esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Material Optimization for Lattice Materials Based on Nash Equilibrium
## 2 Analysis of Equivalent Mechanical Properties of Lattice Materials
### Solution of Base Function  
The solution of truss node displacement \(u^{\prime}\) can be considered a quadratic programming problem with equality constraints based on the principle of minimum potential energy:  
\[\begin{split} find& u^{\prime}\\ \min\Pi&=\frac{1}{2}u^{\prime T}K_{g}u^{\prime}-f^{T }u^{\prime}\\ s.t.& Bu^{\prime}=b\end{split} \tag{4}\]  
where \(\Pi\) is the finite element expression of the potential energy functional; \(K_{g}\) is the global stiffness matrix of the mesotruss structure; \(Bu^{\prime}=b\) is the constraint on displacement \(u^{\prime}\); \(B\) is a constraint matrix of order \(w\times n\); \(w\) is the number of constraints; \(n\) is the total number of degrees of freedom; and \(b\) is the constraint case. This optimization problem can be solved by the Lagrange multiplier method, and the Lagrange function can be constructed by introducing Lagrange multiplier \(\lambda\):  
\[L\big{(}u^{\prime},\lambda\big{)}=\frac{1}{2}u^{\prime T}K_{g}u^{\prime}-f^{T }u^{\prime}-\lambda^{T}\big{(}Bu^{\prime}-b\big{)} \tag{5}\]  
Figure 1: Macrostructure and mesoscopic representative volume element.  
By considering \(\
abla_{u^{\prime}}L(u^{\prime},\lambda)=0\) and \(\
abla_{u^{\prime}}L(u^{\prime},\lambda)=0\), we obtain:  
\[\left[\begin{array}{cc}K_{g}&-B^{T}\\ -B&0\end{array}\right]\left[\begin{array}{c}u^{\prime}\\ \lambda\end{array}\right]=\left[\begin{array}{c}f\\ -b\end{array}\right] \tag{6}\]  
Taking into account Equations (1) and (2), the solution to Equation (5) is as follows:  
\[\overline{u^{\prime}}=Qf+R^{T}b \tag{7}\] \[R=\left(BK_{g}{}^{-1}B^{T}\right)^{-1}BK_{g}{}^{-1}\]  
From Equation (1), the base function can be expressed as:  
\[N=u^{\prime}=\left[R_{1}^{T}b_{1},R_{2}^{T}b_{2},\cdots,R_{i}^{T}b_{i},\cdots, R_{8}^{T}b_{8}\right] \tag{8}\]
# Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
## 3 Results
### Denoising Performance in ASD Measured Dataset  
Compared with the remote-sensing image retrieved \(R_{\text{rs}}\), the field-measured \(R_{\text{rs}}\) were less influenced by complex absorption and the scattering of gas molecular and particulates in the atmosphere. In addition, the field measurement of \(R_{\text{rs}}\) had a longer integration time. This will further decrease random errors. So, the field measured spectra are usually treated as the reference data in research, such as atmospheric correction [49; 50; 51; 52]. However, the field-measured signal is not noise-free. Random factors, such as wind speed, white cap, and platform swing, will all influence the measurement. Some of the related research completed a pre-process to denoise the \(R_{\text{rs}}\) curves and then built \(C_{\text{chla}}\) estimation models. The improved estimation accuracy indicated the necessity of denoising, even for field-measured \(R_{\text{rs}}\)[18; 53].  
We selected four typical \(R_{\text{rs}}\) curves and showed the denoised spectra, together with the original ASD spectra (Figure 7). Different from the simulated noisy spectra (Figure 5), in this dataset, the noises were not evenly distributed along the wavelength. Generally, the spectra curves between 500 and 720 nm are smooth. However, in the shorter and longer wavelength regions, with the decrease in the \(R_{\text{rs}}\) signals, the noises were more obvious. The denoised curves are highly consistent with the original spectra. All of the characteristic peaks and valleys were clearly kept after denoising; for example, the \(R_{\text{rs}}\) valley caused by the chlorophyll-a absorption peak at around 680 nm and the peak caused by chlorophyll-a fluorescence near 700 nm. At the same time, the \(R_{\text{rs}}\) curves at high noise wavelengths became smoother after denoising. The results indicated that, for ASD-measured spectra data, the proposed SR denoising method is also effective. The method is not sensitive to the wavelength distribution of random noise.
# Testing the Galileo High Accuracy Service User Terminal (HAUT) in Static Scenarios +
## 8 Conclusions  
In a record one-year time, Spaceopal and its partners developed the Galileo High Accuracy Algorithm and the Galileo HAS User Terminal (HAUT), which hosts the algorithm for the EU Agency for the Space Programme (EUSPA).  
The Galileo HAS User Terminal is a portable, configurable and autonomous device powered by a triple-frequency Galileo and GPS receiver and calculates a single- (Galileo) or multi-constellation (Galileo + GPS) Galileo HAS and Open Service (OS) positioning, velocity and time (PVT) solution. The User Terminal can be configured to retrieve Galileo HAS corrections either from Galileo E6-B or Internet link corrections over NTRIP in an RTCM3 format and works in all dual- and triple-frequency modes of the algorithm. The User Terminal is a robust device (IP64) with multiple communication and logging capabilities.  
This article has presented the results of the positioning performance provided by the Galileo HAS User Terminal, using Galileo HAS corrections and configuring the User Algorithm to assume static dynamics (as opposed to the configuration used for the Galileo HAS performance characterization presented in the HAS SDD, which considers kinematic conditions) from February to April 2023. The results of the data campaign reach centimeter level at the 68 th percentile.  
Writing: E.G. (Sections 1-5) and P.P. (Sections 6-8); resources: A.S.; validation: P.P. and N.D.; supervision, J.O., C.H., J.V., S.L. and J.d.B. All authors have read and agreed to the published version of the manuscript.  
The HAUT project was funded by EUSPA through the Fundamental Elements program, contract GSA-OP-25-19.  
Not applicable.  
Galileo HAS corrections are publicly available. The data presented in this study are not publicly available as they belong to the administrators of the HAUT project.  
We deeply acknowledge the contribution of the industrial team to the development and validation of the HAS algorithm and User Terminal, as well as the technical supervision of the work by the relevant EUSPA officers and partners.  
The authors and Spaceopal GmbH declare no conflict of interest.
# GPU-Accelerated CNN Inference for Onboard DQN-Based Routing in Dynamic LEO Satellite Networks
## 4 Proposed Method
### Partitioned Weights for Low-Level Operations  
In Figure 3, the weights are systematically divided. Due to the complexity of the input data and the structure of the CNNs, the weights are initially organized into a complex four-dimensional structure, with each dimension representing a specific property of the filter. This is necessary because the filter must learn both the spatial and channel dimensions from the input data simultaneously. To meet the efficient computational requirements of handling weights, we converted the filters into a 3D format for different types of filters, making them more manageable. This conversion provides two main benefits. First, it allows each block to take inputs more efficiently. Inputs in three dimensions are processed using weights also converted into three dimensions, enabling more efficient, low-level computation compared to the traditional structure. Second, we established a mechanism for shared access among all computational threads. Threads within the same block can access shared memory and jointly use the data, reducing memory bandwidth usage and minimizing access to global memory, which speeds up computation. This heuristic approach to weight granularity and structure is not just a superficial adjustment, as it greatly simplifies the indexing mechanism. Multiple threads can access the same data simultaneously, enhancing computation performance. This improved structure allows the system to handle low-level operations more effectively, giving threads faster access to data. The result of this optimization is a method that is fine-tuned for the stringent requirements of GPGPU computing.
For each of its in-cluster nodes, the cluster head node calculates some corresponding coverage redundancy rates (\(CRR\)) by assuming that the in-cluster node is deployed on each position within the maximum communication radius of its basic nodes (_i.e._, nodes that belong to the same cluster head node and that is the next-hop of the in-cluster node). The cluster head node subsequently minimizes the hop number of the in-cluster node to optimize and adjust its position. This algorithm forms a network layout of uneven distribution. In the distribution, the node density and number of cluster head nodes of the area close to the sink node increase; the scale of the cluster in that area becomes small. As a result, the number of nodes near the sink node then increases, and the network reliability improves. The process of optimizing and adjusting the position of the in-cluster nodes further improves the network coverage rate, decreases the hop of in-cluster nodes and decreases the energy consumption of network deployment. The connected topological structure formed during the process of node deployment (_i.e._, the cluster head node farther from the sink node has a greater communication radius, and the cluster farther from the sink node has a larger scale) also balances the energy consumption of network operation. The simulation results show that compared to CDA having a good deployment performance, URSA can improve network reliability, balances and reduces network energy consumption and improves the network coverage rate. The rest of this paper is organized as follows. Section 2 describes the system model, assumptions and definitions considered in this study. Section 3 presents the details of URSA. Section 4 analyzes the complexity of URSA. Section 5 discusses the performance study and provides a detailed analysis of its result. Finally, Section 6 concludes the paper and plans some future works.
## 3 Results
### Changes in Endmember Fractions after Blowdown  
The difference (\(\Delta\)NPV\({}_{\rm first}\)) between the mean NPV in the blowdown first visible image and the mean pre-blowdown raw NPV for both satellites was not significantly different considering a 5% significance level (Figure 5). Most of the \(\Delta\)NPV\({}_{\rm first}\) values were between 0.3 and 0.8, meaning that the blowdown events generally caused a moderate to substantial decrease in NPV relative to the pre-disturbance conditions, with some sites experiencing more severe damage. However, the fact that the majority of the values fall within this range suggests a relatively consistent impact across the study sites. The maximum \(\Delta\)NPV for PlanetScope NICFI is larger than its \(\Delta\)NPV\({}_{\rm first}\), whereas for Landsat-8, the two attributes are not significantly different. Furthermore, the maximum \(\Delta\)NPV of PlanetScope NICFI was relatively higher than the distribution of OLI Landsat-8. This indicates that, while the recovery of reflectance values after a disturbance varies between the two satellites, the blowdown events generally caused a moderate to substantial decrease in NPV, with some sites experiencing more severe damage, highlighting the significant impact of these disturbances on the forest ecosystem.  
Figure 3: Examples of true-color composites of blowdown-disturbed areas, as indicated by red lines. Images (**A1,B1,C1**) refer to PlanetScope NICFI, whereas images (**A2,B2,C2**) correspond to OLI Landsat-8.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{\(\Delta\)NPV \textgreater{} 0.3} & \multicolumn{4}{c}{\(\Delta\)NPV \textgreater{} 0.5} \\ \hline \multirow{3}{*}{**ID**} & **Planet** & **Landsat** & **Planet** & **Landsat** & **Planet** & **Landsat** & **Planet** & **Landsat** \\  & **Pixel** & **Pixel** & **Area (ha)** & **Area (ha)** & **Area (ha)** & **Pixel** & **Area (ha)** & **Area (ha)** \\  & **Count** & **Count** & & & **Count** & **Count** & **Count** & **Area (ha)** \\ \hline
10 & 468 & 40 & 1.06 & 3.60 & 60 & 2 & 0.14 & 0.18 \\
11 & 5957 & 312 & 13.55 & 28.08 & 1853 &
## 3 Results
### Activity, Total Polyphenols Content, and LA Efficiency  
Table 6 shows the results of the activity, total polyphenols content, and LA efficiency of optimised FPE and PE from clove buds.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{4}{c}{**Effect of Plant Raw Material Content and Fermentation Time**} \\ \hline AA & TPC & LA\({}_{\text{e}}\) \\ a\({}_{0}\) & 3.59 & 2.49 & 10.37 \\ a\({}_{1}\) & \(-\)1.63 & \(-\)0.05 & \(-\)0.41 \\ a\({}_{2}\) & 0.18 & 0.02 & 0.35 \\ a\({}_{3}\) & 3.32 & 1.92 & 13.02 \\ a\({}_{4}\) & \(-\)0.06 & \(-\)0.10 & \(-\)0.54 \\ a\({}_{5}\) & 0.19 & 0.00 & \(-\)0.03 \\ R\({}^{2}\) & 0.99 & 1.00 & 0.87 \\ AdjR\({}^{2}\) & 0.97 & 1.00 & 0.84 \\ SD & 0.80 & 0.042 & 14 \\ RSD & 0.055 & 0.0044 & 0.42 \\ PRESS & 5.7 & 0.016 & 2980 \\ \hline \multicolumn{4}{c}{**Effect of Initial Sugar Concentration and Fermentation Time**} \\ \hline AA & TPC & LA\({}_{\text{e}}\) \\ a\({}_{0}\) & 17.77 & 6.68 & 32.00 \\ a\({}_{1}\) & 3.56 & 1.98 & 12.00 \\ a\({}_{2}\) & \(-\)0.49 & \(-\)0.23 & \(-\)0.71 \\ a\({}_{3}\) & \(-\)1.72 & 0.08 & \(-\)5.00 \\ a\({}_{4}\) & 0.27 & 0.01 & \(-\)0.47 \\ a\({}_{5}\) & 0.11 & \(-\)0.02 & 0.74 \\ R\({}^{2}\) & 1.00 & 0.98 & 0.80 \\ adjR\({}^{2}\) & 0.99 & 0.95 & 0.69 \\ SD & 0.30 & 0.12 & 12 \\ RSD & 0.010 & 0.0099 & 0.35 \\ PRESS & 0.80 & 0.13 & 4488 \\ \hline \end{tabular}
\end{table}
Table 4: The most relevant response surface methodology (RSM) optimisation statistics.  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**Functions of the Fermentation Process *} & **Unit** & **Value** \\ \hline AA & mmol Tx/L & 33.89 \(\pm\) 0.04 \\ TPC & mmol GA/L & 11.61 \(\pm\) 0.02 \\ LA\({}_{\text{e}}\) & \% & 96 \(\pm\) 1 \\ \hline \end{tabular}
\end{table}
Table 5: The values for the main functions of the buds fermentation process according to the RSM analysis.  
Table 6 shows the antioxidant activity (via a DPPH radical scavenging assay), total polyphenol content (via the Folin-Ciocalteu method), Fe\({}^{2+}\) chelating activity, Fe\({}^{3+}\) reductivity, and LA efficiency of optimised FPE and PE. Antioxidant activity (AA) tests showed that the fermented plant extract had a higher degree of DPPH radical scavenging (11.60 mmol Tx/L \(\pm\) 0.09) than the plant extract (9.21 mmol Tx/L \(\pm\) 0.05). The results, presented in Table 6, show that the optimised FPE also has a higher content of phenolic compounds (TPC = 33.90 mmol GA/L \(\pm\) 0.14) than the test extract (22.04 mmol GA/L \(\pm\) 0.13). Studies of the chelating activity of Fe\({}^{2+}\) ions and the reduction in Fe\({}^{3+}\) ions in the ferrozine test showed that the activity of the tested FPE chelating and reduction potential is 0.32 mmol Fe\({}^{2+}\)/L \(\pm\) 0.01 and 49.09 mmol Fe\({}^{3+}\)/L \(\pm\) 0.16, while in the case of the PE, the ChA and RA values are lower (0.13 mmol Fe\({}^{2+}\)/L \(\pm\) 0.01 and 35.42 mmol Fe\({}^{3+}\)/L \(\pm\) 0.22). Based on the results obtained by GC-MS, it was found that the lactic acid efficiency was obtained on the 10 th day of conducting the fermentation of clove buds: LA\({}_{\text{e}}\) = 96% (Table 6).
# GPS + BDS Network Real-Time Differential Positioning Using a Position Domain Estimation Method
## 1 Introduction
All the above position domain methods use inter-epoch differenced observations to estimate the coordinates. Considering the strong correlation of the coefficients between two adjacent epochs, this may lead to the ill-conditioned problem in the design matrix. To avoid this problem, in this paper we propose an improved algorithm, where the inter-epoch differenced carrier phase observations are only used to obtain the position variation, and this variation is further used in the motion model of the Kalman filter. In this way, the irregular motion model of a user is simplified to the position variation model. In this paper, we first describe the differential corrections estimation, followed by the traditional position estimation algorithm in the range domain. Then, an improved algorithm in the position domain is proposed. To demonstrate the performance of the improved algorithm, static and dynamic data were collected respectively from Guangdong Province and Jiangsu Province of China, and the positioning performance of the network real-time differential positioning using the traditional and the improved algorithms is evaluated. The results using GPS + BDS combination are also compared with those using GPS-only solution. Finally, some conclusions and further studies are given.
# Integrated Magnetic Management of Stored Angular Momentum in Autonomous Attitude Control Systems
## 3 System Performance
### Model-in-the-Loop Verification
Figure 11 reports the pointing error and the reaction wheels' spin rate for the same simulation scenario described above, but with pure three-axis PID attitude control. The simulation was run for 3.5 h, and it was initialized with the same initial conditions as the previous case. It is evident how the control performance is worse. Indeed, the higher average rotation rates of the rotors induce larger oscillations in the pointing error because of the higher jitter due to the static and dynamic imbalance of the actuators. Moreover, at \(t\sim 2.5\) h, the pointing error rises because of the saturation of two reaction wheels, namely, \(R_{1}\) and \(R_{3}\). In this case, the real spacecraft system would have entered an emergency desaturation mode to avoid this problem. Furthermore, the system level performance would be affected by this trend because higher spin rates of the reaction wheels are typically associated with larger power consumption, which has a negative impact on the spacecraft's power budget.
###### Abstract  
Oil and gas pipeline networks require the periodic inspection of their infrastructure, which can cause gas and oil leakage with several damages to the environment and human health. For this, non-destructive testing (NDT) techniques of low-cost and easy implementation are required. An option is the metal magnetic memory (MMM) method, which could be used for real-time monitoring defects of ferromagnetic structures based on the analysis of self-magnetic leakage fields distribution around each defect. This method only requires magnetic sensors with high resolution and a data processing system. We present a measurement system of tangential and normal MMM signals of three rectangular defects of an ASTM A-36 steel pipe. This system is formed by a magnetoresistive sensor, an Arduino nano and a virtual instrumentation. The measured magnetic signals have non-uniform distributions around the rectangular defects, which have small differences with respect to the results obtained of a 2D magnetic dipole model. The size of each rectangular defect is related to the amplitude and shape of its tangential and normal MMM signals. The proposed system could be used for real-time monitoring of the size and location of rectangular defects of ferromagnetic pipes. This system does not require expensive equipment, operators with high skill level or a special treatment of the ferromagnetic samples.  
rectangular defect; magneto-mechanical memory method; metal magnetic memory method; magnetic sensor; non-destructive testing; oil and gas pipeline +
Footnote †: journal: Applied Physics Letters
## 4 Theoretical and Experimental Analyses
### Reversibility
#### 4.4.2 Error Map  
The error map serves as a detailed comparison between the processed data and the original data on a bit-by-bit basis. If the comparisons are equal, the error rate is zero. If the error maps of the reconstructed image and the original image are zero and the error maps of the extracted data and the embedded data are zero, the scheme is fully reversible. Figure 11 shows the error map of the extracted data and the error map of the reconstructed image after embedding the data in Peppers, from which it can be shown that the scheme is fully reversible.  
\begin{table}
\begin{tabular}{c c c} \hline
**Test Image** & **PSNR** & **SSIM** \\ \hline Jetplane & +\(\infty\) & 1 \\ Peppers & +\(\infty\) & 1 \\ Airplane & +\(\infty\) & 1 \\ Goldhill & +\(\infty\) & 1 \\ Baboon & +\(\infty\) & 1 \\ \hline \end{tabular}
\end{table}
Table 2: PSNRs and SSIMs of the reconstructed images of the test images.  
Figure 10: Comparison of PSNR for different schemes [18; 23; 26; 28; 29; 34].
## 6 Numerical Results
### L-Shaped Structure
#### 6.2.1 SISO System  
The optimal topology at convergence obtained using the SISO model is shown in Figure 19. In the visual representation, blue, green, and yellow correspond to the materials with \(E_{1}=1\), \(E_{2}=2\), and \(E_{3}=3\), respectively. As previously observed, the components of the multi-material structure exhibit varying thicknesses, with stiffer materials typically forming thinner, more localized features.  
To assess the performance of the optimization algorithm, Figure 20 presents the evolution of the objective function over the course of the optimization. Although a total of 50 iterations were executed, convergence was effectively achieved after approximately 25 iterations, demonstrating the efficiency of the Method of Moving Asymptotes in this context.  
A note is in order regarding the influence of the horizontal load \(P\) on the optimal topology. In the standard benchmark formulation of the L-shaped problem, the horizontal load is typically absent. As a result, the vertical leg of the structure is often reduced to a pair of vertical load-bearing chords positioned at the left and right edges.  
The introduction of the horizontal load \(P\), however, significantly alters the structural response. In addition to the need to sustain axial forces (tension and compression), the left vertical leg must now exhibit substantial bending rigidity to effectively counteract the moment induced by \(P\). This additional requirement gives rise to a more complex and articulated topology in that region, aimed at minimizing the compliance associated with the horizontal loading component.  
Figure 19: L-shaped structure: (SISO) Optimal topology at convergence.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 9 Tools for Performance Evaluation of Wi-Fi 6
### Software-Defined Radio (SDR)  
Software-Defined Radio (SDR) is a radio communication system where signal processing features are implemented in software.  
An SDR system brings more flexibility in applying changes. It can operate in an embedded system or a personal computer. However, it faces hardware limitations; in particular, it creates restrictions on the signal bandwidth and the sampling frequency. Thus, it is practically expensive and even challenging to exploit an SDR architecture for Wi-Fi 6, which operates with very high bitrates. Finally, ultra-low-power devices are still better implemented with dedicated hardware. Thus, it is hard to assess the energy efficiency of a protocol or algorithm with SDR.  
Openwifi is an open-source full-stack Wi-Fi based on SDR designed for Linux operating systems [189]. The prototype is based on an FPGA and includes the software drivers used with Linux. Both the hardware and software implementations are freely available. It supports multiple different SDR platforms. Currently, Wi-Fi 6 features are under development but openwifi does not yet support the high bitrate of Wi-Fi 6.  
[PERSON] et al. [190] implemented a cross-technology communication scheme between Wi-Fi 6 and LoRa. They demonstrated the feasibility of their proposition by implementing a prototype in the USRP N210 hardware. However, the focus was on implementing the LoRa mechanisms and most Wi-Fi 6 features are not supported.