esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fractional-Order Creep Model of Water-Immersed Coal
## 2 Experimental Methods
### Specim Preparation  
The tested samples were picked from the Lingxin Coal Mine, which is located in an extremely water-deficient area of western China. This coal mine is being constructed as a demonstration project of CUR with an attempt to improve the utilization of mine water in this area.  
The concept of the experiment involved in this paper aims at understanding the law of the creep performance of water-immersed coal; therefore, the standard coal sample should be prepared firstly by core drilling, cutting, and polishing. Then, water immersion on these samples should be implemented to the target time, and after that is the creep experiment of the immersed coal samples under various durations. The SCQ-300-type automatic stone cutting machine (XinChuan Intelligent Corporation, Suzhou, China), SC-200-type automatic core drilling machine (XinChuan Intelligent Corporation, Suzhou, China), and M250-type surface grinding machine (XinChuan Intelligent Corporation, Suzhou, China) were employed to make the specimen have cylindrical-shaped dimensions with a diameter of 50 mm and a height of 100 mm, as shown in Figure 1a. The processed coal specimens were measured and weighed using electronic vernier calipers and high-precision 7-degree electronic scales, as shown in Figure 1b. The coal samples with similar weights and sizes within the error range were selected as experimental ones. In addition, all the specimens were ensured that the non-parallelism of the two ends of the specimen were \(\leq\)0.05 mm, with an axial deviation \(\leq\)0.25\({}^{\circ}\).  
In order to simulate the water-immersion conditions of the coal pillar dam in a CUR, the immersion experiments were carried out under constant temperature in a vacuum saturation chamber. Specifically, as shown in Figure 1c, all the specimens were firstly put into a vacuum drum and pumped to the vacuum state, then the natural water was injected into the vacuum drum via a conduit until the coal samples were completely immersed.  
Figure 1: Coal sample soaking process with (**a–c**) the standard coal samples, measuring devices and water-immersion facilities, respectively.  
After that, the vacuum drum was opened at the target times. Three groups with seven pieces of the coal samples in total were immersed at different durations as follows: specimen B was immersed for 7 days, specimen C was treated for 14 days, and specimen D was immersed continuously for 30 days. A specimen named A was an unimmersed specimen.
# Telecommunication Systems for Small Satellites Operating at High Frequencies: A Review
## 4 Isara  
ISARA is a CubeSat mission designed to test Ka-Band communication technologies for CubeSat. The spacecraft is a 3U CubeSat carrying a Ka-Band low power transmitter and a Ka-Band reflectarray antenna integrated on the back of the deployable solar panels. Additionally, the spacecraft carries a standard gain antenna and a switch. The standard gain antenna is used to provide a reference gain during operation to allow for the characterization of the performance of the reflectarray [9]. ISARA was launched on 12 November 2017 on the Cygnus Resupply Mission (OA-8) from the Mid-Atlantic Regional Spaceport, Wallops Flight Facility, in Virginia [10]. The spacecraft was deployed from the International Space Station few weeks later and the antenna was successfully deployed right after. Operations occurred over few months during which the pattern of the high gain antenna was characterized [11]. ISARA operated in space for a total of 5 months [12].
## 6 Applications of LLM4 Rec Across Domains
### Educational Resources and Learning Recommendations  
Large language models (LLMs) have shown significant promise in educational applications by enabling personalized, adaptive, and scalable learning recommendations.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Base Model** & **Limitations** & **Scalability** & **Latency** & **Multimodal** & **Training** \\  & & & & & **Suitability** & & **Cost/Eval** \\ \hline MM-Rec [141] & ViLBERT + BERT & Requires & rich image–text & Moderate & Low & Yes & Moderate/ \\  & & & & & & F1 = 0.78 \\ \hline GNR [142] & GPT-2 & Limited support for real-time updates & Low & Low & Yes & High/ \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Base Model** & **Limitations** & **Scalability** & **Latency** & **Multimodal** & **Training** \\  & & & & & **Suitability** & & **Cost/Eval** \\ \hline CoLLM & Frozen LLM + Collaborative Embedding & Needs collaborative history; frozen LLM restricts adaptability & High & Moderate & No & High/ \\ \hline RecLLM & LLM + Retrieval-Augmented & Requires high-quality user input for meaningful adaptation & Moderate & Moderate & No & High/ \\ \hline SocialRec & Context-Aware + Sentiment Classifier & Sentiment clustering may oversimplify user diversity & High & High & No & Moderate/ \\ \hline Prospect [146] & Agent-Based LLM Coordination & Complex multi-agent embedding & High & Moderate & Yes & High/ \\ \hline LLM-BRec & BERT + LLM Fusion & Limited by session context length and user profile noise & Moderate & High & Yes & Moderate/ \\ \hline \hline \end{tabular}
\end{table}
Table 9: Social media-focused LLM4 Rec models. Source: Author’s own design.  
TutorLLM [148] leverages retrieval-augmented generation (RAG) and knowledge tracing to generate tailored learning resources based on learner performance history. Similarly, studies on LLMs in MOOCs [149] demonstrate that prompt-tuned models can outperform traditional recommenders in course suggestions.  
RecMind [80] offers agent-based recommendation capabilities, adapting to evolving learner contexts in unstructured environments. E4 SRec [150], tailored for structured learning settings, excels in curriculum-aligned scenarios using BERT4 Rec architecture. OpenP5 [151] enhances LLM-based feedback generation through an open-source framework, supporting fine-tuning and evaluation.  
TALLRec [152] introduces a tuning-efficient recommendation pipeline to align Transformer-based models with long-term learning goals. These systems highlight the growing capacity of LLMs to support both structured and flexible learning paths.Table 10 presents a detailed comparison of all the models discussed.
## 3 Results and Discussion
### Yoghurt Synessis  
The changes in yoghurt syneresis are presented in Table 13. Synneresis during storage was assessed by the drainage (gravity method) and by centrifugation. There were no significant differences (\(p<0.05\)) in yoghurt syneresis between the treatments and within the same storage day with both examination methods. Synneresis, the separation of the liquid phase from the gel, is undesirable in yoghurt and can adversely affect consumer acceptance of the product. Synneresis can happen spontaneously or be triggered by mechanical actions, such as cutting, agitating, or freezing the gel [93]. The texture of yoghurt and its tendency toward syneresis are closely linked to the microstructure of the product, particularly the structure of the protein matrix [91].  
Synneresis by centrifugation was different from the values reported by [PERSON] et al. [69], who examined commercially available yoghurt of various types. This difference is attributed to the lower centrifugation speed applied by the latter researchers and the fact that the values in this study were derived from different types of yoghurt, such as strained, traditional, full-fat, or low-fat.  
Synneresis decreased during storage in yoghurt from all treatments. This finding is in agreement with the findings of [PERSON] [91]. Regarding syneresis by gravity, [PERSON] reported low syneresis in yoghurt from sheep milk. The total solid and the protein content are related to the extent of syneresis. Although there were differences in the total solid  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Treatment**} \\ \cline{2-6}
**Storage Day/Variable** & \begin{tabular}{c} **Control** \\ **(n = 4)** \\ \end{tabular} & \begin{tabular}{c} **UOP** \\ **(n = 4)** \\ \end{tabular} & \begin{tabular}{c} **POP** \\ **(n = 4)** \\ \end{tabular} & \
\begin{tabular}{c} **SEM** \\ **(n = 4)** \\ \end{tabular} &
\begin{tabular}{c} **Significance** \\ **S** \\ \end{tabular} \\ \hline \multicolumn{6}{c}{_Gravity_} \\ \hline
7 & 3.21 & 0.17 & 0.83 & 0.897 & NS \\
14 & 0.42 & 0.25 & 0.01 & 0.117 & NS \\
21 & 2.48 & 0.02 & 0.35 & 0.824 & NS \\ \hline \multicolumn{6}{c}{_Centrifugation_} \\ \hline
7 & 14.17 & 11.21 & 13.83 & 1.533 & NS \\
14 & 13.72 & 12.26 & 13.49 & 1.510 & NS \\
21 & 13.2 & 9.63 & 10.82 & 1.704 & NS \\ \hline \hline \end{tabular} UOP = unprocessed orange peel; POP = processed orange peel; NS = non-significant.  
\end{table}
Table 13: Changes in yoghurt syneresis during storage (days 7–21).  
content between the treatments (Table 5), this effect was not born out in the syneresis extent during storage. This effect is attributed to the fact that there were no differences in the protein content between the treatments. According to [PERSON] [91], the protein matrix of sheep milk yoghurt produces a stronger gel that is more resistant to deformation.  
The extent of syneresis, determined either by the drainage or the centrifugation method, was not significantly (\(p>0.05\)) affected during storage within the same treatment group.
## 5 Conclusions
Overall, in spite of some profile pairs showing large differences, C2 moisture profiles generally agree well with both ERA5 and MERRA-2 moisture, which confirms the usefulness of C2 as an independent observational dataset for tropical and subtropical tropospheric water vapor research. While the time period chosen in this study is relatively short, neither reanalysis dataset had yet assimilated C2 RO observations. Thus, it is probable that C2 data assimilation would improve the moisture output of both reanalyses. Future studies are likely to look more closely at C2 water vapor retrievals in the presence of deep convection and heavy precipitation to better quantify how tropospheric moisture is impacted by convection throughout the tropics. Conceptualization, B.R.J. and W.J.R. ; methodology, B.R.J., W.J.R. and J.P.S. ; software, B.R.J. ; validation, B.R.J., W.J.R. and J.P.S. ; formal analysis, B.R.J. and W.J.R. ; investigation, B.R.J. ; resources, B.R.J. ; data curation, B.R.J. and J.P.S. ; writing--original draft preparation, B.R.J. ; writing--review and editing, B.R.J., W.J.R. and J.P.S. ; visualization, B.R.J. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Science Foundation through grant AGS-1522830.
## 1 Introduction
[26] used a unified elasto-plastic constitutive model that can describe the liquefaction of sand and the two-phase u-p theory for saturated soils to analyze the dynamic responses of a sandy seabed subjected to cyclic wave loads. Then, some scholars have carried out further research [27; 28; 29; 30]. Due to the huge geometric size of reef islands, existing research on the seismic response of reef-island sites mostly uses simplified calculation models. Although the simplified model has the advantage of being simple and efficient, it is too idealistic and is difficult to quantitatively obtain the seismic ground motion field distribution patterns that can be used to guide actual engineering construction. In view of the shortcomings of the existing models, this paper adopts the fluid-structure coupling algorithm based on the acoustic fluid element [31], the fluid dynamic artificial boundary [32], and the consistent viscoelastic artificial boundary to establish a finite element model of the dynamic interaction of the reef-island-seawater system. Afterward, a numerical simulation of the seismic response of the island reef site was carried out to study the seismic ground motion distribution patterns of the reef-island-seawater site and the reef-island site with a lagoon terrain. This article will focus on analyzing the influence of factors such as the width and slope of the reef platform, the dynamic coupling effect of seawater, and the lagoon on the seismic response of the island reef site. The research framework diagram of this article is shown in Figure 1.  
As mentioned above, the existing seismic response analysis of reef sites mostly adopts a simplified one-dimensional calculation model without considering the influence of reef geometry and fluid-structure coupling. Compared with previous studies, the innovation of this paper is that the influence of reef-island topography and fluid-structure coupling is considered in the analysis when vertical ground motion is input. The purpose of this paper is to reveal the influence of island geometry and fluid-structure coupling on island seismic response. Because the geometric contour of reef islands generally presents the shape of a trapezoidal layered convex, to reveal the general law qualitatively, the reef islands are assumed to be trapezoidal and layered in the subsequent model in this paper. Figure 1 shows the flow framework for the entire analysis. Firstly, a sea-island dynamic interaction model is established according to the reef geometric dimensions, medium parameters and fluid artificial boundary method. Secondly, the representative ground motion records in the sea area are selected as the ground motion input to calculate the dynamic response of the whole model. Finally, according to the calculation results, the general law of seismic response of the reef is analyzed, i.e., the influence law of the bottom slope angle, the width of the reef platform, the fluid-structure coupling, the frequency spectrum of input ground motions on the seismic response of the reef.
## 2 Methodology  
### Virtual Prototype of BPMSM  
The optimized BPMSM prototype is a magnetically levitated centrifugal pump, BPS-200 of Levitronix(tm) (rated voltage = 24 V, rated speed = 8000 rpm, input power = 170 W). As presented in Figure 1a, the virtual prototype of the BPMSM developed in Ansoft Maxwell consists of stators, a slice rotor, levitation windings, and torque windings. The outer stators are folded down to form eight iron-core columns, while the slice rotor is a two-pole permanent magnet ring made of NdFeB. The magnetic circuit of the BPMSM forms a loop through the permanent magnet rotor, the iron-core columns, and the stator yoke. This structure makes one side of the BPMSM completely exposed, which facilitates the installation of the windings. This allows the two winding currents that generate the levitation force to be controlled separately. Furthermore, the two sets of windings are decoupled from each other electromagnetically. The slice shape rotor of the BPMSM can realize full suspension in five degrees of freedom (DOFs): passive levitation in an axial DOF and two axial-tilting DOFs, and active levitation in two radial DOFs on the _xoy_-plane. When the rotor is axially offset or twisted, the magnet flux in the BPMSM changes. With the principle of minimum magnetic reluctance, a magnetic force will be generated to pull the rotor back to its balance position.  
Based on the virtual model, a coarse grid is first used for mesh generation, then the steady-state adaptive mesh is used. As presented in Figure 1b, the grid between  
Figure 1: The structure diagram of the BPMSM. (**a**) The virtual prototype of the BPMSM developed in Ansoft Maxwell; (**b**) the meshing of the BPMSM finite element model on the _xoy_-plane; (**c**) mechanical structural parameters; (**d**) no-load magnetic density cloud map; and (**e**) magnetic density cloud map with 1 A current passing through the suspension force winding.  
different material structures is finer to ensure computation accuracy. Figure 1d,e displays the magnetic field distribution of BPMSM no-load and on-load conditions. For the former condition, the two-pole magnetic flux is symmetrical with the rotor in the equilibrium position. For the on-load condition, the magnetic flux is the combination of the symmetrical quadrupole magnetic field produced by the levitation force winding current and the symmetrical two-pole magnetic flux of the no-load condition. It can be observed that the magnetic density along the +\(x\)-axis is obviously larger than that of other parts, verifying a controllable radial suspension force generated along the +\(x\)-axis.  
Referring to the traditional permanent magnet synchronous motor design method, the candidate mechanical structural parameters for BPMSM performance optimization are presented in Figure 1c. According to the design specifications where the rated power is \(P_{e}\) = 200 W, rated speed is \(n_{N}\) = 8000 r/min, and rated efficiency is \(\eta\) = 65%, the initial values are listed in Table 1.
## 3 Slotted Waveguide Antenna Design
The \(|S_{11}|\) has been computed and is plotted in Figure 7b, showing that the antenna keeps good performances even with the applied twist. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value [mm]** & **Description** \\ \hline \(\text{l}_{slot}\) & 9.92 & Slot length \\ \(\text{w}_{slot}\) & 1.75 & Slot width \\ \(\text{d}_{center}\) & 13.45 & Slot centers distances \\ \(\text{d}_{slot}\) & 3.53 & Longitudinal distance between slots \\ \(\text{d}_{slot}\) & 2.53 & Last two slots distance \\ \(\text{s}_{slot}\) & 2.75 & Transversal distance between slots \\ \(\text{d}_{short}\) & 6.455 & Last slot center to short wall distance \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Slotted waveguide antenna main design parameters. Figure 5: (**a**) Slotted waveguide antenna model with fundamental design parameters highlighted. The structure consists of a steel block (grey object) fed through a waveguide port, highlighted in red. (**b**) Farfield pattern of the presented slotted waveguide antenna.
## 4 Discussion
This constraint is particularly relevant when using machine learning models, which typically perform better with largerdatasets to capture complex patterns and interactions effectively. For instance, the ensemble learning model, which achieved a solid accuracy in this study, may demonstrate reduced robustness when applied to more diverse or significantly larger populations. It also affects the using of Principal Component Analysis (PCA). While PCA is a widely used method for exploratory data analysis and dimensionality reduction, its effectiveness can be limited in smaller datasets, where the stability and generalizability of the extracted components may be reduced. The PCA assumes linear relationships among variables and may not fully capture complex or nonlinear interactions relevant to the constructs studied. To address these limitations, future studies could consider using nonlinear dimensionality reduction techniques such as t-distributed Stochastic Neighbor Embedding (t-SNE) or Uniform Manifold Approximation and Projection (UMAP), which are often better suited to reveal subtle patterns and groupings in high-dimensional data, particularly in small to medium-sized samples. Another limitation of this study is the use of Google Forms as the data collection tool, which, while accessible and efficient, may be subject to self-selection bias and limited control over respondent authenticity, potentially affecting the generalizability of the findings. Future research could strengthen reliability by incorporating follow-up interviews or triangulating responses with other data sources.
###### Abstract
The proposed method was verified using real trajectory data in Lianyungang City in China and the experimental results show that this method can automatically extract intersection locations effectively and efficiently. The following section provides the proposed method. Section 2.1 provides a continuous trajectory segment extraction. Section 2.2 gives a detailed description of the algorithm for the extraction of straight trajectory segments using the maximum reconstruction error. Sections 2.3 and 2.4 describe the method for calculating the intersection point and extracting intersection information by clustering. Experimental results and discussions are presented in Section 3. Conclusions are given in Section 4. Figure 1: The workflow for extracting road intersections.
# Real-Time 3D Reconstruction Method for Holographic Telepresence
## 2 Real-Time 3D Reconstruction Method
### Point Cloud Extraction  
In this research, we used two Kinects, and the multiple sensors were used simultaneously. In order to obtain the 3D points, the [\(x\), \(y\), \(z\)] coordinates of each pixel inside the human body segmentation mask used perspective projection with \(z=depth\). Then, the [\(x\), \(y\), \(z\)] points were projected onto the colour frame using the intrinsic and extrinsic parameters of the colour camera to extract the corresponding colour of each 3D point.  
The 3D-coloured points (\(X\), \(Y\), \(Z\), \(R\), \(G\), \(B\)) of each depth pixel using perspective projection are obtained using the following equation.  
\[\begin{array}{l}X=\frac{depth\times(x-cx)}{f}\\ Y=\frac{depth\times(y-cy)}{f}\\ Z=depth\end{array} \tag{2}\]  
where (\(cx\), \(cy\)) is the principal point and f is the focal length. To fuse the coloured 3D data, the extrinsic parameters of each camera, i.e., the poses between each camera and the reference will be used to transform all the point clouds into a single reference frame. The next step is to refine the initial estimate of the extracted point cloud using iterative closest points (ICP) [24] with Algorithm 1.  
```
Input: F1...n--3D point cloud from each sensor Data: R1...n, t1...n--initialized to identity Result: R1...n, t1...n--refinement transform for each sensor v = 1; \(\tau\) = 0.01; firstPass = true; e = 0; \(\hat{\epsilon}\) = 0 whilev > \(\tau\)do \(e\) = 0 for i = 1 do \(K\) = {1, 2,..., n} \(\backslash\) i /* ICP aligns the \(i\)-th shape to all the other ones, updates \(R_{i}\), it and returns the average error per point */ \(e\) = \(e\) + ICP(\(F_{K}\), \(F_{i}\), \(R_{i}\), \(t_{i}\))  end if if firstPass == true then  firstPass = false \(v\) = 1 else \(v\) = \(\frac{|e-e|}{e}\). end if \(\hat{\epsilon}\) = \(e\) End
```  
**Algorithm 1****Iterative Closest Point (ICP)**  
We want to construct, within each cube, a 3D model that is usually constituted by a triangular mesh that correctly represents the geometry and topology of the isosurface. The surface generation from point cloud data will later be implemented with the extended marching cube algorithm, which requires a discrete set of cubes as mentioned in [PERSON] et al. (2016). ICP has been used to divide the input volume into a discrete set of cubes. This is the primary reason the ICP can avoid degenerate triangles and guarantee topological correctness during the surface generation stage.