esa-sceva/satcom-chunk-collection huggingface dataset:
### Comparison of the Efficiency of the Optimally Configured Networks for A1 and A2  
In this subsection, the results of the comparison between the optimally configured networks used for the implementation of A1 and A2 are presented and evaluated. More specifically, the networks described in Table 7 (A1) and Table 8 (A2) are compared using the percentages of the correct classifications (OA index values) extracted for the samples of the testing subsets. It should be noted here that, as regards A2, the form \"A2/(C2S + C2M)\" is more efficient than the form \"A2/(C3S + C2M),\" as shown in Section 4.2. Therefore, the results extracted by the networks NA1-BB, NA1-RIB, and NA1-IIB (Table 7) are compared to those extracted by the networks NA2C2S-BB/NA2C2M-BB, NA2C2S-RIB/NA2C2M-RIB,  
The results presented in Figure 13 indicate that A2 is clearly more efficient than A1 according to the OA index values for each of the three versions of the selected RC buildings. However, the efficiency of A1 is not unacceptable, since the OA index of the corresponding classifications is greater than 82% for RIB, whereas it is close to 90% for BB and IIB. Certainly, the vast majority of the OA values extracted by means of A2 are greater than 90%, but it must be noted that the implementation of A2 requires the knowledge of the classification of buildings to an SDC for \(\theta=0\). This means that NTHA or the simulation of properly trained MLP networks is required for this classification (N00 networks in Figure 3b).  
Therefore, the implementation of A2 is accompanied by an additional classification for the studied buildings. This additional classification can possibly add errors to the entire procedure. On the other hand, A1 is free of these errors and is more flexible, because it can be implemented regardless of the existence of classification of buildings for \(\theta=0\).  
Finally, the conclusion regarding the greater efficiency of A2 is also confirmed by means of the CMs (i.e., the corresponding values of the R-index and P-index), which are summarized in Figure 14.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Category** & \multicolumn{3}{c}{**A2/C35**} & \multicolumn{3}{c}{**A2/C2S**} & \multicolumn{3}{c}{**A2/C2M**} \\ \hline
**Version of** & & & & & & & & \\
**Buildings** & **BB** & **RIB** & **IIB** & **BB** & **RIB** & **IIB** & **BB** & **RIB** & **IIB** \\ \hline
**Training algorithm** & SCG & RP & RP & SCG & SCG & RP & SCG & SCG & RP \\
**Number of HLs** & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 \\
**Activation** & T/L/L & L/L/T & L/L/T & T/T/T & T/L/T & L/T/T & T/T/T & T/T/T & T/T/T \\
**Functions** & 30/50 & 16/52 & 44/26 & 36/46 & 50/36 & 46/16 & 56/38 & 48/22 & 20/24 \\
**Neurons/HL** & NA2C3S- & NA2C3S- & NA2C3S- & NA2C3S- & NA2C2S- & NA2C2S- & NA2C2M- & NA2C2M- & NA2C2M- \\  & BB & RIB & IIB & BB & RIB & IIB & BB & RIB & IIB \\ \hline \hline \end{tabular}
\end{table}
Table 8: Configuration parameters and names of the optimal networks used for the implementation of A2.  
Figure 13: OA values extracted by the optimally configured networks for A1 and A2.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **X-ray** \\ **Tube** \\ **Voltage (kVp)** \\ \end{tabular} } & \multicolumn{8}{c}{**Mean Exposure (\(\mu\)R)**} & \multicolumn{8}{c}{**Shielding Rate (\%)**} \\ \cline{2-13}  & **X-ray** & **Nothing** & **Pb** & **Oyster** & **Shelf** & **Barium** & **Bismuth** & **Oxide** & **Tungsten** & **Pb** & **Oyster** & **Shelf** & **Barium** & **Bismuth** & **Oxide** & **Tungsten** \\ \hline
40 & 24.6 & 106.90 & 0 & 67.01 & 61.04 & 41.69 & 19.23 & 100 & 37.32 & 42.90 & 61.00 & 82.01 \\ \hline
60 & 28.7 & 381.63 & 6.47 & 278.97 & 236.51 & 160.15 & 114.36 & 98.30 & 26.90 & 38.03 & 58.04 & 70.03 \\ \hline
80 & 32.5 & 799.70 & 48.87 & 630.80 & 550.80 & 463.81 & 351.76 & 93.89 & 21.12 & 31.12 & 42.00 & 56.01 \\ \hline
100 & 48.5 & 1318.33 & 144.83 & 1078.67 & 1001.93 & 870.03 & 672.26 & 89.01 & 18.10 & 24.00 & 34.01 & 49.01 \\ \hline
120 & 54.9 & 1648.33 & 207.43 & 1372.67 & 1318.66 & 1120.76 & 906.05 & 87.42 & 16.72 & 20.00 & 32.01 & 45.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between the shielding efficiencies of eco-friendly shielding sheets. the particles to their edges. Consequently, it is possible to increase the relative density by adding particles within the same space. This approach improved the shielding efficiency, with the shielding efficiency of the oyster shell material produced using plastic working at 1200 \({}^{\circ}\)C being more than double that of the sheet fabricated without plastic working. During plastic working, calcium carbonate changes to calcium oxide within a certain temperature range; therefore, the characteristics of the heat-treated oyster shell powder sheet reflect those of calcium oxide. Therefore, as shown in the results of this experiment, the shielding efficiency of eco-friendly shielding materials other than tungsten is insufficient to be used alone. However, in the case of oyster shell powder, if other materials, such as tungsten, are mixed and used, it is considered that the low weight and cost effectiveness of the shield can be achieved. In addition, it should be noted that barium sulfate is considered adequate for shielding against the lower doses received from scattered rays, and the shielding efficiency of the heat-treated oyster shell powder was approximately 7.2% lower than that of barium sulfate, indicating its potential as a shielding material that can be mixed with other materials.
# Rheological Performance of Bio-Char Modified Asphal with Different Particle Sizes
## 5 Results and Discussions
### Rotational Viscosity Test
#### 5.2.2 Anti-Aging Property Analysis  
To further compare the anti-aging properties of different asphalt binders, the aging index was applied, in this study. The aging index was obtained from Equation (2).  
\[\mathrm{I_{ag}}^{r}=\mathrm{V_{RTFO}}/\mathrm{V_{origin}} \tag{2}\]  
where  
\(\mathrm{I_{ag}}^{r}\)--Aging index of the asphalt binder;  
\(\mathrm{V_{RTFO}}\)--Rotational viscosity of RTFO aged asphalt binder;  
\(\mathrm{V_{origin}}\)--Rotational viscosity of the original asphalt binder.  
The aging indexes of all asphalt binders, at 135 \({}^{\circ}\)C, were calculated and are displayed in Figure 3. The higher the aging index, the worse the anti-aging property of asphalt binder was found to be. The aging indexes of 2% BS, 4% BS, and 8% BS decreased by 29.13%, 17.58, and 17.65% more than those of 2% BL, 4% BL, and 8% BL, respectively. This illustrated that as the bio-char content increased, the anti-aging properties of bio-char modified asphalts increased. This could be explained by the stiffening effect caused by the high content of bio-char, which reduced the aging effects. Meanwhile, the bio-char modified asphalts with smaller-sized particles had better anti-aging properties than those with larger-sized particles. At the same content, the smaller-sized bio-char led to higher numbers of stiff particles, as a result, the aging effects were more likely to be suppressed with this stronger stiffening effect. Moreover, 2% BS, 4% BS, and 8% BS had higher anti-aging property compared to the control asphalt binder. In addition, the 4% BS had a better anti-aging property than 4% GS. The bio-char had lower density and higher volume than graphite, at the same dosage. On one hand, this could be explained by the higher volume filling, which decreased the oxidation. On the other hand, the complex fibrous and porous structure of bio-char might have led to larger adhesion interaction between the bio-char and the base asphalt binder molecules, and thus decreased the oxidation.
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 5 Experiment Results and Analysis
### Experiment Results
Figure 13 shows the average user delay values corresponding to different time points for various values of the maximum number of tasks randomly generated by the user. Clearly, as the number of tasks increases, the average user delay gradually increases. This is because, for a larger number of user-generated tasks, more time is required to offload the data to the cloud or edge servers, which increases the average user delay. Based on the above experiments, the PSO, whale optimization algorithm (WOA), and SO algorithm were compared with our proposed algorithm in terms of latency. Additionally, the algorithms of Refs. [16; 17; 19; 20] were compared with our proposed algorithm in terms of cost and average user latency. The results are reported below. Figure 14 shows the average user latencies obtained for different algorithms and the same number of edge servers at 04:00. Clearly, the ISO algorithm has the smallest average user latency. Compared with other classic algorithms, the ISO algorithm can reduce the average user latency by approximately 5% to 12%. This indicates that the ISO algorithm determines the placement of edge servers more effectively, enabling more users to offload their tasks to the edge servers and thereby reducing the average user latency. Figure 12: Impact of number of tasks on cost. Figure 13: Effect of number of tasks on delay. Figure 15 shows the cost values obtained for the different algorithms at different time points when the average delay was restricted to a certain range. Clearly, the algorithms in Refs. [16; 17] had considerably higher costs than the dynamic placement algorithms; this is because they employ a static placement approach. Compared to the algorithms in Refs. [19; 20], the algorithm proposed in this study minimized the cost by finding the minimum number of dynamic servers, optimal placement location, and minimum movement distance when the user location changed at different time points. Additionally, the latency was satisfactory, being within a reasonable range. Compared to the other algorithms, our approach yielded a cost reduction of 20% to 43%. Figure 16 shows the average user delays corresponding to different time points for the different algorithms. The algorithms in Refs. [16; 17] had considerably higher latency; again, this is because they are static algorithms, whereas the other algorithms are dynamic. Among the dynamic algorithms, the delay obtained for the algorithm developed in this study was consistently the lowest, which indicates that the proposed algorithm more efficiently determines the optimal edge-server placement. Figure 14: Effect of different algorithms on delay. Figure 15: Impact of different algorithms on cost [16; 17; 19; 20].
## 4 Results and Discussion
### Effect of the Rayleigh Number (Ra)  
Cases with varying Rayleigh number were also under investigation. For these simulations, the volume fraction was kept at \(\varphi=0.03\), while the porosity was kept at \(\varepsilon=0.7\). The Hartmann number was set to \(Ha=100\).  
For the low Rayleigh values considered, a large clockwise rotating vortex was formed, as shown in Figure 8. The fluid rose from the left vertical wall, the location of the heat source, and flowed downward along the right vertical wall. By increasing the Rayleigh number, isotherms expanded gradually, as they emerged from the heat source and reached the rest of the walls. Furthermore, the flow regime became stronger, and the streamlines were intensified. For higher values of the Rayleigh number (\(Ra=5\times 10^{8}\)), the two anticlockwise rotating formations appeared in the stream function contour, and the distribution of the isothermal lines changed from conduction-controlled to convection-dominated. For small values of the Rayleigh number, a minute variation on the vorticity was observed near the walls of the cavity. Microrotation contours had a central vortex at the middle of the cavity that elongated diagonally for elevated values of the Rayleigh number.  
Figure 7: Average Nusselt number as a function of the Hartmann number, for characteristic Rayleigh numbers; \(\varepsilon=0.7\); \(\varphi=0.03\).  
The dependence of the average Nusselt number, \(Nu_{ave}\), on \(Ra\) is shown in Figure 9 for two \(Ha\) values, and two porosities of the medium, while the volume fraction was set to \(\varphi=0.03\). The increase of \(Ra\) resulted in an increase of \(Nu_{ave}\). For \(Ha=100\), a smaller increase of the Nusselt number was observed, as the presence of the magnetic field had a negative effect on the strength of the flow field.  
Figure 8: Stream function (\(\Psi\)), temperature (\(\theta\) ), microrotation (\(N\) ), and vorticity (\(\Omega\) ) contours for a range of Rayleigh numbers.
###### Abstract
The low image quality due to speckle noise, ill-defined boundaries of breast masses, and image distortions caused by shadowing or enhancement effects of B-mode images make CNN-based deep learning approaches challenging. In this study, we proposed an end-to-end CNN framework for BUS image analysis using multiple parametric images generated from ultrasound RF signals. We believe that the main obstacles to deep learning techniques for BUS analysis are the low quality of B-mode images and the limitations of the training datasets. Although the virtue of deep learning techniques is an automatic abstraction or extraction of features from raw data (or signal) through the training process, it is challenging to learn some features directly from raw data that are not represented clearly in their original forms because of either the image quality or the size of the training datasets. Therefore, in addition to B-mode images, various other images (called parametric images) generated from raw ultrasound RF signals in the time and spectral domains, which carry information concerning scanned tissue, are applied to the CNN as inputs in the proposed framework. The multiple parametric images generated from RF signals are (1) entropy image, (2) phase image, (3) traditional B-mode image in the time domain, and (4) attenuation image estimated in the frequency domain. While the entropy and phase images, generated in the time domain, represent microstructural features and anatomical information, respectively, attenuation images provide spectral characteristics of tissue that do not directly appear in a B-mode image but are closely related to the pathological status. Because the idea of multiple parametric images is an extension of data augmentation in a broad sense, it releases the limitation of dataset sizes in medical ultrasound applications while providing complementary information concerning distinct aspects of soft tissue in both the time and spectral domains. As one set of RF signals scanned from one patient creates multiple parametric images, the proposed end-to-end framework of a deep learning network adapts an ensemble learning scheme for multiple inputs to determine the final decision. Four well-known CNN models, namely VGG-16, ResNet-50, DenseNet-201, and EfficientNetV2-L, were selected as backbone networks. The experimental results of the traditional CNN-based deep learning network using only B-mode images were compared to those of the proposed ensemble architecture with multiple parametric images. Each backbone network is validated using a five-fold cross-validation method owing to the small size of the dataset. The remainder of this paper is as follows: The following section provides a brief explanation of the dataset and describes its properties. In addition, it presents the signal processing techniques for multiple parametric images (i.e., entropy, phase, and attenuation images) and their properties. Section 3 describes the proposed end-to-end CNN framework and the training parameters. Sections 4 and 5 present and discuss the experimental results, respectively. Finally, Section 6 presents the conclusions and future works.
# A Multi-Plant Height Detection Method Based on Ruler-Free Monocular Computer Vision
## 3 Principles and Methods
### Principle of Monocular Vision Measurement without Ruler  
The traditional monocular measurement of plant height with calibration objects compares the plant in the image screen with the ruler and establishes the relationship between the pixel size and the actual size through the ratio of the pixel height of the ruler to the actual height, so as to convert the pixel height information of the plant into the actual plant height value [7]. In the uncalibrated plant height measurement method, the scale is not set in the image, the scale is discarded as the source of the reference ratio between the pixel and the actual size, and the external parameters of the camera are used to provide the ratio. The monocular camera is set at different positions to shoot, and a proportional relationship is established according to the distance of the camera position shift and the pixel offset distance of the reference point in the image, which is used as a reference to convert the pixel height of the plant in the image to the real plant height value.  
In this study, two shots were used to obtain the image information. There was a displacement in the height direction between the two shots, and a proportional relationship was established between the displacement and the displacement of the reference point in the image. The secondary measurement model with monocular and uncalibrated objects is shown in the following Figures 4 and 5:  
Figure 3: Main process of treatment method.  
Figure 2: Example of acquired images. (**a**) The first shot and (**b**) the second shot.  
Based on the above model, it is possible to establish a relationship between the pixel size and actual size, with a proportion of k:  
\[\mathrm{k}=\frac{\Delta\mathrm{l}}{\Delta\mathrm{h}} \tag{1}\]  
\(\Delta\mathrm{l}\) is the mean of the difference between the two upper edges and the difference between the two lower edges to reduce the impact of accidental errors.  
It can be assumed that there is a scaling factor, k, between the size of each pixel in the image and the actual size, therefore, it is believed that any pixel size e has the following relationship with the actual size d:  
\[\mathrm{d}=\mathrm{k}\times\mathrm{e} \tag{2}\]  
\[\mathrm{H}=\mathrm{k}\times\mathrm{l}=\frac{\Delta\mathrm{l}}{\Delta\mathrm{h }}\times\mathrm{l} \tag{3}\]  
Therefore, by moving the camera to take two images, combined with the reference point pixel distance and the camera displacement distance, the plant height can be calculated without a ruler.
# Compensation Control Strategy for Orbital Pursuit-Evasion Problem with Imperfect Information
## 6 Results and Discussion
### Examples of Compensation Control Strategy
#### 4.2.2 Terminal Time is Fixed
When the evader plays without compensation for time delay, the evader's time-to-go \(t_{go}^{E}\) suddenly increases at 176.43 s. The reason is that the evader's delayed information misleads the calculation of \(t_{go}^{E}\). When the evader plays with compensation for time delay, the time-to-go corresponding to the pursuer and the evader slightly deviates from that of the no-delay scenario. The reason is that the compensation method cannot completely eliminate the effect of delayed information. Figure 14 shows the average control variable of 100 Monte Carlo runs between the players in three scenarios: the evader plays without time delay, without compensation for time delay, and with compensation for time delay. When the evader plays without time delay, the control trajectories are linear during the confrontation. When the evader plays without compensation for time delay, its control switches at 176.43 s. This phenomenon is caused by a sudden change in \(t_{go}^{E}\). The evader's control deviates from the guaranteed cost strategy due to the delayed information, which leads to the system state deviating from the expected trajectory. It thus gives the pursuer a chance to shorten the interception time. The pursuer's control decreases from 176.44 s due to the state change caused by the evader's control switch. When the evader plays with compensation for time delay, the pursuer's control and the evader's control slightly deviate from the no-delay ones due to the delayed information. Figure 12: The average relative distance of 100 Monte Carlo runs in the scenario that the evader plays (**a**) without time delay; (**b**) without compensation for time delay; and (**c**) with compensation for time delay.
# Application and Performance Evaluation of Resource Pool Architecture in Satellite Edge Computing
## 2 Related Works  
### Satellite Edge Computing  
In recent years, cloud and edge computing have been gradually introduced into space-based systems. At present, the research mainly focuses on the system architecture or the key technology in the application of edge computing to satellites.  
Regarding the architecture, [PERSON] et al. propose satellite mobile edge computing (SMEC), which integrates network resources through dynamic network virtualization technology, realizes satellite resource sharing, and improves QoS based on collaborative computing and collaboration technology [7]. [PERSON] et al. define and characterize orbital edge computing, which is similar to satellite edge computing (SEC) [8]. [PERSON] et al. combine mobile edge computing with satellite edge computing. They propose a satellite edge computing framework which realizes the integration of scattered satellite computing power and provides sufficient computing power for auxiliary processing of satellite big data and time-sensitive tasks [9]. [PERSON] et al. use LEO satellites as edge computing nodes to establish an edge computing satellite cooperation network in an emergency and combine the software-defined network model to guide inter-satellite link connection and resource sharing [10]. [PERSON] et al. propose a system architecture for edge computing in LEO networks and implement an LEC prototype system to verify their design [11].  
In addition to the system architecture, other research focuses on solving the problem of the application of edge computing on satellites. [PERSON] et al. are the first to propose a novel online service placement algorithm by leveraging the Lyapunov optimization and Gibbs sampling [12]. [PERSON] et al. focus on the edge server placement problem, that is, where the edge servers should be placed in LEO satellite networks. However, the key problem is that each satellite itself is an edge server. According to their placement algorithm, satellites that are not placed on edge servers will not work [13]. [PERSON] et al. analyze the architecture of satellite edge computing and propose a network slice scheduling for IoT support [14]. [PERSON] et al. discuss the offloading problem when artificial intelligence applications are learned and trained in orbit, but they skip the fact that artificial intelligence applications require a lot of computing resources, which means that such applications may not be able to run on satellites [15].  
These researchers are studying how to apply edge computing to satellites, or how to realize resource sharing through the satellite cooperation. However, they do not consider that the difference between satellites and ground servers lies in the strict limitation and mobility of satellite resources, nor do they optimize resource sharing from the perspective of the whole system.