esa-sceva/satcom-chunk-collection huggingface dataset:
# Attentional Keypoint Detection on Point Clouds for 3D Object Part Segmentation
These methods can capture complex relationships between different parts of the object and are often more efficient than voxel-based methods. Some other methods adopt multiview CNNs to render the 3D object from multiple viewpoints and then apply 2D CNNs to these 2D images [10]. The features extracted from different views are then combined to make a final prediction. This method leverages the strength of 2D CNNs while still capturing some of the 3D structure. Although the above methods have achieved good performance in part segmentation tasks, there are still at least three aspects that can be improved. First, although they can obtain good feature representation capabilities, the features they obtains are relatively sparse, which can make it difficult to satisfy the requirements of subsequent complex tasks. Second, while these works can capture good edge information from the input point cloud, they are unable to model the overall shape information, which is very important for the part segmentation of 3D objects. For instance, prior knowledge that the input point cloud represents a cup with a handle can be very helpful for subsequent part segmentation. Furthermore, although many works attempt to extract highly expressive features, numerous studies have demonstrated that combining multimodal data can provide very strong feature representations for downstream tasks. Given the substantial modal disparity between the edge features and the features provided by keypoints, devising a method to effectively integrate them into a more powerful feature representation has a strong impact on subsequent tasks. Specifically, we introduce a keypoint detection algorithm to provide intrinsic shape information for part segmentation. In this paper, we use PointNet++ as our baseline, which can obtain good point cloud features, but this information is relatively sparse. We propose a feature expansion module to extract global feature representations to address the issue of sparse feature extraction. For the second problem, we propose a keypoint acquisition module that can effectively capture the intrinsic shape information of point clouds, which is of great help for object part segmentation. For the third problem, we introduce a novel feature fusion mechanism that learns weight information for features of different modalities through this mechanism. In summary, the contributions of this paper include the following:  
* We propose an attentional keypoint detection network that leverages inner-shape information for part segmentation. * In this network, a keypoint-aware module, a feature extension module, and an attention-aware module are proposed. * Experiments on the ShapeNet-Part dataset demonstrate the effectiveness and superiority of the proposed modules in improving segmentation accuracy. * In order to demonstrate that the three proposed modules are not limited by the task itself, we conducted experiments on the ModelNet40 dataset to validate the effectiveness of the modules.
# Aerial Grasping of an Object in the Strong Wind: Robust Control of an Aerial Manipulator
###### Abstract  
An aerial manipulator is a new kind of flying robot system composed of a rotorcraft unmanned aerial vehicle (UAV) and a multi-link robotic arm. It gives the flying robot the capacity to complete manipulation tasks. Steady flight is essential for an aerial manipulator to complete manipulation tasks. This paper focuses on the steady flight control performance of the aerial manipulator. A separate control strategy is used in the aerial manipulator system, in which the UAV and the manipulator are controlled separately. In order to complete tasks in environments with strong wind disturbance, an acceleration feedback enhanced robust \(H\infty\) controller was designed for the UAV in the aerial manipulator. The controller is based on the hierarchical inner-outer loop control structure of the UAV and composed of a robust \(H\infty\) controller and acceleration feedback enhanced term, which is used to compensate for the wind disturbance. Experimental results of aerial grasping of a target object show that the controller can suppress the wind disturbance effectively, and make the aerial manipulator hover steadily with sufficient accuracy to complete aerial manipulation tasks in strong wind.  
Keywords: aerial robot; aerial manipulator; robot control; disturbance rejection; robust control; UAV +
Footnote †: journal: Applied Sciences
# Chaotic Vibration Prediction of a Laminated Composite Cantilever Beam
## 5 Conclusions  
The RNN data-driven model has been demonstrated to accurately and robustly predict chaotic vibrations in the laminated composite cantilever beam under various perturbed initial conditions. Unlike conventional numerical simulation methods that rely on explicit governing equations and domain expertise, the RNN model learns the dynamic behavior implicitly from historical time-series data. The results demonstrate that the RNN model can accurately predict the chaotic vibrations of the laminated composite cantilever beam,  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \(5450\text{Pa}\sin(20\pi t)\) & \(3450\text{Pa}\sin(20\pi t)\) & \(4500\text{Pa}\sin(23\pi t)\) & \(4500\text{Pa}\sin(41\pi t)\) \\ \hline MAE & 0.030 & 0.010 & 0.024 & 0.041 \\ \(w_{\text{P}}\) & 0.053 & 0.018 & 0.043 & 0.080 \\ \(\text{d}w_{\text{P}}/\text{d}t\) & 0.008 & 0.003 & 0.005 & 0.003 \\ RMSE & 0.050 & 0.019 & 0.039 & 0.067 \\ \(w_{\text{P}}\) & 0.070 & 0.026 & 0.055 & 0.095 \\ \(\text{d}w_{\text{P}}/\text{d}t\) & 0.010 & 0.003 & 0.006 & 0.004 \\ \hline \end{tabular}
\end{table}
Table 1: Testing result comparison based on four situations.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \(w_{1,1}(0)=0.001\), & \(w_{1,1}(0)=0\), & \(w_{1,1}(0)=0\), & \(w_{1,1}(0)=0\), \\ \(w_{1,2}(0)=0\), & \(w_{1,2}(0)=0.001\), & \(w_{1,2}(0)=0\), & \(w_{1,2}(0)=0\), & \(w_{1,2}(0)=0\), \\ \(providing a valuable complement to traditional simulation techniques in the context of chaos and structural dynamics.  
This study focuses on the prediction of chaotic vibrations in a multi-dimensional nonlinear dynamic system of the laminated composite cantilever beam. Based on the numerical simulation results, the main conclusions and contributions are summarized as follows:  
1. The RNN data-driven model is capable of automatically learning the time-series characteristics of chaotic vibrations from data generated by numerical simulation and accurately predicting the nonlinear vibrations of the laminated composite cantilever beam.  
2. The model's predictive performance was evaluated under various combinations of excitation magnitudes and angular frequencies. The results showed that the maximum MAE and RMSE reached 0.041 and 0.067, respectively, indicating that the RNN model exhibits good prediction accuracy and numerical stability in capturing chaotic vibrations.  
3. Given the strong sensitivity of chaotic systems to initial conditions, the robustness of the RNN model was further tested by introducing multiple perturbed initial states. The results demonstrated that the RNN model maintained reliable performance even under disturbed initial conditions, with a maximum MAE of 0.022 and RMSE of 0.038, confirming its robustness and resistance to initial conditions.  
Therefore, the predictive analysis demonstrates that the RNN data-driven model is a powerful algorithm for predicting the chaotic vibrations of the widely applied laminated composite cantilever beam structures.
## 5 Experimental Results and Analysis
Figure 15 shows simulated grid voltage injected with the 5 th and 7 th harmonics on current control. In this system, the current THD was 6.3% before application of the proposed CFFC and DRC, which exceeds the IEEE 519 standard of 1.3% and precludes it from being connected to the grid. After application of the proposed method, this THD could safely be connected to the distribution system. THD results at the distorted grid voltage conditions are summarized in Table 7. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Transient Response**} & \multicolumn{3}{c}{**Transient Response Characteristics**} \\ \cline{2-4}  & **Without** & **With** & **Reduction Ratio** \\  & **CFFC and DRC** & **CFFC and DRC** & **Reduction Ratio** \\ \hline Peak current & 6.03 [A]/201 [\%] & 5.5 [A]/183.3 [\%] & 0.48 [A]/17.7 [\%] \\ \hline Transient time & 0.06 [s] & 0.03 [s] & 0.03 [s] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experiment results of GCI system phase current THD when line-to-ground fault occurs. Figure 14: When line-to-ground fault occurs, experimental results of the stationary reference frame grid currents and abc reference frame grid voltages (**a**) without CFFC and DRC and (**b**) with CFFC and DRC.
# A New Hybrid Prime Code for OCDMA Network Multimedia Applications
## 5 The Proposed OCDMA System Model  
Figure 20 shows the proposed OCDMA transceiver system architecture based on the HPC. To prevent the pulse spreading during the optical fiber channel, the data is first PPM modulated. Each PPM chip is spread by the proposed HPC sequence of length \(L\)  
Figure 19: Cross-correlation between \(C_{173}\) and \(C_{193}\) sequences from different groups, \(P_{1}=5\) and \(P_{2}=7\).  
Figure 18: Cross-correlation between \(C_{121}\) and \(C_{150}\) sequences from different groups, \(P_{1}=5\) and \(P_{2}=7\).  
and weight \(W\) and then is fed to a star coupler optical channel to broadcast it [54]. The system multiplicity \(M\) is the power control factor that controls the optical power level at the receiver. The total number of available code sequences is the most important factor for the system capacity calculation and throughput. Additionally, the MAI caused by the star coupler optical channel is also the important factor that affects the BER performance and system capacity and is considered in the performance analysis.
# Android Malware Detection Based on Hypergraph Neural Networks
## 2 Related Research
And the APKs were handed over to the downstream classifier for detection and classification. However, their construction of heterogeneous graphs still required domain knowledge. Compared to the approach in [12], a more straightforward approach is to directly combine the FCG and the extracted function features with the graph neural network. [PERSON] et al. [1] used FCGs to extract centrality features of functions (vertices) and directly combine popular graph neural networks such as GCN, GIN, and GraphSAGE for graph-level classification tasks, and enhances the model representation with the Jumping Knowledge technique to achieve an excellent detection accuracy. [PERSON] et al. [14] extracted the properties of the functions in the FCG itself, such as whether they are declared by public or static descriptors, whether the function is an android API, the size of the function bytecode, the opcode contained inside the function, etc. These properties reflect the essential functional characteristics of the function. The FCG with features is provided to a graph convolutional neural network for representation learning, and the graph embedding features obtained from the Readout function are provided to a downstream classifier for classification. [PERSON] [15] separated local subgraphs around sensitive APIs from FCGs and used GNN to classify the subgraphs. In an APK, if a local subgraph was identified as malicious, the APK was treated as a malicious application. The above research has achieved some success in the field of android malware detection; however, considering only the caller-callee relationship between functions is still insufficient to exploit the rich semantic information. Using hypergraphs to reconstruct FCGs, or constructing hypergraphs using properties such as common permissions and common descriptors between functions, and then using hypergraph neural networks for representation learning, is a meaningful research direction. In this paper, we use hyper graphs to construct the multiple forms of topological relationships in android applications to capture higher-order semantic relationships between functions.
## 5 Conclusions  
In this paper, an AI-based model using the DBN method to analyze GPR data obtained from the previously published source was presented. The proposed DBN model with four inputs, namely temperature (T), relative humidity (RH), chloride level (CL), and rebar surface corrosion condition (CS) was employed to predict the amplitude of GPR signals. A total of 288 experimental records were utilized to train and test the proposed predictive DBN model. The model showed excellent performance in the prediction of the output with the value of the coefficient of determination was 0.9681 for the entire database.  
Regarding the sensitivity analysis, the results revealed that the temperature (T) was found as the most influential variable in the prediction of the amplitude of the GPR signal. In the meantime, the relative humidity parameter (RH) was found less sensitive to the output of the predictive DBN model. It is worth noting that the limited amount of available GPR experimental data in developing the predictive DBN model might be a drawback of this study. The performance of this intelligence-based model should be compared to the other traditional model such as the regression model or other data-driven models in future studies.  
T.T.N.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Resources, Writing--original draft, Writing--review & editing; P.T.T.: Project administration, Conceptualization, Investigation, Methodology, Resources, Supervision, Writing--review & editing; N.N.T.: Investigation, Resources, Supervision, Validation, Writing--review & editing; N.N.L.: Investigation, Resources, Supervision, Visualization, Review & editing; T.T.L.: Data curation, Investigation, Resources, Software, Supervision, Validation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used to support the findings of this study are included in the cited article.  
The research group of Development and Application of Advanced Materials and Modern Technologies in Construction would like to thank the Hanoi University of Civil Engineering, Vietnam for funding this research.  
The authors hereby declare no potential conflict of interest regarding the research, authorship, and/or publication of this article.  
Figure 9: Scatter of the predicted and actual amplitude of GPR signals: (**a**) Training; (**b**) Testing; (**c**) Overall.
## 1 Introduction
Therefore, a boosting ensemble learning method was adopted in this paper for the design of an ANC system. Common boosting ensemble algorithms include adaptive boosting machine (AdaBM), gradient boosting decision tree (GBDT) [16], histogram-based gradient boosting machine (Hist-GBM) [17] and light gradient boosting machine (Light-GBM) [18]. Among these, GBM optimizes the loss function in the direction of the fastest gradient decline through orderly iteration. The Hist-GBM and Light-GBM algorithms are derived from GBDT. GBDT is widely used because it is easy to achieve high regression accuracy and generalizability. The GBDT is a decision tree with the introduction of two ideas, gradual lifting and shrink, which improve the generalizability of ordinary decision trees. The core point of GBDT is the accumulation of the predicted values from multiple decision trees, with the residual as the learning goal of each decision tree. Through the above analysis and discussion, this paper uses GBDT to design an ANC method for different noise types. [PERSON] predicted airfoil self-noise using a GBDT with random forest [19]. [PERSON] proposed a new GBDT algorithm based on a Kalman filter to tackle time series prediction problems [20]. Based on GBDT and principal component analysis, [PERSON] proposed a novel seismic trace interpolation method for irregularly sampled spatial data [21]. To address situations where the noise type is unknown, this paper designed an ANC based on VMD and GBDT algorithms with high real-time performance. To identify noise type automatically, a PCA dimensionality reduction algorithm and VMD algorithm were used to extract characteristics of input noise, in order to reduce data size. Then, a k-means clustering algorithm was applied to classify the extracted noise characteristics. Finally, GBDT regressors were constructed for different types of noise, to acheive the objective of an ANC system with high real-time performance and better noise reduction. The rest of this paper is organized as follows: Section 2 presents the noise feature extraction based on VMD, noise type clustering based on PCA and k-meas++ and the ANC method based on GBDT. Section 3 illustrates the pre-processing of a noise dataset and numerical simulation of the ANC. Section 4 presents the results of analyses. The paper's conclusions are in Section 5.
## 3 Classical Methods for Generating OAM Waves in RF Domain
### Spiral Phase Plate (SPP)  
A spiral phase profile is obtained when a plane wave passes through a system of varying phase. Spiral phase plates (SPPs) come from the optical frequency region and are generally made of a material with constant refractive index and a radially varying thickness, as illustrated in Figure 2. Usually, the higher the topological charge is desired, the bigger is the thickness difference in the SPP. The advantages of SPP reside in small OAM beam divergence and low attenuation. The main disadvantages of the SPPs are that they are not operational for low-frequency signal transmission and since the design is fixed, they cannot provide simultaneous generation of several different OAM topological charges. The studies presented in References [28; 29] have been performed in the millimeter wave frequency band at 86 and 60 GHz, respectively. Two different phase plates were designed in [28] and were made of polyethylene having a refractive index of 1.52 at millimeter-wave frequencies. One was utilized to generate the LG\({}_{0}\)\({}^{1}\) mode and the other one to generate the LG\({}_{0}\)\({}^{2}\) mode. The step heights of the phase plates were 6.7 mm and 13.4 mm respectively to give a single and a double wavelength step at 86 GHz. The main characteristic of a vortex OAM beam which is the null of energy at the center of the radiated beam (doughnut shaped radiation pattern) can be clearly observed in the results presented in Figure 2.
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 4 Evolution of Pavement Image Processing Techniques
### Conventional Machine Learning Methodologies  
Machine learning algorithms have significantly improved pavement distress detection by enabling automated feature extraction from large annotated datasets. These methods enhance both accuracy and automation in infrastructure condition assessment.  
Artificial Neural Networks (ANNs), a core machine learning technique, simulate biological neuron connections through interconnected nodes (performing specific functions) and weighted links (defining node relationships). [PERSON] [29] developed a three-layer Back Propagation (BP) neural network (Figure 5) with 6 input nodes, 12 hidden nodes, and 5 output nodes to classify five pavement distress types including transverse and longitudinal cracks. However, this model requires manual preprocessing to extract input parameters (e.g., crack seed count, length) as data lists, limiting its automation capability.  
Classical machine learning algorithms, including decision trees, random forests (RFs), and support vector machines (SVMs), have been extensively applied in pavement analysis  
Figure 4: Treatment of asphalt pavement cracks using OTSU and noise reduction optimization method: (**a**) Asphalt pavement cracks; (**b**) OTSU processing result; (**c**) Median filtering denoising; (**d**) Anisotropic diffusion denoising.  
Figure 5: Three-layer BP neural network.  
alongside ANNs. These methods enable preliminary distress classification and localization by extracting key image features like texture patterns and geometric shapes. Table 1 provides a comprehensive comparison of these techniques.  
While effective for pavement distress identification, traditional machine learning methods face key limitations in automation and scalability. These algorithms require extensive manual data annotation and struggle with computational efficiency for large datasets. Methods like SVMs and decision trees often overfit training data with poor generalization, despite random forests' improved performance through ensemble learning. However, even random forests face computational constraints that limit real-time applications. These challenges drive the need for more advanced frameworks to enable accurate, automated infrastructure monitoring.
## 6 Simulation Results
### Analysis of Communication Complexity  
This subsection evaluates the communication complexity of the proposed HDTC scheme, given a random environment (Scenario 1) with \(\theta=60^{o}\), transmission range = 150 m and an average speed = 21 m/s.  
Referring to the simulation result (Figure 13 (bottom left)), the average communication overheads in the proposed HDTC method can be properly approximated, which yields the following:  
* Source node sends a message to the communication group members (average group size = 14). \[N_{T}=1,N_{R}=14\]
* The communication group members reply a message to the source node. \[N_{T}=14,N_{R}=14\]
* Routing node selection: \[N_{T}=1,N_{R}=1\]
* The relay node replies a message to the source node, which completes the operations in one round. \[N_{T}=1,N_{R}=1\]
* Five rounds are performed in a typical run (_i.e._, the average number of hops = 5).  
Thus, the total number of transmissions and receptions are 85 and 150, respectively.  
Figure 21: The communication complexity of the HDTC scheme, cone-based scheme and distance scheme under Scenarios 1, 2 and 3, respectively.  
Similarly, given the same traffic scenario and referring to Figure 13 (bottom left)), the cone-based method [9] induces a group size = 20, and the number of hops = 4, which generates a total number of transmissions and receptions, 92 and 168, respectively. For the distance method [8], the typical run forms a group size = 22, and the number of hops = 4, which generates a total number of transmissions and receptions, 100 and 184, respectively. A similar analysis can be applied to Scenarios 2 and 3. Referring to Figure 21, the proposed HDTC method is shown to have lower communication complexity compared to the other two methods.