esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap
## 3 Results
### Test Case C--Arm Skin Replica  
The stitched topographic maps of test case C have been gathered in Figure 12, with inversed \(z\)-axis as a replica being measured. Once again, the CC algorithm has not succeeded in stitching the arm skin replica, a stitching chain reaction failure. Other algorithms provide a satisfactory visual aspect. Height amplitude is similar for all successfully stitched topographies.  
Figure 13 shows the quantitative criteria of test case C. First, run time is lower for CC, despite a stitching chain reaction failure, followed in order by the GEO, GOD, NR, TSR and EER algorithms. Note that the latter algorithm is much slower than others. Memory usage is similar for all tested algorithms, although slightly higher for CC and NR. Regarding the quality of the stitching, the reflectance-based stitching algorithms (NR, EER and TSR) are better, with lower mean repositioning error and higher stitching error estimator, followed by the GOD and CC algorithms.  
Figure 11: Bar charts of the quantitative criteria for test case B: run time (**a**), memory usage (**b**), mean repositioning error (**c**) and stitching error estimator (**d**). NA = Not applicable.  
Figure 12: Three-dimensional view of the stitched topography in test case C. Stitching size: \(5\times 5\).
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 3 Materials and Methods
### UAV Orbit  
In this study, a circular orbit for the UAV is assumed. As demonstrated by [8], flying the UAV along a circular trajectory defined by a specified radius and candidate points improves estimation accuracy compared to random placement, where the UAV moves to randomly assigned coordinates. Although more advanced trajectory models exist, such as the traveling salesman problem (TSP) optimization model, this study adopts a circular orbit. It is well known that in TSP optimization problems, computational costs increase exponentially as the number of candidate points increases [9]. In practical estimation system operations, illegal radio stations may move during the simulation time, making it preferable to reduce computational time for analysis. Therefore, a circular trajectory was selected. The configured trajectory is illustrated in Figure 6. The number of sampling points on the trajectory is set to eight to mitigate accuracy degradation by averaging values even if one of the points is in an NLoS condition.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Initial position \(\mathbf{u}_{i}(0)\), Initial velocity \(\mathbf{v}_{i}(0)\) & \(U(0,1)\) \\ \hline \(w\) & \(0.5\) \\ \hline \(c_{1},c_{2}\) & \(U(0,0.14)\) \\ \hline Number of particles & \(100\) \\ \hline \(t_{\text{MAX}}\) & \(10\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: PSO Parameters used in this study.
## 3 System Model
### Acquisition of Belief Function  
#### 4.2.1.  
Sine Mapping Based Membership Function  
The acquisition of belief function is based on membership function, so this section first introduces the establishment process of the membership function. For each node \(i\), the three attribute indexes of its forward neighbor node \(j\) are presented as \(EB(j),RC(j),BI(j),j\in FN(i)\), which correspond to the index \(a=1,a=2,a=3\). All three are benefit indexes, that is, the larger the index value, the more likely it is to be selected as the next hop. The three indexes value of each forward neighbor node can be calculated, and the maximum value of each index is the optimal solution under this index. That is, \(\sigma(j)\gets EB(j),\sigma_{\max}\leftarrow\max\{EB(\alpha)\big{|} \alpha\in FN(i)\}\), \(\sigma(j)\gets RC(j),\sigma_{\max}\leftarrow\max\{RC(\beta)\big{|}\beta \in FN(i)\}\) and \(\sigma(j)\gets BI(j),\sigma_{\max}\leftarrow\max\{BI(\chi)\big{|}\chi\in FN (i)\}\).  
In order to avoid missing the node with the best comprehensive performance due to excessive pursuit of ideal solution, we establish the membership function based on sine function, its expression is as follows:  
\[u_{aj}=\sin(\frac{\pi}{2}\times\frac{\sigma(j)}{\sigma_{\max}}),j\in FN(i) \tag{8}\]  
where \(a=1,2,3,u_{aj}\in[0,1]\).  
The mapping relationship is shown in Figure 3.
## 3 Material and Method
### Experimental Design
#### 3.2.3 Real-Time Data Acquisition Process  
This section provides a comprehensive overview of the real-time data acquisition process, as shown in Figure 6. It outlines the key components involved, including the Arduino microcontroller, sensor, and internet communication module, which, in this instance, is an Ethernet Shield. The Arduino program retrieves data from the sensor and transmits them via HTTP requests by utilizing both POST and GET methods. Subsequently, the Arduino interfaces with a Web API possess an appropriate endpoint for receiving these requests. On the Web API side, the received data are stored securely in a database.  
Figure 4: Proposed monitoring system sketch.  
Figure 5: Voltage and degradation value visualization over time.  
Figure 6: Arduino HTTP Request.
[52] introduced a deep RL layered framework in which planning at the higher level is performed through \(A^{\star}\); then, in proximity of the target, the lower-level policy builds a reachability graph through previously learned policies. In [53] RL was used for long-term planning; ref. [54] shifted the RL paradigm by considering the rollout of the model as a decision-making problem through considering the model itself as a decision maker and the current policy as the dynamics, showing good adaptability while tackling uncertainty. These approaches, however, do not focus on kinodynamic feasibility. Furthermore, existing approaches are often tightly coupled with the specific control technique used in trajectory planning. tracking, while the proposed approach, instead, only requires a few hypotheses about the closed loop model of the system. This work stems out of a series of previous works by the authors, which were focused on kinodynamically feasible trajectory planning for skid-steered tracked mobile robots that had been experimentally validated in [1] and later applied to heterogeneous vehicles, such as cars (see [55]). While the previous works were focused on finding and proving the feasibility of trajectories, this work, instead, assumes that such a procedure exists and focuses on improving planning. Without loss of generality, this work uses the authors' results in trajectory planning for the training of a classifier that can speed up planning by giving precedence to the exploration of the most likely feasible candidate trajectories. The main novelties and enhancements to the existing literature are the following:  
* It embeds provable kinodynamic feasibility in artificial intelligence-planned trajectories;
* It offers a sensitivity analysis of the most important parameters of the AI training with respect to the planning;
* It offers an analysis of some of AI feature effects upon planning. This work is structured as follows. In Section 2, the considered trajectory planning problem is presented. In Section 2.1, a vehicle system model is discussed, highlighting its features that are of interest in terms of trajectory planning, while Section 2.2 formalizes the required mathematical structure of system model and constraints for planning. Then in Section 2.3, the set-based procedure for proving the feasibility of a trajectory is described. In Section 3, the planning algorithm is introduced, while in Section 3.1, the proposed AI-based enhancement is presented, followed by its validation in Section 4 and discussion in Section 5.
# Broadband Passively Mode-Locked Fiber Laser with DNA Aqueous Solution as Saturable Absorber
## 4 Discussion  
A passively mode-locked fiber laser with liquid DNA as the SA was demonstrated in the 1-1.5 \(\upmu\)m broad band. An etched fiber, with low optical loss and better light-SA interaction, immersed in the DNA aqueous solution, functioned successfully as an SA. Pulse laser with a central wavelength of 1563 nm and pulse width of 822 fs was achieved using erbium-doped fiber as the gain medium, whereas a 1034 nm wavelength/250 ps pulse was obtained using yterbium-doped fiber as the gain medium and an identical DNA SA. The broadband mode-locked laser with DNA SA exhibits excellent thermal stability under high-pump power conditions as well as long-term stability because of the large specific heat of the aqueous solution and the natural stability of DNA. The liquid phase DNA SA is also free from bubble/scattering issues thanks to relatively low optical loss from the etched fiber/DNA solution configuration. We confirmed the optical nonlinearity of the aqueous DNA solution in this study, and improved the usability of the material using liquid DNA and etched optical fiber. In particular, because the concentration of liquid-state DNA can be readily manipulated and the DNA molecular orientation can be controlled in the lyotropic chiral nematic state, it can be utilized as a platform for the in-depth study of the optical properties of DNA as well as in various optoelectronic and fiberoptic applications.  
Conceptualization, W.L.; methodology, W.L.; investigation, M.K. and J.J.K.; writing--original draft preparation, M.K. and J.J.K.; writing--review and editing, W.L.; visualization, W.L.; supervision, S.B.L. and D.-Y.K.; project administration, K.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the R&D Program funded by the Korea Institute of Science and Technology (2E31171).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 6: (**a**) Optical spectrum and pulse duration; (**b**) 3 dB bandwidth as a function of time.
## 3 Design of AcPAS  
The AcPAS antenna structure designed in this paper is shown in Figure 1. The figure shows the simulation model of the AcPAS. The antenna was simulated and optimized using ANSYS HFSS EM simulation software (2024 R2) via the finite element method (FEM). The antenna consists of a metal base, a coaxial feeding port, a stepped septum polarizer, a parabolic metal housing, a dielectric substrate, and two metal rings. One of the coaxial feeding ports is for feeding, and the other is matched. CP waves are formed through the waveguide and the septum polarizer. There is a circular opening on the top of the parabolic metal housing, inlaid with a dielectric substrate, and metal rings are printed on the top and bottom surfaces of the dielectric substrate. The circularly polarized waves are radiated through the metal cover and the metal rings, forming a wide-beam coverage CP wave.
# AI, Optimization, and Human Values: Mapping the Intellectual Landscape of Industry 4.0 to 5.0
## 1 Introduction  
Over the years, humankind has increasingly relied on advanced technologies to enhance production efficiency. Each industrial revolution has introduced transformative tools and paradigms. Industry 1.0, emerging in the 1780s, leveraged steam, water, and fossil fuels to power mechanical production systems [1]. This was followed by Industry 2.0 in the 1870s, characterized by the widespread adoption of electrical energy, which enabled mass production and assembly lines. The advent of Industry 3.0 in the 1970s saw the integration of electronics and information technology, laying the foundation for automation in manufacturing.  
Industry 4.0 represents a continuation and intensification of these technological advances. It is defined by the convergence of artificial intelligence (AI), cloud computing, the Internet of Things (IoT), and cyber-physical systems (CPSs), which together facilitate real-time communication between digital and physical systems [2]. This integration led to the development of Cyber-Physical Production Systems (CPSs), where machines collaborate and make decisions autonomously. [PERSON] and [PERSON] [3] (p. 307) describedIndustry 4.0 as a means to achieve industrial competitiveness by embedding CPSs into production processes.  
Despite its groundbreaking potential, Industry 4.0 has been critiqued for overlooking the human dimension. As [PERSON] and [PERSON] [3] noted, the transformation it initiated placed excessive focus on technological automation, often disregarding the centrality of human labor. This shortfall catalyzed the evolution toward Industry 5.0, which seeks to rebalance industrial priorities by reintroducing human values into technological systems.  
Industry 5.0, as described by [PERSON] et al. [2] (p. 530), moves beyond economic growth and job creation to promote societal well-being, sustainability, and resilience. This human-centric vision recognizes the essential role of people in intelligent manufacturing ecosystems and aims to harmonize industrial development with social and environmental considerations.  
To explore this emerging paradigm, the present study undertakes a systematic bibliometric literature review (LRSB) of the transition from Industry 4.0 to Industry 5.0. Applying the PRISMA 2020 protocol, the analysis examines 53 peer-reviewed sources from the Scopus database. This review focuses on the integration of AI, optimization techniques, and human values, offering insights into how these elements are reshaping the future of industry. Key themes include human-machine collaboration, ethical governance, and the alignment of advanced technologies with sustainability and societal needs.
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 4 Design and Performance Simulation of the Adaptive RC-NB-LDPC Coding Scheme
### Design and Performance Simulation of the Adaptive RC-LDPC Coding Scheme  
#### 4.3.1 The Design of the Adaptive RC-LDPC Coding Scheme  
In this section, we take the image transmission as an example, and propose a rate-compatible LDPC coding scheme based on channel clustering, as shown in Figure 20. The basic process of the program is divided into three parts:  
1. Channel clustering  
Using the data set composed of different channel environment parameters and user usage behavior as training data, the k-means++ algorithm is used to divide the wireless channel situation into k different classes.  
2. Rate allocation  
Figure 19: Clustering of simulated wireless channel conditions.  
Figure 20: The adaptive RC-LDPC coding scheme block diagram.  
We use the set partitioning in hierarchical trees (SPIHT) algorithm as the image compression algorithm. The SPIIHT algorithm has extremely low computational complexity and high-quality recovery characteristic and breaks the boundary between coding efficiency and complexity in traditional coding algorithms [51]. It makes reasonable use of multi-resolution characteristics after wavelet decomposition and has good coding performance. The higher compression rate causes the output code stream to be very sensitive to channel errors. Therefore, the worst one of each channel class is selected as the basis for designing the code rate allocation, ensuring that under all channel conditions in the channel class, the compressed code stream is transmitted as accurately as possible. A corresponding code rate allocation scheme is designed according to the worst channel condition in each cluster.
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 3 Parametric Analysis
### Independently Controllable Notch C Band (4-7.2 GHz)  
The elliptical slot at the lower radiating portion of the designed antenna was liable for notch creation at the C band. It is apparent from the Figure 7 that parameter EMIN was accountable for governing the notch at the C band. For EMIN = 4 mm, the notch at the C band was at 6.13 GHz. By increasing the value of EMIN from 4 mm to 4.2 mm, the second notch was shifted from 6.13 GHz to 6 GHz. By further increasing the value of EMIN to 4.4 mm, the second notch was reached at 5.97 GHz. In all instances, the first and second notches stayed unaffected by changes in the factor EMIN. It could be stated from the study of the parametric analysis for EMIN that the middle notch could be independently controlled. It was also ascertained that the EMIN parameter did not influence the first frequency notch band (AWS band) or the third frequency notch band (X band).  
For a split elliptical slot of major axis length EMAX, minor axis length EMIN, and width ER, the design equations for calculating a band-notch at a given frequency can be represented as Equation (4).  
\[E_{c}=D_{e}\pi(0.5E_{MIN}-E_{R})=\frac{\lambda_{g}}{2}=\frac{c}{2f_{notch}\sqrt {\varepsilon_{eff}}} \tag{4}\]  
\[D_{e}=3(1+d)-\sqrt{(3+d)(1+3d)} \tag{5}\]  
\[\varepsilon_{eff}=\frac{\varepsilon_{r}+1}{2}+\frac{\varepsilon_{r}-1}{2} \bigg{[}1+12\frac{h}{W_{F}}\bigg{]}^{-\frac{1}{2}} \tag{6}\]  
where, \(E_{c}\) represents the inner perimeter of the elliptical slot, which could be equal to half of the directional wavelength at the intended notch frequency. The parameter \(D_{e}\), utilized for the estimation of the perimeter of ellipse, is associated with the ellipticity by \"\(d\)\" through Equation (5) (where \(d=E_{MAX}/E_{MIN}=1.94\)), which is obtained as 3.01. The effective dielectric constant is calculated through Equation (6) as 1.84, where \(h\) (1.57 mm), \(W_{F}\) (3.6 mm), \" \(\varepsilon_{r}\)\" (2.2) are the substrate height, width of the microstrip feed, and relative permittivity of substrate, respectively. In the design simulations, the width of the slot was fixed at \(E_{R}\) = 0.2 mm. To obtain this second band-notch at 6 GHz within the C band (4-7.2 GHz), we chose \(E_{MIN}\) = 4.2 mm and \(E_{MAX}\) = 8.16 mm. The theoretically computed elliptical slot value (\(E_{c}\)) for 6 GHz was 18.43 mm, whereas the optimal parameter (\(E_{c}\)) for the slot was recorded as 18.02 mm.  
Figure 7: Parametric variation of independently controllable C band notch by varying \(\mathrm{E_{MIN}}\).
# FBG-Based Monitoring of Geohazards: Current Status and Trends
###### Abstract  
In recent years, natural and anthropogenic geohazards have occured frequently all over the world, and field monitoring is becoming an increasingly important task to mitigate these risks. However, conventional geotechnical instrumentations for monitoring geohazards have a number of weaknesses, such as low accuracy, poor durability, and high sensitivity to environmental interferences. In this aspect, fiber Bragg grating (FBG), as a popular fiber optic sensing technology, has gained an explosive amount of attention. Based on this technology, quasi-distributed sensing systems have been established to perform real-time monitoring and early warning of landslides, debris flows, land subsidence, earth fissures and so on. In this paper, the recent research and development activities of applying FBG systems to monitor different types of geohazards, especially those triggered by human activities, are critically reviewed. The working principles of newly developed FBG sensors are briefly introduced, and their features are summarized. This is followed by a discussion of recent case studies and lessons learned, and some critical problems associated with field implementation of FBG-based monitoring systems. Finally the challenges and future trends in this research area are presented.  
fiber optic sensor; fiber Bragg grating (FBG); geological process; geohazard; field monitoring; geotechnical instrumentation +
Footnote †: journal: Sensors
# A Survey of Advances in Landscape Analysis for Optimisation
## 4 Applications of Landscape Analysis
### Algorithm Performance Prediction  
Although most metaheuristics can be easily understood in terms of their algorithmic elements, the behaviour that emerges is often unpredictable. Landscape analysis can be used to extract general features of problems to be used as input to machine learning models for predicting algorithm performance. These models are an important component in the wider aim of automated algorithm selection. Examples of the application of machine learning to predicting algorithm performance based on landscape features include the following:  
* [PERSON] et al. [113] used one-sided support vector regression to predict the best-performing algorithm from a portfolio of four numerical optimisation algorithms based on ELA features. They showed that the model was able to generalise on new problem instances and predict the optimal or close to optimal algorithm from the portfolio.
* [PERSON] et al. [114] used a neural network regression model to predict the performance of a CMA-ES algorithm based on landscape features and algorithm parameters. Per formance was measured in terms of the number of function evaluation required and they found that the model was able to predict the relative ranking values for given algorithm-parameter combinations effectively.
* [PERSON] and [PERSON] [115] used decision tree models to predict failure of seven variants on the particle swarm optimisation algorithm based on landscape features. The models of five of the algorithm variants achieved testing accuracy levels above 90%.
* [PERSON] et al. [30] used a random forest regression model to predict the performance of multiobjective optimisation algorithms in combinatorial optimisation based on a combination of landscape features and problem-specific features. They later developed a decision tree model for selecting the best performing algorithm out of three multiobjective algorithms [50]. Their model was able to predict the best performing algorithm in more than 98.4% of the cases.
* [PERSON] and [PERSON] [116] proposed a random forest regression model for predicting the performance of CMA-ES algorithms based on ELA features in a fixed-budget setting. They obtained high-quality performance prediction by combining two regression models trained to predict target precision and the logarithm of the target precision.
* [PERSON] et al. [117] used random forest and linear regression models to predict algorithm performance for solving quadratic assignment problems based on landscape features derived from LON sampling. They found that random forest trees performed better at prediction than linear regression.