esa-sceva/satcom-chunk-collection huggingface dataset:
# Retrospective Spectrum-Conversion Method Based on Time-Modulated Van Atta Array
## 2 Van Atta Array Backtracking Principle  
The Van Atta array is composed of multiple symmetrical antenna units. The two symmetrical antenna units are connected by transmission lines of isoelectric length. In this way, the EM waves received by each antenna are radiated by the symmetrical antenna after passing through the transmission line.  
As shown in Figure 1, the antennas are uniformly arranged on the z = 0 plane for a two-dimensional planar Van Atta array. Two symmetrical antenna elements are connected by transmission lines of equal electrical length. Any antenna element located at the position (\(x_{n}\), \(y_{n}\)) is connected to the antenna element located at (\(-x_{n}\), \(-y_{n}\)) through the transmission line of equal electrical length. For incoming waves at any azimuth angles \(\theta\) and elevation angles \(\varphi\) in space, the phases of the electromagnetic waves received by the two connected antenna units are \(\varphi_{n}^{r}(x_{n},y_{n})\) and \(\varphi_{n}^{r}(-x_{n}\), \(-y_{n})\), respectively, which can be expressed as  
\[\begin{cases}\varphi_{n}^{r}(x_{n},y_{n})=k_{in}(x_{n}\sin\phi\cos\theta+y_{n} \sin\phi\sin\theta)\\ \varphi_{n}^{r}(-x_{n},-y_{n})=k_{in}(-x_{n}\sin\phi\cos\theta-y_{n}\sin\phi \sin\theta)\end{cases} \tag{1}\]  
where \(k_{in}\) is the propagation constant of EM waves in space. Assuming that the length of the transmission line connecting two symmetrical antenna units is l, and its phase delay is \(\varphi_{l}\), then the initial phase of the received EM wave re-transmitted is  
\[\begin{cases}\varphi_{n}^{t}(x_{n},y_{n})=\varphi_{n}^{r}(-x_{n},-y_{n})- \varphi_{l}\\ \varphi_{n}^{t}(-x_{n},-y_{n})=\varphi_{n}^{r}(x_{n},y_{n})-\varphi_{l}\end{cases} \tag{2}\]  
Then the phase sum of the signals sent and received by each antenna unit is  
\[\begin{cases}\varphi_{n}^{t}(x_{n},y_{n})+\varphi_{n}^{r}(x_{n},y_{n})=\varphi _{n}^{r}(-x_{n},-y_{n})+\varphi_{n}^{r}(x_{n},y_{n})-\varphi_{l}\equiv-\varphi _{l}\\ \varphi_{n}^{t}(-x_{n},-y_{n})+\varphi_{n}^{r}(-x_{n},-y_{n})=\varphi_{n}^{r}(x _{n},y_{n})+\varphi_{n}^{r}(-x_{n},-y_{n})-\varphi_{l}\equiv-\varphi_{l}\end{cases} \tag{3}\]The phase sum of the signals sent and received by the array antenna unit is a certain value. It satisfies the generalized phase conjugation condition and can realize the direction backtracking function of the signal [42; 43; 44; 45].
# Service for Deploying Digital Twins of QKD Networks
## 4 Implementation
### QKD Network Digital Twin Orchestrator Application
In any of both scenarios presented above, once the machines are ready, the orchestrator leverages Ansible (through the Ansible python API) [48] to configure and start the QKD network nodes. Ansible is a widespread tool in the field of task automation since it allows the execution of tasks in remote hosts without having to deal with the specific configuration details of each system. It should be noted that Ansible performs configuration tasks in parallel in all the machines, so that the machines are configured simultaneously. To this, Ansible assigns a configuration process to each of the machines to be configured. However, the default number of parallel processes Ansible supports is five [49]; which implies that if there are five machines, the configuration tasks will take the same time as if there were one; but if there are six machines, the task will take twice as long, since in that case two rounds of parallel tasks have to be executed. Using Ansible, the orchestrator will download the required software in the machines, including Python and the pip package if the machines were not pre-deployed, along with the QKD Node package that contains the quantum emulator SimulaQron, and that is capable of communicating with client applications through the developed QKD API. Then, the orchestrator will start the QKD network digital twin by connecting through emulated quantum and classical channels the QKD nodes that the user had described as neighbors in the configuration document. From this point on, the QKD network environment is operational and applications can run over it. Both scenarios described in this section can be found in Figure 4. On the left side of the figure, the scenario in which the user has a pre-deployed machine pool is shown. On the right side, there is the case in which the OSM is used to deploy the needed machines.
# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
## 2 Materials and Methods
### Evaluation Methodology
#### 2.3.1 Statistical Parameters  
The statistical parameters of image quality can be divided into two categories according to the number of factors involved in the evaluation [28]: one is the single-factor evaluation index, including gray mean value, standard deviation, sharpness, and various entropy values of the image. The other is the comprehensive evaluation indicators, including spectral distortion, correlation coefficient, bias index, etc.  
All seven parameters' calculation formulas and descriptions, which are used in this study, are shown in Appendix B. Gray mean value is calculated by the sum of the gray values of all the pixels in the image divided by the number of pixels. The closer the gray average value to the multi-spectral image average, the smaller the spectral distortion; standard deviation provides a measure of the dispersion of image gray level intensities. The larger the standard deviation, the more scattered the gray level distribution, and the larger the contrast of the image, the more convenient the information extraction. The concept of information entropy describes how much information is provided by the image. Average gradient measures the gradient magnitude of an image and takes the variation of each of the adjacent pixels into account. The larger the average gradient, the higher the sharpness of the image.  
Correlation coefficient is the correlation between images, which is a statistical measure of the strength of the relationship. The larger the correlation coefficient between the fused image and the multi-spectral image indicates that the higher the degree of integration of high-frequency information, the smaller the degree of image spectral variation. Spectral distortion is a measure of mismatch between two signals based on their spectral properties. The greater the degree of distortion, the higher the degree of spectral distortion of the image. Bias index is an index of the deviation degree between the fused image and the low-resolution multispectral image. The smaller the deviation index, the higher the spectral information retention of the image before and after fusion.
## 6 Numerical Simulation Results  
Figure 25a,b shows the states of convergence of flow rates at the downstream end the upstream end, respectively. It can be seen that \(Q_{d}(t)\) converged to \(Q_{S}(t)\) with three iterative calculations. Figure 26 shows the longitudinal profile of the calculated maximum water level of the third iteration, along with the flood traces and the maximum water level measured by the water gauges installed at points A and B. The final value of Manning's roughness is also indicated in the figure.  
Figure 27 shows the temporal changes in the inundation area and in the inundation depth distribution: (a) inundation began from part of the low levee on the right bank, (b) inflow to the HDP started, (c) the HDP filled before the flood peaked, (d) inundation to the left bank side floodplain started, and (e) the inundation flow from upstream arrived at the second drainage gate.  
Figure 24: Time lag obtained by the preliminary calculation.  
Figure 25: Time lag obtained by the preliminary calculation. (a) Downstream end. (b) Upstream end.  
Figure 28 shows the calculation results of the water level at the second drainage gate at the most downstream end of the floodplain. The arrows above the curve reflect events reported by the River Administration Bureau based on a video taken by neighboring citizens and the gate operation record. The black dotted curve is the water level record on the land side of the gate, and the blue curve is the calculated water level. Before the gate was closed around 12:00, the observed water level rose due to the inflow from the river, and the record stopped then due to the submergence of the water level gauge. The water level became stationary after the gate closed, but it rose rapidly at around 17:00 because flow arrived from the upstream floodplain. Around 18:00, levee overtopping began from the land side toward the river channel, and the embankment above the water gate collapsed around 24:00. The calculated water level variation agreed with the recorded events fairly well.  
Figure 27: Variation of modeled 2019 inundation depth distribution: each depiction time in (**a–e**) is indicated in the hydrograph in (**f**).  
Figure 29 shows the time variation of each overflow component (\(q_{A},\ q_{B},\ q_{C},\ q_{H},\ q\)\(D\), and \(q_{E}\)) together with the river flow rate at the upstream and downstream ends (note the difference in scale on the vertical axes). The inflow into the HDP, \(q_{H}\), ended in the first half of the flood and did not contribute to the lower level of the second flood peak. Instead, the overflow from upstream point A reduced the second flow peak effectively. This inundation flow arrived at the second drainage gate 2 h later, and the embankment above the drainage gate was broken by overtopping, as shown in Figure 28.  
The numerical simulation results described above show good overall agreement with the observations.
# Infrared Dim and Small Target Detection Based on Local-Global Feature Fusion
## 2 Methods  
We present the proposed local-global feature fusion from dual input for dim and small target detection in detail. Shown in Figure 1 is the basic network architecture.  
YOLOv7 strikes a good balance between speed and accuracy, making it ideal for applications that require real-time detection. The structure of YOLOv7 ([PERSON] et al., 2018) is based on the cross stage partial network (CSPNet), which consists of a backbone network and a head network. The backbone network primarily comprises conv-batchnorm-SiLU (CBS), max-pooling (MP), and efficient layer attention network (ELAN) modules. ELAN effectively combines shallow and deep features through a multi-channel branching structure, which captures detailed information of the target at different scales without significantly increasing computational cost. This is especially important for detecting small targets and handling complex backgrounds. The head network consists of three target detectors that use a grid-based anchor mechanism to detect targets across feature maps of different scales. With the infrared dim and small target size of 0.15%, which falls within the small range, using multiple inspection heads to detect targets on feature maps of different resolutions can improve both the comprehensiveness and accuracy of detection. Building upon the YOLOv7 architecture, our model strategically divides the input image into four quadrants, carefully balancing the trade-off between computational speed and detection accuracy. Specifically, we enhance the feature information related to dim and small targets by magnifying the local areas within the image. This localized information is then fused with the global features extracted from the entire infrared image, resulting in improved details for specific objects of interest. Additionally, our approach increases the receptive field, expanding the spatio-temporal context of the target. This is achieved through a dual-branch fusion mechanism that combines multi-scale feature information, allowing the weight distribution of the feature map to be dynamically adjusted. Further elaboration and specific scenarios are detailed in the remainder of this paper.
## 3 Comparison of Different Surrogate Models and Motor Optimization Design  
Considering that the 37 kW,160 rpm low-speed high-torque permanent magnet motor needs to be used on the pumping unit, the output torque of the motor needs to be more than four times the overload multiple. Considering the performance requirements and application scenarios of this motor, the pole-slot ratio scheme of 70P168S is more suitable. The 70P168S pole-slot ratio and motor were selected for the comparison of different surrogate models and the motor optimization design.  
Figure 7 is the cross section of the stator and rotor unit motor of the 70P168S motor. Among them, Wt is the tooth width, B80 is the slot opening, Tp is the thickness of the permanent magnet, \(\alpha_{\text{p}}\) is the pole arc coefficient of the permanent magnet, and \(\delta\) is the air gap length.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter Name** & **70P84S** & **70P168S** & **80P90S** & **80P180S** \\ \hline Rated voltage (V) & 350 & 350 & 350 & 350 \\ Rate output power (kW) & 37 & 37 & 37 & 37 \\ Rated speed (rpm) & 160 & 160 & 160 & 160 \\ Solid loss (W) & 133 & 31 & 106 & 26 \\ Core loss (W) & 483 & 614 & 511 & 723 \\ Copper loss (W) & 696 & 852 & 711 & 785 \\ Efficiency (\%) & 96.14 & 95.64 & 96.11 & 95.7 \\ Power factor & 0.96 & 0.915 & 0.958 & 0.97 \\ Current density (A/mm\({}^{2}\)) & 2.6 & 2.7 & 2.63 & 2.67 \\ Electric load (A/mm\({}^{3}\)) & 25.1 & 26.1 & 23.7 & 24.2 \\ Thermal load (A\({}^{2}\)/mm\({}^{3}\)) & 65.26 & 70.47 & 62.33 & 64.62 \\ Torque ripple (\%) & 7.9 & 1.4 & 1.75 & 3.96 \\ No-load back EMF (V) & 330.3 & 327 & 328.9 & 329.7 \\ PM weight (kg) & 12.2 & 12.2 & 12.2 & 12.2 \\ Maximum torque (kNm) & 7.72 & 9.9 & 7.05 & 8.5 \\ Overload multiples & 3.5 & 4.48 & 3.19 & 3.85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Electromagnetic performance of four pole–slot ratio motors.  
Figure 6: Performance of four pole-slot ratio motors. (**a**) Efficiency and Power factor. (**b**) motor loss.
# A Decentralized Multi-Agent-Based Approach for Low Voltage Microgrid Restoration
## 1 Introduction
### Literature Review
For instance, when new loads or generators are installed, the centralized control schemes may need to be redesigned. To avoid these shortcomings, the decentralized control scheme is introduced. One of the most popular decentralized control solutions is the multi-agent system (MAS). The MAS has the advantage of surviving single-point-failure, and it can do the decentralized data processing which, in turn, leads to task distribution and faster decision-making process [24]. MASs need to share the information process among the agents. The problem of communication of the agents can be solved by the average consensus algorithm (ACA). ACA shares the information among the agents in a distributed way to achieve an agreement on a common decision. This algorithm is widely employed in different areas including the collective behavior of swarms [25], random networks [26; 27], formation flight control of multi-unmanned aerial vehicle (UAV) system [28], cooperative control of satellites [29], networks of cameras [30], and coordination and control of mobile robots [31].
# Finite Element Method (FEM) Modeling of Laser-Tissue Interaction during Hair Removal
###### Abstract  
In this study, a comprehensive and realistic model of laser light interaction with skin and hair was constructed. The model was applied to study the characteristics of laser-tissue interaction for the deeply penetrating Nd:YAG laser. Three types of finite element method (FEM) models were developed. In the first model, the hair shaft grew straight out of the follicle; in the second model, it grew at a variable angle; and in the third model, an array of hair was considered. The transport equation and heat diffusion equation were solved with the mesh-based Monte Carlo method and partial differential equations, respectively. The results of the simulations indicated that the area of necrosis increased with increasing fluence; cooling had a limited effect on the extent of necrosis, particularly at a fluence of \(80\,\mathrm{J/cm^{2}}\). The thermal damage to hair follicles on the periphery of an irradiated array of hair may be insufficient for achieving necrosis. The pulse itself and the short cooling-down period after the pulse contributed the most to the final thermal damage to the hair follicle. The FEM modeling of laser-tissue interaction has proven to be a useful tool for studying the influence of different therapeutic parameters on the resulting hair and skin damage.  
laser; hair removal; finite element method; FEM; modeling; Monte Carlo method; heat diffusion; necrosis; hair follicle +
Footnote †: journal: Applied Sciences
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 3 Methodology
### Automatic Point of Interest Recognition and Tracking
#### 3.2.5 Point of Interest Mask Refinement  
When a POI is recognized, it is important to ensure that the region to be tracked is as accurate as possible. As the initial POI mask and bounding box are derived from two points of the saliency detection, there are instances where the SAM output is suboptimal for tracking. To address this issue, the mask and bounding box of the POI are further refined using the available POI recognition information, thereby enhancing the subsequent tracking process.  
Figure 4: Example visualization of the predicted mask and derived bounding box by the Segment Anything Model (SAM) when prompted with the most and least salient point.  
The POI recognition step yields multiple keypoint matches between the salient object crop and the reference POI image. These keypoints are now used to reprorupt SAM, resulting in an improved segmentation mask. To optimize the refinement process, not all keypoint matches are used. First, only keypoints that fall within the non-refined SAM mask are selected, eliminating keypoints of the environment, such as trees or the road, which can negatively impact the refinement. Second, five keypoints that are maximally apart from each other are chosen. This approach ensures a limited set of points that still provides good coverage over the entire POI area, as using too many prompt points can reduce the segmentation accuracy of the SAM model.  
Figure 6 illustrates an example of this refinement procedure. The left image shows the SAM segmentation based solely on the saliency information, while the right image displays the refined mask incorporating the keypoint matching information. Initially, the mask incorrectly includes part of the road, which is resolved in the refinement step. By employing this refinement technique, the tracking process can be initiated with a more reliable and representative area of the POI.
# Monoterpenoids Evolution and MEP Pathway Gene Expression Profiles in Seven Table Grape Varieties
## 2 Results and Discussion
### Correlation of Genes Expression and Monoterpenoids Accumulation  
To study the relation between the expression of genes in MEP pathway and the accumulation of monoterpenoids in all the seven varieties studied here, the Pearson's correlation analysis was carried out (Table 4). It was found that the _VvDXS3_ expression was strongly positively correlated with most of the 25 monoterpenoids except four of them, including _trans_-furan linalool oxide, _cis_-furan linalool oxide, _cis_-furan linalool oxide, and 4-terpineol. This indicated that the differential expression of the _VvDXS3_ might play an important role in diversifying the monoterpenoids profiles in table grape varieties. The high expression levels of _VvDXS1_ and _VvDXS3_ at the maturity stage might be responsible for the high concentrations of monoterpenoids in 'Xiangfei'.  
_VvDXR_ and _VvGPPS_ showed a positive correlation with all monoterpenoids; _VvHDR_ had a positive correlation with 24 monoterpenoids but a negative correlation with _trans_-furan linalool oxide. In 2021 [PERSON] et al. [23] studied the correlation of the expression of genes in MEP pathway with monoterpenoids in 'Shine Muscat' and found that _VvDXR_, _VvGPPS_, and _VvHDR_ were positively correlated with all detected monoterpenoids, which was contrary to our finding that _VvHDR_ has a negative correlation with _trans_-furan linalool oxide, speculating that was due to the different varieties.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & _VvDXS1_ & _VvDXS3_ & _VvDXR_ & _VvHDR_ & _VvGPPS_ \\ \hline \(\beta\)-Myrcene & 0.482 * & 0.609 ** & 0.410 & 0.351 & 0.426 \\ Limonene & 0.737 ** & 0.652 ** & 0.511 * & 0.391 & 0.576 ** \\ \(\beta\)-_trans_-Ocimene & 0.643 ** & 0.728 ** & 0.498 * & 0.457 * & 0.534 * \\ \(\beta\)-_cis_-Ocimene & 0.599 ** & 0.668 ** & 0.430 & 0.360 & 0.473 * \\ \(\gamma\)-Terpinen & 0.661 ** & 0.651 ** & 0.441 & 0.314 & 0.505 * \\ Terpinolen & 0.587 ** & 0.556 * & 0.346 & 0.232 & 0.443 \\ _trans_-Rose oxide & 0.467 * & 0.573 ** & 0.336 & 0.244 & 0.390 \\ _cis_-Rose oxide & 0.411 & 0.518 * & 0.270 & 0.170 & 0.316 \\ (_trans_,_cis_)-Allo-ocimene & 0.636 ** & 0.598 ** & 0.427 & 0.326 & 0.455 * \\ Allo-Ocimene & 0.682 ** & 0.644 ** & 0.408 & 0.379 & 0.509 * \\ _trans_-furan linalool oxide & 0.713 ** & 0.323 & 0.223 & \(-\)0.006 & 0.370 \\ _cis_-furan linalool oxide & 0.684 ** & 0.365 & 0.264 & 0.029 & 0.378 \\ Citronellal & 0.550 * & 0.749 ** & 0.715 ** & 0.786 ** & 0.733 ** \\ Nerol oxide & 0.518 * & 0.426 & 0.150 & 0.004 & 0.309 \\ Linalool & 0.693 ** & 0.598 ** & 0.441 & 0.301 & 0.503 * \\
4-Terpineol & 0.666 ** & 0.434 & 0.338 & 0.132 & 0.432 \\ Neral & 0.359 & 0.626 ** & 0.472 * & 0.489* & 0.447 * \\ \(\alpha\)-Terpineol & 0.676 ** & 0.619 ** & 0.449 * & 0.330 & 0.506 * \\ Geranial & 0.252 & 0.589 ** & 0.449 * & 0.490 * & 0.402 \\ \(\beta\)-Citronellol & 0.366 & 0.528 * & 0.264 & 0.215 & 0.300 \\ \hline \hline \end{tabular}
\end{table}
Table 4: [PERSON]’s correlation between terpene volatiles and MEP pathway gene expression in seven table grape varieties.
## 3 Results and Discussion
### Image Generation Results
Specifically, the generated images accurately simulate the complex details found in real images, including the uneven distribution of large and small pores, fine cracks between particles, and the intricate mixture of clay and pores. The accurate reproduction of these details not only confirms the model's high precision in simulating real images but also demonstrates the effective guarantee of diversity in the generated images. Combining the quantitative and qualitative evaluation results, we can conclude that this generative model excels in simulating real images and achieves outstanding visual quality. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Total Training** & **Batch Size** & **IS** & **KID** & **LPIPS** \\ \hline \multirow{3}{*}{500,000} & 4 & 1.0120 & 7.4536 & 0.5521 \\  & 8 & 1.4896 & 5.3625 & 0.5405 \\  & 16 & 1.6057 & 4.9057 & 0.5284 \\ \hline \multirow{3}{*}{1,000,000} & 4 & 1.6318 & 4.4064 & 0.5272 \\  & 8 & 1.8869 & 3.3446 & 0.5031 \\  & 16 & 2.0023 & 2.0271 & 0.4399 \\ \hline \multirow{3}{*}{2,000,000} & 4 & 2.0048 & 1.1501 & 0.4385 \\  & 8 & 2.1913 & 0.4203 & 0.4073 \\ \cline{1-1}  & 16 & 2.2428 & 0.2132 & 0.3965 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effects of various key training parameters on the performance of the generative model.