esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 2 Related Works
However, in the field of traffic sign recognition by deep learning methods, to meet the demands of real time application, it is necessary to further study the choice of discriminative features and to research the network structure to improve classification accuracy and processing time. For a long time, there were no public and challenging datasets available in this area, but this situation changed in 2011. [PERSON] and [PERSON] [16] and [PERSON] et al. [17] introduced challenging databases, including annotations for traffic sign detection classification. These databases included the Belgium Traffic Sign Classification (BTSC), the German traffic sign detection benchmark (GTSDB), and the German Traffic Sign Recognition Benchmark (GTSRB) databases. In particular, the GTSDB and GTRSB have attracted more scholars to find new methods to verify through this database, and some of them have achieved good results. The existence of open databases has made the research methods comparable. The above traffic signs databases belong to European traffic laws and regulations. Some Chinese scholars have paid more attention to the problem of traffic sign detection and recognition in the Chinese traffic environment [18; 19; 20; 21]. The construction of a traffic sign database for Chinese traffic environment is very necessary. Fortunately, the Chinese Traffic Sign Dataset (CTSD) was constructed by [PERSON] [10], which has attracted the attention of more Chinese researchers.
# Design and Control Simulation Analysis of Tender Tea Bud Picking Manipulator
## 2 Overall Structure Design of Tender Tea Bud-Picking Manipulator
### Finite Element Analysis of Picking Machine Fingers  
In this study, we utilized the simulation module within SolidWorks 2021 to conduct finite element analysis of the manipulator. Given that the components in contact with tender tea buds and leaf stalks during the picking process primarily include the mechanical fingers, we focused on extracting and subjecting the picking robotic fingers to static stress analysis to validate the rationality of the mechanical structure. In this design, AISI304 stainless steel was uniformly chosen as the material for the mechanical finger, and the cylindrical aperture at the end of the mechanical finger served as the fixed geometry location point. Using the Simulation module in SolidWorks 2021 software, we performed a detailed finite element static stress analysis of the robotic finger to simulate the external load of 4 N that the manipulator experiences during the tea-picking process. By scrutinizing the stress diagram, we elucidated the stress distribution within the mechanical finger under these  
Figure 4: 4-DOF picking manipulator planar schematic diagram.  
working conditions. As depicted in Figure 5, the maximum stress occurs in the red region at the middle and lower end of the mechanical finger, reaching 44.95 MPa. This stress value is considerably lower than the yield strength of AISI304 stainless steel (206.8 MPa), indicating that the mechanical finger will not experience plastic deformation or damage. Additionally, as illustrated in Figure 6, the preset external load is 4 N, which is significantly below the critical yield strength of AISI304 stainless steel (18.4 N). This underscores the stability of the mechanical finger, as the external load remains well below the critical threshold. Examining the displacement in Figure 7, we observe that the maximum displacement of the robotic finger during operation occurs at the red region of the fingertip, measuring 0.1863 mm. This displacement falls within acceptable limits for the overall positioning accuracy requirement of the manipulator, indicating that the manipulator can maintain sufficient displacement accuracy during tea picking to successfully complete the task.  
In summary, based on the comprehensive analyses conducted above, we can conclude that when the robotic finger is exposed to an external load of 4 N, both the stress and displacement of the component fall within a reasonable range. This aligns with the requirements for tea picking by the manipulator and ensures that there is no risk of material damage or performance issues. Consequently, these findings affirm the reliability and stability of the manipulator's performance.  
Figure 5: Stress cloud diagram.  
Figure 6: Maximum stress cloud diagram.  
Figure 7: Displacement cloud diagram.
## 2 Results and Discussion
### The Laveder EO Composition Is Affected by Harvest Time
Etherio EOTherefore, our results suggest that the transcripts of monoterpene synthases are accumulated earlier than the EO compounds. The lavender EO is regulated by the time of  
Figure 3: The percentage of (**a**) Linalool and linalool acetate, (**b**) 1,8 Cineol, Camphor, Borneol, Lavandulyl acetate and \(\beta\)-Caryophyllene in Lavender EO during the day length. Error bars are presented as SEM of three replications. The values that are significantly different from the first day’s 9:00 sample are indicated with asterisks (* \(p<0.05\), ** \(p<0.01\), ** \(p<0.001\)). release, light and temperature variables, and developmental factors [1; 22; 28]. The release of floral scent at a specific time of the day attracts effective pollinators and avoids predators' attraction, indicating an evolutionary benefit to maximize resource efficiency [29; 30]. The emission of linalool from Lilium and orchid flowers also follows diurnal cycles [31; 32]. The VOC biosynthetic genes can be temporally regulated by clock genes encoding transcription factors (TF) [33]. For example, the rhythmic emission of linalool in orchids is controlled by circadian clock genes like CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) through direct regulation of terpene synthases [34]. The morning component LATE ELONGATED HYPOCOTYL (LHY) controls the daily expression of many VOC biosynthetic genes and specific transcription factors, such as ODORANT1 in petunia, by restricting their expression in the evening [35]. Although several TF, including members of MYB and bZIP families, activate gene expression of monoterpene synthases in lavelenders [36], our knowledge of the circadian clock regulation of TFs in lavelnder is limited.
# A Stackelberg Game Approach to Model Reference Adaptive Control for Spacecraft Pursuit-Evasion
## 5 Conclusions  
This study presents a novel framework that synergizes Stackelberg game theory with the Riccati method and model reference adaptive control to resolve dynamic optimization challenges in spacecraft Pursuit-Evasion. By unifying these methodologies, the proposed MSE approach effectively addresses the intricacies of non-zero-sum games and \(J_{2}\) perturbations, ensuring optimal control strategies under realistic conditions. This integration offers a significant advancement over conventional techniques by explicitly accounting for the sequential decision making and dynamic interplay between the spacecraft. Central to this work is a model-referenced adaptive control mechanism, enabling the pursuer to dynamically refine its strategy in real time based on the evader's behavior. The optimal solution for the Stackelberg equilibrium is analytically derived through coupled Riccati equations, with rigorous proofs establishing its existence and uniqueness.  
Extensive simulations validate the efficacy of the proposed method, demonstrating superior computational efficiency, minimal fuel expenditure, high pursuit success rates, and exceptional robustness against perturbations compared to NE and SSE strategies. These results underscore the method's capability to deliver reliable and adaptive solutions for complex PE scenarios.  
Despite the promising results, this work has limitations that open avenues for future research. The robustness analysis was conducted via scenario-based simulations; a direction for future work would be to formally integrate uncertainties like time delays into the system model itself and design a controller with certified robust stability guarantees. Additionally, the performance comparison could be expanded to include other optimal control  
Figure 10: Comparative success rates of MSE, SSE, and NE algorithms under noise, signal loss, and time delay conditions.  
benchmarks like Model Predictive Control (MPC). Future work will focus on addressing these areas to further enhance the practical applicability of the proposed framework.  
Conceptualization, G.G.; methodology, G.G.; software, G.G.; validation, G.G.; formal analysis, G.G.; investigation, G.G.; resources, G.G.; data curation, G.G.; writing--original draft preparation, G.G.; writing--review and editing, M.C., H.Z. and S.L.; visualization, G.G.; supervision, M.C.; project administration, M.C.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of Beijing Municipality grant number 3252017.  
The data presented in this study are available on request from the corresponding author.  
The authors declare that they have no competing interests. They have no financial or personal relationships with other people or organizations that could inappropriately influence their work. The research was conducted independently, and the results are presented without bias.
# Privacy-Preserving E-Voting System Supporting Score Voting Using Blockchain
## 4 System and Security Models
### Network Model  
The following entities are considered in the network as illustrated in Figure 2.  
* _Blockchain._ The Blockchain network is the cornerstone element of our system. The Blockchain is responsible for handling all voting transactions. All business logic is stored and executed on-chain in the system smart contracts;
* _Election Authority._ The election authority can be any entity conducting an election or holding a decision-making vote; this entity is not required to have political goals. An election authority could be a small non-profit seeking to make an important decision amongst its board-members, or it could be a candidate participating in an federal election, or it could be anywhere in between. The election authority is responsiblefor publishing an election contract to the Blockchain. They are also responsible for maintaining the list of candidates and the list of eligible voters;
* _Voter_. The voter is an entity that is making and submitting a selection in the election. The voter must be authorized (i.e., registered) in order to cast a vote. Users who cannot present sufficient proof of registration or authorization must be denied access to the system. The voter is able to verify their ballot and the overall results of the election.
# Flexural and Shear Performance of Prestressed Composite Slabs with Inverted Multi-Ribs
## 4 Analysis of Flexural Behavior and Shear Strength
### Comparison of Test and Analysis Results  
Figure 10 compares the test and non-linear flexural analysis results of the UF and CF specimens. It was found that the analysis model provides very approximate predictions of the flexural strength and behavior of the specimens, and thus, can be applied to the flexural design of the JAS.  
Figure 11 and Table 4 compare the test and analysis results of the shear specimens (US and CS series). As shown in Figure 11a, for the PC unit specimens, the shear strength estimation method, considering only the contribution of concrete (\(V_{cv}\)) without regard for the contribution of the shear reinforcement (\(V_{s}\)), provided the conservative calculation results. For the composite specimens, Method 1 (\(V_{n1}\)), which applies the effective depth (\(d_{p}\)) of the composite member, including the thickness of the topping slab in the upper part of the rib, provided the analysis results of the unsafe side, whereas Method 3 (\(V_{n3}\)), which estimates the shear strength by considering the gross-s  
Figure 10: Comparison of the test and analysis results on the flexural specimens.  
Figure 9: Shear strength estimation methods for composite member.  
the shear strength of the composite specimens to be on the excessively safe side. Meanwhile, Method 2 (\(V_{n2}\)), which calculates \(V_{cw}\) by regarding the distance from the top of the PC unit rib to the center of the strands layer as \(d_{p}\), slightly overestimated the shear strength of the CSt specimen, but provided a much more reasonable evaluation of the shear strength of composite members when compared to Methods 1 or 2. Considering that the safety factor and strength reduction factor are applied in the practical design, the most economical and reasonable design can be derived by using Method 2 when estimating the shear strength of the composite JAS. In this case, it is appropriate to apply 60\({}^{\circ}\) as the angle of the shear crack for estimating the contribution of the shear reinforcement (\(V_{s}\)).
## 4 Experimental Results and Analysis  
### Datasets  
1. BrainWeb: BrainWeb is a well-known benchmark dataset that simulates a brain database and offers phantom images accompanied by ground truth labels [61]. In our  
Figure 7: **(a)** The threshold obtained using Cauchy-TB based on the entire histogram. **(b)** The threshold obtained using Cauchy-TB based on the remaining histogram after removing one category from the original histogram.  
experiments, we utilized phantom brain MRI images generated with the following parameters: modality (pulse sequences) = T1, slice thickness (in-plane pixel size is always 1 x 1 mm) = 1 mm, noise (calculated relative to the brightest tissue) = 3%, intensity non-uniformity (\"RF\") = 20%. We obtained two sets of data from the dataset for our experiments. The first set consists of 120 slices numbered from 30 to 119, and the second set consists of eight Z-plane-cut brain slices (Z1, Z2, Z5, Z10, Z36, Z72, Z108, and Z144).
2. MRBrain513: MRBrain513 [62] is another well-recognized benchmark dataset that primarily provides three types of multi-modal MRI brain images for segmenting gray matter, white matter, and cerebrospinal fluid: T1-weighted images, T1-weighted inversion recovery images, and FLAIR images. The sequence we used is a 3D T1-weighted scan registered to T2 FLAIR (voxel size: 0.958 mm \(\times\) 0.958 mm \(\times\) 3.0 mm).
## 2 Development of the Numerical Model
### Grid Independence and Experimental Verification  
The geometric model was meshed using an unstructured grid, and the reliability of the model was verified through grid independence verification, and a comparison with the experimental data from the literature [18] was conducted to ensure the reliability of the gas leakage model.  
In this paper, the physical model used was divided into five mesh sizes, and the mass fraction was selected as the index to verify the grid independence. The verification results are shown in Figure 3. Considering the accuracy of calculation and running speed, grid \(N=600\),119 was selected. In order to ensure the accuracy and reliability of the simulation, the grid near the leak hole was locally encrypted, and the boundary layer was set on the inner wall of the leak pipeline through which the refrigerant flowed. The mesh size at the leak hole was 0.0001 m, the mesh size of the space was 0.08 m, and the mesh quality was 0.847.  
The physical model was validated against the experimental data from [PERSON] et al. [29] (Figure 4). Using an R290 leakage experiment with a refrigerant mass flow rate of 0.0018 kg/s, the simulated and measured concentration changes at the measuring point showed good agreement in regard to both trend and magnitude, with the final R290 concentration stabilizing between 0.6% and 0.8%.  
Figure 4: The comparison between simulation and experimental results (mass flow rate = 0.0018 kg/s).  
Figure 3: Grid independence verification.
## 2 Materials and Methods
### mRNA Library Construction and Sequencing  
The total RNA was isolated and purified using the TRIzol reagent (Invitrogen, Carlsbad, CA, USA), following the manufacturer's instructions. A NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA) and a Bioanalyzer 2100 (Agilent, Palo Alto, CA, USA) were used to determine the RNA concentration and integrity of each sample. Poly (A) RNA was achieved by using Dynabeads Oligo (dT) 25-61005 (Thermo Fisher, Waltham, MA, USA) and then fragmented into small pieces using the Magnesium RNA Fragmentation Module (New Englnd Biolabs (Beijing, China) LTD. cat. e6150, USA). After reverse-transcribing to cDNA, dUTP Solution (Thermo Fisher, cat.R0133, USA), _E. coli_ DNA polymerase I (New Englnd Biolabs (Beijing) LTD. cat. m0209, USA), and RNase H (New Englnd Biolabs (Beijing)LTD. cat. m0297, USA) were used to synthesize U-labeled second-stranded DNAs, which were assembled in an adapter. After the heat-labile UDG enzyme (New Englnd Biolabs (Beijing) LTD., cat. m0280, USA) treatment of the U-labeled second-stranded DNAs, the ligated products were amplified with PCR.  
Sequencing and analysis: Low-quality bases were removed using Cutadapt software ([[https://cutadapt.readthedocs.io/en/stable/](https://cutadapt.readthedocs.io/en/stable/)]([https://cutadapt.readthedocs.io/en/stable/](https://cutadapt.readthedocs.io/en/stable/)), accessed on 28 October 2024, version:cutadapt-1.9) and then the clean reads were mapped to the reference genome and assembled using HISAT2 ([[https://daehwankimlab.github.io/hisat2/](https://daehwankimlab.github.io/hisat2/)]([https://daehwankimlab.github.io/hisat2/](https://daehwankimlab.github.io/hisat2/)), accessed on 28 October 2024, version:hisast2-2.2.1) and StringTie software ([[http://ccb.jhu.edu/software/stringtie/](http://ccb.jhu.edu/software/stringtie/)]([http://ccb.jhu.edu/software/stringtie/](http://ccb.jhu.edu/software/stringtie/)), accessed on 28 October 2024, version:stringtie-2.1.6). After generating the final transcriptome, the transcript expression levels were then calculated and represented with FPKM (FPKM = [total_exon_fragments/mapped_reads (millions) \(\times\) exon_length (kB)]). Genes with fold change >2 or fold change <0.5 and \(p\)-value < 0.05 were recognized as differentially expressed genes (DEGs), and then KEGG enrichment was applied to these DEGs.
# Emerging Decision-Making for Transportation Safety: Collaborative Agent Performance Analysis
## 1 Introduction
### Motivation and Contributions  
This study employs ABM to explore and validate its impact on decision-making capabilities for autonomous vehicles within ADASs. The system design and architecture focus on program configuration and simulation setup, ensuring accurate representation of real-world scenarios. By leveraging ABM, the research aims to demonstrate its effectiveness in enhancing driver safety and support, particularly in lane merging scenarios. The methodology also examines the practical benefits and considerations of hardware  
Figure 2: Wireless communications environment of an intelligent transportation system [25].  
acceleration for deploying ABM in ADASs. Given the existing literature, we address the need for robust solutions centred around the integration of ADASs and AVs into future transportation systems. By utilizing ABM principles, we aim to analyse the behaviour and interactions of agents to improve decision-making capabilities for autonomous vehicles. The paper validates the effectiveness of ABM in enhancing safety and reliability in automated transportation scenarios, particularly focusing on the scenario of lane merging. Additionally, the paper explores the practical aspects of implementing ABM within ADASs, including the potential advantages and challenges of hardware acceleration to enable its deployment in real-world applications.  
This paper thoroughly examines the essential requirements and technological components necessary for the successful implementation of agent-driven vehicles. This includes an in-depth analysis of sensors, actuators, communication systems, protocols/messaging standards, and control mechanisms crucial for enabling autonomous behaviour in vehicles. This paper as such makes the following contributions:  
* The paper presents a comprehensive system design architecture for agent-driven vehicles, encompassing data flow, functional modules, connectivity mechanisms, and other critical aspects. This architecture serves as a blueprint for building robust and efficient autonomous vehicle systems.
* We introduce a novel approach to modelling agent-driven vehicles using MATLAB Simulink in dynamic systems. It elaborates on the creation of agent-based models and observes emergent behaviours within these models, providing valuable insights into the dynamics of autonomous vehicle systems.
* The paper showcases the practical applications of agent-based modelling in driver-assistance scenarios, such as localization and visualization. Through demonstrations and case studies, it highlights the effectiveness of agent-driven approaches in enhancing various aspects of driving assistance systems.
## 4 Verification Experiments
### Estimation and Analysis of the Experimental Data
#### 4.2.1 Self-verification the Measurement Results  
The accuracy of the proposed method was evaluated by acquiring multiple measurements of a single target point in a known position relative to the transmitter station with the receiver array held in constant contact under four different postures. Theoretically, the coordinate values in the \(O\)-\(XYZ\) frame obtained in this process should be equivalent for all postures because the position of the probe never changes.  
The values of the conversion parameters \(r_{l}\), \(l=1\), 2,..., 9, and \(t_{1}\), \(t_{2}\), and \(t_{3}\) obtained with the receiver array held in the four different postures are listed in Table 3. As expected, these values generally vary considerably for the different array postures adopted. The coordinate values of the target point in the \(O\)-\(XYZ\) frame obtained with the receiver array held in the four different postures and their deviations from the true coordinate values are listed in Table 4, where \(\Delta L=\sqrt{\Delta X^{2}+\Delta Y^{2}+\Delta Z^{2}}\) is the spatial difference between the true and measured target points. It can be seen from the table that the single transmitter station system using the external receiver array generally obtains a greater error in the \(X\)-axis direction than in the other directions, and the maximum error obtained was 2.5 mm, while \(\Delta L\) was not greater than 2.6 mm.  
Because the positions of each control point were directly obtained using the wMPS, and the precision is sub-millimeter [18], we can then consider that the error of the single transmitter station system is mainly caused by calibration error.