esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Calculation Details
### Calculation Parameter  
In order to study the coordination of the pavement structure, three different structures of asphalt pavement were paved on the Yunluo Expressway in Guangdong Province in 2012, namely, reinforced cement stabilized base asphalt pavement structure, roller compacted concrete base asphalt pavement structure, and inverted base asphalt pavement structure. The three structures are hereinafter referred to as structure I, structure II, and structure III, respectively. The structural types are shown in Table 1, and the corresponding material parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Structure I** & **Structure II** & **Structure III** \\ \hline \multirow{4}{*}{Surface} & 5 cm modified asphalt AC-16 & 4 cm modified asphalt AC-16 \\ \cline{2-3}  & - & 6 cm modified asphalt AC-20 \\ \cline{2-3}  & 8 cm \#30 asphalt AC-25 & 8 cm \#70 asphalt AC-25 \\ \hline \multirow{4}{*}{Base} & 19 cm reinforced cement stabilized & 24 cm roller compacted concrete & 20 cm cement stabilized macadam \\ \cline{2-3}  & macadam (strength \textgreater{} 6 Mpa) & (strength \textgreater{} 12 MPa) & (strength 3-5 MPa) \\ \cline{2-3}  & 19 cm reinforced cement stabilized & 19 cm cement stabilized macadam & 20 cm cement stabilized macadam \\ \cline{2-3}  & macadam (strength \textgreater{} 6 Mpa) & (strength 3-5 MPa) & (strength 3-5 MPa) \\ \hline \multirow{4}{*}{Base} & 19 cm cement stabilized macadam & 19 cm cement stabilized macadam & \multirow{2}{*}{20 cm graded aggregate} \\  & (strength 3-5 MPa) & (strength 3-5 MPa) & \\ \cline{1-1} \cline{2-3}  & 19 cm cement stabilized macadam & & \\ \cline{1-1} \cline{2-3}  & (strength 3-5 MPa) & & (strength 3-5 MPa) \\ \hline \multicolumn{3}{c}{Subgrade} & / \\ \end{tabular}
\end{table}
Table 1: Pavement types.  
Figure 1: Simplified model of pavement structures and the selected calculation points.  
As seen Table 1, the design strength (7-day unconfined compressive strength) of cement stabilized macadam in structure II and III was 3-5 MPa, so the modulus, as seen in Table 2, was taken as 3600 MPa. However, for structure I, the strength of cement stabilized macadam of the upper and lower base was greater than 6 MPa, and hence, the modulus was taken as 6000 MPa. In addition, [PERSON]'s ratio was taken as 0.35, 0.25, 0.25, 0.4, and 0.4, respectively, for the asphalt mixture, cement stabilized macadam, roller compacted concrete, graded aggregate, and subgrade. The material parameters shown in Table 2 were adopted from [25; 26].  
For consistency, the thickness of the lower surface layer, base layer, and sub-base layer under structure I were taken as 0.13 m, 0.51 m, and 0.89 m, respectively. Respective thicknesses for structure II were taken as 0.13 m, 0.37 m, and 0.75 m. Similarly, for structure III, respective thicknesses were taken as 0.18 m, 0.58 m, and 0.98 m. Among them, the graded aggregate layer in structure II was a functional layer and was not treated as a structural layer.
## 2 Road Traffic Situation Assessment Model
### Road Traffic Situation Assessment
#### 2.3.2 Road Traffic Trend Prediction Calculation  
Road traffic trend prediction is actually the fusion of monitoring data from road sensors to calculate a score to determine the degree of traffic trends affected by road emergencies. The weights assigned to the various monitoring indicators affect the final calculation results.  
This method is characterized by its data input diversity and indicator assignment method comprehensively.  
The diversity of data input will make the final results more comprehensive and reliable. It is mainly reflected in the types of monitoring indicators and monitoring targets. In the trend prediction, the data of multiple indicators are fused and calculated. The indicators associated with urban road emergencies are selected from three factors: road factors, natural factors and human factors for analysis; the monitoring targets are used for multiple urban road nodes. When the emergency is recognized by the event source node, the collaborative nodes are searched for in the road network, and the collaborative nodes can help to monitor this emergency, just like the UAV cluster monitoring tracks the impact generated by the emergency.  
In this paper, the subjective and objective assignment methods based on game theory are used for assignment. The two indicator assignment methods are combined and considered together to reconcile the contradiction between subjective and objective weights. The indicator weights \(W=\{w_{1},w_{2},\ldots,w_{M}\}\) can be combined with the collaborative node monitoring dataset \(D\) to weigh the road situation assessment indicators and calculate the corresponding road traffic trend value \(F=\{f_{1},f_{2},\ldots,f_{N}\}\) in order to complete the road traffic trend prediction. \(M\) is the number of traffic risk assessment indicators, and \(N\) is the number of collaborative nodes.
## 3 Realization of \"Touch-and-Away\" Behavior
### Calculation of Tangent Points  
After determining the target player's center coordinate, we need to calculate the position of the tangent point towards which the robot tries to move, as shown in Figure 1.  
The parameters are shown in Figure 6. Let the origin of the coordinate system be the center of the robot. The center point of the target player is \(\textbf{{P}}=(x_{P},y_{P})\). We consider a virtual circle around \(P\) with a radius of \(r_{\text{touch}}=1\) (m). When the robot is outside the virtual circle, we can consider two tangent lines from the origin to the circle. Let the tangent points at the left side be \(\textbf{{S}}_{L}=(x_{L},y_{L})\) and that at the right side be \(\textbf{{S}}_{R}=(x_{R},y_{R})\).  
Since we measure the object using the LRF, we obtain the angle of the target player \(\theta_{P}\) and the distance \(d_{P}\). Then, the coordinate of \(P\) is calculated as  
\[\left(\begin{array}{c}x_{P}\\ y_{P}\end{array}\right)=d_{P}\left(\begin{array}{c}\cos\theta_{P}\\ \sin\theta_{P}\end{array}\right). \tag{3}\]  
The distance to the tangent points \(d_{S}\) is:  
\[d_{S}=\sqrt{d_{P}^{2}-r_{\text{touch}}^{2}}. \tag{4}\]  
Since \(\tan\Delta\theta=\frac{r_{\text{touch}}}{d_{S}}\), we can calculate:  
\[\Delta\theta=\tan^{-1}\frac{r_{\text{touch}}}{d_{S}}. \tag{5}\]  
Then, the coordinates of \(\textbf{{S}}_{L}\) and \(\textbf{{S}}_{R}\) are calculated as follows:  
\[\left(\begin{array}{c}x_{L}\\ y_{L}\end{array}\right)=d_{S}\left(\begin{array}{c}\cos(\theta_{P}+\Delta \theta)\\ \sin(\theta_{P}+\Delta\theta)\end{array}\right) \tag{6}\]  
\[\left(\begin{array}{c}x_{R}\\ y_{R}\end{array}\right)=d_{S}\left(\begin{array}{c}\cos(\theta_{P}-\Delta \theta)\\ \sin(\theta_{P}-\Delta\theta)\end{array}\right) \tag{7}\]
## 4 Simulation and Experimental Results
### Simulation Results
#### 4.1.2 Operation of the PQ-MRAS in Closed-Loop Mode  
During further tests, the estimator was operating in the closed-loop mode. Both estimated parameters tracked their real values (Figure 12).The estimated value of the rotor resistance was injected into the control system (to the rotor flux estimator--to the current model) at \(t=12\) s. It resulted in the elimination of the flux error estimation and the decoupling of the control paths (Figure 13).  
Figure 11: State variables of the drive: (**a**) reference and measured rotational speed, (**b**) electromagnetic torque and stator current component \(i_{sy}\), (**c**) estimated rotor flux vector module and stator current component \(i_{sx}\), (**d**) stator current components \(i_{s\alpha,\beta}\); (simulation results), (\(\omega_{m}\textsuperscript{ref}=0.5\)\(\omega mN\), \(t_{L}=0.75\)\(tN\), \(R_{r}=\) var, \(R_{s}=\) var).  
Figure 10: Operation of the PQ-MRAS estimator: (**a**) estimated rotor resistance, (**b**) reference and estimated reactive power, (**c**) estimated stator resistance, (**d**) reference and estimated active power; (simulation results), (\(\omega_{m}\textsuperscript{ref}=0.5\)\(\omega mN\), \(t_{L}=0.75\)\(tN\), \(R_{r}=\) var, \(R_{s}=\) var).  
A similar study was performed with a voltage model [1] instead of the current model as the estimator of the rotor flux (18). This estimator directly depends on the stator resistance value (current model depends on rotor resistance). Therefore, it can be used to evaluate P-MRAS operation in the closed-loop mode. The estimated stator resistance was injected into the control system at \(t=11\) s, which resulted in the improvement of the rotor flux estimation accuracy (Figures 14 and 15).  
Figure 12: Operation of the PQ-MRAS estimator: (**a**) estimated rotor resistance, (**b**) reference and estimated reactive power, (**c**) estimated stator resistance, (**d**) reference and estimated active power; (simulation results), (\(\omega_{m}\)_ref_ = 0.5 \(\omega mN\), \(t_{L}=0.75\)\(tN\), \(R_{r}=\) var, \(R_{s}=\) var).  
Figure 13: State variables of the drive: (**a**) reference and measured rotational speed, (**b**) electromagnetic torque and stator current component \(i_{sy}\), (**c**) estimated rotor flux vector module and stator current component \(i_{sx}\), (**d**) stator current components \(i_{s\alpha,\beta}\); (simulation results), (\(\omega_{m}\)_ref_ = 0.5 \(\omega mN\), \(t_{L}=0.75\)\(tN\), \(R_{r}=\) var, \(R_{s}=\) var).
# A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network
## 5 Experiments
### Ablation Analysis  
We perform several ablation experiments on the COCO _val_ set to better understand the gain of the proposed PPR and improved \(l_{1}\) loss. Here, HRNet is used as the backbone of our network.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Backbone** &
\begin{tabular}{c} **Input** \\ **Size** \\ \end{tabular} & **AP** & **AP\({}^{0.5}\)** & **AP\({}^{0.75}\)** & **AP\({}^{\textbf{M}}\)** & **AP\({}^{\textbf{L}}\)** & **AR** & **Time [s]** \\ \hline CMU-Pose [12] & - & - & 0.618 & 0.849 & 0.675 & 0.571 & 0.682 & 0.665 & 0.5 \\ Mask-RCNN [5] & ResNet-101 & - & 0.631 & 0.873 & 0.687 & 0.578 & 0.714 & - & 0.2 \\ G-RMI [6] & ResNet-101 & 353 & 0.649 & 0.855 & 0.713 & 0.623 & 0.700 & 0.697 & - \\ AssocEmbedding [13] & Hourglass & 512 & 0.655 & 0.868 & 0.723 & 0.606 & 0.726 & 0.710 & 0.19 \\ PiffBet [15] & - & - & 0.667 & - & - & 0.624 & 0.729 & - & - \\ PersonLab [14] & ResNet-152 & 1401 & 0.687 & 0.890 & 0.754 & 0.641 & 0.755 & **0.754** & 0.381 \\ HigherHRNet-1 [16] & HRNet-W32 & 512 & 0.664 & 0.875 & 0.728 & 0.612 & 0.742 & - & 0.052 \\ HigherHRNet-2 [16] & HRNet-W48 & 640 & **0.705** & **0.893** & **0.772** & **0.666** & **0.758** & 0.749 & 0.142 \\ \hline Ours (DLA) & DLA-34 & 512 & 0.634 & 0.864 & 0.693 & 0.575 & 0.739 & 0.698 & **0.039** \\ Ours (ResNet) & ResNet-101 & 512 & 0.651 & 0.868 & 0.703 & 0.642 & 0.737 & 0.721 & 0.073 \\ Ours (Hourglass) & Hourglass & 512 & 0.663 & 0.881 & 0.731 & 0.662 & 0.747 & 0.748 & 0.132 \\ Ours (HRNet) & HRNet-W32 & 512 & 0.668 & 0.883 & 0.740 & 0.665 & 0.748 & 0.751 & 0.078 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of our model to other state-of-the-art models on the MSCOCO test-dev daTable 2080. ti GPU. Superscripts M, L of AP stand for medium and large objects. The highest values are indicated in bold.  
Figure 5: Qualitative results on the MSCOCO dataset. For each pair, we show the predicted human pose (**left**) and intermediate heatmap (**right**). In the predicted human pose, each color corresponds to a particular human instance.  
First, to demonstrate the effect of the proposed PPR, we trained the PCP Network with traditional pose representations (Figure 1b). Here, the body center prediction head was removed. As shown in Table 2, this network achieved an AP of 0.648. Using the proposed PPR, our PCP Network outperformed the above network by +0.12 AP (AP = 0.660). Table 3 shows the performance results from using the original \(l_{1}\) loss and the improved \(l_{1}\) loss. When the improved \(l_{1}\) loss was used, the performance of our model increased from AP = 0.657 to 0.660. These results verify the effectiveness of the proposed PPR and improved \(l_{1}\) loss. Table 3 also shows that the increase in AP for poses of large people is significantly higher than for other methods. This indicates that the improved \(l_{1}\) loss works better on instances of large people.
## Materials and Methods
### Optical Property and Stability Characterization
#### 2.4.2 Absorbance Analysis to Evaluate the Stability or Degradation of Nanofluids  
An innovative approach to determine the stability of nanofluids was proposed in this research. In this technique, nanofluid absorbance was calculated at different intervals for its stability after preparation. The stability or lifespan of a nanofluid depends on the sedimentation rate of nanoparticles. Since sedimentation influences nanofluids absorbance, analyzing the difference in nanofluids absorbance over time may be used to assess its stability. The formulated ionanofluids' absorbance spectra were measured by a Lambda 750 UV-Vis Spectrometer (PerkinElmer) on the first and seventh day after preparation. The obtained results were used to assess the change of absorbance rate over time, illustrating nanofluids stability.
# A Review on Vibration Control Using Piezoelectric Shunt Circuits
## 2 Types, Applications, and Recent Advances of Piezoelectric Shunt Circuits
### Negative Capacitance Shunt Circuits
[PERSON] _et al._ (2016) examined piezoelectric shunts with negative capacitance (NC). In order to balance attenuation and OP-AMP efficiency, they investigated lowering operational amplifier (OP-AMP) demands, improving circuit characteristics, comparing configurations, and offering recommendations for developing NC systems. [PERSON] et al. [PERSON] _et al._ (2016) suggested enhancing vibration suppression and energy harvesting in piezoelectric shunt circuits by utilizing negative capacitance (NC). A self-sustaining system is made possible by the negative capacitor, which enhances suppression while maintaining positive net power.
# Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
## 3 Adaptive Downward/Upward Protocol
### Overview of RRD+ Mechanism
#### 3.1.4 Dynamic Control Message Management  
In mobile scenarios, propagation of control messages needs to be more frequent in order to adapt to topology changes. Maintaining up-to-date information about topology causes high overhead. In our case, similarly to RPL, control messages are broadcast by the sink node and propagated by other nodes until they reach leaf nodes. In RRD+, we designed a dynamic control message management according to Rank values in order to reduce overhead. Nodes that are closer to the sink should send control messages more frequently and the frequency is reduced for nodes with higher Ranks. Control messages coming from lower Rank nodes will help more nodes find parent nodes. When a node changes its Rank value, it automatically adapts its control message interval. The control message interval calculation is shown in Equation (2).  
\[Interval=Base\_interval+RankxTime\_unit \tag{2}\]  
where \(Interval\) dynamically changes due to the change of Rank of nodes in mobility. \(Base\_interval\) is the smallest \(Interval\). \(Rank\) stands for the current Rank value of the node. \(Time\_unit\) is the incremental step in the control message frequency. \(Base\_interval\) and \(Time\_unit\) can be fixed according to the application needs. High densities and high speeds would require smaller values of Interval.
# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
## 5 Conclusions  
In order to address the issues of low efficiency and insufficient accuracy in the deployment of disaster emergency communication UAVs, this paper presents a method for the rapid deployment of disaster emergency communication UAV base stations. This method utilizes efficient algorithms tailored to different scale scenarios, greatly improving deployment efficiency, and the feasibility of the proposed algorithms is validated through simulations. By dynamically adjusting the launch power and deployment height of UAVs to provide emergency communication for different disaster scenarios, the power consumption efficiency of the UAVs is improved, the usage time of the UAV base stations is increased, and the energy-saving deployment of the UAV base stations is realized. From the perspective of data transmission rate in urban, suburban, and rural environments after UAVs' deployment, this method effectively provides emergency communication services for disaster areas with multiple scenarios.  
Although the algorithm in this paper allows for the rapid deployment of UAVs to restore emergency communication in disaster areas, it does have certain limitations. Specifically, it does not consider complex dynamic environmental interference factors. Therefore, further improvement of the proposed algorithm and the consideration of the interrelation between rapid UAV deployment and multi-hop self-organizing communication of relay UAVs should be explored in future research.  
Conceptualization R.G.; methodology, R.G.; formal analysis, R.G.; investigation, R.G.; resources, R.G.; data curation, R.G.; writing--original draft preparation, R.G.; writing--review and editing, R.G.; visualization, X.W.; supervision, X.W.; project administration, R.G.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
Figure 12: The data transmission rate of UAVs base station in rural environment: **(a)** 200 m hover height deployment and 20 dBm launch power; **(b)** 200 m hover height deployment and 60 dBm launch power.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under grant 61861007 and 61640014; in part by the Guizhou Province Science and Technology Planning Project ZK [2021]303; in part by the Guizhou Province Science Technology Support Plan under grant [2022]017, [2023]096 and [2022]264; in part by the Guizhou Education Department Innovation Group Project under grant KY [2021]012; in part by the Talent Introduction Project of Guizhou University (2014)-08.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data used to support the findings of the study is included in this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 1 Introduction
While the current method can automatically identify staircase areas to some extent, representing staircase areas with a single node or a single straight line in the navigation network fails to accurately reflect the actual paths of staircases [6, 27, 28]. And manually extracted detailed staircase nodes [5] are time-consuming and may not be suitable for large-scale indoor navigation network extraction. Using Building Information Modeling (BIM) data, [PERSON] et al. [29] proposed an automatic method to generate comprehensive 3D indoor maps, integrating both floor-level and cross-floor paths through a hybrid grid-topological map approach. Their method effectively adapts to complex building geometries and achieves high accuracy rates in extracting paths across multi-floor structures. However, compared to structured BIM data, point cloud data can be more directly acquired, developing an automatic and efficient method to accurately capture the actual paths of staircases from 3D point cloud and adapt to different staircase designs still requires further research. Therefore, the main challenges in accurately extracting indoor navigation networks from semantic point clouds can be summarized into two key aspects: (1) developing a standardized preprocessing framework to ensure the quality and reliability of the data for navigation network extraction; and (2) effectively representing complex multi-floor transitional spaces, particularly staircases, in a way that accurately and efficiently captures their true paths and layouts. To address the challenges above, in this work, we mainly focused on the following aspects:  
1. Introducing a preprocessing rule to standardize and refine the semantic point cloud data, ensuring its accuracy and consistency for reliable navigation network extraction. 2. Developing a moving window method to ensure that the extracted navigation network aligns more closely with the actual structure of staircases. 3. Designing a lightweight data storage structure in the JavaScript Object Notation (JSON) format [30] specifically to store navigation network information extracted from 3D semantic point clouds. Additionally, for indoor navigable space classification, this work referenced the IndoorGML standard [31], which divides navigable space into general space, connection space, and transition space. For experimental evaluation, in this study, besides using the public Stanford Large-Scale 3D Indoor Spaces Dataset (S3 DIS) [32], we further validated the effectiveness and robustness of our indoor navigation network extraction algorithms on our own dataset. In the following, Section 2 outlines the methodology for extracting JSON-encoded navigation networks from 3D semantic point clouds, including data preprocessing, space classification, and navigation network generation. Section 3 presents the experimental process and results. Section 4 provides a discussion and analysis of the results, and Section 5 concludes the study and suggests future work.
## 3 Metaheuristic Optimization Algorithms
### Transformer Loading and System Losses  
Figure 8 shows the net transformer loading with the optimal addition of the BESS in the network. The curve with BESS\({}^{45}\) reflects the level of reduction in the transformer loading active power when four batteries are placed optimally in the network at four different homes, namely the homes at bus 53 (BESS\({}^{53}\)), bus 50 (BESS\({}^{50}\)), bus 55 (BESS\({}^{55}\)), and bus 45 (BESS\({}^{45}\)). A similar analogy is reflected in the remaining curves. Therefore, the curve with BESS\({}^{52}\) displayed in Figure 8 depicts the transformer loading active power when 21 BESS are installed optimally in the network. From this figure, both export and import peak power are minimized considerably by increasing the number of batteries installed in the network. With the optimal planning of 21 BESS, export and import peaks are reduced by 40.14% (from 195.211 kW to 116.845 kW) and 29.76% (from 320.088 kW to 224.838 kW), respectively.  
Moreover, BESS's optimal operations improved the system losses, as illustrated in Figure 9. As the BESS number increases, the line losses decrease caused by minimizing the reverse power during high PV output and supporting the load during high load. By adding 21 BESS, the peaks of the line losses, due to the reverse power and due to the peak load, are reduced by 65.43% and 53.11%, respectively. Finally, peak shaving is also accomplished by the accumulated optimal discharge of BESS along the feeder. All installed batteries worked collectively to prevent reverse power and improve network voltage profile and peak shaving. Finally, the operation behaviors of BESS are highly impacted by the distribution system voltage magnitudes.
# An Improved Hybrid Routing Protocol Combining MANET and DTN
## 1 Introduction
If virtual source nodes are selected, messages are delivered to the virtual source nodes and virtual source node operates as original source node. By doing this, messages can be efficiently disseminated through virtual source nodes. This paper is organized as follows: Section 2 reviews related works on hybrid MANET and DTN routing protocol. Section 3 proposes an improved hybrid routing protocol. Performance of the proposed protocol is evaluated and compared with conventional protocols in Section 4. Finally, Section 5 concludes this work and presents future work.