esa-sceva/satcom-chunk-collection huggingface dataset:
# V2G Strategies to Flatten the Daily Load Curve in Seoul, South Korea
## 1 Introduction
In other words, according to V2G strategy modeling, a methodology is presented to calculate the daily charge and discharge load curve for each electric vehicle charge and discharge location in Seoul and add it to the existing load curve. More specifically, according to the Seoul electric vehicle market share scenarios in 2030 and 2040, electric vehicles are discharged when going to work in the morning, and electric vehicles are charged in the afternoon after work, which we can use as the basis for calculating the amount of electric vehicle charging and discharging. In addition, the calculation conditions are again divided into uncontrolled type (taking into account the number of vehicles operating by hour), controlled type 1, and controlled type 2 (SMP/TOU rate plan + considering the number of vehicles operating by hour), and the charging and discharging amount for each time period is calculated to calculate the daily charge and discharge for each condition. An optimization method is proposed to equalize the entire daily load curve by calculating the load curve leveling rate. In sum, in this study, an efficient V2G strategy was developed in the direction of leveling the daily load curve of large cities according to TOU and SMP plans for large cities where electric vehicles are charged and discharged. Assuming an EV deployment scenario, this paper presents our work to apply and evaluate a method that induces electric vehicles to be discharged at work in the morning and charged at home in the afternoon. Furthermore, in this paper, a V2G strategy for flattening the load curve for a day is presented. The remainder of this paper is organized as follows. Section 2 explains grid impact analysis according to EV modeling by location and the V2G strategy as a daily load curve evaluation method. Section 3 presents the daily load curve leveling results according to the V2G strategy for each EV share scenario for Seoul in 2030 and 2040.
## 3 Results and Discussion
### Sensitivity of Radar Signal to Soil Moisture  
The sensitivity of X-band SAR signal to soil moisture was studied for two biomass classes: BIO \(<\) 1 kg/m\({}^{2}\) and BIO \(>\) 1 kg/m\({}^{2}\). The BIO = 1 kg/m\({}^{2}\) limit corresponds to a vegetation height of approximately 30 cm, a vegetation water content of 0.8 kg/m\({}^{2}\), and a LAI of approximately 2 m\({}^{2}\)/m\({}^{2}\). First, the mean backscattering coefficients were calculated from calibrated TSX and CSK images over all sampled plots by averaging the linear \(\sigma^{\circ}\) values of all pixels within the non-flooded training plots or within the non-flooded portions of irrigated plots.  
Figure 7 shows that the radar signal for HH and HV polarizations is clearly dependent on soil moisture, with sensitivity to soil moisture for BIO lower than 1 kg/m\({}^{2}\) of approximately 12.64 and 13.40 dB/[cm\({}^{3}\)/cm\({}^{3}\)] at HH and HV, respectively. [PERSON] _et al._[79] showed that when TerraSAR-X data are used after strong rains, the soil contribution (influenced by soil moisture) to the backscattering of sugarcane plots is important when the cane height is less than 30 cm. For BIO higher than 1 kg/m\({}^{2}\), the sensitivity of the radar signal to soil moisture decreases to 8.83 and 6.39 dB/[cm\({}^{3}\)/cm\({}^{3}\)] at HH and HV, respectively. These results demonstrated that the soil contribution to the X-band SAR signal could be high for grassland biomass lower than 1 kg/m\({}^{2}\) in both the HH and HV polarizations; the soil contribution also decreases more quickly in HV than in HH for BIO higher than 1 kg/m\({}^{2}\). These results show that the SAR X-band signal, mainly in HH polarization, can penetrate the canopy and interact with  
the soil even for vegetation with biomass (when BIO>1 kg/m\({}^{2}\)). When biomass is both lower and higher than 1 kg/m\({}^{2}\), the radar response showed more variability for the same soil moisture range in HV than in HH; this is due to the sensitivity of HV polarization to vegetation cover, which has already been observed by [PERSON] _et al._[80], [PERSON] _et al._[81], and [PERSON] _et al._[82].  
In conclusion, these results show that the X-band radar signal at a medium incidence angle (30\({}^{\circ}\)) depends on the soil moisture, regardless of the vegetation conditions. This dependence could be improved with SAR data at lower incidence angles. Indeed, the penetration depth of the radar signal into the vegetation cover is higher at low incidence angles.
###### Abstract  
Three-dimensional point clouds, as an advanced imaging technique, enable researchers to capture plant traits more precisely and comprehensively. The task of plant segmentation is crucial in plant phenotyping, yet current methods face limitations in computational cost, accuracy, and high-throughput capabilities. Consequently, many researchers have adopted 3D point cloud technology for organ-level segmentation, extending beyond manual and 2D visual measurement methods. However, analyzing plant phenotypic traits using 3D point cloud technology is influenced by various factors such as data acquisition environment, sensors, research subjects, and model selection. Although the existing literature has summarized the application of this technology in plant phenotyping, there has been a lack of in-depth comparison and analysis at the algorithm model level. This paper evaluates the segmentation performance of various deep learning models on point clouds collected or generated under different scenarios. These methods include outdoor real planting scenarios and indoor controlled environments, employing both active and passive acquisition methods. Nine classical point cloud segmentation models were comprehensively evaluated: PointNet, PointNet++, PointMLP, DGCNN, PointCNN, PAConv, CurveNet, Point Transformer (PT), and Stratified Transformer (ST). The results indicate that ST achieved optimal performance across almost all environments and sensors, albeit at a significant computational cost. The transformer architecture for points has demonstrated considerable advantages over traditional feature extractors by accommodating features over longer ranges. Additionally, PAConv constructs weight matrices in a data-driven manner, enabling better adaptation to various scales of plant organs. Finally, a thorough analysis and discussion of the models were conducted from multiple perspectives, including model construction, data collection environments, and platforms.  
orgnat segmentation; plant phenotyping; deep learning; plant point cloud datasets 2024
## 2 Research Background  
### Fault Tree Analysis (FTA)  
FTA is a deductive technique for identifying, evaluating, and modeling the interrelationship between events leading to a failure or an undesired state [9]. It is a scientifically sound and proven technique that models complex system interactions in an easy-to-read visual model. Containing qualitative (cut sets) and quantitative (probabilities) sides, the structured, repetitive and methodical construction process results in layers, levels and branches. Once a logical and systematic picture of all immediate and basic causes leading to top event are identified, undesired events are clearly depicted. Though FTA employs Boolean logic to combine a series of lower-level events causing an undesired state of a system, the technique is static and incapable of examining multiple failures [10]. It also is unintuitive and lacks the ability to properly account for dynamic interactions between components. These and many other drawbacks make FTA limited in modeling dynamic complex systems.
# Practical Work of Three-Phased Induction Motor Modeling in a Classical Spreadsheet Software
## 4 Spreadsheet Modeling of the Induction Motor
### Comparison with Other Software and Models
#### 4.3.1 Advantages  
1. Accessibility and Cost: Unlike Simulink and PSCAD, which require specialized software licenses, our spreadsheet-based tool is accessible to anyone with standard spreadsheet software, making it a cost-effective solution for educational and practical applications.
2. Ease of Use: The intuitive interface of spreadsheet software allows users to easily input data and visualize results without extensive training, which is particularly beneficial for students and practitioners who may not be familiar with more complex simulation tools.
3. Pedagogical Value: Our tool effectively bridges the gap between theoretical concepts and practical applications, providing a hands-on learning experience that reinforces understanding of induction motor modeling.  
Figure 9: Operating point determination at the intersection of electromagnetic and load resistive torque.
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 3 Synthesis of Molecularly Imprinted Polymers
### Suspension Polymerization  
Suspension polymerization is one of the promising methods for the production of uniform beads because of the use of monomers that are either insoluble or partially soluble in water. Thus, the method is actually performed through the droplets of the monomer suspended in the aqueous phase [15; 16].  
Even though MIPs prepared by bulk and precipitation polymerization methods demonstrate high recognition property, there might be some difficulty in the diffusion of the template to the imprinted cavities through the highly cross-linked polymeric matrix, which limits their applications for chromatography. That is why suspension polymerization to overcome this drawback through grafting of MIP layers onto the surface of synthesized beads is performed, producing MIP beads with a uniform size.  
Though having many advantages, such as a homogeneous transmission of heat, this method can be used for industrial scale-up; on the other hand, the resulting number of binding sites might be low due to the use of aqueous dispersing agent and water, which might retard the reaction kinetics between the functional monomer and template molecule [1].
# Application of Mathematical Models for Blood Flow in Aorta and Right Coronary Artery
## 3 Results
### Flow Behavior in the RCA  
The same analysis was performed on the coronary model. From the streamlines (Figure 7), related to the Newtonian model, it is evident that the highest velocities are reached at the point of maximum curvature and at the outlet. Note how the flow is laminar under these physiological conditions.  
The velocity magnitude was analyzed in greater detail at the section corresponding to the maximum curvature (Figure 8) and the outlet section (Figure 9).  
\begin{table}
\begin{tabular}{l c c} \hline
**Parameter** & **Value** & **Unit** \\ \hline vel (line average) & 1.25080 & m/s \\ volume-averaged velocity & 0.84300 & m/s \\ surface-averaged velocity (in) & 0.61699 & m/s \\ surface-averaged velocity (out) & 1.45420 & m/s \\ volume-averaged pressure & 12,679.00 & Pa \\ surface-averaged pressure (in) & 13,600.00 & Pa \\ surface-averaged pressure (out) & 9372.50 & Pa \\ Qin & \(4.50\times 10^{-4}\) & m\({}^{3}\)/s \\ Qout & \(4.61\times 10^{-4}\) & m\({}^{3}\)/s \\ \hline \end{tabular}
\end{table}
Table 1: Derived values.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Parameter** & **True Value** & **Newtonian** & **Carreau** & **Casson** & **Oldroyd-B** & **Giesekus** & **FENE-P** \\ \hline Average velocity on surface (in) & 0.617 m/s & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ Average velocity on surface (out) & 1.417 m/s & 0.025 & 0.028 & 0.027 & 0.029 & 0.029 & 0.029 \\ Average pressure on surface (out) & 9332.54 Pa & 0.004 & 0.002 & 0.002 & 0.002 & 0.002 \\ Qin & \(4.50\times 10^{-4}\) m\({}^{3}\)/s & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ Qout & \(4.50\times 10^{-4}\) m\({}^{3}\)/s & 0.025 & 0.028 & 0.026 & 0.042 & 0.029 & 0.042 \\ \hline
**Avg Relative Error**1 & & & **0.011** & **0.012** & **0.011** & **0.015** & **0.012** & **0.015** \\ \hline \end{tabular}  
* Highlighted in bold for each model, which serves as a key comparative indicator.  
\end{table}
Table 2: Relative error—AORTA.  
Figure 7: RCA - Newtonian model: streamlines.  
The same procedure was followed for the other five models as well. In Figure 10, for example, we can see the velocities reached in the outlet section for each model. Compared to the Newtonian model, the shear-thinning models underestimate the maximum velocity, while the viscoelastic models overestimate it. The exact opposite occurs for the shear rate.  
Then all the post-processing values were derived from the software and the relative error was calculated for each model. Relative errors are reported in Table 3.  
Figure 8: RCA - Newtonian model: **(a)** Cut plane, maximum curvature. **(b)** Velocity magnitude.  
Figure 9: RCA - Newtonian model: **(a)** Cut plane, outlet. **(b)** Velocity magnitude.
# State of the Art of Telecommunication Systems in Isolated and Constrained Areas
## 2 Telecommunication Systems in an Isolated and Constrained Environment
### The Flying Systems
#### 2.3.2 The Drones  
Drone history began in 1916 when the English engineer [PERSON] developed the aerial target (Figure 7), a crewless target plane, remotely piloted via radio waves. At the beginning of the 20 th century, the great powers were looking for military applications to carry out air missions without putting their pilots' lives at risk. The drone then underwent a considerable boom in the 21 st century with recreational applications. Currently, the drone is a quadcopter with four brushless motors as a propulsion system, a chassis, and a speed controller.  
The size of drones ranges from a few tens of centimeters to a few meters for military applications. The small ones are used for leisure activities for individuals [42] by carrying a camera. Their weight is low, less than 250 g, and they do not require a specific permit in Canada, for example. From an industrial point of view, their field of applications is growing. Thus, they are used to assess future failures on structures such as bridges or pylons inaccessible to humans. In the same register of inspection, Hydro-Quebec is developing a drone called Line drone for the contact inspection of power lines, as in Figure 8.  
The field of agriculture has integrated drones into its crop improvement processes or track animals [44]. Drones are indeed able to assess contamination in crop fields [45] with, of course, appropriate image processing. The drone field [46] is booming and many  
Figure 8: Hydro-Quebec’s line drone [43].  
Figure 7: The world’s first drone developed by [PERSON] and team [41].  
innovative aspects are being implemented. The British company ISS has unveiled its drone named Sensus (Figure 9). It is powered by a fuel cell [47] using hydrogen. Therefore, the drone takes on its hydrogen tank, thus giving it a flight autonomy of around 2 h. Different sensors can be installed to perform air analysis. A platform called Sensus has been developed to display and interpret the results of measurements made in flight.  
Finally, to carry out operations without pilots, numerous drone applications supplement military drones [49]. The pilot room of these drones brings together trained military pilots. Thales is developing giant drones like the watchkeeper X (Figure 10) to carry out surveillance missions. Its autonomy is 16 h and it is operational in less than 2 h. This autonomy allows it to intervene in places far from its base. These drones are crewless real scale planes. To ensure telecommunications coverage in an isolated sector, one could easily imagine a drone fleet, each drone carrying a telecommunication system flying over the area. This is like the Internet of drones (IOD) [50].
## 4 Discussion
### Effects of Heating Time  
Figure 14 depicts the effects of the heating time on the \(V_{g}\) and \(R_{gw}\) within 1080 days with LWH&CH. With stop heating in advance (SHIA) at 120, 240, 360, and 480 days, the \(V_{g}\) decreases from 3.982 \(\times\) 10\({}^{6}\) to 3.716 \(\times\) 10\({}^{6}\), 3.827 \(\times\) 10\({}^{6}\), 3.890 \(\times\) 10\({}^{6}\), and 3.931 \(\times\) 10\({}^{6}\) ST m\({}^{3}\), decreasing by 6.68%, 3.89%, 2.31%, and 1.28%, respectively. The simulation results indicate that when LWH&CH is applied to the wellbore located in the TPL and stops heating in advance at 480 days, the \(V_{g}\) just decreases by 1.28% during the 1080-day production cycle. However, the cumulative heat input of LWH&CH&SHIA (480 d) is only 44.44% of the LWH&CH heating production. Compared with the WWH&CH production, the \(V_{g}\) only decreases by 3.06%, but the cumulative heat input is only 9.52%. After 1080 days of production, the \(R_{gw}\) in the above cases remains around 200, and there is no significant difference.  
Figure 12: Gas and water production curve with different heating locations within 1080 days. **(a)** Gas production rate and cumulative gas production. **(b)** Water production rate and gas-to-water ratio.  
Figure 13: \(EROI_{custom}\) curve with different heating locations within 1080 days.  
Figure 15 shows the \(EROI_{custom}\) curves of continuous heating and stop heating in advance. Compared with continuous heating, all \(EROI_{custom}\) curves that stop heating in advance show an increasing trend at the time of stopping heating, followed by a decreasing trend, and tend to stabilize in the later stage. Obviously, stop heating in advance has a better energy output-to-input ratio than continuous heating.
## 1 Introduction
successfully used for indoor (or enclosed environment) navigation, it is still very hard to apply for UAV navigation in large open environment. In this paper, we proposed an airborne MEMS INS enhancing method based on cross-modal images matching, which can provide long range autonomous navigation for medium- and high-altitude aircraft under GNSS blocking environment. The main contributions of this work are the following:  
1. A coarse matching algorithm based on the dense structure features is proposed to effectively adapt to nonlinear grayscale variation and noise distortion. In addition, the searching process of the coarse matching is significantly accelerated with fast Fourier transform (FFT). Therefore, the proposed coarse matching algorithm can provide position updates with a rate above 10 Hz on a low power airborne compute. Since the proposed coarse matching method is reliable and time efficient, the visual odometer is no longer required. 2. A fine alignment algorithm based on a deep end-to-end trainable matching network is proposed to precisely align the real-time aerial image with the referenced satellite image despite the local ground feature changes and nonlinear grayscale variation. The result of fine alignment is employed to estimate the precise heading angle of the UAV, which is better than three degrees (1\(\sigma\)) during a typical experiment and can be used to correct the heading angle of the MEMS INS. 3. Based on the cross-modal image matching approach, a precise position and heading can be obtained, which is used to enhance the MEMS INS performances in this research. A filtering model to fuse the vision-based navigation information and the INS information is developed, which can precisely and reliably estimate the position, velocity, and attitude errors of the UAV and avoid the accumulating errors of the MEMS INS. The enhanced navigation approach can provide long-time autonomous navigation for the UAV at medium- to high-altitude at GNSS denied environments, and it can work effectively in both daylight condition (with visible light camera) and night condition (with infrared camera). In the remaining parts of this paper, Section 2 describes the cross-modal image matching approach to achieve the position and heading enhancement information. The data fusion approach to achieve the enhancement to MEMS INS with image matching is discussed in Section 3. The experimental tests are discussed in Section 4, and the concluding remarks are provided in Section 5.
## 3 Result and Discussion
### Performance of the Method in Estimating Fruit Properties Based on the Most Effective Wavelengths
#### 3.5.2 Properties of Total Soluble Solids (TSS)  
As seen in Figure 12, the regression coefficients obtained by data based on 920 to 980 nm and the most effective wavelengths are above 0.95, and thus TSS of Fuji apples can be predicted with both data types. According to Table 4, the mean and standard deviation of the hybrid ANN-BBO at 100 iterations using spectral data of 920 to 980 nm and the effective wavelength is near similar; therefore, the proposed method is highly reliable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Properties** & **Criterion** & **MSE** & **RMSE** & **MAE** & **R** & \(\mathbf{R^{2}}\) \\ \hline \multirow{3}{*}{Spectral data in the range 920–980 nm} & Mean & 0.069 & 0.261 & 0.203 & 0.947 & 0.897 \\  & SD & \(\pm\)0.016 & \(\pm\)0.032 & \(\pm\)0.028 & \(\pm\)0.029 & \(\pm\)0.052 \\ \cline{1-1}  & At best state & 0.034 & 0.186 & 0.145 & 0.956 & 0.914 \\ \hline \multirow{3}{*}{Spectral data of the most effective wavelengths} & Mean & 0.061 & 0.243 & 0.191 & 0.946 & 0.895 \\ \cline{1-1}  & SD & \(\pm\)0.019 & \(\pm\)0.04 & \(\pm\)0.034 & \(\pm\)0.034 & \(\pm\)0.061 \\ \cline{1-1}  & At best state & 0.023 & 0.153 & 0.119 & 0.962 & 0.925 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the mean and standard deviation of criteria of ANN-BBO for estimating TSS of Fuji apple in 100 replicates using spectral data of 920–980 nm and data on effective wavelengths.  
Figure 12: Regression analysis of scatter plot at the best state of training of ANN-BBO to predict starch content of Fuji apple. (**a**) Based on spectral data in the range of 920–980 nm, (**b**) of the most effective wavelengths.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Properties** & **Criterion** & **MSE** & **RMSE** & **MAE** & **R** & \(\mathbf{R^{2}}\) \\ \hline \multirow{3}{*}{Spectral data in the range 800–900 nm} & Mean & 0.043 & 0.203 & 0.156 & 0.975 & 0.950 \\  & SD & \(\pm\)0.014 & \(\pm\)0.035 & \(\pm\)0.027 & \(\pm\)0.020 & \(\pm\)0.038 \\ \cline{1-1}  & At best state & 0.015 & 0.12 & 0.093 & 0.967 & 0.953 \\ \hline \multirow{3}{*}{Spectral data of the most effective wavelengths} & Mean & 0.046 & 0.213 & 0.163 & 0.941 & 0.885 \\ \cline{1-1}  & SD & \(\pm\)0.016 & \(\pm\)0.033 & \(\pm\)0.025 & \(\pm\)0.016 & \(\pm\)0.028 \\ \cline{1-1}  & At best state & 0.025 & 0.16 & 0.119 & 0.961 & 0.923 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the mean and standard deviation of criteria of ANN-BBO for estimating starch content of Fuji apple in 100 replicates using spectral data of 800 to 900 nm and data on effective wavelengths.
# The Development and Evaluation of a Training Monitoring System for Amateur Rugby Union
## 4 Discussion
### Challenges Associated with Monitoring Training  
The player survey results suggest that the majority of players found the online TMS useful, with 80% of respondents rating it as either moderately, very, or extremely useful (Figure 4). However, it is evident from Table 3 that the S&C coaches found several challenges with using a TMS of this nature.