esa-sceva/satcom-chunk-collection huggingface dataset:
# Cutting-Edge Trajectory Optimization through Quantum Annealing
## 4 Lunar Landing and Rendezvous Applications
### Lunar Landing Problem  
The mathematical expressions governing the dynamics of a lunar lander can be derived using [PERSON]'s law in the context of no central forces. In this context, the gravitational influence and the propulsive thrust produced by the propulsion system act as the only forces of motion [37]. Furthermore, a flat ground is considered, thus obtaining linear dynamics. This preference is logically valid, since the present study limits its analysis to only the terminal phase of the landing trajectory. Accordingly, the formulation of the dynamics can be written as follows:  
\[\ddot{\mathbf{s}}(t)=\mathbf{g}_{M}+\mathbf{u}(t), \tag{23}\]  
where the unvarying gravitational acceleration vector of the Moon, \(\mathbf{g}_{M}=[0,0,-g_{M}]^{T}\), exclusively acting along the \(z\)-axis (\(g_{M}=1.62509\) m/s\({}^{2}\)), along with control thrust and acceleration vectors \(\mathbf{T}\) and \(\mathbf{u}\), respectively, contribute to the movement. Upon obtaining the second derivative of Equation (22), it becomes evident that solely time vector \(T\) undergoes differentiation, while the other terms remain held constant. The resulting \(\ddot{\mathbf{s}}(t,\mathbf{\alpha}_{2})\) can be included in Equation (23) to find the expression of control vector \(\mathbf{u}\):  
\[\mathbf{u}(t)=\ddot{\mathbf{s}}(t,\mathbf{\alpha}_{2})-\mathbf{g}_{M}=\ddot{M}\mathbf{\alpha}_{2} +\ddot{N}-\mathbf{g}_{M}=P\mathbf{\alpha}_{2}+R, \tag{24}\]  
where \(R=\bar{N}-\mathbf{g}_{M}\) represents the component that remains independent of the variable optimization. Then, by following the steps reported from Equation (15) to Equation (18) the QUBO problem can be derived.
# A Multi-Scale Contextual Information Enhancement Network for Crack Segmentation
## 4 Experiments
### Effect of Different Settings of the Network Components
#### 4.5.1 Comparison of Different Skip Connection Structures  
We compare the segmentation performance and model complexity of our proposed multi-scale skip connection (MSSC) with the other three skip connection structures, namely the plain skip connection (PSC), the densely connected skip connection (DCSC), and the full-scale skip connection (FSSC), as mentioned in Section 3.1. Experiments are conducted using the four network models shown in Figure 12 with no other changes. IoU, F1 score, parameters, and FLOPs are used as the metrics and the results are shown in Table 8. It can be seen in the table that the segmentation performance of the multi-scale skip connection is better than that of the plain skip connection, and is very close to that of the densely connected skip connection. While the full-scale skip connection achieves the best results in the IoU and F1 score metrics, its result in model complexity is the worst, with the highest FLOPs of \(454.80\times 10^{9}\). Our multi-scale skip connection achieves the best results in parameters and FLOPs, which are \(5.82\times 10^{6}\) and \(101.58\times 10^{9}\), respectively. Obviously,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **IoU (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline Network B & 73.82 & 82.07 & 90.32 & 84.82 \\ MCIE-Net & 86.5 & 95.05 & 90.74 & 92.41 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of our networks with or without CFEM on MTSB.  
Figure 11: Sample results of using our networks with or without contextual feature enhancement module on the mixed test set B. (**a**) Images, (**b**) ground truth, (**c**) Network B, (**d**) MCIE-Net.  
compared to the densely connected skip connection and full-scale skip connection, our multi-scale skip connection structure significantly reduces the computational cost at the cost of only a slight degradation in segmentation performance. Considering all the data in the Table 8, it can be concluded that the proposed multi-scale skip connection structure achieves a good balance between performance and model complexity.
## 1 Introduction
Although much attention has been given to near-ground propagation models, few studies place the transceiver directly on the ground, with the antenna height at the level of a few centimeters, which is a more realistic deployment scenarios for WSNs. Studying the actual performance of protocols through field test-beds requires a tremendous amount of work [17]. Therefore, various simulation tools are used to expediently test and evaluate new protocols. However, these simulation results are strongly influenced by the selected propagation models. Most existing simulators adopt simplistic theoretical models such as Free Space Path Loss (FSPL) model and two-ray model [18]. Although simple models perform well in some cases, unrealistic models with unreasonable physical layer assumptions may lead to unreliable simulation results, which are far from practical applications. We measured the received signal strength indication (RSSI) of several near-ground wireless channels at 470 MHz, with four terrains and three antenna heights. Through comprehensive studies, we derived several statistic propagation models to more accurately reflect the characteristics of near-ground path loss. We also analyzed the impact of antenna height and terrain, and further investigated the propagation model selection strategy in different scenarios. Furthermore, we explored the impact of near-ground propagation models on network connectivity by simulations. The contributions of this paper can be summarized as follows. 1. We derived several statistic propagation models to describe near-ground path loss in four terrains, including two derived scenarios with different terrain factors. 2. We analyzed the impact of antenna height and terrain factors on the radio path loss at ground level, and further investigated the propagation model selection strategy in near-ground scenarios. 3. We implemented the measured propagation models on Cooja simulator [19] and showed that simplistic theoretical models induce great deviation of network connectivity. The rest of this paper is organized as follows. Section 2 describes some popularly used WSN propagation models. Section 3 describes the measurement setup. Section 4 shows the measurement results and analyzes the influence factors of propagation models. Section 5 discusses the impact of path loss models on network connectivity in the simulations. Finally, Section 6 concludes the paper.
## 4 Results and Discussion
### Shap Analysis across 49 Features
#### 4.2.5 Key Findings of SHAP Analysis across 49 Features  
Key findings for four quadrants after SHAP analysis, including all 49 features, can be seen in Table 6. Key features refer to those attributes that hold the highest significance as determined by summary plots. These summary plots provide a visual representation of the importance of each feature in the model. The analysis reveals that among the various attributes examined, saccadic movements stand out as the most crucial. This indicates that saccadic movements have the greatest influence on the model's predictions within the context of the four quadrants.  
Figure 9: Variations in F1-Score Across Different Feature Numbers for Quadrant 3.
## 4 Application Characteristics
### Synergy of Onboard Sensors with Optical Camera and Ship AIS  
The HSC combines an optical camera and ship AIS on a satellite platform for the detection, identification, and recording of dynamic ship information. All four launched satellites are equipped with remote sensing sensors and AIS onboard, enabling the retrieval  
Figure 6: Visualization of detection results, where the types, confidences, and positions of ships were marked.  
Figure 7: The smallest sizes of ships detected in the images with spatial resolutions of 5 m (**left**) and 1.5 m (**right**), where the rough longitudes and latitudes are marked on the right of targets.  
of spatiotemporally collocated information regarding images and AIS. These technical capabilities are advantageous for obtaining ship parameters. After data processing, ships can be detected from images, and ship parameters, such as name, type, position, and speed, can be obtained by combining AIS (including commercial AIS information acquired from other AIS satellites) information with images when the ship position is matched. However, ships identified in the images without any retrieved AIS information require further post-processing analysis. As mentioned, HN-1 01/02 and WC-1 01/02 all have AIS onboard, which can collect approximately 800,000 items of AIS information every day in a 24 h regime.
# An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network
## 6 Conclusions and Future Work  
In this paper, we perform adaptive control of network traffic prediction and modification in the process of slice reconfiguring, and then use the TTV theorem to migrate the traffic if the link traffic exceeds the peak rate. The adaptive migration process completes the reconfiguration of the logical virtual link, and thus completes the reconfiguration of the virtual networks and the allocation of resources more efficiently. Aiming at the eMBB application scenario, the effectiveness of the proposed algorithm is verified from the perspectives of network OPEX and KPIs, such as network run time, delay and throughput. The performance of the configuration is measured and compared with the other two algorithms, and the effectiveness of the proposed TME approximate algorithm is analyzed from a statistical point of view.  
There are many different domains where the advanced optimization algorithms (e.g., heuristics, metaheuristics, machine learning algorithm, deep convolutional neural network) have been applied as solution approaches, such as online learning, scheduling, multi-objective optimization, transportation, medicine, data classification, and others [34; 35; 36; 37; 38]. Inspired by these applications, we will try to use some advanced optimization algorithms to solve the TE-based optimization problems in our future research work. We will also construct the dynamic slicing migration strategy to meet the resource allocation requirements by using the advanced optimization algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(i\) & **Algorithm** & **The Adjusted \(p\)** & \(\alpha i\) & **Hypothesis** \\ \hline
3 & HPR & 0.07074 & 0.1 & Reject \\
2 & TE & 0.00000041 & 0.0025 & Reject \\
1 & A* search & 0.0073 & 0.05 & Reject \\ \hline \hline \end{tabular}
\end{table}
Table 4: Holm test.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Mean Rank for Residual Energy Resources** \\ \hline A* Search & 2.00 \\ TE & 1.00 \\ HPR & 3.00 \\ TME & 4.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: [PERSON] test.  
**Author Contributions:** Conceptualization, Z.Z.; Investigation, Z.Z.; Methodology, X.M.; Supervision, Z.Z.; Validation, X.M.; Writing--original draft, Z.Z.; Writing--review & editing, X.M. and Y.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the Fundamental Research Funds for the Central Universities (No. JB210707) and the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (No. ICT2022B39).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data (including the simulation parameters and simulation program) used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Development of a Novel Deployable Solar Panel and Mechanism for 6U CubeSat of STEP Cube Lab-II
## 3 A PCB-Based High-Damping Deployable Solar Panel Module
### Wire-Tightening Process  
The wire-tightening procedure on the brackets for the solar panel's holding mechanical constraint is shown in Figure 8. Dyneema wire (YGK G-Soul) [29] (diameter 0.20 mm) is wound on the brackets that can bear maximum strength of 88.2 N; the panel-holding capacity can be further increased with the wire-winding numbers. The surgeon's knot is carried out at the bracket corner for the final knotting of the wire, which helps to create  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Input**} & \multicolumn{5}{c}{**Solar Panel Deployment Status**} \\ \cline{3-6}  & & \multicolumn{2}{c}{**Undeployed**} & \multicolumn{2}{c}{**Deployed**} \\ \hline \multirow{2}{*}{V\({}_{\text{in}}\)} & \multirow{2}{*}{Enable} & Pogo pin & Output & Pogo pin & Output \\  & & voltage & voltage & voltage & voltage \\ \hline
1 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Circuit truth table for deployment status of solar panel.  
Figure 7: Mechanical configuration of the burn resistor PCB: (**a**) front view and (**b**) rear view.  
a steady tight tension on the wire knot. For reliable panel release, physical contact of the wire to the resistor is mandatory. Thus, the notching guide rail is made on the bracket's surface to prevent wire misalignment from the resistor surface under launch vibration loads. Additionally, the combination of vertical and horizontal cross-pattern winding of the wire with two additional hole interfaces in the brackets creates a secure holding constraint on the panel. The process of wire tightening to stow the solar panel is therefore significantly simpler and more reliable than conventional mechanisms.
# Analysis of a Collision-Energy-Based Method for the Prediction of Ice Loading on Ships
## 4 Results
### Influence of the Assumed Ice Edge Opening Angle  
The PC design loads are calculated assuming an angular contact geometry with an ice edge opening angle (\(\phi\)) of 150\({}^{\circ}\). To assess the influence of the assumed \(\phi\)-value, we calculated ice load estimates for \(\phi\)-values between 90\({}^{\circ}\)-150\({}^{\circ}\). For the two different load lengths of 0.4 m and 0.8 m, Figure 11 shows how the calculated ice load (\(q\)), in terms of its mean, standard deviation and maximum, depends on the value of \(\phi\), and how the calculated values compare to the measurements. We find that calculated ice load decreases with increasing ice edge opening angle (\(\phi\)) values. In addition, by comparing the calculated line loads with the measurements we find that:  
* For one-frame spacing (load length = 0.4 m), a good agreement with the mean and maximum of the measurements is obtained for \(\Phi\)-values around 150\({}^{\circ}\) and 110\({}^{\circ}\), respectively.
* For two-frame spacing (load length = 0.8 m), a good agreement with the mean and maximum of the measurements is obtained for \(\Phi\)-values around 150\({}^{\circ}\) and 140\({}^{\circ}\), respectively.  
Figure 10: Measured vs. calculated ice crushing pressure-area relationships. Collison type: Ship–ice floe impact. Contact geometries: (**a**) Right-apex oblique; (**b**) Round; (**c**) Symmetric v wedge; (**d**) Angular. The ice crushing pressure-area relationships determined based on measurements conducted on MV Arctic and IB Kigoriak, and on IB Sampo, were determined based on Kujala [8].
# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 6 Conclusions  
This paper presented a novel randomly overlapped dual-component radar signal recognition method under different SNRs based on a convolutional neural network-swin transformer (CNN-ST), for improving recognition performance. The overall model framework was first designed, and the swin transformer was subsequently adopted and integrated into the CNN model. An inverted residual structure and lightweight depthwise convolutions were used to maintain the powerful representational ability. The influences of the different SNRs on the recognition performance of dual-component radar signals were experimentally investigated. The results demonstrated that recognition accuracies of CNN-ST were up to 82.58% at \(-8\) dB, which showed better recognition performance over others. The recognition accuracies of randomly overlapping dual-component radar signals under different SNRs were all more than 90% at \(-2\) dB, which verified the superior recognition performance of the proposed CNN-ST model. The recognition accuracies of 2 FSK-4 FSK, 2 FSK-BPSK, 2 FSK-EQFM, 2 FSK-FRANK, 2 FSK-LFM, 2 FSK-NS, 2 FSK-SFM, LFM-4 FSK, LFM-BPSK, LFM-EQFM, LFM-FRANK, and LFM-SFM are up to 76.25%, 63.75%, 82.625%, 75.625%, 70.625%, 72%, 67.5%, 71.5%, 60.625%, 78.25%, 73.75%, and 70.625% at SNR of \(-12\) dB, respectively. This work provided essential guidance in enhancing recognition performance under different SNRs and promoting the actual applications.  
For considering the more complex scenarios with randomly overlapping radar signals, future research will focus on classifying multi-component signals under different SNRs and promoting the actual applications in modern electronic warfare.  
C.W.: conceptualization, validation, writing--original draft. Q.Z.: resources, funding acquisition, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (Grant No. 62073123), the Key Research & Development and Promotion Project of Henan Province (Grant No. 242102211002), and the High-Level Talent Research Start-up Fund Project of Henan University of Technology (2023 BS040).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **CNN-ST** & **CNN-DQLN** \\ \hline FLOPs (G) & 3.7 & 5.2 \\ Parameters (M) & 20.6 & 34.9 \\ Time (ms) & 33 & 59 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computational complexity of two methods.
###### Abstract
The velocity of the lower wheel, i.e., the vehicle's longitudinal velocity, is changed using a large DC motor coupled with the wheel. The velocity of the upper wheel, i.e., the wheel's angular velocity, is diminished by using a disk-brake system, which is operated by using a small DC motor for which the control input is applied. In the case of the incipient lock-up of the upper wheel, an electronic control unit reduces the pressure in the disk-brake system via an electronic modulator. Recently, various studies used this ABS setup. For example, References [25; 26] compared linear and nonlinear controllers. In [27; 28], an adaptive controller that estimates the tire-road adhesion coefficient was developed. References [29; 30; 31] presented some fuzzy controllers, and reference [32] proposed intelligent control techniques applied to an adaptive neuro-fuzzy controller. Reference [33] provided a nonlinear observer to recreate a vehicle's longitudinal velocity, as was shown in [34], to develop a dynamic controller that imposes a specific wheel-slip ratio. A plain variant of this control was carried out through a linearized event-triggered strategy in [35]. Reference [36] presented their design and application to the ABS laboratory of the Lyapunov-based SMC and reaching-law-based SMC. In [37], the real-time implementation of the super-twisting controller for the ABS laboratory setup was developed using a super-twisting estimation to provide a finite-time estimation of the tire-road friction coefficient. In this article, a modified HOSM controller using a PID-sliding surface is designed and applied to the ABS laboratory setup to track a suitable value of the wheel-slip ratio. Hence, the original contributions of the present paper are as follows:  
1. A modified HOSM controller using a PID-sliding surface was designed. 2. The convergence and stability of the controller were proven rigorously using a theoretical analysis based on the Lyapunov function. 3. The proposed controller was implemented in an ABS laboratory setup, and the results were compared with a PID-like controller. The rest of this paper is organized as follows. Section 2 describes the mathematical modeling of the ABS laboratory setup. Section 3 discusses the control problem and the proposed modified HOSM using a PID-sliding surface. The numerical and real-time simulations are presented in Section 4. Finally, the conclusion summarizes this paper.
## 4 Results
### Individual Plant-Level Segmentation  
The dataset of the plot maize was utilized to validate the Mask3D's application potential for point cloud data acquired using UAV-LS technology. Table 9 presents the  
Figure 8: Performance and training time comparison among segmentation models for various crop types. The figure on the left depicts the analysis of mIoU against training time, while the figure on the right illustrates the analysis of OA against training time.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Tomato** & **Maize** & **Tomato\_** & **Maize\_1** & **Potato** & **Cotton** & **Rapeseed** & **Maize\_2** & **Total** \\ \hline PointNet & 0.67 & 0.62 & 0.17 & 0.35 & 0.18 & 0.28 & 0.25 & 0.43 & 2.95 \\ PointNet++ & 0.70 & 0.63 & 0.35 & 0.63 & 0.50 & 0.67 & 0.38 & 0.28 & 4.15 \\ PointMLP & 0.42 & 0.47 & 0.28 & 0.33 & 0.23 & 0.37 & 0.33 & 0.43 & **2.86** \\ DGCNN & 1.00 & 1.17 & 1.17 & 2.80 & 1.58 & 2.22 & 1.92 & 1.67 & 13.52 \\ PointCNN & 1.10 & 1.33 & 1.00 & 2.73 & 1.50 & 2.17 & 1.90 & 2.26 & 13.99 \\ PAConv & 2.00 & 2.63 & 1.92 & 5.38 & 4.12 & 5.70 & 5.02 & 6.22 & 32.99 \\ CurveNet & 4.00 & 4.50 & 4.63 & 4.65 & 11.30 & 7.00 & 5.63 & 5.83 & 47.55 \\ PT & 4.07 & 4.20 & 7.68 & 10.00 & 4.80 & 8.32 & 6.20 & 1.41 & 46.68 \\ ST & 5.00 & 4.67 & 7.63 & 12.00 & 6.20 & 11.00 & 11.63 & 30.03 & 88.16 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Training duration for nine segmentation models across seven crop datasets. Maize represents the maize data sourced from Pheno4D; Maize_1 refers to the maize data collected in field scenarios using TLS; Maize_2 represents the maize point cloud derived from UAV RGB images; and Tomato_ represents the tomato point clouds derived from DSLR images.  
segmentation results of Mask3D on the plot maize dataset. Figure 9 illustrates the visualization of instance segmentation results produced by Mask3D. Overall, Mask3D achieved satisfactory segmentation performance on the plot maize, with maize accuracy of 0.817 and a mean Average Precision (mAP) of 0.909. The visualization of the Mask3D results demonstrates that Mask3D effectively achieves instance segmentation of plot maize point clouds, except for one plant that was not distinguished. Remarkable segmentation results have been achieved on other individual maize plants. More importantly, compared to traditional segmentation algorithms, Mask3D does not require the configuration of complex parameters, showcasing significant application potential in point cloud segmentation at the individual plant level.
# A Survey on Recent Advanced Research of CPS Security
## 4 Discussion
### Application Scenarios
#### 4.2.3 Smart Transportation  
Smart transportation integrates traffic data collection, transmission, and analysis technologies. These technologies are supported by various sensors and powerful electronic control units (ECUs). Smart vehicles and drones can cooperate in a wireless network and therefore make traffic more efficient. All driving operations require the assistance of sensor information and control commands, which are also vulnerable to attackers.  
In transportation security, it is essential to detect abnormal behaviors and prohibit unauthenticated operation. To achieve this goal, researchers have utilized the statistical movement metrics of a drone [22] and an event-aware finite-state automaton (eFSA) model [18] to detect hidden attackers. Even when nodes in a drone formation are compromised, safe interactions can be guaranteed by data integrity attestation [24].