esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Data Analysis  
#### 2.5.1 Gap Acceptance Analysis  
To evaluate pedestrians' gap acceptance, a Generalized Linear Mixed Model (GLMM) with a logit link function was fitted using R (version 4.3.2). The GLMM analysis was conducted separately for the two eHMI penetration conditions (full and partial). Within the partial penetration condition, separate GLMMs were also fitted for AVs equipped with eHMI (Partial-AVs) and TVs without eHMI (Partial-TVs).  
Gap acceptance was modeled as a binary outcome (cross vs. wait) for each vehicle-pedestrian encounter, with gap size, vehicle speed, and eHMI information content as fixed effects and participant as a random effect by the maximum likelihood method. Gap size (2.0-6.0 s, in 0.5 s intervals) was standardized (M = 0, SD = 1) prior to analysis. For each condition (Full, Partial-AVs, Partial-TVs), we began with a full model including all main effects, two-way interactions, and the three-way interaction. Model selection employed stepwise reduction based on Akaike Information Criterion (AIC), which balances model fit and complexity. AIC was chosen over the Bayesian Information Criterion (BIC) due to its suitability for exploratory analyses, as it imposes a less stringent penalty on model complexity, enabling better representation of intricate pedestrian-AV interactions in our dataset.
# Design and Control Simulation Analysis of Tender Tea Bud Picking Manipulator
## 2 Overall Structure Design of Tender Tea Bud-Picking Manipulator  
### Picking Method and Structural Design  
Tender tea buds, whose fresh leaf standards are generally one bud, one bud, and one leaf, need to ensure the integrity of the leaves [20]. At present, the highest process of plucking tender tea buds is manual plucking, and the resulting tea buds have the characteristics of good quality and high quality. As shown in Figure 1, in the analysis of hand-picked tea shoot manipulation, four steps are generally required in the plucking process: (1) Clamping: the thumb and forefinger clamp the tea leaf stalks (Figure 1a). (2) Rotation: the thumb and forefinger are rotated clockwise (Figure 1b). (3) Lifting: the thumb and forefinger are lifted upwards (Figure 1c). (4) Picking is completed (Figure 1d), and the broken pieces of the plucked tea leaves are flat and smooth. Based on the inspiration of traditional manual picking methods, this paper concludes that a 'rotating and lifting' way can ensure the quality of tender tea buds.  
According to the 'rotary pull-up' clamping and ripping picking method obtained from Figure 1, the design of the tender tea bud-picking manipulator model is shown in Figure 2. As the growth of the tea environment is more complex, the location of the shoots around the branches of the tea tree will block the manipulator's movement. The area of the tea shoots of different heights will be uneven, but overall there is upward growth, so the design of the picking manipulator is similar to the shape of the long mouth poker to pull the leaves and buds effectively.  
The designed manipulator for picking tender tea shoots must efficiently clip the shoots. The power unit of the picking manipulator primarily relies on an electric motor. The front-end clamping fingers are connected to the transmission device via a gear structure. The electric motor imparts power to drive the transmission device, enabling the mechanical fingers to open and close. Moreover, the inner side of the picking fingers is equipped with a PVDF piezoelectric thin film sensor. When the pressure reaches a preset value, the No. 1 motor stops functioning. The rear end is powered by the No. 2 motor, controlling the rotary action of the front-end mechanical finger part. This manipulator employs embedded alignment with the cable threaded through the inner part of the robotic finger and exiting through the tail end. This design ensures both the safety and simplicity of the manipulator's appearance during practical operation.
## 3 Establishment of the Flow Prediction Model
### Traditional Water Measurement Method
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Measured**} & \multicolumn{2}{c}{**Water Level (mm)**} & \multirow{2}{*}{**Gate Width**} & \multirow{2}{*}{**Gate Opening**} & \multirow{2}{*}{**E/H**} & \multirow{2}{*}{**_h/H_**} & \multirow{2}{*}{**Flow Pattern**} \\
**Discharge (m\({}^{3}\).h\({}^{-1}\))** & & & & & & & \\ \hline
475.69 & 1051.9 & 478.2 & 600 & 100 & 0.10 & 0.46 & FOG \\
524.07 & 714.6 & 519.5 & 600 & 200 & 0.28 & 0.73 & FOG \\
942.22 & 1318 & 641.4 & 600 & 150 & 0.11 & 0.49 & FOG \\
1149.08 & 945.1 & 703.3 & 600 & 500 & 0.53 & 0.74 & FOG \\
1905.31 & 1181.6 & 860.1 & 600 & 300 & 0.26 & 0.73 & FOG \\
115.23 & 409.1 & 356.6 & 600 & 100 & 0.24 & 0.87 & SOG \\
145.55 & 433.2 & 397.9 & 600 & 150 & 0.35 & 0.92 & SOG \\
325.32 & 501.6 & 467.2 & 600 & 300 & 0.60 & 0.93 & SOG \\
530.79 & 637.2 & 517.4 & 600 & 250 & 0.39 & 0.81 & SOG \\
2416.81 & 1209.9 & 1027.1 & 600 & 580 & 0.48 & 0.85 & SOG \\
114.79 & 385.9 & 360.9 & 600 & 450 & 1.17 & / & WF \\
525.92 & 555.9 & 508.7 & 600 & 590 & 1.06 & / & WF \\
859 & 679.6 & 614.1 & 600 & 500 & 0.74 & / & WF \\
1169.72 & 849.6 & 785.4 & 600 & 580 & 0.68 & / & WF \\
1424.57 & 814.6 & 706.6 & 600 & 590 & 0.72 & / & WF \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Training sample data for partial flow measurement. Figure 3: Fitting curves of the flow coefficient under different flow patterns: (**a**) free outflow with gate control; (**b**) submerged outflow with gate control; and (**c**) weir flow.
# CT Scanning of Structural Characteristics of Glacial Till in Moxi River Basin, Sichuan Province
## 6 Conclusions  
This paper uses CT scanning technology and SEM technology to study the meso-structure and micro-structure of the intact glacial till in the Moxi River Basin. Meso-structure and micro-structure images of the interior of intact glacial till are obtained and the porosity, particle shape, directivity and structural units are analyzed. The main conclusions are shown below:  
1. The intact glacial till in the Moxi River Basin has a wide range of particle size distribution and exhibits bimodal characteristics, mainly composed of huge particles such as boulders, followed by fine particles such as clay.
2. The bulk porosity of the intact glacial till is 24.09%, the average porosity of the longitudinal and horizontal sections is 24.92% and 24.35%, respectively, and the difference between the bulk and surface porosity is small.
3. The average circularity of the particles in the longitudinal and transverse sections is 0.836 and 0.802 and the average aspect ratios are 2.5 and 3.7, respectively. The shape of the particles in the longitudinal section is more circular than that in the transverse sections. The orientation of the particles in the transverse section direction is more obvious.
4. The main mineral components of the glacial till sample are mica, feldspar and quartz. In the process of transportation and deposition, the mineral particles have undergone different degrees of grinding, crushing and dissolution. The particles show mainly calcareous cementation, and the cementation is dense. The structure is mainly a skeleton structure composed of fine particles that are wrapped or filled. This structural feature of glacial till determines its good mechanical properties, and that it is not prone to damage, even under the conditions of cyclic freezing and thawing in the alpine mountainous area.  
Conceptualization, Y.Z. and Y.T.; methodology, Y.Z.; software, Y.Z. and L.W.; validation, Y.T. and L.W.; formal analysis, Y.Z. and J.L.; resources, Y.T.; data curation, Y.Z. and J.L.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.T. and L.W.; project administration, Y.T.; funding acquisition, Y.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (41772324), the State Key Laboratory of Hydraulics and Mountain River Engineering, and the China Postdoctoral Science Foundation funded project (2021M700608). Geological Survey Project of China Geological Survey (DD20221746).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results  
### Mean Velocity Subsidence and the Time Series Subsidence  
With Sentinel-1 ascending datasets from January 2018 to April 2019, the mean velocity subsidence map on Dadeng Island, as shown in Figure 6a, was generated by SBAS-InSAR method. The negative values mean that the ground is moving away from the satellite. According to previous studies [1, 8], the vertical displacement dominates ground deformation for subsidence on reclaimed land. When the horizontal displacement is much lower than the vertical displacement, the vertical subsidence dominates the LOS displacements [28]. As XXNA was in flat topography, the displacements acquired from the InSAR method were reasonably interpreted as vertical subsidence in this study. As we can see in Figure 6a, severe subsidence can be observed at XXNA (rectangle A in Figure 6a) and the subsidence rates can reach up to 192 mm/year, which is the position with the largest subsidence rate in the entire study area. In Qiaotou (rectangle B in Figure 6a) and Dengqi (rectangle C in Figure 6a), the maximum subsidence rates can reach 169 mm/year and 156 mm/year, respectively. Except for these three regions mentioned, the rest of Dadeng Island is relatively stable. Figure 6b presents the error map of mean velocity results calculated by the standard deviation value as shown in equation 7. It can be seen that there is high precision in most parts of study area (below \(\pm\)5 mm/year), and the precision in a very small part of study area (with red color in Figure 6b) is estimated as 5-12 mm/year.  
To explore the time series subsidence information on Dadeng Island, five points were selected in Figure 6a. P1, P2, and P3 are located in Region A, while P4 and P5 are distributed in Region B and  
Figure 6: The results of interferometric synthetic aperture radar (InSAR) measurements. (**a**) Mean velocity map of subsidence on Dadeng Island; (**b**) the error map of mean velocity results; (**c**) time series of the subsidence at selected points on Dadeng Island.  
Region C, respectively. Except for P3 selected on Runway A of XXNA, the other points are undergoing significant subsidence in these regions. Figure 6c presents the time series of the subsidence on these five points. Maximum and minimum cumulative subsidence were observed at P1 and P3, which can reach 213 mm and 55 mm in 486 days, respectively. Furthermore, we can clearly see that P1 to P5 are subsiding linearly. And subsidence trend on P3 is towards stable in the future, while other points will undergo continuous subsidence, which means that the XXNA will probably suffer from severe subsidence problems continuously in the future.
## 4 Experimental Results and Model Interpretability Analysis
### Susceptibility Evaluation
#### 4.1.1 Analysis of Inception Model Results  
The Inception model was used to predict the landslide occurrence probability for each pixel in the study area, obtaining a landslide susceptibility evaluation map for the Inception model (Figure 9). The susceptibility levels were classified into five levels using the natural break method: low, moderately low, medium, moderately high, and high.  
In the prediction results provided by the Inception model, most of the landslide points fall within the moderately high and high susceptibility levels, while other areas where landslides have not occurred are predicted to have low susceptibility levels. This demonstrates the model's high level of fit accuracy. The Inception model exhibits strong discriminative ability, effectively identifying \"high-risk areas\" from \"low-risk areas\", thus  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Index** & **Dataset** & **Inception** & **BPNN** & **ResNet** & **CNN** & **VGG-16** \\ \hline \multirow{2}{*}{OA/\%} & Training set & 92.5 & 90.0 & 89.8 & 88.3 & 86.1 \\  & Validation set & 87.5 & 88.3 & 89.6 & 87.4 & 85.4 \\ \hline \multirow{2}{*}{Sensitivity/\%} & Training set & 95.0 & 90.0 & 92.5 & 90.0 & 87.5 \\  & Validation set & 90.0 & 87.5 & 87.5 & 92.5 & 87.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy and sensitivity of selected models.  
Figure 9: Landslide susceptibility assessment results using the Inception model.  
reducing the occurrence of false alarms and missed alarms. In conclusion, the Inception model demonstrates outstanding performance in landslide susceptibility assessment.
# Challenges and Potential in Increasing Lutein Content in Microalgae
## 3 Limitation of Lutein Sequestration in Algal Oil Droplets
### Lack of Lutein esterification Mechanism  
Carotenoid esterification implies that one or two of the hydroxyl-group on the carotenoid is esterified with fatty acid. Carotenoid esters are common in flower petals such as marigold,senesent leaves, as well as fruits of potato plant, chili peppers, vegetables and some algal strains such as _Haematococcus_ spp. and _C. zofingiensis_[28; 68; 81; 82; 83; 84].  
The esterification of hydroxyl-carotenoids has received extensive attention in recent years. The ester condition could provide an additional positive attribute that enhances several properties of hydroxyl-carotenoid. The ester condition significantly increases the stability of hydroxyl-carotenoid under UV stress, ROS stress and heat treatment [85; 86; 87; 88].  
Esterification of carotenoids may integrate carotenoids into different kinds of lipid droplets [82]. Studies suggested a relationship between carotenoid esters and lipid droplet development in plants [89]. For example, the xanthophylls could not be esterified in a tomato mutant, which was accompanied by decreased carotenoid accumulation and fewer fully developed plastglobules in petals. Therefore, esterification could increase the stability of carotenoids both in vitro under UV light and heat treatments and could also integrate carotenoids into lipid droplets. Introducing the esterification reaction of lutein in microalgae could better preserve lutein under stress conditions. However, the access to lipid droplets remains another barrier for sequestration of lutein or lutein ester in microalgae.
# Green Strategies for Molecularly Imprinted Polymer Development
## Green Strategies in MIP Development
### Ionic Liquids (ILs) and Deep Eutectic Solvents (DES)
#### 2.2.1 ILs as Solvents (Porogenic Solvents)  
MIP synthesis towards _trans_-aconitic acid, using ILs as porogenic solvents, was reported by the first time by [PERSON] and his co-workers, in which [Bmim][BF4] and [Bmim][PF6] were used, under photochemical and thermal conditions. MAA was used as monomer and EGDMA or TRIM as crosslinker. The impact of ILs on bulk polymerization was evaluated, and both ILs revealed a faster synthesis of polymer microspheres (\(d_{p}<200\) nm). In the performance of the materials, MIP produced using [Bmim][BF4] and thermal initiator was shown to be quite selective [59]. Because of the tunable nature of the ILs, there is the possibility to overcome the lack of template solubility at the beginning of the polymerization, such as, for example, opiates that show very low solubility in conventional organic solvents. Also, ILs properties can be fine-tuned for each specific application, that is why they are called designer solvents.  
Later on, the same group [63] performed the same study, testing not only [Bmim][BF4] and [Bmim][PF6], but also two other ILs, [Hmim][PF6], [Omim][PF6], varying the length of the alkyl chain, on MIP preparation for propranolol. They could see that the length of alkyl chain length had strong impact on solvent viscosity, meaning that [Hmim][PF6] and [Omim][PF6] showed the low binding performance, because they increase the IL's viscosity.
## 2 Related Work
### Energy Consumption Models for UAVs  
Energy consumption modeling is a critical component in UAV mission planning and system design. Many existing approaches rely on simplified assumptions such as constant cruising speed, negligible aerodynamic drag, or uniform energy consumption across phases of flight. These simplifications may be suitable for preliminary analysis, but they fail to capture the variability of real missions, especially in dense urban areas [7].  
More physically informed models have emerged to describe UAV energy consumption based on thrust, drag, payload mass, and trajectory dynamics. These models use first-principle mechanics and include considerations for battery discharge rates, motor efficiency, and energy losses during takeoff and landing [7]. The sensitivity of UAV energy consumption to flight altitude, obstacle proximity, and urban wind conditions has been demonstrated in various simulation studies.  
To further enhance realism, computational fluid dynamics (CFD) [15] has been used to model airflow interactions between UAVs and their environments. CFD enables fine-grained analysis of thrust variation caused by aerodynamic perturbations near buildings or moving vehicles. However, despite its analytical strength, CFD remains largely absent from mission-level energy modeling frameworks, particularly in the context of urban multimodal delivery systems involving PTS coupling.  
This observation motivates the present work, which introduces a scenario-aware energy modeling framework that combines physical modeling, environmental corrections, and mission-level constraints to support realistic energy estimation and system planning for UAV-based delivery integrated with public transport infrastructure.
## 3 Results and Discussion
### Modeling Average Velocity Measured by the Tunnel Anemometers: Moving Average  
When evaluating the simulation results, we to date considered the velocity values measured by tunnel anemometers, which tend to slightly fluctuate, even though in practice it is customary to consider several velocity values when monitoring and evaluating the airflow velocity at a given time. Therefore, in Figures 10-12 we present a 5-point moving average of the corresponding velocity values to illustrate the underlying trends of the measured velocity in the experimental data and in both the simulations. In this way, we eliminated to some extent the tendency of the velocity values to fluctuate slightly, similarly to what the tunnel's CCS does. In Table 8, the maximum and minimum differences between experimental and simulated average airflow velocities determined by moving average are shown. The illustrations confirm the large difference between the results' accuracies  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**AD*** & **Mode 1** & **Mode 2** & **Mode 3** \\ \hline \multirow{2}{*}{\(\text{Sim}_{\text{AG}}\)} & 0.02 m/s & 0.10 m/s & **0.32 m/s** \\  & **-0.50 m/s** & **-0.13 m/s** & \(-0.03\) m/s \\ \multirow{2}{*}{[18]} & **0.86 m/s** & \(-0.39\) m/s & \(-0.45\) m/s \\  & 0.33 m/s & **-0.74 m/s** & **-0.89 m/s** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Maximum and minimum differences \(\text{AD}^{*}\) [m/s] between experimental and simulated average airflow velocities determined by the tunnel anemometers.  
Figure 8: Absolute differences ADs [m/s] between the experimental and simulated averaged airflow velocities determined by the tunnel anemometers for the simulation presented in this paper (red curve) and the simulation in [18] (yellow curve).  
Figure 7: Average airflow velocities v [m/s] measured by the tunnel anemometers AT1–3 within the considered time intervals in the simulation presented in this paper (red curve), in the experiment (black curve) and in the simulation in [18] (yellow curve).  
Figure 9: Relative differences RDs [%] between the experimental and simulated averaged airflow velocities determined by the tunnel anemometers for the simulation presented in this paper (red curve) and the simulation in [18] (yellow curve).  
of the simulation presented in this paper and in [18]. Relatively sufficient (although less) accuracy with respect to the measurement accuracy of the tunnel anemometers it can be observed for modes 2 and 3. The accuracy for model 1 is lower, but can still be considered sufficient for practical purposes. However, relatively high inaccuracies in the simulation results presented in [18] are observed. From this analysis, it can be concluded that the improved representation of the tunnel anemometers significantly increased the accuracy of the simulation, and the achieved accuracy appears to be sufficient for the practical use.
# A Hierarchical Mission Planning Method for Simultaneous Arrival of Multi-UAV Coalition
## 6 Conclusions  
In this paper, a hierarchical mission planning was proposed to decouple and solve the coupled task allocation and path planning problem for a multi-UAV attacking a multi-target. Three phases were involved in the method. In the path estimation process, each UAV utilized the PSO-based method to estimate the path length to targets. Then, a task allocation algorithm based on a negotiation mechanism  
Figure 7: Comparison of these task allocation (TA) algorithms. (**a**) Average mission completion time; (**b**) average system utility.  
was proposed to assign the targets. Finally, a path planning method was proposed to generate flyable and safe paths for the UAVs. Through the corresponding simulation, some key points were illustrated.
* Considering the resource requirement of targets, UAV's resource consumption and task sequence, the proposed task allocation algorithm could obtain feasible solutions for the UAVs.
* The proposed path planning method could generate flyable and safe paths for the UAVs considering several constraints, such as simultaneous arrival, UAV's kinematic and collision avoidance with obstacles.
* The Monte Carlo results showed that path estimation using the PH curve performed better than that using the straight line. The proposed task allocation algorithm could obtain a lower mission completion time and higher system utility compared with the PSO algorithm and the modified resource welfare-based task allocation algorithm.  
Although this paper focused on the problem of cooperative attacking using multiple UAVs, the proposed mission planning method can be applied to some civilian scenarios, such as multi-UAV cooperative fire fighting, where each UAV can carry different fire fighting resources. There are also some limitations in the proposed mission planning approach. The proposed task allocation algorithm assumes that all the UAVs can communicate with each other. Some practical issues that may appear in real environment, such as communication delay or even interruption will be considered in future work.  
Conceptualization, X.Z. and Z.Z.; methodology, F.Y.; software, F.Y.; formal analysis, F.Y.; writing--review and editing, F.Y. and J.C.  
This research was funded by the Project 61703336 supported by National Nature Science Foundation of China.  
The authors declare no conflict of interest.