esa-sceva/satcom-chunk-collection huggingface dataset:
# Prototypical Graph Contrastive Learning for Recommendation
## 4 Experiments
### Uniformity Study (RQ2)  
To clearly demonstrate the uniformity of the embeddings learned by the baseline models, we used t-SNE to reduce the dimensions of 2000 randomly sampled user embeddings and plotted them in the upper half of each image. The lower half of each image shows the corresponding Gaussian kernel density estimation curves to illustrate the density estimates from different angles. As shown in Figure 4, each row is ordered based on the recommendation performance of the models. From left to right, the darker regions in the upper part of the figure decrease, and the curves in the lower part become smoother (indicating more uniformly learned features). This indicates a positive correlation between uniformity and recommendation performance. Both overly clustered and overly uniform feature distributions can hinder performance improvement.  
As shown in Figure 4a,\(c\), compared to ProtoRec and SimGCL, LightGCN, SGL, and NCL learned more clustered embedding features, with density curves exhibiting steeper trends. This phenomenon is caused by the over-smoothing issue from stacking graph convolutional networks, resulting in overly similar embeddings. Additionally, the structural perturbations in SGL make it difficult to train some nodes, also affecting the embedding distribution. As shown in Figure 4b, all baseline models learned uniform embeddings because they could effectively capture the latent relationships between users and items in the small dataset. Intuitively, ProtoRec, by guiding users and items to learn prototypes, generates clustered distributions within the same prototype, while the use of graph convolutional networks also leads to over-smoothing. However, ProtoRec alleviates the over-smoothing problem by constructing refined prototypes, guiding users and items to learn more diverse and enriched preference information. Unlike SimGCL, which adds uniform noise to the embeddings, ProtoRec purposefully directs embeddings to learn from prototypes, achieving a uniform embedding distribution.
# Static Properties of Kaolinite Samples from Different Structures and the Influence of Strain Rate
## 2 Preparation of flocculated and Dispersed Samples  
Commercially available kaolin obtained from Suzhou, China, was used in this study. In the studied samples, 80% of the particles were finer than 10 \(\upmu\)m, and 46% were finer than 2 \(\upmu\)m. The specific gravity and activity were 2.61 and 0.44, respectively. The preparation of the slurry has a major impact on the quality of the sample. High-water contents can make the slurry too thin, easily leading to soil-water separation and long consolidation periods. Conversely, low-water contents make it difficult to mix the slurry evenly. Typically, the initial water content is set to approximately 1.5 to 2.5 times the liquid limit. In this study, the required water content for preparing kaolin slurry was approximately twice that of the liquid limit of the clay sample. Kaolin powder was mixed with distilled and de-ionised water for the flocculated structure, while it was mixed with a dispersant solution prepared by dissolving 2% Calgon [(NaPO\({}_{3}\))\({}_{6}\)] by weight of water for the dispersed structure.  
As shown in Figure 1, a vacuum consolidation device was used. Each sample was placed in an individual mould and several batches of samples were prepared simultaneously by joining all the pedestals as a circuit. Because of the small sample size, both the uniformity of the samples and the efficiency of their preparation were ensured. Using a vacuum pump to create a negative pressure inside the consolidation cylinder, we increased the negative pressure by 15 kPa every 12 h until a vacuum level of 90 kPa was reached. After sediment consolidation, samples with a diameter of approximately 80 mm and a height of approximately 150 mm were obtained.  
Kaolin is a 1:1 sheet silicate composed of two types of layers, negatively charged tetrahedral layer [(Al2[OH]4)+2] and positively charged octahedral layer [(Si2O5)-2]. The consolidation processes of the flocculated and dispersed samples are shown in Figure 2. The faces of the particles became negatively charged while the edges of the particles had positive charges. This arrangement makes the edges of the particles cling to the faces of the other particle, while the faces of the two particles repel each other. This arrangement resulted in the formation of flocculated kaolin clay after aggregation and sedimentation. The presence of a dispersing agent (Calgon) chiefly affected the ionic environment. All the particles have charged surfaces and repel each other as much as possible. Therefore, particles tend to stack in a face-to-face contact manner and form the dispersed kaolin clay after aggregation and sedimentation [27].  
Figure 1: Vacuum consolidation device: (**a**) schematic diagram; (**b**) photograph.
# A Study on the Movement Characteristics of Fuel in the Fuel Tank during the Maneuvering Process
## 2 Research Object and Numerical Method
### Boundary Conditions  
When fuel moves within a fuel tank, it occupies both the fluid domain and the solid domain of the fuel tank structure. As a result, a boundary exists between the liquid phase and the solid phase. At this boundary, the fuel is unable to leave the fuel tank wall or penetrate through it. Assuming negligible influence from fuel surface tension and air pressure within the fuel tank on the free liquid surface, the boundary conditions for the free liquid surface are as follows:  
\[\frac{\partial\
u_{n}}{\partial t}+\frac{\partial\
u_{\tau}}{\partial n}=0 \tag{7}\]  
\[-p+2\mu\frac{\partial\
u_{n}}{\partial n}=-p_{0} \tag{8}\]  
Among them, \(\
u_{n}\) is the normal velocity of the free liquid surface, \(\
u_{\tau}\) is the tangential velocity of the free liquid surface, and \(p\), \(p_{0}\) are the pressures on the liquid side and air side of the free liquid surface, respectively. In terms of boundary treatment, for the internal boundary of the fuel tank, it is defined as a no-slip wall surface. The particle positions on this boundary remain fixed, so the velocity of particles on the boundary is zero. The free surface is represented by free boundary particles, and at the beginning of each time step, it is necessary to traverse and detect the free surface particles.  
Figure 2: Comparison of free surface shapes between experimental (left) and numerical simulation (right). (**a**) The surging process; (**b**) Flip and shatter.
## 3 Challenge of Lignin Fractionation and Depolymerization  
To achieve lignin valorization, fractionation and depolymerization are still challenging research issues due to the complex mixture of lignin's subunits and the oligomers extracted from its biomass. Some procedures, preparing klason, alkali, sulfide lignin, etc., are known to fractionate lignin from biomass [29; 30]. However, separating certain types of \(\beta\)-aryl ether structural units or the monomerized monolignols from lignin remains a challenging task for lignin valorization to obtain products, such as hydrolyzed sugars or oligosaccharides from cellulose or hemicellulose. Nevertheless, two main resources are considered to obtain potentially available lignin as byproducts in the utilization of lignocellulosic. One major source of lignin is the byproducts of the pulp and paper industry [21; 30]. The kraft and sulfite processes generate kraft lignin and water-soluble lignocellulosic as a byproduct, respectively. These two methods can extract lignin by chemical treatments with sodium hydroxide and sodium sulfide for kraft lignin or using aqueous sulfur dioxide for lignocellulosic. The other main source is byproducts of lignocellulosic bio-ethanol production processes [14]. There are various chemical pretreatment processes used to extract cellulose and hemicellulose for fermentable sugars of ethanol production, and lignin is usually solubilized into wastewater or remains in the treated biomass [7; 12; 13]. The residual lignin in the biomass remains as a solid waste after fermentation and the product recovery process [12]. However, these byproducts are still considered waste and are burned to generate energy to operate their process [13; 14]. Recently, pyrolysis or gasification of these biomass byproducts has been alternatively applied to valorize lignin fractions [31; 32].  
Although these lignin fractions would be potentially useful resources, lignin depolymerization is a barrier to producing valuable chemicals from lignin due to its heterogeneous and complex structure, the different physical properties of pretreated byproducts, difficulties associated with linkage cleavages in monolignols, repolymerization of depolymerized products, and so on [2; 16; 25; 29]. Nevertheless, there is increasing interest in developing novel biocatalyst methods for lignin valorization by mimicking the natural processes of biodegradation in microorganisms [17; 18; 19].
## 1 Introduction  
Treatment of skeletal malocclusion in individuals with completed skeletal growth represents a challenge for both the clinician and the patient, as it requires surgical release of the areas of bone resistance. Surgically assisted rapid maxillary expansion (SARME) is a widely recognized treatment modality, which employs conventional dental anchorage or, more recently, a skeletal anchorage [1]. The latter, namely: a bone-borne device (transpalatal distractor, TPD) not only eliminates side effects of using the teeth as an anchorage, but also contributes to a greater component of skeletal than dental expansion [1; 2; 3; 4; 5; 6].  
Maxillary expansion provokes evident skeletal and soft-tissue changes. The assessment of dental and skeletal changes involves a variety of tools, including two- and three-dimensional radiological imaging techniques [4; 5; 6]. Lateral cephalograms have inherent limitations such as image magnification, distortion, overlapping of anatomical structures and errors related to the identification of reference points. Despite these drawbacks,2D images are still widely used in orthodontic diagnostics and monitoring of treatment progress [7]. Rapid development of computed tomography and substantial reduction in the radiation dose in modern imaging have made it possible to introduce three-dimensional craniofacial analysis [8]. Within recent years it has been proven that 3D analysis of the maxill area shows a high interobserver agreement and can be a reliable and reproducible method for both clinical and research applications [9; 10; 11; 12].  
The present study aimed to assess skeletal and dental changes after transpalatal distraction, using 3D cephalometric analysis.
# A Metric for Secrecy-Energy Efficiency Tradeoff Evaluation in 3 GPP Cellular Networks
###### Abstract
The results show that these metrics are useful in identifying the optimum transmit power level for the BS, so that the maximum secure area can be obtained with the minimum energy consumption: particularly, the metrics are useful during the network planning phase since they permit the cell planner to define the power that allows the user to receive the data with a required quality of service (QoS). Given the distance of the legitimate receiver and the secrecy rate to be served to the user, the planner can define the minimum transmit power that maximizes the secure area.
# Scalable Name Lookup for NDN Using Hierarchical Hashing and Patricia Trie
## 2 Background
### Patricia Trie  
A trie is a kind of tree structure and has been used in several IP address lookup schemes [9; 10]. A binary trie is the most basic trie whose degree is two. Each node in a binary trie is expressed by a bit string to represent the path from the root to that node. Figure 3a shows an example of a binary trie where all prefixes (\(a\)-\(d\)) are indicated by filled circles. As shown in this figure, the binary trie needs many node accesses. Note that a node with a single child induces unnecessary node access. The path compression to eliminate such a node can reduce the number of node accesses.  
A Patricia trie is a binary trie in which any node without its sibling is merged into its parent [4]. Figure 3b shows a Patricia trie corresponding to the binary trie in Figure 3a. Note that the degree of each node is 0 or 2 in a Patricia trie. In a binary trie, each bit position is checked in turn. However, in a Patricia trie, only specific bit positions are checked. In Figure 3b, the number in a circle indicates the bit position to be checked. If the incoming key is 00100, the bit positions 1, 2 and 4 are checked in turn. Thus, the corresponding values are 0, 1 and 0 so it reaches the prefix \(b\).  
Figure 3: Two trie structures to contain four prefixes: (**a**) binary trie and (**b**) Patricia trie.  
Figure 2: Forwarding processes in NDN router for: (**a**) interest packet and (**b**) data packet.
# Entropy-Based Anomaly Detection for Gaussian Mixture Modeling
## 3 Results
### Gaussian Distribution with Outliers  
Consider the dataset shown in Figure 1a generated from a bivariate Gaussian distribution with additional outliers added at the outskirts. According to the BIC criterion shown in Figure 1c, the optimal GMM estimated on this dataset is a two-component Gaussian mixture with covariance matrices having equal shape and orientation but different volume (VEE,2). Figure 1b shows the scatterplot with data points marked according to the GMM classification and the ellipses corresponding to the estimated cluster covariances. The presence of anomalies leads to the selection of a larger number of components, with an inflated covariance matrix for the second Gaussian component.  
Figure 1: Scatterplot of first synthetic data example with data points generated from a Gaussian distribution with outliers at the outskirts (**a**). Estimated GMM with points marked by colors and symbols according to the GMM classification, ellipses corresponding to estimated cluster covariances, dashed lines referring to principal axes, and \(\ast\) to the cluster means (**b**). BIC traces for the selection of the optimal GMM model (**c**).  
Figure 2a shows a plot of the entropy contribution values \(h_{i}\) against the probability points \(p_{i}=(i-0.5)/n\), for \(i=1,\ldots,n\). Note that the \(h_{i}\) values are sorted in non-decreasing order, which explains the increasing pattern observed in the graph. The dashed line refers to the entropy contribution from the uniform noise, so observations above that line can be included in the initial set of anomalies. Figure 2b shows a scatterplot with data points marked according to the initial classification of noise and with size proportional to the entropy contribution. It is easily seen that the points with the largest contribution are those far from the bulk of the data.  
We then initialized the noise component using the identified data points as shown in Figure 2. The BIC traces in Figure 3a provide clear evidence that the data can be modeled using a single Gaussian component along with a noise component. Figure 3b confirms that the model successfully identifies the presence of a single Gaussian component despite the presence of some outliers in the data. A comparison of entropy contribution from the model without and the model with a noise component can be seen in Figure 3c. In the latter, the contribution from the outliers appears to flatten.  
Finally, we note that for this simple case the anomaly detection procedure has both sensitivity and specificity equal to 1.  
Figure 3: _Cont_.  
Figure 2: Entropy contribution of each data point in the first synthetic dataset, with dashed horizontal line from uniform noise for reference (**a**). Scatterplot of data points marked according to the initial classification of noise and with size proportional to the entropy contribution (**b**).
## 6 Discussion
### Results of DM Test and Forecasting Effectiveness  
Besides the error evaluation indexes, which have been applied to evaluate the forecasting ability of the proposed hybrid approach, this paper also used the DM (Diebold-Mariano) test and Forecasting Effectiveness to further study the forecasting accuracy.  
In this paper, the DM test is used to study the significant differences in forecasting ability between the new hybrid model and some conventional methods. The results of the DM test are shown in Table 8. The DM statistical values far exceed the critical value at the 1% significance level, at which the value of \(|DM|\) is 2.1017. Thus, the proposed approach performs significantly different with the conventional methods at the 1% significance level. In conclusion, the proposed approach significantly outperforms the conventional methods.  
The results of forecasting effectiveness is shown in Table 9. The first-order forecasting effectiveness is based on the expected value of the forecasting accuracy sequence, while the second-order forecasting effectiveness is related to the difference between the standard deviation and expectation of the forecasting accuracy sequence. In Table 8, the forecasting effectiveness of the proposed model outperforms all the other models in both first order and second order.
## 3 Optical Coupling between the III-V Device Layer and the Silicon Waveguide Layer  
Efficient optical coupling between the III-V device layer and the silicon waveguide layer is critical for the integration of III-V opto-electronic components on silicon PICs. The optical coupling scheme should not only be efficient, it should also work over a broad wavelength range and be fabrication (alignment) tolerant. In order to satisfy these requirements for waveguided III-V opto-electronic devices, an adiabatic taper scheme was developed. For bonded surface illuminated photodetector structures, a diffractive grating structure is used.
## 2 Materials and Methods  
### FEM Model  
The upper canines, premolars, and first molars on both sides were expanded in parallel or in a fan shape (Figure 1). Assuming the dental arch and the expander to be symmetrical, their right half was modeled (Figure 2). A FEM software (ANSYS 16.1, ANSYS Inc., Canonsburg, PA, USA) was used for the present study.  
A three-dimensional model of the dentition was created using cross-sectional images of a dental study model (i21D-400C, Nissin Dental Products Inc., Kyoto, Japan) taken by cone beam computed tomography (CBCT). The surfaces of the teeth were divided into triangular shell elements (SHELL181: element name in ANSYS). The teeth were assumed to be rigid bodies, and their surface nodes were constrained to be rigid. The periodontal ligament (PDL), with a constant thickness of 0.2 mm, was constructed over the root and divided into three-dimensional solid elements (SOLID45). The PDL was assumed to be a nonlinear elastic material with a piecewise linear stress-strain curve [11]. The alveolar bone and teeth were assumed to be rigid bodies because their Young's moduli (approximately 10 GPa) were significantly higher than that of the PDL (approximately 0.1 MPa). Under this assumption, we could exclude the alveolar bone from the FEM model.  
Figure 1: Parallel and fan-shaped symmetrical expansions of the maxillary arch.  
Shapes of the quad-helix and W-arch were determined by referring to [PERSON]'s text [1]. In the quad-helix, the diameters of the anterior and posterior helices were 2.8 mm and 3.2 mm, respectively. The W-arch was made by eliminating the helices from the quad-helix. Both expanders were made of stainless-steel wires with a Young's moduli of 200 GPa. They were divided into three-dimensional beam elements (BEAM188). The expanders were assumed to be soldered on the molar bands and fixed on the crowns with rigid beam elements (MPC184). Contact elements were overlaid on the expanders and crown surfaces with a frictional coefficient of 0.15.  
Figure 2 shows that the total numbers of shell elements and solid elements were 932 and 14,361, respectively. Similarly, the total numbers of beam elements in the W-arch and quad-helix were 96 and 120, respectively. The total number of nodes in all elements became 15,978 for the W-arch and 16,026 for the quad-helix.
## 6 Design Example According to the Proposed Workflow
### Optimization Results  
The collected data are managed by a MATLAB script that enables optimization at various levels. Scalar values can be used to guide optimization and identify a global optimum using response surfaces. Real-time parameter exploration allows observation of their influence on aircraft aerodynamics. This approach facilitates a more robust and informed optimization process because it provides a clear understanding of the physics involved and the contribution of each parameter. For example, it allows for discarding DPs that, despite improving efficiency, lead to greater flow instability.  
Using this approach, an optimal point (see Table 6) was identified that significantly improves performance (see Table 7). In particular, it can be observed that the flow is better guided, and there is less separation at the trailing edge. By examining the pressure contour, the efficiency also improves due to the dual effect of increased pressure on the lower surface (enhancing efficiency) and on the upper surface due to reduced separation (lowering drag). Figure 30 shows a comparison between the baseline and optimized geometries. A comparison between the CFD results and predictions from the ROM is given in Figure 31, where pressure contours are compared; Figure 32, where velocity pathlines are compared; and in Figure 33, where original and optimized configurations are compared.  
Figure 31: Comparison of pressure contours: baseline-upper (**a**), baseline-lower (**b**), optimized-upper (**c**) and optimized lower surface (**d**).  
Figure 32: Comparison of velocity pathlines: baseline (**left**) and optimized (**right**).  
Figure 33: Comparison of CFD (**left**) and ROM (**right**) on the front and rear ends of the wing for the optimized point.
# Nonlinear Seismic Response of Multistory Steel Frames with Self-Centering Tension-Only Braces
## 1 Introduction
Unlike those rigid SCBs, tension-only braces (TOBs) are flexible and can enable the full use of tensile strength of high strength materials without compressive-flexural buckling, thereby reducing the axial stiffness as well as the cross-sectional dimensions of the brace significantly [27; 28]. As a result, a mitigating seismic response can be obtained due to the elongated fundamental period of vibration of the resulting structure. In addition, TOBs are lighter in weight, making them easier to install and relatively simple to replace. In view of this, the authors developed a self-centering tension-only brace (SC-TOB) system, in consideration of the load mitigation characteristics of TOBs, to reduce the seismic demands of SCB structures [29]. Several response parameters were studied to compare the behavior of the SC-TOBFs and BRB frames [30]. This study investigated how the SC-TOB can be implemented in multistory steel frames to improve seismic performance in terms of internal force, drift, and energy dissipation. Pushover analysis subjected to two lateral load distributions and nonlinear dynamic analysis under ground motion ensembles scaled to four hazard levels were performed.