esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Global Model Performance on Test Data  
The test dataset is presented in Figure 12 with the evaluation results of four machine learning models: Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), and K-Nearest Neighbours (KNN). These findings underline, according to a number of standards, the special performance qualities of every model. The impressive accuracy scores of 95% of the KNN and SVM models, especially, show how well they can categorize situations. The SVM model has an accuracy of 94%; thus, it can accurately identify positive scenarios. Additionally, the stunning 96% recall rate is evidence of how well it captures relevant examples of selective forwarding attacks. What the KNN model shows by reaching a balanced precision, F1 score, and recall of 95% is that it has high classifying accuracy,  
Figure 11: NB confusion matrix.  
Figure 10: NB training model.  
implying the detection of patterns that possibly indicate selective forwarding attacks. Despite its somewhat lower accuracy of 93%, the LR model shows good precision, recall, and F1 scores of 92%, 95%, and 93%, respectively. This underlines its capacity to accurately identify positive situations and document noteworthy examples of selective forwarding assaults. On the other hand, while attaining a very high precision of 98%, the accuracy of the NB model is noticeably lower, at 87%. This is why the model reduces false positives really effectively. Still, its 86% F1 score and significantly lower recall rate of 76% suggest that it might be difficult to detect every instance of selective forwarding assaults. We finally show how successfully a number of algorithms detect selective forwarding attacks in RPL-based IoT networks. Their limits and benefits are clarified in a significant way.
## 4 Results  
### Complex Dispersion Relation. SLaTCoW vs. EPWE  
We start by considering a reference case which consists in a classical SC of finite size composed of 21 \(\times\) 21 rigid cylinders arranged in a square lattice and embedded in air, where no losses are considered. In this situation, the imaginary part of the wavenumber is non-zero only because of the evanescent modes created within the band gap. To validate the results obtained using SLaTCoW, we make use of EPWE [9; 10], an already established technique that allows us to retrieve both the real and imaginary parts of the wavenumber for non-resonant periodic structures of simple geometries.  
Figure 3 illustrates the complex band structure of the finite SC presented in Figure 1 using SLaTCoW and the one obtained using EPWE for an infinite SC with the same geometrical and physical properties. The imaginary part of the wavenumber is shown in the right (left) panel in the \(\Gamma\)X (\(\Gamma\)M) direction, while the real part of the wavevector for both directions is shown in the central panel. Both the real and imaginary parts of the wavenumber show an excellent agreement with the EPWE calculations for both directions of symmetry. Moreover, the typical signature of the Bragg interference on the imaginary part of the wavevector is captured, with a maximum attenuation at the Bragg frequency and smooth variations from this point to the upper and lower limits of the band gap. Minor differences are noted in the band gap frequency range along the \(\Gamma\)M direction, where the imaginary part of the wavenumber using SLaTCoW is slightly lower than the one obtained using EPWE. Small differences are also noticed on the real part of the wavenumber in that frequency range. Both are therefore attributed to the finite size of the SC used for SLaTCoW calculations.
## 2 System Reliability Evaluation Method of a Single Failure Criterion
### Copula Theory
#### 2.1.1 Archimedean Copulas  
Archimedean copulas are rather popular because of the mathematical tractability and the capability of capturing wide ranges of dependence. In addition, the statistical inference on Archimedean copulas has been well developed; therefore, the Archimedean copulas are widely used in various fields [37]. The d-dimensional Archimedean copula function is defined as given in Equation (2):  
\[C(u_{1},u_{2},\ldots,u_{d}|\theta)=\begin{cases}\varphi^{-1}(\varphi(u_{1}), \varphi(u_{2}),\ldots,\varphi(u_{d})),&\sum\limits_{i=1}^{d}\varphi(u_{i}) \leq\varphi(0)\\ 0,&\text{others}\end{cases} \tag{2}\]  
where \(C(u_{1},u_{2},\ldots,u_{d}|\theta)\) represents the Archimedean copula function. \(\varphi(t)\) is a generator function of \(C(u_{1},u_{2},\ldots,u_{d}|\theta)\) and satisfies \(\varphi(t)=0\), \(\varphi(t)<0\), and \(\varphi^{\prime\prime}(t)>0\). \(\varphi^{-1}(\cdot)\) is the inverse function of \(\varphi(\cdot)\).  
The Archimedean copula function \(C(\cdot)\) is determined by its unique generator function \(\varphi(t)\). In this study, the Clayton, Gumbel, and Frank copulas were adopted as the candidate Archimedean copulas. The three copulas have different generator functions and copula functions, which lead to some differences in their description of correlation, especially in describing the tail correlation. Table 1 shows their respective copula functions and generator functions.
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 4 Evaluation  
In this section, we present the evaluation of the proposed method for the dynamic vertical mapping from user smartphone data as shown in Table 2. More specifically, in Section 4.1, the difference in calibration between the two phones used in this experiment is presented. Section 4.2 presents the robustness of our algorithm against various human walking velocities. In Section 4.3, the performance of the identification of OITransitions is evaluated. In Section 4.4, the evaluation of the identification of the number of floors and their altitude estimation during various weather conditions is presented. For the evaluation of the stair removal Section 4.2, data were collected from three different human walking velocities. Finally, a detailed evaluation, with datasets collected over a period of 6 months from three different buildings, is presented in Section 4.4.
## 2 Materials and Methods
### Data Sources and Processing  
The decennial population census provides comprehensive data on the size, structure, and characteristics of China's population. According to the research design, rural demographic data such as population size, educational attainment, and population migration are obtained from the Tabulation on the 2000 and 2010 Population Census of China and China Population Census Yearbook (2020). Among these, data on population size and educational attainment are sourced directly from the first part of the census table, while data on population migration are derived using the long-form data in the second part. Specifically, the long-form data table titled \"Population with a hukou registration outside townships and subdistricts, by type of current resident and hukou registration place\" reveals insights into population migration. This table classifies the current resident place into three categories, namely, cities, towns, and villages, while the hukou registration place is categorized into townships (including townships, neighborhood committee of towns, village committee of towns, and subdistricts). According to the National Bureau of Statistics of China (NBS), townships and village committee of towns are classified as rural areas, while neighborhood committee of towns and subdistricts are classified as urban areas. As a result, we obtained the specific direction and scale of interprovincial migration of China's rural population by back-calculating the data on current resident and hukou registration place. Since the long-form data are based on a 10% sample, the migration figures are scaled up by a factor of ten to estimate the total provincial rural migration.  
Socio-economic data, such as per capita GDP, internet penetration rate, and number of beds in health facilities per 1000 people, as well as data on natural disasters are collected from the China Statistical Yearbook of 2001, 2011, and 2021. Straight-line distances between provinces are determined by calculating the distance between the centers of gravity based on the geographic center of each province. The topographic relief data are from the China 1 km topographic relief dataset [65].
# A Self-Supervised Tree-Structured Framework for Fine-Grained Classification
## 3 Proposed Tree-Structured Framework for Fine-Grained Classification
### Tree-Structured Classifier Building
Furthermore, CNN classifiers are applied to perform fine-grained classification in each leaf cluster, e.g., clusters marked in blue in Figure 6.  
Figure 6: An example of classification tree with pruning. ```
0: an initial classification tree (denoted as \(t_{int}\)); machine-learning matcher (denoted as \(g(\cdot)\));
0: A tree-structured classifier
1:functionCreateTreeStructuredClassifier (\(t_{int}\), \(g(\cdot)\))
2: Initialize a queue (\(q\))
3: Put the root cluster of \(t_{int}\) into \(q\)
4:while (\(q\) is not empty) do
5: Fetch the head node (\(n_{h}\)) of \(q\)
6: Calculate the accuracy of \(n_{h}\) (\(acc(n_{h})\))
7: Calculate the comprehensive accuracy of all child clusters of \(n_{h}\) (\(acc(n_{chd})\))
8:if\(acc(n_{h})<acc(n_{chd})\)
9: Put all child nodes of \(n_{h}\) into \(q\)
10:endif
11:endwhile
12: apply CNN classifiers for classification in leaf nodes
13:endfunction
```  
**Algorithm 3** Tree-structured classifier building. Algorithm 3 constructs a tree-structured classifier and removes useless clusters through a hierarchical traversal approach. The inputs consist of an initial classification tree and machine-learning matchers. The process starts by initializing an empty queue and placing the root node of the tree in the queue (Lines 1-2). The construction of the tree-structured classifier is built using the hierarchical traversal method (Lines 3-11). During the traversal process, the head node of the queue is fetched (Line 5), and the classification accuracy is calculated (Line 6). Next, Algorithm 3 iterates over and calculates the comprehensive classification accuracy of all child nodes (Lines 7). According to the pruning criterion, it is determined whether the classification accuracy increases with the increase of levels. If the accuracy increases, the child nodes are placed in the queue. Finally, for the leaf nodes in the pruned classification tree, CNN classifiers are applied to perform fine-grained classification in each cluster (Line 12). This section presents a self-supervised framework for automatically constructing a general tree-structured classifier for fine-grained classification without requiring manual labeling. The process of construction involves classifying each subclass into the belonging cluster based on similarity to eliminate the effect of cluster similarity in feature extraction. Then, a machine-learning classifier is trained to accurately predict the images' belonging cluster using the extracted features. The pruning criterion is proposed to improve the accuracy of the tree-structured classifier by eliminating invalid clusters. Finally, the CNN models are used to identify the belonging subclass of each object in the leaf cluster of the tree-structured classifier.
# Minimization of _n_th Order Rate Matching in Satellite Networks with One to Many Pairings
## 4 Conclusions  
In this paper, considering realistic scenarios with fewer gateways than the users' beams in the HTS system, we present one-to-many capacity allocation schemes for the minimization of the \(n\)th order rate matching for positive and integer \(n\). Due to the high complexity of the optimization problem, a fast convergent two-step heuristic approach is proposed for the first time in the literature. This pairing scheme includes the relaxation of the initial problem and the application of the difference of convex programming theory in the first step, while the continuous to discrete conversion of the pairing solution is made in the second step. Moreover, the existence of multiple stationary points in non-convex functions, as in our case, makes the presented solution approach to depend on the starting feasible point. Thus, different initial points, constant and random, are investigated. Extended simulations show the fast convergence and a generally smaller relative error of the proposed schemes for smaller \(n\) and the very good performance of our approach for small values of \(n\), such as \(n=1\) and \(n=2\) cases. Our algorithm can be used as a benchmark compared to other algorithms for facing similar problems in the future. Finally, even though the rate matching is a widely applied performance metric in the literature, the role of \(n\) has not been investigated. The factor \(n\) can be used as a fairness controller. Greater fairness among UEs' rate matchings, in terms of their absolute differences between the requested and offered capacities, has been depicted as \(n\) increases compared to the \(n=1\) case. The key functionality of \(n\) in UEs' fairness is an important point for operators not only of satellites but also in the rest of the wireless systems.  
Figure 3: Average Relative Error between Exhaustive and Proposed Schemes, with Constant and Random Initial Feasible Points, for Different \(n\)th Order in \(3\ \times\ 6\) and \(3\ \times\ 9\) HTS Systems.  
**Author Contributions:** Conceptualization, A.J.R.; Formal analysis, A.J.R. and C.N.E.; Software, A.J.R.; Supervision, A.D.P.; Validation, A.J.R.; Visualization, A.J.R. and C.N.E.; Writing--original draft, A.J.R. and C.N.E.; Writing--review and editing, A.J.R. and A.D.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Cooling System Design and Cooling Simulation Based on UG and Moldflow  
### Establishment of Finite Element Model  
#### 4.1.1 Model Simplification and Meshing  
Meshing the plastic part is a primary process. Moldflow offers three types of meshing, including mid-plane meshing, double-layer meshing, and 3D meshing. Among these options, the mid-plane mesh model is particularly suitable for injection-molded products with small and uniform thicknesses, simple structural distribution, and various locations of thickness distribution. Additionally, this type of meshing results in a relatively low number of meshes after the division process. A double-layer mesh model is a widely used option in Moldflow, allowing for simulation of both the top and bottom of the melt flow. By using this model, injection molding processes can be accurately simulated to yield high-quality mesh and faster processing times. As a result, it is an effective tool for optimizing the structural and mold design of plastic parts, leading to improved simulation results. The three-dimensional model of the product is depicted in Figure 5. The maximum thickness of this product is 2.11 mm, and the average thickness is less than 2 mm. Subsequently, after performing meshing on the model, we obtain Figure 6.  
Figure 5: Model of the plastic part.  
Figure 6: Product model after meshing.  
Meshing the plastic part using a 3D mesh model in Moldflow is an effective way to accurately simulate the injection process in the entire 3D space, allowing for a realistic representation of the filling process. Since the outer surface of the plastic part typically has a complex structure with an unevenly distributed wall thickness, a 3D mesh model is necessary. The edge length of the mesh is primarily determined by the thickness of the plastic part, the fit quality of the mesh, and the shape accuracy of the plastic part. Generally, it is 1.5-2 times the thickness of the plastic part to ensure analysis accuracy.
## 2 Materials and Methods
### Multilevel Inverter Topology  
Within the array of available multilevel inverter topologies, the Cascade H-Bridge Multilevel Inverter (CHBMILI) stands out by its distinctive composition, where multiple Cascade H-bridge inverters are intricately interconnected in a series configuration. This intricate arrangement amplifies the inverter's potential to generate high-quality output voltage with enhanced levels of precision and reduced harmonic distortions [34]. A distinct input voltage characterizes each inverter, and the resulting output voltage is formed by aggregating all the voltage levels generated by each inverter (Figure 2). The output voltage levels in this inverter are two times the number of the separate direct current (DC) sources plus one [35; 36].  
Within the diverse array of the CHBMILI modulation techniques, one exceptionally fundamental and widely employed method entails fundamental frequency switching. This intricate approach revolves around the meticulous determination of precise switching angles for the semiconductor devices. These calculated angles serve as the blueprint for the obtention of the voltage generation, explicitly targeting the creation of the fundamental voltage waveform [37; 38]. However, it is worth noting that finding the switching angles requires solving nonlinear equations [35].  
Figure 1: MFO algorithm diagram.  
Figure 3 shows the output voltage waveform for a five-level CHBMIL configuration to provide a visual anchor. Within this context, the critical angles that demand meticulous calculation are \(\theta_{1}\) and \(\theta_{2}\), serving as the linchpins for shaping the desired voltage output profile. When extending our inquiry to the seven-level CHBMIL configuration, depicted in the insightful portrayal of Figure 4, the complexity deepens. Now, not only must \(\theta_{1}\) and \(\theta_{2}\) be diligently computed, but an additional angle, \(\theta_{3}\), comes into play, further emphasizing the intricacy and precision required to determine these switching angles.  
Figure 4: Seven-level CHBMIL voltage.  
Figure 3: Five-level CHBMIL voltage.  
Figure 2: General CHBMIL topology.
## 4 Discussion
Although there are enzyme markers that were developed more recently than traditional techniques, owing to the influence of quantity, environment, and biological growth and development stage, the exact identification and evaluation of rice germplasm resources cannot be carried out, hence their application scope is limited to a certain extent. Molecular markers can reveal differences in rice seed resource materials at the DNA molecular level and become a reliable and efficient tool for the identification and analysis of germplasm resources. At present, molecular markers are widely used in indica and japonica subspecies, main cultivars, landraces, backbone parents of hybrid rice, and genetic diversity of rice seed resources [33]. Many studies have been conducted on the classification and identification of rice seed resources using genetic markers. [PERSON] et al. [34] used 48 SSR molecular markers to analyze the genetic similarity of ninety rice varieties from nine different countries. [PERSON] et al. [35] used 34 pairs of SSR primers to conduct genetic similarity and cluster analyses of 313 japonica rice varieties from different geographical sources. [PERSON] et al. [36] analyzed the polymorphisms of 33 American rice and 13 Chinese rice germplasm resources using 24 pairs of SSR primers. As there are only a few polymorphic markers in these studies, the identification results are often not ideal, especially for resources with close relationships. The results of this research can also be used to identify the genetic relationships among different resources. Through the relationships between phenotype and molecular markers and different genetic groups, we can improve the accuracy and reliability of germplasm resource identification, compensate for the shortcomings of traditional phenotypic identification techniques, employ techniques that play an important role in the rational utilization of resources, and promote new breakthroughs in crop breeding.
## 3 ATON Framework
### Presentation Layer
#### 3.6.2 Rendering System  
The core rendering system is based on Three.js (see Section 2), taking advantage of features offered by the open-source library for the presentation of CH objects, archaeological sites or generic 3D virtual environments. As previously anticipated, the interactive rendering fully supports the PBR model, thus providing advanced simulation of materials and how they react to the surrounding environment. The PBR model is widely adopted and well-established, especially within desktop-based content creation pipelines targeting applied games [54; 55] and Web3D platforms like SketchFab. The workflow is well-defined within the gIIF standard, with several properties (e.g., roughness, metalness, emissive, etc.) that--just like desktop-based pipelines--are generally encoded into multiple textures. Three.js PBR model is also compatible with WebXR, thus offering the final user a consistent simulation of the flow of light when consuming content through a stereoscopic device (i.e., high-end HMDs or cardboards).  
Common Web3D solutions (like SketchFab) offer a single light-probe [56] system to simulate a general response of 3D model surfaces to the environment (Figure 8). In ATON, a multiple light-probe system is provided: this allows to place and arrange _multiple_ light-probes in a 3D scene, thus leading to a more consistent simulation of PBR materials in relation to the surrounding environment. Light-probes (LP) independently capture their surroundings, with each mesh automatically assigned to an LP depending on proximity, following a similar policy of game engines like Unreal Engine 4. This drastically improves the final result, with more consistent reflections and overall illumination for 3D items scattered across the scene (detailed discussion later in Section 4.2).  
The lighting system also supports dynamic shadows (provided by Three.js), to further improve the overall quality of the presentation, from small objects (e.g., artifacts) up to large environments (e.g., archeological sites). ATON rendering system also supports dynamic pixel density, to control or fine-tune framerate on devices with poor graphics performances. A similar approach is employed for immersive WebXR sessions through the framebuffer scale factor, while the specification is currently aiming to embrace _foveated rendering_  
Figure 7: Device classes to consume ATON content.  
lowering rendering workload by reducing resolution in the peripheral vision [57]. Web-applications based on ATON automatically adapt and fine-tune these parameters according to the profiler, in order to maintain a consistent framerate, or directly control them for specific requirements. These can be particularly useful for small museums employing low-cost or cheap GPU hardware kiosks to present 3D items.