esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact Monopole Wideband Antenna Based on DGS
## 3 Simulation Results and Analysis  
### Reflection Coefficient (S\({}_{11}\)) and Voltage Standing Wave Ratio (VSWR)  
Figure 8a shows the reflection coefficient graph with a significant null at 16.90 GHz, where the S\({}_{11}\) value is \(-\)48.59 dB. This low S\({}_{11}\) confirms that the antenna is well-tuned, reflecting minimal power. The graph also displays multiple resonant peaks and valleys across the 4.08 GHz to 18.92 GHz range, indicating a broad operational bandwidth for effective performance. Figure 8b presents the VSWR graph, showing a value of 1.01 at 16.90 GHz, which signifies exceptional impedance matching and efficient power transmission with negligible standing waves. Together, the analyses of S\({}_{11}\) and VSWR highlight the antenna's excellent performance. The deep null in S\({}_{11}\) corresponds with the low VSWR, confirming efficient operation at this frequency. The multiple resonances suggest versatility across a wide range, with the VSWR indicating minimal power loss.  
Figure 8: (**a**) Reflection coefficient (S\({}_{11}\)) of the proposed antenna; (**b**) VSWR.  
Design modifications, including slots in the ground plane, have enhanced the antenna's low reflection and high efficiency. Overall, the combined analysis demonstrates that the antenna is optimized for performance, making it a reliable choice for various wireless communication applications.
# ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks
## 3 Problem Modelling  
This section describes the research problems, and our primary goals are explained. Consider that a field \(F(A)\) is monitored by a number of nodes for a time horizon \(T\). The sensor nodes were static and location-aware. The network topology is built on clusters, and sensors are organized as clusters. The cluster head is responsible for gathering data from its specific cluster using a single-hop communication channel.  
Efficient exploration of the sensor network field is represented by the undirected weighted graph \(G\left(V,E\right)\), where \(V\) is the collection of nodes and \(E\) is the set of edges where \(x,y\in V\). The edges represent communication links between nodes. A connection exists between nodes \(x\) and \(y\) only if they are able to communicate. Additionally, the Packet Reception Rate (\(PRR\)) is offered by the MAC layer [29] to measure the connection quality.  
First, we start with one of the paper's goals: Identifying an ideal cluster head that conserves nodes' energy network lifetime extension. To achieve this, the cluster head should satisfy some constraints, including:  
1. Provides the shortest average intra-cluster distance.
2. Provides the highest possible data transfer reliability.
3. Provides the shortest inter-cluster distance (the distance to the sink node).
4. Has the highest energy level.
5. Has the minimum cluster load metric value.  
Secondly, designing a routing technique aims to save energy and optimize the network's lifetime. Furthermore, energy management is considered while designing the proposed protocol. It also examines how to avoid unreliable routing paths. To attain this aim, the proposed route must fulfill the following criteria:  
1. Minimum communication distance.
2. Maximum reliability.
3. In order to establish a better energy balance, the nodes participating in such a path have the highest value resulting from the new proposed energy load function.
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 2 Related Work  
In this section, we address the motivation and the major approaches to the system design. One major issue of the routing protocol between UAVs in a wireless network is that it is difficult to avoid delays on the network because of the altering positions of UAVs [15]. A minor delay between the sending and receiving of data can have a huge impact on the quality of the experience. Numerous network routing protocols have been proposed to solve this problem.
## 5 Discussion  
Self-mixing optical coherent detection is an important sensing method for the assessment of cardiac, respiratory, and muscular activities. \(HRV\) is an important parameter to analyze both the overall health and the ability to tolerate stress [51]. Optical coherent detection techniques have the advantage of being a contactless method, as it becomes very helpful in measurements from critically damaged skin conditions. They are also useful for measurements on the delicate skin of preterm infants. HP can be measured by detecting a periodic feature in the waveform acquired, such as \(V_{1}\) and \(V_{2}\) peaks in the VCG signal. A comparison of R peaks in the ECG with V peaks in the VCG signal was performed and both the measurements were found to be similar, with a less than 1% deviation. The mapping of features of the ECG signal is performed with VCG signal features [52]. In this research paper, measurement results from homodyne and heterodyne techniques are compared with the ECG signal. In future, the accuracy of homodyne and heterodyne measurements can be compared with other signals of a different nature, such as the photoplethysmograph (PPG) and phonocardiogram (PCG). The ECG is the electrical signal, which is a representation of the electrical response of the heart activity. The PCG is the audio signal and represents the acoustic response of the heart activity. The PPG is the optical signal, while the VCG is the mechanical signal. During an arrhythmia, the heart can beat too fast, too slowly, or with an irregular rhythm. The first-degree AV block occurs when the PR interval is more than 200 ms. The AV block can be easily correlated with the VCG signal when there is a delay between \(U_{1}\) and the zero crossing of the VCG signal [53]. The stability of coherent detection can be improved by the construction of a multiple mirror Fabry-Perot resonator in one of the arms of the interferometer, or by implementing a servo motor mechanism for the motion of mirrors [54; 55; 56; 57; 58].
## 6 Validation Using Circuital Simulations
Finally, the waveform of the switching function \(\Psi\) is always trapped inside the desired hysteresis band \([-H,+H]\) with \(H=0.67\) A; this confirms that both the reachability and transversality conditions analyzed in Sections 3.1 and 3.2 are always fulfilled. In conclusion, the previous results validate the global stability of the SMC in presence of large perturbations, and verify the correct design of the voltage controller to impose the desired behavior to the PV voltage. A third simulation scenario was designed to evaluate the performance of the complete PV system, including the action of the P&O algorithm. Such a simulation, reported in Figure 18, considers fast changes on the irradiance condition (i.e., high derivatives of 1000 (W/m\({}^{2}\))/ms) to test the correct performance of the control system. In particular, the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Peak-to-Peak Oscillation** &
\begin{tabular}{c} **Compared with the Oscillation** \\ **Used in the Validation Example** \\ \end{tabular} \\ \hline
[12] & 0.05\% & 500 times smaller \\
[13] & 2.6\% & 9.6 times smaller \\
[11] & 13.1\% & 1.9 times smaller \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Examples of DC-link oscillations reported in literature. Figure 18: Complete PV system operation under changing irradiance conditions. irradiance starts at the highest value possible (1000 W/m\({}^{2}\)), falling to the lowest irradiance considered in this example (250 W/m\({}^{2}\)); then, the irradiance changes to 500 W/m\({}^{2}\) and, finally, to 750 W/m\({}^{2}\). The results of this simulation confirm the correct operation of both the P&O algorithm and control system: the PV voltage (\(v_{pp}\)) exhibits a 3-point behavior around the MPP voltage (\(V_{mpp}\)) for each irradiance condition, thus ensuring that the P&O reference (\(v_{r}\)) reaches the optimal operation condition for every irradiance value as demonstrated in [27; 37]. This optimal condition is also confirmed by the power produced by the PV panel (\(p_{pv}\)), which always reaches the maximum power possible (\(P_{mpp}\)) for every irradiance value. Finally, in the 33.25 ms of this simulation, the complete PV system extracts 99.67% of the maximum energy available, which puts into evidence the correct operation of the system. Therefore, the simulation scenarios presented in this section validate the following conditions:  
* The design process for the NEC boost converter is correct. * The mathematical analysis of the SMC is correct and practically verifiable. * The design process for the SMC is correct, ensuring the global stability of the PV system even under the presence of large perturbations. * The design process for the voltage controller is correct, ensuring the tracking of the reference with the desired settling time. * The designed PV system, based on the NEC boost converter, ensures the extraction of the maximum PV power and provides a continuous current to the DC-link, resulting much better power quality in comparison with the classical boost converter.
# Risk-Based Virtual Power Plant Implementation Strategy for Smart Energy Communities
## 4 Results  
In this section, to verify the effectiveness of the proposed strategy, the performance of the VPP implementation is measured, and the effects of the characteristics of the system parameters are discussed.  
To perform simulations, 10 PV and 10 wind generations were considered as resources that could compose a VPP. The actual generation data used for the resources were recorded at the Lawrence Berkeley National Laboratory in the US for PV [31] and the National Renewable Energy Laboratory in the US for wind [32]. The unit implementation costs of the resources were assumed to be $5.83/kW/month for community-sized PV and $5.21/kW/month for onshore wind, considering a service period of 20 years [33].  
The demand of the SEC was modeled using commercial and residential hourly load profiles in the US [34]. The average daily demands of commercial SECs and residential SECs are about 1500 kWh and 600 kWh, respectively. Figure 3 shows the typical demand of a commercial SEC and a residential SEC for a week. As shown in the figure, the commercial SEC requires high demand on weekdays and low demand on weekends. On the other  
Figure 2: Flow chart of the simple risk-based VPP implementation strategy.  
hand, the demand of the residential SEC shows a similar repetition pattern over a week. In addition, in the case of the commercial SEC, peak demand appears in the daytime, and in the residential SEC, it appears in the evening. The demand characteristics affect the implementation of the VPP.  
Figure 4 shows an example of VPP implementation for a commercial SEC. The black line, the dashed red line and the blue dash-dot line red represent the required demand of a commercial SEC, VPP-served PV generation and VPP-served PV and wind generation, respectively. As shown in the figure, the demand is mainly served by PV generation, and it is supplemented by wind generation. This is because the demand of a commercial SEC and PV generation have a high temporal correlation. However, on weekends, more generation than demand is supplied, and energy mismatching occurs at other times due to uncertainty of resources. This energy mismatching is resolved through the utility grid.
## 5 Results and Discussion
### Performance in Base Condition  
The state property of each point in the design condition is listed in Table 4. As shown, the absorption subsystem provides high-temperature cooling, in which its inlet and outlet temperatures of chilled water are 18.8 \({}^{\circ}\)C and 16.75 \({}^{\circ}\)C, respectively. We found that this high-temperature cooling capacity can be converted into a low-temperature cooling capacity by the subcooling; therefore, it can be employed to fulfill the requirement of the building's cooling. Additionally, low-grade solar energy is utilized adequately owing to the improvement in the evaporator temperature in the absorption subsystem. The absorption subsystem operates efficiently when the inlet temperature of the hot water is only 70 \({}^{\circ}\)C. It is noteworthy that the chilled water temperature of the absorption subsystem in the prototype is less than that in our previous investigation [8]. This is attributed to the evaporator of the absorption subsystem and subcooler not being integrated in the prototype, thus leading to the additional exergy destruction in the subcooler. Nevertheless, the separated evaporator of the absorption subsystem and subcooler is convenient for the modularization of the absorption subsystem and compression subsystem, as well as for the development of the SASCHCS.  
The performance of the system in the design condition is demonstrated in Figure 5. The heat loads of the generator, condenser and absorber in the absorption subsystem, subcooler, condenser, evaporator, and compressor of the compression subsystem are 4.16 kW, 2.71 kW, 3.96 kW, 2.52 kW, 16.89 kW, 16.21 kW, and 3.12 kW, respectively. As shown, the ratio of cooling in the absorption subsystem to that in the hybrid system is 0.155. This low value is attributed to the excessively high cooling capacity of the absorption chiller that not only lowers its performance but also frosts the  
Figure 4: Model validation of absorption subsystem.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Model** & **Experiment** & **Relative Error** \\ \hline \(T_{cw,c,\phi}\) (\({}^{\circ}\)C) & 31.53 & 32.7 & 3.58\% \\ \(T_{chw,c,\phi}\) (\({}^{\circ}\)C) & 10.0 & 9.9 & 1.01\% \\ \(T_{dis}\) (\({}^{\circ}\)C) & 59.68 & 59.4 & 0.47\% \\ \(\Delta T_{sub}\) (\({}^{\circ}\)C) & 14.28 & 14.1 & 1.28\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model Validation of hybrid system.  
subcooler [15]. Furthermore, it is found that the corresponding COP of the absorption and compression subsystem is 0.605 and 5.225, respectively.
# Survey of Artificial Intelligence Model Marketplace
## 3 AI Model Lifecycle
### AI Model Life-Cycle  
AI models in marketplaces transit through a sequence of processes as their life-cycle. The cycle ranges from training, pricing, trading, and tracking the intellectual property's performance in the production environment after leaving the marketplaces. Below is a brief description of the different stages in the model life-cycle as depicted in Figure 4.  
**Problem Definition:** In this phase, a potential model seeker defines and formulates the problem to be solved after understanding the use case requirements, its scope, and the specific questions the model needs to answer. Ideal problem definition guides the data selection required for the use case. Ensuring the data are relevant, sufficient, and of good quality is essential. This definition guides the seeker's decision on the best way of finding a model that satisfies the need.  
**Model Training:** When the model seeker decides to explore the marketplace option to find the best-performing model for the problem, it may be due to a lack of efficient training resources from her end or expertise to train models. This phase enables the seeker to join the marketplace as a model buyer. The broker mediates between the buyer and the data owner(s) to train the required model.  
**Pricing and Trading:** This entails assigning financial value to the trained model to facilitate trading. In this phase, the broker assigns prices to the trained model to compensate the data owners, satisfy buyers' utility requirements, and generate a profit for herself based on the services rendered.  
**Model Tracking:** This entails continuously monitoring the intellectual property's (model) performance in the production environment. This includes tracking its accuracy, handling data drift, and updating the model to maintain its effectiveness. Similarly, the sold intellectual property needs to be tracked against any set of impropriety.  
Figure 4: AI model life-cycle in AI marketplace.  
Significant research focuses on the model training phase, i.e., centralized, decentralized, or distributed (federated) settings. In the subsequent sections of this article, our focus will be narrowed down to the pricing and trading phase, followed by the tracking, and then finally, the marketplace security. Specifically on pricing and trading, we reviewed the existing pricing schemes and trading platforms, developed a problem space to categorize them, and made some remarks to guide their extension in the future in Section 4.
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 4 Bacteria
### Yersinia Species  
Similar to _Serratia_, _Yersina_ represents another genus from the Enterobacteriaceae family that has potential for scarab control. Additionally, isolated from the New Zealand grass grub, _Y. entomophaga_ has been shown to infect the Tasmanian grass grub (_Acrossidius tasmaniae_), redheaded cockchafer (_Adoryphorus couloni_), African black beetle (_H. arator_), and chafer beetles (_Odontria_ species) with high mortality [72; 73]. _Y. entomophaga_ utilizes a toxin complex encoded by five genes; two chitinases that likely degrade the peritrophic membrane and three protein subunits that bind to the gut epithelium [74]. This toxin complex is homologous to the _sep_ genes found in _Serratia_ and also those found in _Photorabbas_ and _Xenorhabditis_, which are bacterial symbionts of entomopathogenic nematodes [75]. Although _Y. entomophaga_ has not been tested against any scarab pests found in the U.S. and has not been developed for commercial use, this bacteria and its toxin complex appears to have a broader toxicity across scarab taxa than _Serratia_ species [76], making it a viable option as a future microbial products. Despite this, no natural epizootics of _Yersinia_ have been documented in scarab species.
# Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance
## 1 Introduction
### Phase Keeping  
When a team of UAVs are utilized to track the ground target in a cooperative manner, the coordinate algorithm is needed. After UAVs reach the desired circle, we need to evenly space UAVs around the circle. In this manner, the localization error of the ground target could be reduced. The desired phase difference between adjoining UAVs could be \(\theta_{d}=2\pi/n\), where \(n\) denotes the number of UAVs. The phase between adjoining UAVs are \(\theta_{1}\),\(\theta_{2}\),\(\theta_{3}\), and the phase difference \(\Delta\theta\) could converge to zero by controlling the speed \(s(t)\).  
\[\Delta\theta=\theta_{d}-\theta_{i}\ \ \ \ \ i=1,2\cdots,\ n \tag{3}\]  
An example of phase keeping with three UAVs is shown in Figure 2.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 7 Discussion  
Table 2's analysis provides valuable insights into the performance of different mobility models under attack and non-attack scenarios within RPL-based IoT networks. Firstly, the PDR demonstrates the CMM's robustness in ensuring efficient data delivery in the absence of attacks, while the PMM excels at tracking moving targets. However, vulnerabilities became apparent with the introduction of the Hello Flood attack, leading to disruptions across all models, particularly in terms of increased packet loss and delays. Secondly, the E2 ED metric reveals that under attack conditions, there is a notable increase in E2 ED across all models, indicating delays in data transmission and routing decisions. While models like the RPGM and NCM show moderate increases, the CMM and PMM struggle to maintain efficient data delivery, highlighting weaknesses in adapting to attack-induced disruptions. Thirdly, the analysis of throughput demonstrates initial efficiency in data transmission with models like the CMM, but the attack leads to congestion and reduced data transfer, particularly evident in increased APC. The RPGM and NCM also exhibit congestion-related issues under attack conditions, emphasizing the impact on network efficiency and performance. Fourthly, the ETX metric highlights the increased need for packet retransmissions and hops under attack conditions across all models, despite their balanced realism. The RPGM and NCM, however, face challenges with increased packet loss and limited scalability, indicating vulnerabilities to attack-induced disruptions. Finally, the examination of APC emphasizes the efficient energy utilization of the CMM but reveals increased energy consumption across all models with the attack. This makes the effect on power efficiency and network resilience even more clear, which is why strong security measures and custom solutions are needed to prevent disruptions and make sure that RPL-based IoT networks will be resilient in changing environments for a long time. On the other side, among the four-group mobility models analyzed, the CMM and the PMM stand out as the most robust and suitable for IoT applications. The CMM demonstrates efficient data delivery and transmission rates in the absence of attacks, thanks to its structured movement patterns that ensure reliable communication. However, it may face challenges with increased E2 ED and ETX in complex scenarios. On the other hand, the PMM excels at tracking moving targets, making it ideal for scenarios requiring dynamic node movements, such as surveillance or tracking applications in IoT networks. Despite its effectiveness, the PMM may introduce overhead and exhibit limited applicability in certain scenarios with stationary or less dynamic node configurations. In conclusion, both the CMM and PMM have their own benefits, but the best one to use depends on the specifics of the deployment. This shows how important it is to think carefully about which mobility model is best for IoT implementations. Figure 21: APC without and with attack using PMM.
# Reconfigurable Intelligent Surface Assisted Target Three-Dimensional Localization with 2-D Radar
## 4 Simulation Analysis
### Target Localization in Multi-Target Scenario  
The multi-target localization capability of the proposed RIS-assisted 2-D radar is considered. The 2-D radar located at [0, 0, 0] km is assisted by three RISs to locate four sparse targets in one scanning beam. The locations of the targets are [20; 40; 9] km, [18; 36; 7] km, [24; 48; 12] km and [16; 32; 10] km, and the locations of the RISs are set as [1.34, 2.68, 0.1] km, [1.34, 2.68, 1.3] km and [1.34, 2.68, 2.5] km, respectively.  
For the first target located at [20; 40; 9] km, its SNR in the direct channel is changing from 5 to 30 dB with a step as 1 dB. Its SNR in the auxiliary channels can be calculated. In addition, it is assumed that the Radar Cross-Sections (RCSs) of the four targets are the same. Then, the SNR of the other targets in the direct channel and the auxiliary channels can be obtained by scaling according to the measured distance information. To obtain reliable simulation results, 5000 Monte Carlo simulation experiments have been carried out, and the simulated GDOP varying with the SNR for each target is given in Figure 7.  
Figure 7: Simulated GDOP for each target in multi-target scenario.  
As shown in Figure 7, the proposed RIS-assisted 2-D radar target position estimation algorithm can obtain acceptable localization accuracy for each target, and the localization accuracy becomes higher with the increasing of the target SNR. For these four targets, target 4 has the highest localization accuracy, which is followed by target 2, target 1 and target 3. This is related to their distances to the 2-D radar, since the distances determine the target SNR. Target 4 is closest to the radar, it has the highest target SNR and the highest localization accuracy, while target 3 is the farthest away from the radar station; it has the lowest SNR and the worst localization accuracy. This is consistent with the analysis of the influence of the SNR on the localization accuracy in Section 4.1.