esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Plate Topology Optimization  
Topology optimization considers the optimal redistribution of the material of a given design domain to find the optimal element restructure controlled by an objective function and constraints. Solid isotropic microstructure with penalization (SIMP) is a topology optimization approach established by [PERSON] and [PERSON], 1999 [35], employed by several commercial software packages such as Abaqus (Dassault Systemes, Waltham, MA, USA) and Ansys (Ansys, Canonsburg, PA, USA) and used in medical applications [36], SIMP aim is to find the optimal design layout considering continuously converging the design variable (material element density, \(\rho_{\mathrm{e}}\)) by either retaining the element (\(\rho\ =1\)) or eliminating it (\(\rho\ =0\)) [37].  
Mathematical description of the topology optimization problem is as follows [35]:  
\[\min_{\rho_{\mathrm{e}}}\mathrm{C}(\rho_{\mathrm{e}})=\mathrm{f}^{\mathrm{T}} \cdot\mathrm{u} \tag{1}\]  
\[\mathrm{subject\ to\ }\begin{cases}\sum_{\mathrm{e}=1}^{\mathrm{N}}(\rho_{ \mathrm{e}})\
u_{\mathrm{e}}\leq\mathrm{V}^{*},\\ \sum_{\mathrm{e}=1}^{\mathrm{N}}(\rho_{\mathrm{e}}^{\mathrm{P}})K_{\mathrm{e} }\ \mathrm{u}=\mathrm{f},\\ 0<\ \rho_{\mathrm{o}}\leq\rho_{\mathrm{e}}\leq 1,\end{cases} \tag{2}\]  
where f is the force vector, u is the displacement vector C is the compliance, \(p\) is a penalization factor (\(p\) = 3), \(\
u_{\mathrm{e}}\) is the volume of each element, V* is the volume fraction, K\({}_{\mathrm{e}}\) is the elemental stiffness matrix and \(\rho_{0}\) is the initial density. The constraint (3) represents the equilibrium equation representing all the stiffness tensors to attain the material properties of a given isotropic material. The inequality equation in (2) expresses the amount of material at the user disposal with a limited volume for the minimum compliance design. The  
Figure 1: Computer tomography of (a) 2D scans and (b) 3D bone model and (c) initial custom-fit distal tibia fixation plate design, dimensions in mm.  
constraint (4) presents the interpolation of the density with minimum density to prevent singularities of the equilibrium problem. This design problem for user-defined design domain is formulated as a sizing problem with continuously modifying the stiffness matrix as a function of the material's density. This function becomes the design variable. Due to the material/no material situation, the artificial density intermediate values are penalized in analogous manner. This penalization factor is usually defined as the value of \(\geq\) 3 to obtain globally optimized designs [35]. SIMP method was considered to redesign the implant in Figure 1c, considering 25%, 50% and 75% volume reductions referred to as 'TO25-BP', 'T50-BP' and 'TO75-BP', respectively [37]. Topology optimization problem was executed in TOSCA module, Abaqus (Dassault Systemes, Waltham, MA, USA).
## 4 Applications of Graph Attention Networks
### Image Analysis  
Image analysis encompasses a range of techniques and methods employed to process an image by breaking it down into its fundamental constituents with the aim of extracting meaningful information. These methods can involve tasks such as shape recognition, edge detection, noise reduction, object quantification, and texture analysis or image quality evaluation. To this end, GATs have been put forward as a formidable tool for diverse forms of image processing. Specifically, GATs have been suggested for applications such as hyperspectral image classification [72; 114; 125; 169], image super-resolution [128; 170], image denoising [171], change detection in remote sensing images [172], image multi-label classification [19; 173], and text-image summarization [19].
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 2 Materials and Methods  
### Research Methodology  
This study collected monitoring data from an actual in situ excavation project and used the finite element software PLAXIS 3D to simulate each excavation stage. The on-site monitoring data were compared with the analysis results to validate the reasonableness of the selected soil parameters, material constitutive laws, and structural parameters. The case study was conducted in an area located at the Sanchong District, New Taipei City, Taiwan. Based on the in situ geological drilling data, relevant retaining wall support data, and empirical formulas for the soil elastic modulus, the study analyzed and compared on-site monitoring data with numerical simulation results. Using actual-case model parameters, the relative positions of a single neighboring building and the deep excavation site were established. The study examined the impact of various types of neighboring buildings and their relative positions on the retaining wall and ground settlement of the excavation site. A numerical analysis model was developed using the PLAXIS finite element method, incorporating soil constitutive laws. To validate the simulation, an actual deep excavation project was analyzed, comparing simulation results with monitoring data to confirm the accuracy of soil parameters and analysis steps. The study then examined the impact of adjacent buildings on diaphragm wall deformation and ground settlement, using the validated model to assess the effects of depth, distance, and load factors.
## 3 Remote Sensors at the SAS
### Geomagnetic sensor
One one hand, visibility problems appear when trying to reach geostationary satellites from polar latitudes, which can be overcome with a skywave link due to its higher angle of departure. On the other hand, in a not \(100\%\) reliable scenario, end-to-end reliability can be significantly increased by transmitting each frame repeatedly throughout the day, that is by using time diversity. In opposite to satellite, using time diversity in skywave does not represent an extra cost as no extra time-slots must be reserved. Moreover, in skywave the time diversity can be exploited at the physical layer by employing similar techniques to those considered in space time coding (STC). This results in larger performance improvement than if diversity is employed at the information bit level.
## 5 Conclusions  
In this paper, we study the resource allocation problem in a multi-user and multi-satellite scenario. Using an auction model, we describe the resource supply and demand relationship between users and satellites. In addition, in order to improve the service satisfaction of the overall satellites of the system, an online multi-round auction mechanism for service satisfaction maximization is proposed for the service matching relationship between satellites and users and to determine the final payment price. The OMARA method can ensure the delay tolerance of user tasks and allocate resources more rationally. Through experimental simulations, the results show that the method proposed in this paper can improve the service satisfaction of resource providers (satellites) in the overall system and increase the number of service users as much as possible.  
In future work, we should consider the cooperation between the satellites. The satellite should not be limited to providing service to users, they should assist the other satellites after the completion of the current service works from users to improve the overall service satisfaction of the satellite networks. The time sequence relationship between multiple satellites should also be included in the influencing factors of resource allocation problem.  
Conceptualization, W.X.; methodology, L.L. and T.L.; software, T.L.; formal analysis, W.X. and H.X.; data curation, T.L.; writing--original draft preparation, T.L.; writing--review and editing, W.X. and H.X.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Scientific and Technological Innovation Foundation of Foshan, USTB, No. BK22 BF002.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
# A Deep Learning Framework for Signal Detection and Modulation Classification
## 3 Deep Learning Framework for Signal Detection and Modulation Recognition
### SSD Networks for Signal Detection
\(N\) is the number of the feature default boxes that are matched to real target borders. Parameter \(\alpha\) is used to adjust the radio between \(L_{conf}\) and \(L_{loc}\), default \(\alpha=1\). \(L_{conf}\) is softmax loss function. \(L_{loc}\) is used to measure the performance of the boundary box prediction, and in our initial research, we use the typical \(smooth_{L1}\) function to calculate  
\[L_{conf}(x,c)=-\sum_{i\in Pos}^{N}x_{ij}^{p}\log\Big{(}\xi_{i}^{p}\Big{)}-\sum _{i\in Neg}\log\Big{(}\xi_{i}^{0}\Big{)}\xi_{i}^{p}=\frac{\exp\Big{(}\xi_{i}^{ p}\Big{)}}{\sum_{p}\xi_{i}^{p}} \tag{25}\]  
\[L_{loc}(x,l,g)=\sum_{i\in Pos}^{N}\sum_{m\in[cx,cy,y,h]}x_{ij}^{k}smooth_{L1} \Big{(}l_{i}^{m}-\delta_{j}^{m}\Big{)} \tag{26}\]  
where \(Pos\) and \(Neg\) represent all positive and negative borders, respectively. \(\xi_{i}^{p}\) represents the confidence degree for \(p\)th feature default matching \(i\)th target. \(l_{i}^{m}\) represents the prediction bias of the i-th feature default box. \((cx,cy)\) is the box center coordinates and \((w,h)\) is the box width and height. \(\delta_{j}^{m}\) represents the deviation between the real target border \(g_{j}^{m}\) and the default box \(d_{i}^{m}\). \(\delta_{j}^{m}\) is calculated as follow:\(\delta_{j}^{cx}=\Big{(}\delta_{j}^{cx}-d_{i}^{xx}\Big{)}/d_{i}^{w}\), \(\delta_{j}^{cy}=\Big{(}\delta_{j}^{cy}-d_{i}^{cy}\Big{)}/d_{i}^{h}\), \(\delta_{j}^{cy}=\log\Big{(}\delta_{j}^{w}/d_{i}^{w}\Big{)}\)\(\delta_{j}^{h}=\log\Big{(}\delta_{j}^{h}/d_{i}^{h}\Big{)}\).
# Response of Global Terrestrial Carbon Fluxes to Drought from 1981 to 2016
## 2 Materials and Methods
### Data Used to Drive the BEPS Model
#### 2.2.1 Time Series of LAI during 1981-2016  
LAI is an input to the BEPS model and critical for the simulation of carbon fluxes. The time series of LAI over the period from 1981 to 2016 was generated through fusion of LAI inverted from MODIS reflectance data and AVHRR GIMMS NDVI data [69]. LAI from 2000 to 2016 was first inverted from the MOD09A1 land surface reflectance and the associated illumination and view angles based on the GLOBCARBON LAI algorithm, which was developed using the four-scale geometric optical model. This algorithm explicitly considers the bidirectional reflectance distribution function (BRDF) effects on observed canopy reflectance. For the fusion of MODIS and AVHRR remote sensing data, the relationships between GIMMS NDVI and MODIS LAI were established pixel by pixel for their concurrent period (2000-2006) [69]. Then AVHRR LAI from 1981 to 2000 was generated using the same relationships. The spatial resolution of the LAI series is 0.072727\({}^{\circ}\)\(\times\) 0.072727\({}^{\circ}\) and temporal resolution varies from 16 days (1981 to 1999) to 8 days (2000 to 2016). In the simulation, these 16- and 8-days LAI values were interpolated to daily values. The BEPS model was driven using a climatological average LAI (1981-1989 data) for the period prior to satellite availability of LAI (1901 to 1980) and then the temporally variable LAI for the period from 1981 to 2016.
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 5 Proposed Work Overview
### Asset-Specific Bridge Selection (ABS)  
The asset-specific bridge selection (ABS) process is essential for determining the most suitable bridge or interoperability protocol for transferring specific assets between different blockchain networks within the DeFi framework. This selection is pivotal to ensure efficient, secure cross-chain transactions, minimizing risks and enhancing user experience. The most suitable bridge selection can enable quick and more effective transfer strategies, ensuring reliable and smooth asset transfers. This mechanism can minimize the attack surfaces by utilizing unique security measures tailored for particular risks. ABS considers a variety of factors including asset type, liquidity, security, and network compatibility to pinpoint the optimal bridge for each asset scenario. The purpose of using an asset-specific bridge is to optimize interoperability, enabling DeFi users to transfer specific assets securely and in a decentralized manner across diverse blockchain networks. This approach enhances the liquidity and accessibility of certain assets within the broader blockchain ecosystem.  
To illustrate the diversity and capabilities of available bridges, we have compiled a comparative analysis of different bridges currently utilized within DeFi ecosystems. Table 6 offers a detailed comparison based on several key characteristics, including asset support, integration with DeFi apps, speed and efficiency, network adaptability, availability of validators, support for bi-directional transfers, security level, and transaction fees. This analysis aids stakeholders in making informed decisions by highlighting the strengths and weaknesses of each bridge, allowing for the selection of the most suitable option based on specific needs and characteristics.
# Dynamic Analysis on the Parametric Resonance of the Tower-Multicable-Beam Coupled System
###### Abstract  
Considering the effect of the bridge deck's bending stiffness and the indirect effect of adjacent cables (CEB), this paper aims to propose a refined model to reliably analyze the complex internal resonance mechanism of the tower-multicable-beam coupled system (MCS) under nonlinear geometric conditions. To accurately analyze the dynamic behavior, the shear difference effect is applied to simulate the continuous rigidity of the single beam. The dynamic equations of the whole resonance system are derived based on the D'Alembert Principle and the Finite Difference Method, the Galerkin Method and verified by the case study. The results of the numerical simulation based on the Fourth Runge-Kutta Method show that the dynamic parameter of each component is closely related to the coupled resonance of the system. The dynamic behavior under two conditions, tower-cele 1:1 resonance (TCR) or cable-beam 1:2 resonance (CBR), is deeply analyzed. Additionally, the excitation effect of the maximum amplitude by two excitation approaches, the initial displacement or initial velocity, both show a linear increase. The mutual transmission process of vibration excitation on the cable through the bridge beam or the tower as the medium is also further discussed.  
c pacs: acble-stayed bridges; multicable system; numerical analysis; parametric resonance; coupled resonance +
Footnote †: journal: Applied Physics Letters
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
###### Abstract  
Network slicing is a powerful paradigm for network operators to support use cases with widely diverse requirements atop a common infrastructure. As 5G standards are completed, and commercial solutions mature, operators need to start thinking about how to integrate network slicing capabilities in their assets, so that customer-facing solutions can be made available in their portfolio. This integration is, however, not an easy task, due to the heterogeneity of assets that typically exist in carrier networks. In this regard, 5G commercial networks may consist of a number of domains, each with a different technological pace, and built out of products from multiple vendors, including legacy network devices and functions. These multi-technology, multi-vendor and brownfield features constitute a challenge for the operator, which is required to deploy and operate slices across all these domains in order to satisfy the end-to-end nature of the services hosted by these slices. In this context, the only realistic option for operators is to introduce slicing capabilities progressively, following a phased approach in their roll-out. The purpose of this paper is to precisely help designing this kind of plan, by means of a technology radar. The radar identifies a set of solutions enabling network slicing on the individual domains, and classifies these solutions into four rings, each corresponding to a different timeline: (i) as-is ring, covering today's slicing solutions; (ii) deploy ring, corresponding to solutions available in the short term; (iii) test ring, considering medium-term solutions; and (iv) explore ring, with solutions expected in the long run. This classification is done based on the technical availability of the solutions, together with the foreseen market demands. The value of this radar lies in its ability to provide a complete view of the slicing landscape with one single snapshot, by linking solutions to information that operators may use for decision making in their individual go-to-market strategies.  
end-to-end network slicing; 5G; smart networks and services; multi-domain orchestration; technology radar; roll-out plan +
Footnote †: journal: Sensors
# Research on the Efficient Space Debris Observation Method Based on Optical Satellite Constellations
## 1 Introduction
This is a very complex combinatorial optimization problem that exhibits multi-time window constraints, multi-resource constraints, and high conflict, and it has become one of the leading issues in the field of space monitoring [10; 11; 12]. The current observation scheduling algorithm is not suitable for rapidly moving observation equipment and observation targets, and there is little research on the observation task scheduling of space-based satellite constellations. The research on the observation task scheduling algorithm of space-based optical satellite detecting for space objects is the key for improving the efficiency of space object detection, and it is also one of the important problems that needs to be solved in the field of space object observation [13; 14; 15]. We designed satellite constellations with different configurations and created a constellation observation task scheduling algorithm. The main purpose was to track and observe LEO space objects, obtain a large amount of observation data, and update the object orbit information in real time. The goal was to catalog a large number of LEO space objects using a constellation composed of a small number of satellites. The space objects orbit data were used for the simulation and verification, and good results were obtained. With the observation of satellite constellation, we can realize space situation awareness, provide emergency response to space emergencies, reduce collision risks, and maintain the safety of space assets.
## 3 Data and Methodology
### Methodology
#### 3.2.3 Base Classifiers: SVM, DT, and LR  
**Support Vector Machine (SVM)**  
An SVM [67; 68] is a discriminative classifier, typically explained as a separating hyperplane. Put another way, this algorithm generates an optimal hyperplane that classifies new instances from labelled training data (supervised learning). The data points or vectors nearest to the hyperplane, which influence the direction of the hyperplane, are referred to as the Support Vector, as these vectors support the hyperplane. SVM has high predictive accuracy and generalization capabilities, particularly for small, nonlinear, and high-dimensional samples [45]. Here, we used linear SVM.  
**Decision Tree (DT)**  
DTs are among the most successful ML algorithms thanks to their intelligibility and clarity [69]. DT approaches are used for estimation, clustering, and classification tasks [70]. The best attribute is placed at the root node. This attribute is selected based on a measure of information gain that is subsequently used at each stage of tree building. The training dataset is then split into two or more subsets based on the values of the chosen attribute. This process is repeated for each of the subsets, selecting the best attribute for each and creating child nodes. The process continues until a stopping criterion is met, such as reaching a maximum depth or all instances belonging to the same class. The leaf nodes in the decision tree reflect the class, while the decision nodes determine the rules. The test data class is predicted by the decision rules. Among the key benefits of Decision Trees are that they offer a model that meaningfully describes the acquired knowledge and enables the extraction of if-then classification rules [36].  
**Logistic Regression (LR)**  
LR as a general statistical model was originally developed and popularized primarily by [PERSON] [71]. LR is conducted when the dependent variable is binary. It is a discriminative classifier that is linear in its parameters, and is used to describe the relationship between a single dependent binary variable and one or more independent variables. LR can handle both nominal and numerical data. It predicts the likelihood of a binary response based on one or more predictor attributes [72].
## 5 Specific Construction of CLR-SS-AIBBE
It randomly chooses \(s\in Z_{N}\) and \(Z,Z^{\prime}\in G_{p_{2}}\) and computes the ciphertext:  
\[CT=(C,Hdr)=(C,C_{1},C_{2})=(Me(g_{1},g_{1})^{as},(h_{1}\prod\limits_{j=1}^{d }u_{j}^{ID_{j}})^{s}Z,g_{1}^{s}Z^{\prime})\]  
The encapsulated key is \(e(g_{1},g_{1})^{as}\). The data owner transmits \((CT,S)\) to the receiver. **Decryption algorithm 1**. For one user \(ID_{i}\), if \(ID_{i}\in S\), it can decrypt the received ciphertext. It divides \(CT=(C,Hdr)\). They run the decryption algorithm  
\(\textbf{Decrypt1}(MP,SK_{ID,k,1},S,CT)\to CT^{\prime}\). If \(ID_{i}\in S\), the algorithm uses \(Hdr\) to calculate part of the plaintext, \(CT^{\prime}\). First, it uses \(SK_{ID_{i},k,1}\) to calculate \(CT^{\prime}=(C,C_{1},C_{2},C_{1}^{\prime},C_{2}^{\prime})\):  
\[C_{1}^{\prime}=e(SK^{1}_{ID_{i},k,1},C_{1})=e(g_{1}^{r_{1}}R_{1}s_{1}^{\beta_{ i,1}+\ldots+\beta_{i,k}},(h_{1}\prod\limits_{j=1}^{d}u_{j}^{ID_{j}})^{s}Z),\]  
\[C_{2}^{\prime}=e(SK^{2}_{ID_{i},k,1},C_{2})=e(g_{1}^{a}(h_{1}\prod\limits_{j=1 }^{d}u_{j}^{ID_{j}})^{r_{i}}Q_{1}g_{1}^{\gamma_{i,1}+\ldots+\gamma_{i,k}},g_{1 }^{a}Z^{\prime}).\]  
**Decryption algorithm 2**. The algorithm inputs \(MP\), \(SK_{ID,k,2}\), the user's identity set \(S\) and the part plaintext \(CT^{\prime}\). Supposing \(ID_{i}\in S\), it first calculates the encapsulated key \(CK\). Next, the plaintext message \(M\) is recovered by \(CK\).
# Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique
###### Abstract  
External corrosion poses a significant threat to the integrity and lifespan of buried pipelines. Accurate prediction of corrosion rates is important for the safe and efficient transportation of oil and natural gas. However, limited data availability often impacts the performance of conventional predictive models. This study proposes a novel composite modeling approach integrating kernel principal component analysis (KPCA), particle swarm optimization (PSO), and extreme learning machine (ELM). The key innovation lies in using KPCA for reducing the dimensionality of complex input data combined with PSO for optimizing the parameters of the ELM network. The model was rigorously trained on 12 different datasets and comprehensively evaluated using metrics such as the coefficient of determination (R\({}^{2}\)), standard deviation (SD), mean relative error (MRE), and root mean square error (RMSE). The results show that KPCA effectively extracted four primary components, accounting for 91.33% of the data variability. The KPCA-PSO-ELM composite model outperformed independent models with a higher accuracy, achieving an R\({}^{2}\) of 99.59% and an RMSE of only 0.0029%. The model comprehensively considered various indicators under the conditions of limited data. The model significantly improved the prediction accuracy and provides a guarantee for the safety of oil and gas transport.  
buried pipelines; kernel principal component analysis; extreme learning machines; particle swarm optimization; corrosion rate prediction 2024 14 5120. [[https://doi.org/10.3390/app1425120](https://doi.org/10.3390/app1425120)]([https://doi.org/10.3390/app1425120](https://doi.org/10.3390/app1425120))