esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fast Approach to Optimize Trad Pattern Shape for Tire Noise Reduction
## Results and Conclusions  
### Semi-Anechoic Chamber Noise Experiment  
To evaluate the prediction results of tire noise, a tire noise experiment was conducted in a semi-anechoic chamber following the measurement standard GB/T 23789-2016 ([PERSON] et al., 2018). The test parameters could be controlled in this chamber, minimizing the impact of external factors on the results. Before testing, the tire was mounted to the rim and was left in the laboratory for over one hour. Then, the tire was installed on the test equipment for the experiment. The M2 microphone was positioned at a distance of 1 m from the tire center and 0.2 m above the ground, as shown in Figure 4b.  
Figure 3: Flowchart of tread pattern shape optimization.  
In this experiment, a radial passage car tire with a size of 225/60R18 was selected. The testing conditions were an internal tire pressure of 230 kPa and a vertical load of 4640 N. The geometric shape of each pitch is illustrated in Figure 5a. There were four pitches, and the pitch length and pitch numbers are listed in Table 1.  
After the test, the tire noise data from microphone M2 were collected under various rolling speed conditions: 60 km/h, 80 km/h, 100 km/h, and 120 km/h; then, the 1/3-octave-band sound pressure levels (dB(A)) were calculated [42].  
Figure 6 illustrates the 1/3-octave-band sound pressure level (dB(A)). There is a significant correlation with velocity when the frequency is over 800 Hz. The tire noise mainly comes from the tread block impact, contact area block slip, and pump noise due to the grooves. With the increase in speed, the impact force increases, which results in a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pitch Type** & **Pitch Len. (mm)** & **Pitch No.** & **Pitch Sequence** \\ \hline A & 45.23 & 10 & DCDDCAABBC \\ B & 51.85 & 10 & ABDDCABCDB \\ C & 58.47 & 10 & AABAA BDDCD \\ D & 65.08 & 11 & BDCBC CAACB D \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of pitch sequence.  
Figure 4: Tire noise test in a semi-anechoic chamber. (**a**) Front view; (**b**) side view; (**c**) the test tire; (**d**) experimental setup in semi-anechoic chamber.  
Figure 5: Pattern shape and pitch arrangement of test tire. (**a**) Pattern geometry of different pitches; (**b**) pitch sequence around the tire. A, B, C, and D represent four different pitches.  
higher noise emission. When the frequency is below 800 Hz, the tire noise is mainly caused by structural vibration, including of the carcass cavity and sidewall; this is related to the tire vibration characteristics and exciting force, and there is no obvious correlation between tire velocity and tire noise.
# Recent Progress on Graphene-Functionalized Metasurfaces for Tunable Phase and Polarization Control
## 4 Polarization Modulation in Graphene Metasurfaces  
Polarization manipulation, usually achieved by birefringence materials, total internal reflection, optical gratings, and the Faraday effect, is instrumental in a wide range of optical applications, such as telecommunications [75], imaging [76], sensing [77], polarimetry [78], and spectroscopy [79]. As compared to those conventional methods, metasurfaces have shown exceptional capabilities for flexible polarization control in a planar, ultrathin, and integrable manner. Introduction of graphene into the metasurfaces opens an exciting route to dynamically and actively taming the polarization in a desired manner. The basic idea is an anisotropic subwavelength pattern made of graphene or hybrid graphene/metal that supports two plasmonic eigenmodes along two orthogonal polarization directions. Therefore, the polarization control generally utilizes the plasmonic property of graphene in Figure 1b. By altering the relative magnitude and phase delay between the two eigenmodes via geometric design and dynamical modulation of graphene conductivity, several outstanding features are proposed, such as wave plates with tunable working frequency, switching between two polarization states, or variation of the polarization along a continuous path in the Poincare sphere, all without readjusting the geometry of the metasurface. The review of this part is classified by the functionality as quarter-wave plates, half-wave plates, polarizers, and general polarization control.
## 5 Results and Discussion
### Efficiency Analysis  
To validate suitability for edge deployment, we benchmarked runtime on an NVIDIA Jetson Nano (4 GB RAM, ARM Cortex-A57 CPU, Maxwell GPU, Nvidia, Santa Clara, CA, USA) and a desktop GPU (NVIDIA RTX 3090). These devices represent constrained and high-performance inference environments, respectively.  
Our model maintains inference speeds of approximately 40 FPS on an NVIDIA RTX 3090 (24 GB VRAM) and 6 FPS on a Jetson Nano (4 GB RAM, ARM Cortex-A57 CPU, 128-core Maxwell GPU) when processing 256 \(\times\) 256 resolution inputs. The model has ~1.3 million parameters and requires ~12.7 GFLOPs per forward pass. In comparison, SID contains ~6.7 million parameters and consumes over 120 GFLOPs, making our model nearly 10\(\times\) more efficient in terms of computation. We also monitored memory usage during inference: our model requires ~180 MB on the Jetson Nano and ~240 MB on the RTX 3090.  
While our model is compact and well-suited for deployment on embedded platforms such as the Jetson Nano, real-time performance (e.g., \(\geq\)30 FPS at 512 \(\times\) 512) may require further optimization or lower input resolutions depending on the use case.
## 2 Description of the Shipboard Microgrid under Study
### Renewable Energy Sources
#### 2.3.2 Photovoltaic Source  
The second RES used in this shipboard system is photovoltaic. The PV converts the solar energy into electricity [45]. The corresponding circuit diagram for the PV cell is shown in Figure 2 and consists of a series resistor (\(R_{s}\)), light current (\(I_{L}\)), and external contacts. Additionally, a parallel resistance (\(R_{sh}\)) was utilized, producing a minor leakage current. The reason for representing the PV cell model using a random power source is due to the irregular nature of the power generated by the system, which is determined by the changing levels of sunlight intensity and temperature. Equation (8) represent the diode current (\(I_{D}\)), while (9) gives the instantaneous current of the PV cell:  
\[i_{pv}(t)=I_{L}-I_{D} \tag{8}\] \[I_{D}=I_{\text{rs}}\big{(}e^{\gamma(v_{pv}(t)+i_{pv}(t)R_{s})}-1 \big{)} \tag{9}\]  
where \(\gamma\) is the diode ideality factor, \(I_{\text{rs}}\) is the reverse saturation current of the diode, and \(i_{pv}\) and \(v_{pv}\) denote the current and the voltage through the PV cell array. In order to connect the PV power plant to the control area, a grid-side inverter and a DC-DC converter were used. The highest power point tracking conditions are produced by the DC-DC converter. Transfer functions were used to model these power electronic circuits [39], as shown in (10):  
\[G_{PV}(s)=\bigg{(}\frac{1}{1+T_{inv2}s}\bigg{)}\bigg{(}\frac{1}{1+T_{\text{c}} s}\bigg{)} \tag{10}\]
# Design of PIDD(r) Controller for Robust Performance of Process Plants
## 1 Introduction
### Power System Applications
#### 1.1.2 Two and Multi-Area Power System  
In a two-area power system, two control areas are connected by a weak tie line. Both areas must generate enough power to meet customers' demands while maintaining the system's predetermined frequency and tie-power levels. Since frequency and active power are closely linked, the system frequency regulates any changes in power. A load frequency controller properly balances generated power and load demand. In conventional and hybrid two-area power systems, researchers in [6; 20; 21] have proposed a fuzzy PIDD\({}^{2}\) controller for voltage and frequency regulation. They have significantly improved the controller's performance compared to its counterpart in transient response characteristics by tuning the controller parameters via a gradient-based optimisation algorithm. To effectively control frequency variations in the system, the authors in [22] have proposed a design that uses a PIDD\({}^{2}\)-PD controller for a two-area linked power system. They have simulated the proposed design by tuning controller parameters using the wild horse optimiser, and it has shown superior settling times, maximum overshoot, and undershoot values compared to other PID controllers.  
In addition, a study by the authors in [23] has suggested a robust load frequency control design utilising PIDD\({}^{2}\). The controller's parameters are fine-tuned with the help of internal model control, which enhances the controller's transient response. Another configuration for PIDD\({}^{2}\) controller design is used in [24], which is applied to a two-area power system. The design is simulated and demonstrates an improvement in ISE by utilising a sine-cosine optimal algorithm. In [25], the authors introduce a PIDD controller with the 2 DOF to be incorporated into a multi-area thermal system. The controller's parameters are adjusted using the cuckoo search method to improve its settling time, peak overshoot, and oscillation rate performance. The suggested design outperforms controllers like I, PI, and PID controllers.
## 2 Methods and Materials
### Experimental Analysis
#### 2.2.2 RubisCO Activity Analysis  
The Ripnose-1,5-diphosphate (RuBP) carboxylase can catalyze the reaction of RuBP and CO\({}_{2}\) to produce phosphoglycer acid, which can be coupled to the oxidative action of nicotinamide adenine dinucleotide (NADH). Based on this chemical reaction process, the spectrophotometric enzyme-coupling method can be used to determine the RubisCO activity [32]. For all of the freeze-dried sediments, samples of 2.00 g were added into 10 mL centrifuge tubes, to which was also added 600 uL of 2.0 ug mL\({}^{-1}\) protease inhibitor (Sigma), 6 mL of 100 mM Tris-HCl (pH 7.8) and 1 mM dithiothreitol. After 30 min of ultrasonic oscillation at 0 \({}^{\circ}\)C, the mixtures in the tubes were separated by centrifugation for 15 min at a speed of 14,000 r-min\({}^{-1}\) and at 4 \({}^{\circ}\)C. The centrifugation was repeated to ensure the maximum particle removal rate. Ammonia sulfate was added to the suspensions until a saturation rate of 80% was reached. After stirring and centrifugation, the formed pellets were then dissolved in 50 uL buffer of 100 mM Tris-HCl (pH 7.8) and 1 mM dithiothreitol. Subsequently, 50 uL of 100 mM ATP, 100 mM phosphoercatine, 40 U-mL\({}^{-1}\) phosphoercatine kinase, 80 U-mL\({}^{-1}\) 3-glycerophosphate dehydrogenase (Sigma), 80 U-mL\({}^{-1}\) 3-phosphoglycerate phosphokinase (Sigma), and 10 mM RuBP were  
Figure 1: Diagram of the four constructed wetland pots. Image **(a)** shows the variables, and **(b)** shows the scene picture.  
added into 700 \(\upmu\)L of the above-mentioned buffer, respectively. The absorbance of the buffer at a wavelength of 340 nm was detected by ultraviolet spectrophotometer, and it was recorded as E\({}_{0}\). Then, 50 \(\upmu\)L of 25 mM RuBP and 50 \(\upmu\)L of 4 mM NADH were added to the buffer, and the absorbance was determined after a reaction time of 30s, which was recorded as E\({}_{\text{t}}\). The formulation of RubisCO activity (mmol CO\({}_{2}\) g\({}^{-1}\) min\({}^{-1}\)) was listed as follows.  
\[\text{RubisCO activity}=\frac{\text{F}(\text{E}_{0}-\text{E}_{\text{t}})\text {V}}{\epsilon\text{dtw}} \tag{1}\]  
In this formulation, F means the converted coefficient of the molecular ratio from CO\({}_{2}\) to cofactor NADH, and the value is 0.5. V is the total volume of the reaction mixture (mL). \(\varepsilon\) means the absorptivity, the value of which is 6.22 \(\times\) 10\({}^{-3}\) mL nmol\({}^{-1}\) cm\({}^{-1}\), and d is the optical path length of the cuvette (cm). t and w are the reaction time (min) and soil weight (g), respectively. This experiment was repeated three times and the average values of absorbance were calculated as the final data of the RubisCO activity.
## 6 Conclusions  
Four full-scale load tests on large-diameter drilled shafts are reported in this paper to investigate the differences in axial behavior of shafts with and without grouting under compressive loading. Based on the test results and discussions, the following conclusions can be summarized.  
1. Post-grouting drilled shaft has great advantages in terms of economy and safety. The bearing characteristics of the shaft can be effectively enhanced by using the combined tip and side post grouting technique. The load was determined to be 10.08 MN, the corresponding normalized displacements of control shaft (TS1) and grouted shaft (TS2, TS3 and TS4) were 5.56% \(D\), 2.18% \(D\), 5.39% \(D\) and 3.00% \(D\), respectively.
2. The index of shaft tip grouting has a significant influence on the performance of the test shaft. Pre-mobilization that tends to stiffen the response of post grouted drilled shaft but not increase the ultimate tip resistance, whereas ground improvement at the shaft tip and enlargement of the shaft tip would increase the ultimate tip resistance.
3. The SPT blow counts of soil increased after grouting, and it is nonlinear with the increase of soil strength parameters. The SPT blow counts of cohesive soil increased by about 12.5-160%, and the SPT blow counts of cohesionless soil increased by about 69.2-200%.
4. The pressurized cement grout can improve the physical and mechanical properties of the soil layers surround the shaft and the shaft-soil interface, and the enhancement effect is more significant for cohesionless soil.
5. Tip resistance of shaft can be mobilized more rapidly and fully after grouting, and the side and tip resistance are mobilized in a more synchronized and coordinated manner due to the pre-mobilization of the grouted cement.
6. Although the designed tip grouting quantity of test shaft TS3 was not reached, the bearing capacity required for the design load of shaft TS3 was fully met.  
Figure 11: The percentage of the tip resistance and side resistance of the shaft.  
**Author Contributions:** Data curation, Z.Z.; Funding acquisition, W.G. and G.D.; Investigation, Z.Z. and G.D.; Project administration, W.G.; Software, X.C.; Writing--review & editing, Z.Z., G.D., Y.Z. and H.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China (Nos. 51878160; 52008100), Natural Science Foundation of Jiangsu Province (No. BK20200400), and Six Talent Peaks Project in Jiangsu Province (No. XNY-047). The authors are grateful for their support.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors' gratitude goes to the reviewers for their helpful comments and constructive suggestions in improving this paper.  
**Conflicts of Interest:** The authors declare that there is no conflict of interest. They have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Optimal Siting and Sizing of FACTS in Distribution Networks Using the Black Widow Algorithm
## 5 Numerical Results
### Evaluating the Scalability of the Proposed Master-Slave Methodology  
The IEEE 85-bus grid was used as a test feeder to demonstrate the proposed optimization methodology's effectiveness and robustness. This is a medium-voltage distribution network operated with 11 kV at the substation bus. The electrical configuration and all electrical parameters of this test feeder are presented in Figure 4 and Table 7.  
Figure 4: Grid configuration of the IEEE 85-bus system.  
The numerical results in Table 8 show the following:  
1. The proposed BWO algorithm finds the exact nodal locations for each FACTS device, i.e., nodes 12, 34, and 67. In addition, the maximum reactive power injection per node is observed in the SVC, with values of 249, 393, and 328.9 kvar, respectively.
2. The minimization of the annual operating costs varies from USD 35,363.6 (when UPFCs are installed) to USD 41,031.98 (when SVCs are installed). These values imply a difference of about 5668.38 USD per year of operation in favor of the SVC. In addition, the expected annual costs reduction using FACTS in the IEEE 85-bus grid are 22.87, 24.91, and 26.53% when considering UPFCs, TCSCs, and SVCs, respectively.
3. Regarding processing times, the proposed BWO approach takes between 255 and 266 s on average to reach an efficient solution to the problem under analysis. These times can be considered to be efficient, in light of the fact that only the size of the discrete component of the optimization model is about 95284 possible options, each with infinite possibilities regarding the expected nominal size of the FACTS.  
To confirm the effectiveness of the proposed BWO algorithm in siting and locating FACTS, Figure 5 presents the reductions in the objective function value after 100 runs for each compensation device.  
Figure 5 shows the variations in the objective function with each compensation system inside a little band. In the case of the UPFC, these variations are between 22.30% and 22.87%, i.e., a difference of about 0.57%. In the case of the TCSC, these margins are about 24.27% and 24.91%, which represents a difference of about 0.64%. In contrast, the reduction margins with SVCs are between 25.91% and 26.53%, i.e., a difference of about 0.62%. Note that the small difference between the best and the worst solution reached with the proposed BWO algorithm per compensation device (lower than 0.64%) confirms that, in each run of the solution methodology, an adequate solution for the studied problem is found, ensuring an excellent annual expected operating costs reduction. This is very attractive for distribution companies, as the BWO algorithm can be considered to be an adequate solution methodology to establish their investment plans regarding reactive power compensation, with different options for locating and sizing FACTS in their grids.
## 3 Results and Discussion
### High-Risk Zones Created by Cruise Ships' Routes  
Figure 9b is a fragment of Figure 9a; it shows high-risk zones created by cruise ships' routes. Figure 9b shows cruise ships' traffic in the central and south part of the Adriatic east coast (black lines), the position of four marine protected areas (red curves) and eight areas of high navigational and environmental risks (blue circles). Susac island was selected as a reference point being the closest to the Central Adriatic Separation Scheme (grey star). Four marine protected areas are Kornati National Park, Mljet National Park, Lastovo Nature Park and Susac Island Nature Park. Eight areas of high navigational and environmental risks were selected based on cruise ships' routes data (the vicinity that cruise ship routes pass from the islands' shores and marine protected areas and cruise routes' mutual interaction). Designated areas of high navigational risks are the islands of Svetac, Bisevo, Susac and Lastovo, as well as the Svetac-Bisevo passage, Hvar channel and the Susac-Lastovo and Lastovo-Mljet passages.  
The comparison of cruise traffic density in the Central Adriatic Separation Scheme and cruise traffic density in the vicinity of the island of Susac provide valuable information on cruise ships' navigational principles in newly established and navigationally less defined cruise regions. The research showed that cruise ships used the Central Adriatic Separation Scheme 187 times, while cruise ships kept north of the Central Adriatic Separation Scheme and passed south of Susac island 237 times.  
Figure 9b shows that cruise ships frequently pass through areas of high navigational and environmental risk which are geographically restricted, navigationally challenging and environmentally sensitive areas. Cruise ships' routes that pass very close to outer island shores as well as between the islands have become standard navigational practice for cruise ships. Additionally, longitudinal and transversal routes' interaction in coastal navigation  
puts cruise ships in crossing, head on and overtaking situations, which increase the risk of collision grounding and pollution, especially in areas of high navigational risks.  
During the research period, cruise ships' traffic inside designated high-risk areas was thoroughly analyzed. The results are presented for each high-risk zone separately.
# Preparation Techniques of TiO\({}_{2}\) Nanofluids and Challenges: A Review
## 2 Preparation Methods
### pH Adjustment  
The dispersion environment can be adjusted by adjusting the pH value of the base fluid. This technique raises the zeta potential of the suspended nanoparticles. The significance of zeta potential is that the stability of the fluid can be directly related to the value of the zeta potential. Colloids with a higher value of zeta potential are electrically stabilized as compared to colloids with a lower value.  
[PERSON] and [PERSON] [76] investigated the effect of pH value on the sedimentation and aggregation of TiO\({}_{2}\) nanofluids prepared in a base fluid of (SRFA, Fe(III)). A solution of Suwannee River fulvic acid (SRFA) and Fe(III) were magnetically stirred for 1 h, after that the TiO\({}_{2}\) particles were dispersed in the solution of SRFA or/and Fe(III) and stirred for 24 h at a speed of 200 rpm. The stability of the experimentally prepared solution of TiO\({}_{2}\) nanofluids was enhanced by adjusting the pH value with the addition of 0.1 M HCl or NaOH. By using the mono-system of Fe(III) and bi-system of SRFA and Fe(III), aggregation was reduced at the pH value of 4 in the case of the mono-system and at the pH values of 6 and 8 in the case of the bi-system.  
[PERSON] et al. [77] mixed the nanoparticles of TiO\({}_{2}\) in distilled water and ultrasonicated for 30 min in order to break the agglomerations. The processed solution was then passed through the medium mill to further reduce the agglomeration size. In order to make the suspension more stable, its pH value was adjusted to 11 to prevent any type of re-agglomeration. They used this technique to produce nanofluids with 1.0%, 2.5%, and 4.9% by weight concentration.  
[PERSON] and [PERSON] [78] carried out the pH adjustment method to stabilize the TiO\({}_{2}\)-water nanofluid and performed studied its heat transfer performance. Figure 13 shows the variation of the zeta potential of TiO\({}_{2}\) nanofluid for a particle concentration of 0.024 %vol. with pH adjustment.  
Some other related studies that have been carried out with pH adjustment of the formulated solution will be discussed in the combination usage section because it is an additional process proven to improve the stability of nanofluids.
# Analysis of Elastic Nonlinearity Using Continuous Waves: Validation and Applications
## 4 Efficiency and Robustness of the Approach
### Frequency Dependence  
Robustness of the approach was tested by first verifying that results are independent from the choice of the testing frequency. To this purpose, quadratic nonlinearity on both \(S\) and \(\lambda\) were considered and the procedure was applied varying the frequency of the excitation around the first resonance mode. The linear resonance frequency was at \(\omega=\omega_{r}=25.82\) kHz. Plots in Figure 7 report velocity and attenuation variations as a function of the averaged quadratic strain, as defined in Equation (19).  
The results show that both amplitudes and phases vary with different functional dependences on the averaged quadratic strain. This is expected as a result of the shift of the resonance frequency which could give an apparent increase in amplitude, even though damping is increasing. On the contrary, variations of velocity and attenuation coefficient, calculated using the proposed method, result to be frequency independent.