esa-sceva/satcom-chunk-collection huggingface dataset:
# The Value of Fractal Analysis in Ultrasound Imaging: Exploring Intricate Patterns
## 3 Results
### Salivary Glands  
Elastography is a noninvasive imaging technique that measures tissue stiffness and provides valuable insights into various pathological conditions. It is particularly useful for assessing the submandibular glands, where changes in tissue elasticity can indicate the presence of diseases, such as inflammation, sialadenitis and tumors [22]. Fractal analysis applied to elastographic images helps to identify pathological changes in the submandibular gland by quantifying the complexity of tissue structures. Higher fractal dimension values in elastographic images are indicative of greater structural complexity due to inflammation or other pathological processes. This advanced analysis enhances the diagnostic accuracy of elastography, making it a powerful tool for noninvasive diagnosis of salivary gland disease [22]. Elastographic studies have shown that higher fractal dimension values correspond to areas of increased stiffness and complexity in inflamed submandibular glands. This correlation helps diagnose conditions like acute or chronic sialadenitis by distinguishing between healthy and diseased tissues [23]. Fractal analysis can differentiate between benign and malignant tumors in the submandibular gland. Malignant tumors generally exhibit more chaotic and complex structures, reflected in higher fractal dimension values compared to the more regular structure of benign tumors. This distinction aids in accurate diagnosis and appropriate treatment planning [23]. Moreover, elastography combined with fractal analysis is effective in assessing the involvement of salivary glands in [PERSON]'s syndrome [23; 24]. [PERSON] et al. [23] reported that patients with [PERSON]'s syndrome exhibited higher fractal values in their salivary glands, indicating significant structural changes due to the autoimmune process.
# A Lightweight Graph Neural Network Algorithm for Action Recognition Based on Self-Distillation
## 4 Experiments and Discussion
### Denser Representations
#### 4.3.2 Self-Distillation vs. Supervision  
**The representations learned by self-distillation are denser than representations trained under supervision.** As shown in Figure 9, the cluster densities of representations by self-distillation are better than the representations under pure supervision. For ST-GCN 20 and ST-GCN 12, the Dunn index and silhouette coefficients of self-distillation are larger than the supervision, and the Davies-Bouldin index of self-distillation is smaller than the supervision. The training under supervision only adopts action labels to drive the predicted labels to be close to the ground truth labels, while the self-distillation also emphasizes the similarities between predicted distributions and feature distances. By limiting the predicted distributions to be close to the ground truth (well compacted clusters), each cluster's density is limited. The forcing of close feature distances ensures that the learned representations are geometrically close to the ground truth.  
Figure 8: The (**a**) PCA; (**b**) T-SNE; (**c**) 20-Nearest Neighbor Graph projections for representations learned by self-distillation. The features come from ST-GCN 20 with _pos_ as input data, performed on NTU xsub, captured from the first block.  
Figure 9: The cluster densities of representations estimated by **(a)** Davies–Bouldin index, **(b)** Dunn index and **(c)** silhouette coefficients. The representations are trained by self-distillation or pure labels, which are labeled as kd, and sup in the legend, respectively. The brown color, which is not shown in the legend, is the overlapped region between kd and sup. For each subgraph, each row shows the densities on different datasets, and each column shows different blocks. The \(x\)-axis denotes trained backbones and input data. All instances are evaluated here.
## 2 Materials and Methods
### Analytical Methods  
The total solids (TS) and volatile solids (VS) concentrations were determined using the gravimetric method (part of EPA Standard Method 2540). Dried biomass samples (at 105 \({}^{\circ}\)C) were assayed for their total carbon (TC), total organic carbon (TOC), and total nitrogen (TN) using a Thermo Flash 2000 organic elemental molecule analyzer (Thermo Scientific, Waltham, MA, USA). The determination of the total phosphorus (TP) was carried out colorimetrically in ammonium metavanadate (V) and ammonium molybdate, after prior mineralization in a mixture of sulfuric (VI) and chloric (VII) acids at 390 nm using a DR 2800 spectrophotometer (Hach-Lange GmbH, Dusseldorf, Germany). The total protein (TP) concentration was calculated by multiplying the TN value by a protein conversion factor of 6.25. Saccharides were determined as reducing sugars colorimetrically using an anthrone reagent at 600 nm and a DR 2800 spectrophotometer (Hach-Lange GmbH, Dusseldorf, Germany). Lipids analyses were carried out using Buchi extraction apparatus (B-811, Buchi AG, Flawil, Switzerland) with the Soxllet method. The dissolved chemical oxygen demand (COD) was determined using a DR 5000 spectrophotometer with a HT 200 s mineralizer (Hach-Lange GmbH, Dusseldorf, Germany), and the dissolved total organic carbon (TOC) was determined using a TOC-L analyzer TOC-L (Shimadzu, Kyoto, Japan). The pH in the samples was determined using the following procedure. In total, 10 g of the homogenized air-dried sample was weighed in a 100 mL beaker with 50 mL of distilled water, and then the pH of the sample was measured with calibrated apparatus (VWR 1000 L, Germany). The methane in biogas was analyzed using a GC Agilent 7890 A gas chromatograph (Santa Clara, CA, USA) with a thermal conductivity detector (TCD), two Hayesep Q columns (80/100 mesh), two molecular sieves columns (60/80 mesh), and a Porapak Q column (80/100). The temperatures of the injection and detector ports were set at 150 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively. Helium and argon were used as carrier gases at a flow rate of 15 mL/min.
## 3 Results and Discussion  
### Relative Density  
The relative density results using different methods including the Archimedes methods and image analysis were obtained, and were plotted into the curve of relation between relative density and energy density in Figure 2. Figure 3 presents the merged microscopies of the cross-section of AlSi10 Mg SLMed samples. According to the merged microscopies of the cross-section, the relative densities of the samples were calculated with ImageJ software.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Liquid** & **Density (g/cm\({}^{3}\))** & **Viscosity (10\({}^{-6}\)m\({}^{2}\)/s)** & **Surface Tension (10\({}^{-3}\)N/m)** \\ \hline
96\% Ethanol & 0.8075 & 0.740 & 22.39 \\ Tetrachloroethylene(C\({}_{2}\)Cl\({}_{4}\)) & 1.622 & 0.549 & 31.74 \\ Pure Silicone Fluid & 0.97 & 50 & 20.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical properties of liquid used in measurement.  
Figure 2: _Cont._  
Figure 3: Merged micrographs of the cross-section of AlSi10 Mg selective laser melted (SLMed) samples.  
Figure 2: Comparison of results of relative density using different methods: (**a**) comparison of method 2-land method 2-2; (**b**) comparison of method 3-1, method 3-2; (**c**) comparison of method 2-2, method 3-2 and method 4; and (**d**) comparison of method 1, method 4, and image analysis.
# Iterative MLFMA-MADBT Technique for Analysis of Antenna Mounted on Large Platforms
## 4 Numerical Results
### Wire Antenna Mounted on a Trihedral Corner Reflector  
As displayed in Figure 10a, a wire antenna is mounted on a trihedral corner reflector whose side length is 1 m. Its working frequency of the antenna is 3 GHz and the magnitude is 1 \(Idl(Am)\). A part of the trihedral corner reflector near the antenna is divided into the transition region. For the proposed iterative MLFMA-MADBT method, the mesh size of the remaining trihedral corner (BT region) is about 3\(\lambda\).  
The far-field radiation patterns in the XOZ cut-plane obtained from the MLFMA, the MLFMA-PO and the proposed iterative MLFMA-MADBT methods are plotted in Figure 10b. The proposed method gets smaller value of mean and standard deviation than the MLFMA-PO, which indicates that the proposed method has a higher accuracy and is more advantageous than the MLFMA-PO especially for the platform with multiple reflectors.  
Furthermore, for the wire antenna mounted on a trihedral corner reflector, the number of mesh elements, memory usage and total calculation time of the three methods are listed in Table 1. It is obvious that the iterative MLFMA-MADBT method has the best performance on memory usage and time consumption among the three methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Mesh Elements**} & \multirow{2}{*}{**Memory (MB)**} & \multirow{2}{*}{**Time (s)**} \\ \cline{2-2} \cline{5-5}  & **MLFMA** & & & \\ \hline MLFMA & 119874 & / & 4270.14 & 66.02 \\ MLFMA-PO & 171 & 13907 & 85.90 & 56.47 \\ MLFMA-MADBT & 171 & 115 & 29.29 & 8.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculation time and memory requirements of different methods for the wire antenna mounted on a trihedral corner reflector.  
Figure 10: The model and far-field radiation patterns in XOZ plane of the wire antenna mounted on a trihedral corner reflectors. (**a**) Model (**b**) Comparison result.
# Cooperative Power-Domain NOMA Systems: An Overview
## 3 Types of NOMA
### Power Domain NOMA
#### 3.2.6 Clustered-Based MIMO-NOMA
The authors used average transmit power and OP to numerically and analytically assess H-NOMA's performance. Projection Hybrid (PH)-NOMA:In [129], PH-NOMA was proposed to reduce intra- and inter-cluster interference by combining H-NOMA precoding and the traditional ZFBF method. The features of quasi-degradation hlwere used to combine PH-NOMA with a pairing algorithm with inversion and projection to achieve minimal complexity and reduce overall interference. The diversity and OP gain were used to evaluate the performance of these methods.
## 6 Conclusions  
Mobile communication is increasing day by day, and the requirement for high EE and low latency is also growing, particularly in industry 5.0. 6G provides the solution to this problem; however, the deployment of 6G is challenging. RIS is the primary solution in deploying the 6G, and this article considers an industry scenario and formulates an MINLP optimization problem. We propose an OAA algorithm for the formulated problem. The performance of the proposed algorithm is evaluated in terms of EE, throughput, latency,  
Figure 5: Performance analysis of the proposed algorithm. (**a**) Average \(\Omega\) (Mbps) vs. No. of \(U_{E}\); (**b**) No. of \(U_{E}\) vs. \(\mathbb{EE}\) and Latency (ms); (**c**) Channel efficiency (\(\mathbb{CE}\)) vs. \(\Omega\); (**d**) Resource allocation to \(U_{E}\).  
and resource allocation. The optimal EE obtained by OAA is 9 MbJ\({}^{-1}\), whereas the optimal average \(\Omega\) achieved by OAA is 90 Mbps. The latency of the network using the OAA algorithm is 0.5 milliseconds. We also compare the performance of the proposed algorithm with several optimization algorithms. The experimental results verify that the proposed OAA outperforms all the other algorithms. In we future, we aim to implement the proposed algorithm in other domains such as in a smart city, heterogeneous networks, human to robots communication.  
Conceptualization, S.J.; Data curation, S.J. and M.S.A.; Formal analysis, S.J. and M.S.A.; Investigation, M.S.A.; Methodology, S.J.; Project administration, M.R.; Supervision, M.R. and F.; Validation, M.R.; Visualization, F.; Writing--original draft, S.J.; Writing--review and editing, M.R. and F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
Principal component analysis (PCA) is usually part of data visualisation. PCA converts the features of datasets from high dimensional data and can help convert data to low-dimensional data with the aid of covariance, eigenvalues, and eigenvectors in identifying and sorting the strength of the predictive power of all features [9]. PCA allows for the extraction of critical feature vectors from multidimensional datasets in terms of data visualisation and loses some crucial information along the process [10], which is a considerable disadvantage, primarily if it is used extensively in the field of machine learning. This study used PCA not as a dimension reduction but as a sample reduction in order to remove the unwanted noisy samples in multidimensional datasets. This paper proposes an application of a PCA-sample reduction process (SRP) to improve the prediction accuracy of ANNs. Using publicly available biomedical datasets from a different field provides qualitative analysis and demonstrates the effectiveness of said method in improving the accuracy of ANNs. By cleaning the publicly available biomedical datasets before training and testing the ANN, the classification problem is expected to increase the accuracy and to lower the computational cost, which is a great help in dealing with the the analysis and prediction of big multidimensional datasets. A study to reinforce the methodology of an ANN using PCA-sample reduction is proposed in this paper to determine the proposed system's accuracy and performance with different sizes of multidimensional biomedical datasets. Furthermore, we investigate heart disease, voice and speech analysis for gender recognition, breast cancer classification, and cancer patient datasets to prove the versatility and flexibility of the proposed data-cleansing technique. The rest of the paper is structured as follows. Section 2 provides a literature review of data cleaning in biomedical applications. Section 3 discusses the basic concepts of principal component analysis and related topics. Section 4 discusses how the PCA-SRP is integrated with an ANN. Section 5 provides the recommended \(Sc\) ranges. Section 6 interprets the result of publicly available biomedical datasets. Lastly, Section 7 provides a discussion, the conclusion, and future research directions.
## 5 Evaluation
### Average Cache Hit Ratio  
After a cache hit, the cache node immediately retransmits the lost data. The cache hit ratio is defined as the number of NACKs recovered divided by the total number of NACKs received in a cache node, which reflects the overall cache resource utilization of the multicast tree nodes. Figure 5 shows the variation of the average hit ratios of all MTNs against different cache space sizes in the combinations of different cache allocation mechanisms and different cache strategies.  
As can be seen from Figure 5, the cache hit ratios of all combinations become greater with the cache size increasing, which is in line with the expectation that more chunks  
Figure 5: Comparison of average cache hit ratio. (**a**) Comparison of average cache hit ratio under LCE; (**b**) comparison of average cache hit ratio under CAPC; (**c**) comparison of average cache hit ratio under Prob; (**d**) comparison of average cache hit ratio under ProbCache.  
can be cached in larger cache space. Among them, the combinations of DCAM and all cache strategies achieve the highest cache hit ratio, because the DCAM evaluates the cache requirements of the multicast group according to the change of the packet loss probability, node depth, and transmission rate, and can adjust the allocation results in time. Based on the allocation result of AARM, the cache hit ratios achieved by each cache strategy are the second. The cache hit ratios achieved by each cache strategy based on the allocation result of Equal are the worst.  
Based on the same cache quota, LCE shows the same trend as CAPC, but it has a lower cache hit ratio. ProbCache and Prob have the worst performance because they just randomly cache chunks and do not care if cached chunks will be lost. Under the same cache quota, CAPC can cache chunks that experience congestion loss with a high probability, and most of the lost data can be recovered through MTNs instead of multicast source. Therefore, CAPC has the highest cache hit ratio.  
Therefore, this section verifies the effectiveness of DCAM in the scenario where multiple multicast groups share cache resources, and the combination of DCAM and CAPC can achieve optimal cache resource utilization.
## 5 Case Study: Users Play with Animal Dolls in Mobile AR Application
### Mutual Occlusion  
Most AR applications simply overlay the virtual objects on top of the real scene. Such a method might be useful when overlaying information in a scenario where the visibility of the information is crucial. However, for the illusion of a virtual object existing in the real world, mutual occlusion between the real and virtual objects should be provided [48]. In the case of a virtual human walking among real animal dolls, occlusion should be applied to the virtual human when behind dolls. Conversely on dolls, when the virtual human is in front of them. Since we created our silhouette mesh from the real object rendered on the image plane, the shape of the silhouette is matched with the shape of the real object on the image plane. We also placed all silhouette meshes at the corresponding positions of real objects. Therefore, by applying a texture that excludes the virtual object's layer to silhouette mesh, we could achieve a realistic mutual occlusion.  
Figure 9 shows results of three different occlusion strategies. As seen in the first image, a simple overlay of the virtual object on the real scene, for example, used in Pokemon Go, may result in spatially incorrect placement of the virtual object. Figure 9b simulates the occlusion based on object detection without segmentation. Most object detection algorithms return such bounding boxes around recognized objects, which might be faster compared to generating a segmentation map as our method does. However, in this case, the virtual human can be incorrectly cropped in midair even if the spatial relation between virtual and real might be kept. On the other hand, since our silhouette mesh has the shape of the real object, it can provide a delicate mutual occlusion for sophisticated objects.
# Analysis of Earth Pressure Variation for Partial Displacement of Retaining Wall
## 1 Introduction
However, most of above-mentioned studies barely conducted research about the horizontal soil arching effect of the retaining wall in the case of partial displacement, and also did not consider the influence of movement modes such as T, RB, and RT modes. Figure 1: Disaster caused by partial displacement of retaining wall. (**a**) longitudinal cracking, (**b**) displacement of part retaining wall. In this study, horizontal trap-door tests were carried out to investigate the influence of the displacement of the partial wall. The width of the trap-door and three different movement modes (T, RB, and RT) were taken as the influence factors in these tests. The distribution of lateral earth pressure under three movement modes are discussed in detail. The variation of horizontal earth pressure with displacement and along the height were also researched, respectively. The soil arching effect during the experiment was studied to explain the variation of earth pressure. The influence of the trap-door width and the load sharing ratio were considered to further illustrate the effect of the partial moveable wall.
# NFT-Based Framework for Digital Twin Management in Aviation Component Lifecycle Tracking
## 5 Conclusions  
This paper presents a comprehensive framework for implementing NFT-based decentralized algorithms for digital twin management in aviation, focusing on component lifecycle tracking. The proposed system uses blockchain technology, smart contracts, and decentralized storage to create a robust, transparent, and efficient ecosystem for managing aviation components throughout their lifecycle.  
The integration of NFTs with digital twins offers new opportunities for enhancing traceability, authenticity, and data integrity in the aviation industry. By creating unique digital representations of physical components, this approach addresses critical challenges in supply chain management, maintenance, certification, and regulatory compliance. The system's ability to provide real-time, immutable records of a component's entire history--from manufacture to retirement--has the potential to significantly improve safety, reduce fraud, and optimize operational efficiency.  
The key benefits of this NFT-based approach include enhanced traceability and authenticity verification, streamlined certification processes, efficient supply chain management, and data-driven maintenance decisions. The system also facilitates comprehensive digital twin integration, enabling real-time monitoring, simulation, and performance optimization of aviation components.  
However, the implementation of such a system is not without challenges. Addressing these challenges will be crucial for the successful implementation and widespread adoption of NFT-based systems in aviation.  
Future research directions should focus on blockchain optimization for improved scalability, integration of AI and machine learning for enhanced predictive capabilities, development of interoperability standards, and exploration of smart components directly linked to their digital NFT twins.  
The NFT-based approach to aviation-component lifecycle management represents a significant advancement in how the industry can manage its critical assets.
# Subspace Detours Meet Gromov-Wasserstein
## 2 Background
### Subspace Detours and Disintegration
#### 2.2.1. Disintegration  
Let \((Y,\mathcal{Y})\) and \((Z,\mathcal{Z})\) be measurable spaces, and \((X,\mathcal{X})=(Y\times Z,\mathcal{Y}\otimes\mathcal{Z})\) the product measurable space. Then, for \(\mu\in\mathcal{P}(X)\), we denote \(\mu_{Y}=\pi_{\#}^{Y}\mu\) and \(\mu_{Z}=\pi_{\#}^{Z}\mu\) as the marginals, where \(\pi^{Y}\) (respectively \(\pi^{Z}\)) is the projection on \(Y\) (respectively \(Z\)). Then, a family \((K(y,\cdot))_{y\in\mathcal{Y}}\) is a disintegration of \(\mu\) if for all \(y\in Y\), \(K(y,\cdot)\) is a measure on \(Z\), for all \(A\in\mathcal{Z}\), \(K(\cdot,A)\) is measurable and:  
\[\forall\phi\in C(X),\;\int_{Y\times Z}\phi(y,z)\mathrm{d}\mu(y,z)=\int_{Y}\int_ {Z}\phi(y,z)K(y,\mathrm{d}z)\mathrm{d}\mu_{Y}(y),\]  
where \(C(X)\) is the set of continuous functions on \(X\). We can note \(\mu=\mu_{Y}\otimes K\). \(K\) is a probability kernel if for all \(y\in Y\), \(K(y,Z)=1\). The disintegration of a measure actually corresponds to conditional laws in the context of probabilities. This concept will allow us to obtain measures on the whole space from marginals on subspaces.  
In the case where \(X=\mathbb{R}^{d}\), which is our setting of interest, we have existence and uniqueness of the disintegration (see Box 2.2 of [19] or Chapter 5 of [23] for the more general case).