esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of ZIF-8@AuNPs  
AuNPs exhibit significant enhancement of the Raman signal, primarily attributable to their unique LSPR, which typically varies according to the size of the AuNPs. Moreover, the chemical bonding between ZIF-8 and AuNPs within the ZIF-8@AuNPs composite can alter the original LSPR of the AuNPs. The impact on the sizes of AuNPs in the ZIF-8@AuNPs was thoroughly investigated. Figure 5a illustrates the size effect of AuNPs in ZIF-8@AuNPs, which agrees with that of bulk AuNPs. Notably, AuNPs with a diameter of 42.1 nm exhibit stronger SERS signals. Zn ions exhibit multiple coordination modes owing to their outer electronic structure. A straightforward means of influencing the coordination mode and consequential structural attributes of MOFs involves meticulous adjustment of the quantity of ligands employed in experimental procedures. Particle sizes of ZIF-8 were varied by manipulating the zinc nitrate and 2-MIM ratio (1:3, 1:5, and 1:7), using Val as the probe molecule. The results are shown in Figure 5b. ZIF-8 synthesized at a 1:5 ratio demonstrated superior SERS performance.  
The quantity of ZIF-8 used is a critical factor in determining the maximum adsorption capacity of the target molecule. However, an increased quantity is not inherently advantageous, as the consensus postulates that the more distantly positioned the target molecules are from the LSPR center, the more diminished the influence of LSPR on their SERS signals. The quantity of ZIF-8 additionally modulates the thickness of the external MOF layer encompassing the AuNPs, consequently impacting the SERS intensity of the  
Figure 4: SERS spectra of Cys, Trp, Val, Phe, Gin, His, and Ser.  
Figure 5: SERS spectra of Val with different sizes of AuNPs (**a**). SERS spectra of Val with different sizes of ZIF-8 (**b**). SERS spectra of Val with different quantities of ZIF-8 (**c**).  
adsorbed molecules. As shown in Figure 5c, discernible SERS signals were observed for ZIF-8 quantities of 1, 5, 8, and 10 mg. This observation highlights the delicate equilibrium between the adsorption capacity and the modulating influence of LSPR.  
Consequently, AuNPs with a diameter of 42.1 nm were selected. The ligand solution ratio for the preparation of ZIF-8 was 1:5, and a composite substrate with a loading capacity of 5 mg was formulated for ensuing discussions concerning the SERS performance of ZIF-8@AuNPs.
## 1 Introduction
Most prior simulation-based studies have overly simplified the test scenarios to one-to-one or one-to-many linear arrays, thus overlooking the influence of intricate and dynamic large-scale traffic environments on the communication performance of the IEEE 802.11 bd standard. This oversight has resulted in inaccuracies in the assessment of the practical communication performance of IEEE 802.11 bd. Moreover, not all the key technical improvements are integrated into the prior studies. The impact of some novel technologies applied in IEEE 802.11 bd, such as the 256-QAM modulation and coding scheme (MCS) and Low-Density Parity Check (LDPC) coding, still needs to be simulated and tested. Motivated by the research gaps mentioned above, this research proposes a comparative study of IEEE 802.11 bd and IEEE 802.11p within dynamic traffic environments. This paper first investigates the technical improvements of 802.11 bd and analyzes their impact on V2X performance, which helps to refine the technical evolutions that highly correlate with the data transmission performance. These key technical improvements of 802.11 bd are implemented in an IoV simulation framework, Veins [26]. Simulation tests are conducted to evaluate the communication performance in two large-scale dynamic traffic scenarios. Meanwhile, we conduct a series of simulations to investigate the factors influencing the data dissemination effectiveness in IoV. Compared to prior research, this work is one of the first efforts to provide an analytical and simulation-based hybrid approach in investigating the improvements of the novel 802.11 bd in large-scale and complex traffic environments. The improved Internet of Vehicles simulation framework integrates new MCS and LDPC mechanisms, and large-scale dynamic traffic simulations based on it can more accurately represent the communication performance of 802.11 bd in reality than prior research. Moreover, our analysis of the properties of data dissemination reveals the limited impact of the traffic volume on the data propagation speed. The proposed research contributes to a better understanding of the technical characteristics of this newly released standard. Hence, it will contribute to both the literature and practical applications. The remaining sections are organized as follows. Section 2 introduces the technical improvements that we analyze from the two aspects of the data transfer rate and transmission reliability, including newly introduced enhanced data transmission modes, new antenna modes, new coding schemes, etc. Section 3 focuses on the implementation process of these key technical improvements, including a partial theoretical validation. Section 4 presents the design of the experiment for the V2X performance comparison and the properties of data dissemination, with an analysis and further evaluation of the simulation results. Conclusions and a discussion follow in Section 5.
## 2 System Modeling  
This study utilized a general AGV system, mechanical four-wheeled mobile robot, DC motors, DC/DC buck converters and fuel cell. The system block diagram is shown in Figure 1. The inputs and the outputs of all subsystems are clearly shown in Figure 1. It shows that voltage and current are supplied by the PEM fuel cell (V\({}_{\text{cell}}\) and i\({}_{\text{cell}}\), respectively). With the power fed from the fuel cell, the necessary voltages and currents of motors, which are determined by the Optimizer-1-optimized PID controller, are sent to the motors of the mecanum wheels. However, the voltage value at the fuel cell output is a high value for the engines operating in the \(\pm\)24 V range that we use in the system. For this reason, it needs to be lowered with the help of the DC/DC converter. However, the voltage value required by each motor throughout the motion varies depending on its speed. Wheel speeds are changed during the course with the PID controller, which is designed for full tracking of the route, and accordingly, the voltage value required by the motors changes in the range of \(\pm\)24 volts. For this reason, DC/DC converters need to adjust the voltage value from the fuel cell as needed by the engine. For this purpose, an integral (I) controller was designed for each DC/DC converter. After the necessary inputs for the motors are tracked successfully by the DC/DC converters, the output signals of the converters are sent to motors. These voltage inputs generate rotational motions and torques of the motors. Then, the speeds of the motors and torque values are used in the dynamics of the AGV system, and the actual position and rotation of the mobile robot are determined. After that, these actual and reference values of the mobile robot are compared and are evaluated in Optimizer-1, and the optimal PID parameters are obtained to track the reference route with minimum errors. Then, the calculated control inputs for the motors are sent to the DC/DC converters as reference voltages for tracking. Optimizer-2 tries to minimize the power consumption and voltage-tracking errors at the output of the converters by comparing the determined control inputs for motors and actual output voltages of converters. By using these data, Optimizer-2 determines the values of the resistor, inductance and capacitance of each converter and I coefficient of the controller in the converters. The coefficients of both PID and I controllers and the components of the DC/DC converters were determined on the basis of maximum route tracking and minimum power consumption with the PSO technique. By providing the desired voltage at the DC/DC converter output, the motors rotate at the desired speed, and the route tracking is successfully achieved.
## 3 Network Model
### Latencies  
Fronthaul data flows in 5G RANs have stringent latency requirements, which are imposed by low-latency 5G services and the processing requirements of functional split options. The one-way latency budget may be as low as some tens of \(\upmu\)s for specific fronthaul traffic profiles and functional split options [12; 13]. To account for these requirements, in the study, we assume a 50 \(\upmu\)s propagation latency limit, which constrains the allowable length of a TP to 10 km. Consequently, the TPs that are longer than the maximum permissible path length are not included in the sets of candidate paths.
## 5 Applications Across Diverse Sectors
### Inventions in 3D Printing Technologies
#### 5.2.5 AI-Assisted AM  
AI integration in additive manufacturing enhances design capabilities, optimizes part performance, and refines process monitoring [88]. MIT researchers developed a system combining AI and computer vision to improve 3D printing by making real-time adjustments to printing parameters, which enhances accuracy and allows seamless incorporation of new materials [89]. Machine learning also expedites design optimization, with [PERSON] et al. using CNN to replace heavy parts with lightweight components, achieving results 20 times faster than conventional methods [90].  
AI methods are set to transform the development of complex metal mixtures like high-entropy alloys (HEAs). Machine learning and data-based models quickly identify and refine alloy compositions, enabling the exploration of new alloys with outstanding features [46]. AI enhances the understanding of material structure-property relationships, identifying key components that improve performance. Overall, AI accelerates the development of new materials and advancements in materials science for aerospace, energy, and medical industries [46; 91; 92].  
AI holds significant potential in advancing materials science, particularly for complex alloys like high-entropy alloys (HEAs), where traditional trial-and-error methods are impractical due to the vast number of possible compositions [93; 94]. AI techniques use data-based models and machine learning to identify optimal mixtures and understand the relationship between structure and properties in HEAs, speeding up the discovery of new alloys with specific features [46; 95].  
In additive manufacturing, AI is promising for process monitoring and control. [PERSON] and [PERSON] utilized neural networks to classify powder bed images into seven defect types during laser powder bed fusion (L-PBF) processes, enabling real-time problem correction and improved manufacturing quality [96]. AI leverages extensive data from materials databases and research papers to systematically develop complex alloys, accelerating the creation and refinement of HEAs with exceptional properties [46; 95].
# Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach
## 5 Simulation Experiments and Analysis
### Performance of the Spacing Policy
#### 5.2.2 Leading Vehicle Deceleration Situation  
The safety is the most important performance of the platoon. In addition to the amplification impact of disturbance in platoon, the emergency braking of a vehicle can also cause huge safety risks. In this section, the initial state of the vehicles is shown in Equation (36). The leading vehicle will brake to stationary state to verify the safety of the proposed method. The deceleration is chosen to be \(-6\) m/s\({}^{2}\) at 0 s. To easily observe the final spaces, the abscissa of the position-time curve is limited to 5\(\sim\)10 s. The results of the simulation are shown in Figure 9.  
\[\left\{\begin{array}{l}X=\left[\begin{array}{cccc}24&16&8&0\\ 40&40&40&40\end{array}\right]^{T}\\ \end{array}\right. \tag{36}\]  
Figure 9: The performance of the multiple policies in deceleration condition: (**a**) Position; (**b**) Velocity; (**c**) Longitudinal space.  
We can notice that the although the leading vehicle performs a strong braking, the proposed control methods ensures a stable space between the vehicles. Unlike the CD policy, there is no significant acting lag in the change of the velocity. Figure 9a,b shows that the vehicle controlled with the normal spacing policy and AL value are both basically equal to their nearest vehicles throughout the whole process. However, the space of vehicle 4 controlled with Shapley value cannot align with the previous vehicles. The reason for it is because the third vehicle's profit caused by center gathering principle is distributed to vehicle 4 as marginal contribution. The difference cannot be eliminated unless the error of the center gathering quickly drops to zero in the process of braking. In fact, the analysis is support by the performance shown in Figure 9c. The policy of the Shapley value makes the gradual enlargement of the space more obvious which is good for safety. When the vehicles stop under the emergency braking conditions, the increasing of the static interval instead of the decreasing can ensure that the safety will not be weaken as the number of vehicles in platoon increases. The performance of CD policy shows that the final behaviors of the platoon result from the following performance of the individual vehicle and the errors will increase with the accumulation of each vehicle upstream the platoon. Intuitively, the actual spaces will gradually decrease until a collision occurs.
## 2 Materials and Methods
### Methodology for Tocopherol Detection and Quantification  
A sample of 100 mg of seed extract was subjected to alkaline hydrolysis using 5 mL of a 2% sodium hydroxide solution in ethanol. Subsequently, a standard cholesterol solution (Sigma-Aldrich, Poznan, Poland) was added into the mixture. Following an hour of reflux heating, the resultant mixture was allowed to cool, after which 6 mL of a 2% hydrochloric acid solution in methanol was introduced. The sample was stirred, and then 4 mL of hexane along with 2 mL of distilled water was added. The hexane phase was collected into a screw-capped tube and subjected to centrifugation. To 1 mL of the resulting solution, 200 uL of the silylation reagent N,O-bis-(trimethylsilyl) trifluoroacetamide combined with 1-trimethylchlorosilane (Sigma-Aldrich, Poznan, Poland) was added. The samples were analyzed via gas chromatography-mass spectrometry with flame ionization detection (GC-FID-MS). The quantitative analysis of tocopherols was conducted independently, utilizing external calibration curves. The peak areas corresponding to individual tocopherol isomers were adjusted by their previously established response factors. The content of \(\beta\)-tocopherol was expressed as a percentage (_w/w_) of the dry extract.
## 3 Methodology
### System Reliability Modeling  
As discussed in Section 2, the RBD method is an effective tool; therefore, it was selected in this paper to develop a system reliability model, compare subsystems reliabilities, identify weak subsystems, and estimate system reliability using subsystem reliability [45]. The RBD method converts the physical system into a graphical system, where the components/subsystems (called blocks) are connected based on their logical relationship to create a system reliability model. It facilitates the estimation of system reliability, availability, and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reliability**} & \multicolumn{4}{c}{**Reliability**} & \multicolumn{4}{c}{**Design Life (h)**} & \multirow{2}{*}{**MTTF (h)**} \\ \cline{2-2} \cline{4-9}
**Distribution** & **100 h** & **500 h** & **1000 h** & **3000 h** & **R = 0.95** & **R = 0.9** & **R = 0.8** \\ \hline Exponential & 0.98 & 0.91 & 0.83 & 0.58 & 281 & 578 & 1226 & 5493 \\ \hline Weibull 2-parameter & 0.94 & 0.816 & 0.71 & 0.45 & 80 & 209 & 564 & 4844 \\ \hline Weibull 3-parameter & 0.98 & 0.80 & 0.68 & 0.44 & 136 & 223 & 503 & 5373 \\ \hline Lognormal & 0.97 & 0.81 & 0.67 & 0.41 & 148 & 266 & 538 & 7485 \\ \hline Normal & 0.79 & 0.77 & 0.74 & 0.60 & – & – & 10 & 4418 \\ \hline Gamma & 0.92 & 0.79 & 0.69 & 0.45 & 44 & 141 & 461 & 4733 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reliability metrics for a failure data.  
maintainability metrics. The RBD model is then used to identify critical paths, weak points, and potential bottlenecks affecting system performance and reliability.
## 5 Interference Distribution Characteristics
### Soil Resistivity  
Soil resistivity influences not only the grounding current flow path but also the polarization. Four soil resistivity types (10, 30, 100, 1300 \(\Omega\)-m) were chosen for study. The HVDC  
Figure 12: Calculation results under different distance between electrode and pipeline. (**a**) leakage current density along the pipeline; (**b**) PSP along the pipeline.  
Figure 13: Corrosion risk area distribution for different distances between HVDC electrode and pipeline.  
grounding current is 3000 A. The other parameters are the same as those listed in Table 1. The polarization curves were measured under different soil conditions. Figure 14 shows the test polarization curve data.  
The calculation results of leakage current density and PSP are shown in Figure 15.  
The maximum leakage current density and PSP increase as the soil resistivity increases. Regression analysis shows that the relationship between the maximum leakage current density \(J_{\mathrm{max}}\) and the soil resistivity \(\rho_{\mathrm{s}}\) satisfy logarithmic function, that is \(J_{\mathrm{max}}=a_{3}\cdot\ln(\rho_{\mathrm{s}})+b_{3}\). \(a_{3}\) and \(b_{3}\) are fitting constant coefficients.  
Figure 16 shows the interference range for the anode area and cathode area along the pipeline for different distances between the electrode and pipeline.  
Figure 14: Polarization curve under different soil resistivity.  
Figure 15: Calculation results under different soil resistivity. (**a**) leakage current density along the pipeline; (**b**) PSP along the pipeline.  
The results indicate that the anode area range extends as the soil resistivity becomes higher. Both the maximum leakage current density and corrosion risk area are positively correlated with the soil resistivity. Therefore, it is very important to evaluate the interference level accurately when dealing with highly resistive soil conditions.
# Inverse Kinematics of Large Hydraulic Manipulator Arm Based on ASWO Optimized BP Neural Network
## 3 SWO Algorithm and Its Improvement
### SWO Algorithm  
The SWO algorithm is an optimization technique inspired by the behavior of spider wasps in nature. It effectively balances the exploration and exploitation processes by simulating the hunting, nesting, and mating behaviors of female spider wasps, as shown in Figure 5.  
Figure 4: Robotic arm inverse kinematics error.  
Figure 3: The process of establishing the forward kinematics error model.  
Where \(r_{1}\) is a 0-1 random number, \(TR\) is the trade-off rate, comparing the two to determine the current execution of hunting and nesting behaviors or mating behaviors. i is the current index value of the agent to be optimized, \(N\) is the population size, and \(k\) gradually decays from 1 to 0 with the growth in the number of iterations as shown in Equation (4), which determines the execution of hunting or nesting behaviors. p is a 0-1 random number, and the result of comparing it with the value of \(k\) determines whether the search agent executes the search or follows behavior. In addition, in the mating behavior, the crossover rate \(CR\) plays an important role in the decision.  
\[k=1-(t/T_{\text{max}}) \tag{4}\]  
Among the behavioral strategies of the four algorithms mentioned above, different judgment conditions can be used to select suitable individual position updating methods in order to explore the solution space more efficiently. In the early stage of the search, the algorithms focus on extensive exploration to prevent prematurely stagnating at local optimum solutions. In the later stages of the search, they focus on utilizing known information to improve search efficiency, targeting the global optimum solution more quickly.  
In addition, the algorithm introduces Levy flights. By adjusting the scaling factor or stability index of the step size, it can balance global exploration and local exploitation of the search process. With its long-range step size, the algorithm can escape local optima and explore search spaces far from the current position, helping to avoid getting stuck in local optima for extended periods in multi-peak problems. The continuous reduction of population size with population iteration can effectively reduce the computational burden brought by high-dimensional problems.  
These features make the SWO algorithm show unique advantages in dealing with multi-peak and high-dimensional problems, which can adapt to the variable search space and locate the global optimal solution effectively. However, there are problems, such as the algorithm parameters are fixed and the local optimum cannot be jumped out in time in some iteration stages.