esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems
### Social Recommendation
#### 3.4.3 Social Recommendation Based on GNN
Following GraphSAGE, the GAT [143] further refined the spatial GNN concept, addressing the limitations of the previous spectral methods regarding key issues such as model generalization. Notably, the GAT introduced an attention mechanism, enabling the model to allocate different weights based on the importance of neighboring nodes, leading to a more selective neighborhood feature aggregation process. This strategy enhanced the model's discriminatory capacity, achieving commendable results in social recommendation tasks. For dealing with heterogeneous graph structures, HetGNN [144] emerged as a representative technique. Distinct from traditional GNN models, HetGNN devises specific aggregation strategies for nodes and edges that are present in heterogeneous graphs. Initially, it segregates neighbors based on their types and then employs LSTM and MEAN operations to individually process these subsets, effectively capturing the abundant structural and attribute information inherent in heterogeneous graphs. Furthermore, it is noteworthy that HetGNN implements spectral GNN methods on hypergraph structures, propelling recommender system research in a novel direction. Regarding the exploration of social network structures, DiffNet and DiffNet++ stand as pivotal milestones. Both methods emphasize delving into higher-order social structures to achieve enhanced recommendation accuracy and efficacy. DiffNet accentuates capturing higher-order neighbor information within the network to boost its recommendation results [145], while DiffNet++ further refines and extends this base [46]. Following DiffNet and DiffNet++, GraphRec emerged as an innovative approach, emphasizing not only capturing interactions between users and items but also jointly capturing opinions about both users and items [146]. This dual capture strategy is designed to ensure recommendation accuracy while maintaining system interpretability. With the continuous evolution of social media and platforms, the significance of social recommender systems is set to expand further [147]. Advanced technologies such as deep learning and graph neural networks have notably enhanced the accuracy and efficiency of social recommendation [148]. Moreover, striking a balance between user privacy and the quality of recommendations will emerge as a pivotal research direction [149]. In the future, we may also witness an increase in cross-platform social recommendation solutions, ensuring that users receive consistent and high-quality recommendation experiences across diverse platforms.
# Damage Location Diagnosis of Frame Structure Based on a Novel Convolutional Neural Network
## 1 Introduction
However, due to the large number of times of manual data processing required for signal classification of multiple measurement and control positions with multiple 1 DCNNs and the poor anti-noise interference ability of 1 DCNN, efficient fault diagnosis cannot be carried out. Facing the problem of poor anti-noise ability of the ordinary one-dimensional convolutional neural network, [PERSON] proposed a one-dimensional convolutional neural network model with strong anti-noise ability, named WDCNN (first layer wide resolution kernel deep convolution neural network) [29]. This model increased the number of extracted effective features by increasing the overall perception field of the neural network, thus improving the recognition ability of the neural network. However, it is difficult to effectively identify the fault type when WDCNN is used to classify the vibration data with similar fault conditions. In order to accurately diagnose the damage location of the frame structure in the strong noise environment according to the weak vibration signal, this paper proposed a novel fault diagnosis method based on the convolutional neural network. The basic principle of this method is: firstly, wavelet decomposition and reconstruction [30] is used to denoise the vibration data collected by the acceleration sensor; then CEEMDAN [31] (complete ensemble empirical mode decomposition with adaptive noise analysis) is used to decompose the reconstructed signal at multiple scales, highlight the characteristic information of the vibration signal, and increase the length of sample data; finally, WDCNN and LSTM [32] (Long Short-Term Memory Network) are used to extract the features of reconstructed signals and the correlation features between signals. Taking the building frame structure model of Columbia University [33] as the research object, the damage location diagnosis research was carried out in the strong noise environment by using the method proposed in this paper, and compared with 1 DCNN and WDCNN under the same experimental conditions. The research results verified the high accuracy and strong anti-noise ability of the method proposed in this paper. The contribution and innovation of this paper is to propose a novel fault diagnosis method based on the convolutional neural network, which solves the problem of accurate damage location diagnosis of the frame structures under a strong noise environment.
# A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications
## 4 Proposed Mechanism  
### Assumptions  
As shown in Figure 1, the system compares RSS power from all base stations when the vehicle is in motion. According to the compared RSS report from the vehicle, the system allocates the matched antenna resource to them. Although the base station takes the broadcast form to transmit signals, a user can select only one base station as their own transmission base station [27].  
In this paper, we take the dedicated bandwidth-allocation strategy. The bandwidths of the macrocell and femtocell base stations are different. Adjacent femtocell base stations are also allocated different amounts of bandwidth. Thus, transmissions from macrocell to femtocell base stations do not interfere with each other. We mainly discuss the base station selection of vehicles in the case of a macrocell and a femtocell base station [28].  
Suppose there are nvehicles in the macro-femtocell system. The transmission power of the macrocell base station is \(E_{M}\), and the transmission power of the femtocell base station is \(E_{F}\). The RSS of the \(j\)-th (\(j=1,2,3,...\)\(n\)) vehicle could be presented as [29]:  
\[RSS_{M_{j}} = E_{M}d_{M_{j}}^{-\beta}h_{M}10\frac{\xi}{10} \tag{1}\] \[RSS_{F_{j}} = E_{F}d_{F_{j}}^{-\beta}h_{F}10\frac{\xi}{10} \tag{2}\]  
where \(RSS_{M_{j}}\) represents the signal strength that the \(j\)-th vehicle could receive when the vehicle treats the macrocell base station as its transmission station. When the \(j\)-th vehicle selects the femtocell base station, the signal strength that the vehicle received could be presented by the \(RSS_{F_{j}}\). \(d_{M_{j}}\) represents the distance between the vehicle and the macrocell base station; \(d_{F_{j}}\) could also show the distance from  
Figure 1: Macro–femtocell system.  
the vehicle to the femtocell base station; \(h_{M}\) and \(h_{F}\) denote the channel gains from the macrocell and femtocell base stations, respectively; \(\beta\) denotes the path-loss exponent. In the pass loss model, we apply the Okumura-Hatamodel in the large-scale fading channel, which represents the shadow fading, subject to a Gaussian distribution with a mean of zero and a standard deviation up to 12 dB. Among them, 12 dB is the fading tolerance. Therefore, \(\xi\) could be presented as a Gaussian-distributed random variable with a mean of zero and a standard deviation of up to 12 dB [30].
## 4 Cybersecurity-Based MLP Trainer
### Computational Complexity and Deployment Potential of Anomaly Detection Algorithm
#### 4.3.2 Deployment Potential and Latency in IPS/IDS  
In a real-time Intrusion Prevention/Detection System (IPS/IDS), minimizing latency is essential to ensure the timely detection and prevention of cyber threats. The evolutionary nature of the anomaly detection algorithm introduces latency challenges due to the significant number of function evaluations and iterations across multiple generations.  
The training phase of the algorithm focuses on optimizing the weights and biases of the MLP using differential evolution, a process that is computationally intensive. Due to the high complexity, as indicated in Equation (16), this phase is more appropriate for offline training environments, where the algorithm can be allowed sufficient time to converge toan optimal solution. Conducting training in an online or real-time setting would result in considerable delays, rendering it unsuitable for real-time intrusion detection applications.  
Once the training phase is completed, the deployment phase utilizes the trained MLP for real-time anomaly detection. In this stage, the computational complexity is significantly reduced, as making predictions only requires forward propagation through the network. The complexity of this operation is linear with respect to the number of inputs \(n\) and outputs \(m\), as illustrated in Equation (17):  
\[O(n\cdot m) \tag{17}\]  
This lower complexity makes the deployment phase more suitable for real-time detection, where latency must be minimized.
###### Abstract  
To explore the effect of ultraviolet (UV) aging on the adhesion properties of warm mix asphalt (WMA), WMA binders were prepared by adding Evotherm M1 and Sasobit into base asphalt. The base asphalt and two WMA binders were aged using a self-made UV aging box after a rolling thin-film oven test (RTFOT). As a comparison, pressure aging vessel (PAV) tests were performed for asphalt binders after RTFOT. The contact angles between the WMA binder and distilled water, glycerol, and formamide, before and after UV aging, were measured using the sessile drop method. On the basis of the surface free energy (SFE) theory, the SFE and its components, cohesion work, adhesion work, peeling work, and compatibility rate, before and after the UV aging of WMA, were obtained. The results show that the contact angle between WMA and water gradually increases with the degree of UV aging, which indicates that the aging increases the hydrophobicity of asphalt. After the RTFOT aging, UV aging at different times, and PAV aging, the SFE and its component, as well as cohesion work for all asphalt binders were reduced. The attenuation was severe after UV aging, followed by PAV aging and RTFOT aging. With the extension of UV aging time, the adhesion work, hot mix asphalt, and limestone aggregate compatibility rate gradually reduced, the peeling work increased, and the overall adhesion performance became poor. Under the aging effect of RTFOT and PAV, the Evotherm M1 WMA had the strongest anti-peeling ability, better compatibility, and the best adhesion. When the UV aging time of Sasobit WMA reached 100 h, the adhesion performance increased.  
p  
pavement materials; warm mixed asphalt; surface free energy; adhesion; ultraviolet aging
# Optical Design and Stray Light Analysis of Underwater Spectral Radiometer
## 3 Underwater Spectral Radiometer Design  
### Overall Design  
The hardware design of the underwater spectral radiometer consists of four main components, as illustrated in Figure 2. These components include the front-end optical system, spectrometer system, electronic system, and upper-level software. Except for the upper-level software, all other parts are integrated into the instrument's mechanically sealed structure. The optical module serves as the core unit of the underwater spectral radiometer and is the focus of this study.
## 4 Results and Discussion  
### Meaning of Effective Parameters  
The method developed for determining effective parameters of nanocomposite waveguide structures has been tested with a number of samples differing in NW morphology and annealing temperature. In this section, we present some of these results (dispersion curves and tables of effective parameters) illustrating the influence of these two factors on the effective constants of the HSQ-NW composites and the waveguide properties of the related sandwich structures.  
It is worth to note that the obtained sets of parameters should not be treated as the \"real\" parameters of the corresponding materials. First, because such \"real\" parameters do not exist in the nature at all, while the material constants introduced within one or other model of a solid medium are just a tool to simulate its stress-strain state. Second, the effective parameters are obtained to simulate wave phenomena in a layered structure as a whole. Therefore, a change of properties of one lamina (e.g., its thickness) affects the properties of all others. Moreover, the borders between the layers, in fact, also do not exist but are introduced manually or obtained by minimizing the goal function. For example, the density of some layers does not correspond to these real mass divided by the volume but is just a constant in governing Equation (1) providing the required SAW characteristics. The same reasoning is valid for all other effective parameters.  
On the other hand, if a specimen were fabricated strictly from \(M=3\) or other number of homogeneous layers used in the mathematical model with the elastic properties described by the effective constants obtained, it would exhibit the same wave properties as the sample under study. And most importantly, since the SAW eigenfrequencies and wavenumbers calculated within the mathematical model are the same as those measured in the experiments, their eigenforms should be very similar to those in the real samples. The eigenforms determine the laws of deformation in the sample at the corresponding frequencies, thus, its electrical output due to the deformation of the embedded piezoelectrical NWs. This can be used for the design and optimization of piezogenerators based on III-nitride NWs.  
As for the mechanical properties of the microstructural material HSQ-NW itself, the narrower ranges of the parameters of other layers are specified, the more stable its effective moduli are obtained. Therefore, to analyze the tendencies of their change, e.g., depending on the annealing temperature, the best is to fix the properties of the coating and substrate as well as all thicknesses even if they do not provide the global minimum in the space of all input parameters of the model. The results of such estimation of the temperature influence obtained with fixed layer thicknesses are discussed in Section 4.4 below.
# Valorization of Waste Tires by Pyrolysis and Activation Processes
## 2 Materials and Methods
### Liquid and Gas Fraction Characterization  
The liquid obtained from the pyrolysis tests was subjected to a qualitative chemical characterization to identify the main compounds and the group to which they belong: aromatic, aliphatic, heterocyclic aromatic, and compounds with heteroatoms. The characterization of a representative replica from each process was performed using a gas chromatograph 7890A coupled to a selective mass detector MSD 5975C (GC-MS; Agilent Technologies, Santa Clara, CA, USA). The experimental procedure is described in [33]. Although this characterization technique is useful and commonly used, detection limitations should be considered. Compound identification by this technique could be uncertain as isomers, homologues, or altogether different compounds have a similar structure. In addition, search results below 80% accuracy indicate that compounds are not presented in the library.  
As depicted in Figure 1, an in-line micro-GC (Agilent 3000A, Santa Clara, CA, USA) was used to determine the composition of the gas. The detection of CO\({}_{2}\), C\({}_{2}\)H\({}_{4}\), C\({}_{2}\)H\({}_{6}\), C\({}_{2}\)H\({}_{2}\), H\({}_{2}\)S, H\({}_{2}\), N\({}_{2}\), CH\({}_{4}\), and CO was performed in each sample injection conducted every 160 s. Other gases such as SO\({}_{2}\) and H\({}_{2}\)SO\({}_{3}\) were not considered for discussion, as previous studies reported its presence on insignificant amounts [34]. The data provided  
Figure 2: Four-point methodology (upper panel: schematic representation of the four-point probe device; lower left panel: visualization of the sample thickness distribution; and lower right panel: average longitudinal thickness of the sample).  
by the micro-GC were normalized and quantification of the gas species was performed by a numerical integration using N\({}_{2}\) as the internal standard. The volume composition was expressed on an N\({}_{2}\)-free basis. The lower heating value of the gas produced was calculated as a weighted average of the heating value of each compound (Equation (1)):  
\[LHV_{gas}=\sum X_{i}\times LHV_{i} \tag{1}\]  
where \(LHV_{gas}\) is the lower heating value of the gas produced during the process, \(X_{i}\) is the molar fraction of each chemical species \(i\) in the gas mixture, and \(LHV_{i}\) is the lower heating value of each chemical species \(i\).
# Secrecy Rate of Resource-Constrained Mobile Relay Model under Two-Way Wiretap Channel
## 5 Conclusions  
In the current study, the secrecy rate of a ground mobile vehicle-based relay system was investigated. Firstly, an integrated graph mobility model and a secrecy rate of the mobile relay system model were proposed. The joint secure transmission and graph mobility management problem was formulated to achieve secrecy rate maximization, subject to resource constraints and mobility graph structure constraints. Then, algorithms that achieve a local optimal solution were proposed. Simulation results were presented to demonstrate that our scheme outperforms the fixed relay scheme and that the mobile relay trajectory highly depends on noise power.  
Conceptualization and methodology: H.W.; software: H.W. and Y.W.; validation, formal analysis, investigation, resources, visualization, and writing--original draft preparation: H.W.; writing--review and editing: H.W. and Y.W.; supervision and project administration: M.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Overseas Study Fund of Jiangsu University of Science and Technology grant number just-2018-05.  
The authors are grateful to [PERSON] at the University of Hong Kong for providing the visiting researcher project; thank [PERSON] (Southern University of Science and Technology) technique help; and acknowledge the funding support from Jiangsu University of Science and Technology.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MINLP & Mixed integer nonlinear programming \\ UAV & Unmanned aerial vehicles \\ CSI & Channel state information \\ SNR & Signal-to-noise ratio \\ NSM & Neighborhood search method \\ \end{tabular}
## 4 Conclusions  
This work demonstrated that the modification method combining OBC blending and MAH/DVB co-grafting could be used to prepare highly wood flour-filled waste polypropylene-based composites with excellent toughness and rigidity. In the MAH grafting modification system, the addition of DVB not only inhibited PP degradation,but also promoted MAH grafting and introduces branched structures. Therefore, in the composites, grafting modification of the matrix enhanced interfacial compatibility among rPP, OBC, and wood flour, promoting the formation of embedded structures. The extent of embedding of wood into maleated elastomer-OBC was proven by the higher tensile modulus of the composites. The synergistic effect of the effective interfacial layer and the branched structure enhanced the shear yielding process of the OBC-initiated matrix. Compared with the unmodified composites, the tensile strength, flexural strength, impact strength, and tensile modulus of the modified ternary hybrid composites increased by 75%, 39%, 125%, and 60%, respectively, after elastomer blending and MAH/DVB co-graffing modification. This approach is significant for recycling waste plastics and enhancing the application value of wood-plastic composites.  
Methodology, S.Y., W.L., M.W., and Q.W.; Formal analysis, S.Y., W.L., S.X., R.H., Q.L., and Z.H.; Data curation, S.Y., M.W., and Z.H.; Writing--original draft, S.Y. and Q.L.; Writing--review and editing, S.X., R.H., Q.W., and Z.H.; Funding acquisition, S.X. and Z.H. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by Specific Research Project of Guangxi for Research Bases and Talents (Grant No. GuikeAD23026309), Guangxi Key Technologies R&D Program (Grant No. GuikeAB24010240), 2023 Guangxi Postdoctoral \"Two Stations\" Project Subsidy Funds and Guangxi Natural Science Foundation Program (Grant No. 2021 GXNSFBA196056).  
Not applicable.  
**Data Availability Statement:** Available data are presented in the manuscript.  
The authors declare no conflict of interest.
## 4 Taxonomy of Clustering Schemes
### Classification of Clustering Attributes in WSNs
#### 4.1.4 Entire Proceeding of Algorithm  
_Algorithm Stages_: In general, a complete clustering routing algorithm comprises two basic stages, _i.e._, cluster construction and data transmission, but the consideration degree of algorithms may differ in different stages. Based on algorithm stages of whole process of clustering algorithms, clustering routing protocols in WSNs can be classified into cluster construction based and data transmission based ones. In the former algorithm, cluster construction is mainly discussed, while data transmission is concerned less or performed by a relatively simple way. As contrary to it, the latter one chiefly takes data transmission into account, but care less about cluster formation.