esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 3 Satellite Ground Verification System and Algorithm Hardware Acceleration Strategy
### The HISEA-1 Satellite and Its On-Board Image Processing Load  
At 12:37 noon Beijing time on December 22, 2020, the HISEA-1 satellite that was developed by Spacety was successfully launched on the CZ-8 rocket. The HISEA-1 satellite represented the first SAR remote sensing satellite constructed for scientific observation of oceans and coastal zones by a domestic university. It is also the first star of the planned HISEA series of small satellite constellations. The constellation will include multiple small and light SAR satellites and multi-spectral satellites that can be utilized by various field observation systems. Table 1 lists the requirements for the HISEA-1 mission. On 25 December 2020, the first batch of images from the HISEA-1 satellite were successfully obtained. To reduce the pressure of data transmission, the satellite platform is equipped with an intelligent information processing module for technical exploration of on-board intelligent image processing. This module is designed to extract effective information from remote sensing images and, thereby, reduce the data load sent to the measurement and control station and improve the efficiency of data acquisition.  
Figure 5: Schematic of the ground verification system.  
The on-board image processing load of the HISEA-1 satellite includes two parts, one is the SAR image preprocessing module and the other is an intelligent application module. Pre-processing includes data decoding, radiometric calibration, and geometric correction. The intelligent application module is responsible for real-time ship detection and detection of ground changes. Figure 6 shows the processing flow. Using the pre-processed images, the on-board ship detection process will extract the target image patches and position information, producing the effective information that will be transmitted to the ground receiver through the downlink. At the same time, the ground control system can monitor satellite transmission data in real time and communicate ground instructions to the satellite and, by continually updating the algorithm model parameters in the satellite's intelligent processing unit through the satellite upload channel, it can be tasked to complete more on-board processing tasks.
## 5 Experiments and Results
### Payload  
Payload is a variable feature in the proposed data-hiding scheme, as it depends of entropy in each sub-band of each audio clip. Figure 14 shows the payload results for the audio dataset.  
The payload mean, median, and variance were calculated as \(184.54\), \(195\), and \(3,088\), respectively; the minimum and maximum payload values were \(59\) and \(298\), respectively, for \(5\)-s audio clips.
## 3 Experimental Results and Discussions
### Experimental Results
Figure 14: Simulated and measured normalized radiation patterns at 20 GHz in the (**a**) x–z plane and (**b**) y–z plane. Figure 15: Simulated and measured normalized radiation patterns at 28.5 GHz in the (**a**) x–z plane and (**b**) y–z plane. To highlight the advantages of the proposed antenna, we compared the proposed antenna with other similar designs and summarized the comparison in Table 4. The dual-band, dual-CP array reported in [7] has a wide AR bandwidth due to the sequentially rotated feed network. However, the higher- and lower-frequency elements of this array are coplanar, resulting in a significantly larger electrical aperture size than a stacked structure design similar to our proposed approach. Compared with other dual-band, dual-CP arrays implemented using PCB lithography in [8,9], the proposed array has a higher efficiency and power capacity due to the full metallic-body construction that helps avoid dielectric losses. Moreover, the combination of dual-band, dual-CP capability with high efficiency, compact aperture size, and low-cost fabrication emphasizes the significance of our proposed design in comparison to various GW-based antenna works [19, 20, 21, 22]. Indeed, the antenna presented in the current work provides the highest efficiencies among all GW-based, dual-band, dual-polarization antennas listed in Table 4. The only work reporting a slightly higher efficiency than our current study is [19], which presented a single-band, single-CP antenna. Furthermore, the SLA 3D printing process significantly reduces the cost, fabrication complexity, and weight of the antenna structure when compared to using the conventional CNC machining method.
# A UAV-Based Visual Inspection Method for Rail Surface Defects
###### Abstract  
Rail surface defects seriously affect the safety of railway systems. At present, human inspection and rail vehicle inspection are the main approaches for the detection of rail surface defects. However, there are many shortcomings to these approaches, such as low efficiency, high cost, and so on. This paper presents a novel visual inspection approach based on unmanned aerial vehicle (UAV) images, and focuses on two key issues of UAV-based rail images: image enhancement and defects segmentation. With regards to the first aspect, a novel image enhancement algorithm named Local Weber-like Contrast (LWLC) is proposed to enhance rail images. The rail surface defects and backgrounds can be highlighted and homogenized under various sunlight intensity by LWLC, due to its illuminance independent, local nonlinear and other advantages. With regards to the second, a new threshold segmentation method named gray stretch maximum entropy (GSME) is presented in this paper. The proposed GSME method emphasizes gray stretch and de-noising on UAV-based rail images, and selects an optimal segmentation threshold for defects detection. Two visual comparison experiments were carried out to demonstrate the efficiency of the proposed methods. Finally, a quantitative comparison experiment shows the LWLC-GSME model achieves a recall of 93.75% for T-I defects and of 94.26% for T-II defects. Therefore, LWLC for image enhancement, in conjunction with GSME for defects segmentation, is efficient and feasible for the detection of rail surface defects based on UAV Images.  
Keywords:rail surface defect; UAV image; defect detection; gray stretch maximum entropy; image enhancement; defect segmentation +
Footnote †: journal: Applied Sciences
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
###### Abstract
[PERSON] [28] evaluated the performance of de-speckle filters by using a dataset of 64 B-Mode liver ultrasound images based on three kinds of measurements. [PERSON] et al. [29] made a comparison of eleven de-speckle filters for the BUS images and simulated images based on several comparing methods, such as blind image quality metric (NIQE). However, it did not discuss the computational performance of the filters and the filtering effect for the subsequent processing, such as segmentation. [PERSON] et al. [30] made a comparison of eleven spatial de-speckling filters for Ultrasound based on some performance metrics, such as mean square error. However, there are a lot of de-speckling filters which are not spatial filters, such as Wavelet Transform Filter (WTF). Motivated by the application of ultrasound breast images, we systematically evaluate eight de-speckling filters according to the categories of filters discussed above. The evaluation consists of the performance of visual measurement, the filtering effect on tumor region segmentation accuracy and computational performance. In relation to the BUS images, the degree of approximation of the estimated image cannot be measured due to the inaccessible original image. Hence, reasonable parameters should be set to compare and analyze the performance among different types of filtering algorithms. In most cases, not only us, but also the doctors pay close attention to the visual measurement. This includes the degree of smoothness in the homogeneous region and the clarity of detailed features in the edge region. In this paper, Mean to Variance Ratio (VMR) [31] which is also named as equivalent number of looks and Laplace Response of Domain (LRD) [32] are chosen to evaluate the key performance, i.e., the degree of smoothness in the homogeneous region and clarity of edge detailed features in the edge region, respectively. Moreover, we show the filtering effect on tumor region segmentation accuracy. Referring to experimental results, a general conclusion discusses the cause of different performances between different filters, parameter settings and improvement of some certain filters.
## 3 Finite Element Setup
### Simplified Welding Simulation  
Full FE simulation of welding is a coupled thermo-mechanical simulation, which includes mechanical deformation and captures grain growth within the FE calculation. Such an approach delivers numerous important outputs, such as residual stresses. In practice, supporting ultrasonic inspection only requires approximate local grain orientations. Moreover, the coupled thermo-mechanical simulation is costly, hindering its implementation in imaging workflows. To address this, this paper proposes a simplified approach in which full FE simulation is used to determine thermal gradients, and a phenomenological loop (analogous to that in MINA [9]) captures grain growth in the post-processing stage. In this approach, the deformation resulting from welding is not taken into account, and grain growth effects are only approximated. Consequently, significant computational savings are possible since the thermal calculations are less time-consuming. In general, the heat source applied during welding produces beads in which the grains grow along the maximum thermal gradient during cooling. This approach is sufficient to determine a realistic grain orientation, ignoring the effect of geometric distortion. Moreover, several welding processes and complex geometries can be investigated using this approach. By neglecting mechanical effects, the computational cost is significantly reduced, allowing large-scale 3D calculations to be implemented. User-specified subroutines were used to implement grain orientation and growth calculation models for the entire welded area. The grain orientation calculation is based on the MINA model, as mentioned above  
\[n=0,\ \alpha_{0}=\alpha_{\text{lower box}} \tag{6}\]  
\[t_{n}=\begin{cases}cos(\alpha_{n-1}-\alpha_{S}),&\text{if }\alpha_{n-1}- \alpha_{S}<\pi/2\\ 0,&\text{otherwise}\end{cases} \tag{7}\]  
\[\alpha_{n}=t_{n}\alpha_{n-1}+(1-t_{n})\alpha_{S} \tag{8}\]  
where \(\alpha_{n}\) is the grain angle, \(\alpha_{S}\) is the direction of the effective temperature gradient, \(t_{n}\) is the parameter representing the change in orientation, and \(n\) is the number of iterations used. This parameter \(n\) represents the transition between microscopic and macroscopic scales. Unfortunately, the value of \(n\) in the FE calculation depends on the cooling rate, so its value is fixed with an equal change in temperature. However, the changes in the temperature gradient are only considered between the beginning and the end of solidification.
# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 3 Methodologies  
### Iga  
In order to adapt to TTSA, the genetic selection operator, genetic crossover operator, and genetic mutation operator are improved, as follows:  
1. The selection operator (SO) adopts the elitist retention strategy and increases the parameter of selection factor. When selecting the population, it retains a certain proportion of excellent parents, and enters the selection range together with the offspring, so as to achieve the optimal individual survival rate in the process of genetic evolution. In order to ensure the diversity of the population, the algorithm replaces the duplicate individuals in the new population during the execution of the selection operator in each iteration.
2. The improved crossover operator (ICO) adopts the traditional genetic crossover processing according to the arrangement sequence that each individual is assigned to the equipment based on the task. Usually, the crossover point is determined, the sequence before the crossover point is reserved, and the sequence after the intersection point of two individuals is exchanged to generate new individuals. However, for two permutation sequences, there is a high probability of conflict after cross-processing, which is reflected in the issue of TTSA; that is, one task is assigned to two equipment items or two tasks are assigned to one equipment item. This paper proposes a method of the cyclic exchange of packets. The two sequences are divided into multiple packets according to the cyclic exchange calculation between two parents using a tracking calculation, and then one or more packets whose size is close to half of the length of the individual sequence are selected as the reservation or exchange reason, thus avoiding the occurrence of node gene conflict after the crossover operation and achieving the purpose of preserving all excellent genes. The specific operation is shown in the example provided in Figure 3.
3. The improved mutation operator (IMO): In order to ensure the diversity of the population and avoid falling into the local optimal trap, the mutation operator uses the random selection of two positions in the sequence to exchange the transmission tasks performed by the two positions of the transmission equipment to generate new mutation individuals. The specific operation is shown in the example provided in Figure 3.  
Figure 2: Process of ICO.
# Airfoil Optimization Design Based on the Pivot Element Weighting Iterative Method
## 5 Conclusions  
The CST method is widely used in the airfoil parameterization for the airfoil aerodynamic design. The low order Bernstein polynomial, which can reach the requirement of the fitting precision, can be applied to fit the symmetric NACA0012 airfoil and small camber RAE2822 airfoil. But the low order Bernstein polynomial cannot reach the fitting precision for the large camber S1223 airfoil. So the number of control parameters needs to be added. The more the number of control parameters is, the closer the coefficient matrix of CST is to morbidity. Therefore, the PEWI method is proposed to improve the ill-conditioned matrix and this method is suitable for the optimization design of airfoil. And the robustness of the CST method can be enhanced.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(C_{\text{L}}\) & \(C_{\text{D}}\) & \(C_{\text{L}}\)/\(C_{\text{D}}\) & \(\Delta C_{\text{L}}\) & \(\Delta C_{\text{D}}\) & \(\Delta C_{\text{L}}\)/\(C_{\text{D}}\) \\ \hline Initial airfoil & 1.5119 & 0.0321 & 47.1 & & & \\ Optimized airfoil (RBF) & 1.5247 & 0.03165 & 48.17 & 0.85\% & \(-1.40\%\) & 2.27\% \\ CFD & 1.5244 & 0.03165 & 48.16 & 0.83\% & \(-1.40\%\) & 2.25\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Aerodynamic performances of the initial and optimized airfoils.  
Figure 26: Pressure coefficient distribution of the initial and optimized airfoils.  
Figure 25: Velocity streamlines of the initial and optimized airfoils near the trailing edge at \(\alpha=4^{\circ}\).  
Setting the range of design parameters is the crucial step for the airfoil optimization. So the normalization method of design variables is put forward in this paper and it is helpful for the airfoil optimization to obtain the suitable design parameters.  
The samples of aerodynamic coefficients for the S1223 airfoil are generated by the optimal Latin hypercube design method. The RBF neural network is trained by these samples. The S1223 airfoil is optimized to obtain the maximum lift-drag ratio by GA. The optimum airfoil has the higher camber by comparison with the initial airfoil. The optimum results in which the lift-drag ratio increases by 2.27% and the drag coefficient decreases by 1.4% are good values for the airfoil design.  
Methodology, X.L.; Validation, X.L.; Writing-original draft, X.L.; Writing-review & editing, W.H.  
This research received no external funding.  
The authors declare no conflicts of interest.
## 3 Design Process
### Data Analysis Methods and Statistical Considerations
#### 3.6.3 Planned Statistical Considerations for the Concept Validation  
Descriptive statistics (mean, standard deviation, and range) will be used to summarize the baseline characteristics of the study participants, including age, gender, injury history, and pre-intervention performance measures. For within-group comparisons, changes from baseline to post-intervention will be assessed using paired \(t\)-tests or Wilcoxon signed-rank tests, depending on the normality of the data. For between-group comparisons, differences between the experimental group (DAid(r) biofeedback system) and the active comparator group (FIFA 11+ program) will be analyzed using independent \(t\)-tests (for normally distributed data) or Mann-Whitney U tests (for non-normally distributed data). These comparisons will assess differences in injury rates, biomechanical changes, and performance improvements. Repeated measures Analysis of Variance (ANOVA) will be used to assess changes over time (e.g., pre-intervention vs. post-intervention) for continuous outcomes like biomechanical parameters (COP1x, COP1y) and performance metrics, considering within-subject correlations and tracking changes across multiple time points. Cohen's d will be calculated to measure the magnitude of differences between groups, particularly for key performance and biomechanical outcomes. An effect size greater than 0.8 will be considered large, indicating a significant practical difference between the groups. Pearson or Spearman correlation tests will be used to assess the relationships between biomechanical parameters (e.g., COP1x) and injury rates. A strong negative correlation would suggest that improvements in biomechanical alignment are associated with lower injury incidence. If necessary, multivariate regression models will be used to adjust for potential confounding variables, such as baseline performance levels, age, gender, or previous injury history. This will allow for a more comprehensive understanding of the relationship between the DAid(r) biofeedback system and injury prevention outcomes.
# Analysis of the Energy Efficiency of a Pneumatic Booster Regulator with Energy Recovery
## 3 Energy Efficiency Evaluation Method for Booster Regulator with Energy Recovery (VBA-R)
### Pressure Response of VBA-R
#### 3.3.1 Flow Rate Characteristics Equation  
The air flows in and out of the chambers through the check valve or throttle valve, and all of the mass flow rate of air can be calculated based on the equations given in ISO6358 [18]. However, when we used these equations in the numerical simulation, it was unstable for an accurate simulation and we had to set the steps to be very small in order to avoid divergence. Because of this numerical inaccuracy, the pressure ratio may surpass 1 when it is close to 1, which then leads to a negative value under the radical sign. This might lead to unknown errors or uncertain results, which would stop the simulation. To avoid these problems, we introduced the laminar flow model proposed by [PERSON] [19] for use when the pressure ratio is approximately 1. In that model, subsonic flow is taken as laminar flow when the pressure ratio is greater than \(\beta\) (\(\beta\) ranges 0.995 to 0.999). Then, a redefined flow rate characteristic equation can be given as:  
\[G=\left\{\begin{array}{ll}k_{1}p_{1}\Big{(}1-\frac{p_{2}}{p_{1}}\Big{)} \sqrt{\frac{\theta_{0}}{\theta_{1}}}&\frac{p_{2}}{p_{1}}\geq\beta\\ Cp\rho_{0}\sqrt{\frac{\theta_{0}}{\theta_{1}}}\Bigg{(}1-\Big{(}\frac{\frac{p_ {2}}{1-b}}{\frac{p_{1}}{1-b}}\Big{)}^{2}\Bigg{)}^{0.5}&b<\frac{p_{2}}{p_{1}}< \beta\\ Cp_{1}\rho_{0}\sqrt{\frac{\theta_{0}}{\theta_{1}}}&\frac{p_{2}}{p_{1}}\leq b \end{array}\right. \tag{7}\]  
where \(k_{1}\) is the linear gain:  
\[k_{1}=\frac{1}{1-\beta}\cdot C\cdot\rho_{0}\sqrt{1-\Big{(}\frac{\beta-b}{1-b} \Big{)}^{2}} \tag{8}\]
## 4 Experimental Results
### Power Spectrum  
Looking at the strength of the GA power spectrum on the 3-40 Hz frequency interval (Figure 8a), we notice higher power for Uni perception for the \(\alpha\) band (8-12 Hz) for all channels (Figure 8b) and lower for the perception in the \(\beta\) and \(\gamma\) bands, between 20 Hz and 35 Hz for the temporal channels (Figure 8c). The effect is in line with the literature stating that more complex processes decrease in frequency. The fact that the parietal sites do not show visible differences in higher bands for Nat and Frac perceptions might relate to the fact that the thought process of evaluating images complexity is comparable. The visible differences in frequency for the temporal sites might indicate, access to memory, since it's natural for the human mind to correlate forms and structures with already known objects [74; 75]; process which is easier for Nat images since they represent natural textures and they are recognizable to some extent (as stated by subjects). While for Frac images perception, the correlation is far to be straightforward and if one would imagine a recog  
Figure 7: GA ERDs over all blocks, for the 3 types of images: Uni (blue), Nat (red), Frac (orange), considering all channels and the: (**a**) \(\alpha\) (8–14 Hz) and (**b**) \(\beta\) (20–28 Hz) frequency bands. The shaded grey area marks the considered baseline period (\(-\)200,0) ms.  
nizable form, the thought process will be more difficult (therefore the decrease in power in the temporal sites, more pronounced for Frac images, in Figure 8c). For example, one subject specified that for Frac images he was thinking at sand grains; but of course, for a conclusion to be made, this fact need to be analyzed separately in a study on each subject, relating to its exact thought process.  
For more details on the neural modulations modulations within different frequencies over time, as related to perception and decision-making, see the Event-Related Spectral Perturbations (ERSP) investigation in Figures A4-A6 in Appendix F.
## 5 Results and Discussion
### Overall Performance Evaluation
#### 5.3.1 Overall Residual Energy of the Network  
As shown in Figure 20, the proposed method significantly outperforms other methods in terms of residual energy. This is primarily due to the careful selection of relay nodes and the minimisation of energy consumption during inter-cluster communication. In particular, the proposed method maintains higher residual energy compared to RLEACH, CRPFCM, and EERPMS, thereby ensuring prolonged network effectiveness.  
In heterogeneous sensor networks, cluster heads consume significantly more energy than normal nodes due to additional responsibilities such as data aggregation and relaying. As a result, they deplete their energy more quickly. However, by considering nodes with greater resources for relaying operations and minimizing overall energy consumption, the proposed method efficiently balances the network load and extends the operational lifetime of the network.  
Figure 19: Comparison of residual energy of nodes with the proposed relay-node rotation scheme: (a) alpha = 0.6; (b) alpha = 0.7.  
Figure 20: Comparison of residual energy of network.