esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapid Target Extraction in LiDAR Sensing and Its Application in Rocket Launch Phase Measurement
## 4 Conclusions  
This paper addresses the challenge of target extraction from 3D point cloud data collected by LiDAR and presents a fast and accurate method for 3D point cloud target extraction. The approach starts by using cylindrical unfolding projection to convert the 3D data into a 2D grayscale image, with the conversion relationship between the two being calculated. Two-dimensional target extraction is then performed on the grayscale image. Finally, the target 3D point cloud is extracted by leveraging the conversion relationship between the 3D data and the grayscale image, using a target retrospective search algorithm. The core of this method is to perform target extraction in the 2D image and then, using the computed conversion matrix, map the 2D targets back into the 3D point cloud through reverse search. This enables fast and efficient processing of 3D point clouds without sacrificing extraction accuracy. In the experimental section, data were collected in a real-world environment using a hybrid solid-state LiDAR. A near-field detection experiment was conducted to validate the feasibility of the proposed method. The results demonstrate that the method can accurately extract the target point cloud from hundreds of thousands of points in just 0.53 s, significantly outperforming traditional point cloud target extraction techniques. Furthermore, the rocket launch segment experiment also confirmed the applicability and reliability of the proposed target extraction algorithm.  
In summary, this study presents an efficient and reliable technical solution for target extraction from LiDAR point cloud data, offering a new research direction and methodology for the field of LiDAR point cloud processing. Future work will focus on further optimizing the algorithm to improve both processing speed and accuracy, as well as applying it to a broader range of practical scenarios.  
Figure 12: Three-dimensional trajectory of rocket vertical launch phase.  
**Author Contributions:** Conceptualization, X.L.; methodology, X.L.; data curation, X.L.; writing original draft preparation, X.L.; writing--review and editing, X.L., H.S., F.W., M.Y. ([PERSON]), W.H. and M.Y. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by The Youth Innovation Promotion Association CAS Fund Project (No. 2021406), Key Laboratory of Space Precision Measurement Technology CAS Fund Project (No. 29J21-063-III), and Key Laboratory Fund of the Chinese Academy of Sciences (CXJ)-225017).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available upon request from the author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 3 Data Sources and Quality
### Data Completeness and Accuracy  
The most fundamental challenge in crash analysis is underreporting, where a significant number of crashes are not captured in official databases. Sweden's Transport Agency (Transportstyrelsen) has studied this phenomenon extensively and provided a diagram, as seen in Figure 4, showing the varying coverage of different crash data sources. The extent of underreporting varies by crash type and jurisdiction, with [PERSON] et al. [20] finding that motorcyclists, cyclists, and young people are particularly under-represented in official reports.  
Beyond missing cases entirely, the accuracy of reported data presents another significant challenge. Location data prove especially problematic, with [PERSON] et al. [21] finding inaccurate locations in approximately one-third of their studied crash reports. Such inaccuracies can severely impact spatial analysis and the identification of high-risk areas.
# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
## 3 Global Path Planning Algorithm Based on the Improved Genetic Algorithm  
A distinctive feature in dynamic environments is the lack of complete map information; an AGV may only have the coordinates of a few target points. Compared with other global path planning algorithms, genetic algorithms require less map accuracy and can define a limited number of target points as chromosome variants to accomplish path planning between target points and incorporate constraints into the calculation process of the algorithm by improving the fitness function. Therefore, we design an improved genetic algorithm based on path smoothing factor and node busy factor as the global path planning algorithm. In addition, we integrate the scheduling policy into the global path planning and complete the queuing of AGVs by with a weight-based polling algorithm.
# Enhancing the Accuracy of Advanced High Temperature Mechanical Testing through Thermography
###### Abstract
Rolls-Royce plc developed a variety of novel thermal indicating paints and high-emissivity thermal paints, for use with gas turbine engine development which display an array of permanent colour changes in proportion with the temperature they are exposed to, and typically encompass a temperature range of 140-1330 \({}^{\circ}\)C [30; 31]. The emissivity of the HE23 TP was found to be stable over a wide temperature range up to 1300 \({}^{\circ}\)C using pyrometers. This was maintained over a range of incidence angles as a result of its high stability and lower angular dependency on emissivity. Effectively, HE23 TP can deliver a constant, very high emissivity value across an extremely wide temperature range and has the potential to prove invaluable in delivering accurate temperature measurement over an extensive range of both high and low temperatures under non-invasive techniques [30]. In this paper, HE23 TP is used in combination with the thermography technique to demonstrate its stability, accuracy, and repeatability of temperature measurement and control in comparison to traditional spot welded TCs. This paper demonstrates the inaccuracy of traditional techniques in dynamic temperature testing using thermography. Furthermore, the use of thermography allows for comprehensive temperature visualisation. Unique TMF testing is undertaken upon a non-metallic ceramic composite material (CMC), using thermography for temperature control on a bespoke purpose built TMF system.
# Experimental Investigation of Free-Motion Task Implementation on a Serial Metamorphic Manipulator
### Motivation and Contribution  
While previous research of the authors [28; 29; 30] focused on structural and kinematic optimization, this paper extends the optimization framework by incorporating simple SMMdynamics formulation and dynamic performance evaluation based on experimental data, which is essential for understanding the robot's behavior under real operational conditions. The primary scientific claims of this study are articulated as follows:  
* Assessing system modeling accuracy: This study provides insights into the accuracy of the proposed system model for SMMs. In order to test modeling accuracy, experimental validation is conducted to bridge the gap between theoretical predictions and real-world performance. Robot performance varies along the task path, necessitating the use of theoretical foundations and an accurate dynamic model to identify regions of higher and lower performance. This understanding is critical for the optimal configuration of task execution strategies and the precise tuning of controller parameters.
* Dynamic performance evaluation: Reconfiguration introduces additional variables that significantly affect kinematic and dynamic characteristics. The primary focus is to study the achieved performance along the free-motion task path execution for the optimized robot configurations. Optimized robot configurations should achieve higher efficiency in robot joints' motions and achieve higher acceleration capabilities with reduced joint effort.  
By integrating established performance evaluation tools, the analytical dynamic modeling of SMMs, and experimental validation, this research makes a key contribution to advancing the understanding of SMM dynamics and establishing a foundation for the development of more sophisticated control algorithms for SMMs. The insights gained from the dynamic performance evaluation, along with the recognition of the limitations in task execution performance, serve as a basis for designing control algorithms aimed at enhancing system performance under conditions of higher dynamic demands and load uncertainties, addressing critical challenges in optimizing task execution capabilities of SMMs.  
The remainder of this paper is arranged as follows. Section 2 outlines the fundamentals of dynamic modeling for SMMs, the performance measures employed and the methodology applied for experimental analysis. Section 3 provides a theoretical validation of the proposed dynamic model, while Section 4 presents the experimental case study with a detailed discussion of the results. Section 5 summarizes the main conclusions and outlines the direction for future scientific work.
# The Sensitivity Analysis of Parameters in the 1D-2D Coupled Model for Urban Flooding
## 5 Conclusions
### Identification of Key Sensitive Parameters  
The analysis confirms that minimum infiltration rate and Manning's coefficient of permeable surfaces exert a dominant influence on surface water accumulation under all rainfall conditions. In contrast, pipe roughness and surface roughness show conditional sensitivity, depending on specific rainfall patterns. These findings emphasize the importance of carefully calibrating highly sensitive parameters to enhance model accuracy and predictive reliability.
## 3 Results
### Evaluation of Aging Schemes  
The second simple functionalization scheme (2-bin), assuming no addition or production of bSOA and neglecting fragmentation, resulted in a predicted average OA equal to 2.6 \(\upmu\)g m\({}^{-3}\) (Table 2). Its root mean square error was higher than that of the 1-bin case, its fractional bias was similar and its fractional error a little less. Overall, its performance was quite similar to that of the 1-bin scheme.  
To explore the role of fragmentation, the 1-bin simple functionalization scheme was first used, assuming production of bSOA during aging combined with a fragmentation probability equal to 15% for all aging reactions as estimated previously [20]. The average predicted OA mass was equal to 2.9 \(\upmu\)g m\({}^{-3}\) with a fractional bias of 17% and a root mean square error equal to 1.69 \(\upmu\)g m\({}^{-3}\). This scheme tends to overpredict OA in contrast to the simple scheme, but the deterioration in performance is small to moderate.  
The 2-bin functionalization scheme, neglecting bSOA aging and assuming a fragmentation probability \(b\) = 0.1, performed a little better (lower bias and root mean square error) than the 2-bin scheme. However, the improvements were small (reduction of bias by 5% and of root mean square error by 0.1 \(\upmu\)g m\({}^{-3}\)) while the absolute error was almost the same. The 2-bin functionalization, with bSOA and more rapid fragmentation (\(b\) = 0.4), performed a little worse than the previous schemes with a tendency to overpredict OA (fractional bias 22%).  
In the next set of simulations, the detailed functionalization scheme (DET case) was used, assuming a fragmentation probability equal to 30%. This scheme performed quite well with a fractional bias of only 2% and a fractional error of 33%. Its root mean square error was just 0.05 \(\upmu\)g m\({}^{-3}\) higher than that of the simple 1-bin scheme. Combining the detailed functionalization, bSOA aging and intense fragmentation (\(b\) = 0.7) reduced the fractional error to 26% but increased the bias to 15%.  
Overall, the results are rather encouraging because the optimized schemes based on the Po Valley simulations appear to perform reasonably well in the very different environment of southern Finland.  
Figure 4: Predicted contribution of (**a**) ASOA-\(v\), (**b**) BSOA, (**c**) SOA from oxidation of intermediate-volatility organic compounds, (**d**) FPOA, (**e**) SOA from oxidation of evaporated POA, and (**f**) OA from long-range transport using the seven aging parameterizations.  
For all three aging schemes, fragmentation of the organic molecules led to a better prediction of the OA concentration, while in some cases more fragmentation than the probabilities calculated previously [20] is probably needed.
# Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks
## 5 Simulations and Results Analysis  
### Parameter Settings  
Experiments are conducted on a simulation server equipped with one NVIDIA GeForce RTX 3060 graphics card, one 12 th generation 6-core processor, and two 8 GB RAM modules. The software environment involves Python 3.9.13, PyTorch 1.13.0, and satellite tool kit (STK 11.6). Detailed parameter settings are presented in Table 1.  
In particular, to acquire position information of LEO satellites across 100 time slots, three LEO satellites are simulated at an orbital height of 900 Km using STK, thereby obtaining position information reports. The CPU's computing power is configured at 1000 cycles/bit [51], making the computing resources on the LEO satellite and cloud server reach \(3\times 10^{7}\) bit/s and \(1\times 10^{8}\) bit/s, respectively [40]. Therefore, the magnitudes of computing resources in the figures below may appear smaller than those in Table 1, but in reality, equivalent, with the only difference in units.  
Figure 2: Flowchart for joint computation offloading and resource allocation with P-DQN.  
Furthermore, for the \(Q\) network, the input layer's dimensionality equals the sum of state space and parameter action dimensions, while the output layer's dimensionality matches that of the action space. Hidden layers comprise three fully connected ones with 256, 128, and 64 neurons, respectively. Moreover, rectified linear unit (ReLU) activation functions are utilized for each hidden layer for nonlinear mappings. Next, for the parameterized policy network, its input layer matches with the dimensionality of state space, hidden layers mirror those of the \(Q\) network, and the output layer's dimensionality corresponds to that of the parameter action, utilizing the hyperbolic tangent (tanh) function as its activation.
## 3 Results and Discussion  
### Summary of Experiment Results  
The experimental results, presented in Figure 2, summarize the CR (MPY) values obtained across various factor combinations listed in Table 3, measured using both a commercial LPR sensor and a potentiostat. The data showed a clear correlation between temperature, flow velocity, and CR: higher temperatures and faster flow velocities resulted in increased MPY values. The most severe MPY values were recorded under the highest levels of temperature and flow velocity (No. 21, 24, and 27).  
In contrast, pH levels between 6 and 8 did not exhibit a significant impact on MPY under the tested conditions. This suggests that the CR of carbon steel remains stable within the tested pH range, confirming that temperature and flow velocity are dominant factors influencing corrosion under seawater conditions. The results also demonstrate the agreement between the commercial LPR sensor and the potentiostat measurements, with both techniques yielding MPY values within the standard deviation. This consistency demonstrates that the use of two-electrode LPR sensors as a reliable alternative to three-electrode systems for real-time corrosion monitoring.
## 5 Results
### Validation of Optimal Feature Subset  
To validate the efficiency of the RF-RFECV feature selection algorithm, the optimal feature subset derived from the RF-RFECV algorithm was compared with the complete feature dataset. XGBoost served as the classifier in the subsequent analysis, with the test set employed to assess the model's classification performance based on predefined evaluation metrics. The results of this comparison are presented in Table 6.  
The utilization of XGBoost as the classification model yields remarkable results, as shown in Table 6, with the accuracy, precision, and F1 score surpassing 90%. This indicates the effectiveness of the identification capability of XGBoost in determining the grade of surrounding rock. The RF-RFECV feature selection algorithm was employed to construct the XGBoost classification model. Compared with the complete feature set, the optimal subset of features as input variables enhanced the four evaluation indexes by 8.26%, 8.03%, 7.67%, and 7.89%, respectively. Hence, this demonstrates that RF-RFECV feature selection is efficacious in reducing feature dimensions and that it is the optimal feature subset to use as input for the subsequent classification models to achieve an accurate recognition of surrounding rock grades.
## 4 Conclusions  
The future 5G and 6G technologies require the installation of several base stations and access points at closer distances to achieve the required higher data rates. The transparent antenna is a possible solution that can be installed in windows of buildings and car windscreens to provide seamless wireless connectivity. This review paper presented a comparison of thin-film and mesh-grid technologies that enables the transformation from an opaque antenna to a transparent antenna. The performance parameters of both technologies are compared in terms of gain, bandwidth, efficiency, transparency, and transmittance for different applications and frequency bands.  
The efficiency results of Tables 1 and 2 indicate that optical transparent antennas designed from thin-film material show better efficiency as compared to mesh grids. The conductive fabric tissue using polydimethylsiloxane (PDMS) substrate provided 90% efficiency; in addition, the antennas designed using ITO, FTO material with glass substrate had an efficiency of 86%, and the ITO thin film with Pyrex glass showed 92% efficiency.  
In terms of transparency, the ITO thin-film technology provided the highest transparency of 90%. The mesh grid using quartz substrate showed a transparency of 95%. Therefore, mesh-grid technology performs a little better in terms of transparency, with a low-cost and simple fabrication technique.  
Future directions include the addition of a metasurface-based Fourier lens to achieve wide-angle beam steering, which will be useful for future wireless communication, and a holographic three-dimensional display. Moreover, improvement of directivity and efficiency in a nanoantenna system may be considered. Losses in metal structures need to be reduced. Improved optical antennas may be designed for the detection of certain diseases inside the human body.  
Conceptualization, A.R.C. and A.A.; validation, A.M.A. and A.Z.; formal analysis, A.R.C., A.A., and R.H.; investigation, A.R.C. and M.N.A; resources, A.M.A.; data curation, N.H.; writing, M.A.Q. and A.R.C.; preparation, A.R.C.; writing--review and editing, A.R.C., M.N.A., and M.A.Q.; visualization, A.A.; supervision, A.A.; project administration, R.H.; funding acquisition, A.M.A. and A.Z. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful to the Deanship of Research Oversight and Coordination (DROC) of King Fahd University of Petroleum & Minerals (KFUPM) for sponsoring this research under project number INCS2102.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
OTA Optical transparent antenna CP Conductive polymer AgNWs Silver nanowire AZO Aluminum zinc oxide Cu Copper MMMC Micro metal mesh conductive MM Micro metal mesh MWCNTs Multiwall carbon nanotubes AgGL Silver grid layer
## 3 Foundations of MANETs
### Introduction  
MANETs are self-configuring networks established by mobile wireless nodes. In order to transmit messages to nodes which are not in direct transmission range, nodes between source and destination have to operate as router and forward the messages. No preexisting infrastructure is required. Like P2P networks, MANETs work in a distributed and decentralized way, thus avoiding single points of failure as the routing tasks of each node may be performed by nearby nodes. On the other hand, MANETs require special routing algorithms and several challenges regarding security occur due to the characteristics of the network. These challenges have to be addressed to achieve a reliable availability of the network which is required in envisioned application scenarios where no infrastructure is available, like first responder scenarios (e.g., _DUMBO_[5], _HiMoNN_[6]), development projects (e.g. _One Laptop per Child_[7]) or car-to-car communication (e.g., _CAR 2 CAR Communication Consortium_[8]).
## 2 Related Work  
### Space-Air-Ground Integrated Network  
The Space-Air-Ground Integrated Network (SAGIN), as a heterogeneous network architecture for achieving global seamless coverage, has received widespread attention in recent years. SAGINs provide multi-layered communication services by integrating satellite, unmanned aerial vehicle (UAV), and ground networks, aiming to enhance network coverage and service quality. However, due to its multi-layered architecture and heterogeneous characteristics, SAGINs face numerous challenges in network architecture design, resource management, and security.  
Research related to SAGINs has made significant progress in recent years. Considering that fifth-generation (5G) mobile communication networks struggle to meet the demands of emerging technologies and applications such as the Industrial Internet of Things (IIoT), Digital Twin (DT), and ubiquitous Artificial Intelligence (AI), [PERSON] et al. [11] proposed a new flexible, low-latency, and flat SAGIN architecture to support the new requirements of sixth-generation (6G) mobile communication networks. With the widespread application of Artificial Intelligence (AI) technologies, constructing deep learning service-oriented SAGINs has become essential. The integration of AI technologies with SAGINs is essential for applications requiring global coverage and distributed intelligence. Global climate modeling demands AI analysis of datasets from satellites, aerial sensors, and ground stations worldwide--a task only SAGINs can support with its comprehensive coverage. Autonomous maritime navigation requires continuous AI processing across oceanic regions beyond traditional network reach. Disaster response applications rely on AI to process data from satellites detecting events, UAVs providing situation assessment, and ground sensors--all requiring a SAGIN's seamless integration. [PERSON] et al. [12] proposed a hierarchical intelligent computing structure and optimization strategy for processing deep learning tasks in future SAGINs, aimed at improving the Quality of Service (QoS) for deep learning tasks. The heterogeneous, time-varying, and multi-dimensional information sources in SAGINs make it difficult for traditional network architectures to support resource allocation in large-scale complex network environments. [PERSON] et al. [13] proposed a service provision method based on Service Function Chain (SFC) to address resource efficient allocation and utilization issues in SAGINs. Space-Air-Ground Integrated Networks face significant challenges in security and privacy. [PERSON] et al. [14] proposed a Quantum-secure SAGIN (Q-SAGIN), which utilizes quantum mechanics to protect data channels in the integrated network and presented a universal Quantum Key Distribution (QKD) service provision framework to reduce the cost of QKD services.