esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Surface Roughness  
In Figure 7, the measured values of the roughness \(R_{a}\) on all top and side samples are summarized. Overall, clear trends are visible. With an increase in the linear energy density of the top surface, the \(R_{a}\) decreased up to a certain point (Approx. 0.40 J/mm) before it increased once again. This can be attributed to the fact that at low LED, there is insufficient energy to achieve the complete melting of the powder particles on the top surface, which results in spatter and balling due to the unsteady melt pool. This is known to create high surface roughness [5]. The surface roughness was seen to drop when the LED increased and operated closer to a stable melt-pool condition, such as in a conduction mode [20]. A higher LED can lead to keyhole melt mode of the melt pool that can lead to further surface defects caused by the unstable melt pool [21]. While for the side surface, it can be seen that the LED did not have a significant trend and the \(R_{a}\) remained constant throughout with only minute variations. This could be explained by the applied layer thickness, which was the same for all samples. Hence, the roughness profile on the side is similar. Using the LED of 0.39 J/mm, a lower surface roughness was achieved on the top surface in comparison to the standard parameter within the same component.  
Figure 7: Measured surface roughness \(R_{a}\) on the top and side surface.
## 1 Introduction
### Our Contributions  
Heatwaves denote prolonged periods of exceptionally hot weather, often coupled with high humidity, exerting severe repercussions on human health and the environment [1]. Despite extensive research endeavors [3; 4; 5; 6; 7; 8; 9; 10; 11; 12], including investigations into urban areas vulnerable to the 'urban heat island (UHI)' effect [14; 15], our current grasp of heatwave effects remains incomplete. This gap underscores the need for comprehensive studies focusing on diverse environments to ensure a thorough assessment of heatwave impacts across varied areas.  
Our contribution addresses this critical gap by introducing a novel approach for understanding heatwave dynamics, particularly in data-deficient regions vulnerable to severe heatwave events in Thailand. We aim to elucidate the complex temperature dynamics in such regions, encompassing urban (Bangkok), suburban (Pathum Thani), and rural (Saraburi) locations. Leveraging LST as a vital indicator for heatwaves, we propose an innovative, integrated RF model that combines satellite-derived LST data with air temperatures and spatial and temporal features. Unlike previous methods [16; 37; 40; 41; 42; 43; 44; 45; 46] reliant solely on remote sensing data or ground-based observations, our model bridges the gap between the two sources, enhancing the accuracy and reliability of heatwave predictions.  
Satellite remote sensing holds promise for studying heatwaves due to its high spatial resolution and temporal frequency [47; 48]. However, cloud cover often obstructs LST data acquisition, leading to widespread data gaps [57]. Our proposed method overcomes this challenge by integrating MODIS-LST with other surface variables, such as surface topography and land cover conditions, through RF regression. This integration enhances the predictive performance of our model, enabling accurate predictions of various extreme climate phenomena and facilitating a comprehensive evaluation of spatial-temporal heatwave patterns across diverse environments. Thus, our study represents a significant advancement in understanding heatwave dynamics and provides valuable insights for effective heatwave risk management and climate resilience strategies.  
Overall, our findings underscore the significant role of earth observation and machine learning in shaping sustainable climate strategies. Notably, recent advancements in machine and deep learning approaches have markedly enhanced prediction accuracy and research outcomes over traditional methods. These developments are pivotal in informing governmental policies and guiding decision-making processes, leading to improved resilience in both urban and non-urban environments against climate extremes and driving efforts toward a more sustainable future.
# Management Platforms and Protocols for Internet of Things: A Survey
## 8 IoT Device Management Platforms
### Management for the IoT (ManIoT)
#### 8.1.1 Local Management  
The Local Manager acts based on information on the context within a scenario, i.e., the Local Manager can control and monitor the events, such as turning a lamp on or off.  
The functions performed for each layer, as shown in Figure 22, are described below:  
* **Application Layer:** The first layer consists of applications that use data provided by one or more devices, as well as platform services. Network users access applications through a Web interface, and these applications, in turn, interact with ManIoT using function calls. Each application requests the platform to perform actions on the sensors based on the implemented scenario, e.g., an energy management application requests turning an air conditioner on or off to reduce consumption.
* **Service Layer:** The second layer is formed by the services that support the applications and use the abstractions implemented by the drivers to communicate with the devices. Among the items in this layer are Storage, Scheduling, Authentication, Settings, Communication, Events, Conflict Management, and Context Management.
* **Adaptation Layer:** This layer is divided into two parts, the first one being responsible for standardizing the data and the second for dealing with the specificities of each device. Each device type has a specific driver that abstracts the specificities of access to its sensors and actuators, which allows the management of the services in an integrated way.
* **Communication Layer:** The layer consists of the different device access protocols. As mentioned earlier, the network may consist of devices that can use different application protocols (i.e., UPnP or proprietary protocol) and different networks (ZigBee, Wi-Fi).
* **A Layer of Things/Devices:** The last layer has the \"Things\". There are two devices type: the real devices and the virtual devices. The actual devices are sensors and physical actuators, e.g., an intelligent lamp (actuator), a pressure sensor (sensor). Virtual devices have already captured  
Figure 21: Illustration of the Management for the IoT.  
information from a server connected to a TCP/IP network, e.g., a calendar or email service, or a social networking server.
# An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs
## System Model  
### Node Properties  
In this work, it is considered that sensor nodes and sinks have the following properties:  
1. There is one static sink located in the center of the area with unlimited energy supply.
2. Sensor nodes cannot move after they are deployed.
3. Sensor nodes are powered by limited energy supplies.
4. Transmission range of sensor nodes is adjusted after determining the size of clusters and based on the distance between nodes and their CHs.
5. The network is homogenous. All sensors have similar properties, such as data transmission range, initial energy, etc.
6. Multi hop data transmission model is applied for inter cluster communication.
7. Member nodes transmit their data packets directly to their local CHs.
8. The number of mobile sinks is equal to the number of coronas.
9. Mobile sinks are resource-rich devices.
10. Mobile Sinks and static sink are able to communicate with each other.  
Static sink determines and sets the velocities of mobile sinks.
## 3 Vertical Displacement of Cantilever Beam by IHAM
### Vertical Displacement at Free end of Cantilever Beam
#### 3.3.2 Control Parameter \(\varepsilon_{1}\in[0,1]\) for Convergent Region  
Combining Equation (2.3b) with Equation (3.3) gives  
\[\sin\theta=\theta-\frac{\varepsilon_{1}}{3!}\theta^{3} \tag{3.32}\]When \(\theta=\frac{\pi}{2}\), we have  
\[1=\frac{\pi}{2}-\frac{\varepsilon_{1}}{3!}\Big{(}\frac{\pi}{2}\Big{)}^{3} \tag{3.33}\]  
Accordingly, the optimal convergence parameter for the full coverage is derived as  
\[\varepsilon_{1}=\frac{24(\pi-2)}{\pi^{3}} \tag{3.34}\]  
When \(n=2\), \(h=-1\), \(\varepsilon=8/\pi^{2}\), the 2 nd-order approximation of \(V_{b}\) by IHAM is  
\[\begin{array}{l}V_{b}=\frac{1}{3}\alpha+2.3159\ \times 10^{-2}h_{1}\alpha^{3}+ 1.9048\times 10^{-3}h_{1}\varepsilon_{1}\alpha^{3}\\ +9.5238\times 10^{-3}h_{1}^{2}\varepsilon_{1}\alpha^{3}-2.0430\times 10^{-3}h_{1} \varepsilon_{1}\alpha^{5}\end{array} \tag{3.35}\]  
For example, the 2 nd-order approximation of \(V_{b}\) with \(h_{1}=-1\), \(\varepsilon_{1}=24(\pi-2)/\pi^{3}\) is  
\[V_{b}=\frac{1}{3}\alpha-3.1575\times 10^{-2}\alpha^{3}+1.8053\times 10^{-3} \alpha^{5} \tag{3.36}\]  
When the relative error \(\Delta\leq 1\%\), the convergent region of the 2 nd-order approximation by IHAM is  
\[\alpha\in[0,1.5],\ V_{b}\in[0,0.41],\ 0\leq\delta_{v}\leq 0.41L \tag{3.37}\]  
Table 2 shows 2 nd order numerical solutions \(V_{b}^{[n=2]}\) by IHAM with different values \(\varepsilon_{1}\) compared with exact solutions \(V_{b}\). The relative error in Table 2 is calculated by  
\[\Delta=\frac{\left|V_{b}^{[n=2]}-V_{b}\right|}{V_{b}}\times 100\% \tag{3.38}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(a\) & Exact Solution & \(\varepsilon_{1}=0\) & \(\varepsilon_{1}=24(\pi-2)/\pi^{3}\) & \(\varepsilon_{1}=1\) \\  & & (HAM) & (IHAM) & (IHAM) & (IHAM) \\ \hline  & \(V_{b}\) & \(V_{b}\) & \(\Delta\) & \(V_{b}\) & \(\Delta\) & \(V_{b}\) & \(\Delta\) \\ \hline
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline  
*1.36**\% & 0.43717 & 2.16\% & **0.43510** & **2.62\%** \\ \hline \hline \end{tabular}
\end{table}
Table 2: 2 st-order numerical solutions \(V_{b}^{[n=2]}\) by IHAM (\(h_{1}=-1\)) with different values \(\varepsilon_{1}\) compared with exact solutions \(V_{b}\).  
After two iterations, when \(\varepsilon_{1}=0\), the convergence region of \(V_{b}\) is \(0<\alpha\leq 0.5\). When \(\varepsilon_{1}=24(\pi-2)/\pi^{3}\), the convergence region of \(V_{b}\) is \(0~{}<\alpha\leq 1.5\), whence the convergence region is expanded by 200% compared to the case with \(\varepsilon_{1}=0\).  
When \(0<\alpha\leq 0.5\), \(\varepsilon_{1}\) increases from \(0\) to \(1\), the series of solutions of vertical displacement converge to the exact solution with relative error to the exact solution is less than \(1\%\), so that the effective region of \(\varepsilon_{1}\) is identified to be \(R_{\varepsilon_{1}}=[0,1]\).  
Thus, the optimal control parameters for the convergence region of the improved homotopy analysis method is identified to be \(\varepsilon_{1}=24(\pi-2)/\pi^{3}\).
# A Credit-Based Congestion-Aware Incentive Scheme for DTNs
## 5 Performance Evaluation
### Performance Comparison
#### 5.2.1 Cumulative Utility Comparison  
Figure 7 shows the distribution of cumulative utilities, where only even nodes are selected due to the space limit. We can see that in CBCAIS, most of the nodes can get more utilities than the SMART, Multicenter, and GTA schemes. In the SMART, Multicenter, and GTA schemes, many messages are forwarded to nodes with higher connection degree, and discarded due to node congestion. In SMART, the total credits paid for a message are divided by all the forwarding nodes. Therefore, all the forwarding nodes will lose their credits and get fewer utilities than CBCAIS when a message is discarded. In Multicenter, the message destination node has to pay the forwarding nodes even when a message is discarded, so the destination node will get fewer utilities due to the discarding. In GTA, many messages are only sold to the high connection degree and congested node, therefore the nodes with more buffer and low connection degree will have no chance to get more credits. However, CBCAIS can effectively reduce the number of discarded messages by the check and punishment mechanism. In addition, by adjusting the message acceptance threshold according to congestion degree, a node can buffer the messages with more benefits. Therefore, most nodes in CBCAIS can get more utilities than other schemes.
## 2 Materials and Methods
### Case Study and Data Availability
**Data Source III:** CMEMS Marine Data store to download the in situ data near real time from the \"copernicusmarinetoolbox\" API ([[https://help.marine.copernicus.eu/en/articles/7949409-copernicus-marine-toolbox-introduction](https://help.marine.copernicus.eu/en/articles/7949409-copernicus-marine-toolbox-introduction)]([https://help.marine.copernicus.eu/en/articles/7949409-copernicus-marine-toolbox-introduction](https://help.marine.copernicus.eu/en/articles/7949409-copernicus-marine-toolbox-introduction)), last accessed online 28 October 2024), product: Mediterranean Sea--near real-time (NRT) in situ quality-controlled observations, hourly updated and distributed by INSTAC within 24-48 h from acquisition on average ([[https://data.marine.copernicus.eu/product/INSTU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035/description](https://data.marine.copernicus.eu/product/INSTU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035/description)]([https://data.marine.copernicus.eu/product/INSTU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035/description](https://data.marine.copernicus.eu/product/INSTU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035/description)), last accessed online 28 October 2024). **Data Source IV:** For the initialization of WRF with external atmospheric forcing, the NCEP operational GFS analysis and forecasts were used as the initial meteorological fields for the simulation. The data are 0.25-degree spatial resolution that correspond to approximately 27 km. More information about the dataset can be found in [22]. The WRF model is initially run without the application of DA and is henceforth referred to as the Control run, with downscaling of the computational grid at D01 (9 km grid) and at D02 (3 km grid) in the domains outlined in Figure 1.  
As seen, the data available in the coarser domain D01 that measure oceanic variables (Data Source III, Section 2.1) are limited to 4 locations, which drop down to 2 for the finer resolution subdomain D02, and that can be used for validation. This makes the creation, calibration, and validation of such a system extremely difficult. To overcome this, additional data that have never been used before to the authors' knowledge have been added in the form of atmospheric data (Data Source I, Section 2.1), and the observational network uses meteorological stations similar to [23]. Figure 1: The two downscaled domains of interest for the WRF application in southern Greece. Ground METAR stations are indicated by blue dots. Red dots are buoy locations, and orange signifies additional CretaWeather Meteorological stations used for DA. Coordinates of the bounding box are shown around the domain, with indications on inner and outer domain D01–D02, as well as (**a**) a zoomed map of Antikythera Island with the 3 km grid (grey) and 9 km grid (red) overlay.
# Effects of _Gynura bicolor_ on Glycemic Control and Antioxidant Ability in Predicates
## 3 Results
### Anthropometric Measures, Blood Pressure, Lipid Profile and Liver Function
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Control** & **GB** \\ \hline Body weight (kg) & & \\ Week 0 & 62.56 \(\pm\) 3.10 & 64.03 \(\pm\) 3.10 \\ Week 4 & 62.38 \(\pm\) 2.67 & 64.00 \(\pm\) 2.94 \\ Week 8 & 62.70 \(\pm\) 2.84 & 63.66 \(\pm\) 3.11 \\ Week 12 & 59.51 \(\pm\) 1.92 & 63.31 \(\pm\) 3.11 \\ BMI (kg/m\({}^{2}\)) & & \\ Week 0 & 24.36 \(\pm\) 0.62 & 25.96 \(\pm\) 1.35 \\ Week 4 & 24.32 \(\pm\) 0.41 & 25.94 \(\pm\) 1.25 \\ Week 8 & 24.41 \(\pm\) 0.42 & 25.54 \(\pm\) 1.17 \\ Week 12 & 24.00 \(\pm\) 0.61 & 25.64 \(\pm\) 1.24 \\ Body fat (\%) & & \\ Week 0 & 31.83 \(\pm\) 1.46 & 32.41 \(\pm\) 2.47 \\ Week 4 & 30.84 \(\pm\) 1.34 & 32.47 \(\pm\) 2.46 \\ Week 8 & 30.33 \(\pm\) 1.22 & 31.38 \(\pm\) 2.34 \\ Week 12 & 30.88 \(\pm\) 1.62 & 31.19 \(\pm\) 2.34 \\ Fat-free mass (kg) & & \\ Week 0 & 42.82 \(\pm\) 2.75 & 42.98 \(\pm\) 1.81 \\ Week 4 & 43.29 \(\pm\) 2.51 & 42.98 \(\pm\) 1.89 \\ Week 8 & 43.88 \(\pm\) 2.66 & 43.40 \(\pm\) 1.82 \\ Week 12 & 41.11 \(\pm\) 1.60 & 43.29 \(\pm\) 1.88 \\ Waist circumference (cm) & & \\ Week 0 & 85.61 \(\pm\) 1.29 & 87.33 \(\pm\) 3.00 \\ Week 4 & 84.33 \(\pm\) 1.39 & 87.56 \(\pm\) 3.22 \\ Week 8 & 83.00 \(\pm\) 1.90 & 87.39 \(\pm\) 2.48 \\ Week 12 & 82.38 \(\pm\) 1.29 & 84.89 \(\pm\) 2.85 \\ Hip circumference (cm) & & \\ Week 0 & 99.61 \(\pm\) 1.50 & 99.56 \(\pm\) 2.57 \\ Week 4 & 100.11 \(\pm\) 1.56 & 101.00 \(\pm\) 2.77 \\ Week 8 & 98.89 \(\pm\) 1.30 & 98.39 \(\pm\) 2.57 \\ Week 12 & 95.50 \(\pm\) 1.88 & 98.44 \(\pm\) 2.19 \\ Waist to hip ratio & & \\ Week 0 & 0.92 \(\pm\) 0.01 & 0.93 \(\pm\) 0.02 \\ Week 4 & 0.91 \(\pm\) 0.01 & 0.94 \(\pm\) 0.02 \\ Week 8 & 0.91 \(\pm\) 0.01 & 0.93 \(\pm\) 0.01 \\ Week 12 & 0.91 \(\pm\) 0.01 & 0.93 \(\pm\) 0.01 \\ \hline \end{tabular} Values are mean \(\pm\) SEM (n = 9).  
\end{table} 
Table 4: Effects of _Gynura bicolor_ on anthropometric and body composition measures. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Control** & **GB** \\ \hline Systolic (mmHg) & & \\ Week 0 & 122.89 \(\pm\) 2.80 & 135.33 \(\pm\) 5.06 \\ Week 4 & 120.89 \(\pm\) 5.78 & 136.78 \(\pm\) 6.28 \\ Week 8 & 115.22 \(\pm\) 6.09 & 133.44 \(\pm\) 4.19 \\ Week 12 & 110.88 \(\pm\) 7.18 & 120.56 \(\pm\) 5.23 \\ Diastolic (mmHg) & & \\ Week 0 & 71.22 \(\pm\) 5.17 & 78.22 \(\pm\) 3.05 \\ Week 4 & 71.78 \(\pm\) 5.25 & 80.22 \(\pm\) 3.03 \\ Week 8 & 67.67 \(\pm\) 4.66 & 77.11 \(\pm\) 3.12 \\ Week 12 & 63.25 \(\pm\) 4.63 & 72.00 \(\pm\) 3.54 \\ \hline \end{tabular}
\end{table} 
Table 5: Effects of _Gynura bicolor_ on blood pressures.
## 2 Antenna Design Procedure and Methods
### Design of the Dual-Band, Dual-CP Antenna Unit  
Figure 3 shows the 3D structural view of a CP antenna unit together with cross-sectional views of its Ka- and K-band cavities. The K-band cavity is stacked above the Ka-band cavity to share the common antenna aperture. The input ports of the antenna unit are on the sides of the two cavities. Each cavity has a cylindrical shape with two chamfered edges. The chamfered edges are created by placing two parallel metallic blocks that make an angle of \(\theta\) with the feeding channel (see Figure 3). Owing to the perturbation effect of these angled blocks, two orthogonal modes of the cavity are excited with the same amplitude and a \(90^{\circ}\) phase difference. A 2.6 mm high cylindrical post with a radius of \(r1\) is placed at the center of the lower cavity to optimize the impedance of the Ka-band units.  
The influence between the K- and Ka-band antennas is discussed as follows. For the K-band antenna, the Ka-band cavity can be regarded as a total reflection boundary. Hence, the radiation performance of the K-band antenna is not much affected by the Ka-band cavity below it. For the Ka-band antenna, the K-band cavity operates as a circular waveguide transmission line. The angled blocks of the K-band have some effect on the phases of the two orthogonal electric field components. However, this can be compensated by optimizing the dimensions and positions of the two angled blocks.  
The field distributions inside the Ka- and K-band cavities at different time steps within one period T are shown in Figures 4 and 5, respectively. It can be seen that the E-field vectors are periodically rotated by \(90^{\circ}\) after each T/4 interval in the respective frequency band inside both cavities. The electric field vectors rotate clockwise at Ka-band and counterclockwise at K-band, which means LHCP and RHCP are realized at different frequency bands.  
Figure 4: Electric field vectors inside the Ka-band cavity at different time steps within one period T. (**a**) t = 0. (**b**) t = T/4. (**c**) t = T/2. (**d**) t = 3T/4. The direction of wave propagation is coming out of the page.  
Figure 3: Illustration of an antenna unit. (**a**) 3D view of the antenna unit showing the stacked structure. (**b**) Sectional view of the K-band cavity. (**c**) Sectional view of the Ka-band cavity. The parameters of the antenna unit follow: \(r1\) = 1.4 mm, \(r2\) = 3.3 mm, \(r3\) = 4.75 mm, \(r4\) = 5.4 mm, \(b1\) = 3.44 mm, \(b2\) = 6.4 mm, \(\theta 1\) = 46\({}^{\circ}\), and \(\theta 2\) = 37\({}^{\circ}\).
## 2 Preliminary
### Multi-Agent Deep Deterministic Policy Gradient (MADDPG)  
In a multi-agent environment, when other agents are treated as part of the environment, applying the single-agent RL algorithm directly to a multi-agent setting can be problematic, because, from the perspective of an agent, the environment appears to be non-stationary, which violates the Markov assumption that is required for convergence. Especially in DRL, using a neural network as a function approximation, the non-stationary problem is even more serious. In the case of 1-vs.-1 WVR air combat, it is impossible to determine the next state of an enemy UCAV according to the current agent state and its actions, because the opponent UCAV can use different maneuvers to cause the opponent's next state to differ. Particularly, in a multi-agent competitive environment, the non-stationary problem is serious.  
To address the problem of reinforcement learning in a multi-agent environment, [PERSON] et al. [22] proposed the MADDPG algorithm and applied the DDPG algorithm to a multi-agent environment. The MADDPG algorithm presents the idea of \"centralized training, decentralized execution\" and learns a centralized Q function for each agent. A MADDPG algorithm that is based on global information can address the non-stationary problems and stabilize training.  
Concretely speaking, the study considers the two-player ZSMG as an example of a multi-agent environment and assumes \(\mu_{a_{1}}\) to be the agent policy and \(\mu_{a_{2}}\) to be the opponent policy. Subsequently,we can write the gradient of the expected return for the agent and opponent with polices \(\mu_{a_{1}}\) and \(\mu_{a_{2}}\),\(J_{a_{1}}(\theta_{a_{1}})=\mathbb{E}_{s\sim\mu_{a_{1}},\mu_{a_{2}}}[G_{a_{1}}]\), \(J_{a_{2}}(\theta_{a_{2}})=\mathbb{E}_{s\sim\mu_{a_{1}},\mu_{a_{2}}}[G_{a_{2}}]\) as:  
\[\
abla_{\theta_{a_{1}}}J_{a_{1}}(\theta_{a_{1}})=\mathbb{E}_{s\sim\mathcal{D}}[ \
abla_{\theta}\mu_{a_{1}}(s)\
abla_{a_{1}}Q^{\mu_{a_{1}}}(s,a_{1},a_{2})|_{a= \mu_{a_{1}}(s)}] \tag{5}\]  
where, \(Q^{\mu_{a_{1}}}(s,a_{1},a_{2})\) is a centralized action-value function of an agent that uses the agent and opponent as input and \(Q^{\mu_{a_{2}}}(s,a_{1},a_{2})\) is a centralized action-value function of the opponent. The experience replay buffer \(\mathcal{D}\) contains the tuples \((s,s^{\prime},a_{1},0,2\mathcal{R})\) recording experiences of the agent and opponent, where \(s^{\prime}\) denotes the next state from \(s\) after taking actions \((a_{1},a_{2})\). Centralized action-value functions are updated using the temporal-difference learning. Taking UCAV 1-vs.-1 WVR air combat as an application scenario, both UCAVs will have a Q network and a policy network, respectively. The input of the Q network is the flight status, air combat situation features and action vector of both UCAVs, and the output is Q value of own UCAV. The input of the policy network is the flight status, and the air combat situation features of both UCAVs, and the output is the action vector of own UCAV. A schematic of MADDPG's idea of \"centralized training, distribution execution\" of UCAV 1-vs.-1 WVR air combat is illustrated in Figure 1.