esa-sceva/satcom-chunk-collection huggingface dataset:
# A Global Continuous Multi-Scale Terrain Contour Simplification Method Based on the Level Set
## 2 Methodology
### Extracting Characteristics  
Linear features are considered basic elements in maps and suitable features that need to be retained during multi-scale transformation [23]. Therefore, when constructing the CCS, the optimum extraction of morphology characteristics and the constraint of characteristics are critical to obtain the final global continuous multi-scale simplified results. The characteristics of CCS include characteristic points, characteristic sub-polylines, and characteristic links.  
(1) Characteristic points. The boundary of the CCS needs to be constructed with the initial contour \(L_{0}\) and the target contour \(L_{n}\). \(L_{0}\) represents the initial contour with a large scale, whereas \(L_{n}\) represents the target contour with a small scale. These two contours served as boundaries for building the CCS.  
(2) Characteristic sub-polylines. \(L_{0}\) and \(L_{n}\) were resampled via equidistant sampling to ensure that they have the same number of points. To maintain the morphology shape of the contour line, the number of resampling points should exceed the number of characteristic points extracted from the initial contour. All resampling points were considered characteristic points. Then, two NURBS curves (\(L^{\prime}{}_{0}\) and \(L^{\prime}{}_{n}\)) were defined using the characteristic points from \(L_{0}\) and \(L_{n}\), respectively. It is worth noting that \(L_{0}\) and \(L^{\prime}{}_{0}\) are highly similar in shape and size, as are \(L_{n}\) and \(L^{\prime}{}_{n}\). Meanwhile, the B-spline curve was built based on the control points \(P_{i}(i=0,1,\ldots,m)\), which are the characteristic points of \(L_{0}\) or \(L_{n}\), where  
Figure 1: CCSM methodological framework.
# Revising the Observation Satellite Scheduling Problem Based on Deep Reinforcement Learning
## 3 Solving Method
### Task Preprocess: Graph Clustering
#### 3.1.2 Clique Partition Algorithm  
Based on the graph model, each independent cluster represents a clustering task. The purpose of the clique partition algorithm is to minimize the number of clustered tasks and ensuring more original tasks are contained in each divided clique. The algorithm is described as follows:  
Firstly, the edge \(e_{uv}\) with the largest number of common neighbors on the edge set \(E(G_{i})\) in the graph is selected. Secondly, the edge which needs to delete the least number of edges is screened out after merging. Thirdly, the edge which has a larger evaluation parameter \(p\) of the corresponding vertices is selected. Finally, the two vertices are merged into a new virtual vertex, and the edge associated with the merged vertex is deleted. Repeatedly applying the procedure to the updated edge set, the process is stopped when the original \(E(G_{i})\) becomes empty.  
In the algorithm, evaluation parameter \(p\) can be calculated as follows:  
\[prio^{c}=\frac{1}{m}(prio_{1}+prio_{2}+\cdots+prio_{m}) \tag{14}\]  
\[\theta^{c}=\frac{1}{2}\left(\inf\left(\bigcap\limits_{j=1}^{m}\theta_{j} \right)+\sup\left(\bigcap\limits_{j=1}^{m}\theta_{j}\right)\right) \tag{15}\]  
\[p=\frac{prio^{c}}{\max(prio)}+\frac{\max\theta}{\theta^{c}} \tag{16}\]  
where \(prio^{c}\) and \(\theta^{c}\) are the priority and the minimum slewing angle of the generated clustering task, respectively. The pseudocode of the improved minimum clique partition algorithm process is shown in Algorithm 1:  
```
Input: Original observation tasks Output: Clustered observation tasks
1 Initialize \(A_{n\times n}=zeros(n)\)
2 while\(v_{u},v_{v}\) in \(V(G_{i})\)do
3 if constraint conditions are satisfiedthen
4\(A_{uv}=1\)
5 else
6\(A_{uv}=0\)
7 Update \(A_{n\times n}\) and \(G=\langle V,E\rangle\)
8 end if
9
10 end while
11 while\(E(G_{i})\
eq\varnothing\)do
12 Select the edge \(e_{uv}\) with the largest number of common neighbors in the edge set of the graph
13 if\(e_{uv}\) is not uniquethen
14 Select the edge that needs to delete the least number of edges after merging
15 if\(e_{uv}\) is not uniquethen
16 Select two vertices with larger \(p\)
17 end if
18
19 end while
20 Combine the two vertices into \(v_{uv}\)
21 Delete the edges associated with the merged vertex to create a new edge
22 end while
```  
**Algorithm 1**Improved minimum clique partition algorithm In this paper, an improved clique partition algorithm is adopted by taking the priority and the minimum slewing angle of clustering tasks into consideration. The generated clustering tasks are used as the input of the following DRL algorithm to calculate the scheduling result.
###### Abstract  
In addition to the conventional laboratory tests of input materials and construction mixtures, the technically correct design of pavement structure involves more frequent functional tests of the input materials and mixtures. Our study summarizes findings from a long-term monitoring of pavement on selected road sections with lower traffic intensity. The research involved the comparison of resilient moduli as a design parameter of unbound pavement base layers' unbound granular materials (UGMs), determined in a laboratory by a test device for a cyclical triaxial test and in situ by impact load test using a falling weight deflectometer (FWD). In particular, the resilient moduli determined in the laboratory by a cyclic triaxial test (M\({}_{\text{\tiny{c}FT}}\)) were compared with the real values of resilient moduli measured by FWD (M\({}_{\text{\tiny{c}FWD}}\)). A new, unique, and innovative FWD device was used to determine the M\({}_{\text{\tiny{c}FWD}}\) of the UGMs. The innovation lay in the dual-axis deflection measurement--measurement performed not just in the axis parallel to the road, but also in the axis in the transversal direction. It was found that, especially on roads with lower traffic load, such dual-axis (spatial) determination of M\({}_{\text{\tiny{c}FWD}}\) moduli was of great importance because it often allowed the identification of local pavement sections with low bearing capacities, especially at the edges, which have a significantly lower value of M\({}_{\text{\tiny{c}FWD}}\).  
resilient modulus; pavement design; unbound granular material (UGM); cyclic triaxial test (CTT); non-destructive testing; bearing capacity; falling weight deflectometer (FWD); base layers; subbase layers +
Footnote †: journal: Applied Sciences
# Properties of the Quadratic Transformation of Dual Variables
## 2 The Problem Formulation  
Consider the convex programming problem:  
\[\underset{x\in Q}{\text{min}}f(x),\ Q\subset R^{n}, \tag{1}\]  
\[g_{i}(x)\leq 0,\ i=1,2,\ldots,m. \tag{2}\]  
In contrast to [15], where linear equality constraints are used, constraints (2) have the form of inequalities, which imposes restrictions on the positivity of dual variables.  
**Assumption 1**.: _In problem (1)-(2), assume that \(f\) is a strongly convex function, \(g_{i}\) are convex functions, set \(Q\subset R^{n}\) is a convex closed bounded set, and the Slater condition is satisfied, i.e., there is an interior point \(x^{0}\) of the set \(Q\), such that the following conditions are satisfied:_  
\[g_{j}(x^{0})<0,\ j=1,2,\ldots,m. \tag{3}\]  
We form the Lagrangian function  
\[L(x,y)=f(x)+\langle y,g(x)\rangle,\ y\in R^{m},\ y\geq 0,\ x\in Q, \tag{4}\]  
where \(y^{\text{T}}=(y_{1}\),\(y_{2}\),...,\(y_{m})\) are the Lagrange multipliers and \(g^{\text{T}}(x)=(g_{1}(x),g_{2}(x),\ldots,g_{m}(x))\). Here and in the following, we denote by \(\langle y,g\rangle\) the scalar product of vectors \(y\),\(g\).  
According to [34], we introduce the dual function  
\[\psi(y)=\underset{x\in Q}{\text{inf}}L(x,y)=\underset{x\in Q}{\text{min}}[f(x) +\langle y,g(x)\rangle],\ y\geq 0 \tag{5}\]  
and formulate the dual problem  
\[\text{max}\psi(y),\ y\geq 0. \tag{6}\]Let us introduce the function \(\phi(y)=-\psi(y)\), \(y\geq 0\). Instead of (6), we get the problem of minimizing a convex function  
\[\min\,\phi(y),\ y\geq 0. \tag{7}\]  
If Assumption 1 holds, then the function \(\psi(y)\) is differentiable [34]. The gradients of the functions \(\psi(y)\) and \(\phi(y)\) are [19]:  
\[\
abla\psi(y)=g(\ x(y)\ ),\ \
abla\phi(y)=-g(\ x(y)\ ),\ y\geq 0, \tag{8}\]  
where \(x(y)=\underset{x\in Q}{\mathrm{argmin}}L(x,y)\). The gradient \(\
abla\phi(y)\) in the area \(y\geq 0\) satisfies the Lipschitz condition [34; 54]  
\[||\
abla\phi(y_{1})-\
abla\phi(y_{2})||\leq L_{\phi}||y_{1}-y_{2}||. \tag{9}\]  
Here and below, \(||a||=(a,a)^{0.5}\) is the norm of the vector \(a\).  
For \(m<<n\) and given a simple method for finding the minimum point of the Lagrangian with respect to \(x\), the transition to the dual problem may simplify the solution. The complexity of solving the problem (7) lies in the presence of the constraint \(y\geq 0\), which prevents the use of efficient numerical methods of unconstrained optimization. In this paper, we consider two ways to reduce the constrained optimization problem (7) to the unconstrained minimization problem and study their properties theoretically and experimentally.
# Adaptive, Observer-Based Synchronization of Different Chaotic Systems
## 1 Introduction
On the other hand, the system must be transformed into a special canonical form depicted in Figure 1.  
Finally, the problem of chaotic systems synchronization is solved under the following assumptions:  
* Master and slave systems may be different;
* Master and slave systems contain (different) unknown, constant parameters;
* Only the single output of the master system is available;
* The slave system is controlled by a single input located in the last state equation. According to our knowledge, the problem of chaos synchronization under such a set of assumptions has never been investigated previously. Additionally, the design methodology applied here is a new approach. First, we construct an adaptive observer based on a nonlinear state transformation and K filters. In contrast to the original observer shown in [33], we introduce a component related to the control algorithm into the observer's equations. This new approach enables observer-controller cooperation, improving synchronization performance as the final effect. The tuning function technique [37] is smartly included in the observer-controller design to avoid overparameterization. The paper is organized as follows: Output nonlinear parametric (ONP) systems are defined in Section 2, and the observer for such systems is described and modified to enable cooperation with the tracking controller. Next, we discuss chaotic systems transformable into the ONP form by a linear transformation. Several popular systems such as Arneodo [11], Arneodo-Coullet [38], Genesio-Tesi [39], and Lur'e [35] belong to this class. The general form of transformation is provided in the Appendix A. In Section 4, we define a slave system and the synchronization problem. We concentrate on third-order systems, although generalization of the proposed design technique is possible. Section 5 is devoted to the closed-loop adaptive controller design. The stability of the whole system is discussed in Section 6. Finally, examples, discussion, and conclusions are presented.
# Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost
## 2 Method  
### XGBoost (Extreme Gradient Boosting)  
Integrated learning methods are used to obtain better predictions by fusing multiple base learners and combining them [22]. Boosting is the most dominant class of integrated learning methods. Boosting first uses the dataset for training to obtain a base learner. It then observes the results of this base learner training and assigns more attention to the training samples that do wrong in it. This adjusted training set is then used to train the next base learner, and so on until the set number of base learners is reached. Finally, these base learners are weighted together [23]. The XGBoost algorithm is one of the best-performing algorithms in the Boosting family. The loss function of the XGBoost algorithm uses a second-order Taylor expansion to improve the accuracy [24]. For each parameter (learning_rate), its value range is generally from 0 to 1. For parameter (n_estimators), its value range needs to be set appropriately based on sample size and feature count. For parameter (max_depth), it can be unrestricted when the sample size and feature count are small, but should be set within reasonable bounds when they are large. For parameter (min_child_weight), its value cannot be too large or too small; too small will result in overfitting, while too large will result in underfitting.
## 3 Results and Discussion
### Daily, Monthly and Yearly Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Months**} & \multirow{2}{*}{**\(\mathbf{\eta_{sys}}\)**} & \multirow{2}{*}{**Sunny Days**} & \multicolumn{2}{c}{**E\({}_{\text{el}}\) (kWh)**} & \multicolumn{2}{c}{**E\({}_{\text{sol}}\) (kWh)**} \\ \cline{5-8}  & & & **Day** & **Month** & **Day** & **Month** \\ \hline January & 12.90\% & 13 & 192 & 2491 & 1485 & 19,305 \\ February & 15.96\% & 10 & 263 & 2630 & 1648 & 16,485 \\ March & 19.17\% & 15 & 345 & 5177 & 1800 & 27,003 \\ April & 21.65\% & 18 & 418 & 7516 & 1929 & 34,723 \\ May & 22.63\% & 20 & 455 & 9108 & 2012 & 40,246 \\ June & 22.75\% & 22 & 464 & 10,202 & 2038 & 44,844 \\ July & 22.69\% & 29 & 456 & 13,224 & 2010 & 58,283 \\ August & 22.11\% & 29 & 427 & 12,373 & 1930 & 55,971 \\ September & 20.19\% & 20 & 366 & 7324 & 1813 & 36,268 \\ October & 16.96\% & 18 & 282 & 5070 & 1660 & 29,888 \\ November & 13.65\% & 17 & 206 & 3499 & 1508 & 25,643 \\ December & 11.95\% & 15 & 170 & 2549 & 1422 & 21,332 \\
**Year** & **19.80\%** & **226** & - & **81,164** & - & **409,991** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Monthly and yearly performance of the system at the default design scenario. Figure 18: Monthly electricity production and monthly system efficiency for the default design scenario.
## 4 Numerical Examples
### Example 2  
The second example is a numerical test for the modified Fornberg-Whitham equation [3; 5; 6; 7; 9]. The exact solution is given as follows:  
\[u^{ext}(x,t)=\frac{3}{4}\Big{(}\sqrt{15}-5\Big{)}\text{sech}^{2}\Big{(}c\Big{(} x-\Big{(}5-\sqrt{15}\Big{)}t\Big{)}\Big{)}, \tag{25}\]  
Figure 5: The comparisons of the FDM-RK4 and proposed ST-GFDM. (**a**) The numerical solution at \(t\) = 5 and (**b**) the MAEs curves in the entire simulation of Example 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(x\) & \(u^{ext}\) & **HAM [10]** & **ADM [7]** & **RKHSM [7]** & **MVIA-I [5]** & **FDM-RK4** & **ST-GFDM** & **MAEs** \\ \hline \(-4\) & 0.00482795 & 0.00487526 & 0.00317192 & 0.00482795 & 0.00482795 & 0.0048810 & 0.00482958 & 1.626 \(\times\)\(10^{-6}\) \\ \(-2\) & 0.01312373 & 0.01325233 & 0.00862217 & 0.01312373 & 0.01312373 & 0.0127930 & 0.01312294 & 7.893 \(\times\)\(10^{-7}\) \\ \(0\) & 0.03567399 & 0.03602358 & 0.02343750 & 0.03567400 & 0.03567399 & 0.0349080 & 0.03566866 & 5.337 \(\times\)\(10^{-6}\) \\ \(2\) & 0.09697197 & 0.09792224 & 0.06370972 & 0.09697195 & 0.09697197 & 0.1008894 & 0.09696298 & 8.992 \(\times\)\(10^{-6}\) \\ \(4\) & 0.26359714 & 0.26618027 & 0.17318095 & 0.26359729 & 0.26359714 & 0.3172236 & 0.26359021 & 6.925 \(\times\)\(10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons of the numerical results of Example 1 with the exact solution and the other approximation methods at various locations at \(t\) = 5 (n = 10, \(N_{\text{T}}\) = 8421, and \(n_{s}\) = 23).  
where \(c=\frac{1}{20}\sqrt{10\Big{(}5-\sqrt{15}\Big{)}}\). The initial condition and boundary conditions are denoted as follows:  
\[\left\{\begin{array}{l}\mathcal{U}_{0}(x)=\frac{3}{4}\Big{(}\sqrt{15}-5\Big{)} \text{sech}^{2}(cx),\\ f_{1}(t)=\frac{3}{4}\Big{(}\sqrt{15}-5\Big{)}\text{sech}^{2}\Big{(}c\Big{(}a- \Big{(}5-\sqrt{15}\Big{)}t\Big{)}\Big{)},\\ f_{2}(t)=\frac{3}{4}\Big{(}\sqrt{15}-5\Big{)}\text{sech}^{2}\Big{(}c\Big{(}b- \Big{(}5-\sqrt{15}\Big{)}t\Big{)}\Big{)}.\end{array}\right. \tag{26}\]  
The following parameters are used in this numerical example: \(a=-10\), \(b=10\), dt = 0.2, \(T=1\), \(N_{D}=10\), \(N_{T}=40\),421, and \(n_{s}=20\). The numerical solutions are displayed in Figure 6. In Figure 6a, the comparison of the exact solutions and the numerical solutions at specific time levels (\(t=0.2\), 0.4, 0.8, and 1) is shown. These numerical results are in good agreement with the exact solutions. In addition, the MAEs at specific points (\(x\),\(t\)) are listed in Table 6 with the errors of the MVIA [5] and VIM [8]. Obviously, the error performance of the proposed numerical scheme is better than that of the other two approximation algorithms. Also, it verifies that the proposed ST-GFDM is fully capable of handling such complex PDEs.
# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 3 Construction of Domain-Specific Lexicon for Maritime Judgment Documents Via Feature Analysis  
### Structural and Content Characteristics of Maritime Judgment Documents  
Taking a first-instance judgment document for a dispute over contract of freight forwarding by sea as an example, we categorize the discourse structure of maritime judgments into six core segments through systematic analysis: cause of action, basic information, litigation proceedings, judicial decision, judicial personnel, and appendices.  
Cause of action defines the legal nature and dispute type of the case. Based on 2023-2024 data from the China Maritime Trial Network ([[https://cmt.court.gov.cn/](https://cmt.court.gov.cn/)]([https://cmt.court.gov.cn/](https://cmt.court.gov.cn/)), accessed on 12 March 2025), Table 1 lists the top nine causes of action in maritime cases.  
Basic information includes court details (court name, document type, case number), litigation parties (names/types/quantities of plaintiffs and defendants), and representation (law firms and attorneys involved).  
Litigation proceedings records adversarial claims and defenses, including plaintiff's claims and evidentiary materials, defendant's rebuttals and count-evidence, and key disputed issues.  
Judicial decision comprises three components. Fact-finding reconstructs factual chains through evidentiary examination, including timelines, contractual obligations, and breach behaviors. Legal application cites applicable maritime laws, international conventions, and judicial interpretations to establish legal ground. The operative part of judgment specifies liability allocation, compensation calculations, and enforcement requirements.  
Judicial personnel lists presiding judges, collegiate panel members, court clerks, and judgment date.  
Appendices attach full texts of invoked laws and regulations, and evidentiary exhibits.  
It is found that the litigation proceedings and judicial decision, as the core content of judgment documents, serve as the primary sources for summary generation; meanwhile, the legal application in the judicial decision and the legal provisions in the appendices provide a foundational corpus for extracting and incorporating legal terminology related to statutory clauses into the maritime judgment lexicon.
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 3 Interleaver
### Proposed Interleaver
#### 3.7.2 Criteria for the Chaotic Interleaver  
The interleaver is an important element in defining the minimum distance and the performance of the turbo code [20; 37].  
Two main properties that characterize any interleaver are dispersion (randomness), which is the length of the set of all possible spreading factors for that interleaver, and spreading, which is the distance between indexes that were close before being permuted by the interleaver. Good spreading improves the BER performance of the system [38]. However, if some criteria are incorporated, it is possible to ensure the characteristics of the interleaver.
# NOMA-Based VLC Systems: A Comprehensive Review
## 4 Potential Challenges and Open Research Issues
### Signaling and Processing Overhead  
When compared to its orthogonal variants, NOMA incurs a higher cost from a variety of signaling and processing sources. Specific time intervals, for instance, must pass in order to gather the CSI from various receivers and update the receivers of the SIC order. Because of this, NOMA's data transfer rates degrade. NOMA signal processing also has a higher energy need because of dynamic power allocation and SC/SIC encoding/decoding.
# High-Speed Photodetectors for Microwave Photonics
## 3 Integrated Photodiode-Antenna Emitters  
Recently, significant research has been devoted to the integration of photodiodes with antennas, or microwave photonic wireless transmitters [15; 16]. Due to the everlasting demand for high data rate wireless transmission, the low frequency bands are already too crowded to cope with the requirements. Higher frequency bands well into the range of millimeter-waves are needed to be deployed for next generation high data rate wireless networks. However, the hardware for the wireless data transmission at millimeter-wave frequency is far from being mature. The traditional way of implementing the hardware using electronic systems has its drawbacks, such as the high signal propagation loss inside coaxial cables, the limited bandwidth of electronics and the susceptibility to electromagnetic interference, which are incompatible with system operation at millimeter-wave frequencies. The realization of these systems using photonic techniques is advantageous, mainly due to the low-loss signal propagation in optical fibers, broad bandwidth of photonic components, and immunity to electromagnetic interference.  
For a photonic wireless transmitter, the data is imposed on an optical carrier and transmitted to the antenna site through optical fiber. Then, the optical signals are converted to the electrical domain by a PD. Finally, the signals are broadcast to the users wirelessly by the antenna. The PD should maintain high speed at high optical input power in order to achieve sufficient radiated power, which is why the MUTC PD is well suited for this application.  
Besides the PD performance, system integration is also an important factor. For reliable operation, the system needs to have high mechanical strength, good thermal dissipation, and carefully designed RF characteristics for optimal coupling between PD and antenna. Moreover, compact planar structures are preferred owing to their small footprint and ease of large system integration such as in phased arrays.  
In the following sections, we describe integrated photodiode-antenna emitters for applications in the V- and W-bands. The effective isotropic radiated power (EIRP) is among the highest in the literature due to the high power-handling capability of the MUTC PD, and its careful integration with the antenna. V-band frequencies are of particular interest since the Federal Communications Commission allocated a 7 GHz unlicensed spectrum (57-64 GHz) for 60 GHz band communication [17]. At 100 GHz and above, a growing number of applications including high-capacity wireless communication, non-destructive sensing/imaging, and radio astronomy continue to motivate the development of photonic components [18].  
Figure 2: (**a**) Layer stack of waveguide MUTC PD; doping concentrations are given in cm\({}^{-3}\) (**b**) Frequency responses of PDs with different active areas; inset: schematic layout.