esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapid Synthesis of Silver Nanowires in the Polyol Process with Conventional and Microwave Heating
## 2 Materials and Methods
### Methods  
Silver nanowires were synthesised in a bath process with conventional and dielectric heating. Experiments with conventional heating were carried out in a 250 cm\({}^{3}\) Erlenmeyer flask heated up on a magnetic stirrer (Heidolph MR, Schwabach, Germany) at a stirring intensity of 150 1/min. The microwave-assisted synthesis was performed in a laboratory installation shown in Figure 1, which is a modification of the previously used single-mode microwave system [28].  
In this setup, AgNPs were prepared inside a 30 mm ID glass cylinder placed inside a microwave applicator. The reaction mixture was mixed with a magnetic stirrer placed in the lower part of the cylinder. In both experimental setups, the temperature was controlled with an accuracy of \(\pm\)0.5 \({}^{\circ}\)C. During a typical synthesis, 0.892 g of PVP was dissolved in 90 cm\({}^{3}\) of polyol, subjected to extensive mixing and its temperature was 80 \({}^{\circ}\)C. Then, 0.91 cm\({}^{3}\) of stock solution of 50 mM CuCl2 was added and heated up to 170 \({}^{\circ}\)C. When the preset temperature had been reached, 0.910 g of AgNO3 in 0.5 cm\({}^{3}\) of deionised water was injected with a syringe into the reactor. Using a concentrated precursor solution guaranteed  
Figure 1: Single-mode laboratory setup for microwave-assisted synthesis of AgNWs.  
identical conditions for starting the nucleation and the growth of nanostructures in the course of syntheses. Following the addition of the precursor, the reaction mixture changed its colour to yellow, and with the progress of the reaction it became light grey and non-transparent. At the final stage of the process, the reaction mixture became grey with wispiness, which indicated the presence of one-dimensional nanostructures. After 5 min, the synthesis was completed by cooling the reactor in the water at room temperature. The final product was subjected to centrifugation at a rate of 20,000 1/min for 15 min. The supernatant was removed, and the remaining part was dispersed in DI water, centrifuged three times at 5000 1/min and stored in isopropanol for further characterisation.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 2 Existing Surveys
### Surveys on AI-Assisted Image Encryption  
As a branch of AI-assisted image processing, AI-assisted image encryption has received a research focus in recent years. A few researchers have conducted surveys on existing research works in this area. As an example, one may refer to [31], wherein the applications of neural networks in image encryption for optical security in the healthcare sector were studied. Image encryption is an important component in the healthcare sector for improving the security of patient images gathered from sources such as ultrasounds, MRI scans, and X-rays. Neural networks are heavily used to provide security and privacy through encryption, although the algorithms are currently limited by their complexity and speed, and therefore, much research in the field is focused on optimization.  
Table 1 summarizes the surveys discussed above in order to highlight their shortcomings, which motivated the work of this paper.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Survey** & **Year** & **Chaotic** & **Ecosystem** & **Roadmap** \\ \hline
[17] & 2021 & No & No & No \\ \hline
[19] & 2020 & No & No & No \\ \hline
[32] & 2020 & No & No & No \\ \hline
[18] & 2020 & No & No & No \\ \hline
[16] & 2018 & No & No & No \\ \hline
[20] & 2018 & No & No & No \\ \hline
[33] & 2015 & No & No & No \\ \hline
[21] & 2014 & Yes & No & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Existing Surveys.  
In Table 1, each entry in the first column contains a survey. The second column states whether or not the survey is focused on chaotic image encryption. The third column demonstrates whether or not the survey establishes an ecosystem for chaotic image encryption. Finally, the fourth column contains \"Yes\" if the survey develops a future roadmap for the field. It contains \"No\" otherwise.
## 8 Conclusions  
Extensive efforts have been made to develop reliable and efficient onboard generation systems for electric aircraft. Due to the inherited robustness, Switched Reluctance Machines (SRMs) have attracted attention as a promising candidate for an integrated starter/generator in aerospace applications. This paper has presented two modes of operation: Constant Current (CC) and Constant Voltage (CV). A Particle Swarm Optimization (PSO)-based tuning approach is employed to optimize the controller of a Switched Reluctance Generator (SRG). Both CV and CC control have been achieved separately by controlling the turn-on/off angles. PSO can tune the PID controller easily and efficiently while experiencing good dynamic results. For CV, controlling the turn-off angles is faster with less overshoot than controlling the turn-on angle. Overshoot values of 10% and 75% have been obtained when controlling the turn-off and turn-on angles, respectively. Besides, the steady-state settling time was 0.03 s and 0.15 s when controlling the turn-off and turn-on angles, respectively. The controlling turn-off angle has a 10% peak overshoot for the constant current and 0.1 s for steady-state settling. The response has a high-magnitude ripple for controlling the turn-on angle, and the settling time depends on the reference current. The optimal PID tuning has been carried out for both CV and CC modes. The proposed control system also has the option to generate the maximum current by varying the turn-on angle or turn-off angle. The proposed optimization technique sets the maximum voltage not exceeding the nominal voltage. The control-Hardware-in-the-loop (CHiL) has been used to validate the theoretical findings of the proposed control strategy.  
Conceptualization: M.M.S. and A.S.A.-K.; methodology: M.M.S. and W.E.A.-A.; software: M.M.S. and W.E.A.-A.; validation: M.M.S. and W.E.A.-A.; formal analysis: M.M.S., W.E.A.-A. and A.S.A.-K.; investigation: M.M.S., W.E.A.-A. and A.M.M.; resources: M.M.S., W.E.A.-A. and A.S.A.-K.; data curation: M.M.S., W.E.A.-A. and A.S.A.-K.; writing-original draft preparation: M.M.S., W.E.A.-A., A.S.A.-K. and A.M.M.; writing-reiveive and editing: M.M.S., W.E.A.-A., A.S.A.-K. and A.M.M.; visualization: M.M.S., W.E.A.-A., A.S.A.-K. and A.M.M.; supervision: [PERSON] and A.S.A.-K.; project administration: A.S.A.-K. and A.M.M.; funding acquisition: A.S.A.-K. and A.M.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Information Technology Industry Development Agency (ITIDA)'s Information Technology Academia Collaboration (ITAC) collaborative funded project under the category type of advanced research projects (ARP) and grant number ARP2020.R29.7.  
Not applicable.  
Figure 23: Opal-RT real-time simulator response for turn-on angle, (**a**) constant current response at different references currents, (**b**) effect of turn-off angle on maximum constant current (solid line is experimental, and dashed line is simulation).
## 2 Methods
### GPS PPP Observation Models
#### 2.1.2 Troposphere-Constrained Single-Frequency GPS PPP Observation Model  
According to the tropospheric delay product published by IGS, a virtual observation is used in the observation equation, and a troposphere-constrained SF GPS PPP is then constructed. The observation equation is expressed, as follows:  
\[\left[\begin{array}{c}p_{r}^{1}\\ l_{r}^{1}\\ \vdots\\ p_{r}^{m}\\ l_{r}^{m}\\ \frac{l_{r}^{m}}{\Lambda}\\ T_{r}\end{array}\right]=\left[\begin{array}{ccccc}-\mu_{r}&\mathbf{I}_{t}&M_{w} &\mathbf{K}&\mathbf{R}\\ \end{array}\right]\left[\begin{array}{c}\mathbf{x}\\ \tilde{d}_{r}\\ Z_{w}\\ \mathbf{I}_{r}^{s}\\ \overline{\mathbf{N}_{r}}\end{array}\right]+\left[\begin{array}{c}\varepsilon_{ r}\\ \varepsilon_{r}\\ \varepsilon_{r,trop}\end{array}\right],\quad\mathbf{Q}_{L},\quad\mathbf{Q}_{T}. \tag{8}\]  
where \(\mathbf{I}_{T}\) is a unit vector of m rows and one column, corresponding to the zenith wet delay parameter \(Z_{w}\); \(\hat{\overset{\wedge}{T}}_{r}\) is derived from external tropospheric products, the products for each IGS station at 5 min intervals provided by the IGS Analysis Center; \(\varepsilon_{r,trop}\) is the corresponding noise; \(\mathbf{Q}_{T}\) denotes the stochastic model of virtual troposphere observables; and, \(\mathbf{0}\) is a zero matrix.
## 7 Performance Analysis
### Simulation Results
#### 7.2.2 Performance Comparison under Different Link Average Loads  
In order to simulate the impact of the average link load on the algorithm's performance, we changed the rate of the uplink traffic of the satellite node, but its ratio still met the traffic model in Section 2, so that the average link load ranged from 0.23 to 0.42, and fixed the duration of sun outage to 120 s. It can be seen from Figure 12 that, the packet loss rate of the three algorithms all increase with the increase in link load, and the packet loss rate of LPRA is always the lowest. Among them, the packet loss rate of the IADR algorithm increases significantly when the link load reaches 0.35. This is because the load at this time has reached the link bottleneck. After reaching the link bottleneck, the statistics of the link cannot truly reflect the network status. At this time, the performance of algorithms that rely on link information drops significantly. While the packet loss rate increased significantly, the delay decreased slightly. The main reason for this is that some originally high-delay data packets were not included in the final statistical results due to packet loss. In termsof latency, the SPF algorithm is always the highest. After the load exceeds 0.35, the delay of the LPRA algorithm exceeds that of the IADR algorithm. This is because the LPRA algorithm uses more detour paths, and the paths with higher queuing delays in IADR have already caused packet loss, which is not included in the statistics. For the actual arriving data packets, the average delay of the IADR algorithm is better than LPRA.
## 5 Conclusions
First, indoor real-time location technology is still in its early stages and there is much room for improvement in terms of accuracy and cost. As a result, an indoor RTLS cannot be readily implemented in most buildings; thus, push notifications of nearby checkable facilities and indoor navigation could be functions that may not be available in most buildings, and users will need to find facilities on their own using the BIM model, even a 2D map, as a reference. Second, the open nature of the crowdsourcing approach presents an opportunity for individuals who exhibit antisocial behavior to participate in the inspection process. For example, people may upload incorrect inspection reports deliberately. Therefore, the quality control of such a crowdsourcing inspection system is the next logical topic of study. Third, considering that non-expert general public many have limited ability to perform inspection tasks, local authorities may use a customized version of Table 3 in the early stage of implementation. Last but not least, the effectiveness of the incentive model employed in this research needs to be validated by implementing the iInspect app on a large scale. The prototype application was tested by only a few users, and it is not possible to examine the effectiveness of the incentive model when using such a small sample size. Conceptualization, J.Z. and D.Z. ; funding acquisition, J.Z. ; investigation, D.Z. and J.Z. ; project administration, J.Z. ; software, Z.C., D.L. and H.X. ; writing--original draft, D.Z. and J.Z. This work was supported by the National Natural Science Foundation of China under Grant 71272147 and Tianjin Science and Technology Committee under Grant 15 ZXHLSF00070. The authors declare no conflict of interest.
## 4 Design of the DRFTM
### Access to Evidence of Trust
#### 4.1.2 Data Trust  
Data trust is an assessment of the credibility and reliability of data, typically encompassing considerations of data fault tolerance and consistency. In a certain period, data detected by adjacent nodes have temporal and spatial correlations, and the trustworthiness of node \(n_{i}\) is judged by the difference between the data transmitted by node \(n_{i}\) and the data perceived by neighboring node \(n_{j}\). Suppose data collected by various sensor nodes over a defined time period are denoted as \(\{d_{1},\ d_{2},\ \ldots,d_{m},\ \ldots,d_{n}\}\), we introduce a normal  
Figure 3: DRFTM architecture diagram.  
distribution to model the probability density of data items detected by adjacent nodes as [28; 29]:  
\[f(x)=\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{(x-\mu)^{2}}{2\sigma^{2}}}, \tag{4}\]  
where \(\mu\) represents the mean of this set of data and \(\sigma\) represents the variance of this set of data.  
The mean value of data obtained from a normal distribution is primarily influenced by the majority of data points, making it considered the most representative value that effectively reflects the similarity of data values. Consequently, it is regarded as having the highest trustworthiness. Trustworthiness evidence based on the received data values is calculated as  
\[T_{i}^{data}=\begin{cases}2\left(0.5-\int\limits_{\mu}^{d_{m}}f(x)dx\right)= 2\int\limits_{d_{m}}^{\infty}f(x)dx,&d_{m}\geq\mu\\ 2\left(0.5-\int\limits_{d_{m}}^{\mu}f(x)dx\right)=2\int\limits_{-\infty}^{d_{ m}}f(x)dx,&d_{m}<\mu\end{cases}. \tag{5}\]
# Research on Interval Optimal Scheduling Strategy of Virtual Power Plants with Electric Vehicles
## 2 Structure and Principle of Virtual Power Plant with Electric Vehicles  
### Structure of Virtual Power Plant with Electric Vehicles  
The structure of the virtual power plant incorporating electric vehicles studied in this paper is shown in Figure 1. This virtual power plant can realize the coupling of a power grid and natural gas network, and the composition includes wind power generation, photovoltaic power generation, a gas turbine, power-to-gas equipment, power storage equipment, gas storage equipment, residential electric load, and an electric vehicle [30; 31]. Under the influence of wind and light generation as well as load and other uncertainties, how to ensure the economic, safe, and environmentally friendly operation of this virtual power plant is an urgent problem that needs to be solved [32; 33].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Interval Number**} & \multirow{2}{*}{**Probability-Based**} & **Time-Domain Rolling** & **Multiple** & **Combinatorial** \\  & & & **and Multiple Time Scales** & **Scenarios** & **Optimization** \\ \hline \multirow{2}{*}{advantage} & simple practicality and efficient & \multirow{2}{*}{effective} & \multirow{2}{*}{better results} & \multirow{2}{*}{good robustness} & \multirow{2}{*}{high accuracy} \\  & & & & & \\ \hline \multirow{2}{*}{ disadvantage} & \multirow{2}{*}{low accuracy} & difficult to obtain engineering practice & \multirow{2}{*}{complex process} & \multirow{2}{*}{high dependence} & complex process and difficult \\ \hline \hline \end{tabular}
\end{table}
Table 1: Taxonomy table to compare the advantages of this work with recent research.  
Figure 1: Structure diagram of gas-electric interconnection virtual power plant incorporating an electric vehicle.
## 5 Numerical Analysis of the Roadbed Heating and Frost Heaving Prevention
### Analysis of the Effect of Frost Heaving Prevention  
Figure 13a shows the cross-sectional profile of the temperature field in the natural roadbed on February 1 in winter. The top and slope surfaces of the roadbed and the surrounding ground surface formed a continuous region of negative temperature that was affected by frost heaving. The freezing depth was 0.83 m at the center of the roadbed and up to 1.24 m at the road shoulders on both sides, indicating serious frost-heaving damage. Figure 13b-d show the characteristics of the roadbed temperature field distribution on 1 February of the second year for heating temperature target points of 40, 50, and 60 \({}^{\circ}\)C, respectively. It can be seen that, under the effect of the heat supplied by the heat pumps, two symmetrical warming regions were created beneath the roadbed surface, and only a substantially shallow sub-zero temperature region remained at the surface layer, with a freezing depth of less than 0.2 m. This indicates that the frost damage was effectively controlled. The higher the heating temperature target point was, the larger the warming region of the roadbed and the higher the resultant temperature rise were. At the same time, a visible cooling region emerged around the heat pump evaporator. Hence, it was evident that the heat pump effectively collected, transformed, and transferred the heat energy from the stable stratum surrounding the roadbed to the frost-heaving affected layer of the roadbed.  
Figure 12: Roadbed computational model.  
Since the evaporator in the computational model was 2.0 m away from the foot of the roadbed slope, the heat absorbed by the evaporator would not have a secondary effect on the roadbed. In practical applications, the size and placement of the heat pump heat exchangers should be reasonably designed to suit the roadbed freezing depth and extent, as well as the geothermal energy distribution in the underlying foundation.
# Phase Compensation Technique for Effective Heat Focusing in Microwave Hyperthermia Systems
## 2 The Antenna Array System for Microwave Hyperthermia Treatment
### Antenna Array Configuration for Effective Electric Field Concentration  
As mentioned in the previous section, the designed TSA antenna is linearly polarized along the radiation direction, and each antenna should be aligned along the surface of the cylindrical phantom for effective E-field synthesis. The number of radiators, which should be determined for the successful E-field concentration on the target area, was set at 12 due to the SAR accumulation in the breast phantom, in accordance with our previous study [25]. The array configurations for inducing microwave hyperthermia along the xz- and xy-planes are presented in Figure 4a,b, respectively. To feed the array antenna, simultaneous excitation is induced with an EM simulation tool. To apply the input power to each antenna, a discrete port is assigned to each TSA and the open boundary condition in CST is set to analyze the antennas' performances. The narrowband antennas are arranged at regular intervals for vertical linear polarization along the z-axis.
# Study on Compaction Characteristics and Compaction Process of an Unsaturated Silt Based on PFC3D
## 4 Research on the Rolling Compaction Process
### Analysis of the Compaction Results
#### 4.3.1 Relationship between the Compaction Effect and Rolling Times
Analysis of the Relationship between the Compaction Effect and the Rolling Process  
Using the above discrete element model, the rolling simulation with a water content of 15% and rolling amount of five times was carried out. By changing the amplitude of the excitation force, the compaction effect of strong vibration and weak vibration rolling was analyzed. In the rolling process, the model is first subjected to static load rolling, and then strong vibration rolling and weak vibration rolling. When the weak vibration is applied, the amplitude of the excitation force is 1, and the magnitude of the excitation force is 928 N. When the strong vibration is applied, the amplitude of the excitation force is 2, and the magnitude of the excitation force is 1856 N.  
It can be seen from Figure 12 that compared with the static rolling effect, both strong vibration and weak vibration rolling can greatly improve the compaction effect of subgrade rolling. The larger the amplitude of the roller vibration, the more obvious the improvement of the compaction effect. The main reason for this phenomenon is that the eccentric roller will produce an exciting force when it rotates, which will exert a short-term continuous pulse impact on the compacted soil, so that the effect between the soil particles changes from static friction to dynamic friction, the soil pores are reduced, and the particle skeleton is denser. After the fifth rolling, the compaction degree of strong vibration + weak vibration combination rolling is significantly higher than that of only strong vibration rolling or only weak vibration rolling, and it is found that the compaction effect of strong vibration first and then weak vibration is stronger than that of weak vibration first and then strong vibration. For the soil samples with a water content of 15%, the best rolling compaction process obtained by discrete element simulation is static compaction once + strong vibration twice + weak vibration twice.
# Liquid-Crystal-on-Silicon for Augmented Reality Displays
## 2 LCoS Working Principles
### Vertical Alignment Mode  
In the VA mode [24], the initial LC directors are nearly perpendicular to the substrates (pretilt angle \(\alpha\sim 88^{\circ}\)), as Figure 3 shows. For intensity modulation, the VA cell is sandwiched between two crossed polarizers, whose absorption axes are \(\pm 45^{\circ}\) with respect to the x-axis. In the voltage-off state (V = 0), the incident light propagating along the z-direction experiences a negligible phase retardation. That means that the linearly polarized incident light remains unchanged and cannot pass through the crossed analyzer. As the applied voltage exceeds a threshold, the LC molecules with a negative dielectric anisotropy (\(\Delta\varepsilon<0\)) would be reoriented perpendicular to the longitudinal electric field. Because the surface anchoring force decreases as the distance from the alignment layer increases, the LC molecules keep the pretilt angle near the substrates and get fully reoriented in the central layer (Figure 3b). A pretilt angle \(\alpha\
eq 90^{\circ}\) gives a preference of x-z plane reorientation. Balancing between the competitive surface anchoring force, electric force and elastic restoring force, the LC tilt angle can change from \(\sim\)88\({}^{\circ}\) towards 0\({}^{\circ}\) as the voltage increases. The associated phase retardation can be tuned from 0 to \(\pi\), thus, the output light intensity can be progressively changed from dark to bright. The VA mode produces an excellent dark state and high contrast ratio (CR > 5000:1) regardless of the cell gap, wavelength, and operation temperature. However, the fringing field effect on the VA mode is more severe, which will be addressed in detail in the next section.  
In principle, a VA cell can also be used for phase-only modulation, if the incident light polarization is parallel to the LC directors (in the voltage-on state). However, the negative-\(\Delta\varepsilon\) LC materials usually exhibit a higher viscosity and a smaller \(\left|\Delta\varepsilon\right|\) than the corresponding positive LCs because the dipoles are in the lateral positions. As a result, homogeneous cells using a positive-\(\Delta\varepsilon\) LC are preferred for phase-only spatial light modulators.
## 5 DRL-Based Online Resource Allocation  
The original problem in Equation (10) is first formulated through MDP in this section. Afterward, a DRL-based resource online allocation algorithm called DRL-QoS-RA is elaborated, including action space, state space, immediate reward, and operating process. We also propose a transfer learning-based deployment mechanism in Section 5.4 to reduce computational complexity and promote efficiency.