esa-sceva/satcom-chunk-collection huggingface dataset:
# Logically-Centralized SDN-Based NDN Strategies for Wireless Mesh Smart-City Networks +
## 3 Proposed SDN-Based System
### Proactive NDN Path Selection Strategy  
The proactive strategy is also SDN-based and it is grounded on the implementation described in Figure 2. Specifically, each node in the network collects information from its neighbors related to RSSI and delays, periodically. The collected monitoring data is transferred to the SDN controller through the control channel. Then, the controller performs the clustering and chooses the best path by adjusting the NDN on the wireless nodes accordingly. Compared to the reactive strategy, in this implementation, the SDN controller forwards all the content requests on the selected path until it re-estimates the best path again. For this reason, we consider this strategy proactive.  
More precisely, the proactive strategy is based on partitional clustering and similarity-based distance measures. The main goal is to determine the best NDN path in terms of performance and reliability for end-to-end NDN communication (e.g., between an NDN consumer and NDN producer). In summary, our implementation has the following objectives: (i) the data collection among the network nodes, regarding the RSSI and delay, in a central node of the network; (ii) the clustering of the measurements, and (iii) the determination of the end-to-end NDN best paths among the wireless links.  
In Figure 3, we illustrate the NDN path selection according to the clustering results, considering 4 clusters, sorted from best to worst based on the average value within the cluster. The color gradations indicate the cluster each link belongs to, i.e., the colors green, blue, black, and red symbolize the clusters from best to worst, respectively. Then, each provided path is characterized by its worst link, e.g., the worst path contains at least one link clustered as the worst link. For example, in Figure 3, the best path is that containing the nodes 1-2-3-5-8, since all the including links are clustered as best-links (notated with green color).  
Here we describe the clustering method, we choose the dynamic time warping (DTW) [31] to estimate the similarity between the time series. Exploiting the nonlinear and time-independent nature of DTW, compared to typical measures (e.g., Euclidean distance), which provides a more robust and \"global\" (less sensitive to local time-series shifts) similarity estimation, improving the clustering performance. Moreover, we utilize the k-medoids [32] algorithm, since it is a representative object technique, more resilient to outliers [33] than k-means. The fundamental steps of the proposed clustering approach, are given below:  
* Step 1: Calculate DTW, pairwisely, between all the available links.
* Step 2: Apply the k-medoids algorithm to the vector that describes the pairwise similarity of all the available links (computed in Step 2). The number of clusters is pre-defined (in Section 4. We chose 4 clusters).
* Step 3: Sort the clusters, according to a specific Quality of Service (QoS) metric, for example, RSSI or delay.  
Figure 3: NDN path selection using the proactive strategy.
## 3 Materials and Methods
### Information Sources and Data Collection  
The procedure for collecting the information contained in the different official repositories (public data) relating to the RMSC was developed through the compilation of the documentation via the websites of the main official bodies, whether national, regional or provincial. The main sources of information consulted are listed below (Table 1):  
* General Directorate of Traffic (DGT): data on the vehicle fleet linked to the RMSC were obtained from this body, as well as the demand for state-owned roads that run through the area under study.
* National Centre for Geographic Information (CNIG) and the Territorial Information System of Extremadura (SITEX): these were the public tools that made it possible to obtain geographic information on the municipalities in the study area, both in terms of transport networks and municipal and provincial boundaries.
* National Statistics Institute (INE) and the Extremadura Statistics Institute (IEEX): both official sources were used to provide information on socio-demographic data, making it possible to obtain data on the population of each municipality in the RMSC, as well as different socio-demographic and economic indicators.  
Figure 2: Methodology flowchart.  
Based on the parameters obtained through the different calculations, geographic information system tools (specifically the free and open source software QGIS 3.16.16) were used for the processing, analysis, visualisation and interpretation of the data. This resource allows the materialisation of information on the territory by means of the elaboration of thematic maps as a geographic information system and is an indispensable tool for managing the dynamics of georeferenced data and information for any form of territorial planning [53]. Thus, through the different analyses carried out, the aim is to show the situation of the municipalities that make up the area under study. Therefore, data from different public bodies were compiled, obtaining information at the municipal level for the period 2014-2020, with the aim of observing the existing variation of different aspects related to the mobility patterns of these municipalities.
## 3 Results
### Short-Lasting Effect of Real rPMS  
Because of the interaction effect of groups and time found through MANOVA, we performed a paired \(t\)-test as a post hoc analysis to identify at which timepoint (i.e., before stimulation vs. right after stimulation, before stimulation vs. 10 min after stimulation, before stimulation vs. 20 min after stimulation) the rPMS effect was statistically significant, multiple comparisons was corrected by [PERSON], and \(\alpha\) level was set to 0.167. In the real rPMS group, for perceived hand length of where the ring finger locates, before stimulation (10.46 \(\pm\) 2.53) compared with right after stimulation (11.19 \(\pm\) 2.47), \(p\) = 0.017, compared with 10 min after stimulation (10.98 \(\pm\) 2.92), \(p\) = 0.115, compared with 20 min after stimulation (11.28 \(\pm\) 2.68), \(p\) = 0.015; for perceived hand length of where the little finger locates, before stimulation (8.69 \(\pm\) 2.54) compared with right after stimulation (9.17 \(\pm\) 2.51), \(p\) = 0.074, compared with 10 min after stimulation (8.76 \(\pm\) 2.90), \(p\) = 0.848, compared with 20 min after stimulation (9.08 \(\pm\) 2.63), \(p\) = 0.208; for the percentage of perceived hand length of where the middle finger locates to corresponding real length, before stimulation (64.89 \(\pm\) 14.54) compared with right after stimulation (68.03 \(\pm\) 13.59), \(p\) = 0.075, compared with 10 min after stimulation (66.63 \(\pm\) 16.02), \(p\) = 0.310, compared with 20 min after stimulation (68.47 \(\pm\) 14.54), \(p\) = 0.035; for the percentage of perceived hand length of where the ring finger locates to corresponding real length, before stimulation (60.93 \(\pm\) 13.70) compared with right after stimulation (65.17 \(\pm\) 13.19), \(p\) = 0.017, compared with 10 min after stimulation (63.99 \(\pm\) 16.12), \(p\) = 0.110, compared with 20 min after stimulation (65.80 \(\pm\) 15.00), \(p\) = 0.014; for the percentage of perceived hand length of where the little finger locates to corresponding real length, before stimulation (60.16 \(\pm\) 16.47) compared with right after stimulation (63.38 \(\pm\) 15.95), \(p\) = 0.085, compared with 10 min after stimulation (60.55 \(\pm\) 18.78), \(p\) = 0.868, compared with 20 min after stimulation (62.92 \(\pm\) 17.23), \(p\) = 0.207. See Figure 3. In the sham rPMS group, paired \(t\)-test of before stimulation compared with right after stimulation, 10 min after stimulation, and 20 min after stimulation, none of the comparisons were of statistical significance.
## 6 Conclusions  
BDS PPP have been implemented and employed to retrieve seismic displacements of the 2015 Mw 7.8 Gorkha, Nepal, earthquake. In order to obtain positioning results with higher precision, 1 Hz BDS satellite clock corrections were generated based on precise orbit products from WHU. They were utilized for BDS PPP solutions and their results were compared with the counterpart of GPS PPP. It is demonstrated that the accuracy of BDS PPP could reach 1 cm in horizontal and 2 cm in vertical components in retrieving seismic displacements of this earthquake event, which is comparable to the result of GPS PPP.  
We have also analyzed the contributions to BDS PPP of clock corrections with different intervals. It is shown that PPP with 300 s clock intervals is the worst and that with 1 s clock interval is the best, while the precision of PPP solutions for the 5 s clock intervals is almost the same as 1 s results. Considering the time consumption of clock estimates, we suggest that the 5 s clock interval is adequate with regard to the requirement of high-rate BDS solutions.  
It should be noted that the approach of clock generation in this study is not suitable for real-time estimation. For future research we plan to investigate the real-time clock estimation approach for real-time applications, such as rapid determination of earthquake magnitude [28], earthquake early warning [29], and rapid hazard assessment [30].  
**Acknowledgments:** The IGS MGEX and ILRS are greatly acknowledged for providing the GPS, BDS and SLR tracking data. This research is partially supported by the National Natural Science Foundation of China (Grant No. 41231174, 41674004, 41325015, 41404032, 41631073) and the Fundamental Research Funds for the Central Universities (No. 2042016 kf0185, 2042015 kf0194).  
**Author Contributions:** [PERSON] provided the initial idea for this study. [PERSON] designed and performed the experiments and wrote the article. [PERSON] led the study and wrote the article. [PERSON] prepared and analyzed the data and drew figures. [PERSON] and [PERSON] supervised the experiments.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: The displacement waveforms from BDS PPP with 1 s, 5 s, 30 s and 300 s clock intervals over the period from 6:14:00 to 6:17:00 (UTC).
## 3 Results and Discussion
### Determination of Relevant Visual Information
#### 3.2.3 Information Gained from Test Clips
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Video Clip**} & **Typically** & **High Level of** & \multicolumn{3}{c}{**Participant Preference for Visual Information**} \\ \cline{3-6}  & **Viewed (\%)** & **Importance * (\%)** & **Live Video (\%)** & **Graphical** & **No Substantial** \\  & & & & **Display (\%)** & **Impact (\%)** \\ \hline
1 & 97 & 79 & 55 & 21 & 24 \\
2 & 93 & 83 & 62 & 17 & 21 \\
3 & 10 & 18 & 21 & 10 & 69 \\
4 & 3 & 14 & 41 & 10 & 48 \\
5 & 7 & 10 & 28 & 21 & 48 \\
6 & 0 & 10 & 28 & 7 & 66 \\
7 & 28 & 3 & 34 & 0 & 59 \\
8 & 10 & 41 & 24 & 31 & 41 \\
9 & 3 & 31 & 52 & 14 & 31 \\
10 & 14 & 31 & 41 & 3 & 52 \\ \hline \hline \end{tabular}
* Proportion of participants who rated the level of importance as either “very important” or “extremely important” to the operation of the sprayer.  
\end{table} 
Table 2: Summary of responses from experienced sprayer operators (n = 29) who completed the video clip questionnaire.
## 3 Analysis of AI Techniques in SVAD
### Algorithms
#### 3.2.1 Statistics-Based Algorithms  
Two main algorithms are used in video anomaly detection: parametric and non-parametric [43].  
Parametric algorithms assume the data follow a specific probability distribution, such as a Gaussian distribution. These algorithms estimate the parameters of the distribution using the data and then use these parameters to calculate the likelihood of new data points. One popular parametric algorithm for video anomaly detection is the Gaussian Mixture Model (GMM). The GMM is a probabilistic model representing a dataset as a mixture of multiple Gaussian distributions. The algorithm estimates the parameters of the Gaussian distributions using the data and then uses these parameters to calculate the likelihood of new data points. If the likelihood of a new data point is below a certain threshold, it is considered an anomaly.  
Non-parametric algorithms do not make any assumptions about the distribution of the data. Instead, these algorithms rely on the empirical distribution of the data, which is estimated using Kernel Density Estimation (KDE) [44]. One popular non-parametric algorithm for video anomaly detection is the Local Outlier Factor (LOF) [45]. The LOF is a density-based algorithm that calculates the local density of a data point by measuring the distance to its k-nearest neighbors. The algorithm then compares a data point's local density to its neighbors' density. The data point is considered an anomaly if the ratio is below a certain threshold. Several studies have been conducted on statistical-based algorithms, some of which are listed below: Gaussian Mixture Model (GMM), selective histogram of optical flow, Histogram of Magnitude and Momentum (HoMM), Histogram of the oriented Swarm (HoS), Histogram of Gradients (HoG), Bayesian, Fully-Convolutional-Network (FCNs)-based models, and Structural Context Descriptor (SCD). Some statistics-based studies are presented in Table 2.
## 5 Conclusions  
In this study, a new method of conducting the EMI technique was proposed and tested to evaluate its possibility for detecting debonding of composite structures. The idea of using a roller device by attaching a PZT transducer was experimented with on a glass fiber epoxy plate with two different thicknesses. The results were compared to another temporary attachment method to evaluate its performance, and the results showed that the newly proposed idea could detect debonding of composite plates. For the actual experiment, two composite structures were created using 150 mm squares with different thicknesses of 0.2 mm and 0.4 mm. With each composite structure, half of the area was adhered, using a commercial adhesive, where the newly proposed roller PZT device was used to measure impedance signatures on the bonded and debonded areas. Ten impedance signatures were randomly measured at each area where this was analyzed using a statistical metric known as root mean square deviation (RMSD). Here, the re-attachable device that was created in a previous study was compared to the newly proposed roller PZT device in this study. With the roller PZT device, the signature variations were very small, and it was virtually impossible to visualize the difference between the bonded and debonded area. However, when the impedance signatures were averaged and zoomed in to take a look at the signature closely, the results were promising, as the RMSD values of 2.47% and 2.68% were calculated when comparing the averaged impedance signatures from the bonded and debonded areas. Although that the thickness of the top composite plate did not significantly affect the RMSD values, one can see that the resonance peak increased when moving from the bonded to debonded area as the top plate was free to vibrate, allowing resonance with higher amplitude. Thus, using this fact, the roller PZT device could detect debonding of composite structures as the defect areas would result in higher resonance peaks. In addition, since the roller device can easily travel over any surface, the proposed idea could also be made into a robot device with wheels to develop a system that can automatically detect structural damage.  
[PERSON] was responsible for conceptualization and methodology of the work; [PERSON] and [PERSON] carried out the investigation and conducted the experiments; [PERSON] and [PERSON] participated in writing the manuscript and analyzing the data. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Department of Structural Engineering Research, Korea Institute of Civil Engineering and Building Technology (KICT), project number 20210097.  
Not applicable.  
Figure 5: Averaged impedance signature signatures from (**a**) Figure 3; (**b**) Figure 4.
## 5 Experimental Setup and Results
### Node Selection Criteria  
Initially, we selected the best CHs using the K-medoids algorithm over 100 nodes. For this selection, we coded the algorithm in Python and executed it on a Python Jupiter notebook (Version: Python 3 (ipykernal)) in a Linux operating system. Now, we apply our proposed algorithm (see Algorithm 1) for selection of CHs for the next round, depending  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Cluster Head** & **Weights** \\ \hline
**CH13** & **0.4968** \\
**CH5** & **0.4965** \\
**CH10** & **0.4962** \\
**CH6** & **0.4958** \\
**CH1** & **0.4953** \\
**CH4** & **0.4947** \\
**CH11** & **0.4946** \\
**CH0** & **0.4940** \\
**CH12** & **0.4933** \\
**CH2** & **0.4919** \\
**CH3** & 0.4918 \\
**CH9** & 0.4883 \\
**CH8** & 0.4879 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The 14 cluster heads along with their corresponding weights.  
Figure 6: Distribution of 100 nodes into top 10 selected clusters based on their weights.  
on different parameters, such as reliability, residual energy, the distance from the sink (BS) and the average distance of cluster nodes. Algorithm 1 is given below:  
```
Step 1: Initially, i.e., in the first round, create \(N_{c}\) number of clusters using the K-medoids algorithm. Step 2: In the next round, select nodes and calculate their distance from the sink node as well as calculate average distance of cluster nodes, reliability and residual energy. Step 3: Repeat Steps 4 to 9 for the next round for every cluster until all the nodes diminish their residual energy for finding the next cluster node in every new round. Step 4: For all nodes in a cluster, compare its residual energy with other nodes in a cluster and increment the counter it encounters as a greater value in comparison to another node. Step 5: For all nodes in each cluster, compare its distance from the sink with other nodes in a cluster and increments the counter it encounters small value in comparison to other node as the nodes are movable. Step 6: For all nodes in a cluster compare the average distance of nodes in a cluster with other nodes in a cluster and increment the counter it encounters small value in comparison to other node as the nodes are movable. Step 7: Choose the node in a cluster as a cluster head with the highest counter value. Step 8: If a cluster has fewer than three nodes, nodes will be added to the nearest cluster based on the reliability of each cluster. Step 9: Jump to the next cluster. Step 10: Stop.
```  
**Algorithm 1** CHs Selection Algorithm  
The term which has been used in the algorithm can be calculated as follows:  
1. [label=()]
2. Average distance of nodes: distance of one node with respect to all other node in a cluster/number of neighbor's node. If \(d\) is the distance between CH and a designated node, then \(d\) can be measured using the formula as follows: \[d=\sqrt{\left(C_{x}-N_{x}\right)^{2}+\left(C_{y}-N_{y}\right)^{2}}\] (11)
3. Reliability: \(1-\frac{\text{No. of Neighbors in a cluster}}{\text{Total number of nodes in the experiments}}\)
## 4 Hybrid Fiber Composition
### Steel--Steel Hybrid Fiber
#### 4.1.2 Mechanical Properties
Neither increasing the fiber content to 1.5% nor increasing the fiber length resulted in a higher modulus. This was attributed to the reduced workability of the mixture and the difficulty in achieving uniform fiber dispersion. Table 5 presents the results of static tests on UHPC reinforced with hybrid steel fibers. \end{table}
Table 5: Static properties of UHPC reinforced with hybrid steel fibers. In Figure 2, the compressive strength results of UHPC reinforced with hybrid steel fibers are presented. The compressive strength ranges from 120 MPa to more than 220 MPa, depending on the initial mixture and the type and number of fibers in the hybrid combination. It can be observed that it is not necessary to use more than 2% of the fibers, as increasing their proportion did not result in a significant increase in compressive strength. Determining the appropriate combination of fibers is more important. The highest results exceeding 195 MPa were achieved by mixtures combining straight steel fibers of 13 or 19.5 mm and 30 mm in length, as well as straight fibers of 19.5 mm combined with twisted or hooked fibers of 30 mm. The lowest compressive strength values were obtained for straight fiber combinations with lengths of 6, 10, 13, or 15 mm, with a total content of 1.5-2.5%. On the basis of the results, it can be concluded that for hybrid steel combinations, the addition of longer fibers is beneficial. Figure 3 presents the results of the flexural strength tests. The strengths obtained ranged from 20 to 50 MPa. Increasing the fiber content above 2% did not improve the results, indicating that for flexural strength, planning the appropriate fiber combination is more important than simply increasing its amount, which negatively affects workability. The highest results were achieved with combinations of straight fibers of 13 or 19.5 mm and 30 mm, as well as straight fibers of 19.5 mm combined with twisted or hooked fibers of 30 mm. The addition of straight fibers of 6, 13, and 15 mm, along with a combination of straight fibers of 13 mm and hooked-end fibers of 30 mm, resulted in the lowest strength increases. Fibers that are too short tend to slip out of the matrix without fully using their properties.
# MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion
## 2 Related Works  
### Camera-Based 3D Object Detection  
Achieving low-cost 3D object detection has emerged as a significant challenge within autonomous driving technologies. Given the advantages of camera sensors--namely their low cost, low power consumption, and ease of integration--numerous researchers have concentrated their efforts on utilizing cameras for 3D object detection. In the field of monocular 3D object detection, SMOKE [4] employs a simplified monocular 3D object detection approach, leveraging keypoint estimation and an anchor-free strategy. M3 DSSD [17] integrates multi-scale feature extraction with a feature enhancement module, designing an adaptive depth fusion strategy to optimize the joint regression of depth and other 3D attributes. MonoRUn [18] improves detection performance in complex scenes by combining 3D reconstruction with uncertainty propagation. MonoFlex [19] achieves enhanced accuracy and flexibility through adaptive center point regression, flexible 3D bounding box modeling, and an anchor-free design. However, these methods primarily rely on depth estimation [4; 17], geometric constraints [19], and shape priors [18] to infer 3D information. Due to the inherent lack of depth information in monocular images, their 3D localization accuracy remains limited. To address the limitations of monocular detection, multi-view 3D object detection methods have been developed. BEVDepth [20] enhances the accuracy of the transformation from multi-view images to bird's-eye view features by employing improved depth estimation strategies and a depth-assisted view transformation module. BEVstereo [21] generates high-quality depth-aware BEV features by integrating explicit depth estimation with an advanced stereo matching strategy. DETR3D [22] introduces a transformer-based, anchor-free 3D object detection framework, facilitating end-to-end 3D bounding box prediction through the interaction between 3D object queries and multi-view features. However, these methods still rely on transformations from camera-view to BEV, which are constrained by the lack of depth information, making it difficult to ensure robustness in complex scenarios. In contrast, multimodal fusion methods integrate the geometric information of point clouds with the texture and color information of RGB images, demonstrating superior detection accuracy and adaptability in challenging environments.