esa-sceva/satcom-chunk-collection huggingface dataset:
# Multicriteria Autonomous Vehicle Control at Non-Signalized Intersections
## 2 Motivation  
With the development of intelligent transport systems and the expansion of the applications of highly automated vehicles, transport scenarios are emerging that require the development of special methods in terms of control planning. The introduction of autonomous vehicles in traffic calls for a review of a number of traffic rules which, in addition to established habits and the ethical behavior of drivers, currently provide a solution in most traffic situations. However, for highly automated vehicles without a driver, these sets of rules are not applicable in all traffic scenarios. The control of autonomous vehicles at intersections is a significant challenge from the point of view of control planning. Currently, traffic lights, traffic signs and rules known to drivers ensure that vehicles pass through intersections without accident. When designing an intersection, engineers make a decision appropriate to the size of the traffic flow to use one of the above methods to reduce the number and magnitude of congestion or accident-prone situations. With the proliferation of autonomous vehicles, the spread of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies and sensor fusion procedures, it is possible to trigger traffic rule-based management of intersections with intelligent intersection control. As an expected result of these research and development, in addition to increasing traffic safety, the development of congestion and the energy consumption of individual vehicles can also be reduced. In order to increase safety and comfort of the passengers, various conditions/limits (such as speed limits, acceleration limits) need to be incorporated into the control during design. A key task in planning the crossing is to determine the purpose of the intersection management. The primary goal is the safe passage of vehicles, which can be accompanied by performances such as minimizing transit time to avoid congestion and energy-optimal control to reduce fuel consumption and emissions. A balance between these often contradictory performances can be reached with multicriteria control procedures. The first step in planning the control of an automated intersection is to determine the passing order of the autonomous vehicles. Next, the control intervention for the implementation of the given order must be designed.
# Reinforcement Learning Decision-Making for Autonomous Vehicles Based on Semantic Segmentation
## 3 Experimental Procedure and Results
### Simulation Results and Discussion
#### 3.5.2 Agent Training Results
The experiment combined a lower-accuracy semantic segmentation model with the decision agent, with the setup being identical to that of the PPO-SS and DDQN-SS environments. The experimental results are shown in Figure 10a,b. The results indicate that PPO-SS consistently achieved higher reward values than PPO-SS(d) with lower segmentation accuracy throughout the training process, suggesting that a decrease in segmentation model accuracy impacts the decision performance of the proposed framework. The reward value increase for PPO-SS(d) was slower compared to PPO-SS, indicating that lower segmentation accuracy interfered with the decision process. Similar to PPO-SS, DDQN-SS had higher overall rewards than DDQN-SS(d), and although DDQN-SS(d) improved in the later stages, it did not reach the performance level achieved with higher segmentation accuracy. The comparison of results clearly shows a significant correlation between segmentation accuracy and the RL decision-control framework, with lower segmentation accuracy leading to a decrease in the quality of decision-making outcomes. Based on the reward values observed in the experiments, particularly for DDQN-SS and PPO-SS, the results showed that, compared to traditional algorithms (DDQN and PPO), the reward trajectory during the training process exhibited relatively smaller fluctuations. This indicated that, even in complex and dynamic traffic scenarios, the framework enhanced by semantic segmentation led to more stable decision-making behavior. The smaller  
Figure 10: Comparison of reward values between PPO-SS(d), DQN-SS(d) with low semantic segmentation accuracy and PPO-SS, DDQN-SS. (**a**) Reward Values of DDQN-SS and DDQN-SS(d). (**b**) Reward Values of PPO-SS and PPO-SS(d). the standard deviation of the reward values, the higher the reliability and robustness of the method, especially in complex environments with varying traffic density, pedestrian interactions, and dynamic road conditions. The performance of DDQN-SS and PPO-SS at different stages of the training process demonstrated the adaptability of the framework. As training progressed, the reward values for DDQN-SS and PPO-SS increased at a more stable rate, highlighting the model's ability to adapt to various traffic conditions, road complexities, and environmental changes. This adaptability was further reflected in the model's performance when handling different levels of traffic density and challenging driving scenarios, such as intersections. By providing higher and more stable rewards during the training process, semantic segmentation enhanced the environmental perception and decision-making capability of the reinforcement learning agent. Reward values, as a key indicator, showed that the integrated approach contributed to achieving more precise and stable decision-control, enhancing the framework's reliability, robustness, and adaptability to real-world driving conditions.
# Preparation and Application of ZIF-8 Thin Layers
## 4 Discussion  
In order to create a thin and homogeneous ZIF-8 layer as a host material in liquid infused porous surfaces (LIPS) using electrochemical anodization process, various synthesis conditions were evaluated. As the best conditions, a linker concentration of 1 M of 2-methylimidazole and 0.1 M of sodium sulfate as conductive salt were used. We applied a potential of \(-1.1\) V (vs. MSE) over 20 min. The selected conditions resulted in a \(-60\)-\(\upmu\)m thin mechanically stable ZIF-8 layer, with an average ZIF-8 particle size of 1-1.5 \(\upmu\)m. However, compared to the ZIF-8/PVDF composite layer, the electrochemically deposited ZIF-8 layer is still significantly thicker (min. 60 \(\upmu\)m thickness) and has a higher roughness. This is mainly due to the larger particles, as the long incubation time did not allow a high nucleation rate, which would result in smaller particles and a simultaneous layer deposition. Additionally, material purity is an issue, as impurities due to crystallization from a solution are significantly greater than would be expected from crystallization from a melt [43]. In our case, the formation of Cu-containing admixtures arising from the Cu substrate reaction with the electrolyte additionally impedes the synthesis procedure. The usage of the blade technique has some advantage for the creation of the ZIF-8 layers, like an increased smoothness of the layer and a more precise control over the layer thickness. The successive two-layer composite does not reveal any significant advantages in the apparent contact angles or contact angle hysteresis compared to the monolayer. Furthermore, the upper ZIF-8 layer can be detached from the polymer layer over time, so that due to this disadvantage, the monolayer coating was favored. By comparing the contact angles of electrochemically created coatings with ZIF-8/PVDF composite layers and the ZIF-8 pellet, it can be noticed that the advancing contact angles (for GPL 105) have similar values around \(118^{\circ}\) for water and \(96^{\circ}\) for diiodomethane. The apparent (as placed) contact angles of these coatings are also similar to each other (apparent contact angles are shown in Table S1). However, the composite layers show a significantly lower water contact angle hysteresis of \(9^{\circ}\) compared to the electrochemically deposited samples with \(23^{\circ}\) hysteresis for ZIF-8. It seems clear that the surface chemistry and surface structure of the host layer has only little influence on the advancing contact angle, but a stronger influence on the resulting contact angle hysteresis. This can also
## 2 Construction Method of EMR-SF along the Road Collection Data
### Construction of the Grid Points in the LRBF  
Before the LRBF method is used, the grid points are constructed within the sampling range. The sampling data boundary is first obtained. Then, the grid points with a certain interval distance are constructed within the boundary range (Figure 2).  
In the process of constructing grid points, the accuracy of constructing an EMR-SF based on the LRBF is affected by the density of grid points. Finding the appropriate grid point interval distance effectively improves the interpolation accuracy of the LRBF. In this paper, a method is proposed to obtain the grid point interval distance, which calculates the maximum influence distance of the sample points based on the Gaussian basis function and then obtains the density of the grid points according to the maximum impact distance.
## 3 Results and Discussion
### Analysis of Grade 5 (Ti-6 Al-4V) Titanium Vessel  
Grade 5 (Ti-6 Al-4V) titanium with a yield strength of 930 MPa exhibited favourable results in terms the safety factor, particularly with a wall thickness of 25 mm: at this thickness, the safety factor remains above one when applying pressures ranging from 35 MPa to 70 MPa. Specifically, at 35 MPa, the deformation is 1.454 mm, which decreases to 1.0771 mm at 50 MPa and increases to 2.4926 mm at 60 MPa. The stresses generated start at approximately 412.85 MPa and increase with pressure up to 825 MPa. When increasing the wall thickness from 25 mm to 50 mm, the deformation appears slightly reduced, as indicated in Figure 7. With a thickness of 50 mm, the deformations at 35, 50, 60, and 70 MPa are 0.754, 1.0781, 1.29, and 1.50 mm. These deformations are relatively low, particularly considering the safety factor. Additionally, the stress levels generated range from 229 MPa to 459 MPa across pressures from 35 MPa to 70 MPa. Given that titanium has a yield strength of 930 MPa, these stress levels are manageable for the material. Consequently, a titanium hydrogen vessel is suitable for applications involving both lower and higher pressures. Moreover, the safety factor remains above 1 for all parameters across various pressure and thickness configurations, as illustrated in Figure 8. The deformation of the titanium vessel is presented in in Table A5. The equivalent stress for titanium vessel is presented in Table A6.  
Figure 8: Safety factor vs. pressure for Grade 5 (Ti-6 Al-4V) titanium vessel.  
Figure 7: Deformation vs. pressure for Grade 5 (Ti-6 Al-4V) titanium vessel.  
Three different materials of hydrogen vessels were analysed. The titanium vessel showed impressive structural integrity across various pressure ranges. For a thickness of 25 mm, titanium showed good results, along with an acceptable safety factor, for applied pressures between 35 MPa and 70 MPa. The deformation is moderate whilst maintaining a greater-than-unity safety factor even at higher pressures. We conclude that titanium will be well suited for high- and low-pressure applications in aircraft.  
At higher pressures of 50, 60, and 70 MPa, the aluminium vessel suffered from both unsatisfactory levels of deformation, reaching up to 4.25 mm and sub-unity safety factors: aluminium is therefore not suitable for higher pressure application for aviation. However, at lower pressures, aluminium showed reasonable performance especially at 35 MPa, the deformation is small 1.0859 mm and the safety factor is above unity making aluminium suitable for lower pressure aviation applications.  
The 304 Stainless steel vessel showed low deformation with a below unity safety factor lower at both lower pressure and higher pressures, making it the least favourite choice for aviation.
## 3 Results and Discussion
### Separation Performance
The CO\({}_{2}\) permeability coefficient reached a maximum value of 92 Barrer and 90 Barrer at 95% RH, for cmNFC-PVAm-AEAPTMS-A and cmNFC-PVAm-AEAPTMS-B respectively. This improvement was due to the presence of the amino group which could react with CO\({}_{2}\) molecules. As the EDX and elemental analysis showed, the percentage of grafted amino group were similar for the two processes, which was consistent with the fact that the sample A and B had a similar CO\({}_{2}\) permeability coefficient. Similarly to the cmNFC-PVAm membrane, the presence of water in the membrane induced a large increase in the CO\({}_{2}\) permeability, but with a lower impact as pure cmNFC, due to the hydrophobicity of PVAm and aminosilanes. The selectivity for CO\({}_{2}\)/N\({}_{2}\) was also improved by the crosslinking with values 2.3 times larger than what observed in pure MFC. In particular, at the high humidity content, the selectivity grew from 56 to 130. Actually, the presence of water enhanced the ideal selectivity. As said before, the N\({}_{2}\) permeability was less affected by the water content within the membrane.
## 2 Materials and Methods  
### Ice Charts  
This study used the weekly ice charts released by the Canadian Ice Service (CIS) as the baseline for classifying the training data and validating the test data used for the semantic segmentation of sea ice via deep learning. The CIS, an essential component of the Canadian Meteorological Service (CMS), provides sea ice charts that were compiled through in situ collection and satellite and aerial reconnaissance, which cover the five study hotspots in the North Pole. The ice charts contain one-week sea ice concentration data for the study area, the development stage of sea ice coverage in the area, and the percentage of sea ice coverage; these data not only covered our area of study but have also been proven over the years to be relatively reliable and accurate, supporting their validity.  
The ice charts published by the CIS follow the sea ice classification standards of the World Meteorological Organization (WMO). The ice charts, published in the shapefile format, were extracted and matched with SAR images using Python software to obtain the original uninterpreted ice chart information. We also set corresponding reading rules, such as using sea ice development stages and proportions in determining the main sea ice types; labels for open water and land areas were also extracted based on the information on sea ice concentration and the development stages. Meanwhile, the places identified as unknown and glacial areas were cropped out as effectively as possible.
# Assessing Traffic-Flow Safety at Various Levels of Autonomous-Vehicle Market Penetration
## 3 Methodology
### Human-Driving Vehicles (HDVs) Input Variable Estimation
#### 3.4.2 Optimizing VISSIM Input Variables with Genetic Algorithms  
A genetic algorithm is an algorithm that finds a more appropriate combination of variables to satisfy an objective function and is often used to find the optimal value among a large number of cases. In this study, the process of using genetic algorithms to find the optimal values of the VISSIM input variables is shown in Figure 3.  
First, VISSIM 2023 was used to build the same network as the NGSIM data and input data such as vehicles. Second, using Python (v. 3.7), the values of each input variable are randomized and entered into VISSIM, and the effectiveness measures MAPE (mean absolute percentage error), RMSPE (root mean square percentage error), and GEH statistic are calculated. Equations (1)-(3) show the formulas for these three measures of effectiveness. The third step is to examine the convergence conditions of the impact measures and select the combination of input variables that gives the best impact measure values. The convergence conditions of the impact measures are 0.05 or less for MAPE and RMSPE and 5 or less for the GEH used in traffic analysis models, and if the conditions converge, the level of simulation is considered reasonable [16]. If the convergence condition is not met, a new combination is generated and returned to the second step until the convergence condition is met.  
In the genetic algorithm analysis, the unit of value for finding the optimal solution was changed to 0.01 to generate a large number of cases, and the number of generations was  
Figure 3: VISSIM input variable optimization process using genetic algorithm.  
Figure 2: Example of parameter set for Wiedemann 99 model.  
set to 10 generations, 20 populations, 10% population mutation probability, and 50% gene mutation probability.  
\[MAPE=\frac{1}{2}\left(\left|\frac{\vartheta-v}{\vartheta}\right|+\left|\frac{ \hat{t}-t}{\hat{t}}\right|\right) \tag{1}\]  
\[RMSPE=\sqrt{\frac{1}{2}\left[\left(\frac{\hat{v}-v}{\vartheta}\right)^{2}+ \left(\frac{\hat{t}-t}{\hat{t}}\right)^{2}\right]} \tag{2}\]  
\[GEH=\sqrt{\frac{2\left(\hat{v}-v\right)^{2}}{\hat{t}+t}} \tag{3}\]  
where, \(\vartheta=\) average traffic speed derived from NGSIM data, \(v=\) average traffic speed from simulation, \(\hat{t}=\) traffic volume derived from NGSIM data, and \(t=\) traffic volume derived from simulation.  
The results of the analysis of the optimal values for the five input variables using the genetic algorithm are shown in Table 2. The analysis resulted in a CC0 of 2.75, CC1 of 3.28, CC2 of 13.07, maximum deceleration for cooperative braking of \(-\)3.18, and safety-distance reduction factor of 0.46. After analyzing the effectiveness measures using these input variables, the MAPE and RMSPE values are 0.002, and the GEH is 0.130, indicating that the traffic volume and average traffic speed are very similar to the actual driving behavior of the NGSIM data.
# Risk Assessment in Supplier Selection for Intelligent Manufacturing Systems Based on PLS-SEM
## 4 Empirical Analysis  
### Data Collection and Analysis  
In this study, data were collected by means of a questionnaire survey. The subjects of the survey were experts, scholars, graduate students and experienced employees of intelligent manufacturing enterprises, in the field of intelligent manufacturing supplier risk. Most of the respondents to this questionnaire received higher education, and a small number of scientific researchers had no practical experience, but they had more than one year of research experience in this field. Therefore, the information fed back by the respondents through the questionnaire is of high quality and can be used in this study. In addition to the general questions, the questionnaire adopts the form of a Likert five-level scale. The respondents choose and score each question item; \"very unimportant\" represents 1 point and \"very important\" represents 5 points. A total of 98 questionnaires were collected and 90 valid questionnaires met the requirements of statistical PLS-SEM small sample analysis. In this paper, Smart-PLS 3.0 is used to build the PLS-SEM model, and a PLS algorithm is used for a single dimension test to fit various index data (as shown in Figure 4). In this model, the evaluation item IMS suppliers risk evaluation is represented by \"EV-IMSS suppliers\", and the six primary indicators are represented by V1-V6; V1.0 is used for 23 secondary indicators 1-v6. As such, 3 indicates that the data input before running the model comes from the actual data. When using the PLS algorithm, Smart-PLS 3.0 software standardizes the original data by default. The results output by the software are standardized results. The following are analyzed with standardized results. As can be seen from Figure 4, the path coefficients of each level I index are sq (0.229), SS (0.158), SCT (0.165), SSC (0.257), SP (0.181), and SISS (0.145). The square value (R2) of the coefficient determined by the multiple regression equation of EV-IMSS suppliers for the six primary indicators is 1.000, which reflects that the risk assessment of IMSS suppliers summarizes the six primary indicators quite well, indicating that the primary indicators have strong interpretation ability for the risk assessment of IMSS suppliers, and the interpretation proportion has reached 100%. Run the PLS algorithm to obtain the factor loads (see Table 1 for details). The factor loads are positive and greater than 0.6, indicating that each index supports IMSS suppliers risk assessment. The greater the load coefficient, the greater the impact.
## 4 Design Analysis
### Finite Element Analysis  
For a CubeSat to function properly, it is expected to withstand the harsh conditions during launch in addition to the extreme thermal cycles while in orbit. During launch to space, the CubeSat will be exposed to accelerations several times larger than the gravitational acceleration in addition to extreme dynamic loads. The dynamic loads include low and high frequency along with random vibrations. The failure of CubeSat caused by vibrations during launch can result in damage of the CubeSat and the launch vehicle.  
In this section, the focus is on the Light-1 assembly's response to the quasi-static and vibrational loads during launch. It is deemed necessary for a successful CubeSat mission that the structure sustains these loads without failure. Therefore, it is necessary to predict the response of the launch vehicle loads to avoid these possible failures. The commercially available finite element software ABAQUS was used to conduct the structural analyses. An analysis procedure similar to [8] was followed.  
Figure 10: Quaternion response for (**a**) Vilnius and (**b**) Aalborg.
# 3R Composites: Knockdown Effect Assessment and Repair Efficiency via Mechanical and NDE Testing
## 3 Results and Discussion
### Low-Velocity Impact Tests  
#### 3.2.1 Knockdown Effect on Mechanical Properties  
Low-velocity impact tests were performed at two energy levels (9 and 15 J) for conventional and 3R specimens. The knockdown effect between the 3R and the conventional composites was evaluated in terms of maximum impact load (kN) and absorbed energy (J). Figure 8 depicts representative load-time and load-displacement plots of both impact energies. The 3R composites exhibited a negligible knockdown effect of -1.8% of the maximum load at the 9 J impact and a knockdown effect of \(-\)3.7% at the 15 J impact (Table 1).  
Figure 8: Representative load/time and load/displacement plots of impacts at 9 J and 15 J of conventional and 3R specimens.  
Figure 7: Impedance measurements of the 3R ILSS specimens before (**left**) and after (**right**) the repair process.  
The reduced maximum load suggested that the 3R specimens had decreased bending stiffness against the conventional [42; 43]. On the contrary, in terms of energy absorption, the 3R panels absorbed more energy than the conventional composites. The 3R specimens exhibited 4.1% more energy absorption than the conventional specimens at the 9 J impact and 2.2% more energy absorption at the 15 J impact. The results showed that the 3R specimens were tougher than the conventional specimens. They also showed that the 3R specimens had more extensive damage, as confirmed by the phased array ultrasonic technique. The damage was not visible by bare eye in all cases. Further evaluation of the impacted plates (mean diameter of impact damage) was performed via a phased array (c-scan) and IR thermography, and the results are presented below.