esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed h-POQL MPPT Control
### Methodology of the h-POQL MPPT Control  
Following the previous review on the MPPT methods, this work proposes a simple hybrid MPPT control method, which is the combination of Q-learning and P&O, to overcome the disadvantages of each technique. In MPPT based on the P&O as shown in Figure 14, the oscillation with large step perturbation around the maximum power point and the low response to the change of weather conditions are the main constraints. On the other hand, the method following the Q-learning algorithm can just handle the discrete states and actions, so longer computational time in case of large states states is the major limitation. Details of the h-POQL method will be described below.  
The proposed h-PORL MPPT method can be shown in Figure 15. As shown in Figure 16, it can be divided into eight control zones based on the temperature and irradiation. In each control zone, the Q-leaning-based MPPT method will learn the responses of the PV source for the optimal values of the duty cycle. Then these optimal values will be used as the inputs for the P&O MPPT controller. This study aims to reduce the learning time by decreasing the number of discrete state spaces, and to improve the P&O MPPT method by lowering the variable step size. As shown in Figure 17, the testing model built in Simulink is the combination of the Kyocera solar KT200 GT module, one boost converter, and one resistor which acted as the load.  
Figure 14: Flow chart of the perturb and observe algorithm.
## 5 Experiemental Result and Analysis
### Error Analysis and Limitation  
By observing the samples with incorrect predictions in the test set, we note a notable ratio of the samples containing less information in the images. For example, they are featured with only a few words in the image, blurry visuals, and a single scene, as exhibited in Figure 5, which makes the model fail to focus on essential positions in the image and unable to extract adequate information from the image. As a result, the model heavily relies on text modality alone. The subsequent work can alleviate these issues by extracting words from images and using techniques such as image defogging to enhance the visual quality. Research limitation: the inability to extract sufficient information from blurred images and images with less information and inadequate adaptability in the absence of a certain modality.  
Figure 4: Case study and visualization.
## 4 Discussion
Levan-degrading enzymes including _levB_ and _sacA_ confer to the rapid hydrolysis of levan to levan-type fructologosaccharides [20], which are supposed to be inhibited due to high sucrose concentration as described previously in _B. subtilis_[49]. Biochemical and structural analysis of EPS confirmed that EPS produced by _B. velezensis_ VTX20 contained predominantly levan, accompanied by an extremly low level of dextran polymer. These results led to a hypothesis that _ls_ is highly expressed in presence of high sucrose concentration, yielding levan and releasing free glucose. Released glucose activated transcription of the _epsA-O_ operon, resulting in the production of dextran polymer. A previous study proved that the presence of glucose strongly inhibits the enzymatic activity of levansucrase, causing a decrease in levan production [50]. Our result proved that obtained EPS is a functional biopolymer. The EPS from _B. velezensis_ VTX20 showed great solubility and water-holding capacity, which are in accordance with the presence of fructose residues. It is consistent with levan produced by other strains [42; 51]. In terms of bioactivities, EPS obtained in this study exhibited \(64.0\pm 4.0\%\) of DPPH radical scavenging activity at 4 mg/mL, which was significantly higher than that of EPS from _B. tequilensis_ PS21 [43], _Lactobacillus heleticus_ MB2-1 [52], and _Leuconostoc mesenteroides_ WiKim32 [45]. This indicated that _B. velezensis_ EPS had strong scavenging activity on DPPH. Since the DPPH free radical scavenging activity of antioxidants is based on the ability to transfer either an electron or a hydrogen atom to DPPH, neutralizing its free radical character [53], the different antioxidant activity could be attributed to chemical features discrepancy. On the other hand, hydroxyl radical is the most reactive and toxic reactive oxygen species, which is generated via Fenton reaction and iron-catalyzed Haber-Wiss reaction, causing aging, cancer, and other diseases [22; 53]. Thus, it is of great importance to characterize the hydroxyl radical scavenging potential of antioxidant agents. EPS from _B. velezensis_ VTX20 showed moderate scavenging activity on hydroxyl radicals, which was nearly similar to that of _B. amyloliquefacis_ C-1 [25]. EPS is able to scavenge hydroxyl radical because of the hydrogen and electron abstraction mechanism. These findings concluded that _B. velezensis_ EPS could be considered as a promising candidate that has potential uses in the biomedical and food industries, such as drug delivery, food hydrocolloids, and as bioabsorbents.
## 4 Conclusions  
This paper employed CFD with a volume of fluid (VOF) method to implement high-precision simulations of the co-currently downward gas and liquid flow between catalyst particles inside TBRs. Traditional experimental methods have intrinsic limitations on liquid holdup and wetting efficiency measurement and most empirical equations which were concluded from these methods do not consider all effective factors such as the contact angle and surface tension coefficient. The detailed effective factors were investigated in this paper and remarkable findings are presented below.  
1. Four different catalyst particle shapes including sphere, cylinder, single-hole cylinder, and 4-hole cylinder were simulated to study their characteristics about liquid holdup and wetting efficiency. The 4-hole cylinder obtained both the highest liquid holdup and wetting efficiency. Spherical and cylindrical particles of the same volume have similar liquid holdups, but the wetting efficiency of cylindrical particles is higher than that of spherical particles. In addition, the liquid holdup of cylindrical particles increases with the number of inner holes (surface area), while the wetting efficiency does not change significantly.
2. The variation of the contact angle and surface tension coefficient directly affects the accumulation and flow of liquid in voids between the particles inside the 4-hole cylinder packing, which further affect the liquid holdup and wetting efficiency. Both liquid holdup and wetting efficiency increase with the surface tension coefficient. The wetting efficiency decreases with the increase of contact angle, but a moderate contact angle can result in a high liquid holdup.
3. Liquid viscosity and density affect the liquid holdup and wetting efficiency by affecting the liquid-solid shear force and gravity force, respectively. A higher viscosity or lower density of liquid can result in an increase of both liquid holdup and wetting efficiency.
4. Gas superficial velocity affects the liquid holdup and wetting efficiency by affecting the inertial force and has the opposite effect on the surface tension and liquid-solid shear force. While liquid superficial velocity affects those two factors mainly by varying the liquid input quantity,  
Figure 10: Liquid distributions with different gas superficial velocities (\(v_{L}=0.008\) m/s).  
vector from particle center of gravity to contact point [-]  
\(a_{i}\) volume fraction of phase \(i\) [-]  
\(\beta_{L}\) liquid saturation [-]  
\(\varepsilon\) porosity [-]  
\(\varepsilon_{L}\) liquid holdup [-]  
\(\eta_{L}\) wetting efficiency [-]  
\(\theta\) contact angle [\"]  
\(\kappa\) the curvature of the interface [-]  
\(\mu\) viscosity [Pa-s]  
\(\rho_{i}\) density [kg/m\({}^{3}\)]  
\(\sigma\) surface tension coefficient [N/m]
## 4 Example
### Constructing the Knowledge Graph
#### 4.1.1 Knowledge Graph Ontology Construction  
The construction of the knowledge graph ontology is the core element of knowledge graph construction. As a domain knowledge abstraction model, the ontology needs to be iteratively optimized in combination with expert experience and domain knowledge characteristics to provide a foundation for applications. In this paper, we constructed two knowledge graphs for spacecraft anomaly detection: the spacecraft equipment knowledge graph and the spacecraft abnormal event graph. The ontology models are shown in Figures 8-10.  
Figure 8: Ontology design of a spacecraft equipment knowledge graph.  
In the tree-like ontology structure of the spacecraft equipment knowledge graph, different hierarchical relationships, system equipment connection relationships, task state switching conditions, and parameter correlation relationships are defined. The reference feature data of telemetry parameters are integrated as anomaly detection-matching templates.  
The ontology design of the spacecraft abnormal event graph includes types, such as abnormal phenomena, causes, equipment, and parameters, and uses reference feature data for precise matching. In the event chain ontology, subgraphs describing the transition from normal to abnormal states are incorporated, and the abnormal development process is intuitively described using the graph model. The event chain subgraph contains normal states (white nodes) and abnormal states (gray nodes), with state-switching conditions as relationships. The subgraph of normal states is strongly connected, while recoverable abnormal state nodes and the subgraph of normal states are strongly connected. Irrecoverable abnormal state nodes have only unidirectional relationships with other nodes.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 4 Electrical System Design
### Power Distribution  
When all power usages are determined, the adequate DC power supply rating and power distribution are selected [29]. Here, DC was used rather than AC because it is simpler and more accessible for the portable ROV to use power supply by the generator. The total available power is computed as follows:  
\[\text{Total available power = Power supply power -- Tether power loss} \tag{15}\]  
The ROV electricity consists of the following components: T100 thruster, ESC, cameras, and Arduino digital video recorder (DVR). Table 1 tabulates the power distribution for each component and the tether power loss due to temperature. The table shows that the power consumption is lower than the available power [2].
## 4 Discussion  
Since we used open-source codes, CTs for all the hysteresis models are evaluated for the single iteration (the final one), without accounting for the identification procedure, which could depend on the adopted strategy affecting the comparison. Moreover, for the CPM, the number of sampling points for the ascending and descending branches affects not only the accuracy but also the CT [27; 31; 32]. Therefore, a number of \(N_{asc}\) = 100 samples for each branch has been deemed as a good trade-off between accuracy and CT.  
As for the CPM, the number of sampling points \(N_{asc}\) is shown to affect the accuracy, even for the D-D-D model, the number of points \(N_{c}\) = 2 \(N_{asc}\) used to discretize the phase shift vector \(\omega t\) would affect the accuracy. Indeed, it has been experienced that a number \(N_{c}\) < 100 yielded unclosed and asymmetric loops. This could happen with coarse measurement points (\(N_{m}\) < 100) in evaluating the Pearson correlation coefficient (i.e., \(N_{c}\) = \(N_{m}\)). In such a case, an oversampling of both measured and simulated points will avoid this issue.  
To further improve the accuracy of the D-D-D model, it is also desirable to refine the sampling step where the gradient of the magnetization is steepest and relax it where it is smoothest, as expected from (6). Once again, this is somewhat not controllable during measurement campaigns but could be overcome in the post-processing phase. Another improvement that could be made is to add a reversible term, as for the CPM, which will give a slope to the magnetization after saturation when needed, such as for _Fe-Si_ loop 11.  
Finally, we noted that a tentative approach has been undertaken for the parameter identification of the D-D-D model, but in the future, an automated optimization procedure could be easily afforded.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Model**} & \(\mathbf{e}\) & \multirow{2}{*}{\(\mathbf{r^{2}}\)} & \(\mathbf{CT}\) \\  & & **(\%)** & & **(s)** \\ \hline \multirow{3}{*}{\(MnZn\) ferrite} & CPM & 4.05629 & 0.99785 & 0.09182 \\  & J-A & **1.51375** & **0.99948** & 0.15134 \\  & D-D-D & 4.53686 & 0.99718 & **0.00685** \\ \hline \multirow{3}{*}{\(Fe\)-\(Si\) loop 11} & CPM & 15.24575 & 0.91456 & 0.08678 \\  & J-A & 5.0458 3 & **0.99212** & 0.22523 \\  & D-D-D & **4.91653** & 0.97532 & **0.00976** \\ \hline \multirow{3}{*}{\(NdFeB\) at 80 \({}^{\circ}\)C} & CPM & **0.19651** & **0.99927** & 0.07080 \\  & J-A & 9.62853 & 0.93845 & 0.26254 \\ \cline{1-1}  & D-D-D & 2.03716 & 0.99921 & **0.00766** \\ \hline \multirow{3}{*}{\(NdFeB\) at 27 \({}^{\circ}\)C} & CPM & 2.18167 & 0.99584 & 0.07430 \\ \cline{1-1}  & J-A & 31.31527 & 0.79565 & 0.35150 \\ \cline{1-1}  & D-D-D & **2.16800** & **0.99872** & **0.00660** \\ \hline \end{tabular}
\end{table}
Table 2: Numerical comparison of the considered models. Bolded values are the most performant metrics.
## 3 Results and Discussion
### Analysis of Direct Shear Test Results
#### 3.1.3 Effect of Grouting Parameters on Shear Strength Index  
An increase in shear strength is reflected in the change in \(c\) and \(\varphi\). To make the variation in the shear strength index of the soil around piles more intuitive, the variation curve of the shear strength parameter with the cement content is drawn in Figure 8.  
The shear strength indices of the two soils increase with the cement content from the variation characteristics of the curve, and the muddy clay is more sensitive to the cement. The silty clay's value of friction angle fluctuates within a narrow range, but its friction angle under different grouting parameters is greater than that of muddy clay. The cohesion of silty clay increases slowly with the cement content. The cohesion of muddy clay is greater than that of silty clay after grouting, but it begins to decline when the cement content reaches more than 13%. This is because there is an optimal cement content in engineering practice. Cement soil with high content shows brittle failure, which is similar to the research results of [PERSON] et al. [35]. The slurry of permeation grouting contributes to the shear strength index of the soil around piles. For the soil around the pile with a low cement content (\(w\leq 15\%\)), the fitting formula given in Figure 8 can be used to reveal the strengthening mechanism of the soil around piles with permeation grouting. Therefore, the grouting parameters can be adjusted appropriately according to different types of soft soil on site to achieve the best strengthening effect.
## 3 Application of Uncertainty Quantification Methods and Results
### Results of Quantitative Radar-Based Precipitation Estimation
Illustrated in Figure 4d-f are the AWS-observed precipitation and the radar-based precipitation estimates produced by applying the G/R ratio and LGC algorithms, respectively, for case 6, which was a case of Changma that formed along the east sea coast to the southern sea at 05:30 LST on 13 July 2012. The precipitation observed by the AWSs (Figure 4d) shows very intense precipitation in regions 4 and 5; the most intense precipitation was 53.5 mm/h in region 5. Precipitation estimates produced by applying the G/R ratio method (Figure 4e) were underestimated considerably throughout the entire area. In contrast, in Figure 4f, precipitation estimates produced by applying the LGC method were more intense than those produced using the G/R ratio method; especially, in relation to the occurrence of the intense precipitation in regions 4 and 5 that was relatively well estimated. The results presented in Figure 4 show that the application of the fuzzy algorithm during quality control and of the LGC method during post-processing produces results that are more accurate for generating radar-based precipitation estimates. Figure 4: Comparison of precipitation estimates: between automatic weather stations (AWS) and quality control methods (**a**) Observation from AWSs, (**b**) fuzzy, (**c**) ORPG at 1020 LST on 28 August in 2012; between AWS and bias correction methods (**d**) AWS, (**e**) G/R ratio, (**f**) LGC at 0530 LST on 13 July in 2012.
# DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
## 2 Related Works
### Tracking Technologies  
Since the onset of coronavirus pandemic, many countries have used technology-based solutions, to inhibit the spread of the disease [12; 27; 28]. For example, some of the developed countries, such as South Korea and India, use GPS data to monitor the movements of infected or suspected individuals to find any possible exposure among the healthy people.  
The India government uses the Aarogya Setu program to find the presence of COVID-19 patients in the adjacent region, with the help of GPS and Bluetooth. This may also help other people to maintain a safe distance from the infected person [29]. Some law enforcement agencies use drones and surveillance cameras to detect large-scale rallies and have carried out regulatory measures to disperse the population [30; 31].  
Other researchers such as [PERSON] et al. [32] perform human detection using wireless signals by identifying phase differences and change detection in amplitude wave-forms. However, this requires multiple receiving antennas and can not be easily integrated in all public places.
## 1 Introduction
At the same time, the system load should not exceed 5% of the total power in every single step. Also, when a blackout occurs, the status of the system is reported to the ERCOT ISO by the transmission system operators, and the system operators carry out the restoration procedure using the specified black start units. The system is divided into small islands and all islands are synchronized after completing the restoration process of each island. Many studies related to the methodology of restoring the islands have studied the ERCOT ISO [23]. On the other hand, the Korean Power Exchange (KPX) in Korea  
Figure 2: The general procedure of the black start service. divides the whole system into subsystems and restores the black start units and pre-assigned paths in each subsystem first. Afterwards, there is no specific restoration methodology. It is important to perform the restoration process considering the system condition and understanding the characteristics of the blackout. The Electrical Research Institute (EPRI) proposed a generic restoration milestones (GRM) technique to restore the system depending on various system conditions [24; 25]. In the GRM, there are six milestones and system operators can select a series of milestones to restore the system depending on system conditions. The Power System Engineering Research Center (PSERC) suggested restoration procedures based on optimization techniques [6]. During the power system restoration process, the optimization technique Mixed Integer Linear Programming (MILP) is used to determine the generators' start-up sequence [26; 27]. In this paper, we propose a sequential restoration strategy based on the enhanced Dijkstra algorithm for Korean power systems. In the methodology, we determine the generator start-up sequence (GSS) using generator characteristic data and an enhanced Dijkstra algorithm. To determine the GSS, we compare the characteristic data of generators, including the cranking power, start-up time, and ramping rate. Additionally, we create an adjacency matrix and consider the charging current as a weighting factor to establish power grids quickly [28]. The proposed method is verified by Korean power systems.
# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 6 Empirical Evaluation of the Cycle Approach's Utilization in EMRs
### Data Preparation and Cleaning  
The dataset was first in an Excel file and inspected for consistency, missing values, and outliers. Variables considered in this study (such as treatment_cycle, post_treatment_glucose, and treatment_success_rate) were identified and cleaned to ensure uniformity. Any erroneous entries were corrected, and the data were tagged based on the treatment cycle, creating a structured approach for subsequent analyses.
# Comparison of Machine Learning Methods to Up-Scale Gross Primary Production
## Materials and Methods
### Data and Data Processing  
#### 2.2.1 Remote Sensing Data  
The MODIS land surface reflectance (LSR) products (MOD9A1) with a spatial resolution of 500 m were provided on an 8-day basis [43]. In this study, MODIS LSR products from May to September in 2014 were used. These data contained the best possible observation during an 8-day period after atmospheric correction, which could provide an estimate of the surface reflectance as it would be measured at ground level in the absence of atmospheric scattering or absorption [44]. Then 8-day NDVI were derived using the red and near-infrared reflectance from LSR products, and were aggregated to 1 km to train the up-scaling model of GPP. Additionally, 8-day fractional vegetation cover (FVC) from May to September in 2014 were also obtained from 8-day NDVI by using the dimidiate pixel model [45], and were also aggregated to 1 km.
## 5 Applications of Thermal Images to Future Space Missions
### Operation at Nighttime  
During the proximity phase, optical images observed only the sunlit surface of Ryugu, while thermal images observed almost the whole surface regardless of day and night. This capability is important and applicable to operate and work on the planetary surfaces in the night and in the shadowed area, if the surface temperature is higher than 160 K in the case of TIR on Hayabusa2 [31]. For example, when a rover works on the polar regions of the Moon where there are many shadows of boulders, optical images need lights if it is used in the shadow, but suffers from overexposure from the sunlit surface within the image. A thermal imager can be used more easily even in the dark side of the Moon. Thermal images are also applicable for in situ resource use such as mining on near-Earth asteroids where day and night changes rapidly because of fast rotation.