esa-sceva/satcom-chunk-collection huggingface dataset:
# Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression
## 1 Introduction
Secondly, existing graph-based coding schemes predominantly utilize geometry correlations for graph node identification and edge weight determination, thereby overlooking the potential benefits of texture-guided analysis in enhancing graph construction accuracy. We aim to enhance graph representations by integrating texture-guided analysis into our approach. Lastly, the oversight of sparsity optimization in existing schemes results in high computational complexity. Our solution addresses this challenge by optimizing sparsity within the graph, effectively reducing computational overheads in point cloud attribute compression. Unlike previous works solely relying on geometry information for graph sparsity optimization [16; 18], we introduce a novel texture-guided graph transform optimization scheme to overcome the limitations of current graph-based attribute codecs and achieve more efficient point cloud attribute compression. To address the challenges above, we present a texture-guided graph transform optimization scheme tailored for point cloud attribute compression. Our contributions can be delineated as follows:  
* We design a novel point cloud graph transform coding framework, which incorporates region-adaptive graph construction and R-D optimized transform to improve coding performance. By formulating attribute transform as a graph optimization problem, our scheme achieves optimal transform coding performance by effectively leveraging attribute correlation, thus outperforming state-of-the-art methods. * We propose a texture-guided graph optimization scheme to fully capture underlying attribute correlation in point clouds. Texture analysis is performed in point cloud local regions to guide graph optimization. By optimizing graph construction with a tailored optimization function, the scheme improves the fidelity of graph representations and consequently enhances transform performance. * We introduce a R-D optimized graph transform scheme with graph sparsity constraints. Our optimization framework considers both the decorrelation capability of the transform and the sparsity of the constructed graph. This enhancement not only bolsters coding performance but also augments the efficiency of graph-based point cloud transform processing. The subsequent sections of this paper unfold as follows: Section 2 provides a comprehensive literature review on point cloud attribute compression. Section 3 presents the details of the proposed framework. Section 4 offers a detailed exposition of the experimental results and analysis. Finally, concluding remarks are presented in Section 5.
# Mission-Oriented Real-Time Health Assessments of Microsatellite Swarm Orbits
## 3 Real-Time Health Assessment Algorithm
### Single Satellite Health Assessment Algorithm
\[\omega(v_{i})=\frac{I(v_{i})}{\sum\limits_{i=1}^{N}I(v_{i})} \tag{7}\]  
To model satellite reliability, the failure rate is usually used as an indicator to characterize reliability, and the factors affecting health generally include sudden failure and attrition failure. According to the statistical data, the failure probability of a spacecraft satisfies the \"bathtub curve\" [17], so the Weibull distribution and the normal-terrestrial distribution are used here for failure simulation. The satellite random failure reliability obeys the Weibull distribution as follows [17]. \[R(t,\eta,\beta)=e^{-\left(\frac{t}{t}\right)^{\beta}} \tag{8}\]where \(\eta\) is the scale parameter, indicating the characteristic lifetime, and \(\beta\) is the shape parameter. The satellite loss failure reliability obeys a normal distribution as follows. \[R_{loss}(t)=\int_{t}^{\infty}\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{(t-\mu)^{2}}{ 2\omega^{2}}}dx \tag{9}\]  
where \(\mu\) is the expected value and \(\sigma\) is the standard deviation. Considering the reliability distribution of sudden failure and loss failure together, the satellite reliability model is as follows. \[R_{vi}(t)=e^{-(\frac{t}{t})^{\beta}}\int_{t}^{\infty}\frac{1}{\sigma\sqrt{2\pi }}e^{-\frac{(t-\mu)^{2}}{2\omega^{2}}}dx \tag{10}\]  
where \(R_{vi}\) denotes the reliability value of satellite \(v_{i}(i=1,2,\cdots,N)\) in the swarm system. Combining the satellite reliability with the satellite weight values, the single-satellite health degree can be further collated as follows. \[H_{sat}=\sum_{i=1}^{N}R_{vi}\times\omega(v_{i}) \tag{11}\]
# Temperature Effects on Vibration-Based Damage Detection of a Reinforced Concrete Slab
## 2 RC Slab and Vibration Testing
### Variation of Damping Ratios  
Based on the theory of structural dynamics, that the damping in the RC slab is proportional and viscous, the first four damping ratios are extracted and the relationships between temperature variations and modal damping ratio are shown as in Figure 9.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Mode** & **Minimum/Hz** & **Maximum/Hz** & **Error/\%** \\ \hline
**1 st** & 6.884 & 8.198 & 16.52 \\
**2 nd** & 26.093 & 29.467 & 11.45 \\
**3 rd** & 58.292 & 65.764 & 11.36 \\
**4 th** & 99.051 & 114.398 & 13.42 \\ \hline \end{tabular}
\end{table}
Table 1: The maximum and minimum natural frequencies.  
Figure 8: Frequencies vs. temperature. (**a**) 1 st mode. (**b**) 2 nd mode. (**c**) 3 rd mode. (**d**) 4 th mode.  
It can be observed that the damping ratio decreases with the increase of temperature. The linear fitting coefficients \(R^{2}\) of damping ratios and temperatures for the first four modes of RC slab were 0.0954, 0.0647, 0.3795, and 0.298, respectively. It was found that the first modal damping was relatively not sensitive to the temperature, and the other three modes had a negative correlation with temperature. This result was probably due to the fact that a linear viscous model was used. For solids and especially for concrete the models of dissipations to be used are different [34; 35].
# Effect of Heat Treatment on Some Titanium Alloys Used as Biomaterials
## 3 Results and Discussions
### Microhardness Results  
For the Vickers microhardness method, at least ten tests are carried out on the material under test. For each trace, the average value of the diagonal is calculated based on the sizes of the two measured diagonals. The difference between the dimensions of the diagonals is allowed to be within a maximum margin of error of 2%. The samples were evaluated before and after the thermal treatment. The results are presented in Table 4 and Figure 7.  
The results showed that the microhardness is significantly influenced by the heat treatment, with the exception of the Ti15 Mo7 Zr15 Ta0.5 Si and Ti20 Mo7 Zr15 Ta alloys.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Alloy**} & \multicolumn{2}{c}{**Microhardness (HV)**} \\ \cline{2-3}  & **Before Treatment** & **After Treatment** \\ \hline Ti15 Mo7 Zr15 Ta & 237.27 \(\pm\) 5.3 & 375.75 \(\pm\) 2.5 \\ Ti15 Mo7 Zr15 Ta0.5 Si & 343.98 \(\pm\) 2.2 & 310.44 \(\pm\) 3.6 \\ Ti15 Mo7 Zr15 Ta0.75 Si & 370.07 \(\pm\) 1.5 & 373.63 \(\pm\) 2.3 \\ Ti15 Mo7 Zr15 Ta1 Si & 364.24 \(\pm\) 3.3 & 383.00 \(\pm\) 3.1 \\ Ti20 Mo7 Zr15 Ta & 305.34 \(\pm\) 4.1 & 278.00 \(\pm\) 5.2 \\ Ti20 Mo7 Zr15 Ta0.5 Si & 341.10 \(\pm\) 2.8 & 360.38 \(\pm\) 2.4 \\ Ti20 Mo7 Zr15 Ta0.75 Si & 318.20 \(\pm\) 9.2 & 483.50 \(\pm\) 6.7 \\ Ti20 Mo7 Zr15 Ta1 Si & 274.64 \(\pm\) 1.8 & 391.13 \(\pm\) 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measured microhardness before and after the thermal treatment.  
Figure 7: Hardness comparison (before and after the thermal treatment).  
It can be observed that with the increase of the percentage of silicon and molybdenum by 20%, the hardness value after heat treatment is significantly increased, visible in the Ti20 Mo7 Zr15 Ta0.75 Si and Ti20 Mo7 Zr15 Ta1 Si alloys.  
Microhardness studies on the surface show the depth of work hardening of the machined surface. The heating and cooling phenomena are the main cause of the microhardness growth on the surface and subsurface. The environment and cutting conditions impact the heating and cooling phenomenon. Due to grain refinement, the microhardness of machined surfaces rises. Since titanium alloys and nickel-based superalloys have a greater range of applications in the aerospace and biomedical areas, they have received a great deal of attention from researchers over the past ten years. Many of these articles have focused on the surface hardness of machined surfaces [11; 16; 17].
# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 1 Introduction
We propose a low-light image enhancement method for deep-space background based on multi-image fusion, which effectively solves the problem of Earth halo interference and color distortion through the steps of calculating segmentation thresholds and binarization processing, calculating halo ranges, extracting and separating halos, contouring analysis, and region optimization, and mask generation;
2. We design an adaptive enhancement module based on luminance distribution detection, which effectively suppresses and eliminates the noise and artifacts introduced in the image during the preprocessing of RetinexFormer;
3. We design an edge detection extension module, which extends the edges by expansion and intersection detection, effectively avoiding incomplete extraction of the target region and solving the problem of incomplete satellite target region;
4. By combining the features of different regions, the extraction of the satellite target region, the Earth region, and the deep-space background is realized, which significantly improves the detailed visibility of the satellite target while ensuring high fidelity of the Earth region. The remaining sections of the paper are organized as follows. Section 2 introduces the existing low-light image enhancement methods. Section 3 describes the proposed method framework and key techniques. Section 4 presents the experimental details and results. Section 5 gives the conclusion and future directions.
# Structural Design and Analysis of Multi-Directional Foot Mobile Robot
## 4 Design and Analysis of Steering Mechanism
### Analysis of Steering Mechanism
#### 4.2.2 Kinematics Analysis  
The core structure of the steering mechanism to achieve steering adopts the parallelogram structure, as shown in Figure 15.  
Therefore, the parallelogram structure is taken for further analysis. As shown in Figure 16, we take point A as the origin to establish a rectangular coordinate system. We suppose that the length of member 1 is \(l_{1}\), its azimuth angle is \(\theta_{1}\), and \(l_{1}\) is the rod vector of member 1, namely \(l_{1}\) = AB. The rest of the components in the mechanism can all be expressed as corresponding rod vectors, so that a closed vector polygon composed of each  
Figure 14: Movement diagram of steering mechanism.  
Figure 13: Overall structure.  
Figure 15: Steering mechanism.  
rod vector is formed, namely ABCDA. In this closed vector polygon, the sum of its vectors must be equal to zero, i.e.,  
\[I_{1}+I_{2}-I_{3}-I_{4}=\mathbf{0}_{2\times 1}. \tag{50}\]  
(1) Location analysis  
The closed vector Equation (50) of the mechanism is written as a projection on two coordinate axes and then rewritten in a form with only an unknown quantity on the left side of the equation. Thus, we have  
\[\begin{cases}I_{2}-l_{3}\cos\theta_{3}=l_{4}-l_{1}\cos\theta_{1}\\ -l_{3}\sin\theta_{3}=-l_{1}\sin\theta_{1}.\end{cases} \tag{51}\]  
It can be found that \(\theta_{1}=\theta_{3}\).  
(2) Velocity analysis  
Taking the first derivative of Equation (53) for time, we can obtain the following results:  
\[\begin{cases}l_{3}\omega_{3}\sin_{3}=l_{1}\omega_{1}\sin_{1}\\ -l_{3}\omega_{3}\cos_{3}=-l_{1}\omega_{1}\cos_{1}.\end{cases} \tag{52}\]  
It can be found that \(\omega_{1}=\omega_{3}\).  
(3) Acceleration analysis  
Taking the derivative of Equation (52) for time, we can obtain the acceleration formulas  
\[\begin{cases}l_{3}\alpha_{3}\cos\theta_{3}=l_{1}\alpha_{1}\cos\theta_{1}\\ l_{3}\alpha_{3}\sin\theta_{3}=l_{1}\alpha_{1}\sin\theta_{1}.\end{cases} \tag{53}\]  
It can be found that \(\alpha_{1}=\alpha_{3}\). The analysis shows that the velocity and acceleration relationship between input link AB and output link CD can be easily controlled to control the motion parameters of the steering structure.
## 2 Materials and Methods
### Analytical Model  
For a comparison of the results obtained with the sensory tool, an experiment was conducted where the temperature is measured on the workpiece side. Therefore, a thermocouple type K with an electrically insulated measuring tip with a diameter of 1 mm was inserted into the workpiece. The principle sketch in Figure 5 shows the positioning of the thermocouple. The thermocouple was inserted in a 50 mm deep hole, where the radial position was set exactly to the position of the outer cutting edge of the deep hole drilling tool. For better heat transfer, thermal paste was added to the tip of the thermocouple. With the use of superglue, the thermocouple was fixed in such way that it would not be pressed out by the cooling lubricant pressure when reaching the measuring channel. The experimental setup remains mostly unchanged. Merely a non-sensory but otherwise identical SLD-tool was exchanged for the experiments. Boundary conditions, such as the process parameters from the experimental design, also remain unchanged.  
The temperature at the cutting edge cannot be measured directly at the affected point in experiments. Thus, an analytical model is created to perform a back-calculation to the actual temperature. For the calculation of the temperature distribution, a differentiation can be made between the start of the drilling process and the stable drilling process. Since the start of the drilling process only represents a short period of the entire process, the following evaluation refers to the stable drilling process. The temperature distribution is to be approximated via exponential functions defining the temperature at certain nodes, divided into different areas of the cutting edge. In a subsequent heat transfer simulation, analytical fields are used to apply the temperature with the corresponding temperature distribution via boundary condition to the cutting edge. The analytical fields defined by  
Figure 4: Experimental setup.  
\(T_{i}(y)\), \(T_{a}(y)\), \(T_{f}(z)\) containing the e-functions describing the temperature as a function of the coordinates \(y\) and \(z\) along the cutting edge. These boundary conditions can be seen in Figure 6, and are able to vary by a factor \(k\) that shifts the function linearly to other temperature levels.
## 3 Methodology of Complementary Filter Calibration  
Finding the optimal time constant for a complementary filter processing signals from a gyroscope and other sensors, such as accelerometers, using mathematical modeling methods is an extremely difficult task. This system works iteratively, which significantly complicates the analysis and evaluation of solutions to stochastic differential equations. Developing a model that also takes into account the variability of various parameters, including the technical specifications of gyroscopes and accelerometers, is an impossible task. Simplifying the theoretical model and/or idealizing the input signal undermines accurate error assessment and control.  
In this situation, the most effective approach to optimizing this system is to implement numerical and statistical analyses. This was implemented in the R environment. This method allowed us to select a time constant for the complementary filter that minimizes errors, eliminates residual systematic errors, and improves the accuracy of the system orientation and ensures operational stability.
## 7 Conclusions  
The infrastructure of the Internet is continuously evolving to support new services. Intelligent transportation systems will play a fundamental role in the future, helping to preserve lives and making transportation safer and efficient. eHealth, if supported by vehicular networks, could be one of the applications improving vehicle passengers' safety.  
This paper describes the integration process of vehicular and eHealth testbeds. The vehicular network is designed to work over a fully deployed IPv6 network. eHealth testbed collects, stores and sends health-related measurements to a PHR server located in the infrastructure, where the results can be viewed remotely by a doctor. Performed experimentation demonstrates the capability of eHealth specific data to be sent on IPv6 from a vehicular setting using an experimental IPv6 deployment over high-speed packet access (HSPA) (3G\(+\)). The underlying network communication protocols used were relying exclusively on IPv6. The application layer protocols included, but were not limited to, HTTP and HTTPS. The hardware used in this configuration is deployable, as is, in a vehicle.  
In the future, two scenarios are possible based on the availability of LTE coverage. Live demonstrations can be done thanks to LTE's IPv6 support [13] when deployed by the operators. In the meantime, in-vehicle demonstrations are possible over existing IPv4 3G networks thanks to encapsulating and decapsulating gateways on the edges of the link for network traversal.
# Wiener Filter Using the Conjugate Gradient Method and a Third-Order Tensor Decomposition
## 4 Simulation Results
### Iw-Tot-Cg versus Iwf-Tot  
The second set of experiments focuses on the comparison between the proposed IWF-TOT-CG and its counterpart recently developed in [18], namely IWF-TOT. This iterative Wiener filter is also based on TOT decomposition but uses direct matrix inversion to solve the associated sets of Wiener-Hopf equations. Therefore, in terms of their decomposition, both algorithms will use the same setup, i.e., \(L=L_{11}L_{12}L_{2}\), with \(L_{11}=L_{12}=16\) and \(L_{2}=2\). Besides, the IWF-TOT-CG involves its specific CG cycles, using the settings \(K_{1}=12\) and \(K_{2}=1\) (as in the previous set of simulations).  
In Figure 8, the two TOT-based algorithms are compared for different values of the decomposition parameter \(P\) under favorable conditions, i.e., \(N=5L\) data samples (to estimate the statistics) and \(\text{SNR}=20\) dB. While for \(P=1\) the performances are very similar, the CG-based version achieves a better accuracy for a larger value of \(P\). This supports the advantage of the line search methods (like CG), as compared to the traditional matrix inversion approach, which is also indicated in other previous works [33; 34; 35]. As shown in the experiment related to Figure 6, increasing the value of \(P\) beyond a certain value does not lead to performance improvement, while increasing the computational complexity on the other hand. In fact, this trade-off relies on the low-rank approach. As indicated in [18], for network impulse responses, the rank of the corresponding matrices is much lower than \(L_{12}\), e.g., usually less than \(L_{12}/5\). In our scenario, this leads to a range for \(P\) between 1 and 3. This was also previously supported in Figure 6, where we can notice that the misalignment curves for \(P=2\) and \(P=3\) are very similar. Consequently, there is no reason for choosing \(P\) beyond these values, since there is no performance gain while paying in terms of computational complexity.  
The robustness of the line search methods in noisy environments is also an important feature to be considered. The experiment provided in Figure 9 outlines this gain. As we can notice, even for \(P=1\) (which led to similar results in good SNR conditions), the IWF-TOT-CG performs better as compared to the previous IWF-TOT [18] in low SNR environments.  
Figure 8: Normalized misalignment of the IWF-TOT [18] and IWF-TOT-CG (with \(K_{1}=12\) and \(K_{2}=1\)) using different values of \(P\). The required statistics are obtained by averaging across \(N=5L\) data samples, \(L=512\), and \(\text{SNR}=20\) dB.
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 4 Experimental Evaluation
### Ablation Studies
#### 4.3.1 Effect of Food Safety Knowledge Graph  
The addition of Food Safety Attributes improved the ZSD performance by 1.6 percentage points (56.8% to 58.4%) compared to the baseline RRFS method. Incorporating the Food Safety Knowledge Graph further improved performance, which reached 60.3%, demonstrating the value of structured domain knowledge for zero-shot food safety hazard detection.
## 3 Workflow and Validity Analysis of HDE Algorithm
### Validity of \(G(\xi,\eta,r_{P})\) function decomposition  
Considering that the convolution and integration of Equation (25) are too computationally intensive, the \(G(\xi,\eta,r_{P})\) is decomposed, and Equation (30) is obtained so that the convolution calculation in Equation (30) is independent of \(\eta\). The validity constraints of this decomposition will be analyzed next.  
Since \(|\lambda\eta/4\pi|\ll 1\), it is easier to keep \(G_{B}(\xi,\eta,r_{P})\) approximately constant compared to \(G_{A}(\xi,r_{P})\) in the bandwidth of \(\Psi(\xi,r_{P})\) along \(\xi\). As described in Section 2.3.2, when \(\Psi(\xi,r_{P})\) the spectral expansion along the \(\xi\) axis is much more minor than the minimum period of oscillation \(T_{\xi}\) of the \(G_{B}(\xi,\eta,r_{P})\), the \(G_{B}(\xi,\eta,r_{P})\) approximates that it does not vary along the \(\xi\) axis within the \(\xi\) bandwidth of the \(\Psi(\xi,r_{P})\) so that the \(G_{B}(\xi,\eta,r_{P})\) can be separated from the convolution calculation along the \(\xi\).  
Therefore, the minimum oscillation period \(T_{G_{B},\xi}\) of \(G_{B}(\xi,\eta,r_{P})\) is required to be much larger than the bandwidth \(\Omega_{\Psi,\xi}\) of the \(\Psi(\xi,r_{P})\) along \(\xi\):  
\[T_{G_{B},\xi}\gg\Omega_{\Psi,\xi}\longrightarrow\Omega_{\Psi,\xi}\Omega_{G_{B},\xi}\ll 2\pi. \tag{42}\]  
Based on [PERSON]'s rule [35] and Equation (42), the bandwidth of \(\Psi(\xi,r_{P})\) along \(\xi\) and the maximum frequency of the oscillations of \(G_{B}(\xi,\eta,r_{P})\) can be obtained. Likewise, an equivalent substitution of the Equation (42) can be achieved through the limitation of the maximum trajectory deviation \(d_{max}\) according to [34]:  
\[d_{max}\ll\frac{2L_{r}}{\Omega_{d}L_{a}}\frac{f}{\Delta f}, \tag{43}\]  
where \(\Omega_{d}\) is the maximum spatial frequency of the trajectory deviation \(d(x^{\prime})\) in a synthetic aperture length. It can be observed from Equation (43) that constraint holds for both slow, larger-amplitude airborne trajectory deviations and fast, smaller-magnitude airborne trajectory deviations. For the typical X-band airborne SAR systems in Table 1, Equation (43) requires that \(d_{max}\ll 7.07\)m.