esa-sceva/satcom-chunk-collection huggingface dataset:
# Region Pixel Voting Network (RPVNet) for 6D Pose Estimation from Monocular Image
## 2 Materials and Methods
### Proposed Method
#### 2.2.2 Keypoints Selection  
Keypoints can be selected manually, but it lacks scalability. The farthest sampling strategy is adopted to select keypoints on the prior point cloud model: the center of the point cloud is initially selected; then, the farthest point from selected keypoints is repeatedly chosen from the point cloud. The detail of farthest sampling is shown in Algorithm 1. As suggested by PVNet [29], \(N_{k}\) is set to 8 in our model.  
In Figures 2-5, the white dots show the selected keypoints of several types of objects in LINEMOD [35]. LINEMOD is a standard dataset; more details can be found in Section 3.1.  
Figure 1: The structure of the network for keypoint estimation.  
```
Input:Model a point cloud model of target object; \(N_{k}\) the number of keypoints to be selected; \(center\) the center of Model; Output:\(K=K_{i},i=1,2,...,N\) the \(N\) keypoints in Model;
1 Initialize:\(K\leftarrow\emptyset,\text{{checked}}\leftarrow\emptyset,\text{{maxdis}} \gets 0,\text{{times}}\gets 0\);
2 putcenter into checked;
3 whiletimes\(N\)do
4\(maxdis\gets 0,curK\leftarrow\text{{none}}\);
5 for\(P\in\text{{Model}}\)do
6 curdis\(\leftarrow\) 0 for\(T\in\text{{checked}}\)do
7 curdis\(\leftarrow\) curdis\(+\|P-T\|\);
8
9 end for
10 ifcurdis\(>\) maxdisthen
11\(maxdis\gets\) curdis\(,curK\gets P\);
12
13 end if
14
15 end for
16
17 put curK into \(K\)put curK into checked;
18
19 end for
20 return\(K\)
```  
**Algorithm 1**Farthest Sampling  
**Input:**_Model_ a point cloud model of target object; \(N_{k}\) the number of keypoints to be selected; \(center\) the center of Model; \(K=K_{i},i=1,2,...,N\) the \(N\) keypoints in Model;  
Initialize:\(K\leftarrow\emptyset,\text{{checked}}\leftarrow\emptyset,\text{{maxdis}} \gets 0,\text{{times}}\gets 0\);  
putcenter into checked;
# A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet
## 2 Materials and Methods
### An Emotion Recognition Method from Multi-Band EEG Topology Maps Using ERENet
#### 2.3.1 Multi-Band EEG Topology Maps
Let \(p_{i}=\left\{p_{i}^{\theta},p_{i}^{\alpha},p_{i}^{\beta},p_{i}^{\gamma}\right\}\) denote the frequency domain features extracted from \(x_{i}\), and \(p=\left\{p_{1},p_{2},\ldots,p_{M}\right\}\) denote the frequency domain features of \(M\) electrodes extracted from EEG sample \(X_{S}^{i}\in\mathbb{R}^{M\times R}\). Figure 2: The mapping relationship between the spatial distribution of electrodes and the 2D topology of electrodes in the International 10–20 system: (**a**) The spatial distribution of the 32 electrodes in the International 10–20 system; (**b**) 9 \(\times\) 9 electrode topology matrix; (**c**) Topological positions of the 32 electrodes. According to the mapping rule described in Figure 2, the topological positions of 32 electrodes can be obtained by relocating the electrode channels to their topological positions, as shown in Figure 2c. The spectral power \(p^{\theta}=\left\{p_{1}^{\theta},p_{2}^{\theta},\ldots,p_{M}^{\theta}\right\}\) of theta band in \(p\) is mapped into a 2D matrix \(f_{\theta}\in\mathbb{R}^{h\times w}\) according to their respective locations, where \(h\) and \(w\) represent the height and width of the 2D matrix, respectively, and \(h=w=9\) in this paper. The remaining positions in the matrix are filled with zero. There are two reasons why zero is used to fill the remaining positions: matrix data more suitable for model input can be obtained, and the relative position of electrodes on the brain can be better simulated without introducing excess noise. This process is repeated for alpha, beta, and gamma bands. Therefore, the frequency domain features \(\left\{p^{\theta},p^{\alpha},p^{\beta},p^{\gamma}\right\}\) of the four frequency bands extracted from the time slice \(X_{S}^{i}\) are converted into four 2D topological maps \(\left\{f_{\theta},f_{\alpha},f_{\beta},f_{\gamma}\right\}\) through mapping, as shown in the lower left corner of Figure 3. \(\left\{f_{\theta},f_{\alpha},f_{\beta},f_{\gamma}\right\}\) will be used as an input for ERENet for learning and emotion evaluation of emotion generation mode.
## 3 Experiments and Results
### Evaluation of Experimental Results and Image Quality  
Common ways to evaluate images are Entropy (EN) [22], Mutual information (MI) [23], Structural similarity (SSIM) [24], Multi-scale SSIM [25], Visual information fidelity (VIF) [26], Spatial Frequency (SF) [27], Image Quality (Quality,\(\mathcal{Q}^{ab//}\)) [28], Noise(\(N^{ab//}\)) [29], Definition (DF) [30], Standard Deviation (SD). Except for the image noise method, the lower the value obtained, the better, the higher the value obtained by other methods.
# HH-NIDS: Heterogeneous Hardware-Based Network Intrusion Detection Framework for IoT Security
## 6 Discussion  
The HH-NIDS framework has trained lightweight NN models on the UNSW-NB15 and IoT-23 datasets using different schemes aimed at two hardware acceleration platforms. Although the eleven inputs used in this prototype are static features, the trained models have achieved high accuracy compared to the related works. To improve the results (higher accuracy and lower the false alarm rate), the Data Pre-processing block in the HH-NIDS framework needs to be upgraded to extract a more comprehensive input feature set.  
In Section 5.3.1, CPU and GPU implementations have an advantage in pre-process and post-process times thanks to high operating frequencies and rich memories for buffering input data; the data transfer buses in these platforms are also optimised for a broader range of applications. The GPU also has extra overhead for loading data into its on-chip memory, which explains why it starts to perform better with a greater input buffer size setup.  
The MAX78000 microcontroller with NN accelerate processors performs well, requiring only 15 us to infer an input. The power consumption is ultra-low at 18 mW, while it draws from 15 W to 17 W on the NVIDIA GeForce GTX 1650 GPU to infer inputs. Besides, the NN models on FPGA have shown high overhead in data transferring between the PS and the PL; the actual inference time was explained in the waveform simulation results in Section 5.3.2.  
While the HLS implementation is more flexible in changing the NN architecture, the Verilog implementation with pipelining is optimised for the best processing performance. Even though the pipelined design on the PL has no bottle-neck points that possibly operate at the on-chip frequency with a fixed 28 clock cycles overhead, the pre-process and post-process blocks on the PYNQ-Z2 SoC FPGA are run on the PS (ARM-based) with limited speed.
# Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading
###### Abstract  
The aging pipeline infrastructure around the world necessitates immediate rehabilitation. Internal replacement pipe (IRP) is a trenchless system offering a versatile and cost-effective solution across a variety of industries, including oil, natural gas, water, and wastewater. As a structural pipeline repair system, IRPs are subject to lateral deformation because of surface traffic loading. The present study evaluates the impact of adhesion between the host pipe and the IRP, with a focus on assessing the debonding effect on the behavior of the repair system under lateral deformation and bending. This was achieved using a comprehensive approach, including experimental, numerical, and analytical techniques. Varying levels of adhesive strength resulting from different methods of surface preparation were considered. The effectiveness of the IRP system on both discontinuous host pipes with various crack widths and continuous host pipes was also investigated. The results demonstrate that adhesive strength exerts a significant influence on the repair system, especially in the case of narrow circumferential cracks, while its impact on the continuous system is minimal. For optimal performance, it is essential to choose adhesives that possess sufficient shear strength while also accounting for the required debonding length. This approach ensures that minor discontinuities are effectively controlled, thereby enhancing the system's fatigue life. The reliable determination of the maximum allowable shear strength for the adhesive or the debonding length can ensure that it does not negatively affect fatigue life. The findings presented in this study offer new insights into the development of trenchless repair techniques that can enhance system performance and extend service life.  
internal replacement pipe; trenchless; discontinuous; adhesive; surface preparation; bonding; debonding; shear strength 20241410509 14 November 8, 2021
## 1 Introduction
However, the current research results have certain limitations: (1) most of the studies are based on ideal conditions; that is, the field operation terrain is flat, or the undulating degree is minor, the operation area is large enough that the harvester does not need to change direction frequently, or the harvesting needs of scattered rice fields in the southern area's hilly, mountainous terrain are not being met [18; 19]; (2) the design does not consider the impact caused to the system's detection accuracy and control effects by the high moisture characteristics of rice harvesting materials from southern China; (3) in addition to the high cost of promoted automatic control technology, some complex integrated control systems struggle to meet the current market demand for affordable, stable, or modifiable small- and medium-sized harvester-traveling speed control systems [20]. To solve the above limitations, we designed an adaptive algorithm for the feeding volume-traveling speed coupling system for a rice harvester in southern China. We calculated the feeding volume by detecting the torque and speed of the power shaft of the harvester's tilting conveyor. We also developed a traveling speed optimization strategy to calculate the optimal traveling speed, which was used as an input quantity to automatically adjust the stepper electric actuator action online based on fuzzy PID. We discussed and optimized the signal analysis method and controller algorithm design for rice harvesting conditions in southern China. The stability, accuracy, and real-time performance of the algorithm were verified through simulation experiments and field trials. Our results showed that this can provide a reference for rice harvesters' feeding volume-traveling speed coupling systems in southern China. In summary, the main contributions of this paper are as follows: (1) We proposed an optimal speed optimization strategy based on torque and detection of the tilting conveyor power shaft to improve the stability and accuracy of the harvester's traveling speed control; (2) We developed an adaptive algorithm for feeding volume-traveling speed coupling systems that adapts to rice harvesting conditions in southern China; (3) A control system with low hardware requirements was designed. The system has a high practical value. It can be used to develop new small- and medium-sized rice harvesters in hilly and mountainous areas. It can also be used for the modification of traditional harvesters. We propose that the variation of traveling speed during harvesting causes threshing loss. Our experimental results can provide rice harvesters with a reference for designing threshing and loss reduction systems in southern China. The rest of the paper is organized as follows: Section 2 discusses and determines the feed rate detection scheme. Section 3 outlines the overall framework of the control system. Section 4 presents the design of the algorithm. Section 5 presents experiment related content and discusses the simulation and field trial results. Section 6 summarizes our proposed approach and the direction of future work.
# Context-Aware Pending Interest Table Management Scheme for NDN-Based VANETs
###### Abstract  
In terms of delivery effectiveness, Vehicular Adhoc NETworks (VANETs) applications have multiple, possibly conflicting, and disparate needs (e.g., latency, reliability, and delivery priorities). Named Data Networking (NDN) has attracted the attention of the research community for effective content retrieval and dissemination in mobile environments such as VANETs. A vehicle in a VANET application is heavily reliant on information about the content, network, and application, which can be obtained from a variety of sources. The information gathered can be used as context to make better decisions. While it is difficult to obtain the necessary context information at the IP network layer, the emergence of NDN is changing the tide. The Pending Information Table (PIT) is an important player in NDN data retrieval. PIT size is the bottleneck due to the limited opportunities provided by current memory technologies. PIT overflow results in service disruptions as new Interest messages cannot be added to PIT. Adaptive, context-aware PIT entry management solutions must be introduced to NDN-based VANETs for effective content dissemination. In this context, our main contribution is a decentralised, context-aware PIT entry management (CPITEM) protocol. The simulation results show that the proposed CPITEM protocol achieves lower Interest Satisfaction Delay and effective PIT utilization based on context when compared to existing PIT entry replacement protocols.  
NDN; context-aware naming; VANETs; NDN; PIT; safety content dissemination; Pending Information Table; non-safety content; Named Defined Networking (NDN) Article
## 5 Performance Assessment
### Model Ranking  
We conclude the models' evaluation with a statistical ranking. The overall performance of a model was calculated on the basis of the linear ranking method [5]. Thus, the mean rank of a model is calculated as a weighted average:  
\[m_{i}=\sum_{k=1}^{7}p_{k}\cdot k \tag{7}\]  
where \(m_{i}\) is the mean rank of the \(i\)-th model (\(i=\) PB, E2, S1, S3, Y4, M1, and M2), \(p_{k}=n/36\) with \(n\) being the number of stations where the model \(i\) is in the \(nRMSE\) hierarchy at position \(k\). For each station, the best model is ranked 1 st, and the most ineffective model is ranked 7 th.  
Table 2 presents the ranking of the new and reference models based on the estimation of \(DNI\) and \(DHI\). Each column in the table corresponds to the mean ranking (Equation (7)) of a particular model. The proposed model M1 is ranked first in estimating both \(DNI\) and \(DHI\). For \(DNI\) estimation, M1 achieves the first position with a mean rank of \(m_{\text{M1}}=2.02\), and is closely followed by S1 (\(m_{\text{S1}}=2.30\)). For \(DIF\) estimation, M1 also achieves the first position with a mean rank of \(m_{\text{M1}}=2.05\), closely followed by S3 (\(m_{\text{S3}}=2.11\)). The detailed ranking of the models at the stations from D_TEST, on which the mean ranking (Table 2) was built, is displayed in Figure 5. Visual inspection shows that M1 performs best at the largest number of stations, taking first place at 16 stations for DNI estimation and at 17 stations for DHI estimation. The high accessibility of the M2 does not compromise accuracy. Figure 5 emphasizes M2 as a real competitor among the best-performing current models, taking second place at three stations for DNI estimation and first place at five stations for DHI estimation.  
Figure 5 also displays the models' sensitivity to climate. The stations are clustered by climate zones, according to the Koppen-Geiger climate classification [27]. Excepting the tropical climate A, where S3 is the most accurate model (with a mean rank (Equation (7)) \(m_{S3}=2.0\) for DNI estimation and \(m_{S3}=1.2\) for DHI estimation), in all the other climates, the proposed model M1 performs with the highest accuracy. Thus, for DNI estimation, M1 achieves the following mean rank: \(m_{M1}=2.0\) in arid climate B, \(m_{M1}=1.5\) in both temperate climate C and in continental climate D. For DHI estimation, M1 achieves the following mean rank: \(m_{M1}=2.3\) in arid climate B, \(m_{M1}=1.6\) in temperate climate C and \(m_{M1}=1.1\) in continental climate D.
## 4 Results and Discussion
### Friction Torque  
As shown in Figure 5(a), the contact torque first decreases rapidly, then decreases slowly in the boundary and mixed lubrication stages. As shown in Figure 5(b), the hydrodynamic torque first increases dramatically, then decreases significantly, and fluctuates with the fluctuation of friction pair gaps, finally increasing slowly with the increase of speed difference in the mixed and hydrodynamic lubrication stages.  
As the ATF temperature increases from 60 \({}^{\circ}\)C to 140 \({}^{\circ}\)C, the average decrease rate of contact torque increases sequentially during the boundary and mixed lubrication stages, which are 7753 N\(\cdot\)m/s, 10298 N\(\cdot\)m/s, 12264 N\(\cdot\)m/s, 13626 N\(\cdot\)m/s and 14824 N\(\cdot\)m/s, respectively. Furthermore, the maximum value of hydrodynamic torque during the disengaging process decreases sequentially, which are 25.6 N\(\cdot\)m, 9.89 N\(\cdot\)m, 4.89 N\(\cdot\)m, 2.54 N\(\cdot\)m, and 1.35 N\(\cdot\)m, respectively. Moreover, the hydrodynamic torque at the disengaging moment also decreases sequentially, which are 3.75 N\(\cdot\)m, 1.24 N\(\cdot\)m, 0.54 N\(\cdot\)m, 0.26 N\(\cdot\)m, and 0.14 N\(\cdot\)m, respectively. Therefore, the increase of ATF temperature significantly enlarges the decrease rate of contact torque (enlarged by 91.2%) and dramatically decreases the hydrodynamic torque (decreased by 94.7%) during the disengaging process.
# Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites
## 4 Experiment Analysis and Results
### Influence of Flood on L4 for GPS Satellites
#### 4.1.3 Influence of Rainfall on L4  
In order to eliminate the influence of rainfall on L4 observation, data sets collected on DOY 198 and DOY 200, 2021 are used. The rainfall on DOY 200 is 73.91 mm, but there is no rainfall on DOY 198. This information can be observed in Figure 3. There is no flood in these two days. Thus, whether the increase of L4 residuals is caused by the rainfall can be analyzed by using data sets from these two days. The comparison results of the G10 satellite on DOY 198 and DOY 200, 2021 are presented in Figure 9.  
From Figure 9, it can be seen that the fluctuation of L4 residuals on DOY 200 is almost the same as that of DOY 198. The slight difference between them is mainly caused by the random noise. This result can be further reflected by the RMS of these two series, which are 0.18 and 0.22 cm for DOY 198 and DOY 200, respectively. In addition, it is obvious that there is a large fluctuation both at the beginning and the end of these two series. The reason for this phenomenon is that the elevation angle of these two stages is low, and the multipath error and random noise perform more seriously than that at high elevation. This result can  
Figure 9: Residuals of L4 on DOY 198 and DOY 200, 2021 for G10 satellite. The blue and red lines denote the residuals of L4 on DOY 198 (no rainfall) and DOY 200 (with rainfall), respectively. The green line means the elevation angle.  
also be found in [PERSON] et al. [28]. Moreover, it should be noted that the same phenomenon can also be found on other satellites. Based on the above analysis, it can be concluded that the influence of rainfall on L4 is very small and can be ignored. Thus, the increase of L4 residuals during the flood is not caused by rainfall.
## 4 Experimental Results
### KITTI Benchmark
The beginning or initial submap data are overlapped with the final submap data, and thus we can see two identical walls overlapping in the starting area. Meanwhile, the AMDS had a difference of one meter from the known ground truth, and the map, as shown in Figure 6c.2, displays that the overlapping structure is almost invisible. A similar result also applies for the 60 m scan distances in Figure 6b.2, where it had a difference of one meter from the known ground truth. Although the AMDS (60 m/25 m) has a similar result as Cartographer (60 m scan), it successfully utilized better time performance in pose generation. In KITTI validation, the local AMDS (60 m/25 m) required 25 ms to generate pose while the local Cartographer (60 m scan) required 27 ms which shows approximately an eight percent reduction in the time performance.
# Efficient Coverage Path Planning for a Drone in an Urban Environment
###### Abstract  
Multirotor drones play an increasingly significant role in smart cities and are among the most widely discussed emerging technologies. They are expected to support various applications such as package delivery, data collection, traffic policing, surveillance, and medicine. As part of their services, future drones should be able to solve the last-mile challenge and land safely in urban areas. This paper addresses the path planning task for an autonomous drone searching for a landing place in an urban environment. Our algorithm uses a novel multi-resolution probabilistic approach in which visual information is collected by the drone at decreasing altitudes. As part of the exploration task, we present the Global Path Planning (GPP) problem, which uses probabilistic information and the camera's field of view to plan safe trajectories that will maximize the search success by covering areas with high potential for proper landing while avoiding no-fly zones and complying with time constraints. The GPP problem is formulated as a minimization problem and then is shown to be NP-hard. As a baseline, we develop an approximation algorithm based on an exhaustive search, and then we devise a more complex yet efficient heuristic algorithm to solve the problem. Finally, we evaluate the algorithms' performance using simulation experiments. Simulation results obtained from various scenarios show that the proposed heuristic algorithm significantly reduces computation time while keeping coverage performance close to the baseline. To the best of our knowledge, this is the first work referring to a multi-resolution approach to such search missions; further, in particular, the GPP problem has not been addressed previously.  
unmanned aerial vehicles (UAVs); autonomous systems; coverage path planning 2025  
2
# Intelligent Transportation System Technologies, Challenges and Security
## 3 The Impact of Innovative Technologies on Intelligence Transportation Systems
### Generation (5G) Mobile Networks  
Generation (5G) mobile networks will form the mobile communication infrastructure of the future. It will have much higher capacity and less latency than existing 4.5G systems. The wireless broadband infrastructure that these networks will provide connectivity not only for smart transportation services but also for many IoT applications [38].