esa-sceva/satcom-chunk-collection huggingface dataset:
# News Recommendation Method Based on Candidate-Aware Long- and Short-Term Preference Modeling
## 2 Related Works
### Graph Neural Networks  
Graph neural networks (GNNs) have recently attracted attention in various fields due to their power of processing and feature extraction on non-Euclidean structured data [21,22]. The seminal work in the field of GNN is often seen as a graph convolutional neural network (GCN) used for semi-supervised node classification. It achieved a breakthrough in performance by performing and propagating constant convolutional operations on the graph. Considering the similarities and differences in the degree of influence of neighboring nodes on the central node, GATs [23] were proposed. They achieved another breakthrough in performance by exploiting the neighborhood information. Graph Sample and Aggregate (GraphSAGE), proposed by [PERSON] et al. [24], greatly improves generalization by setting broader learning goals for the model. The nodes in the graph aggregate the features of neighbor nodes and continuously propagate and update them so that the nodes can fully exploit the high-order structure information of the graph. Some news recommendation methods utilize the powerful feature extraction capabilities of GATs to enhance user representations. A novel graph-based news recommendation method called GERL [25] obtains user representations by combining both one-hop interaction features and two-hop neighborhood features. [PERSON] et al. proposed a novel method called MVL [26]. This method constructs an overall user-news graph to learn user representations through first-order interactions (user-news) and second-order interactions (user-news-user). Different from the above methods, [PERSON] et al. [27] represented each user as a separate graph, and then proposed a novel heterogeneous graph pooling technique to obtain user representations. Although the above methods achieve precise user modeling, they ignore the temporal features contained in the graph. [PERSON] et al. [28] proposed a heterogeneous graph neural network that captures temporal features through an improved Long Short-Term Memory (LSTM) and connectivity between users and news topics through GATs. [PERSON] et al. [29] found that entity information in the clicked news can effectively correlate users' potential interests. Therefore, they explored users' potential interests through entity graphs. However, the above methods independently model user preferences and candidate news without considering the dynamic impact of candidate news on user preferences, which makes it difficult to match user preferences with candidate news accurately.  
Unlike the above works, NRCLS incorporates candidate news to achieve accurate modeling of user preferences. We have also organized and summarized in Table 2 what the shortcomings of the methods mentioned above are, as compared to NRCLS.
# Efficient Approximation for Restricted Biclique Cover Problems
## Overview of New Results
### Model II: A One-Side Stochastic Model  
In this model, we capture the fact that nodes represent independent observations, but the bipartite graph has more structure than a standard random bipartite graph. This structure is dictated by the semi-random assignment of factors to nodes.  
Formally, we define the \((f,\bar{p},k)\)-biclique cover problem, for a constant \(f\), a vector of \(k\) probabilities \(\bar{p}\), and an arbitrary large number of factors \(k\). An input to the \((f,\bar{p},k)\)-biclique cover problem is a bipartite graph \(G=(L,R,E)\) generated by the following process: Factors are assigned arbitrarily to nodes in \(L\), subject to a limit of \(f\) factors per node. Nodes in \(R\) are assigned factors by a random process in which each node is assigned factor \(i\) with probability \(p_{i}\), independent of any other choice. A node \(u\in L\) and a node \(v\in R\) are connected if they share a factor. Given the observed graph \(G=(L,R,E)\), and the parameters \(f\), \(\bar{p}\) and \(k\), we want to find a \(k\)-biclique cover of the graph.  
**Results 2**.: _We show that for an arbitrary large constant \(f\), and under some simple conditions on the distribution \(\bar{p}\), there is a \(O((|L|+|R|)^{O(1)})\) time algorithm that w.h.p. obtains a \(k\)-biclique cover of the graph, as long as \(|R|\geq c\log(|L|)\) for some large enough constant \(c>0\). Here, the \(O(1)\) depends only on the constant \(f\)._  
For an arbitrary probability vector \(\bar{p}\), we present an algorithm that in \(O((|L|+|R|)^{O(1)})\) time with high probability finds a set of bicliques of size \(k\) covering at least a \(1-\frac{1}{e}\) fraction of the edges, or \(O(k\log(|R|))\) bicliques covering all edges of \(G\), as long as \(|R|\geq c\log(|L|)\) for some large enough constant \(c>0\). Here, the \(O(1)\) depends on the distribution \(\bar{p}\) and \(f\) but not on the size of the graph.
## 2 Related Work
### Registration Methods
#### 2.1.1 Original-Point-Information-Based Methods  
ICP (Iterated Closest Point)-like methods optimize the distance error between points and achieve point cloud registration through iterative processes. These methods directly utilize the original point cloud information, specifically the three-dimensional coordinates, for registration. The Iterative Closest Point algorithm (ICP), proposed by [PERSON] et al. [26], reformulates the point cloud registration problem as a least-squares optimization task. It iteratively computes the optimal registration transformation matrix T until the error is minimized. ICP has numerous variants, such as employing KD-tree [27] to partition the point cloud subspace for accelerated nearest-point queries and integrating the Random Sample Consensus (RANSAC) algorithm [17] to eliminate mismatched point pairs, thereby enhancing registration accuracy. Additionally, [PERSON] et al. introduced Trimmed ICP [28], which determines the number of points retained based on the overlap ratio of the point clouds. By performing transformation calculations using only the retained point pairs, Trimmed ICP improves the registration accuracy of partially overlapping point clouds. Beyond ICP-like methods, [PERSON] et al. [29] proposed a point cloud registration method based on a global probabilistic model. This approach reformulates the point cloud registration problem as the maximization of a probabilistic model. Specifically, the Von Mises Distribution is used to model the direction of the point cloud, while the Gaussian Mixture Model (GMM) represents the position of the point cloud. The Expectation-Maximization (EM) algorithm [30] is employed to iteratively optimize the maximum posterior probability of the entire mixture model, thereby obtaining the optimal matching transformation matrix T between the two point clouds. The primary advantage of this method is its consideration of global probabilistic consistency and independence from initial values. However, the need to compute a global probability distribution poses limitations when dealing with point clouds that have low overlap rates. Moreover, despite improvements in computational efficiency compared to ICP-based methods, this approach still faces challenges related to high computational complexity. The limitations of data volume and processing time must also be considered when handling large-scale point clouds.
## 4 Applications of AI in Modern Communication Networks
### Security and Privacy  
AI plays a pivotal role in enhancing the security and privacy of communication networks by enabling intrusion detection, anomaly detection, encryption methods, and privacy-preserving techniques [71]. As cyber threats become more sophisticated, traditional security measures are often insufficient to detect and mitigate emerging risks [73]. AI technologies, particularly machine learning algorithms, can continuously analyze network traffic and identify suspicious patterns that might indicate an attack [74; 77]. These systems can adapt to new and evolving threats, improving the ability to detect zero-day vulnerabilities and preventing unauthorized access [62; 79].  
Moreover, AI-based encryption techniques help ensure that data remains secure while optimizing network performance [103]. By dynamically adjusting encryption methods based on network conditions, AI ensures a balance between robust security and efficient resource utilization. Additionally, AI enhances privacy-preserving techniques such as federated learning and differential privacy [104], which enable data analysis without exposing sensitive information, thereby ensuring compliance with privacy regulations like GDPR [105].  
Through these advanced security mechanisms, AI contributes significantly to building more resilient communication networks that can quickly respond to threats while safeguarding user privacy.
# Iterative Refinement of Uniformly Focused Image Set for Accurate Depth from Focus
## 2 Proposed Depth from Focus  
The proposed method consists of three steps. In first step initial depth of each pixel is estimated. The second step is iterative refinement building uniformly focused image set. The last step is post-processing that improves depth estimation in both textureless and object boundary regions.
# On the Control of Low-Frequency Audible Noise from Electrical Substations: A Case Study
## 2 Methods
### Preliminary Validation  
The validity of the application of Ramsete to the simulation of outdoor propagation has been verified in a similar case concerning the reduction of noise generated by a transformer (TR4) pertaining to an electrical substation located in a rural area (Figure 8).  
Night measurement at the receiver, whose building is zoned in Class III, gave an equivalent SPL of 49.9 dB(A) with the presence of a tone at 200 Hz. The Ramsete model returned an equivalent SPL of 49.6 dB(A) in the same position and the correct amplitude of the 200 Hz clonal component. Due to the penalization on the commission level deriving from the presence of the tone, the implementation of a noise control strategy was necessary. A noise barrier was designed respect  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Frequency (Hz)**} & **Grass** & **Ballast** & **Asphal (from [32])** & **Plaster (from [32])** \\  & **(Ground)** & **(Ground Around the Source)** & **(Roods)** & **(Facades)** \\ \hline
31.5 & 0.04 & 0.11 & 0.02 & 0.01 \\
63 & 0.09 & 0.34 & 0.02 & 0.01 \\
125 & 0.12 & 0.58 & 0.02 & 0.01 \\
250 & 0.15 & 0.96 & 0.03 & 0.01 \\
500 & 0.42 & 0.96 & 0.03 & 0.01 \\
1000 & 0.49 & 0.89 & 0.03 & 0.03 \\
2000 & 0.58 & 0.70 & 0.03 & 0.06 \\
4000 & 0.64 & 0.70 & 0.02 & 0.06 \\
8000 & 0.70 & 0.07 & 0.02 & 0.07 \\
16,000 & 0.83 & 0.70 & 0.02 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sound absorption coefficients for different model elements.  
Figure 8: **Left**: area involved in the simulation for the fully validated case (Imagery ©2020 Google, Imagery ©2020 Maxar Technologies, Map data ©2020 Google). **Right**: digital terrain model with transformer TR4 indicated in red and the receiver in blue.  
linked to the electrical equipment: a 5.35 m high, complexly shaped wall was devised around transformer TR4 and a new simulation with the barrier was run. According to the software calculations, the barrier should have decreased the equivalent SPL at the receiver to 37.7 dB(A), with a reduction of the 200 Hz tonal component by 5 dB with respect to the original situation.  
The noise barrier has been completed in early 2018 (Figure 9) and a final experimental campaign has been performed. The measured equivalent SPL at the receiver was found to be 37.3 dB(A) with a reduction of the 200 Hz tonal component of 5 dB, exactly as predicted by the simulations.
## 2 Field Test
### Monitoring Results
#### 2.2.2 Horizontal Displacement  
Figure 10 shows the effects of RDC on horizontal displacement (including the left and right sides of the embankment) at different depths. The horizontal displacement at all depths of the soil increased as the number of passes increased. The maximum horizontal displacement occurred essentially at the ground level, due to the fact that RDC has the greatest extrusion effect on the ground soil. The difference in horizontal displacement between the two sides of the embankment was small. After compaction, the difference in horizontal displacement at 0 m depth between the two sides of the embankment was about 5 mm. Soil horizontal displacement decreased with increasing depth because the energy generated by the impact dissipated with depth. After 40 passes, the cumulative horizontal displacement at a depth of 3.5 m was \(<\)2 mm, so it can be assumed that the effective impact depth of RDC was \(<\)3.5 m.  
Considering that RDC has the greatest effect on the ground, the average horizontal displacement at the surface is plotted versus the number of passes (Figure 11). As the number of passes increased, the increments of horizontal displacement decreased. Horizontal displacement of the soil occurred mainly within the first 20 passes. The last 20 passes induced only 19.4% of the total horizontal displacement. The pattern was similar to the variation of soil settlement with the number of passes.  
Figure 10: The effect of RDC on horizontal displacement at different depths (**a**) Left side of the embankment (IPI-L); (**b**) Right side of the embankment (IPI-R).  
Figure 9: The variation of the uneven ground settlement value with the number of passes.
## 3 Results and Discussion
#### 3.1.4 Free Fatty Acids
Likewise, to help identify variation within apricot fruit specimens, another PCA model (Supplementary Figure S2A was employed. The main principal component (PC) to differentiate specimens in PCA, that is PC1 to account for 62% of the total variance, with apricot fruit (mild bitter seeds cultivar) cultivated in sandy soil (AP-M\({}_{3}\)-F) found positioned at one side (positive PC1 score value), while the other 3 fruit samples in muddy soil segregated on the left side (negative PC1 score value) and in the middle. Examination of the loading plot (Supplementary Figure S2B revealed that variables corresponding to sugars _viz._ fructose was found to be most enriched in AP-M\({}_{3}\)-F specimen. Whereas, malic acid, glucose and sucrose were more abundant in other fruit specimens. ANOVA (Supplementary Table S1) revealed significant differences in the percentile levels of organic acids i.e., malic acid, amino acids i.e., L-Alanine, L-Valine, L-Leucine, L-Isoleucine and Glutamic acid, sugars _viz._ glucose, sucrose and galactofractose among the different apricot specimens with a confidence level of 95%. AP-M\({}_{3}\)-F cultivar contained organic acids i.e., malic acid and sugars i.e., sucrose at a significantly higher level compared to other cultivars at \(p<0.05\). AP-B\({}_{1}\)-F cultivar contained a significantly higher level of sugars _viz._ glucose and sucrose at significantly higher levels than other cultivars at \(p<0.05\). AP-M3-S cultivar contained amino acids _viz._ valine, leucine, isoleucine & glutamic acid at significantly higher levels than other cultivars at \(p<0.05\). Modelling of primary metabolites suggest that soil type overcomes other variables in apricot fruit segregation with fruits grown in sandy soil being most distant from others. 2.2 OPLS-DA Analysis of Apricot Fruit Cultivar Propagating in Sandy Versus Muddy Soil Primary Silylated Metabolites Dataset  
The supervised orthogonal partial least square discriminant analysis (OPLS-DA) was further employed to assess apricot fruit classification based on soil type (sandy and muddy), which failed to separate in PCA analysis. Hence, a model of apricot fruit cultivars cultivated in sandy soil (AP-M\({}_{3}\)-F) against apricot fruit cultivars cultivated in muddy soil (AP-B\({}_{1}\)-F, AP-B\({}_{2}\)-F and AP-S\({}_{2}\)-F) in another class was constructed (Figure 3A). OPLS model exhibited Q\({}^{2}\) = 0.64 indicating the model predictability, and total variance coverage of 82% (R\({}^{2}\) = 0.82). The respective loading S-plot (Figure 3B) revealed that glucose and sucrose (peaks 26 and 39) were enriched in apricot bitter fruit cultivars compared to other ones, though with non-significant \(p\) value of 0.32.
## 3 Methods
### Shallow Seismic Reflection Survey  
According to the requirements of the \"Qujiang New District Phase II Project\", a shallow seismic reflection survey line CX1 was deployed across the surface scarp on the Shaoling Loess Tableland to detect the buried fault (Figure 2a). Line CX1 started from an unmarked lane in the south and spread along Yanxiang Road to the north, with a total length of 2.109 km. The thickness of the Quaternary deposits in this area reaches to ~1000 m, including about 100 m thick loess layer on top. The strata are relatively loose, and the water level is low. The average water level is 70 m under the Earth's surface.  
The shallow seismic reflection data were collected using the Canadian Aries digital seismic instrument, and the large-scale SM28B with a capacity of 28 T was selected as the controllable seismic source. Based on field tests, 704 geophones were used for data collection, with a shot interval of 9-15 m. The scanning frequency of the seismic source ranged from 10 to 110 Hz, with a scanning length of 12 s. The geophone spacing was 3 m and we recorded data on >40 of these sensors per source location. The sampling interval was set to 0.5 ms, and the recording length was 2 s.  
The 2D seismic reflection data were processed by a workstation and a PC-Cluster (672-core) system. The data processing included datum statics, amplitude compensation, refraction first-break time statics, pre-stack noise suppression, surface consistent deconvolution, post-stack pipeline wave suppression, residual statics, velocity analysis, de-noising after stack, post-stack migration and so on. The shallow seismic reflection related work was carried out by China Coal Technology Engineering Group, Xi'an Research Institute.
# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 2 Related Works
### Touch-gesture Features for Authentication  
Touch-gesture authentication verifies users based on screen-touch behaviors, summarized by touch trajectory, device motion, and orientation. Prior works either utilize the screen touch coordinates, pressure, or motion sensor data captured by the accelerometer and gyroscope [10; 11; 12; 13; 14; 15; 24; 25; 26; 27; 28].  
Most existing methods have relied on hand-crafted features such as length of trajectory, the median of finger pressure, etc. [11; 12; 13; 14; 15; 24; 25; 26; 27; 28]. This is mainly attributed to touch-gesture being sensitive to behavioral changes. Typically, the systems first extract hand-crafted features and train prediction models, such as the one class-SVM [12; 15], kernel ridge regression [26], random forest [27], temporal regression forest [25], SVM [11; 13], and MLP [14; 24; 28]. Criticisms for hand-crafted features are that they are task- or user-dependent and require expert knowledge to design, which are not trivial. Furthermore, hand-crafted features lack delicate features and are unlikely to characterize refined impostor patterns.  
Several works attempted to combine hand-crafted and deep learned features [23] or build an end-to-end feature learning model [10; 31]. To address the limitations of hand-crafted features, we opt to learn the features from raw touch-gesture data directly with a deep network.
## 3 Performance Analysis of UOWC Links under Air Bubble-Induced Fading
### Diversity Order Analysis  
As mentioned before, the average BER and the outage probability tend to \(P_{b}\doteq(G_{c}\gamma)^{-G_{d}}\) and \(\text{OP}\doteq(O_{c}\overline{\gamma})^{-O_{d}}\). As stated in [32], the BER and outage performance exhibit identical diversity orders for sufficiently large SNR, hence \(G_{d}=O_{d}\). The resulting diversity order is useful for evaluating in a unifying manner and building insights about the impact of the underwater channel parameters on the UOWC system performance. From Equations (12) and (19), it can be shown that the diversity order, \(G_{d}\), when considering small air bubbles is given by  
\[G_{d_{small}}=\frac{d}{2}. \tag{23}\]  
In the case of large bubbles, both the average BER and outage probability tend to \(P_{b}\doteq(G_{c_{1}}\gamma)^{-G_{d_{1}}}+(G_{c_{2}}\gamma)^{-G_{d_{2}}}\) and \(\text{OP}\doteq(O_{c_{1}}\overline{\gamma})^{-O_{d_{1}}}+(O_{c_{2}}\overline{ \gamma})^{-O_{d_{2}}}\), where \(G_{d_{1}}=O_{d_{1}}\). Therefore, the diversity order refers to the exponent of \(\gamma\) and \(\overline{\gamma}\) in Equations (13) and (22), which determine the slope of the average BER and outage probability at high SNR, respectively. Mathematically, the smallest exponent dominates the behavior of these expressions. Thus, the diversity order can be expressed as the minimum of \(G_{d_{1}}\) and \(G_{d_{2}}\). Consequently, the diversity order can be obtained from Equations (13) and (22) as follows:  
\[G_{d_{large}}=\frac{\min(d_{1},d_{2})}{2}. \tag{24}\]
# Melting of Paraffin Waves Embedded in a Porous Matrix Made by Additive Manufacturing
## 6 Conclusions  
This paper explored the coupling between a porous matrix and phase change materials. The porous matrix was made by additive manufacturing, and it is made of AlSi\({}_{10}\)Mg, which is an aluminum alloy with a thermal conductivity of 175 W m\({}^{-1}\) K\({}^{-1}\). Modified BCC cells with an edge of 5 mm, form the matrix. Three different paraffins, having different melting temperatures (42, 55, and 64 \({}^{\circ}\)C), were used as phase change material. First of all, the thermal conductivities and diffusivities of the three PCMs and of AlSi\({}_{10}\)Mg were measured with the hot disk transient plane source (TPS) method.  
The entire structure was laterally heated with an electrical heater for the phase change tests, applying three different heat fluxes: 10, 15, and 20 kW m\({}^{-2}\). The experimental results were given in terms of the temperature profile of both the heated side and of the paraffin during the heating process. The main findings are the following:  
* The melting front is almost parallel to the heating side, and this is confirmed by the temperatures of the PCM at different heights, which are approximately equal at each time;  
Figure 9: **(a)** Temperature reached by the heated side at the end of the melting process for different structures (case with RT42); **(b)** time needed to completely melt the paraffin for different structures (case with RT42).  
* The higher the heat flux, the higher the temperature of the heated side, and the shorter the time needed to completely melt the paraffin;
* Compared to the case without PCM, adding paraffin leads to lower temperatures of the heated side for all the heating processes; the benefit is increased when the phase change process starts;
* A proper selection of the melting temperature is to be carried out in the case of a heat transfer device, and it depends on the time during which the device is supposed to work;
* The comparison against an aluminum foam and a copper foam revealed that the modified BCC structure outperforms the considered structures under the same working conditions.  
Conceptualization, A.D.; validation, A.D.; investigation, A.D. and L.M.; writing--original draft preparation, A.D. and L.M.; visualization, A.D. and L.M.; supervision, L.R.; funding acquisition, L.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by MIUR through PRIN Project 2017F7 KZWS_005 and by Universita degli Studi di Padova through Project CPDA107382 and Project BIRD172935/17.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
[PERSON] is gratefully acknowledged. [PERSON] is gratefully acknowledged for the fruitful discussion.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.