esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Controller Design
### Novel Smooth Reference Governor  
Despite the advantages of the nominal reference governor, the revised trajectory is discontinuous or even jitters if the trajectory \(x_{d}\) changes rapidly [35]; therefore, when the discontinuous revised trajectory is applied to the closed-loop system, unmodeled high-frequency plant dynamics will be invoked, and the plant model used in the reference governor will not be accurate, which will decrease the plant behavior prediction accuracy. In this paper, a novel smooth reference governor is proposed to handle this problem, which can smooth the fast-changing trajectory by introducing a feedback mechanism based on the nominal reference governor; i.e., the nominal reference governor is augmented with a dynamic rate limiter, the threshold of which is closely related to \(\alpha_{k}\). The feedback mechanism of the dynamic rate limiter is shown in Figure 3 and is given as:  
\[\dot{x}_{df}=S_{Mf}\Big{[}K_{f}\Big{(}x_{d}-x_{df}\Big{)}\Big{]} \tag{26}\]  
Figure 2: Bisectional search algorithm to optimize \(\alpha_{k}\).  
where \(x_{df}\) is the pressure trajectory used as the reference governor input, \(K_{f}\) is the feedback gain and \(S_{M\!f}(\cdot)\) is the saturation function given by:  
\[S_{M\!f}(x)=\left\{\begin{array}{ll}\alpha_{k}M_{f}&\text{if }x\geq\alpha_{k}M_{f} \\ x&\text{if }|x|<\alpha_{k}M_{f}\\ -\alpha_{k}M_{f}&\text{if }x\leq-\alpha_{k}M_{f}\end{array}\right. \tag{27}\]  
where \(M_{f}\) is a constant.  
The revised trajectory from the dynamic rate limiter \(x_{df}\) is then fed to the reference governor (23). If the actuator constraints are violated, then \(\alpha_{k}\) is decreased; as a result, the dynamic threshold of \(S_{M\!f}(\cdot)\) is then decreased, and therefore, the change rate of \(x_{df}\) is slowed down, making \(x_{df}\) smoother than that of the nominal reference governor. The smooth trajectory helps prevent the unmodeled high-frequency dynamics from happening, which improves the plant model credibility in the model prediction. Therefore, the actuator constraint violation prediction accuracy is improved.  
The block diagram of the proposed control structure is shown in Figure 4. As shown, the proposed smooth reference governor transforms the pressure \(x_{d}\) to a revised reference signal \(x_{d}^{rep}\). The nonlinear feedback controller treats \(x_{d}^{rep}\) as the command, and then a virtual flow command \(Q_{in,d}\) is generated. Finally, \(Q_{in,d}\) is divided into \(\alpha_{d}\) and \(u_{v}\) by the mid-range controller, which drive the pump and valve, respectively.
## 2 Materials and Methods
### Polytetrafluoroethylene (PTFE)  
PTFE, [-CF\({}_{2}\)-CF\({}_{2}\)-]\({}_{n}\), is a thermoplastic polymer formed by polymerising tetrafluoroethylene monomer (CF\({}_{2}\)=CF\({}_{2}\)) at high temperatures and pressures. Depending on its molecular weight, PTFE melts at around 330 \({}^{\circ}\)C. When it cools from its melting phase, it starts to crystallise at around 325 \({}^{\circ}\)C and becomes solid, opaque, and waxy in appearance [26, 27, 28, 29].  
PTFE is utilised in many applications due to its chemical inertness and stability [27, 28]. Due to its non-polar structure, low dielectric constant, low dielectric loss, and high resistivity, it is a well-suited material for cable insulation or microwave applications [30, 31]. PTFE has high thermal stability and can maintain all its useful properties up to its melting point. With a very low coefficient of friction, PTFE is commonly used as a non-stick coating [27, 28]. This polymer is known for its biocompatibility and finds extensive use in medical applications, including medical tubing and advanced catheters, among others [32, 33].  
In this study, the microwave monopole antenna was constructed from a Qaxial Semi-Rigid RG405 coaxial cable [34]. The selected coaxial cable, with a PTFE dielectric, was chosen for its biocompatibility, thermal stability, and consistent dielectric properties with  
Figure 1: Schematic representation of PTFE expansion following an ablation procedure and an actual zoomed microscope image of the region; (A) outer conductor, (B) dielectric-PTFE, (C) inner conductor, (D) quarter-wavelength, (E) immersion length, and (F) PTFE expansion after ablation.  
temperature. Moreover, the reason behind the selection of RG405 is its ability to endure high temperatures (up to 165 \({}^{\circ}\)C) under powered conditions (\(\approx\)92 W at 2.45 GHz), well below its melting point at 330 \({}^{\circ}\)C. However, following ablation procedures, experimental observations of untreated coaxial cables displayed noticeable, permanent PTFE deformation (expansion) [25]. The observed expansion did not correlate with the theoretical expansivity or the true thermodynamic coefficient of linear thermal expansion, \(\alpha_{1}\), defined by Equation (1).  
\[a_{1}=\bigg{(}\frac{1}{L_{0}}\bigg{)}\bigg{(}\frac{\Delta L}{\Delta T}\bigg{)}, \tag{1}\]  
where \(L_{0}\) is the initial length in m, \(\Delta L\) is the change in length or expansion in m, and \(\Delta T\) is the change in temperature in \({}^{\circ}\)C [27,28]. [PERSON] [28] showed that PTFE samples, through different forming techniques (moulded and extruded), manifested congruent expansivity following annealing between \(-\)190 \({}^{\circ}\)C and 350 \({}^{\circ}\)C. In the case of RG405 coaxial cable, the impact of PTFE annealing becomes more intricate due to the outer conductor constricting the PTFE core. Consequently, this study extensively investigates the influence of coaxial annealing pre-fabrication on the antenna's performance.
## 2 Related Works
Studies using real-life and synthetic datasets confirm the efficacy of the technique. The findings showed that the suggested approach, DANS, surpassed current benchmarks in terms of QoL by up to 52% on the synthetic dataset and up to 75% on the real-life dataset. Furthermore, using a simulated dataset, DANS was found to accomplish more for small groups of active nodes, outperforming benchmarks by up to four times in terms of QoL. Finally, side-by-side analyses of state-of-the-art IoT node selection methods are shown in Table 1.
## 3 CO2 Emission Analysis and Indicators in Overall Project
### Overall CO\({}_{2}\) Emissions Distribution  
By analyzing the preliminary design budget documents along with the CO\({}_{2}\) emissions factor database, and applying the accounting methods outlined in Section 2.4, As illustrated in Figure 7, we compared the CO\({}_{2}\) emissions proportions attributed to materials, labor, and machinery across six projects. Among them, 7a,b show the CO\({}_{2}\) emission proportions for a 15.0m diameter shield tunnel, including Projects 1, 2, 3, and 4, while 7c shows the CO\({}_{2}\) emission proportions for an 11.4m diameter shield tunnel, including Projects 5 and 6. In Figure 7a,b, the CO\({}_{2}\) emission proportions for materials, labor, and machinery in Projects 1 through 4, each with a diameter of 15 m, demonstrate minimal variability, with a maximum disparity of 3.4%. Specifically, CO\({}_{2}\) emissions from materials represent approximately 85.0%, from machinery about 10.4%, from transportation around 3.7%, and from labor close to 1%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Shield** & **Segment** & **Structure Type** & **Shield Balance** & **Number of** & **Tunnel Length** \\  & **Diameter (m)** & **Thickness (mm)** & **Structure Type** & **Form** & **Lanes** & **(m)** \\ \hline Project 1 & 15.0 & 650 & single-tube & & & \\  & & double-deck & & & & \\ Project 2 & 15.0 & 650 & single-tube & & & \\ Project 3 & 15.0 & 650 & double-deck & & & \\ Project 4 & 15.0 & 650 & single-tube & & & \\ Project 5 & 11.36 & 500 & double-tube & & & \\ Project 6 & 11.36 & 500 & double-tube & & & \\ Project 6 & 11.36 & 500 & single-deck & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Project overview table.  
Figure 7: \(Cont\).  
In Figure 7c, the distribution of CO2 emissions for materials, labor, and machinery in Tunnels 5 and 6 exhibits a close resemblance, with the largest difference being in Machinery, accounting for 2.6%. The key difference is that the shield excavation in Project 5 has CO2 emissions per unit of length 1100 kgCO2e/m higher than in Project 6. As a result, Project 5 has a higher proportion of CO2 emissions from materials, while Project 6 has a transportation CO2 emission proportion that is 2.3% higher than Project 5. This notable difference is due to Project 6 having transportation CO2 emissions per unit of length 500 kgCO2e/m lower than Project 5, leading to a higher overall proportion of CO2 emissions in Project 5 and lower transportation emissions. Furthermore, when compared to the 15 m projects, it is observed that as project length decreases, the proportion of transportation CO2 emissions increases.  
Additionally, when comparing the proportions of CO2 emissions from shield projects with diameters of 15.5 m and 11.36 m, it is observed that as the diameter increases, the proportion of CO2 emissions from machinery gradually increases, while that from materials decreases. From the perspective of controlling CO2 emissions at the construction site, designing shield projects with smaller diameters could be considered.
## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Conventional Approaches
#### 8.1.5 Landing Spot Concept  
Most of the energy optimization techniques considered in the preceding paragraphs require the UAV to be in constant flight or hovering position in order to serve user requests. This greatly limits the amount of energy savings that can be obtained as the UAV consumes a significant amount of energy during flight or hovering [13; 19]. Hence, alternative deployment approaches need to be developed which can reduce the flying or hovering time of the UAV-BSs in order to further enhance the energy saving obtained from these energy optimization techniques. To address this issue, the concept of a landing spot was introduced in [265] such that the UAVs can land on some designated locations such as roof top of tall buildings, lamp post, or some specially designed platforms which can also be equipped with charging pods, rather than having to hover continuously to serve user request and expend so much energy, which is a major challenge for battery limited UAVs. The few studies that consider the landing spot concept are discussed in the following paragraph.  
However, due to the very limited studies on this concept, the established taxonomy of categorizing the studies based on conventional and ML approaches is not followed here.  
The authors in [31] performed a capacity comparison between landed and hovering UAV with the aim of determining which approach will be suitable for adoption. Their finding reveals that the choice of a suitable deployment option depends on certain factors including the number of UAVs deployed, the distance between the charging stations and service area, and the capacity of the UAV battery. The work in [32] proposed a deep \(Q\)-learning approach for optimizing UAV trajectories using the land spot where the UAVs do not have to continuously fly along the trajectory but can land at some locations along its path in order to minimize energy consumption while meeting user demands. More research works need to be conducted in this direction to determine the optimal locations where UAVs can land along their trajectory as well as the optimal separation distances between the UAV optimal hovering positions and the suitable landing spots in order to improve the amount of energy savings while respecting the QoS constraints of the users.
# An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter
## 5 Simulation Results
Figure 10: The simulation results of Experiment 2: (**a**) the azimuth resolution; (**b**) the ground-range resolution; (**c**) the imaging result of the simulated targets; (**d**) the contour of P\({}_{2}\). Table 3 provides specific imaging performance indicators, where PSLR and ISLR represent peak sidelobe ratio and integrated sidelobe ratio, respectively. In Experiment 1, the theoretical values of the ground-range resolution of P\({}_{1}\), P\({}_{2}\), and P\({}_{3}\) are 0.354 m, 0.331 m and 0.312 m, respectively, and the theoretical values of the azimuth angular resolution are 0.608\({}^{\circ}\), 0.596\({}^{\circ}\) and 0.588\({}^{\circ}\). According to the result data in Table 3, it is concluded that the performance imaging result of the point target is basically consistent with the theoretical analysis value, which verifies the effectiveness of the algorithm proposed in this paper. In Experiment 2, the theoretical values of the ground-range resolution of P\({}_{1}\), P\({}_{2}\), and P\({}_{3}\) are 0.569 m, 0.543 m and 0.448 m, respectively, and the theoretical values of the azimuth angular resolution are 0.608\({}^{\circ}\), 0.595\({}^{\circ}\) and 0.587\({}^{\circ}\). Compared with the data in Experiment 1,  
Figure 11: The simulation results of Experiment 3: (**a**) the azimuth resolution; (**b**) the ground-range resolution; (**c**) the imaging results of the simulated targets; (**d**) the contour of P\({}_{2}\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment Number**} & \multirow{2}{*}{**Target**} & \multicolumn{4}{c}{**Range**} & \multicolumn{4}{c}{**Azimuth**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**Resolutions (m)**} & \multirow{2}{*}{**PSLR**} & \multirow{2}{*}{**ISLR**} & \multicolumn{2}{c}{**Resolutions (\({}^{\circ}\))**} & \multirow{2}{*}{**PSLR**} & \multirow{2}{*}{**ISLR**} \\  & & **Theoretical** & **Actual** & & & & & & \\ \hline \multirow{4}{*}{\(1\)} & P\({}_{1}\) & 0.354 & 0.357 & \(-\)13.273 & \(-\)9.754 & 0.608 & 0.613 & \(-\)12.210 & \(-\)8.618 \\  & P\({}_{2}\) & 0.331 & 0.339 & \(-\)12.965 & \(-\)9.39  
it can be concluded that when the transmitter position changes, there is almost no impact on the azimuth resolution of the system, however, it will have a great impact on the ground-range resolution. In Experiment 3, the theoretical values of the ground-range resolution of P\({}_{1}\), P\({}_{2}\), and P\({}_{3}\) are 0.352 m, 0.330 m, and 0.312 m, respectively, and the theoretical values of the azimuth angular resolution are 0.529\({}^{\circ}\), 0.528\({}^{\circ}\), and 0.527\({}^{\circ}\). Compared with the data in Experiment 1, it can be seen that the velocity of the transmitter has almost no impact on the range-ground resolution, however, it will impact the azimuth resolution of the system, when the transmitter velocity is increased from 50 m/s to 300 m/s, the azimuth resolution has improved.
# Efficient Sizing and Layout Optimization of Truss Benchmark Structures Using ISRES Algorithm
## 6 Conclusions  
In conclusion, this study demonstrates the efficacy of the Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm for the sizing and layout optimization of truss benchmark structures. The results showcase the algorithm's ability to efficiently converge to optimal solutions, effectively minimizing truss weight while satisfying complex design constraints.  
The ISRES algorithm proves to be a valuable optimization tool for structural engineers and designers, as it provides practical and reliable solutions for a wide range of truss-design problems. Its ability to handle multiple design variables, stress constraints, and nodal displacement limits make it a versatile and powerful tool for complex engineering optimization tasks.  
Furthermore, the comparison with other optimization methods and the validation through a finite element analysis highlight the ISRES algorithm's competitiveness and robustness in tackling complex structural design challenges. The algorithm's performance indicates its potential for broader applications in various engineering and architectural contexts.  
Overall, this research contributes to the advancement of optimization techniques in structural engineering, enabling the development of more efficient and cost-effective truss designs. The findings offer valuable guidance to practitioners, empowering them to make informed decisions when selecting appropriate optimization strategies for complex truss-design scenarios.  
As future work, it may be beneficial to extend this study to include additional structural benchmarks and explore the application of the ISRES algorithm in other engineering optimization problems. Additionally, investigating the algorithm's performance on real-world truss-design projects and further fine-tuning its parameters for specific applications could yield even more promising results.  
In summary, the ISRES algorithm proves to be a reliable and efficient tool for the sizing and layout optimization of truss benchmark structures. This study highlights its potential for enhancing engineering design processes, promoting the development of more sustainable and optimized truss solutions for practical applications.  
Conceptualization, M.S.A. and A.N.; methodology, A.N.; software, M.S.A.; validation, A.N., D.Y., E.E. and M.S.A.; writing--original draft preparation, A.N., D.Y., E.E. and M.S.A.; writing--review and editing, A.N., D.Y., E.E. and M.S.A.; visualization, D.Y. and M.S.A.; supervision, A.N. and E.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ongoing studies. The authors declare no conflicts of interest.
# On the Stability of Graphene-Based Aqueous Dispersions and Their Performance in Cement Mortar
## 3 Results and Discussion
### Workability Analysis  
In Table 4, workability data taken for selected admixtures are reported. As expected, the increasing in the amount of graphene-based nanomaterials negatively affected the workability of the mortars [6,15,16].  
However, it is worthy to note that workability is also significantly affected by the type of additive. Gr-SG showed a reduction of about 46% in workability even when 0.3 wt% was added. As expected, this is due to its higher SSA, as reported in Table 1. An increase in the amount of Gr-SG leads to an unworkable mortar, which is why the concentration of this additive was limited to 0.3 wt%. Mortars containing GO showed values of workability  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen** & **W (\%)** & **\(\Delta\)W** \\ \hline C & 119.05 & 0.05 \\
03 SG & 64.76 & 0.05 \\
03 TG & 116.19 & 0.13 \\
01 GO & 106.19 & 0.20 \\
05 GO & 108.04 & 0.08 \\
1 GO & 70.89 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Acquired data of workability test with corresponding errors.  
Figure 4: Results of TSI values for D-TGb (HT + LPE), D-TGb (LPE), and D-GOb specimens. (**a**) Graph on logarithmic scale with the stability index variation measured every 5 min for a total time of 180 min; (**b**) histogram on logarithmic scale with TSI values at 5, 60 and 180 min. The colour scale bar shows the corresponding grade of suspension.  
mildly affected by the addition of up to 0.5 wt% GO. This result can be attributed to poor exfoliation and dispersion of GO flakes, as only ultrasonic treatment was carried out on those suspensions. However, at the highest concentration of GO, the increased number of flakes, and therefore the hydrophilic surface, reduces the workability by about 40%.
# The Modified Void Nucleation and Growth Model (MNAG) for Damage Evolution in BCC [PERSON]
## 3 Results and Discussion
### The MNAG Model
#### 3.2.2 Stress Evolution  
To derive the damage evolution from Equations (1)-(12), it was necessary to measure the stress evolution as a function of time. Typically, the damage was modeled as a stress-dependent quantity ([PERSON] et al., 1999). Here, instead, we treated stress as an independently varying state and proposed the following expression to simulate the stress evolution observed in MD simulations:  
\[\sigma_{m}=\frac{\sigma_{spall}}{t_{d}*\left(t-t_{start}-t_{offset}\right)^{2}+1} \tag{13}\]  
where \(\sigma_{spall}\) is the spall strength calculated from the pull-back signal in the free surface velocity profile, \(t_{start}\) is the time at which void nucleation starts, \(t_{offset}\) is the delay in attaining peak tensile stress following void nucleation, and \(t_{d}\) controls how fast the stress decays and is unitless in this form. It was set to be 0.04 based on Figure 2 (to match the modeling data). Figure 2 shows a simulated stress evolution vs. time as compared to the average stress in the spall region measured in MD simulations. The goal here was to use a general analytical form to obtain a faithful representation of the stress evolution in the system, based on data that was easily accessible from experiments or other platforms. It was noted here that the rate at which stress decays would depend on the loading rate, and hence, \(t_{d}\) was specific to the loading condition used in the set of simulations in this work. In addition, while \(\sigma_{spall}\) was easily calculated from experiments, advanced experiments using diagnostics such as phase-contrast imaging will probably be required to get \(t_{start}\) and \(t_{offset}\)  
Figure 2: Stress evolution in (001) single-crystal Ta as a function of time under shock loading along with the simulated stress evolution.
## 3 Results  
### Time Series Results of Measured Concentrations  
Table 1 shows the averaged concentrations during the study period. The PM\({}_{2.5}\) concentration was 21.87 mg/m\({}^{3}\), meaning that it was observed at less than 25.00 mg/m\({}^{3}\) of its Korea Environmental Standard. PM\({}_{2.5}\) compositions accounted for 80.10% of the total PM\({}_{2.5}\), including 24.09% carbons of OC and EC, 49.34% ions of SO\({}_{4}\)\({}^{2-}\) and NO\({}_{3}\)\({}^{-}\), and 6.63% trace elements. Based on results of previous studies, it was presumed that 19.90%  
of compositions except OC, EC, ions, and metals consisted of H, N, S, and O (which are organic matter except for carbon), unmeasured metals, and a small amount of water [4; 5; 6]. Gaseous compounds measured in the study area had seven compositions including CO, CO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\), NO, NO\({}_{2}\), and NH\({}_{3}\) at average concentrations of 202.18 ppb, 446.74 ppm, 4.95 ppb, 38.14 ppb, 0.95 ppb, 3.77 ppb, and 6.97 ppb, respectively. The characteristic of the study area was investigated using the PM\({}_{2.5}\) compositions. OC can be divided into water-insoluble organic carbon (WIOC) and water-soluble organic carbon (WSOC). WIOC is mainly generated through the combustion of fossil fuels as a primary origin, and WSOC is released as the primary particulate from sources of biomass burning (BB) or generated by creating a secondary organic aerosol (SOA) [22; 23; 24; 25; 26]. WSOC and WIOC accounted for 66% and 34% of the total OC in the study area, respectively. Considering that the study area was located in an agricultural site and the sampling period was in the winter season, it was presumed that WIOC was caused by the use of fossil fuels for heating in winter and that WSOC was caused by biomass burning and SOA formation. From these data, the ratios of K\({}^{+}\)/EC and K\({}^{+}\)/Cl, which are indicators of biological combustion, were inspected to understand the reason for WSOC. According to previous studies, the ratio of K\({}^{+}\)/EC generated by biomass combustion is >2.25 for soft plants such as rice straws, 1-2 for corn, and >0.19 for hard plants such as wood [27; 28; 29; 30]. In