indicating the word to be corrected or suggesting substitutes)? \\ \hline \hline \end{tabular}
\end{table}
Table 1: Survey questions of National Aeronautics and Space Administration (NASA) Task Load Index (TLX) subscales and perceived trust on automation.
# Output Filter Design for a Novel Dual-Input PV-Wind Power Converter by Energy Balance Principle
## 1 Introduction  
Nowadays, renewable energies, such as photovoltaic (PV), wind energy and hydroelectric, have been widely adopted as alternatives to fossil fuels. However, output power of a renewable-energy generator is highly affected by atmospheric conditions. Therefore, a hybrid power system--including two or more input sources--has become the design trend for renewable energy processing, in which a constant output voltage and sustained power supply can be completed [1; 2; 3; 4; 5; 6].  
A dual-input converter (DIC) can simultaneously deal with two inputs and obtains a regulated voltage [7; 8; 9; 10; 11; 12]. For renewable power applications, a DIC should have the ability to process renewable energy for each individual input. Even though neither of the inputs has the power to feed, the DIC can still function well. In addition, the DC-bus voltage of a grid-connected system is normally up to 380 V. That is, the DIC must be capable of achieving a high step-up feature [13; 14; 15; 16; 17; 18; 19; 20; 21]. In order to meet the marketable requirements, a DIC should have the features of high efficiency, cost-effectiveness, a low electromagnetic interference (EMI), small size, minimum component counts, and a low current ripple. In [22; 23], an isolated DIC with multi-windings, based on the flux additivity concept, is proposed to accomplish some of the mentioned features. However, each power MOSFET in the DIC has to be in series with a reverse-blocking diode, which results in the energy stored in the leakage inductors not being recycled and causes a high voltage spike on MOSFET during turn-on and turn-off transitions. Adopting a clamp circuit or snubber may be an approach to alleviate the mentioned voltage spike and to reduce power loss [24]. Nevertheless, this approach increases the power component count and cannot improve the voltage conversion ratio.  
In this paper, a novel power DIC is presented, which can process PV power and wind-turbine energy simultaneously or individually and its so called novel dual-input PV-wind converter (DIPWC). For continuous current operation and output voltage regulation, the output port adopts a second-order LC filter in cascade connection to the converter. The inductance of the output filter will dominate the feature of the DIPWC. However, the determination of the inductance has a sophisticated procedure. Therefore, this paper will first describe the operation of the DIPWC and then design the inductance theoretically.  
Following the introduction in Section 1, the remainder of this paper is organized as follows. Section 2 briefly introduces the characteristics of the proposed DIPWC. The circuit operation principle of the converter and control mechanism are discussed in Section 3. A theoretical analysis for determining an optimal value of the output inductor is presented in Section 4. To verify the correctness of the theoretical analysis, a DIPWC prototype with the designed filter is built. Key experimental results will be illustrated in Section 5. Finally, Section 6 summarizes the conclusions.
## 4 Results and Discussion
### Discussion  
In recent trends, there has been an increased interest in MANET research, and cross-layer design is one of the more intriguing applications. In this paper, the ECTSA, its multiple essential signals, and its qualities are presented. A novel dynamic routing strategy is offered that builds on cross-layer design. In order to reduce the energy consumption produced by the contentions, an Adaptive Competition Window (ACW) adjustment is also implemented. The overall analysis clearly shows that the proposed ECTSA provided better results than the existing CEELBRP [24], EECRP-PSO [25], CLPDM-SI [26], and HFFGSO [28] in terms of energy consumption, delay, lifetime, PDR, PLR, routing overhead, and throughput.
# Random Forest-Based Stability Prediction Modeling of Closed Wall for Goaf
## 2 Experimental Data Description
### Variable Influence  
The Pearson correlation coefficient is used to measure the linear relationship between fixed-distance variables, and its value is between \(-1\) and \(1\). Figure 3 displays the Pearson correlation coefficients for each parameter. The positive correlations between the damage  
Figure 3: Distribution of the input parameters. The figure consists of two subplots designed to illustrate the distribution of damage levels across different datasets. The horizontal stacked bar chart on the left shows the number of samples for each of the three damage levels—No_damage, Slight_damage, and Severe_damage—within the dataset (167 total samples), the training set (134 total samples), and the test set (33 total samples), respectively. The length of each bar represents the total number of samples, with different colors indicating the number of samples for each damage level. The pie chart on the right displays the proportion of each damage level within the entire dataset, clearly presenting the percentage of each damage level in the overall dataset.  
degree of the closed wall and the shock wave speed, compressive strength (MPa), and shear strength (MPa) indicate that the damage degree is positively correlated with these three input variables, with the strongest correlation observed between the shock wave speed and damage degree. Figure 4 displays the heatmap of the correlation coefficients between each variable. As can be seen from the figure, the correlation between each variable is relatively high. Specifically, a very strong positive correlation exists between the shock wave speed (SPEED), compressive strength (obc), and shear strength (\(\tau\)) (correlation coefficients close to 1), indicating a high degree of linearity among these variables, where an increase in one often accompanies increases in the others. A positive correlation also exists between the shock wave speed (SPEED), compressive strength (\(\sigma\)bc), and shear strength (\(\tau\)) and the damage level (Damage) (correlation coefficients ranging from 0.80 to 0.87), suggesting that increases in these variables can lead to an increase in the degree of damage. Therefore, it can be stated that the damage to the closed wall is not determined by a single variable, but rather by the combined effects of multiple variables. This demonstrates that the parameters we used are useful for making meaningful and reasonable predictions.
# Study on Components Determination and Performance Evaluation of LS Pre-Maintenance Agent
## 5 Road Performance Test Results Analysis
### Light Aging Resistance  
The UV aging mass loss rate (_MLR_) calculation method is shown in Equation (2), the average _MLR_ results are shown in Figure 9.  
\[MLR=\frac{G_{1}{-}G_{2}}{G_{1}}\times 100\% \tag{2}\]  
_MLR_--Mass loss rate, %;  
\(G_{2}\)--The residual mass of the sample after light aging, g;  
\(G_{1}\)--The initial mass of sample before light aging, g.  
From Figure 9, the _MLR_ of the LS pre-maintenance agent was obviously less than the Rhinophilt, moreover, its trend was more stable; after 48 h it reached 32.4%. The results of the Rhinophilt changed fast within 8 h and stabilized after 12 h, finally reaching 73.5%.  
The appearance comparison results are shown in Figure 10.  
As shown in Figure 10, the two materials were all black liquid before aging and their surfaces were smooth and consistent. For the LS pre-maintenance agent, the surface formed thin film with slight wrinkles after 8 h, the folds were further deepened with slight cracks after 12 h, the film shrunk, lower liquids became harder and brittle after 24 h, and the surface completely hardened after 48 h. For Rhinophilt, on the specimen appeared small holes and the material continuously reduced and began to harden after 8 h. The crack appeared after 12 h. The specimen was completely hardened and had lost its consistency and viscosity after 24 h.  
Figure 9: Mass loss rate (_MLR_) results versus UV aging time.  
In summary, the LS pre-maintenance agent was completed aged after 48 h UV radiation, which was 2 years of natural UV radiation in Lhasa, and much longer in other lower radiation areas. The anti-light aging time of Rhinophilat was only 25% of the LS pre-maintenance agent. From the light aging resistance effect, the UV aging \(MLR\) of the LS pre-maintenance agent in 48 h was 32.4%. The film formed during the early aging stage can hinder the UV effect on a lower structure, thus the result of Rhinophilat was 73.5%, more than two times the LS agent. Most of the components had lost, only leaving a little rock asphalt. We can see that the LS pre-maintenance agent had better anti-light aging effects than the Rhinophilat, and can meet the requirements of pre-maintenance in strong UV areas. We can choose to spray a year and a half after the first pre-maintenance work, supply the function loss, and repair micro-cracks due to UV radiation and low temperature shrinkage, and thus prevent the disease development of the asphalt layer.
## 3 Results and Discussion
### PPF Model's Results
#### 3.1.1 Persistence Model  
Based on the deviation between actual and predicted energy demand values, Table 1 shows the RMSE and R\({}^{2}\) indexes for the training and validation steps.  
Moreover, these indexes' values were used as the baseline benchmark for quantifying the accuracy improvement obtained by other programmed PPF models' by using Equation (25),  
\[\text{Improvement}\left(\%\right)=\ \frac{Index_{ref}-\ Index_{model}}{Index_{ ref}}*100. \tag{25}\]  
where \(Index_{ref}\) is always an error metric computed through the persistence model and \(Index_{model}\) is the error metric computed through the model whose improvement needs to be quantified.
## 4 Results and Discussion
### Application of Methods  
This study integrated SBAS-InSAR technology with spatial statistical methods, including hotspot analysis and kernel density analysis, to develop a rapid and accurate approach for the early identification of geological hazards. The SBAS-InSAR technology was employed to extract surface deformation information and analyze its temporal evolution, while the spatial statistical methods focused on examining the spatial distribution patterns and clustering of deformation. This combination overcame the limitations of relying on a single method, reduced the manpower requirements, and enhanced the hazard identification efficiency, scientific validity, and reliability.  
The proposed methodology has been successfully applied to the early identification of geological hazards along power transmission lines in the regions surrounding Xi'an and Weinan. The reliability of the results is strongly correlated with the quality of the  
Figure 8: (**a**) UAV optical image of the deformation area on the north side of Taiping Village; (**b**,**c**) on-site investigation phenomenon map.  
data and monitoring outcomes, making this method highly applicable. In addition to its suitability for the Loess Plateau regions, such as Shanxi and Gansu, this approach can also be effectively used in challenging mountainous areas, such as those in southwestern China. By appropriately selecting InSAR parameters to obtain reliable deformation data, this method can facilitate the rapid identification of potential geological hazards.
## 3 Microwave NDT Techniques
### Far-Field Method
In this method, the device is placed on a surface and transmits  
Figure 6: A demonstration of far-field testing. An antenna located at the far-field region of the specimen transmits an electromagnetic wave toward a specimen. At the same location, the reflected wave from the specimen with the defect is measured. The antenna is moved at various locations surrounding the target. electromagnetic pulses. The reflected signals, measured by the device, are processed to create a subsurface image. GPR is commonly used in various industrial applications such as archaeology [65], civil engineering [66], and geology [67]. GPR has several advantages, such as providing high-resolution, real-time results. Nevertheless, the GPR results are highly affected by surface properties such as its conductivity and roughness. Additionally, operator expertise may be required to interpret the GPR images in some cases. In summary, despite the advantages of far-field MNDT, the multidimensional and complex nature of the data may necessitate expertise and sophisticated algorithms to interpret them. Such sophistication hardens the analysis of the results, thus requiring advanced knowledge in signal processing [68], which might make other MNDT methods more favorable in specific applications.
## 2 Monte Carlo-Based Channel Parameter Characteristics Analysis
### Underwater Channel Modeling Based on Monte Carlo
The main function of Monte Carlo is to model a complex probability density function based on its known individual components, which provides a feasible way for studying the characteristics of the channel under different water types, link distances, link geometries and transceiver parameters. By tracing the fate of a large number of photons according to the probability of absorption and scattering, this method can obtain a solution of the RTE. In 2008 [34], the robustness of Monte Carlo for underwater channel modeling was validated in experiment, as the results of Monte Carlo shown reasonable agreement with the water-tank experimental results. In 2014 [35], [PERSON] and [PERSON] also validated the accuracy of Monte Carlo simulation by comparing the simulation results with the experimental data from [36; 37]. The major process of Monte Carlo simulation for photon tracking is detailed in Appendix A. In our simulation, we consider a standard underwater point-to-point link which is performed in an ideal isotropic and homogeneous seawater without flowing and turbulence, as many underwater optical communication analyses [31; 35; 38]. The practical underwater optical channel could be more complex if we consider water turbulence caused by the variations of water refractive index, temperature, salinity and pressure. However, in deep sea the level of sanity and temperature variations are very small and the channel fading caused by water turbulence is negligible [39]. Similarly, there is no blockage caused by bubbles, fish, or large suspended particles in deep sea. The wavelength of the light is defined as \(\lambda=520\) nm because that the seawater has a relatively low attenuation property for light with a wavelength of 450 nm to 550 nm [40]. Monte Carlo simulation is essentially a statistical method and the results rely on calculating of a large number of photons, so we generate \(10^{6}\) photons in each experiment and repeat each experiment at least \(10^{3}\) times to obtain reliable results.
## 2 Data  
This study uses a GeoEye-1 satellite image captured from the city of Hobart, Australia, in February 2009 and a QuickBird image of Fredericton, Canada, acquired in 2002. Generally, the GeoEye and QuickBird imagery comprise a Panchromatic band (Pan) and four multi-spectral (MS) bands (Blue, Green, Red, Near Infrared), all of which are used in this study. Ground Sampling Distance for GeoEye-1 (GSD) at nadir is 41 cm for the Pan band and 1.64 m at nadir for MS bands. In addition, for QuickBird images, GSD at nadir is around 61 cm for Pan band and 2.4 m for MS bands [21]. These two satellites produce typical high-resolution imagery, which approximately have similar spatial and spectral resolutions with the other VHRSI. Therefore, these two satellite products are selected for this research. The coverage of images are so that they include urban or suburban structure types with typical one or two story buildings in order to prevent facing huge relief distortions which reduce classification accuracy. Sun Angle Azimuths are 59.58 and 141.95 degrees for GeoEye and QuickBird images, respectively. These angles form shadows in the southern or western or both mentioned sides of a building--depending on the building orientation--in GeoEye image and in northern or eastern or both sides in QuickBird image. This information is going to be used for building detection (see Section 3.2.4).  
In order to take the advantage of the Pan and the MS bands in image classification, initially the Pan band and MS bands are fused. In the output, MS bands will have as high spatial resolution as the Pan band. This is done using the UNB (University of New Brunswick) pan sharpening method available in the Fuze-Go software. More details about the method can be found in [22].
# Simultaneous Determination of 12 Marker Components in Yeonkyopaedok-san Using HPLC-PDA and LC-MS/MS
## 4 Conclusions  
In this study, a simultaneous analysis for the quantification of the 12 marker components in YPS was carried out using an easy and convenient HPLC system, and a rapid and sensitive LC-MS/MS system, to construct basic data with which to assess the analytical quality of a commonly used, traditional Korean medicine. For the quality assessment of YPS, a simultaneous analysis was carried out using our established and optimized analytical method. These methods are expected to be very useful in quality assessments of YPS and other chemically related herbal prescriptions.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/5/1713/s1](http://www.mdpi.com/2076-3417/10/5/1713/s1)]([http://www.mdpi.com/2076-3417/10/5/1713/s1](http://www.mdpi.com/2076-3417/10/5/1713/s1)), Figure S1: Chemical structures of 12 biomarker components in YPS, Table S1: Composition of YPS, Table S2: Chromatographic parameters for HPLC analysis of the 12 marker components in YPS, Table S3: MRM parameters for LC-MS/MS analysis of 12 marker components in YPS, Table S4: System suitability of the 12 marker compounds using HPLC-PDA, Table S5: The linear range, regression equation, \(r^{2}\), LODs, and LOQs of the analytes from YPS using LC-MS/MS (n = 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{3}{c}{**Amount (\(\mu\)g/g)**} \\ \cline{2-4}  & **Mean** & **SD** & **RSD (\%)** \\ \hline
3-CQA & 466.95 & 14.45 & 3.10 \\ CFG & 35.70 & 0.49 & 1.37 \\ FA & 26.91 & 0.78 & 2.89 \\ LIQA & 310.68 & 6.89 & 2.22 \\ NAR & 137.57 & 2.13 & 1.55 \\
5-MVS & 19.29 & 0.27 & 1.42 \\ NAG & 756.78 & 16.17 & 2.14 \\ NHES & 721.47 & 14.93 & 2.07 \\ OPDH & 56.13 & 1.48 & 2.64 \\ GA & 853.11 & 60.47 & 7.09 \\ ARC & 107.85 & 1.52 & 1.41 \\
6-GIN & 2.35 & 0.21 & 9.03 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Amount of the 12 marker components in YPS by LC–MS/MS MRM mode (n = 3).  
Figure 2: Total ion chromatograms of the reference standard (**A**) and YPS sample (**B**) by UPLC–MS/MS MRM mode. 3-CQA (**1**), CFG (**2**), FA (**3**), LIQA (**4**), NAR (**5**), 5-MVS (**6**), NAG (**7**), NHES (**8**), OPDH (**9**), GA (**10**), ARC (**11**), and 6-GIN (**12**).  
**Author Contributions**: Conceptualization, C.-S.S. and H.-K.S.; performing experiments and analyzing data, C.-S.S.; writing--original draft preparation, C.-S.S.; funding acquisition, H.-K.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by a grant from the Korea Institute of Oriental Medicine (No. KSN1812240).  
**Conflicts of Interest**: The authors declare no conflict of interest.