esa-sceva/satcom-chunk-collection huggingface dataset:
# Risk and Complexity Assessment of Autonomous Vehicle Testing Scenarios
Traffic environments are composed of multiple factors whose influence is not merely additive but involves complex interactions. Therefore, scientific methods are necessary to accurately determine the complexity and risk levels of the testing scenarios. The current method mostly quantizes the scenarios using the potential field method, which is complicated and not universal, to assess the nature of different scenarios in the scenario library [15; 16; 17]. It cannot be implemented under the conditions of the continuous iterative updating of scenario libraries. To address these research gaps, this study aims to evaluate the risk and complexity of AV testing scenarios across two dimensions. First, we developed a method for deconstructing scenario elements into five distinct layers. Using the Spherical Fuzzy-Analytical Network Process (SF-ANP), we then proposed a quantitative evaluation model to assess the risk and complexity of each scenario. Concurrently, we determined the weights of the risk and complexity for each scenario element. Next, we applied this method to evaluate real-world scenarios from the China In-depth Traffic Safety Study-Traffic Accident (CIMSS-TA) database, using a fuzzy comprehensive evaluation approach. After eliminating accidents with incomplete statistical information or more fuzzy accidents, we screened 700 accidents as research objects. Finally, the proposed model's validity was tested through virtual simulations. This study contributes to the research in two significant ways: (1) It introduces a novel five-layer test scenario deconstruction method and applies an innovative Fuzzy-ANP technique to determine the weights of the 17 scenario sub-elements. The magnitude of the weights differs according to the degree of their contribution under different scenario metrics. Lighting and weather have the largest weights under both metrics; (2) Based on these weights, a quantitative assessment model is developed for evaluating the risk and complexity of audiovisual security test scenarios. By virtue of normalization, setting the values of different elemental parameters provides a general framework. This approach provides new insights for selecting and constructing audiovisual security test scenarios. (3) Simulation results are used to validate the assessment methodology, and correlation analysis demonstrates that in the unavoidable scenario, the complexity and risk of the scenario are positively correlated with the actual metrics, with 0.21 and 0.36, respectively, and negatively correlated with the avoided scenario, with \(-0.31\) and \(-0.35\), respectively. The remainder of this paper is structured as follows: Section 2 provides a literature review. Section 3 outlines the testing scenario deconstruction and the SF-ANP-based comprehensive evaluation approach. Section 4 presents the results of the scenario evaluations and simulation validations. Section 5 discusses the significance of the scenario elements in the testing process. Finally, Section 6 concludes and suggests directions for future research.
## 3 Intelligent Congestion Control Model Based on DRL in Heterogeneous V2X
### Design of State Space
#### 3.2.2 Number of ACK Packets Fed Back  
If the sender receives three consecutive duplicate ACK packets, the sender determines that the packets are lost and resends the packets immediately. Obviously, this parameter indirectly reflects the state of network congestion. Receiving three duplicate ACK packets indicates that network congestion occurs, and the size of the congestion window should be maintained or reduced.
## 3 Results and Discussion
### Effect of Spacer Length
#### 3.3.2 Friction Factor  
The influence of the spacer length on the friction factor is shown in Figure 6a,b. Apparently, the friction factor increases with the decrease of spacer length. The BTs with \(S\) = 40, 51.77, and 62.5 mm increases the friction factor by around 11.27, 9.98, and 8.99 times that of the plain tube. The reason can be explained by the fact that the BT with a smaller spacer length generates higher dissipation of dynamic pressure of the fluid because of a larger contact surface area. The contours of velocity in the longitudinal sections of the enhanced tube are presented in Figure 7d-f for different spacer lengths, respectively. It can be observed that using a smaller spacer length between the BTs is more effective to frequently disturb the boundary layer, which leads to stronger disturbance upon the fluid. Furthermore, it is noted that the friction factor ratio (\(f/f_{\text{P}}\)) increases with the increase of the Reynolds number. In addition, the increase in the Nusselt number ratio (\(Nu/Nu_{\text{P}}\)) is significantly higher than the friction factor ratio (\(f/f_{\text{P}}\)), under certain operating conditions.
# On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection
## 3 Performance Evaluation
The proposed method is validated through field experimentation in the subsequent sections. Figure 4: The satellite signal quality assessment of GNSS set to quad-constellation mode in different types of environments denoted by P1, P2, P3 and P4 in Sukkur city. (**a**) Elevation angle, (**b**) Carrier-to-Noise Ratio (CNR) and (**c**) Range Residuals.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 5 Experimental Results and Discussion  
For the proposed hybrid compression and encryption algorithm, security tests and compression performance tests were performed. The experiments were implemented in the MATLAB R2016b programming environment developed by mathworks is an American privately held corporation, which was running on a personal computer with an Intel Corei3 3.5-GHz processor and 8-GB memory.
## 4 Performance Analysis  
In this section, we analyze the AUV-BS distance by varying the direction and magnitude of BS motion in order to investigate its effect on handover decision errors. For this purpose, the parameters related to AUV-BS distance are defined in Table 2. Next, we describe the simulation conditions and assumptions. Then, we analyze the overall simulation results and provide several handover considerations for B-UWANs.
## 6 System Application Verification
#### 6.1.1 Scene Construction  
Using PQ Factory software as the core platform, the digital twin production line construction method integrates digital twin models seamlessly for each unit. Moreover, we develop operation programs, map trajectories for industrial robots, and create comprehensive processing and testing protocols. Additionally, digital twin technology facilitates detailed offline simulations of the production process and aids in the formulation of plans for the physical production line. The simulation process verified the accuracy of the digital twin production line equipment model, and conducted an in-depth analysis of the discrepancies in the control logic between the physical production line and its digital twin,which provided preparation for the real-time mapping of manufacturing data and process optimization of the physical production line of automotive wheels.  
Figure 11 shows a schematic diagram of the intelligent manufacturing production line for automotive wheels based on digital twins. Firstly, the CATIAV5-6R2021 software was utilized to create the 3D digital models for each individual module of the production line. Secondly, these 3D models were seamlessly imported into the PQ Factory software to successfully construct the digital twin of the intelligent manufacturing production line. Then, in the software platform of PQ Factory, the 3D digital model of the intelligent production line of automobile wheels is successfully constructed, the sensors, state machines, and event management of each production unit are defined in detail, and the configuration of the signals and PLC variables is completed.  
In addition, the virtual environment of PQ Factory is used to implement a detailed process plan for the intelligent production line of automotive wheels, encompassing key aspects like path optimization, collision detection systems, and reachability assessments for industrial robots. Through the comprehensive debugging of the PLC program and the simulation test of the production line before the establishment of the production line, the accuracy of the industrial robot trajectory and the construction efficiency of the production line are improved, and the risk of improper equipment layout and equipment interference after the completion of the production line is effectively avoided.
# A Rapid Method for the Determination of SBS Content Based on the Principle of Orthogonal Testing
## 4 Conclusions  
In this paper, based on temperature shear test and bending beam rheological test, the influence of SBS content on the high and low temperature performance of modified asphalt is preliminarily analyzed. The calibration curves of SBS content based on rutting factor and creep slope are presented and its defects are pointed out. Meanwhile, the calibration curve of determining SBS content by FTIR is established, and the orthogonal test is designed to explore the influence of additives sulfur and rubber oil on the determination results. The main conclusions are as follows:  
1. The rutting factor \(G^{*}/sin\delta\) and the creep slope \(m\) both have a good linear relation with the SBS content. The calibration curves of the two indexes can be used to determine the SBS content in modified asphalt. However, the linear fitting of these two factors is related to temperature, and the determination results are also susceptible to other modifiers in the asphalt, so it is not worth promoting.
2. It can be seen from the infrared spectrum that the SBS modified asphalt retains the characteristic absorption peaks of SBS modifier at 699 cm\({}^{-1}\) and 966 cm\({}^{-1}\), as well as the characteristic peak of matrix asphalt at 1377 cm\({}^{-1}\). The area of the absorption peaks at 699 cm\({}^{-1}\) and 966 cm\({}^{-1}\) has a certain relationship with the SBS modifier content. Thus, the characteristic peak area at 1377 cm\({}^{-1}\) can be used as a reference to respectively establish the linear regression curve of SBS content and the ratio of the absorption peak area at 699 cm\({}^{-1}\) and 966 cm\({}^{-1}\) to that at 1377 cm\({}^{-1}\). It can well predict the content of the modifier in SBS modified asphalt.
3. In the orthogonal tests, MANOVA is used to analyze the effect of different additives on the determination results. It is found that at the significance level \(\alpha=0.05\), the additive sulfur and rubber oil have significant effect on the determination of SBS content in modified asphalt by FTIR, and the influence of sulfur content is greater than that of rubber oil. Therefore, the FTIR method for rapid determination of SBS content in modified asphalt needs to be further improved.  
Conceptualization, G.X. and X.C.; methodology, X.G.; validation, X.C., Y.Y.; formal analysis, X.G.; investigation, G.X.; resources, X.C.; data curation, X.G.; writing--original draft preparation, G.X.; writing--review and editing, X.G. and Y.Y.; supervision, X.C.; project administration, X.C.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (No. 51778136), Technology Research and Development Program of China State Railway Group Co., Ltd. (K2020G032). And the APC was funded by Technology Research and Development Program of China State Railway Group Co., Ltd., Beijing, China.  
Not applicable.  
The authors declare no conflict of interest.
# Enhancing Asphalt Binder Performance and Storage Stability with Trinidad Lake Asphalt (TLA)
## 2 Experimental Design
### Evaluation of Binder
#### 2.3.3 Multiple Stress Creep Recovery (MSCR) Test  
The Multiple Stress Creep Recovery (MSCR) test, conducted on a modified asphalt binder using Dynamic Shear Rheometer (DSR) equipment in accordance with AASHTO TP 70 standards [26], is essential for evaluating the rheological properties of the binder. This test is performed on unaged, short-term-aged (RTFO), and storage stability samples. It is executed at two distinct stress levels, 0.1 kPa and 3.2 kPa, with cycles of creep loading and unloading lasting 1 s and 9 s, respectively. Each stress level undergoes a total of ten cycles.  
The MSCR test generates two critical parameters: nonrecoverable creep compliance (J\({}_{\text{nr}}\)) and percent recovery (%rec). These metrics quantitatively assess the rutting susceptibility of asphalt binders.  
Nonrecoverable creep compliance (J\({}_{\text{nr}}\)) is calculated by dividing the nonrecoverable shear strain by the applied shear stress. J\({}_{\text{nr}}\) serves as an indicator of the material's resistance to permanent deformation, or rutting, under repeated traffic loading. A higher J\({}_{\text{nr}}\) value suggests a greater susceptibility to rutting, thus making it a crucial factor in material selection for road construction. A lower \(\mathrm{J_{nr}}\) value, conversely, indicates better resistance to deformation, signifying a more durable binder under traffic-induced stress. The percent recovery (%rec) measures the binder's ability to recover its original shape after deformation, providing insight into its elastic behavior. Together, these parameters enable a comprehensive evaluation of the binder's performance, guiding the selection of materials for enhanced road longevity and safety.
## 1 Introduction
The disadvantage here is that the scale of modelling is reduced to overcome the computational cost. In other words, a specific location rather than a whole turnout is selected to investigate the dynamic behavior. There have been recent attempts to deal with computational costs [27; 30] which, while promising, are still insufficient to expand the simulation scale. As discussed, most of the previous studies are focused on the wheel-rail interface. There also exists other research interest on turnout components such as sleepers and bearers [31; 32]. Nevertheless, these models apply the simple moving load approach with a dynamic impact factor to simulate the worst-case scenarios of loading conditions over the turnout. This approach is incapable of capturing actual impact forces and may result in inaccurate results and interpretations. The current design codes for sleepers and bearers use similar quasi-static approaches. They rely on only static analysis with a dynamic factor to estimate the dynamic effects. In fact, the European standards did not have a design code until very recently they have introduced a design guideline resembling other design codes. Nonetheless, a recent study has questioned whether it is appropriate to use these design codes or similar methodology [33], since neglecting such impulsive forces may cause either under or over estimation of the dynamic behavior of components. As an example, the most critical part was incorrectly found between the crossing nose and switches [31], which contradicts actual field observations [7] and many of the other aforementioned studies. Impact forces, including both P1 (high frequency) and P2 (moderate frequency) forces, appear to be the decisive factor when assessing the reserve strength mechanism of a sleeper so as to achieve optimum sleeper lifecycle [26]. The aim of this study is to critically determine the dynamic behavior of a turnout system and to evaluate the effect of impact loadings on the bearers and sleepers by using an FEM-MBS coupled model that is capable of capturing the impact forces. According to a critical literature review, the dynamic behaviors of clustered turnout bearers and sleepers in a turnout system have not been fully investigated. The new insight stemmed from this study demonstrates the critical effects of dynamic loadings on the behavior of railway turnout bearers and sleepers. It could be used as a new reference indicator to develop a new design standard for railway sleepers and bearers. The paper demonstrates the development of a numerical model, followed by model verification, parametric studies, results, and finally conclusions. The effects of different speeds, axle loads, rail pads, and sleeper types are investigated, as well as the dynamic behavior of a turnout.
## 6 Implications, Limitations, and Future Work
### Future Work  
Building on the existing research, this paper highlights several avenues for future research directions. Expanding the evaluation datasets to include a more diverse and comprehensive range of tasks will provide a deeper understanding of the model's capabilities and limitations. Integrating real-time and longitudinal data assessments can offer insights into the model's adaptability and performance stability over extended periods. Further refinement of the few-shot learning techniques is essential, especially for tasks with high variability. Exploring advanced prompting strategies and incorporating a more contextual understanding could enhance performance in these areas [65]. It is thus important to also investigate the impact of prompt quality on model performance. Additionally, understanding the reasons behind the model's low performance and conducting thorough error analysis are crucial. This involves examining how and why the model failed in specific tasks to inform targeted training and fine-tuning efforts. Such analysis will provide valuable insights into the model's limitations and guide improvements to enhance its utility in nuanced language-understanding tasks.  
Future work should also prioritize creating and adopting new, comprehensive benchmarks that evaluate models across diverse tasks and datasets, addressing the current gap in robust model evaluation. This approach will ensure a holistic understanding of the model's performance, guiding improvements and encouraging the development of more reliable AI systems. The current multimodal evaluation only investigated image and text inputs, highlighting the necessity to explore other inputs and their combinations. For instance, incorporating audio, image, and text together could significantly contribute to cross-domain applications and arts [66], enhancing the model's utility in various fields. Lastly, incorporating qualitative assessments and human judgment in the evaluation process will provide a more nuanced understanding of the model's practical applicability and contextual performance. This can help identify areas where the model performs well in real-world scenarios and where it may require further enhancement.
## 2 Materials and Methods
### In Vitro Antioxidant Activity Assays  
For the antioxidant activity assays, all dried CS extracts were re-dissolved in 50% ethanol (\(v/v\)) and diluted ten times to achieve an absorbance that was within linear limits. The assays described below were executed in compliance with methods that were already described by [PERSON] et al., adapted to microplate readers [29].  
Radical scavenging activity of CS was examined using ABTS\({}^{+}\) radicals and expressed as milligrams of Trolox Equivalents (TE) per gram of lyophilized extract (mg TE/g extract). Briefly, 200 \(\mu\)L of 2,2\({}^{\prime}\)-azino-bis(3-ethylbenzothazoline)-6-sulphonic acid (ABTS) were mixed with 20 \(\mu\)L of CS sample, followed by an incubation time of 6 min. The absorbanceswere measured at 760 nm against a solvent blank. This method has also been used as a response during DoE, where it has been expressed as mg TE/mL extract.  
The ferric-reducing antioxidant power of CS was examined using a reagent that consists of ferric chloride (20 mM) mixed with hydrochloric acid (40 mM) and tri-2-pyridyl-s-triazine (TPTZ, 10 mM), using a ratio of 1:1:10 (\(v/v/v\)). The reaction mix was left to incubate for at least half an hour at room temperature, deprived of light. 175 \(\upmu\)L of the obtained FRAP reagent were mixed with 25 \(\upmu\)L of CS sample, followed by half an hour of incubation at room temperature in the dark. The samples' absorbance was measured at 593 nm. Results were reported like the aforementioned methods, as mg TE/g extract.  
The 2,2-Diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging ability of the CS samples was quantified as follows: 30 \(\upmu\)L of CS samples were left to incubate in the dark for 30 min with a methanolic radical solution of DPPH reagent (0.002%). Afterwards, the absorbances were read at 517 nm against the solvent blank. Results were conveyed as mg TE/g extract.
## 4 Evaluations of Prediction Results Using Accuracy Metrics
### Evaluations of Prediction Accuracy of Different Training Datasets  
#### 4.2.1 Evaluations of Prediction Accuracy Using Scale-Dependent Metrics  
The ANN prediction model was trained with the training algorithm of \"TRAINGLM\" using three different group datasets: group 1 was based on the 300 AE signals collected from the three pile-installed sensors (S1, S2 and S3), group 2 was based on the 300 AE signals collected from the three platform-installed sensors (S4, S5 and S6), and group 3 was based on the 600 AE signals collected from the six sensors (S1, S2, S3, S4, S5 and S6).  
Evaluation results of the prediction accuracy of the three groups using scale-dependent metrics are illustrated in Table 5 and Figure 8. The values of R and R2 of the three groups were higher than 0.90, indicating that the correlations of the three regression models were very strong. However, the difference in correlations between the three groups were very small.  
Figure 8: _Cont._  
The evaluation results of MSE, RMSE and MAE for group 3 were 315.45 cm\({}^{2}\), 17.76 cm and 13.62 cm, respectively. The group 3 was a reference for group 1 and group 2, because dataset 3 (dataset of group 3) was a combination of dataset 1 and dataset 2. The evaluation results of MSE, RMSE and MAE for group 1 were 249.02 cm\({}^{2}\), 15.78 cm and 12.77 cm, and the evaluation results of group 1 were smaller than the evaluation results of group 3. The evaluation results of MSE, RMSE, and MAE for group 2 were 402.15 cm\({}^{2}\), 20.05 cm and 15.12 cm, and the evaluation results of group 2 were greater than the evaluation results of group 3. According to the evaluation results, the prediction errors of the three groups can be ranked as follows: group 2 > group 3 > group 1.
## 5 Results and Experimental Discussion
### Analyzing Test Metrics of the Fine-Tuned Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fine-Tuned Model** & **Test Accuracy** & **Test Precision** & **Test F-1 Score** & **Test Recall** & **Test MCC** \\ \hline _BERT-multilingual_ & 0.5942 & 0.5944 & 0.7452 & 0.9985 & 0.0057 \\ _BETO_ & 0.4855 & 0.6881 & 0.3670 & 0.2456 & 0.0991 \\ \hline _RoBERTa-multilingual_ & 0.5942 & 0.5942 & 0.7455 & 1 & 0 \\ _RoBERTa-BERTIN_ & 0.5489 & 0.6368 & 0.5963 & 0.5607 & 0.0908 \\ _RoBERTa-BNE_ & 0.4383 & 0.5938 & 0.2687 & 0.1737 & 0.0005 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Metrics assessed during testing of the base BERT and RoBERTa models, without fine-tuning, using previously unseen data, which constitute 20% of our Spanish political fake news dataset. differences in token distributions, which a linguistic model like BERT and its optimized version RoBERTa could pick up on. Although our experimental procedure was correctly applied, with a separate test split of 20%, this split was derived from the same underlying generating process. Therefore, there might be no assurance that the results will scale to actual fake news detection. To address this issue, we conducted an additional evaluation using an independent dataset that is described in the next section.