esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsharp Mask Guided Filtering for Acoustic Point Cloud of Water-Conveyance Tunnel
## 4 Results
### Parameters
#### 4.2.2 Simulation Experiment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **Error** \\ **Metrics** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Guided 3D** \\ **Point-Cloud** \\ **Filter** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Bilateral** \\ **Filter** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Moving** \\ **Least-Squares** \\ **Filter** \\ \end{tabular} } & \multirow{2}{*}{**Our Method**} \\ \hline Block & \(D_{mean}\) & 0.57 & 0.58 & 0.56 & **0.45** \\  & \(\sigma\) & 0.21 & 0.22 & 0.23 & **0.18** \\ Cactus & \(D_{mean}\) & **0.66** & 0.67 & 0.76 & 0.71 \\  & \(\sigma\) & 0.28 & 0.30 & 0.34 & **0.26** \\ Chair & \(D_{mean}\) & 2.55 & **2.10** & 2.69 & 2.23 \\  & \(\sigma\) & 1.50 & 1.24 & 1.55 & **1.21** \\ Elephant & \(D_{mean}\) & 0.57 & **0.49** & 0.63 & 0.72 \\  & \(\sigma\) & 0.31 & **0.28** & 0.41 & 0.36 \\ Hippo & \(D_{mean}\) & 0.78 & **0.34** & 0.37 & **0.34** \\  & \(\sigma\) & 0.34 & **0.22** & 0.29 & **0.22** \\ Horse & \(D_{mean}\) & 0.81 & 0.94 & 0.74 & **0.58** \\  & \(\sigma\) & 0.48 & 0.56 & 0.55 & **0.28** \\ Monster & \(D_{mean}\) & 0.78 & 0.80 & 0.92 & **0.64** \\  & \(\sigma\) & 0.34 & 0.34 & 0.49 & **0.26** \\ Skull & \(D_{mean}\) & 0.14 & 0.14 & **0.12** & 0.13 \\  & \(\sigma\) & 0.08 & 0.09 & 0.07 & **0.05** \\ Bunny & \(D_{mean}\) & 0.60 & 0.59 & 0.62 & **0.52** \\  & \(\sigma\) & 0.24 & 0.23 & 0.45 & **0.20** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Error metrics for different methods and the best results are displayed in bold. The proposed method can achieve more satisfactory results on real site models and simulated models than many other competing methods. And the model obtained by our method is more suitable for the subsequent object recognition or 3D reconstruction tasks of water-conveyance tunnel.
# Reducing Model Complexity in Neural Networks by Using Pyramid Training Approaches
## 4 Experiments
### Dataset and Task  
The experiments span three principal areas: classification, segmentation, and object detection.  
* Classification: we utilized the CIFAR-10 dataset [37], a benchmark for image classification tasks, to evaluate the effectiveness of our pyramid training and feature reduction techniques.
* Segmentation: for segmentation tasks, the KITTI dataset [38], which is broadly employed for object detection, tracking, and segmentation, was used.
* Object detection: the Penn-Fudan Database for Pedestrian Detection [39] was chosen for object detection experiments. This dataset, comprising diverse urban scenes, focuses on pedestrian detection, offering a challenging testbed for our methodologies.
## 6 Result and Discussion  
### Optimization Result  
The proposed framework's verification by optimizing parameters in \(p\) for each actuator failure possibility in the platform and chosen post-failure operation performance requirement is presented. The two common operations, hovering at the location and following an obstacle-free trajectory to return home, are considered. The required moment data to accomplish these operations and reject the associated disturbance in the nominal condition were logged from the simulation and used as a performance requirement for optimization in faulty conditions. If the framework is implemented correctly, the parameters must converge to a value that gives a maximum cost function and the least exclusion ratio for a given marginal demand. If this is violated, the parameters should not be accepted as optimum, and we recommend that the operation and parameter constraints be revised. In order to limit the maximum vertical thrust loss due to tilting to 5% and consider installation constraints, the domain of parameters is defined as follows:  
\[\mathcal{D}=\{\mathbf{p}|0\ <\ \beta\ <\ 30,\ -20\ <\ \alpha_{i}\ <\ 20,\ \mathrm{i}=1,2\ldots 6\}\]  
To perform the optimization, the particle swarm optimization (PSO) algorithm was implemented to search for a combination of parameters that maximizes the cost function. The algorithm used randomly distributed population sizes of 500 and 400 iterations.
## 3 Real-Time Control Strategy Considering Cost per kWh of Storage Batteries
### Simulation Results and Analysis  
#### 3.2.1 Data and Parameters  
The simulation data are historical meteorological data and historical load data of a typical year of the demonstration microgrid system. The data are shown in Figure 4.  
Figure 4a shows the historical irradiation data of the microgrid system demonstration project location. The abscissa is the hours and the ordinate is the irradiation value. Figure 4b shows the annual load historical data of the demonstration community. The abscissa is time, the unit is hour, and the ordinate is the load power. From the typical annual meteorological data and load data, we can see that the local light resources are very rich, and the maximum annual irradiation can reach 1060 W/m\({}^{2}\). The local load varies greatly between seasons and between day and night. The annual maximum load is 138.6 kW, the minimum load is 31 kW, and the average load is 83.78 kW.  
The data in the above case are used to verify the energy management algorithm. The simulation parameters are shown in Table 1.  
Figure 4: Simulation data: (**a**) annual irradiation data; (**b**) annual load history data.  
Time-of-use electricity price parameters are shown in Table 2. After considering the charging and discharging efficiency of the ESS, the actual cost per kWh of the ESS is equivalent to 0.58/0.8 = 0.725 yuan. Therefore, when the electricity price is 0.9402 yuan during peak hours, the electricity price of the utility is higher, so the ESS is preferentially selected to discharge, and the utility is preferentially used in other periods.
###### Abstract  
In this paper, a novel internal folded hardware-efficient architecture of multi-level 2-D 9/7 discrete wavelet transform (DWT) is proposed. For multi-level DWT, the unfolded structure is more extensively used compared with the folded structure, because of its low memory consumption and low time delay. However, a set of input data valid every few clock cycles caused the mismatch between clock and data in the unfolded structure. The mismatch usually needs to be solved by multi-clock or complex data adjustment, which increases the consumption of hardware resources and the complexity of the overall system. To solve the above problem of the unfolded structure, we adjust the data input timing by using a single clock domain and folding the DWT architecture of different levels in varying degrees, according to their own clock-to-data ratios. For an image of size of N \(\times\) N pixels and 3-level DWT, the proposed architecture requires only 6N words temporal memory. For 3-level DWT with an image of size \(512\times 512\) pixels, the hardware estimation and comparison of the existing architectures show that, the hardware estimation result shows at least 30.6% area-delay-product (ADP) decrease, and at least 22.4% transistor-delay-product (TDP) decrease for S = 8, and 25.77% transistor-delay-product (TDP) decrease for S = 16.  
discrete wavelet transform (DWT); lifting scheme; internal folded architecture; parallel architecture +
Footnote †: journal: Applied  
1
Footnote 1: School of Microelectronics, Tianjin University, Tianjin 300072, China; [EMAIL_ADDRESS] (W.Z.); [EMAIL_ADDRESS] (C.W.); [EMAIL_ADDRESS] (P.Z.)
## 3 Results and Discussion
### Annual Energy Loss  
In this section, the analysis of the annual energy loss will be carried out. For this, Equation (13) will be used. The annual energy loss, for the movement limits under study, is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Movement Limit** & **Basir** & **Miraflores** & **Canredondo** \\ \hline \(\mathbf{\beta_{\text{max}}}\) (\({}^{\circ}\)) & \(\mathbf{H_{t}}\) **(kWh/m\({}^{\text{2}}\))** & \(\mathbf{H_{t}}\) **(kWh/m\({}^{\text{2}}\))** & \(\mathbf{H_{t}}\) **(kWh/m\({}^{\text{2}}\))** \\ \hline \(\pm 50\) & 2249.16 & 2219.79 & 2007.89 \\ \(\pm 51\) & 2249.88 & 2220.77 & 2008.22 \\ \(\pm 52\) & 2250.48 & 2221.62 & 2008.46 \\ \(\pm 53\) & 2250.97 & 2222.33 & 2008.63 \\ \(\pm 54\) & 2251.36 & 2222.92 & 2008.74 \\ \(\pm 55\) & 2251.66 & 2223.41 & 2008.80 \\ \(\pm 56\) & 2251.88 & 2223.79 & 2008.82 \\ \(\pm 57\) & 2252.03 & 2224.09 & 2008.80 \\ \(\pm 58\) & 2252.12 & 2224.31 & 2008.76 \\ \(\pm 59\) & 2252.16 & 2224.46 & 2008.70 \\ \(\pm 60\) & 2252.17 & 2224.55 & 2008.64 \\ \hline \end{tabular}
\end{table}
Table 4: Incident solar irradiation on the PV field at the locations under study.  
Figure 6: Representation of \(k_{\text{d}}\) at the locations under study.  
The following conclusions can be drawn from Figure 7:  
1. In the three \(PV\) power plants analysed, the energy loss per square metre is small, but as the surface of the \(PV\) field is large, these losses cannot be neglected. The choice of a movement limit different from the optimum produces energy losses that cannot be neglected, taking into account the large surface area of the \(PV\) field. The surface area of the \(PV\) field at the Miraflores \(PV\) power plant is 105,109.81 (m\({}^{2}\)), and at the Basir and Canredondo \(PV\) power plant it is (m\({}^{2}\)) 95,616.64 and 105,012.67 (m\({}^{2}\)), respectively.
2. According to Figure 7a, the use of movement limits lower than \(\pm\)60 (\({}^{\circ}\)) increases the annual energy losses. Therefore, the Miraflores \(PV\) power plant uses the optimal movement limit. The annual energy loss would be 120.06 (MWh) per year if the movement limit of this \(PV\) power plant were \(\pm\)55 (\({}^{\circ}\)).
3. Based on Figure 7b, the annual energy loss increases for movement limits below \(\pm\)60 (\({}^{\circ}\)). Therefore, the Basir \(PV\) power plant uses the optimal movement limit. Using a limiting movement of \(\pm\)55 (\({}^{\circ}\)) would result in an energy loss per year of 48.76 (MWh).
4. According to Figure 7c, the maximum annual incident solar irradiation on the \(PV\) field corresponds to a movement limit of \(\pm\)56 (\({}^{\circ}\)). Therefore, as the movement limit of the Canredondo \(PV\) power plant is currently \(\pm\)60 (\({}^{\circ}\)), the movement limit is not optimised. Therefore, the annual energy loss at this \(PV\) power plant is 18.49 (MWh).  
It can be concluded that it is necessary to calculate the optimal movement limit for each location under study, as the annual losses of the incident energy are not negligible.  
Figure 7: Annual energy loss.
# NMR Evaluation of Shale Oil Mobility: Combined Pyrolysis and CO\({}_{2}\) [PERSON]
## 3 Results and Discussion
### Development of Shale Fluid Component Templates  
Using pyrolysis-NMR and CO2 huff-n-puff-NMR joint testing, the distribution of various components within the \(T_{1}\)-\(T_{2}\) spectrum was ascertained. This facilitated the creation of a fluid component recognition template for the study area by segmenting the \(T_{1}\)-\(T_{2}\) spectrum based on these distribution positions. The \(T_{1}\)-\(T_{2}\) spectrum ranges for these components are detailed in Table 2. Additionally, a comprehensive analysis of the sample components yielded a recognition template for the shale oil reservoir in the study area, encompassing the following six distinct components: movable oil, adsorbed oil, asphaltene, clay-bound water, structural water, and kerogen (Figure 8). The shale component template reveals the composition characteristics of the study area. Various components exhibit distinct temperature change characteristics, reflecting the diversity and complexity of oil and water compositions. Through the template, it can be seen that both oil and water coexist in the clay space of the shale reservoir. This coexistence makes the identification of components by NMR \(T_{1}\)-\(T_{2}\) spectrum difficult and complex, and also increases the research difficulty of oil and water component saturation in the study area. Despite the low free water content in the study area, a large amount of clay-bound water is adsorbed in the clay, which also increases the water saturation. The template obtained in this study can identify components with \(T_{2}\) < 0.2 ms, including kerogen, structural water, asphaltene, and clay components. This enables a more refined classification of shale components and precise localization on the \(T_{1}\)-\(T_{2}\) spectrum. This template provides a foundation for subsequent qualitative and quantitative analyses of shale oil reservoir components.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & \(\mathbf{T_{1}}\)**(ms)** & \(\mathbf{T_{2}}\)**(ms)** & \(\mathbf{T_{1}}\)**/\(\mathbf{T_{2}}\)** \\ \hline Kerggen & 20–160 & 0.02–0.2 & 280–2000 \\ Structural water & 0.36–1.4 & 0.04–0.1 & 5–25 \\ Clay-bound water & 0.5–2.6 & 0.04–0.2 & 7–50 \\ Asphaltene & 170–660 & 0.25–0.6 & 340–1650 \\ Adsorbed oil & 2.3–60 & 0.5–7.5 & 3.6–20 \\ Movable oil & 40–300 & 2–15 & 5–100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The NMR \(T_{1}\)-\(T_{2}\) spectrum range of different components.  
Figure 7: Comparison of oil production after each cycle for three shale samples.
## 4 Results and Discussions
### Effect of Falling Height under Different Submergence Conditions
#### 4.1.4 Effect of Falling Heights  
Six different falling heights were calculated for each initial submergence condition, for a total of 18 cases. The change of maximum wave height with falling height is shown in Figure 9. The maximum wave heights measured at G1 are marked as the solid lines in Figure 9 and that at G2 are marked as the dotted lines. The triangle, square and circle, respectively, represent the subaerial landslide, partial subaerial landslide and submarine landslide in Figure 9. It is worth noting that, in general, the tsunami wave height of the partial subaerial landslide is the largest, followed by that of the subaerial landslide, and the smallest is that of the submarine landslide. However, when the falling height of the submarine landslides is less than \(-0.5\) m, the maximum wave height at G2 is close to that of partial subaerial landslides and even slightly exceeds that of subaerial landslides. The variation of maximum wave height caused by the subaerial landslide with the falling height of the slide block is relatively simple, that is, the greater the falling height, the smaller the maximum wave height. This phenomenon is reflected in many reservoir landslides, and there is an optimal falling height range within which landslides cause the maximum surge height. On the contrary, generally speaking, the maximum wave height caused by a submarine landslide and partial subaerial landslides increases with the increase in initial falling height of the slide block, except for the accidental elevation of submarine landslide-induced tsunamis at smaller falling heights. When the tsunami wave propagates from G1 to G2 (from the solid lines to dotted lines in Figure 9), the attenuation phenomenon of the maximum wave height of subaerial landslide has no significant change, while that of the partial subaerial landslide will be reduced with the increase in initial submersion range. For submarine landslides, the attenuation phenomenon almost disappeared when the initial falling height was higher. However, when the initial falling height is small (\(h=-0.4\) m \(\sim\)\(-0.6\) m), the maximum wave height of the wave train increases unexpectedly during the propagation. This unexpected increase in wave height may be due to the fact that the landslide occurs in deep sea area and the landslide disturbs the high-pressure seawater while the upper low-pressure seawater is less affected. Then, with the propagation of the tsunami, the disturbed high-pressure seawater from the lower layer is gradually released to the upper layer and affects the upper layer of the seawater. As a result, in the beginning, the wave height of the tsunami is small because the surface water is less disturbed and then increases because the effect of the disturbed high-pressure seawater.
## 3 Results and Discussion
### Changes in VFCs in Pickled BLS under Different Salt Concentrations
 \\
8 & Aldehydes & 3-Methylbutylraldehyde & 0.74 & \textless{}0.1 & 0.85 & \textless{}0.1 & 0.18 & \textless{}0.1 & 1.12 & \textless{}0.1 & 0.15 & 0.21 \\
11 & Nonanal & 1.15 & 0.11 & 1.03 & 0.11 & 0.48 & 0.16 & 2.93 & 0.24 & 0.42 & 2.06 & Fat, grassy \\  & & & & & & & & & & & & \\  & & & & & & &Flavor formation is a complex process that occurs during co-fermentation with various microorganisms, such as LABs [56]. While this process produced lactic acid and FAAs that gave the product a variety of flavors, it also produced VFCs, such as esters, isothiyanates, alcohols, ketones, aldehydes, acids, nitriles, sulfides, and heterocyclic compounds, during fermentation. There were many kinds and quantities of ketones and aldehydes. The main VFCs of pickled BLS were mainly dimethyl trisulfide, phenylacetaldehyde, ethyl caprate, ethyl palmitate, 1-octen-3-ol, \(\beta\)-ionone, furan, 3-methyl butyraldehyde, nonanal, undecanone, 2-decanone, furanone, ethyl laurate, and methyl palmitate. These VFCs constituted the unique flavor of pickled vegetables. The contributions of VFCs to the overall flavor perception depended on not only the amount of the components but also their odor threshold values.
# Real-Time Surveillance System for Analyzing Abnormal Behavior of Pedestrians
## 4 Experimental Results
### Intrusion and Loitering  
The test result of intrusion succeeded in detecting 144 events out of 150 videos, and an example of the resulting image of the test result of intrusion is shown in Figure 11(a). Most of the videos have been successfully detected, but some of the videos have failed due to a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Event** & **Recall Rate** & **Precision Rate** & **F1 Value** \\ \hline Intrusion & 0.947 & 0.959 & 0.953 \\ Loitering & 0.967 & 0.967 & 0.967 \\ Violence & 0.960 & 0.960 & 0.960 \\ Fall-down & 0.880 & 0.880 & 0.880 \\ \hline Average & 0.938 & 0.941 & 0.940 \\ \hline \hline \end{tabular}
\end{table}
Table 6: KISA Datasets Experimental Results.  
Figure 11: VLC environment video transmission server.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
\begin{tabular}{c} 2 seconds before the action \\ occurs, within 10 s after the \\ action. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: KISA performance evaluation criteria.  
false detection in a low-light environment at the ROI. This is a chronic problem of most detection algorithms and can be overcome by additionally improving the performance of the network and training data in a low-light environment in the future.The results of the loitering experiment were successfully detected in 145 out of 150 images, and an example of the resulting image is shown in Figure 12b. In the case of loitering, there is a detection failure in a low-light environment and false detection in the ROI. Since both intrusion and loitering use detection and tracking methods to determine abnormal behavior based on the acquired coordinates, we expect that performance can be improved by improving the performance of the detection network and training additional data as in the case of intrusion.
## 2 Materials and Methods
### Experiment: EDEM Discrete Element Simulation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Corn Kernel** & **Corn Cob** & **Corn Kernel–Corn Cob** \\ \hline Normal stiffness per unit area/(N\(\cdot\)m\({}^{-3}\)) & \(8.7\times 10^{8}\) & \(1.12\times 10^{9}\) & \(5.5\times 10^{10}\) \\ Shear stiffness per unit area/(N\(\cdot\)m\({}^{-3}\)) & \(4.79\times 10^{8}\) & \(1.29\times 10^{9}\) & \(6.0\times 10^{10}\) \\ Critical normal strength/Pa & \(1.57\times 10^{5}\) & \(5.0\times 10^{6}\) & \(3.0\times 10^{6}\) \\ Critical shear strength/Pa & \(8.59\times 10^{5}\) & \(5.2\times 10^{6}\) & \(1.675\times 10^{6}\) \\ Bond disc scale & 1.2 & 1.2 & 1.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Corn kernel bonding parameters. Figure 5: Discrete element modeling of corn ears. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Density/(kg\(\cdot\)m\({}^{-3}\))** & **Poisson Ratio** & **Shear Modulus/MPa** \\ \hline Corn kernel & 1394 & 0.4 & 131 \\ Corn cob & 627 & 0.4 & 109 \\ Steel & 7850 & 0.3 & \(8\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Intrinsic parameters of materials and contact materials. Figure 6: Comparative shear testing and experimental results of the discrete element model for corn ears: (**a**) shear test (Here the yellow is the corn kernels and the green is the corn cob); (**b**) load–displacement relative error diagram. To enhance the efficiency of the computer simulation, the HCME crushing apparatus was streamlined, with only the essential components, namely, the primary and secondary crushing devices, retained. The models of the primary and secondary crushing devices were created using SolidWorks 2022 and saved as STEP files for importation into EDEM software. The rotary motion with the center axis as the centerline was added to each crushing roll to simulate the operational dynamics of the crushing roll. The material of the crushing roll was steel 45, and the simulated physical parameters of the steel model were presented in Table 2. A particle plant was positioned above the initial crushing device to facilitate the unimpeded descent of the corn ear into the crushing apparatus. The simulation time was set to 1 s to reduce the computational burden, and the number of corn ear granule factories generated was set to three ears. This was carried out to ensure the stability of the bonding, and the fixed time step for the simulation was determined through the preliminary experimentation as \(7.5\times 10^{-8}\) s. The post-processing module of statistical EDEM software was employed to calculate the qualification rate of corn ear crushing. The simulation test was repeated five times, and the resulting experimental data are averaged to determine the final test results. A box is set in the effective crushing area of the device to prevent the splashing of corn particles after crushing from affecting the crushing results.
## 3 Implementation
### Object-Based Image Fusion  
To achieve visual augmentation here, we need to fuse multiview sensor images from adjacent vehicles. This section estimates fusion region and functional form necessary for achieving image fusion. In order to realize mapping objects from forward vehicle image A to host image B, firstly, we need to figure out some information related to the geometric configuration. The information includes size, shape, and location of the fusion region. All detected street objects' bounding boxes in image A will be the candidate fusion objects. Only those objects occluded by vehicle A will merge to the fusion regions in image B. Epipolar \(e_{a}\) and \(e_{b}\) can be used to eliminate those objects that are not occluded by vehicle A. Here, the fusion region in image B is a circle area (rectangle and other shapes are also available). The center and radius of the circle depends on the location and size of the detected vehicle region (vehicle A).  
Secondly, we need to estimate a functional form to map pixels from the front image to the back one. The mapping matrix \(H_{new}\) between two images is estimated in Section 3.4. The affine transformation regarded as the mapping relationship has the following matrix representation:  
\[P_{B}=H_{new}P_{A} \tag{10}\]  
The fusing location will certainly be determined by affine mapping. The blending method is similar to [18]. The blending weight is adjusted to use more color from the front, image B, close to fusion center and more color from the back, image A, away from the center which is toward the edge of the circle. The transparency parameter controls the mixture of two images.