esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Intersections and Connected Autonomous Vehicles for Sustainable Smart Cities: A Brief Review
## 4 Keys Technologies for Smart Intersections
A better understanding of these tradeoffs can help select appropriate AI techniques for specific smart intersection applications. As depicted in Figure 4, smart intersections for urban environments are designed for efficiency and technological progress. The core of the structure is the Smart Intersection Hub which uses adaptive methods for traffic control dynamic management and sensor-based monitoring to adjust signals continuously. The setup integrates AI and edge computing to support decision-making through scenario evaluation and data-based forecasts for traffic flow. Data collected from vehicle-mounted sensors and environmental monitoring devices are vital in enhancing AI-powered traffic control methods. The system includes adaptive traffic light systems, pedestrian safety protocols, and dynamic lane adjustments to optimize traffic flow, promoting sustainability and operational efficiency. Furthermore, it seamlessly integrates into the smart city infrastructure, working cooperatively with traffic management hubs, cloud services, public transport networks, and emergency response units. This ensures Vehicle-to-Everything (V2X) communication, air quality monitoring, and real-time data exchange across the city. By fostering a data-driven traffic infrastructure, this framework aligns with the overarching goal of establishing eco-conscious urban transportation networks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Key Technologies** & **Functionality** & **Key Reference(s)** \\ \hline Autonomous vehicle & Coordinated CAV interactions & Enhances traffic coordination for & \\ integration & and lane-level & autonomous and & [55; 56; 57; 58; 59; 60; 61] & \\ navigation assistance & connected vehicles & & \\ \hline \multirow{3}{*}{Traffic optimization} & Dynamic lane allocation and & Improves traffic efficiency & \\  & emergency & through adaptive & [62; 63; 64; 65] & \\  & vehicle prioritization & lane management & \\ \hline \multirow{3}{*}{Safety} & Collision prediction and & Enhances road safety by & \\  & incident detection systems & predicting and & [66; 67; 68; 69; 70; 71; 72; 73; 74] & \\  & & preventing accidents & \\ \hline \multirow{3}{*}{Environmental impact} & Traffic signal optimization and & Reduces emissions, optimizes fuel & \\  & emission control & use, and integrates EV charging & [75; 76; 77; 78; 11] \\ \hline \multirow{3}{*}{Equity and accessibility} & Extended pedestrian crossings & Improves accessibility for & \\  & and public transit & vulnerable users and prioritizes & [79; 80; 81; 82; 83; 84; 85] \\  & prioritization & public transit & \\ \hline \multirow{3}{*}{Multi-modal integration} & Public transit synchronization & Supports integrated mobility & \\  & and ride sharing support & solutions for seamless & [86; 87; 88; 89] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)
# Dynamic Bending Behaviour of Sandwich Structures for Marine Applications
## 4 Results and Discussion
### seawater Effects on Single Low-Velocity Impact Bending Performance of FRPSS  
#### 4.2.1 Load-Time Behaviour for Single Impact  
During a single impact, the load-time impact behaviour of FRPSS before and after seawater exposure can be divided into two main sections (I and II) as depicted in Figure 5. The global peak load is known as the _Hertzian failure point_, at which damage begins mainly in the form of matrix cracking, with the potential for through-thickness delamination and core/facesheet disbonding. From Figure 5, at an impact energy of 1.93 J, the initial loading profile was characterised for a short period (values \(<\) 2.5 ms) by a smooth curve until the peak load (Section I) was attained in all specimens before and after exposure to seawater. This could be attributed to the initial damping effects of the specimen in contact with the hammer's striking head. Although not visible in the control sample (a little more obvious in the exposed samples), failures such as low-level dents and matrix cracks dominated the damage morphology at this stage. A similar damage response was reported in the literature [18]. In Section II, characterised by significantly longer duration, there was a sudden drop in the load after the peak load, associated with more severe damage modes such as delamination, facesheet/core debonding, and fibre fracture. A purely elastic impact would have a symmetric shape with equal duration of Sections I and II. Generally, all samples suffered a reduction in their global impact bending properties after seawater exposure. GSP experienced the largest reduction in force with 39.1% after this exposure, while the responses of GSV and GSH reduced by 7.2% and 15.9%, respectively. This is correlated to the absence of adhesive epoxy layers in the core of the GSP specimen.  
Figure 5: Load–time behaviour for single impact.  
Remarkably, the larger deterioration of impact resistance in GSH compared to GSV was mainly due to the limited additional stiffness the horizontal layers provided during impact. This was caused by the vertical epoxy layer, serving as a stiffener connecting the top and bottom facesheets through the core. Furthermore, it provided an additional barrier to free moisture movement in the through-thickness direction, hence limiting the plasticization of the matrix to the in-plane direction while retaining its additional stiffness.
# Kolmogorov-Arnold Finance-Informed Neural Network in Option Pricing
## 3 Methodology
### Machine Learning-Based KAR Decomposition  
The mathematical elegance of KAR representation lies in its ability to break down a high-dimensional function into a sum of lower-dimensional components, which can then be modeled independently. This is particularly useful in machine learning and numerical approximation, where the goal is to learn and optimize the parameters of these univariate functions to best approximate a complex target function. The decomposition also facilitates the understanding of how different input variables contribute to the system, which is essential for applications such as financial modeling, where understanding variable interactions is key to predicting outcomes.  
When applying KAR to time-dependent systems, such as in financial modeling, a function \(\mathbf{u}(\mathbf{x},t)\), depending on input variables \(\mathbf{x}\) and time \(t\), can similarly be decomposed as  
\[\mathbf{u}(\mathbf{x},t)=\sum_{i=1}^{L_{2}}g_{i}\left(\sum_{j=1}^{L_{1}}h_{ij} (x_{j},t)\right),\]  
where \(L_{1}\) and \(L_{2}\) represent the number of inner and outer units, respectively.  
In a parametric modeling framework, \(\mathbf{u}(\mathbf{x},t)\) can be approximated by a parameterized machine learning system \(\mathbf{u}_{\theta}(\mathbf{x},t)\) with learnable parameters \(\theta\). Using the KAR-inspired structure, it can be formulated as  
\[\mathbf{u}_{\theta}(\mathbf{x},t)=\sum_{i=1}^{L_{2}}g_{i,\theta}\left(\sum_{j =1}^{L_{1}}h_{ij,\theta}(x_{j},t)\right),\]  
where \(g_{i,\theta}\) and \(h_{ij,\theta}\) are parameterized univariate functions learned during training.  
The goal of the machine learning process is to determine the parameters \(\theta\) such that the network output \(\mathbf{u}_{\theta}(\mathbf{x},t)\) closely approximates the true system solution \(\mathbf{u}(\mathbf{x},t)\). This is achieved by minimizing a total loss function \(\mathcal{L}(\theta)\), which measures the deviation from governing system rules:  
\[\theta^{*}=\arg\min_{\theta}\mathcal{L}(\theta).\]  
The network architecture, inspired by the Kolmogorov-Arnold representation, utilizes distinct components. The inner functions \(h_{ij,\theta}\) transform input variables \(x_{j}\) and time \(t\), capturing their localized contributions to system dynamics. These are aggregated by the outer functions \(g_{i,\theta}\), which synthesize these contributions into a global representation via summation. Learnable parameters \(\theta\) control the weighting, scaling, and interactions, enabling the model to effectively adapt to complex relationships while maintaining interpretability.
## 3 Biosynthesis of Tanshinones in _S. miltiorrhiza_
GGPP is the universal precursor of all diterpenoids and can be catalyzed by different terpenoid synthases to form various diterpenoid skeletons. Terpenoid synthases are the key enzymes for the biosynthesis of the tanshinone skeletons. 12 _SmCPS_ and nine _SmKSL_ homologs have been reported in _S. miltiorrhiza_[29]. The functions of four _SmCPSs_ and two _SmKSLs_ were identified through the expression of the protein in _Escherichia coli_ and enzymatic catalysis _in vitro_ combined with GC-MS measurements, which characterized the role of _SmCPS1_, _SmCPS2_, and _SmKSL1_ in tanshinone biosynthesis to form the skeleton miltiradiene [51,53], and kinetic analysis showed that _SmCPS1_ had both higher affinity and activity with GGPP than _SmCPS2_. RNA interference was employed to down-regulate the expression of _SmCPS1_, which resulted in a decrease in the accumulation of tanshinones, further verified the key role of _SmCPS1_ in tanshinone biosynthesis [16,53]. According to the structure of tanshinones, the genes involved in the downstream pathway involved in tanshinone biosynthesis are P450s, dehydrogenases, demethylases, and other structural modifying enzymes, which always begin with P450s [27,63]. [PERSON] _et al._[27] used comparative transcriptome analysis to identify six candidate P450s genes that were co-regulated with the diterpenoid synthase genes in both the root and _S. miltiorrhiza_ hairy roots. _CYP76 AH1_ was identified to catalyze militradiene to produce ferruginol both _in vitro_ and _in vivo_. Recently, based on co-expression analysis, the authors further identified two new P450s which can work sequentially to catalyze ferruginol into the metabolic intermediate, which with two keto in C11 and C12 is more close to the tanshinones [54]. Now, the post-modification from ferruginol to tanshinones is still ongoing. The identified enzymes involved in biosynthetic pathway could provide reference genes for post-modification characterization.
# Comparative Analysis of Data Detection Techniques for 5G Massive MIMO Systems
## 6 Conclusions  
In this paper, free matrix inversion methods have been exploited to initialize the AMP detector. Compared with the traditional AMP detector, initialized detectors have attained the target performance with a small number of multiplications and hence, a low computational complexity. The detector based on the AMP and GS method has the best performance over several iterations. However, the computational complexity of the AMP-JA is the lowest. In addition, the AMP-SOR and the AMP-Gs have extremely low complexity compared to the AMP algorithm. It is also shown that an efficient initialization impacts the performance and the number iterations and hence, the computational complexity.  
Recent studies show that the machine learning (ML) can provide a significant improvement in image recognition and wireless communications. Recently, there is a remarkable trend to exploit the ML approach in large-scale MIMO detector's design where the most expensive process is the \"learning\", which can be conducted off-line. However, there are many attempts to do it online. Therefore, the proposed AMP-JA, AMP-GS, and AMP-SOR could be exploited with the ML approaches to achieve a remarkable low computational complexity and high performance.  
**Author Contributions:** Conceptualization, M.A.A.; methodology, M.A.A.; software, M.A.A.; validation, M.H.A.; formal analysis, A.K.; project administration, I.K.; funding acquisition, B.J.C.; review and editing, M.H.A. and B.J.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported under the National Research Foundation (NRF), Korea (2019R1C1C1007277) funded by the Ministry of Science and ICT (MSIT), Korea.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Literature Review
### Graph Convolutional Neural Network  
A graph is a data structure that consists of a series of nodes and links between nodes (called edges). Unlike traditional images, which are composed of a series of neatly arranged pixels, the links between nodes in graph data can represent more relationships. For example, the links between human joint nodes can represent the human trunk and limbs. This laid the foundation for human action recognition using GCN, which borrows from the concept of convolution in CNN and can adaptively extract the information in the graph structure. Since [PERSON] et al. [28] leveraged GCN to extract the spatio-temporal information of human action, compared with CNN-based and RNN-based methods, the effect has been significantly improved. Many scholars have paid attention to the excellent performance of GCN and the powerful expressiveness of graph structure.  
[PERSON] et al. [33] introduced a novel structure-induced graph convolutional network framework that defines a set of internal graphics for each input human skeleton. Then, to improve the performance of the action recognition, they developed an inter-graph model to model the relationship between different parts of the graph. [PERSON] et al. [34] proposed a method based on deep progressive reinforcement learning to extract key frames, and used the GCN to capture the dependency between the joints for action recognition. The GCN demonstrates the ability to efficiently combine highly complex and non-differentiable rules, which is necessary to extract spatial and temporal information in the video.  
The existing deep-learning-based action recognition methods are essentially based on CNN, RNN, or GCN or realize the recognition through a reasonable combination of the above three methods. Although some achievements have already been made, they pay little attention to operational recognition in industrial workflows, which is very important and urgent to study.
# Active Tunable Elastic Metasurface for Abnormal Flexural Wave Transmission
## 2 Mechanisms of Actively Tunable Elastic Metasurface
### Generalized Snell's Law  
According to the generalized Snell's law, the relationship between the incident and transmitted angles of a flexural wave passing through the metasurface is as follows  
\[\frac{\sin(\theta_{\mathrm{t}})}{\lambda_{\mathrm{t}}}-\frac{\sin(\theta_{ \mathrm{i}})}{\lambda_{\mathrm{i}}}=\frac{1}{2\pi}\frac{\mathrm{d}\varphi_{ \mathrm{y}}}{\mathrm{d}y} \tag{13}\]  
where \(\lambda_{\mathrm{i}}=c/f,\theta_{\mathrm{i}}\) and \(\theta_{\mathrm{t}}\) are the incident and transmitted angles, respectively; \(\lambda_{\mathrm{i}}\) and \(\lambda_{\mathrm{t}}\) are the wavelength of the incident and transmitted flexural waves, respectively; and \(d\varphi_{\mathrm{y}}/\mathrm{d}y\) is the phase gradient along the \(y\)-axis.  
The wavelengths of the incident and transmitted regions are the same when the same material is applied, and hence \(\lambda_{\mathrm{i}}=\lambda_{\mathrm{t}}\). Equation (13) can be organized as  
\[\frac{\sin(\theta_{\mathrm{t}})-\sin(\theta_{\mathrm{i}})}{\lambda_{\mathrm{i }}}=\frac{1}{2\pi}\frac{\mathrm{d}\varphi_{\mathrm{y}}}{\mathrm{d}y} \tag{14}\]  
In particular, if the wave is incident vertically, the magnitude of the angle of refraction is calculated according to the following formula:  
\[\theta_{\mathrm{t}}=\arcsin(\frac{\lambda_{\mathrm{t}}}{2\pi}\times\frac{d \varphi_{\mathrm{y}}}{dy}) \tag{15}\]
# An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks
###### Abstract  
With the rapid development of smart handheld devices, wireless communication, and positioning technologies, location-based service (LBS) has been gaining tremendous popularity in mobile social networks (MSN). Users' daily lives are facilitated by the applications of LBS, but users' privacy leaking hinders the further development of LBS. In order to solve this problem, techniques such as k-anonymity and l-diversity have been widely adopted. However, most papers that combine with k-anonymity and l-diversity focus on the security of users' privacy with little consideration of service efficiency. In this paper, we firstly treat the relationship between k-anonymity and l-diversity in the clustering process from a dynamic and global perspective. Then a service category table based algorithm (SCTB) is designed to identify and calculate l-diversity securely and efficiently, which promotes the cooperative efficiency of users in LBS query, especially when the preference privacy that users request in the clustering process have similarities. Finally, theoretical performance analysis and extensive experimental studies are performed to validate the effectiveness of our SCTB algorithm.  
location-based service (LBS); k-anonymity; l-diversity; preference privacy +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
PZT-BYS: 0.99 Pb(Zr\({}_{0.53}\)Ti\({}_{0.47}\))O\({}_{3}\)–0.01 Bi[Y\({}_{(1-x)}\)Sb\({}_{x}\)]O\({}_{3}\). In Figure 2, uniform, fine-grained, and almost pore-free microstructures can be seen for Sb\({}_{2}\)O\({}_{5}\)-doped specimens when sintered in the air atmosphere and for Sb\({}_{2}\)O\({}_{3}\)-doped samples when sintered in the O\({}_{2}\)-rich atmosphere. On the other hand, Sb\({}_{2}\)O\({}_{3}\)-doped specimens sintered in the air have small (approx. 1.4 \(\upmu\)m) and loosely bonded grains. Since the dielectric properties are directly associated with grain size, the air-sintering of the Sb\({}_{2}\)O\({}_{3}\) sample has a smaller average grain size and shows a higher value of \(\varepsilon_{33}\)T. A similar observation was also explained by [PERSON] et al. [24]. Subsequently, higher \(\varepsilon_{33}\)T produces a lower FOM according to Equation (1), therefore, doping Sb\({}_{2}\)O\({}_{3}\) and sintering in the presence of air is not favorable or advantageous in the present case. On the contrary, Sb\({}_{2}\)O\({}_{5}\) is relatively expensive and does not follow the stoichiometric reaction mechanism when doped in PZT-BY. Therefore, according to the experimental results and discussions above, Sb\({}_{2}\)O\({}_{3}\) is considered as a substituent for Y\({}^{3+}\) in the PZT-BYS(\(x\)) and also an O\({}_{2}\)-assisted sintering atmosphere was selected. Hence, a detailed analysis of the crystal structure, microstructure, piezoelectric/dielectric properties, and ferroelectric properties of the Sb\({}_{2}\)O\({}_{3}\)-doped PZT-BYS(\(x\)) specimens sintered in the O\({}_{2}\) atmosphere will be discussed in the next sections.
# On Blockchain-Based Cross-Service Communication and Resource Orchestration on Edge Clouds
## 5 Network Service Lease and Orchestration
### Network Service Lease Orchestration  
The main interactions between the Service Orchestrator component of the proposed architecture, the MANO, the Dashboard and the Blockchain layer are presented in Figure 4. In this subsection, we describe these interactions, from the scope of the NFV layer, in order to clarify the key functionality of the proposed Service Orchestrator. As a first step, the interested client for CSC instantiation, posts a request on the Service Orchestrator API. The request consists of the NST descriptor (NSTD) name, which is onboarded in the OSM and describes the service chain of the client, alongside with the identifier of the specific service of the provider, for which the user is interested in cross-service communication, among the set of the registered services in the data store. At the second step, the Service Orchestrator requests the provider's NSTD for the CSC-available service and extracts the necessary data from the descriptor file. Looking back on Listing 1, where an example of a NSTD of a service provider is depicted, we assume that a client is interested in the network service with identifier _\"provider-service-2\"_. In order to update the client's NSTD, we need  
Figure 3: Lease and Orchestration Lifecycle.  
the information stored in the _netslice-subnet_ object, which matches with the _id_ attribute, and the _netslice-vld_ objects for the corresponding connection points of the desired service. Listing 3 illustrates the extracted data in a JSON file for the _\"provider-service-2\"_ service. After retrieving the mandatory data from the provider's NSTD, the Service Orchestrator requests the onboard client's slice NSTD. Then, using the extracted data from the provider's NSTD, the Service Orchestrator updates the client's NSTD accordingly, in order to be able to access the shared service. The updates in the descriptor refer to the addition of the shared-service object of the _netslice-subnet_ field in the JSON file, and the corresponding connection points attachment at a management-network and a data network of the client's slice. The additions take place in the YAML NSTD file of consumers, in the corresponding fields. Finally, the updated NSTD is used for the instantiation request. For the instantiation, the Service Orchestrator constructs a configuration object, with parameters that refer to the VIM layer, in order to enable the network connections between the services of the client and the provider. After a successful instantiation, every component of the proposed architecture receives an orchestration success message.  
Figure 4: CSC interactions and operations.
# nmODE-Unet: A Novel Network for Semantic Segmentation of Medical Images
## 2 Related Works
### Neural Ordinary Differential Equation  
In 2018, [PERSON] et al. [22] introduced the concept of the Neural Ordinary Differential Equation (neuralODE), treating neural networks as approximations of ODEs and enabling continuous modeling of neural network. Since then, research on neuralODE has been consistently expanding. [PERSON] et al. [23] introduced an innovative approach that combines Graph Neural Networks (GNNs) with ODEs, enabling the modeling of dynamic evolution in graph data within continuous time. By incorporating ODEs into graph data modeling, Graph Neural ODEs provide a more flexible and powerful modeling tool for adapting to the dynamic nature of graph data. It offers a completely new approach to handling dynamic graph data. [PERSON] et al. [PERSON] et al. (2018) introduced an innovative approach that is based on discretization techniques within stochastic differential equations (SDEs) and employs gradient estimation to enhance computational efficiency. This method significantly improves the gradient computation efficiency of SDEs, thereby accelerating their application in machine learning and other fields. [PERSON] (2018) proposed a novel neuralODE model named nmODE. Regardless of external input, nmODE possesses a unique global attractor, thus embedding a memory mechanism within it. nmODE demonstrates universality in image classification or segmentation tasks, laying the foundation for practical applications of neuralODE.  
In this paper, we proposed a network structure named nmODE-Unet for the segmentation of DR lesions in fundus images.