esa-sceva/satcom-chunk-collection huggingface dataset:
# Ceramic Matrix Composites for Aero Engine Applications--A Review
## 7 Manufacturing Processes for CMCs
### Melt Infiltration  
The melt infiltration process has been successfully used for the creation of reaction-bonded ceramics for ultra-high-temperature applications. C/SiC and SiC/SiC CMCs are mainly fabricated with this process, where Si melts are infiltrated in the porous carbon-based preforms for SiC to be formed [211]. MI produces a dense matrix in a shorter cycle time than other infiltration applications, although the needed temperatures are relatively high (up to 1414 \({}^{\circ}\)C) [212]. SiC fiber twos are coated with boron nitride fiber interface and silicon nitride coating. Both the unidirectional fiber and matrix types are created by using a filament winding process and have polymer binders along with commercial SiC and Cf powders. After the tape layup is finished, lamination occurs in a part preform, leading to pyrolysis. After the polymer is turned into carbon, densification of the pyrolyzed preform with SiC and melt infiltration occurs.  
Research shows the fabrication of carbon fiber with SiC-ZrC matrix CMCs. This resulted in the creation of a SiC-ZrC matrix with the SiC matrix adjusting to the carbon film coating fibers, allowing for high bending strength, oxidation resistance, and better ablation resistance in oxyacetylene flame, better than the one that ZrC CMCs have [213].  
Other research shows the fabrication of carbon fiber-reinforced C-SiC-Ti\({}_{3}\)SiC\({}_{2}\) hybrid matrix composites by implementing TiC slurry and molten Si infiltration processes. This resulted in a successful CMC manufacturing process due to the use of high stability and low viscosity of TiC ball milling. The mechanical properties of the fabricated CMCs were increased with the conversion of TiC to Ti\({}_{3}\)SiC\({}_{2}\). On the downside, the research demonstrated that the use of Ti\({}_{3}\)SiC\({}_{2}\) grains in the CMC matrix led to fiber-matrix decohesion, intergranular cracking, and delamination [214].
## 3 Results and Discussion  
### Characterization of SA/PVP/TiO\({}_{2}\) Nanocomposite  
The TEM image of TiO\({}_{2}\) shown in Figure 2a proves the formation of the tubular shape with nanoscale size. After the TiO\({}_{2}\) nanotubes were embedded in the blended SA/PVP polymers, SA/PVP/TiO\({}_{2}\) nanocomposite beads were obtained, as confirmed from EDS data (Figure 2b). As shown in the SEM micrograph of Figure 2c, the beads presented a rough and wrinkled morphology. By taking a higher magnification SEM image, a good dispersion of the TiO\({}_{2}\) nanotubes was observed with visible pores in the polymer matrix (Figure 2d).  
Figure 2: (**a**) TEM image of the TiO\({}_{2}\) nanotube and (**b**) EDS data of SA/PVP/TiO\({}_{2}\) nanocomposite. SEM micrographs of the (**c**) spherical-shaped bead and (**d**) higher magnification of bead’s surface.  
The FTIR spectra of TiO\({}_{2}\) nanotubes and SA/PVP/TiO\({}_{2}\) nanocomposite are shown in Figure 3. The band at about 500 cm\({}^{-1}\) for the TiO\({}_{2}\) nanotube seen in Figure 3a is characteristic of Ti-O stretching vibration modes. The FTIR spectra of SA/PVP/TiO\({}_{2}\) nanocomposite samples exhibit bands around 1600 cm\({}^{-1}\) assigned to O-H stretching mode, as well as absorption bands at 1419 cm\({}^{-1}\) ascribed to COO symmetric stretching vibration in SA. The band at 1030 cm\({}^{-1}\) corresponds to C-O stretching [25], the band at 2178 cm\({}^{-1}\) is related to PVP's C-N bond stretching vibration, and the band located at 2170-2300 cm\({}^{-1}\) represents the polymers' C-H bonds' bending vibration [22].  
The XRD patterns in Figure 3b show the crystalline features of TiO\({}_{2}\) nanotubes, with characteristic peaks at 20 values of 28, 36, 41, and 54, whereas characteristic spectra of an amorphous structure are obtained for the prepared beads. The amorphous nature of the nanocomposites is related to the low Ti content (e.g., 2.7 wt.% Ti in SA/PVP/TiO\({}_{2}\)-3), as determined by EDS analysis.
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 3 Description of the GeoHyTE Platform
### Approximation Bases  
The approximation bases of conventional finite elements are centred on the concept of _nodes_. Nodal displacements and seepages are the degrees of freedom of the problem and all unknown fields are completely determined by their values. The choice of nodes in each element determine the expressions of the (polynomial) approximation functions. Consequently, the redefinition of the number of nodes in one finite element typically requires the recalculation of all approximation functions in all elements of the mesh.  
Hybrid-Trefftz displacement-seepage elements approximate not only the displacement and seepage fields in the domain of the element, but also the tractions and pore pressure fields on its essential (i.e., non-Neumann) boundaries. This property causes the elements to be coined as _hybrid_.  
All approximations abandon the concept of nodes altogether. The nodal displacements are not the main unknowns of the problem and the redefinition of the nodes does not call for the redefinition of the approximation functions. Since nodes lose their significance, hybrid-Trefftz meshes need not be conforming. Bounded to satisfy all domain equations, the displacement and seepage approximation functions embody relevant physical information on the phenomenon they model. They are tailored for each problem and account for most super-convergent features of the hybrid-Trefftz elements. However, they are more difficult to handle numerically than their conventional element counterparts, especially when their order is high.  
Mesh and Basis Refinements  
Finite element solutions can be improved in two ways: by reducing the size of the finite elements (h-refinement) or by increasing the order of the approximation bases (p-refinement). Both strategies are available for conventional and hybrid-Trefftz elements.  
For conventional finite elements, mesh refinement is the main means of improving the solution (and indeed the only means in some commercial software). Mesh refinement can be localised (e.g., in zones where larger solution gradients are expected), as long as it secures the conformity of the mesh. However, the p-refinement of a single element generally requires all elements to be similarly refined. In practice, this means that localised p-refinement is unaffordable.  
Both mesh and basis refinements are important means of improving the solution in hybrid-Trefftz finite elements. Due to the physical information built in the domain bases, the elements are super-convergent under p-refinement. Since hybrid-Trefftz finite elements are not defined by their nodes, the orders of approximation on each finite element and essential boundary can be defined separately (i.e., localised p-refinement is affordable). Mesh refinement may be slightly less convergent than for conventional finite elements, but can be performed without securing the conformity of the mesh since the nodes of adjacent elements do not need to match.
# Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil
## 2 Materials and Methods
### Visualization  
Improvements in personal computing as well as internet access becoming a near-ubiquitous aspect of daily life in many countries, also present the potential of scientific data sharing, visualization and collaboration through digital means. We illustrate straightforward examples of temporal and immersive visualization options for both the satellite imagery and SfM-MVS products. For dynamic visualization of land cover change around the HPPS we examined the newly launched Google Earth Timelapse. This option only requires access to a web browser and an internet connection. Cloud free satellite images can be viewed as a time lapse animation, qualitatively illustrating the changes on the surface of the Earth over a 36-year period (1984-2020).  
We also explored the use of immersive virtual reality (VR) technology for visualization of satellite imagery and SfM-MVS products. Virtual reality is the combination of hardware and software to simulate an environment with the user experiencing a high'sense of being present' in the VR representation [83]. VR experiences are immersive and can be a cost effective way to visualize scientific data. Gaining popularity in fields such as medicine [84], and ecology [85], among others, it is also beneficial for geospatial sciences [86]. We tested two readily available VR head mounted displays (HMD), the Oculus Quest 2 (Facebook Technologies LLC, Menlo Park, CA. USA) and a second generation Google Official 87002823-01 Cardboard viewer (Google, Mountain View, CA, USA).  
The Oculus Quest 2 is a low-cost, consumer-grade standalone HMD with 6 GB RAM, an Android based operating system and internal storage. The lenses have a resolution of 1832 \(\times\) 1920 pixels per eye and are capable of a refresh rate of 72-90 Hz. By connecting the HMD to a computer and initializing the Oculus Link software, the HMD can access additional software and data that require more computational power to run than is possible from the HMD alone. We accessed the satellite imagery archive on Google Earth VR for the HPPS study through this manner. We also illustrate the immersive nature of the SfM-MVS point clouds by interacting with them in the Oculus Quest 2 through the free VRifier software (accessed through Oculus Link). As a lower cost alternative compatible with the Google Cardboard viewer, we uploaded the SfM-MVS models to Sketchfab, a web based VR viewer ([[https://sketchfab.com](https://sketchfab.com)]([https://sketchfab.com](https://sketchfab.com)), accessed on 29 May 2021). The web based VR viewer allows users to interact with content through their smartphone's browser and a basic VR viewer.
## 1 Introduction
The aim of this paper is to offer a comprehensive survey of optimization methods that are showing promising results and can be used in the context of LEO-system design and also to provide examples and design recommendations for chosen scenarios, covering both the LEO space components (i.e., constellation optimization) and the Earth and vicinity-to-Earth components (i.e., ground segment and receiver optimization for terrestrial and aerial vehicles). The chosen application area is the area of the intelligent transportation systems, as this is a broad-encompassing area covering multi-mode receivers (terrestrial, maritime, airborne), all-speed scenarios (from stationary receivers to ultra high-speed receivers), and challenging constraints in terms of communication, positioning, and sensing target metrics. To sum up, this paper's contributions are:  
* An overview of LEO system design considerations for various applications, including high-speed intelligent transportation;
* A comprehensive survey of optimization methods for LEO system design, targeting challenging application scenarios, such as future autonomous transportation;
* The target optimization metrics and typical optimization problems involved in the three-segment architecture of any LEO system presented in a compact form;
* Addressing in detail the space segment and constellation optimization by taking into account aspects not widely addressed so far in the current literature, such as launch and maintenance costs and payloads, constellation management and scaling, and topology models;* Summarizing, in a concise form, **the optimization trade-offs** related to space, ground, and user segments in LEO design, targeting the performance metrics specific to all-speed (and in particular to high-speed) scenarios of autonomous vehicles;
* Design recommendations for future LEO systems for navigation, sensing, and communication purposes. The remainder of this paper is structured as follows: Section 2 covers an overview of the related works from the literature that either have a similar purpose to our paper or showcase the general status of the LEO networks. Section 3 introduces the general architecture of LEO systems and the different segments where optimization can be utilized. Section 4 shows the mathematical formulation of generic optimization problems and classifies the optimization methods. Section 5 introduces high-speed scenarios that arise within the LEO networks and explains the requirements of such systems. Section 6 details the optimization problems of interest within the space segment and provides examples of optimization objectives, criteria, parameters, and methods. Sections 7 and 8 repeat this process for ground and user segments. A few illustrative simulation-based examples are also included here. Last but not least, we provide our recommendations for selecting optimization tools for LEO networks in Section 9 before finalizing the paper in Section 10.
## 5 Experiments
### Experimental Results

Table 9: Response MSCS comparison before and after correction for three types of perturbations (SwapAlpha, SwapWord, CaseAlpha) at 100% intensity across four datasets (MMLU, LogiQA-en, LSAT, and SAT-en) using four LLMs (GLM4, Llama3.1, Qwen2.5, and Falcon3-Lambda). For each perturbation type, the table presents the MSCS values before correction (“Before”), after correction (“After”), and the improvement percentage (“Improv.”). The self-correction reflection demonstrates significant improvements in ACC across different perturbation types, as shown in Table 10, which indicates improved model performance through better handling of perturbed inputs and their corrected representations. In particular, among different types of perturbation, the SwapAlpha perturbation shows the highest average improvement rate (+10.22%), significantly outperforming both CaseAlpha (+3.35%) and SwapWord (+2.89%), suggesting that self-correction reflection is particularly effective in addressing character-level swap perturbations. The ACC improvements of all of these models empirically confirm that self-correction enables better performance by processing corrected input rather than noise-degraded input. As shown in Figure 10, self-correction reflection increases the radar chart area across all models, with improvements observed in each individual radar chart. The radar charts also reveal that SwapAlpha perturbations consistently show larger improvements compared to SwapWord and CaseAlpha perturbations, which aligns with the average improvement rates in Table 10, respectively. This comprehensive chart suggests that self-correction reflection demonstrates varying degrees of effectiveness across different perturbation types, showing the best enhancement in SwapAlpha perturbation (+10.22%), followed by better enhancement in CaseAlpha (+3.35%), and relatively good enhancement in SwapWord perturbation (+2.89%), indicating that the method's effectiveness varies depending on the specific type of perturbation encountered. Figure 9: Illustration of MSCS improvement through self-correction reflection, comparing the semantic recovery before and after correction for each LLM. The radar chart plots normalized MSCS percentages (before/after correction relative to 0% perturbation MSCS) on axes representing distinct perturbation scenarios.
## 2 Multi-Class Traffic Assignment
### Model Formulation  
Let **c** and **f** be the vectors of route costs \(\big{(}\dots,c_{km^{\prime}}^{rs}\dots\big{)}^{T}\) and route flows \(\big{(}\dots,f_{km^{\prime}}^{rs}\dots\big{)}^{T}\). Then, the multi-class TAP with asymmetric vehicle interactions can be formulated. A variational inequality (VI) problem is shown as follows:  
Find a route flow solution \(\textbf{f}^{*}\in\Omega\), such that  
\[\textbf{c}(\textbf{f}^{*})^{T}(\textbf{f}-\textbf{f}^{*})\geq 0,\forall\textbf{ f}\in\Omega \tag{6}\]  
where \(\Omega\) represents the feasible set defined by Equations (7)-(9):  
\[\sum\limits_{k\in\mathrm{K}_{m}^{rs}}f_{km}^{rs}=q_{m}^{rs},\;\forall rs\in \mathrm{RS},\,m\in\mathrm{M} \tag{7}\]  
Figure 2: Graphical illustration of travel time and capacity change. (**a**) Link travel time function and parameters. (**b**) Travel time and capacity change as a function of the proportion of CAVs.  
\[x_{a}^{m}=\sum_{rs\in\text{RS}}\sum_{k\in\mathbb{K}_{rs}^{m}}f_{km}^{rs}\delta_{ km}^{rs},\;\forall a\in\text{A,}m\in\text{M} \tag{8}\]  
\[f_{km}^{rs}\geq 0,\;\forall rs\in\text{RS},m\in\text{M},k\in\mathbb{K}_{rs}^{m} \tag{9}\]  
Equation (7) is the travel demand conservation constraint; Equation (8) is a definitional constraint that sums up all route flows that pass through a given link \(a\); and Equation (9) is a non-negativity constraint on the route flows. The VI formulation meets [PERSON]'s equilibrium conditions [21] based on the Karush-Kuhn-Tucker (KKT) conditions of the VI formulation [22].  
\[c_{km}^{rs}(\mathbf{f}^{s})-o_{m}^{rs}\Bigg{\{}\begin{array}{l}=0\;if\;(f_{ km}^{rs})^{*}>0\\ \geq 0\;if\;(f_{km}^{rs})^{*}=0\end{array},\;\forall m\in\text{M},k\in \mathbb{K}_{rs}^{m}\,rs\in\text{RS} \tag{10}\]  
where \(o_{m}^{rs}\) is the minimum O-D cost of class \(m\) between O-D pair \(rs\).
## 4 Experimental Setup and Results Analysis
### Comparative Analysis of Results from Different Experimental Methods and Conclusions  
In this experiment, we tested different line recognition algorithms and clustering algorithms, including Hough line detection, hierarchical clustering, DBScan clustering, spectral clustering, and improved K-means++ clustering. The results of each algorithm's line recognition are shown in Figure 12.  
Figure 11: Prediction on new samples. (**a**) Visualization of the labels representing the vertical lines in the new samples; (**d**) predicted results.  
Figure 10: Spectrograms of training samples. (**a**) A spectrogram which can be used as training samples after preprocessing; (**b**) visualization of the labels representing the vertical lines in the samples.  
According to the experimental results, we can observe the following: the Hough line detection method detects both horizontal and vertical lines as line segments; the hierarchical clustering model recognizes many false detections; the DBScan clustering model misses many line detections; spectral clustering and the improved K-means++ approach used in this study have similar identification performance. However, K-means++ demonstrates higher time efficiency. The statistical analysis of spectral clustering and K-means++ clustering is shown in Table 1.  
In terms of time efficiency, K-means++ outperforms spectral clustering because K-means++ can be pre-trained, eliminating the need for training the model from scratch each time. This makes K-means++ more time-efficient than spectral clustering. Therefore, in this experiment, we combined the dimensionality reduction concept of spectral clustering with the high time efficiency of K-means++. This algorithm not only achieves high recognition accuracy, but also demonstrates high efficiency.
# Adaptive Coherent Receiver Settings for Optimum Channel Spacing in Gridless Optical Networks
## 4 Experimental Results and Discussions
### Results and Discussion  
The results below are showing an experimental comparison versus the \"state of the art\", labeled as \"Legacy Method\", presented in [8]. Our test circuit, implemented in 28 nm complementary metal-oxide-semiconductor (CMOS) technology, has our new circuit and the typical architectures implemented. Under the exact same conditions, we compare the system level tolerance to channels spacing in gridless setup. We investigated both the SNR in steady state and cycle slip in acquisition state. When trying to reduce spacing between optical channels into the super-Nyquist mode of operation the penalty from linear crosstalk, which appears similar to white noise, is inevitable as discussed in [29]. Our method allows for pushing the bounds at which receiver can carry traffic. We were able to operate without any errors with the same 2 dB OSNR penalty as non-optimal method but with 1 GHz less spacing. In Figure 11, we show how the optimized method improves the jitter margin. \"Legacy Method\" is the optimization performed using circuits described in Figure 2. It has a fundamental limitation on the minimum filtering as it needs to reject the VCO high-frequency noise. In contrast, the main reason for extending the locking margin at the Rx is that we can filter the noise induced by channel, manifested by increased \(\mathrm{J_{channel}}\), due to reducing the spacing below 40 GHz without being limited by \(\mathrm{J_{rx}}\) as the two sources are decoupled in our new architecture.  
Figure 11: Optimization of jitter through setting the proper tracking  
Figure 10: Experimental setup.  
We then show, in Figure 12, the system level benefit in terms of channel spacing with the associated OSNR penalty. The penalty from linear crosstalk is unavoidable; hence the increase in Required-OSNR penalty but it is shown that our method allows for tighter spacing, 1.5 GHz per size at 2 dB OSNR penalty, in a gridless setup.
## 1 Introduction
Some relatively simple machine learning models, such as backpropagation (BP) networks, long short-term memory (LSTM) networks, and other prediction models, can often provide further improvement based on traditional  
Figure 1: Data coverage of the HF radar net system. Blue points mark locations with radar acquisition rates of more than 60%. Orange points mark locations with radar acquisition rates of more than 80%. Green stars mark locations with single radar stations. methods. [PERSON] et al. [20] and [PERSON] et al. [21] enhanced the prediction accuracy of significant wave height in shallow water using LSTM models. [PERSON] et al. [22] also achieved an enhancement effect using a BP model. There have also been many attempts to apply machine learning methods in the field of HF radar, especially in the process of radar echo inversion [23; 24; 25]. The inversion of wind speed from radar echo, which is difficult for traditional electromagnetic inversion algorithms to solve, is continuously explored using machine learning methods [26; 27]. Machine learning has also been used in the mitigation of sea clutter on radar, angle estimation, and target detection [28; 29; 30; 31; 32]. Hence, we have reasons to believe that machine learning methods can integrate more reliable ADCP observational data into HF radar data with a wider coverage area. This work is based on six newly established HF radar stations (Figure 1) within the Pearl River Estuary (PRE). We are dedicated to enhancing the quality of full-field radar data through machine learning methods to obtain a high-quality HF radar dataset. [PERSON] et al. [33] demonstrated the feasibility of a single-point correction using machine learning. However, due to limited in-situ data and the high costs of deploying and maintaining mooring ADCPs, it is imperative to develop a neural network-based method for cruising ADCP measurements. Therefore, in this study, we use an LSTM network model and introduce three physical factors--wind, tides, and river discharge--to control the training and prediction process in order to integrate cruising observation data into the radar network data and improve data quality. Through a series of numerical simulation experiments, we show improved data quality and feasibility by incorporating fixed-point and navigation observational data. The results of this study have implications for guiding future in-situ field observations.
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 4 Discussion and Recommendation
### A Recommended Alternative Approach to Establish an ITU Noise Model  
Considering the insufficient coverage of this (large) data set over the entire parameters space, one must conclude that it is impossible to generate a purely empirical model from this data. However, even though we do not know the difference between each country that we discussed, we can assign parameters to them. And if we understand the physical nature of the propagation and accumulation of the noise from a large number of noise sources, we can make a physical model of the man-made noise. When this model sufficiently approaches reality, the collected measurements in the RNDB can be used to validate the model. This approach will be described in the next section.