esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Experimentation of Tensegrity Jumping Robots
## 4 Experiment Verification  
### Prototype of the Robot  
In order to validate the proposed kinematic modeling approach, we built a two-module robot prototype for experimentation, as shown in Figure 10. The driving laws of the motors and the values of all parameters of the prototype, including the stiffness coefficients and the original lengths of the elastic cables, agree with the theoretical optimization results. Each module consists of two elastic storage mechanisms and four elastic cables. Each module consists of two elastic storage mechanisms and four elastic cables. The robot's individual module measures 8.0 cm \(\times\) 10.4 cm \(\times\) 13.4 cm and weighs 470 g. The robot is designed for use in a variety of applications. The main structure of the robot is made of 3D-printed photosensitive resin material. The control system of each module consists of a controller (Model: STM32F103 ZET6) and two motor drivers (Model: L298N), two geared motors with large reduction ratios (Model: 25 GA310), and two small geared motors (Model: GM12-N20).  
In order to ensure the smooth running of the experiment and to obtain accurate and reliable data, it is crucial to build an appropriate experimental environment. We built the experimental environment as shown in Figure 11; the specific parameters of the device camera are the customized 48-megapixel sensor OV48C from Howell Technologies as the main camera (f/1.9, 1/1.32\"). Among other things, it is important to make sure that the lab bench stays level and is not tilted. Before the experiment is formally carried out, the robot model, power supply, measurement equipment, and so on should be thoroughly checked to avoid accidents during the experiment.  
Figure 9: Step-up jumping simulation. (**a**) Vertical jump height. (**b**) Horizontal jump distance.
# A Review of Artificial Intelligence in Enhancing Architectural Design Efficiency
## 3 Overview of AI Technology
### Application of AI in Architectural Design
#### 3.2.3. Building Energy Consumption Forecast
\begin{table}
\begin{tabular}{c c c c} \hline
**Author** & **Position** & **AI Technology Used** & **Conclusions** \\ \hline \multirow{8}{*}{[PERSON], [PERSON] [41]} & America & RF algorithm & Propose a multi-objective optimization (MOO) method to enhance machine learning models for building energy load prediction, outperforming traditional methods by reducing time complexity and improving accuracy. Highlight the importance of feature selection and model optimization, contributing to energy management research and supporting industry development. \\ \cline{1-1}  & America & RF algorithm & \\ \cline{1-1}  & Switzerland & Non-dominated Sorting & \\ \cline{1-1}  & Germany & Genetic Algorithm II & \\ \cline{1-1}  & Germany & (NSGA-II) algorithm & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{8}{*}{[PERSON], S.L., [PERSON], [18]} & Hong Kong, & Feedforward Multilayer Perception (MLP) neural networks & Develop an ANN model for energy analysis of office buildings with daylighting systems in subtropical climates. With 9 input variables and 4 power consumption outputs, the model achieves high prediction accuracy, particularly for lighting power. It effectively captures nonlinear relationships, supporting energy-efficient building design. \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{8}{*}{Milion, R.N., [19]} & \multirow{8}{*}{Brazil} & \multirow{8}{*}{ANN} & Propose an ANN-based method for estimating electrical material consumption, outperforming traditional methods in handling multidimensional nonlinear problems and proving suitable for early project stages. Address data limitations by integrating BIM for improved quality, with future potential for applying other algorithms to broader material estimation tasks. \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{8}{*}{Macas, M. [19]} & \multirow{8}{*}{Italy} & \multirow{8}{*}{ANN} & Focus on improving neural network performance in predicting building heating variables. Training sample size and input dimensions significantly affect performance, with overfitting addressed through early stopping. The study suggests refining input selection strategies and enhancing comfort prediction for future improvements. \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._ANN is a common AI technology for predicting building energy consumption [39; 40; 41]. A study showed that ANNs demonstrate strong performance in predicting daily electricity
# Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks
## 3 System Model
### Algorithm Design
We explored the value of MinPts and found that network performance was the best when MinPts \(=2\). Due to the randomness of the distribution density, the capacity of the cluster may be too large. So the radius of the cluster and the number of devices in the cluster must be limited. To simplify the process, when the cluster radius exceeds the distance \(R_{0}\), or the number of cluster members exceeds \(Z\), the left devices directly connect to the base station as a separate device.
## 4 Problem Definition
### Multi-Objective Optimization Problem  
A MOP can be stated as follows [24]:  
\[\left.\begin{array}{ll}Minimize/\,Maximize\,\,f(\varepsilon),&\quad m =1,\,2,...,\,M\\ \\ subject\,\,to:\,\,g_{j}(\varepsilon)\geq 0,&\quad j=1,\,2,...,\,J\\ \\ h_{k}(\varepsilon)=0,&\quad k=1,\,2,...,\,K\\ \\ \varepsilon_{i}^{(L)}\leq\varepsilon_{i}\leq\varepsilon_{i}^{(U)},&\quad n =1,\,2,...,\,n\\ \end{array}\right\} \tag{3}\]  
where \(M\) is the number of objective functions subject to \(J\) inequalities and \(K\) equality constraints. A solution \(\varepsilon\) is a vector of \(n\) decision variables, i.e., \(\varepsilon=(\varepsilon_{1},\varepsilon_{2},...,\varepsilon_{n})^{T}\). Each variable is subjected to a constraint called variable bounds. \(\varepsilon_{i}^{(L)}\) represents the lower (\(L\)) bound, and \(\varepsilon_{i}^{(U)}\) corresponds to the upper (\(U\)) bound. The set of all variable bounds defines the decision variable space \(\Omega\).  
A solution \(\varepsilon\) that satisfies all constraints and variable bounds is named a feasible solution. The set of all feasible solutions is called the feasible region (or search space \(\mathbf{S}\)).  
Definition 1: Domination: A solution \(\varepsilon^{(1)}\) is said to dominate the other solution \(\varepsilon^{(2)}\), if the following conditions are verified:  
* The solution \(\varepsilon^{(1)}\) is no worse than \(\varepsilon^{(2)}\) in all objectives, or \(f_{m}(\varepsilon^{(1)})\) is no worse than \(f_{m}(\varepsilon^{(2)})\) for all \(m=1,2,...,\,M\);
* The solution \(\varepsilon^{(1)}\) is strictly better than \(\varepsilon^{(2)}\) in at least one objective, or \(f_{\overline{m}}(\varepsilon^{(1)})\) is better than \(f_{\overline{m}}(\varepsilon^{(2)})\) for at least one \(\overline{m}=1,2,...,\,M\);  
Definition 2: Non-dominated set: Among a set of solutions P, the non-dominated set of solutions P' is comprised of those that are not dominated by any member of the set P.  
Definition 3: Globally Pareto-optimal: This refers to the non-dominated set of the entire feasible space \(\mathbf{S}\).
## 5 Conclusions  
In the present study, a new LAI formula (based on the BIOME-BGC ecosystem model) was implemented in the RegCM4 model. Then, two 13-year simulations (LAIorg and LAImod) were conducted to examine the potential influence of the vegetation cover changes (represented by LAI) on the terrestrial carbon fluxes of Africa. The results were compared with a reanalysis product and Fluxnet measurements. The results showed that switching between the two LAI formulas led to a notable influence on the ecological indicators as well as the terrestrial carbon fluxes. Furthermore, LAImod outperforms the LAIorg in simulating the LAI and terrestrial carbon fluxes with respect to observational-based datasets. Compared with the results reported by [3; 4], LAImod reduces the LAI and GPP bias concerning reanalysis/satellite products. Additionally, ref. [19] tested the new modifications (implemented in the CLM45 land surface model) on LAI, GPP, Evapotranspiration (ET) and biomass. However, the influence on the other terrestrial carbon fluxes was not discussed.  
The present study gives a first insight into the simulated LAI and the terrestrial carbon fluxes with respect to reanalysis/satellite products and Fluxnet measurements. Moreover, it can be noted that LAI parameterization has a notable influence on the ecological indicators which can explain the documented biases of the terrestrial carbon fluxes. In addition, the RegCM4 source bias can be attributed to the following reasons: (1) RegCM4 physical parameterization, (2) uncertainty associated with the lateral and initial boundary condition, (3) uncertainty associated with the CARDAMOM reanalysis product ([20]) and Fluxnet measurements ([14]) and (4) the representation of LAI and terrestrial carbon fluxes in the CN module. Nevertheless, the LAImod shows the improved performance of the RegCM4 compared to the CARDAMOM reanalysis product and Fluxnet measurements. A future study will consider the following points:  
1. Using a nitrogen plant model of [19] to simulate the GPP and other terrestrial carbon fluxes realistically.
2. Examining the influence of various environmental factors affecting the simulated terrestrial carbon fluxes as in [17].
3. Using the Variable Infiltration Capacity (VIC) as a land-surface hydrology scheme and dynamic vegetation (DV) with the new LAI parameterization [6].
4. Addressing the RegCM4 sensitivity to climate forcing as in [10].  
Conceptualization, S.A.A.; methodology S.A.A.; software, S.A.A.; validation, S.A.A.; formal analysis, S.A.A. and Y.K.; investigation, S.A.A.; resources, S.A.A.; data curation, S.A.A.; writing--original draft preparation, S.A.A. and Y.K.; writing--review and editing, S.A.A. and Y.K. All authors have read and agreed to the published version of the manuscript.  
No fund received for this study.
## 3 Potential Security Implications of IoT in 6G Environment
### Network- or Device-Level Vulnerabilities  
This section addresses the security risks associated with the network and device layers within the Internet of Things (IoT) environments. The key areas of concern include device authentication, firmware vulnerabilities, and the scalability challenges inherent in securing large-scale IoT deployments. These vulnerabilities can be exploited by attackers to gain unauthorized access or to disrupt services.
## 3 Results and Discussion
### Transmission Contribution to GIS Pattern
A matrix of the 2D scattering signal was multiplied with a vector, which provided more intense scattering in the upper part of the TS pattern (intensity ratio of 2.5:1 was taken, which depended on the scattering geometry and beam size). Simulating the lower transmission part is also a means of judging possible scattering contribution from the environment and the sample cell. If a simulated GIS pattern differs from the experimental observation additional contributions to the background should be considered. Figure 2 shows the experimental and the simulated 2D GISANS signal including the contribution of the transmitted scattering in the direct beam direction [26]. The simulated 2D pattern as well as line cuts at \(q_{y}\) = 0 (along \(q_{z}\)) and at \(\alpha_{c}\) (parallel to the sample surface) were in good agreement with the experimental data. There are still some remaining differences in the diffuse scattering at the borders of the upper detector part (Figure 2a,b), which might be attributed to the instrumental effects not included into the BornAgain simulation, or sample effects such as the particle size distribution, which were also not part  
Figure 3: Schematic illustration of the transmission in grazing incidence geometry: difference in the footprints of the upper- and lower- beam parts due to the beam divergence. of the simulation. The chosen beam parameters allowed an estimation of lateral correlation length, while resolution in the direction normal to the surface led to some crossing over of specular and Yoneda peak. Here it should also be noted that reproduction of profile of direct beam, such as simulation of the beam collimation, angular divergence etc., is a difficult task and is not too relevant in this case. We focus more on regions of large \(q\), and diffuse scattering fluctuations, while the beam profile could have a stronger influence on low-\(q\) scattering or Bragg-peaks. This is still to be improved for future investigation of larger real space distances.
# Semi-Global Stereo Matching Algorithm Based on Multi-Scale Information Fusion
## 1 Introduction
Their results demonstrated that the accuracy of disparity map obtained by fusing the stereo matching algorithm and multi-scale information was significantly higher than those obtained by the algorithms that only considered single scale. However, inevitably, as the image scales increase, CSCA has high complexity and low matching efficiency. [PERSON] et al. [21] improved CSCA by adding Laplace pyramid transform in the cross-scale cost aggregation stage, and obtained a better disparity map. [PERSON] et al. [22] introduced fast guided filter into CSCA, and used different cost aggregation methods in different scale spaces, which not only improved the quality of disparity map, but also reduced the running time. Although the aforementioned algorithms improved the quality of disparity map to varying extents, there still exists the problem of low matching accuracy when dealing with low-texture regions, and the real-time performance needs to be improved. In this paper, an SGM algorithm based on multi-scale information fusion (SGM-MSIF) is proposed to improve the matching accuracy of low-texture regions and processing efficiency simultaneously. Compared with classic SGM and CSCA algorithms, we propose a novel cost aggregation method and an internal left-right consistency check method. These approaches can significantly improve the matching accuracy of the algorithm in low-texture regions and reduce the complexity of SGM-MSIF. The rest of this paper is organized as follows. In Section 2, the flow chart and specific steps of the proposed algorithm are described. The calculation method of the initial matching cost volumes, the proposed cross-scale multi-path cost aggregation method and the principle of internal left-right consistency check in disparity optimization are illustrated. Then, experiments are designed, and the matching accuracy and processing efficiency of the algorithm are tested in Section 3. Conclusions of this article are summarized in Section 4.
# Optimal Design and Control of MMC STATCOM for Improving Power Quality Indicators
## 6 Proposed Optimization Methods  
In the past decades, with the increasing development in the fields of society, economy and industry, numerous complicated and hardly-solved optimization problems have arisen in all fields. Recently, metaheuristic techniques of optimization have been widely implemented to solve complicated engineering problems, as they possess high capabilities of exploration and exploitation to reach global solutions in a short time when compared with traditional optimization methods [31; 32; 33; 34].  
Taking into account the No Free Lunch Theorem of Optimization [35], the best solution of different optimization problems cannot be fulfilled based on one optimization technique. Based on this theorem, the field of creating and developing better optimization algorithms is still active and full of great achievements. As a result of these achievements, a recent swarm algorithm inspired by physics, called Atom Search Optimization (ASO), is proposed for tackling the optimization problem provided in this study. ASO presented superior performance over conventional and recent optimization techniques when validated on numerous mathematical problems. In addition, the proposed ASO algorithm provided a successful performance when utilized in the optimization problem of hydrogeologic parameter estimation.  
In a related context, like the most of existing optimization methods, the searching process in the proposed ASO algorithm is performed in two phases; exploration and exploitation stages [30]. In the first phase, the optimization algorithm should make utilization and promotion of the randomly initialized operators for the deep exploration of the optimal solution within the search space. Thus, the exploratory behavior of a well-developed optimization algorithm should be randomly enriched to generate more random solutions to different areas of the addressed problem formed in the early stages of the research process [36].
## 6 IoT Leveraging SDN
### Real World Applications  
IoT security is a hot research topic; in literature, machine learning is applied to SDN to improve IoT security. SDN provides an easy approach to handle simple and distributed DDoS attacks [204; 205]. Flow-based dynamic security schemes can be implemented at the network edge [206]. A deep learning network-based approach is proposed in paper [207] for anomaly detection at the edge server. In paper [208], a support vector machine is applied to analyze sensing data and detect abnormal activities. In paper [209], deep learning is applied to edge networks for the detection of IoT attacks. SDN provides significant advantages in the domain of network security, especially in large-scale IoT networks. In summary, EC, SDN, and machine learning can enable intelligent and sustainable IoT solutions. IoT real-world applications of IoT and machine learning leveraging SDN are given in Table 6.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Literature** & **Approach** & **Complementary Technologies** & **Relative Merits** \\ \hline Heterogeneous IoT SDN [210] & Intent-based management and orchestration & NFV and VNF & Orchestration and high level management capabilities to improve scalability and latency. \\ Virtualized IoT network [186] & NFV and SDN based programmable information technology infrastructure & NFV & Interoperability, security, but too general and insufficient in the components working details. Lack of new scenarios discussions, and presentation of cooperation functionalities between SDN and NFV. \\ Network architecture for IoT [208] & Software-Defined Fog based IoT architecture & Fog Computing & Supports mobility, flexibility, real-time delivery, fast latency resolving services and increase efficiency. \\ IoT-fog based systems [211] & SDN and Fog based IoT systems & Fog Computing & Decrease latency, enabling IoT nodes leverage cloud capabilities and increase bandwidth. \\ Trustable Framework for IoT Devices [212] & QoE based Trustable SDN approach & Mobile edge computing & Orchestrate based on resource abstraction, security using authentication mechanism, coordinate management. \\ IoT edge cloud architecture [213] & SDN based IoT mobile edge cloud architecture & Mobile edge computing & Improve latency of control and data plane, reduce overhead and improve flexibility. \\ IoT monitoring framework [214] & Software defined 5G mobile networks based framework & NFV & Data filtering using MQTT, custom monitoring system, handle load balance through NFV, and improve network intelligence. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Real world applications.
## 2 Low-Dimensional Chaotic System  
### Chaotic Systems  
Chaotic phenomena are non-linear observations that can be defined by determined systems. They have a great appeal in the security fields essentially for their ideal pseudo-randomness, broad spectrum and their sensitive dependency on their initial conditions [19]. Therefore, research on the chaotic secure communications has been developed extensively [11; 20; 21]. Chaotic systems are defined as dynamical systems that are characterized by a high sensitivity towards initial conditions and show unpredictable observations from the exterior of the system. This means that the smallest change in the initial conditions of the system can cause an ostensible random and unpredictable behavior of the results; a behavior that nevertheless obeys precise rules [19]. This property is mainly the reason that chaotic systems are introduced to the application of secure communication: when properly used, chaotic systems produce cryptographically secure pseudo-random number generators [22]. In addition, without the right initial conditions, the correct pseudo-random sequence cannot be regenerated.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling  
The fundamental equations governing flow and solute transport were pivotal for the advancement of modelling tools [364]. However, accurately modelling the fate of solutes presents significant challenges, as classical equations often fail to represent reality at the field scale [365]. Factors such as variable water velocity, soil heterogeneity (including double porosity), and variations in the transport equation arising from the predominance of advection or hydrodynamic dispersion contribute to substantial uncertainty in the initial deterministic solute models.  
At this stage, the modelling of solute transport, particularly reactive transport, starts to diverge from expectations and needs. One approach is to simplify the process to make it more accessible for modelers and users. 1D screening models with intuitive user interfaces have been developed based on highly simplified site conditions. These tools have aided in comprehend the fundamental processes influencing hydrocarbon transport. While they may not precisely replicate all contaminant concentrations, these models offer practitioners a rough understanding of contamination extent and timing with minimal computational effort.  
In cases requiring more intricate models, modular 3D flow models with reaction modules have been employed. These models offer greater accuracy in describing the plume, and account for the variability of parameters affecting flow and reactivity across space and time. Another method that effectively captures the sub-surface's randomness distribution is stochastic modelling. Initially, this approach was reserved for modelers with a profound understanding of the mathematical intricacies of solute transport. However, it is now gaining recognition for its high predictive accuracy. Between these approaches--simple 1D screening tools, complex deterministic models, and stochastic models--there exists an opportunity for hybrid models that leverage and integrate the best attributes of each approach.