esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Obstacle Avoidance Strategy for Fixed-Wing UAVs Based on Quaternion Method
## 7 Conclusions  
In this work, we present a guidance framework that can enable the UAV to move along the collision-free smooth reference trajectory. In particular, we extend the traditional 3D-VO methods to kinodynamic constrained nonholonomic UAVs. The approach was to limit the set of feasible velocities to those that can be achieved with a position error below a predefined value. A reference collision-free trajectory resulting from the 3D-VO approach  
Figure 11: (**a**) Trajectories of fixed-wing UAVs in Inertial coordinate system for several values of \(\delta\) (Simulation 2), (**b**) distance between the fixed-wing UAV and obstacle for several values of \(\delta\).  
can be retained by integrating the updated avoidance velocity. The simulation results verified that the UAV can implement collision avoidance maneuvers under allowable trust and torque. We discussed fixed avoidance plane and updated avoidance plane implementations of the method and showed that the latter guarantees the trajectory to be more optimized but processed slower. Compared with other VO-based algorithms, the main innovation of this method is reflected in the extension of the 3D-VO to underactuated systems. The approach is to map underactuated variables to existing actuators. The method we proposed in this paper allows for fast online control and low computational cost, and flexible task assignment was presented in the detailed simulation experiments.  
Although the guidance framework proposed in this paper is desirable, there is still large room for improvement. This algorithm was not tested in a multi-obstacle scenario. The extension to cooperative obstacle avoidance of UAV formation is yet to be considered, which will become our next research focus. The robustness against external disturbances, such as wind gusts, was not discussed.  
Writing--original draft preparation, Y.Q.; writing--review and editing, Y.Q.; supervision, W.Y.; funding acquisition, W.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by the financial support from the National Natural Science Foundation of China (11472136), China Postdoctoral Science Foundation Project (2019M651838) and Jiangsu Postgraduate Research and Practice Innovation Program (KYCX20_0327).  
Not applicable.  
The authors declare no conflict of interest.
# Smart Parking: A Literature Review from the Technological Perspective
## 3 Smart Parking Solutions
### Types of Sensors
#### 3.1.3 Cellular Sensors  
Mobile phones have become a tool of great use in people's daily activities, and so they have been widely used in systems for smart parking. One of the approaches involves scanning WiFi networks so as to establish the person that is in the parking lot both when the vehicle is being parked and when the user is coming back to it. The mobile phone's accelerometer and gyroscope determine if the car is moving when the user is inside it [66]. In the same way, another approach involves establishing an exact position by creating a location profile [67]. Both approaches are used to determine if client parks his vehicle in a parking slot in a specific area that could be an indoor or outdoor parking lot.  
GPS sensors are also used as an application that sends the user's location and allows the system to create a heat map with the acquired information showing free and unavailable parking slots [30].  
Also, the use of QR codes that are meant to be scanned by users' mobile phones, would be helpful for finding their parking spaces and their information would be sent to the solution's server so the status can be updated, allowing users to acquire their vehicles location easily in case they need it [60]. Using this type of sensors gives more possibilities in terms of using sensors, but creates the need of a study to determine which combination is the best to detect a person inside a car and when the car was left in a parking slot.  
Smartphones include sensors like accelerometer, gyroscope and magnetometer. These devices are part of our daily life and widely used. Drivers are carrying them all the time so they could be used as a great source of information. The data they produce comes georeferenced which helps identify specific places in a map. In short, a smartphone represents a sensor with many embedded sources of information that also allows drivers to view information collected through different smart parking systems.
## 2 Experiments  
### Material and Experiment Procedures  
The detailed chemical compositions of Inconel 718 superalloy are as follows (wt %): Ni 52.5, Cr 18.9, Nb 5.2, Mo 3.2, Ti 1.0, Al 0.6, Co 0.02, C 0.08, and balance Fe. From the raw material, totally 18 cylinders with diameter of 10 mm and height of 12 mm were machined as the specimens by wire-electrode cutting. The experimental schedule of isothermal compression tests was shown in Figure 1. According to the schedule, the specimens were firstly heated to 1373 K and held for 12 h in a furnace to obtained homogenized microstructure. In order to accurately control the temperature in the tests, two K-type thermocouple wires were welded on the specimens to record the real-time temperature of the specimens. Then the specimens were respectively fixed at the exact center of the anvils on a computer-controlled, servo-hydraulic Gleeble 3500 testing machine (in Figure 2a), heated to designated temperatures (1203, 1253, 1303, and 1353 K) with a fixed heating rate of 5 K/s and kept in these temperatures for 180 s to reduce anisotropy and eliminate internal temperature gradients. Subsequently, these specimens were respectively compressed to 40% height with the strain rates of 0.01, 0.1, 1 and 10 s\({}^{-1}\). The realistic experimental process was shown in Figure 2b. Besides, two extra experiments under the temperature of 1153 K and strain rates of 0.1 and 1 s\({}^{-1}\) were appended. The corresponding results would be used to validate the predictability of ANN and accuracy of FE model. After the isothermal compression tests, the specimens were water quenched immediately to keep the deformed microstructure at elevated temperature. In these tests, a computer control automatic data acquisition system was applied to monitor the experimental data continuously. The recorded normal stress and normal strain were converted into the homologous true stress and true strain according to the formulae in Equations (1) and (2).  
\[\sigma_{\mathrm{T}}=\sigma_{\mathrm{N}}(1+\varepsilon_{\mathrm{N}}) \tag{1}\]  
\[\varepsilon_{\mathrm{T}}=\ \ln(1+\varepsilon_{\mathrm{N}}) \tag{2}\]  
where \(\varepsilon_{\mathrm{N}}\) and \(\varepsilon_{\mathrm{T}}\) are respectively the nominal strain and true strain, \(\sigma_{\mathrm{N}}\) and \(\sigma_{\mathrm{T}}\) are the nominal stress and true stress [21].  
Figure 1: Experimental procedures for the isothermal compression tests of Inconel 718 superalloy.
## 4 Conclusions  
In the present study, we quantitatively assessed the structural condition of a composite road box-type structure in a reclamation area to investigate the influence of environmental factors (e.g., tide levels, rainfalls and temperature) and vehicle induced structural vibration. The following are the major conclusions:  
* The change of tide levels has little influence on the change in crack width.
* Variations in temperature can significantly influence the crack width (e.g., 120% increase in crack width) with Pearson correlation coefficients of 0.65, 0.84 and 0.86 for August 1, 2 and 3 in 2009, respectively.
* There is a strong negative correlation between rainfall and the change in crack width.
* A relatively low frequency of vibration (i.e., less than 25 Hz) can result in a relatively large increase in crack width.  
**Author Contributions:** Conceptualization, C.Z. and Z.L.; methodology, C.Z. and Z.L.; software, Z.L. and P.L.; validation, C.Z., Z.L., P.L.; formal analysis, Z.L., P.L.; investigation, Z.L., P.L.; resources, C.Z. and Z.L.; data curation, Z.L. and P.L.; writing--original draft preparation, Z.L. and P.L.; writing--review and editing, C.Z., Z.L., P.L.; visualization, Z.L. and P.L.; supervision, C.Z. and Z.L.; project administration, C.Z. and Z.L.; funding acquisition, C.Z. and Z.L.  
**Funding:** The research is supported by the Special Fund Key Project of Applied Science and Technology Research and Development in Guangdong (Grant No. 2016B010124007), the Science and technology planning project in Guangzhou (Grant No. 201803030005).  
**Acknowledgments:** The authors are greatly thankful to assistant editor for consultation, to Sensors Editorial Office for editing and reviewing the manuscript and to the experts of external audit for proposing valuable suggestions to this paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 16: Influence of vehicle induced vibration on the time-dependent change in crack width in Section 3 on 28 September, 2009.
# CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation
## 4 Proposed CKGAT Method
### Overall Framework of CKGAT  
The core ideas of CKGAT are as follows. On the basis of the heterogeneous propagation strategy, CKGAT uses the knowledge-aware graph attention network to extract the topological proximity structures of entities in the multi-hop ripple sets and then learn the high-order entity representations, thereby generating refined ripple set embeddings. CKGAT further uses the attention aggregator to perform weighted aggregation on the ripple set embeddings, the user/item initial entity set embeddings, and the original representations of items, so as to generate accurate user embeddings and item embeddings.  
Figure 1 shows the overall framework of CKGAT, which consists of three layers: the heterogeneous propagation layer, the knowledge-aware GAT-based attentive embedding layer, and the user-item interaction probability prediction layer. These layers are briefly described as follows.  
* **Heterogeneous propagation layer.** This layer is composed of the collaboration propagation module and the knowledge graph propagation module. The first module propagates collaborative signals through the user-item interactions to obtain the user initial entity set and the item initial entity set. On the basis of the two initial entity sets, the second module propagates knowledge associations along the links in the knowledge graph to obtain the user's multi-hop ripple sets and the item's multi-hop ripple sets. Then, this layer outputs these sets to the next layer;
* **Knowledge-aware GAT-based attentive embedding layer.** For each input ripple set of the user/item, this layer uses a knowledge-aware graph attention network to capture the topological proximity structures of the entities (i.e., items) in the ripple set to learn the high-order entity representations, thereby generating the ripple set embedding (i.e., vector). This layer also generates the user initial entity set embedding, the item initial entity set embedding, and the original representation (i.e., vector) of the item. Finally, all these embeddings (vectors) are output to the next layer;  
Figure 1: The overall framework of CKGAT.  
* **User-item interaction probability prediction layer.** For the input embeddings, this layer uses the attention aggregator to learn the weight of each embedding, and performs weighted aggregation on these embeddings to generate the user embedding and the item embedding. The two embeddings are then used to calculate the predicted probability that the user will interact with the item.
## 5 In Situ Evaluation Methods
### CPT/CPTU-Based Evaluation Methods and Models  
After the construction of the slurry wall is completed, in situ evaluations of the strength, permeability, and stress state of the wall are typically conducted using cone penetration testing (CPT) or cone penetration testing with pore pressure measurement (CPTU). [PERSON] et al. [131; 132] proposed combining CPT with the vibratory shear test (VST) technique to assess the continuity and quality of soil-bentonite used as backfill material for vertical cut-off walls. Their study indicated that the ratio of the shear strength of the backfill to the typical strength-consolidation stress ratio could predict permeability changes based on the stress values obtained from in situ tests.  
[PERSON] et al. [133] estimated the permeability change in soil-bentonite vertical cut-off walls by analyzing pore pressure dissipation during pore pressure tests, finding that penetration yielded permeability values greater than those obtained from laboratory experiments. For calculating the shear strength of soil-bentonite, [PERSON] et al. [131] recommended using CPTU data in conjunction with Equations (3) and (4) to determine the shear strength and horizontal effective stress of the backfill. In these equations, q\({}_{\rm c}\) represents the original tip resistance, a is the area ratio, u\({}_{2}\) is the pore pressure measured at the shoulder of the cone, and the tip coefficient \(N_{\rm ke}\) is set at 11.5.  
\[{\rm s_{u}}=((q_{\rm c}+(1-{\rm a})\cdot u_{2})-u_{2})/N_{\rm ke} \tag{3}\]  
\[\sigma_{\rm h}^{\prime}=((q_{\rm c}+(1-{\rm a})\cdot u_{2})-u_{2})/0.3\cdot N_ {\rm ke} \tag{4}\]  
[PERSON] et al. [134] analyzed the Hydraulic conductivity of soil-bentonite vertical cut-off walls as influenced by the stress state and established a stress model based on force balance using [PERSON]'s theory. The model demonstrated good agreement with static touch test results from actual cases, allowing for general estimations of horizontal stress.  
[PERSON] et al. [133] synthesized a variety of empirical equations for the hydraulic conductivity during the pore water pressure dissipation process, as shown in Table 7. They analyzed the non-monotonic characteristics of the dissipation curves, illustrated in Figure 14. By examining these non-monotonic pore water pressure dissipation curves using various interpretation methods, they found that the hydraulic conductivity estimated based on consolidation theory closely matched the results from flexible wall permeability tests. In contrast, the hydraulic conductivities obtained from empirical formulas were lower, while those derived from penetration-based methods were significantly higher than the laboratory test results.
# Backstepping Based Formation Control of Quadrotors with the State Transformation Technique
## 3 Backstepping-Based Quadrotor Formation Control
### Position Control of a Quadrotor with the State Transformation Technique
Using the \(x,y\) position dynamics of the \(i\)-th follower in (21), which is expressed in terms of \(P_{i}\), \(V_{i}\) and \(T_{i,2}(e_{zi,1},e_{zi,2},\phi_{fi},\theta_{fi},\psi_{fi})\) given by (23), we can consider the \(x,y\) position control input of the \(i\)-th follower defined as:  
\[u_{i,p}= \begin{bmatrix}u_{i,pitch}\\ u_{i,roll}\end{bmatrix} = \begin{bmatrix}\tan\theta_{fi}\\ \sec\theta_{fi}\cdot\tan\phi_{fi}\end{bmatrix} . \tag{33}\]  
The motion along the \(x\)- and \(y\)-axis is related to the pitch and roll angles, respectively. The reference roll(\(\phi_{d,fi}\)) and pitch(\(\theta_{d,fi}\)) are the attitude angles of the \(i\)-th follower, which allow the \(i\)-th follower to maintain the desired relative distance to the leader and are derived by the \(x,y\) position control input of the \(i\)-th follower in (33) as follows:\[\theta_{d,fi} = \arctan\left(u_{i,pitch}\right), \tag{34}\] \[\phi_{d,fi} = \arctan\left(\frac{u_{i,roll}}{\sec\theta_{d,fi}}\right). \tag{35}\]  
If the quadrotor completely tracks the derived reference attitude angles, the formation control can be done well. In other words, the formation control is achieved through the attitude tracking control of quadrotors under the assumption that quadrotors tracks the reference attitude (trajectory) perfectly. **Assumption 3**: _To derive the reference attitude angles for the formation control, the attitude of the quadrotor is assumed to track perfectly._
## 4 Methods
### Charging Process Condition  
Equation (10), to simulate the charging process, should take the following form:  
\[(\kappa-\beta_{ch}U_{ch}C_{0})\frac{dq_{int}}{dt}+U_{b}^{ch}C_{0}\gamma_{1}( \mu_{1}+q_{int})=0, \tag{13}\]  
where \(q_{int}\) is the inlet state of charge that should be added to increase the battery state-of-charge level. The battery will reach the full charging state when  
\[SOC(t)=\mu_{1}+q_{int}\implies q_{int}=SOC(t)-\mu_{1}. \tag{14}\]  
To maintain the SOC in [%], we consider that \(0.01=1\%\) and the new expression of \(U_{b}^{ch}\) obtained from Equation (11) is introduced with Equation (14) into Equation (13) to provide  
\[(\kappa-\beta_{ch}U_{ch}C_{0})\frac{d}{dt}(SOC(t)-\mu_{1})+C_{0}\gamma_{1}A \ SOC(t)(SOC(t)-1)SOC(t)=0. \tag{15}\]  
Rearranging Equation (15) yields  
\[\frac{dSOC(t)}{dt}-\alpha_{1}[1-SOC(t)]SOC^{2}(t)=0, \tag{16}\]  
where  
\[\alpha_{1}=\frac{\gamma_{1}A}{V_{n}-\beta_{ch}U_{ch}},\quad\text{and}\quad V_{ n}=\frac{\kappa}{C_{0}}. \tag{17}\]  
In Formula (17), \(V_{n}\) is the nominal voltage of the battery. Moreover, \(\alpha_{1}\) represents the charging rate of the battery. It indicates how quickly the battery is being charged. A higher value of \(\alpha_{1}\) means a faster charging rate, while a lower value means a slower charging rate. To solve Equation (16), the initial condition which represents the initial state of the battery should be given. In fact, at \(t=0\), when the charging process has not yet started, the battery is in the following state:  
\[SOC(0)=(\mu_{1}+q_{int})(0)=\mu_{0}+q_{int}(0).\]  
However, when the charging process has not yet started, we assume that \(q_{int}(0)=0\) and \(\mu_{0}=20\%\). It follows from this that the initial condition of Equation (16) is  
\[SOC(0)=\mu_{0}=20\%. \tag{18}\]
###### Abstract  
In this study, we analyse incremental cooperative communication for wireless body area networks (WBANs) with different numbers of relays. Energy efficiency (EE) and the packet error rate (PER) are investigated for different schemes. We propose a new cooperative communication scheme with three-stage relaying and compare it to existing schemes. Our proposed scheme provides reliable communication with less PER at the cost of surplus energy consumption. Analytical expressions for the EE of the proposed three-stage cooperative communication scheme are also derived, taking into account the effect of PER. Later on, the proposed three-stage incremental cooperation is implemented in a network layer protocol; enhanced incremental cooperative critical data transmission in emergencies for static WBANs (ElnCo-CESat). Extensive simulations are conducted to validate the proposed scheme. Results of incremental relay-based cooperative communication protocols are compared to two existing cooperative routing protocols: cooperative critical data transmission in emergencies for static WBANs (Co-CESatat) and InCo-CESat. It is observed from the simulation results that incremental relay-based cooperation is more energy efficient than the existing conventional cooperation protocol, Co-CESat. The results also reveal that ElnCo-CESat proves to be more reliable with less PER and higher throughput than both of the counterpart protocols. However, InCo-CESat has less throughput with a greater stability period and network lifetime. Due to the availability of more redundant links, ElnCo-CESat achieves a reduced packet drop rate at the cost of increased energy consumption.  
wireless body area networks; energy consumption; reliability; relaying; incremental cooperative communication; routing +
Footnote †: journal: Sensors
## 3 Transport System Modelling and Optimization
### Description of Research  
Figure 2 shows a schematic of the methodology of the research conducted. After developing the transport network model, three scenarios were defined for the size of traffic flows. The all the constraints and conditions that the solutions must meet were determined. For the Nash equilibrium and the Stackelberg approach, the corresponding objective functions were established. Using non-linear programming, the optimal solutions for both methods were determined. On the basis of these, the traffic congestion on the individual links in the road network was calculated. Differences in total travel times were used to calculate the Price of Anarchy.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
## 2 Background
### Location Privacy
#### 2.3.3 K-Anonymity  
K-anonymity is achieved after the precise location of the users is extended to the enclosed regions so that there are at least k-users in each region. In location k-anonymity, protection of the privacy of the user is achieved by using the current location instead of using the historic locations. References [24; 25; 26] provide position k-anonymity strategies.
# Skeleton Motion Recognition Based on Multi-Scale Deep Spatio-Temporal Features
## 4 Experimental Results and Analysis
### Effectiveness Comparison of Time Multi-Scale Modules  
In this paper, we propose a time multi-scale sampling module. First, we preprocessed the dataset and divided the dataset into four scales according to the time sampling strategy described in Section 3. We named them scale 0, scale 1, scale 2, and scale 4, respectively. Secondly, we directly input the features of four times scales into the AGCN. To make the experimental results more fair, we took the node flow in the dual flow adaptive graph convolution network as a reference and experimented only on a single graph from the adaptive convolution model. As shown in Table 1, we compared the effects of the characteristics of four different time scales on the same model on NTU-RGB+D and the dynamics datasets.  
The result of the basic network used in this paper for the Kinetics dataset was only 34%. The effect of our time multi-scale sampling strategy on scale 1 was 1% better than that of the basic network. As for why the accuracy of the kinetic dataset was very low, we suspect that it is because the types of datasets were increasing. The number of classification types in the Kinetics dataset used in this paper is up to 400, whereas the NTU-RGB+D dataset has only 60 classification types. From the results shown in Table 1, it can be seen  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Cross-View\%** & **Cross-Subject\%** & **Kinetics\%** \\ \hline Scale 0 (J-Stream) & 93.1 & 86.3 & 34.0 \\ Scale 1 & 93.9 & 87.1 & 35.0 \\ Scale 2 & 92.7 & 86.2 & 33.2 \\ Scale 4 & 91.5 & 85.2 & 33.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the accuracy of features of different time scales on the NTU-RGB+D and Kinetics datasets.  
that the multi-scale sampling module proposed in this paper achieved the best effect at an interval of one frame (scale 1) and exceeded that of J-Stream (one of AGCN's branches). However, the effect achieved on the adaptive graph convolution model's scale 2 and scale 4 decreased gradually. For NTU-RGB+D dataset and the Kinetics dataset, scale 1 produced 0.8%, 0.8%, and 1% higher accuracy than scale 0, respectively, but scale 2's results were 0.4%, 0.1%, and 0.8% lower than those of scale 0, and scale 4's results were 1.6%, 1.1%, and 1.5% lower than those of scale 0. After this analysis, we believe that although the time multi-scale module can obtain a larger receptive field by spacing a certain number of frames, a side effect is that it loses part of the semantic information, resulting in a decline in the recognition performance of the model. However, to make up for this part of the lost semantic information, we have introduced the deep spatiotemporal feature module. This module is introduced in detail in Section 3.3, and the experimental results of the module are given in Section 4.5.