esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Second, it was found that with increasing tree cover and waveform extent, the phase centre becomes increasingly displaced upward into the canopy as more radar energy is reflected from canopy components and less from the ground. In this investigation, the estimation of VB in NASADEM and the computation of CRDTM2020 were achieved in several steps. Nine 1\({}^{\prime\prime}\) and 3\({}^{\prime\prime}\) publicly available DEMs were evaluated for the first time over Costa Rica using bare-earth elevation data from Laser Vegetation Imaging Sensor (LVIS) surveys and a levelling dataset. The 1\({}^{\prime\prime}\) NASADEM, a recent DEM based on original SRTM data, was chosen as a base DSM for the study. The novelty of this investigation of analytical removal of VB is mostly due to the arrival of the 0.9\({}^{\prime\prime}\) Global Forest Canopy Height 2019 (GFCH2019) model, which has much potential for VB removal from global DEMs. We first validate GFCH2019 with observations of canopy structure from the LVIS 2019 survey and then use it in combination with canopy densities provided by the Global Forest Change model to reproduce canopy heights for the year 2000, when the SRTM data were collected. Then, four analytical approaches are tested to eliminate VB using LVIS 2005 survey data, and the best-performing one was used to correct the NASADEM for VB. We compared our results and strategy to other studies of VB correction. Finally, the resulting CRDTM2020 was validated using levelling data and the LVIS 1998 and 2019 datasets. The analytical approach that we found to provide good results in Costa Rica for VB elimination from NASADEM can be applied in other regions and for other DEMs. Therefore, we believe that this work will be of interest to researchers and users interested in DEMs' accuracy in vegetated areas, researchers working on the elimination of VB from global and regional DEMs, as well as to DEM users in Costa Rica.
## 2 Related Work
### QGS Based on KGs and KBs  
KGs and KBs provide the structured representation foundation for our Multi-Examiner system, enabling contextually relevant question generation, inspired by the work of [PERSON] [12]. One advantage of KGs and KBs is their ability to provide strong relevance among distractors, which enhances the quality of questions generated [13]. However, a notable limitation is their insufficient capability to generate higher-order thinking questions. This section examines how these technologies have been applied to educational question generation and identifies these limitations that our approach addresses.  
Structured knowledge representation in education has progressed from basic taxonomies to advanced semantic networks [14; 15], giving rise to KGs and KBs, each with unique benefits for educational QGSs. KGs create flexible, dynamic networks of interconnected entities, enabling contextually relevant assessments [16]. In contrast, KBs use structured schemas for precise content retrieval, ensuring consistency in assessment accuracy [17; 18].  
Three development phases have marked QGS evolution: template-based systems with static KBs, graph-based representations enabling advanced question generation, and the current convergence of KGs with LLMs, significantly enhancing linguistic naturalness and factual accuracy [16; 19]. KG-based QGS applications have expanded across narrative learning, achieving notable accuracy improvements [20] and enhancing MOOC content evaluations [21]. Advances in graph convolutional networks have further improved temporal reasoning tasks in QGS, achieving substantial accuracy in generating questions about sequential and causal relationships [22]. Meta-analyses show these architectures boost question generation significantly over traditional methods [23]. KB technology has similarly evolved, showing promise in educational customization. Recent KB systems achieve considerable accuracy in creating MCQs aligned with learning objectives, thanks to advancements in semantic parsing [24]. Integrating KBs with LLMs has enhanced domain-specific processing compared to conventional approaches, particularly for interdisciplinary questions [25].  
However, several limitations in existing KG/KB approaches directly inform our Multi-Examiner design. Current systems struggle with synonym recognition and generating questions that engage higher-order thinking, with limited effectiveness for complex cognitive skills [2]. This limitation is particularly relevant to our third research question on supporting higher-order thinking. Additionally, redundancy in question format and cognitive demand limits question diversity, especially for advanced learning objectives. Our Multi-Examiner system addresses these limitations by integrating domain-specific KGs with specialized agents that enhance both cognitive alignment and question diversity.
## 4 Discussion
### Effect of \(\varphi_{2}\)  
The weight factor \(\varphi_{2}\) determines the relationship between the delay and energy costs and the total utility function, thereby affecting the system resource optimization decision. The UAV is at energy level 3, and the reliability index is fixed.  
In Figure 8, the weight factor \(\varphi_{2}\) is 0.2, 0.4, 0.6, and 0.8. The changing trend of the system utility function is shown as the task increases (Figure 8). The task is medium-sized, and the smaller weight factor corresponds to a higher system utility. Moreover, when the task is large, the larger weight factor corresponds to a higher system utility. This situation arises because when the UAV is at energy level 3, the UAV has more available energy, and the higher weight of the delay cost will lead to a larger total cost and a lower utility function. Second-level offloading increases the system's multiple energy consumptions compared with first-level offloading, and the high delay cost weight will reduce the system cost caused by high energy consumption. When switching offloading methods, the switching slope also varies because the weight factor has different effects on the system utility.  
Figure 9 illustrates the choice of the task offloading policy in relation to weight and task size. As shown in Figure 8, the system pays different attention to the delay and energy consumption under different weight values, so the utility of the system varies, thereby resulting in a selection strategy calculated according to the MDP, which also varies. When \(\varphi_{2}\) has the intermediate values of 0.4 and 0.6, the systems are more likely to select first-level offloading.  
Figure 8: Effect of \(\varphi_{2}\) on system utility function.  
Figure 10 shows the effect of the weight factor \(\varphi_{2}\) on the five scenarios with average utility functions in the case of a fixed task size with random UAV levels. In the UAV offloading scenario, the utility function decreases as the weight factor \(\varphi_{2}\) increases. In off-uav, the utility function increases with the increase of the weight factor \(\varphi_{2}\). The utility function of off-MENROM is superior to the utility function of the rest of the scenarios and is less affected by \(\varphi_{2}\). The off-MENROM is less influenced by weight factor \(\varphi_{2}\), and the utility function is more stable. Whether the system has a tendency to delay or energy, off-MENROM can identify a suitable offloading strategy and ensure system performance and reliability.
# Low-Thrust Nonlinear Orbit Control for Very Low Lunar Orbits
## 3 Nonlinear Orbit Control
### Formulation of the Problem  
Nonlinear orbit control is proposed as an effective strategy to drive a spacecraft toward a specified orbit (with subsequent maintenance), starting from a different initial orbit. As described in Section 2, the main effect the \(j_{2}\) harmonic is to yield a secular variation in the orbital elements \((\Omega,\omega,\theta)\). Therefore, the state is integrated numerically by assigning the time variation of the desired orbital elements \((a_{d},e_{d},\dot{i}_{d},\Omega_{d}(t),\omega_{d}(t),\theta_{d}(t))\),  
\[\dot{a}_{d}=\dot{e}_{d}=\dot{i}_{d}=0, \tag{23}\]  
while \(\dot{\Omega}_{d}\), \(\dot{\omega}_{d}\), and \(\dot{\theta}_{d}\) are given according to Equations (10)-(12). Then, through analytical differentiation, it is straightforward to convert the time variation of the classical orbital elements into equinoctial elements,  
\[\dot{x}_{1d}=\dot{a}_{d}(1-e_{d}^{2})-2e_{d}\dot{e}_{d}a_{d}=0, \tag{24}\] \[\dot{x}_{2d}=\dot{e}_{d}\cos\left(\Omega_{d}+\omega_{d}\right)-( \dot{\Omega}_{d}+\dot{\omega}_{d})e_{d}\sin\left(\Omega_{d}+\omega_{d}\right) =0,\] (25) \[\dot{x}_{3d}=\dot{e}_{d}\sin\left(\Omega_{d}+\omega_{d}\right)+( \dot{\Omega}_{d}+\dot{\omega}_{d})e_{d}\cos\left(\Omega_{d}+\omega_{d}\right) =0,\] (26) \[\dot{x}_{4d}=-\tan\left(\frac{id}{2}\right)\dot{\Omega}_{d}\sin \Omega_{d}+\frac{1}{2\cos^{2}i_{d}}\dot{i}_{d}\cos\Omega_{d},\] (27) \[\dot{x}_{5d}=\tan\left(\frac{id}{2}\right)\dot{\Omega}_{d}\cos \Omega_{d}+\frac{1}{2\cos^{2}i_{d}}\dot{i}_{d}\sin\Omega_{d},\] (28) \[\dot{x}_{6d}=\dot{\Omega}_{d}+\dot{\omega}_{d}+\dot{\theta}_{d}, \tag{29}\]where subscript \(d\) refers to the desired value of the equinoctial element. Equations (24) through (29) can be integrated to yield the time history of the desired orbit.
###### Abstract  
The development and improvement of the dual-band 802.11 ac standard Wi-Fi and wide bandwidth satellite communication devices are currently research subjects that have garnered significant interest. In this paper, double C-shaped two split-ring resonator (SRR) bounded unit cells were developed, which are applicable for S, C, and X band applications, including dual-band Wi-Fi communication devices and satellite communication applications for its effective medium ratio (EMR) of 15.6, which results in a 2.4 GHz resonance frequency and wide bandwidth (S21 \(<-10\) dB) of 1650 MHz at an 11.5 GHz resonance frequency. A copper resonator and the popular substrate material Rogers RT 5880 (thickness of 1.575 mm) were adopted for analyzing the characteristics of this unit cell. The \(8\times 8\) mm\({}^{2}\) structure was designed and simulated using a commercially available electromagnetic simulator CST (Computer Simulation Technology) Studio Suite 2019, which was utilized at four resonance frequencies: 2.4 GHz, 5.6 GHz, 8.93 GHz, and 11.5 GHz. The electric field, magnetic field, and surface current distribution were examined by modifying the metamaterial unit cell design structure, showing effective results. To confirm the CST simulation results, the newly designed double C-shaped double-negative metamaterial (DNG) was also simulated with the Ansys High-Frequency Structure Simulator (HFSS) and compared with the extracted results. The suggested metamaterial is advised for usage in Wi-Fi and satellite communication applications for superior long-distance communication performance and efficiency with the compactness of the structure.  
double negative metamaterial; double C shape; effective medium ratio; Wi-Fi application; satellite application +
Footnote †: journal: _crystals_
## 3 DQN-Based Offloading Design  
In this section, we develop a DQN-based offloading framework for minimizing the long-term processing delay cost. With the development of the traditional Q-learning algorithm, DQN is particularly suitable for high-dimensional state spaces and possesses fast convergence behavior. The MEC system constructs the DQN environment in the considered DQN offloading design framework. A central agent node is set up to observe status, perform actions, and receive feedback rewards. The center can be the cloud server or an MEC server.  
The DQN-based offloading framework is introduced in the following, in which the corresponding state space, action space, and reward are defined. In the overall DQN paradigm, it is assumed that the instantaneous CSI is estimated at MEC servers using the training sequences and then delivered to the agent. The CSI observed at the agent is the delayed version due to the channel estimation operations and feedback delay. Only local CSI of intelligent terminals, which connect to this MEC server, is acquired for each MEC server.
## 3 Results and Analysis
### Resistance to Sulfate Attack  
Figure 13 illustrates the weight loss rates of cement mortar samples with different replacement levels of TPSW material under a fixed water-to-cement ratio of 0.5: At 3 days, the weight loss rates for the replacement levels of 0%, 5%, 10%, and 15% were \(-8.4\%\), \(-7.5\%\), \(-6.9\%\), and \(-6.4\%\), respectively, with a 23.8% difference observed between the control group and the 15% replacement level. At 28 days, the weight loss rates decreased to \(-6.3\%\), \(-5.3\%\), \(-4.6\%\), and \(-4.3\%\) for the same replacement levels, where the difference expanded to 31.7%. At 91 days, the corresponding weight loss rates were \(-4.9\%\), \(-4.7\%\), \(-3.9\%\), and \(-3.6\%\), with a 26.5% difference between the control group and the 15% replacement level.  
As the replacement level of the TPSW material increased, the weight loss rate of the cement mortar consistently decreased across all the curing periods. This indicates an improvement in the resistance to environmental degradation. The decreased weight loss rate was attributed to the enhanced sulfate resistance of the TPSW material compared with natural sand. The inert nature of the TPSW material reduced the susceptibility of the mortar to sulfate attack, which led to less material degradation over time. The findings suggest that incorporating the TPSW material into the cement mortar can significantly improve its resistance to weight loss due to external chemical attacks. However, this benefit must be balanced against potential impacts on other mechanical properties to ensure  
Figure 13: Sulfate resistances of cement mortars with different TPSW amounts and W/C ratios.  
optimal performance in practical applications. In conclusion, using the TPSW material as a sand replacement in the cement mortar not only enhanced the durability against sulfate attack but also contributed to sustainable waste management by recycling post-consumer plastics effectively.
# Force Performance Analysis and Numerical Simulation of Assembled Ribbed-Slab Abutments
## 5 Conclusions  
In this study, the force performance of an ARBA was compared with different structural connections, and the following theories were derived from the test and FE simulation results:  
1. Compared with the cast-in-place bridge abutment model, the ultimate bearing capacity and ultimate deformation capacity of the two types of ARBA were significantly reduced, in which the bearing capacity of the flange-connected ARBA was significantly better than that of the ox-leg bolts, and the bearing capacity of the flange-connected ARBA reached that of the \"cast-in-place equivalent.\" However, the deformation capacity of the flange connection was slightly weaker than that of the clevis bolts, and the deformation capacity of the two types of ARBA was significantly weaker than that of the cast-in-place bridge abutment.
2. The flange joint structure is recommended to use the assembly of ribbed-plate bridge deck reasonable joint structure, due to which the performance of the assembly of ribbed-plate bridge deck in the force became \"equivalent to that of the cast-in-place,\" and later, maintenance was convenient.
3. Compared with the overall ARBA, under ideal conditions, the ultimate bearing capacity of the ribbed slab abutment could be better than that of the overall cast-in-place ribbed-slab abutment, whereas the ARBA deformation capacity of the structure was relatively weaker than that of the overall cast-in-place ribbed-slab abutment, and the damage to the two structures was the same. When the structure was damaged, the ARBA connection device of the ARBA did not reach the yield state.
4. Based on a comparative analysis of the ARBA connecting device, its stiffening rib thickness, the number of bolts, and the length of flange plate anchoring steel plate  
Figure 17: Load–displacement curves of flanges with different plate lengths.  
under different working conditions, the comprehensive consideration of the economy, structural stress performance, and other factors, the following combination of the parameters are recommended: the thickness of the stiffening rib is 30 mm, the number of bolts is 12, and a length of 50 cm of the length of flange plate anchoring steel plate.  
Conceptualization, Q.H. and T.W.; Method, Q.H.; Software, T.W. and Q.H.; Verification, Q.H., Y.S.; Data Management, Q.H.; Writing--Original manuscript preparation, Q.H.; Writing--Review and editing, Y.S. and Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was sponsored by the National Natural Science Foundation of China (No.52278159).  
Not applicable.  
All data, models, and codes generated or used in the study are found in published articles.  
The authors declare no conflict of interest.
# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model
## 3 Methods
### SAR Image Preprocessing  
Figure 3 shows a flow chart of the SAR image preprocessing performed to improve the ship detection performance using deep learning. Through preprocessing, the ship detection performance was expected to be improved by reducing the noise in the SAR images and increasing the contrast between the ship and the sea. As shown in Figure 3, the images used for comparison in this study were: (1) intensity image, (2) decibel image, and (3) intensity difference image and texture image. Unlike the intensity and decibel images, the intensity difference image and texture image were used in combination.
## 4 Experimental Simulation and Results Analysis
### Analysis of the Impact of Different Improvement Strategies on Algorithm Performance  
To analyze the impact of different improvement strategies on the performance of the Chimp Optimization Algorithm (ChOA), this paper designs a series of ablation experiments and conducts comparative studies on the following strategies: ChOA refers to the original Chimp Optimization Algorithm; CChOA is the improved algorithm that only applies the hybrid good point set and chaos initialization strategy; GChOA is the improved algorithm that only applies the benchmark weight strategy and Gaussian-modulated cosine factor strategy; EChOA is the improved algorithm that only applies the Global Exploration Enhancer (dynamic opposition-based learning + sine cosine strategy); MSECHOA is the multi-strategy enhanced Chimp Optimization Algorithm that integrates all the above strategies. The results are shown in Table 2.  
The results in Table 2 provide qualitative insights into the impact of each improvement strategy on ChOA's performance. The hybrid initialization strategy (CCHOA) combines good point sets and chaotic mapping, enhancing population diversity, and uniformity for comprehensive exploration of the search space. This reduces the risk of premature convergence in the early stages but lacks refinement mechanisms for precise local exploitation in later stages. The Gaussian-modulated cosine factor and benchmark weight strategy (GChOA) dynamically adjust the convergence factor, balancing global exploration and local exploitation. This approach maintains population diversity in early iterations, facilitating \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **Function** & **Dimension** & **Domain** & **Remarks** \\ Number & **Name** & **Dimension** & **Domain** & **Remarks** \\ \hline F1 & Sphere & 30 & \([-100,100]\) & Unimodal \\ F2 & Schwefel 2.22 & 30 & \([-10,10]\) & Unimodal \\ F3 & Schwefel 1.26 & 30 & \([-100,100]\) & Unimodal \\ F4 & Schwefel max & 30 & \([-100,100]\) & Unimodal \\ F5 & Rosenbrockp & 30 & \([-30,30]\) & Unimodal \\ F6 & Step & 30 & \([-100,100]\) & Unimodal \\ F7 & Quartic & 30 & \([-1.28,1.28]\) & Unimodal \\ F8 & Schwefel 2.26 & 30 & \([-500,500]\) & Multimodal \\ F9 & Rastrigin & 30 & \([-5.12,5.12]\) & Multimodal \\ F10 & Ackley & 30 & \([-32,32]\) & Multimodal (Fixed \\  & & & & Dimension) \\ F11 & Griewank & 30 & \([-600,600]\) & Multimodal (Fixed \\  & & & Dimension) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of Benchmark Test Functions. the discovery of high-quality solutions, and strengthens local exploitation in later stages, significantly improving solution precision. Consequently, GChOA excels in unimodal and fixed-dimension functions where high convergence accuracy is crucial. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \ \begin{tabular}{c} **Standard** \\ **Function** \\ \end{tabular}  & **Average Fitness Value** & \begin{tabular}{c} **Standard Deviation of** \\ **Fitness Value** \\ \end{tabular}
## 4 Investigation of Scalable Configurations Using Containerization
### Testbed Description  
Our test case was set up using the same hardware as described in Section 3.1.1 along with the application model and software. However, the proposed architecture is different. In Section 3, clients were directly sending notifications to one instance of the middleware server. In the new scenarios we are changing the number of nodes running the middleware server, all of them connected through a load balancer which is built upon an nginx Docker image. Load balancing uses the round-robin algorithm and the nginx configuration is organized as follows:  
* two server elements--one for a front-end service and one for an authorization service,
* SSL enabled on the server side,
* worker connections and backlog equal to 9000,
* \(n\) application nodes defined,
* connection between the load balancer and application nodes is plain HTTP (without SSL).  
The load balancer is running on another machine with 24 cores (including HT) of 2 Intel Xeon E5-2640 CPUs and 64 GB of RAM. It is important to notice that setting SSL connections also between the load balancer and application nodes will significantly decrease the performance [42] but in a typical environment load balancer and nodes can be assumed to be located in a safe network so there is no need to set up SSL there.  
The middleware server side is built upon \(n\) Docker nodes running within one physical machine. Each node is running a Docker container with the DeviceHive middleware--backend, front-end and authorization services. Containers share resources within a physical machine. There were no limits concerning RAM usage. No CPU limits are set per container but core affinity is controlled using _taskset_ inside a Docker container. Common data services as Kafka, Hazelcast and Postgres database are located outside of the tested environment. It is also important to notice that configuration of databases differs between the two test cases (Sections 3 and 4) so the results should not be compared directly between them. In Figure 9 we provide an overview of the proposed architecture.