esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The shifting pattern of the Ganges-Padma River in Bangladesh is examined using satellite images. The meandering Ganges-Padma River has recently transformed into a braided river. Satellite imageries from 1973-2016 are used to compute the sinusity ratio and braiding index (two measures that allow for the trend analysis). It turns out that both the sinusity ratio and the braiding index went up with time, surpassing the cut-off for meandering. Overall, the braiding index variation exceeds the sinusity variation.  
braiding index; changing rate; sinusity ratio; Ganges-Padma; Bangladesh 2022 12 11604 14 November 2022
# Design, Manufacture and Wind Tunnel Test of a Modular FishBAC Wing with Novel 3D Printed Skins
## 2 Design Requirements  
The main objective of this design and manufacturing process is to build a wind tunnel wing model for a quasi-2D wind tunnel test (i.e., a wing that spans the test section). In this application, the wing has to span a 1-m test section; therefore, it was designed to have a wingspan of \(b=995\,\mathrm{mm}\), so that a 2.5 mm gap exists between the wing-tips and the tunnel walls to avoid interference. A NACA 23012 airfoil modified with a trailing-edge tab with a 270 mm chord length was selected. This airfoil matches that of a Bo-105 helicopter, which is the baseline rotor system of the EU Horizon 2020's Shape Adaptive Blades for Rotocrraft Efficiency (SABRE) project [25], for which this work was carried out. The FishBAC device is located in the rear 25% of the airfoil section (i.e., starting at the location \(x/c=0.75\)). Therefore, the front 75% of the airfoil section is rigid and should be robust enough to sustain a maximum testing speed of 50 m s\({}^{-1}\) (with a factor of safety of 1.5).  
In terms of stiffness and actuation requirements, the composite-spine second generation FishBAC device was used as a baseline [26]. However, one limitation of this design was the significant amount of spanwise elastic washout observed when the device was deflected, which was caused by a lack of spanwise stiffness combined with the presence of only two actuation points spread out toward each wing-tip. To address this limitation, further work was performed to select the spine stacking sequence, and one additional actuation point was added at the wing's midspan. Another important limitation of the previous wing design was the inability to adjust the tension applied to each tendon once it was fixed to the morphing device, which was addressed in this design by creating an  
Figure 2: Second generation of FishBAC devices designed around a carbon fibre composite spine [23]. The copyright of this photograph belongs to [PERSON] (co-author of this article), and has been used with his permission.  
adjustable tensioning mechanism. A detailed explanation of how these limitations were addressed are presented in the following sections of this paper.
# Dynamic Performance of Soil-Tunnel System under Transverse Sinusoidal Excitations
## 4 Response of FF Ground under Sinusoidal Excitation  
### Acceleration along Depths  
#### 4.1.1 Time Histories  
Time histories of acceleration responses with depths are shown in Figure 12 for the ground of FF model. One should be sure that only the middle five stationary cycles that correspond to the middle of five cycles of input are of significance. Based on the records of acceleration responses from the bottom to the top surface of the FF ground, the variation of waveform, phase, and peak of acceleration at elevated position can be addressed.  
Basically, the waveform of acceleration responses at each monitoring position is approximately sine function as input. In the case of the Sin-2 (Figure 12a), the waveforms of acceleration response of the upper soil layer are in concordance with that of the lower soil layer. In the case of the Sin-3 (Figure 12b), the peak accelerations are showing obvious differences between the upper and lower soil layer. The acceleration responses attenuate slowly with time. In the case of the Sin-6 (Figure 12c), acceleration responses are showing obvious phase difference (shown in Figure 13), especially between the surface (AZ5) and bottom (AZ1) soil layer. The phase difference of response waveforms at a monitoring position is defined as the time lag with respect to that at the bottom of the ground (AZ1). In the case of the Sin-9, the waveforms of acceleration responses become irregular.  
In the cases of the Sin-11, Sin-14, and Sin-17, acceleration responses are different, whether phases or amplitudes. In the case of the Sin-14, the acceleration responses also attenuate slowly with time. The accelerations responses attenuate gently with time shows that the seismic energy excited by the sinusoidal excitation attenuates slowly. This probably due to resonance effects. When the frequency of excitation approaches the first or second natural frequency of the model ground, the decay time becomes much longer.  
With the input frequency of 17 Hz, in case Sin-17 (Figure 12g), the phase difference reaches a maximum of 13/7\(\pi\). Figure 13 gives the phase difference for all the cases. It can be seen that phase difference increases progressively with the input frequency increasing, but decreases with the depths. This is because the phase difference is proportional to the distance between the two stations, and inversely proportional to the wave period, which is consistent with that deduced by [PERSON] and [PERSON] [20].  
To sum up, from the respect of waveform, phase, and peak, the differences of the acceleration responses between the upper soil layer and the lower soil layer obviously increase with the input frequency increasing.
# A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data
## 3 Results
### Rotating Disk Experiments
#### 3.2.2 Rotational Vessel Wall Displacements  
In Figure 10, results for the rotating disk experiments are visualized when processed as vessel wall. Similar as in the vessel wall simulation study, the performance of the four methods is comparable for the axial displacement estimation. Furthermore, the axial accuracy is higher as the lateral displacement estimation accuracy. The lowest median lateral displacement error can be appreciated for the PSF_2 Dcub method, although interquartile ranges of all methods show overlap. Furthermore, the precision of the lateral displacement estimation is highest for the PSF_2 Dcub and conv_2 Dcub methods. Overall, the PSF_2 Dcub method shows the most stable performance with respect to the PRFs.
## 4 Test Results  
### Aerodynamic Wind Force Coefficients and Derivatives  
The wind force can be decomposed into three components, the drag force, lift force and pitch moment, along the wind and body axes, as shown in Figure 6. At the initial wind attack angle \(\alpha_{0}\), the girder experiences an additional wind attack angle \(\alpha_{1}\) produced by the wind load, and the sum of \(\alpha_{0}\) and \(\alpha_{1}\) is the effective wind attack angle \(\alpha\). Because the segment model is attached to the load balance by a pair of rigid connectors and the model must have sufficient stiffness in the wind tunnel test, the additional wind attack angle \(\alpha_{1}\) is always so small that it can be ignored, i.e., \(\alpha=\alpha_{0}\). The instantaneous aerodynamic wind force per unit span can be written as a function of the effective wind attack angle along the wind axes:  
\[\text{Drag force coefficient}:\ \ \ C_{\text{D}}(\alpha,t)=\frac{F_{\text{D}}(t)}{0.5\rho U^{2}H} \tag{10}\]  
Figure 5: Comparison between experimental and target PSDs: (**a**) longitudinal wind fluctuations; (**b**) vertical wind fluctuations.  
\[\text{Lift force coefficient}: C_{L}(\alpha,t)=\frac{F_{L}(t)}{0.5\rho U^{2}B} \tag{11}\] \[\text{Pitch moment coefficient}: C_{M}(\alpha,t)=\frac{F_{M}(t)}{0.5\rho U^{2}B^{2}} \tag{12}\]  
where \(\alpha\) is the effective wind attack angle and \(\rho\) is the air density. The positive direction of the wind force is shown in Figure 6, where the pitch moment is determined according to the right hand rule. The test results of the average aerodynamic force coefficients for the service and construction stages at wind attack angles of \(-10^{\circ}\)\(\sim\)\(+10^{\circ}\) are shown in Figure 7, and these wind tunnel tests were performed by the authors before the tests in this paper [29]. Usually, the aerodynamic force coefficients are fit by a polynomial, such as \(y=\sum\limits_{i=1}^{N}a_{i}x^{N-i},\ N=1,2,3,\ldots\), where \(N=7\) in this paper, and the fitting curves of the aerodynamic force coefficients are also shown in Figure 7. Tables 2 and 3 show the aerodynamic force coefficients and derivatives for the service and construction stages at wind attack angles of \(0^{\circ},3^{\circ}\) and \(5^{\circ}\).
## 2 Materials and Methods
### Data and Processing
#### 2.2.2 ALS Data and Processing  
Airborne laser scanning data was available for the study area in the European Alps from 2013. This was used to investigate the accuracy of the calculated DEM based on Pleiades imagery. The ALS data were acquired on 5 September 2013, with an average point density of more than 2.5 points per square meter. For more information on the ALS data, the reader is referred to [PERSON] et al. [38], where data from the same acquisition was used and is described in more detail.  
The generation of the DEM based on the available ALS data was straightforward. Data was received as a filtered and georeferenced point cloud. Those points were imported into a Laserdata Information System Database [39], and a DEM with a cell size of 1 m was created by using the average elevation value of the points within a potential raster cell as the cell value. Due to the high point density of the available ALS data, the resulting DEM had gaps in less than 1% of the cells, which were filled using the mean value of the 20 closest cells containing data as the new cell value. This resulted in an accurate representation of the terrain and its features. The accuracy of this DEM was evaluated on the basis of a comparison to differential Global Navigation Satellite System (dGNSS) measurements in the study area and to a flat and even reference surface close to the study area [40; 41].
# Hyperspectral Super-Resolution Technique Using Histogram Matching and Endmember Optimization
###### Abstract  
In most hyperspectral super-resolution (HSR) methods, which are techniques used to improve the resolution of hyperspectral images (HSIs), the HSI and the target RGB image are assumed to have identical fields of view. However, because implementing these identical fields of view is difficult in practical applications, in this paper, we propose a HSR method that is applicable when an HSI and a target RGB image have different spatial information. The proposed HSR method first creates a low-resolution RGB image from a given HSI. Next, a histogram matching is performed on a high-resolution RGB image and a low-resolution RGB image obtained from an HSI. Finally, the proposed method optimizes endmember abundance of the high-resolution HSI towards the histogram-matched high-resolution RGB image. The entire procedure is evaluated using an open HSI dataset, the Harvard dataset, by adding spatial mismatch to the dataset. The spatial mismatch is implemented by shear transformation and cutting off the upper and left sides of the target RGB image. The proposed method achieved a lower error rate across the entire dataset, confirming its capability for super-resolution using images that have different fields of view.  
hyperspectral image; super-resolution; histogram equalization +
Footnote †: journal: Applied Physics Letters
# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 3 Method
### Enhancement of Satellite Images Containing Celestial Bodies  
Satellite images that contain celestial bodies, where \"celestial bodies\" mainly refer to the Earth planet. Unlike the images with only a single satellite, these images contain the Earth region, which radiates light outward [4], thereby enhancing the detailed information in the dark areas of the satellite to a certain extent. However, when enhancing such images, the radiation of this light will cause a strong halo effect around the Earth, which in turn affects the visual quality of the image. Especially in low-light environments, the strong halo formed after enhancement often does not conform to the real scene, and may even interfere with the accurate identification and judgment of targets such as satellites. In response to this special situation, this work designs a halo extraction and separation algorithm to effectively suppress the influence of the halo, making the image enhancement effect more realistic and in line with actual observation needs.  
The fusion strategy adopted for this type of image is to extract the Earth area and deep-space background part (other backgrounds after eliminating the halo) based on the original image and fuse it with the satellite target that has been enhanced again after RetinexFormer preprocessing.  
The processing flow for this type of image is illustrated in Figure 4. Initially, the halo in the original image is extracted and removed based on brightness information, creating a mask for the Earth area to prevent enhancement of that region. After preprocessing the image using RetinexFormer, noise and artifacts introduced during the preprocessing stage are reduced and eliminated through an adaptive image enhancement module that detects brightness distribution. Subsequently, the enhanced satellite target area is extracted, and a mask for this target area is created. Finally, these masks are used to map and generate a fused image.  
Figure 3: The comparison of several single satellite images before and after enhancement.  
During processing, determining the Earth area and locating the target range are critical components of the method. The Earth area determination module includes technologies such as expanding the connected component and suppressing the halo effect to accurately locate the Earth area and remove the interference of the halo. The target range positioning module uses methods such as adaptive image enhancement, edge detection expansion, and dynamic calculation of noise thresholds to remove noise and artifacts, thereby ensuring the integrity, clarity, and accuracy of the target area.
## 1 Introduction
In this study, the TOPSIS combined with Grey Relational Analysis (GRA) was applied, which not only retains the objective advantage of the TOPSIS method but also takes the advantages of the similarity and dynamicity of GRA to make up for the shortcoming of the TOPSIS method. Finally, the ranking results of the candidate consolidation centers are obtained based on the combination of the TOPSIS and GRA. This paper makes the following contributions. Firstly, the consolidation centers of an integrated transportation network, which includes highway, railway, air, and water routes, is considered as the research target under the BRI to attempt to solve the high logistics costs resulting from disorderly competition, poor efficiency, and so on among the different transportation modes. Secondly, considering the links among the candidate consolidation centers, the TOPSIS and GRA are combined to comprehensively evaluate the ranking of candidate consolidation centers within the integrated transportation network constructed based on complex network theory. Thirdly, the local and global evaluation indicators were integrated to evaluate the candidate consolidation centers of the integrated transportation network. The remaining sections are as follows: Section 2 introduces the criteria and the steps for selecting node cities. Section 3 establishes transportation networks of air, water, railway, and highway routes. The related evaluation indicators are selected in Section 4. The related approach is depicted in Section 5. A case application is analyzed in Section 6. Section 7 implements a comparative analysis. Finally, Section 8 is the conclusions.
# Compensation Control Strategy for Orbital Pursuit-Evasion Problem with Imperfect Information
## 1 Introduction
Unlike missile guidance, spacecraft's dynamic is more complicated due to the gravity difference, i.e., the difference in the gravitational accelerations of the pursuer and the evader, which leads to the failure of Assumption 1 in Ref. [17]. Therefore, we introduce the knowledge of matrix analysis theory to overcome this issue and drive a near-optimal feedback control strategy to guarantee the terminal cost (i.e., the miss distance). This guaranteed cost strategy is superior to the existing near-optimal feedback strategies in two aspects. First of all, the miss distance can be guaranteed by adopting this strategy. Second, it does not need to solve numerous open-loop solutions before or during the game. Research on the pursuit-evasion problem with time delay can be traced to the work of [PERSON] [18]. In his work, a reachability set method was proposed to deal with the fixed-time pursuit-evasion problem with delayed information for the pursuer. In Refs. [19; 20; 21; 22; 23; 24; 25], this method was applied to missile guidance, in which the state was replaced by the center of the uncertainty set (reachability set) created by the information delay. Simulation results showed that this method could partially compensate for the information delay and effectively improve guidance accuracy. However, these previous studies worked for the pursuit-evasion problem on the assumptions of two-dimensional situations, partial delay, and no gravity difference, which are invalid in the orbital pursuit-evasion problem. Without these assumptions, the calculation of the uncertainty set is more complicated. To get the geometric center of spacecraft's uncertainty set, we analyze the uncertain set's characteristics based on the linear system theory rather than calculate the uncertainty set. Then, the state is replaced by the uncertainty set's center. Thus, a compensation method for the orbital pursuit-evasion problem with delayed information is proposed. The main work of this paper is as follows. First, the orbital pursuit-evasion model is established based on the Clohesy-Wiltshire (C-W) equations [26]. Based on this model, a guaranteed cost strategy with perfect information is proposed. Second, an uncertainty set based time delay compensation method is proposed to compensate for delayed information. Combining the guaranteed cost strategy with the time delay compensation method, a compensation control strategy for the orbital pursuit-evasion problem with imperfect information is proposed. Finally, the effectiveness of the proposed strategy is verified by simulations.
# Experimental and Numerical Analysis of an Innovative Combined String-Cable Bridge
## 4 Numerical Method and Comparison between Experimental Results  
### Modeling and Calculations  
The innovative combined string-cable bridge structure was modeled and calculated using the finite element program [50]. The structure is modeled in one plane (2D). The upper and lower chords (string and cable) are modeled as the only tension truss elements, which takes over only the tensile forces. The struts are modeled as truss elements. The top chord (string) is divided into many finite elements and modeled like a \"cable\" element (see Figure 10). The static calculations of the combined bridge model were performed as geometrically nonlinear systems.  
The dimensions and cross-sections of the elements of the FEM bridge model were selected according to the geometric and physical parameters of the experimental model (see Section 3). Additionally, the bridge model was loaded as in the experiment with symmetric and asymmetric loads and at three different prestressing forces: \(T_{1}=0.472\) kN, \(T_{2}=1.084\) kN, and \(T_{3}=1.506\) kN. For loading options, see Section 3, Figures 6 and 7. As in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Load Type**} & \multicolumn{3}{c}{**Axial Forces of String, \(H_{string}\), kN**} \\ \cline{2-4}  & \(T_{1}\) = 0.472 kN & \(T_{2}\) = 1.084 kN & \(T_{2}\) = 1.506 kN \\ \hline Asymmetrical \(\gamma=1\) & 3.20 & 3.79 & 4.26 \\ \hline Asymmetrical \(\gamma=2\) & 3.86 & 4.44 & 4.92 \\ \hline Asymmetrical \(\gamma=3\) & 4.88 & 5.45 & 5.93 \\ \hline
**Load type** & \multicolumn{3}{c}{**Axial forces of cable, \(H_{cable}\), kN**} \\ \hline Asymmetrical \(\gamma=1\) & 3.03 & 3.03 & 2.97 \\ \hline Asymmetrical \(\gamma=2\) & 3.42 & 3.40 & 4.10 \\ \hline Asymmetrical \(\gamma=3\) & 3.77 & 3.76 & 4.50 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Axial forces in the string and cable by asymmetrical loading.  
Figure 10: Bridge numerical model and FE numbering. Here: 1, 2, 3... FE numbers.
## 4 Automated Zero Trust Risk Management with DevSecOps Integration (AZTRM-D)
### Phases of AZTRM-D
#### 4.1.2 Development Phase  
The Development Phase directly integrates security into the coding process. This stage focuses on secure coding practices [22], real-time security scanning, and automated policy checks. The goal is to prevent vulnerabilities from being introduced [42]. Developers authenticate using strong, risk-based identity verification. AI-powered Just-in-Time (JIT) access controls grant temporary, very specific privileges for particular tasks. This significantly reduces the risk of credential compromise [65].  
As depicted in Figure 8, the AI Analytics and Orchestration Engine receives data from code commits, repositories, and developer activity logs. To secure the code itself, AI-driven Static Application Security Testing (SAST) tools are built right into the developer's Integrated Development Environment (IDE). They provide immediate feedback on potential vulnerabilities. Additionally, AI-enhanced Software Composition Analysis (SCA) tools constantly scan dependencies for known weaknesses. This is a crucial step in securing the software supply chain [28]. The AI engine generates real-time SAST/SCA findings, offers AI-assisted secure code suggestions, and can trigger adaptive authentication challenges for developers. If a developer tries to commit code with hardcoded secrets or a vulnerable dependency, the automated system can block the commit until the issue is fixed, enforcing a \"secure by default\" approach [18].