esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of the IEEE 802.11p Multichannel MAC Protocol in Vehicular Ad Hoc Networks
## 6 Conclusions and Future Work  
In this paper, we proposed two Markov chain models for safety-related messages and WSA messages to analyze the real-time and reliability of broadcasting of safety-related messages on CCH and to analyze the efficiency of throughput and utilization of SCHs under both unsaturated and saturated conditions. We analyze in detail packet transmission delay, PRR and throughput by considering virtual collision, AIFS differentiation, CW, the retry limit, frame blocking probability, channel busy probability, frame failure probability and channel switch. We employ M/G/1 queuing model to derive a more accurate analytical result.  
By simulations, we validate the proposed models, and the analytical and simulation results match well. From the proposed analytical models and the numerical results, in addition to some straightforward observations, we obtained several important conclusions: (1) Under vehicular environments, IEEE 1609.4 standard is able to meet the transmission delay requirement of safety-related messages, however, it is difficult to meet the reliability requirement due to the transmission collision and harsh channel fading. (2) Hidden terminal problem is the main factor affecting the external collision which degrades the PRR significantly. (3) High channel data rate leads to higher reliability for transmitting both safety-related and WSA packets. (4) IEEE 1609.4 standard results in inefficient utilization of both CCH and SCH, i.e., utilization cannot inherently exceed 50%. (5) The fixed channel switching between CCH interval and SCH interval prohibits adaptive and intelligent allocation of time interval in response to variable traffic demands, and thus, leads to inefficiency throughput and underutilization of SCHs. (6) On the one hand, the potential ways to improve reliability for broadcast safety-related messages on the CCH include using bigger CW to reduce the possibility of concurrent transmissions, choosing larger carrier-sensing range to mitigate the hidden terminal problem, increasing the transmission data rate, increasing the number of packet retransmission, etc. On the other hand, the potential ways to improve the throughput and utilization of SCHs include employing the higher transmission data rate, increasing the number of SCHs, using the larger non-safety packet payload to carry more data, increasing the SCH interval, providing the contention-free transmission, etc.  
In our future research, we will focus on how to ensure the reliable transmission of safety-related messages, and improve the throughput and utilization of SCHs by adjusting system parameters dynamically. we will also exploit an efficient method to solve the hidden terminal problem, and explore appropriate method to decrease the BER caused by fading channel. Finally, the proposed model performance under real field test will be considered.
## 3 Control System Design
Therefore, the control stability performance of the system and the bandwidth can be assured as long as there is a reasonable choice of controller parameters (\(K_{p}\), \(K_{i}\), \(K_{d}\)). Here \(K_{p}\) = 10, \(K_{i}\) = 0.01, \(K_{d}\) = 0.
## 2 Configuration of the Hybrid Test Hardware  
The overall flow chart of the RTHT (Real-Time Hybrid Test) system used in this study is shown in Figure 1. It consists of three areas: FEAPH window, Flextest, and DAQ (Data acquisition). These three areas are connected to a shared common RAM network (SCRAMNet) to share data in real time. The FEAPH window is the area where the finite element program is implemented, and the analysis results are stored directly in SCRAMNet. The results stored in SCRAMNet provide real-time testing by providing command displacement to the actuator via Flextest and sharing the feedback values obtained from the displacement meter and load cell via SCRAMNet. The system is configured so that the results obtained in each system area can be intercommunicated almost simultaneously usingshared memory (SCRAMNet). In order to run this optimized real-time hardware system, the company developed and executed a dedicated analysis program for the hybrid system in the FEAPH window. The FEAPH program includes the algorithm of what Simulink has so that the program can run tests without any additional execution processes.  
Three WorkStation T5400 (2.33 GHz quad-core, 1 GB RAM) devices manufactured by Dell International (Round Rock, TX, USA) were used for the system. High frequency dynamic performance is optimized using dynamic actuators (250 kN, \(\pm\) 125 mm). In order to minimize the error value due to the difference between the measured variable and the desired set point, the PID (Proportional-Integral-Derivative) controller algorithm is used.
###### Abstract
[26] proposed a joint system of cascaded PCMs and multilayered solid PCMs, which was applied to the thermal energy storage of a concentrated solar power plant. The results indicated that a design configuration that was filled with a high melting point PCM in the top 25% of the heat storage tank, sensible concrete in the middle 50% and a low melting point PCM in the bottom 25% of the tank had the best performance among all the design alternatives studied. [PERSON] et al. [27] analyzed the thermal performance of three different PCM capsules in a packed bed solar energy storage system, and the research indicated that the storage capacity and utilization rate of three-stage PCM energy storage tanks are higher than others. To sum up, the cascaded phase change heat storage method has been studied and applied in many fields. However, up to now, in the humidification and dehumidification solar desalination system, the research on recovering the latent heat of vapor condensation in the dehumidification process by means of cascaded phase change heat storage has not been reported. The recovery of vapor condensation latent heat is the key problem in humidification and dehumidification solar desalination technology. The use of cascaded phase change heat storage is conducive to improving the uniformity of phase change and the phase change heat storage rate so as to improve the utilization rate of phase change materials and the water production performance of a dehumidifier. In this paper, the cascaded phase change heat storage dehumidifier in the humidification and dehumidification solar seawater desalination system is the research object, with an exergy analysis and comparison on the exergy utilization rate for the dehumidifier with different phase change material stages to determine the applicable number of stages. Through experiments, the effects of the hot and wet air temperature and the volume flow at the inlet of a dehumidifier on the melting process of phase change materials and the effects of the number of phase change material stages on the water output, the gain output ratio and the water production cost of a dehumidifier will be studied in order to determine the appropriate operating parameters and the number of phase change material stages so as to achieve the goal of improving the exergy efficiency, water output and gain output ratio of the dehumidifier and reducing its water production costs.
## 3 Results
### Microwave-Assisted Extraction Optimization Results
#### 3.1.4 Extraction Time  
The next studied factor was the duration of the extraction process. For this purpose, experiments were performed for 5 and 10 min of extraction time, and the calculated RE% are presented in Figure 4. According to the results, the differences in recovery values between the two tested factors were not statistically significant. However, for a percentage of the extracted factors, the results are not statistically significant.  
Figure 3: Recoveries (%) of analytes when MAE extraction was performed on 10 g and 5 g of tissue: (**a**) abamencin (B1a), acetaminophen, acrimathrin, boscalid, bupirimate, carbendazim, chlororyrifos, chlororyrifos-methyl, clofentezine, clothianidin, cyproconazole, cyprodinil, deltamethrin, difenoconazole, etofenprox, fenbuconazole, fenpyroximate, flonicamid, fluidoxonil, fluopyram, fluquinconazole, flutriafol, hexythiazox, imazaili; (**b**) indoxacarb, kresoxym-methyl, lambda-cyhalothrin, methoxyfenzoide, myclobutanil, penconazole, primicarb, pyracolostrobin, pyrimethanil, pyriproxyfen, spinosyn A, spinosyn D, spirodiclofen, tau-fluvalinate, tebuconazole, tebbufenzoide, tebufenpyrad, thiabendazole, thiacloprid, thiamethoxam, trifikovstrobin, alternarol (AOH), alternarol monomethyll ether (AME). The values for each substance followed by the same letter do not differ significantly statistically (\(p<0.05\)).  
of 21%, the recoveries decreased significantly when the extraction process lasted for 10 min but maintained their acceptable levels (70-120%). Exceptions were acrinathrin, lambda-cyhalothrin, and spinosyn A, whose recoveries were reduced from 119%, 102%, and 89% in 5 min extraction time to 53%, 66%, and 65% in 10 min extraction time, respectively. For this reason and considering that the increased speed in the method is desirable, the extraction time of 5 min was chosen as the optimum extraction time. The results confirm that the increase in time has a different effect on each chemical group, as it has been previously reported [30].  
Figure 4: Recoveries (%) of analytes when MAE extraction was carried out for 5 and 10 min: (**a**) abamectin (B1a), acetaminophen, acrinathrin, boscalid, bupirimate, carbendazim, chlororyrifos, chlororyrifos-methyl, clofentezime, clothianidin, cyproconazole, cyprodinil, delamethrin, difenoconazole, etofenprox, fenbuconazole, fenpyroximate, flonicamid, fluidoxonil, fluopyram, fluquinconazole, flutrifad, hexythiazox, imazalli; (**b**) indoxacarb, hexoszym-methyl, lambda-cyhalothrin, methoxyfenzo, myclobutanil, penconazole, primicarb, pyraclostrobin, pyrimethanil, pypriproxyfen, spinosyn A, spinosyn D, spirodiclofen, tau-fluvalinate, tebuconazole, tebufenozide, tebufenpyrad, thiabendazole, thiaacloprid, thiamethoxam, trifikovstrobin, alternariol (AOH), alternariol monomethyl ether (AME). The values for each substance followed by the same letter do not differ significantly statistically (\(p<0.05\)).
# Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review
## Security Issues and Challenges in WSN-IoT
### Attacks  
\"The IoT frameworks hold a vast volume of information; the network layer is particularly vulnerable to attacks, creating much network congestion.\" The network's data integrity and authentication are critical security problems [73]. A significant problem is an attack by hackers and rogue nodes that damage the network's computers. The current security restrictions applied to IoT render them susceptible to attacks. Based on the particular design and features of the WSNs, these attacks usually follow new tactics [74]. Indeed, in the Open System Interconnection (OSI) model, attacks can be characterized according to the targeted protocol layer. Another method of grouping classifies assaults depending on the existence of the offender. We may also describe attacks as internal or external, passive or active. Nodes outside the network execute external attacks [75].  
Passive threats are confined exclusively to the study, capture, and data snooping of traffic. Active attacks, however, usually exploit the data by disrupting the connection between the nodes and affecting the nodes' availability, so attacks can also be carried out [76]. On the other hand, internal attacks are initiated by valid network nodes that function against their requirements. Table 3 shows the security issues and challenges in IoT.
## 4 Implementation
### Network Architectures
#### 4.2.2 Ddpg  
The network architecture used is the default implementation that the OpenAI Baselines library offers, which is very similar to the architecture proposed by [PERSON] et al. [33]. As in PPO, to parameterise both the policy \(\pi_{\theta}(s_{t})\) and the action-value function \(Q(s_{t},a_{t})\), a four layered MLP was used. This algorithm aims to learn a deterministic policy so each state will be mapped with a concrete action. The actor's input layer also depends on the state encoding and it is followed by two hidden layers, composed of 64 neurons each. Concerning the output layer, it is composed of three neurons, one per action, and each neuron is fully connected with each neuron of the last hidden layer.  
With respect to the activation functions, the _Rectified Linear Unit_ (ReLU) function is used in each neuron of the hidden layers. In the output layer, instead, _Tanh_ is used to bound the actions between \(-1\) and \(1\). In addition, a process called _layer normalisation_[41] is applied to the output of each hidden layer to simplify the learning process and to reduce the training time. DDPG is an off-policy algorithm, hence it uses a more exploratory policy to get experience. As explained in the algorithm's description (Section 3.1.2), there are multiple types of noise but, in this application, the noise is added in the neural network's parameter space.  
The action-value function \(Q_{w}(s,a)\) receives as input the state and the action and outputs the expected future reward of executing action \(a_{t}\) in state \(s_{t}\). The input layer depends on the size of the state codification. The input layer is followed by two hidden layers of 64 neurons each and actions are not included until the second hidden layer. The output layer only has one neuron and outputs the \(q\)-value. The activation function used in the critic network is ReLU for each neuron of the hidden layers. The last layer's neurons do not have any activation function, because the action-value does not have to be bounded.  
To minimise the complexity of the critic and to avoid over-fitting, a \(L2\) regularisation term is added to the critic's loss function [42]. This penalises the model for having parameters with high values.
# Imaging Microstructure on Optically Rough Surfaces Using Spatially Resolved Acoustic Spectroscopy
## 2 Materials and Methods
### Materials and Samples
#### 2.3.1 Roughness Comparators  
Table 2 lists the roughness comparators used in this study, along with their minimum and maximum \(R_{a}\) values. The cast comparator was originally specified in microinch RMS and has been converted to \(R_{a}\). Comparators were manufactured by Rubert + Co., Ltd. (ACRU Works, Cheadle Hulme, Cheadle, UK), and GAR Electroforming (Danbury, CT, USA). Examples of two comparators, shot blast and spark erosion, are shown in Figure 3.  
The comparators are made by nickel electroforming and are assumed to have a grain size much smaller than the resolution of the SRAS instrument and free from significant  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Figure No.** & **Material** & \begin{tabular}{c} **Surface** \\ **Finish** \\ \end{tabular} & \
\begin{tabular}{c} **Detector** \\ **Iution \(\upmu\)m** \\ \end{tabular} \\ \hline Comparators & Figures 4 and 5 & Nickel &
\begin{tabular}{c} Various see \\ Table 2 \\ \end{tabular} & Quartet & 400 \\ WAM 1 & Figure 7 & Ti-6 Al-4V & As deposited & Quartet & 500 \\ Ti-6246 & Figure 8 & Ti-6 Al-2 Sn- & Etched & Quartet & 400 \\ L-PBF 1 & Figure 9a & Ti-6 Al-4V & As deposited & SKED & 500 \\ L-PBF 2 & Figure 9b & AlSi10 Mg & As deposited & Quartet & 500 \\ CPT1 & Figure 10 & Titanium & Various & Quartet & 400 \\ T164 & Figure 11 & Ti-6 Al-4V & Milled & SKED & 500 \\ WLAN & Figure 12a & TiB2 & As deposited & Quartet & 500 \\ WAAM 2 & Figure 12b & Ti-6 Al-4V & As deposited & Quartet & 500 \\ L-BPF 3 & Figure 12c & Ti-6 Al-4V & As deposited & Quartet & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of samples.  
Figure 3: Photographs of two roughness comparators, (**a**) shot blast and (**b**) spark erosion.  
orientation texturing, such that the microstructure may be considered homogeneous across each comparator.  
Small 4 \(\times\) 4 mm regions were scanned using the Quartet detector on the grit blast sample with 100 \(\upmu\)m step size. To obtain the data on SNR and dropout, all of the comparators were scanned with 10 lines and a step size of 100 \(\upmu\)m across the full width of the surface area, covering 1 \(\times\) 10 mm to 1 \(\times\) 20 mm depending on the width of each comparator. For each point in the scan area, the returning light level and velocity were calculated. The SNR was calculated by comparing the signal height in the Fourier domain to the average height of a noise trace taken at the same time and expressed in decibels. The number of lost data points (dropout) varies with surface roughness and was calculated by setting a threshold on the optical return of 0.1 (arbitrary units), which excludes data where insufficient light returns to the detector to give usable data.  
For the grit blast sample, this process was repeated with a different number of averages, so the change in SNR and velocity standard deviation with averages could also be determined.
## 2 Mathematical Model
### Piezoelectric Coupled Beam  
In order to find the electromechanical responses of the piezoelectric beam in an open-circuit condition, the Erturk and Inman [35] method is used here. The constitutive relation for piezoelectric materials is,  
\[D_{3}(x,t)=d_{31}E_{p}\epsilon_{1}(x,t)-e_{33}^{\varepsilon}\frac{V(t)}{h_{p}} \tag{21}\]  
where \(D_{3}\) is the electrical displacement, \(d_{31}\) (Coulomb/N or m/V) is the piezoelectric coefficient with polarization in direction 3 due to the external stress in direction 1, \(\epsilon_{1}\) is the axial bending strain along 1-axis, \(E_{\text{p}}\) is the piezoelectric Young's modulus, \(e_{33}^{\varepsilon}\) is the permittivity at constant strain, \(V(t)\) is the electric voltage over the piezoelectric area, \(h_{\text{p}}\) is the thickness of the piezoelectric layer, and \(E_{3}(t)=V(t)/h_{p}\) is the electrical field along 3-axis. It should be noted that 1, 2, and 3 directions are aligned with x, y, and z axes, respectively.  
The longitudinal bending strain \(\epsilon_{1}(x,t)\) in the substructure can be calculated as,  
\[\epsilon_{1}(x,t)=-h_{\text{c}}\frac{\partial^{2}v(x,t)}{\partial x^{2}}=- \bigg{(}h(x)+\frac{h_{p}}{2}\bigg{)}\frac{\partial^{2}v(x,t)}{\partial x^{2}} \tag{22}\]  
where \(h_{\text{c}}\) is the distance between the center of the piezoelectric layer and the beam's neutral axis and \(h\) is thickness of substructure. Consequently, the electric displacement becomes,  
\[D_{3}(x,t)=-d_{31}E_{p}\bigg{(}h(x)+\frac{h_{p}}{2}\bigg{)}\frac{\partial^{2} v(x,t)}{\partial x^{2}}-e_{33}^{\varepsilon}\frac{V(t)}{h_{p}} \tag{23}\]  
The electric displacement \(D_{3}(x,t)\) is related to the output electric charge \(q(t)\) by integration over the electrode area as,  
\[q(t)=-\int_{0}^{L}\bigg{[}d_{31}E_{p}\bigg{(}h(x)+\frac{h_{p}}{2}\bigg{)} \frac{\partial^{2}v(x,t)}{\partial x^{2}}+e_{33}^{\varepsilon}\frac{V(t)}{h_{ p}}\bigg{]}b\,dx \tag{24}\]  
It is noted that the piezoelectric layers should cover the whole surface of the beam.  
According to Ref. [36], the capacitance of the piezoelectric is represented by \(C_{p}\). Additionally, the goal of this research is to estimate the open circuit voltage across the piezoelectric tric layers. Hence, the backward coupling in the relations is ignored. Finally, the generated voltage \(V(t)\) can be calculated as,  
\[V(t)=-\frac{d_{31}E_{p}}{C_{p}}\int_{0}^{L}b\left(h(x)+\frac{h_{p}}{2}\right) \frac{\partial^{2}v(x,t)}{\partial x^{2}}dx \tag{25}\]
# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## 1 Introduction
In subtropical regions like southeast China where cloudy weather conditions are prevalent during the rice growing season, a combination of optical and radar imagery is highly desirable for a more efficient mapping of rice fields. A number of studies have been conducted on rice mapping, but gaps remain on the combined use of microwave and optical data for rice area estimation in fast urbanizing landscapes. This study could be regarded as one of the inaugural studies on the application of the new Sentinel-1 constellation in paddy rice mapping. The combination of these new SAR data with traditional Landsat optical data in mapping urban rice fields which are typically at subsistence or smallholder commercialization scales, further stresses the significance and potential contribution of the current study in the emerging field of agricultural remote sensing.
## 1 Introduction  
Extended Reality (XR) is an umbrella concept encompassing the entire spectrum of methods able to alter reality through immersive technology, such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) [1]. In robotics, XR applications can be developed on top of robot-specific simulators or game engines [2]. Robot-specific simulators are generally used for offline testing of robotic systems. As presented in [3, 4], certain robot-specific simulators enable the creation of virtual reality environments. However, robot-specific simulators can limit the content, graphics quality, and supported hardware of developed applications. To explore the capabilities of XR technologies, researchers require software platforms compatible with new XR hardware that facilitates the generation of content for these devices as well as other programming and integration tasks [2]. In this context, game engines such as Unity and Unreal Engine provide several tools to facilitate the creation of 3D environments for the latest XR hardware devices. Moreover, a growing community is engaged in supporting and expanding these tools. Thus, in many cases game engines are considered a cost-effective and flexible solution for developing XR applications [5]. Nowadays, the ability of game engines to create advanced user experiences and facilitate usage of the latest XR hardware is motivating researchers and practitioners to extend the use of game engines for non-gaming applications [6], such as social and industrial robotics [7]. However, integrating game engines with robotic systems can be a complex task [8]. Moreover, many robotics researchers can have difficulties working with the novel concepts, frameworks, and tools required to use game engines in robotics applications. Therefore, the main objective of this article is to present relevant concepts, tools, and frameworks used or proposed in the literature for enabling the creation of human-robot XR experiences through general-purpose, accessible, and popular game engines, specifically Unity and Unreal Engine.
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 4 The 3D-LUNA Localization Framework
### Localization of Nodes with Known Depth  
In our previous work [4], we demonstrated the localization process of stationary underwater nodes with known depth. The assumption was that the underwater node is equipped with a pressure sensor that can measure the underwater depth of the node. So, rather than getting a 3D contour as depicted in Figure 3, we end up with a single circle in the \(x\)-\(y\) plane which shows the position of the underwater node after receiving a VLC transmission and measuring its intensity. As the airborne node is transmitting cell by cell and the VLC transmission is spreading underwater due to its beam angle, the underwater node may end up receiving multiple transmissions and multilaterate the ranges to find out its \(x\) and \(y\) coordinates. In other words, in the case of known depth, the multilateration process is simple as the sensor position is represented by a 2D circle in the \(x\)-\(y\) plane. Upon receiving multiple transmissions, the intersection between the measured circles would represent the sensor position. To localize the sensor to a single point, three or more transmissions are needed. As the absence of depth information leads to a 3D contour as opposed to a 2D circle, the multilateration process will be more complicated, as explained below.