esa-sceva/satcom-chunk-collection huggingface dataset:
# Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
## 3 Experiments and Results
### Performance Comparison
#### 3.4.2 Comparison of Weekends and Workdays  
As a result of people's different travel intentions, the fluctuation range of traffic flow is more obvious on weekdays than on weekends. The main difference is that traffic flow increases more rapidly during the morning and evening peaks on weekdays. In order to compare the differences between algorithms regarding the prediction for these two periods, we divided the prediction results into working days and non-working days for the statistics. Table 3 shows the metrics of T-GCN, ASTGCN, and the heterogeneous graph convolution network for workdays and weekends. The prediction results of all these three algorithms for non-working days were better than those for working days. Among them, our proposed model reached the optimal prediction results, and the accuracy for non-working days was higher than that for working days.  
In order to better reveal the prediction accuracy and distinction of the heterogeneous graph convolution model on different types of days, we selected a working day from PeMSD4 and a non-working day from PeMSD8 for in-depth analysis. As shown in Figures 9 and 10, the heterogeneous graph convolution model obtained good traffic flow prediction results. It can be seen from the curve overlap degree of Figures 9 and 10 that the prediction effect for non-working days was slightly better than that for working days. The prediction effect in the morning and evening peaks was not good enough, which means that stability of traffic conditions also affects the final prediction results, i.e., the prediction accuracy is high when the traffic conditions are relatively stable but the gap between the prediction result and the real value becomes large when fluctuation occurs in the traffic flow. In addition, it can be observed that the prediction error at noon was large, as the fluctuations and interleaving of the curves are quite different. Furthermore, the model has the defects of failing to predict the maximum flow value in the morning and evening peaks.  
Figure 8: Performance comparison charts. The left subfigure is the bar chart, and the right subfigure is the box chart.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Working Days**} & \multicolumn{3}{c}{**Non-Working Days**} \\ \cline{2-7}  & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline T-GCN & 27.546 & 32.135 & 16.165\% & 23.348 & 30.398 & 14.418\% \\ ASTGCN & 20.125 & 25.972 & 17.214\% & 17.835 & 28.142 & 14.186\% \\
**Ours** & **17.981** & **25.031** & **14.155\%** & **14.013** & **22.489** & **12.410\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of working days and nonworking days.
## 3 Results and Discussion
### Analysis of Salt Bridges in bR Protein to Determine the Stability of the bR-Graphene Complex

Table 1: Salt bridges within bR in the absence of graphene (the typical distance of 3.2 Å between Oxygen and Nitrogen atoms is considered as a criterion to determine the status of the salt bridges where distances less than 3.2 Å reflect formed salt bridges, greater than 3.2 Å reflect broken salt bridges and if the distances were lesser than 3.2 Å all throughout the simulation, it reflects the ‘Sustained’ category). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Salt Bridges * & **Broken** & **Formed** & **Sustained** \\ \hline
**ASP96-LYS41** & \(\surd\) & & \\ \hline
**GLU166-ARG227** & & & \(\surd\) \\ \hline
**GLU9-ARG7** & & \(\surd\) & \\ \hline
**ASP212-ARG82** & & & \(\surd\) \\ \hline
**ASP212-LYS216** & & & \(\surd\) \\ \hline ASP38-LYS41 & \(\surd\) & & \\ \hline
**ASP104-LYS159** & & \(\surd\) & \\ \hline
**GLU166-ARG164** & \(\surd\) & & \\ \hline
**ASP85-LYS216** & \(\surd\) & & \\ \hline GLU74-ARG7 & & \(\surd\) & \\ \hline GLU204-ARG134 & \(\surd\) & & \\ \hline
**ASP85-ARG82** & \(\surd\) & & \\ \hline
**GLU194-LYS129** & \(\surd\) & & \\ \hline \hline \end{tabular}  
* Data in bold represent the common salt bridges between control and experimental simulations.  
\end{table} 
Table 2: Salt bridges within bR in the presence of graphene (experimental simulations). the proton to the extracellular medium through the proton diode gate at SER193 is seen to break its salt bridge with ARG134 in the presence of graphene (Table 2). The same ARG134, however, is seen forming a salt bridge with GLU194 in the absence of graphene (Table 1). This indicates that ARG134 may be the residue through which the proton is transported from GLU194 to GLU204 and then to the extracellular medium via SER193.
## 3 Results and Discussion
### Adsorption Performance
#### 3.2.3 Adsorption Isotherms
reported that the NDA-99 polymeric adsorbent achieved a maximum adsorption capacity of approximately 200 mg g\({}^{-1}\) for m-cresol [18]. Acetamido-functionalized hyper-crosslinked polymers with a high surface area of 756 m\({}^{2}\) g\({}^{-1}\) and tailored porosity demonstrated a phenol adsorption capacity of 214.5 mg g\({}^{-1}\)[37]. In a study focused on phenolic compound recovery from brewery wastewater, the strong base resin SCAV4 exhibited an adsorption capacity of 35.4 \(\pm\) 0.8 mg g\({}^{-1}\)[38]. Activated carbons derived from spent tires have shown capacities of up to 270 mg g\({}^{-1}\) for phenolic compounds under optimized conditions [39]. In contrast, the XDA-1G resin demonstrated a significantly higher adsorption capacity of up to 833.3 mg g\({}^{-1}\) at an initial m-cresol concentration of 20 g L\({}^{-1}\), which notably surpasses most values reported for other adsorbents. This exceptional performance is likely due to the synergy of its high specific surface area (\(\geq\)1100 m\({}^{2}\) g\({}^{-1}\)), non-polar surface chemistry, and amorphous structure that facilitates multilayer adsorption and hydrophobic interactions. While many reported materials are optimized for trace-level or dilute wastewater treatment, XDA-1G stands out for its effectiveness in handling highly concentrated organic contaminants. These comparisons underscore the strong potential of XDA-1G resin as a practical and high-performing candidate for industrial wastewater treatment applications, particularly where high organic loads are involved.
# Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
## 1 Introduction
### Reinforcement Learning
Q-learning converges to the optimal Q-value \(Q^{*}\) when: (a) the environment has discrete and finite state and action spaces; (b) each state-action pair is observed infinitely; (c) the sum of the learning rate goes to infinity; and (d) the sum of the squares of the learning rates is finite [17; 18; 19]. There are two main types of RL, namely, temporal difference (TD) [20] (i.e., Q-learning) and Monte Carlo (MC). Both TD and MC are model-free and learn based on experience without the need for prior knowledge or the transition probability, and each state and action must be visited often to ensure convergence. In an MC method, such as the policy gradient method, the value function \(v_{\pi}(s_{t})\) is estimated based on the average return received at the end of each episode. In the TD method, the value function is estimated based on the reward received at each step, and so it achieves faster convergence [7]. ```
1:Procedure
2: Observe current state \(s_{t}^{i}\)
3: Select action \(a_{t}^{i}= \begin{cases}\text{random},&\text{if exploration}\\ \operatorname*{argmax}_{a\in A}Q_{t}^{i}(s_{t}^{i},a),&\text{if exploitation} \end{cases} \)
4: Receive immediate reward \(r_{t+1}^{i}(s_{t+1}^{i})\)
5: Update Q-value \(Q_{t+1}^{i}(s_{t}^{i},a_{t}^{i})\) using Equation (1)
6:End Procedure
```  
**Algorithm 1**Q-learning algorithm.
# Energy Reduction Multipath Routing Protocol for MANET Using Recoil Technique
## 5 Results and Discussion
### Packet Delivery Ratio  
In Figure 12a, in all protocols the packet delivery ratio (PDR) decreases with mobility; specifically in AOMDV, there is a decrease from 94 to 65%; in AOMR-LM, a decline from 95% to 78%; SRMP decreases from 80% to 47%; and our proposed AOMD-ER decreases from only 97% to 82%. The AOMD-ER performs better because transmission is on strong and stable link.  
Figure 12b displays the PDR decreases with packet size. The PDR in AOMDV decreases from 90% to 72%, AOMR-LM reduces from 96% to 80%, SRMP decreases from 85% to 65% and our proposed AOMD-ER only decreases from 98% to 86%, due to a smaller drop ratio (using strong link).  
Figure 11c, shows PDR increasing with simulation time. The PDR of AOMDV increases from 70% to 76%, AOMR-LM increases from 75% to 78%, SRMP increases from 67% to 70%, and in AOMD-ER from 77% to 86%. The Figure 11c shows the PDR% with varying simulation of time. All these comparisons show that the performance of AOMD-ER is best among all because it uses the stable link nodes and optimal path during transmission.
# Study on Mechanical Properties of Multi-Cavity Steel-Concrete Composite Floor
## 6 Conclusions  
This paper puts forward a new type of composite floor--the multi-cavity steel-concrete composite floor. In order to understand its basic mechanical properties, a static load test was carried out to study the bearing capacity and failure of composite floor under the load. Numerical simulation was performed using ANSYS software to understand the mechanical properties of the composite floor. Additionally, the influence of steel thickness, composite floor thickness, cavity size and span length on the bearing capacity of composite floor was investigated. The following conclusions can be drawn from this paper:  
1. The multi-cavity steel-concrete composite floor has relatively large bearing capacity, stiffness and good ductility. Moreover, the multi-cavity steel-concrete composite floor has good crack resistance and integrity. The porous steel plates are well bonded with self-compacting concrete to form an integrated structure. The cavity steel plate provides good shear performance for the composite floor, and effectively restricts the horizontal movement of concrete under load, which can ensure that the cavity steel plate and self-compacting concrete work together under loading.
2. Compared with certain existing precast floors, the total weight of steel used in the multi-cavity steel-concrete composite floor is nearly twice as much as that of certain existing precast floors, but the bearing capacity of multi-cavity steel-concrete composite floor increases to more than four times.
3. The multi-cavity steel-concrete composite floor is still in the elastic stage when it reaches the limit deflection.
4. The steel thickness, composite floor thickness, cavity size and span length have a great influence on its bearing capacity. The bearing capacity and section rigidity of composite floor can be improved by increasing steel thickness and floor thickness and by reducing cavity size and floor span length.  
Conceptualization: C.L., G.L., R.Y., X.M., P.Q. and X.W.; Software: C.L.; Writing--Original draft preparation: G.L.; Writing--Review and editing: C.L., G.L., R.Y., X.M., P.Q. and X.W.; Supervision: R.Y.; Funding acquisition: P.Q. 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, grant number 41907239.  
We kindly thank Research Center of Civil Engineering, China University of Petroleum (East China) for providing laboratory and experimental devices.  
The authors declare no conflict of interest.
###### Abstract  
Cylindrical shells are widely used in pipelines, pressure vessels, and aircraft fuselages due to their efficient internal pressure distribution. However, axial cracks caused by fatigue, environmental effects, or mechanical loading compromise structural integrity, requiring effective reinforcement. This study presents a finite element modeling approach integrating \(p\)-refinement techniques for the efficient analysis of axially cracked pipes reinforced with composite patches. The proposed method unifies equivalent single-layer and layer-wise theories into a single finite element type, improving computational efficiency and eliminating the need for multiple element types in transition elements. Benchmark studies show that the proposed model accurately predicts mechanical behavior, with maximum displacement and stress intensity factors (SIFs) deviating by less than 5% from reference solutions. Fracture analysis using the virtual crack closure technique confirms the accuracy of the SIF calculations. In patched cracked pipes, the proposed model achieves a 67% reduction in degrees of freedom compared to conventional \(p\)-refinement layer-wise models, while maintaining computational accuracy. Additionally, boron-epoxy composite patches reduce SIFs by up to 40%, demonstrating effective crack reinforcement. These findings support computationally efficient damage-tolerant design strategies for pressurized cylindrical structures in aerospace, marine, and mechanical engineering.  
Keywords:axial crack; composite patch; \(p\)-refinement FEM; stress intensity factor; virtual crack closure technique +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Inversion Model Construction, Accuracy Verification, and Salinity Visualization  
First, the samples were sorted in ascending order of salinity. Then, one sample every four samples was selected to form a test set; the remaining samples were used to form a training set. The ratio of the training set to the test set was 3:1. During the model training, the training set was further divided into a training set and a validation set with a ratio of 4:1.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Spectral Indicators** & **Formula** & **References** \\ \hline
**S5 [Salinity Index]** & \(\frac{\text{B-R}}{\text{G}}\) & [23] \\
**S6 [Salinity Index]** & \(\frac{\text{R-NIR}}{\text{G}}\) & [23] \\
**SAIO [Salinity Ratio Index]** & \(\frac{\text{R-NIR}}{\text{G}+\text{NIR}}\) & [24] \\
**CLEX [Clay Index]** & \(\frac{\text{SWIR1}}{\text{SWIR2}}\) & [24] \\
**CYEX [Gypsum Index]** & \(\frac{\text{SWIR1}-\text{NIR}}{\text{SWIR1}+\text{NIR}}\) & [24] \\
**BRI [Brightness Index]** & \(\sqrt{\text{G}^{2}+\text{R}^{2}}\) & [24] \\
**CAEX [Carbonate Index]** & \(\frac{\text{R}}{\text{G}}\) & [24] \\
**SR [Simple Ratio Vegetation Index]** & \(\frac{\text{NIR}}{\text{G}}\) & [25] \\
**CRSI [Canopy Response Salinity Index]** & \(\frac{\sqrt{\left(\text{NIR:R}\right)-\left(\text{G:R}\right)}}{\sqrt{\left( \text{NIR:R}\right)+\left(\text{G:R}\right)}}\) & [26] \\
**NDVI [Normalized Difference Infrared Index]** & \(\frac{\text{NIR}-\text{R}}{\text{NIR}+\text{R}}\) & [22] \\
**EVI [Enhanced Vegetation Index]** & \(\frac{\text{NIR}-\text{R}}{\text{NIR}+\text{6}+\text{R}-7.5\text{B}+1}\) & [27] \\
**DVI [Difference Vegetation Index]** & \(\text{NIR}-\text{R}\) & [28] \\
**MSAVI [Modified Soil Adjusted Vegetable Index]** & \(\frac{\text{(2:NIR-1)}-\sqrt{\left(2\text{:NIR}+1\right)^{2}-8\left(\text{NIR }-\text{R}\right)}}{2}\) & [29] \\
**ARVI [Atmospherically Resistant Vegetation Index]** & \(\frac{\text{NIR}-\text{2.R}-\text{B}}{\text{NIR}+\left(\text{2:R}+\text{B} \right)}\) & [29] \\
**GDVI [Generalized Difference Vegetation Index]** & \(\frac{\text{NIR}^{2}-\text{R}^{2}}{\text{NIR}^{2}+\text{R}^{2}}\) & [30] \\
**EVI2 [Two-hund Enhanced Vegetation Index]** & \(\frac{\text{NIR}-\text{R}}{\text{NIR}+\text{2.4R}+1}\) & [30] \\
**ENDVI [Extended NDVI]** & \(\frac{\text{NIR}+\text{SWIR2}-\text{R}}{\text{NIR}+
## 4 Numerical Evaluations
### Numerical Results
#### 4.3.2 Coverage Measurement  
Coverage characteristics are investigated where we assume that an access UAV with a downward-facing antenna is hovering at a fixed altitude and a ground user terminal with an upward-facing antenna gradually changes its horizontal distance. Similar to Section 4.3.1, it is assumed that the antenna on the ground does not receive ground-reflected waves since it is located at an altitude of 0 m, and only the LOS path from the access UAV is received. Figure 17 shows the numerical evaluation at different UAV altitudes of 10 m, 30 m, and 50 m with varying horizontal distances. In Figure 17b, the numerical results show that even at a close horizontal distance of 10 m, the performance degrades significantly. It means that the degradation is not due to distance attenuation but instead due to the fact that the angle of incidence did not face the main lobes of the antennas, which deteriorates the effective antenna gains. In addition, those with higher antenna gain (or narrower beam width) are revealed to be more susceptible to the angular shift. Hence, the steepness of throughput deterioration against the saturated rate is more remarkable for those with narrower beam widths. Our analyses at different UAV altitudes also reveal that the higher the UAV is, the longer the coverage distance (that can achieve the saturated rate) that can be realized. On the other hand, since propagation attenuation increases against longer distances, it is clear that higher antenna gain is effective to maintain better communication quality (higher throughput) when evaluated at the same distance.  
Figure 16: Numerical results of direct experiment.
# Designing and Simulating the Control Process of a Feed Pusher Robot Used on a Dairy Farm
## 3 Results
### Control System Design  
Figure 5 shows a mathematical model of a pusher robot, obtained using the Simulink Matlab library.  
The central block of the mathematical model is the robot's kinematic model, the input of which receives the angular velocities of the driving wheels \(\hat{\beta}_{1}\) and \(\hat{\beta}_{2}\).  
The aforementioned signals are also fed to the stepper motor controllers of the driving wheels, which generate control actions to operate them. Within the framework of the dynamic model, ready-made control blocks for stepper motors of the Simulink library are used. The motor control logic contained in the Stepper Motor Driver 1-2 blocks is a controller. The developed model makes it possible to change the geometric data of the mobile platform of the robot and design the transient processes of the dynamic and kinematic structural components.  
Figure 6 shows a functional control diagram of the robot's physical model described mathematically.  
Figure 5: Mathematical model of the feed pusher robot.  
Figure 6: Functional diagram of the pusher robot.  
The control circuit includes a microprocessor-based controller that processes and analyzes input telemetry from inductive sensors and a binocular vision system. In turn, the control is carried out by the output signals of the mobile robotic system. All elements are battery-powered; visual control is carried out using the display on the remote control.  
The system of automatic control of the robot's motion implies determining the deviation from a given motion trajectory by signals from two inductive sensors and the binocular vision unit, and analyzing control pulses based on fuzzy-rules logical inference. The authors have established that stereo vision increases predictive properties of the sensor system. The structural diagram of the automatic robot motion control system is shown in Figure 7.
## 4 Empirical Analysis
### Research Area  
Using ArcGIS geographic information analysis software (v.10.8.2), this paper first established a spatial database and took Lixia District, Shizhong District, Licheng District, Tianqiao District, and Huaiyin District as the research areas by referring to the official administrative division definition of National Basic Information Geographic Center--Administrative Division of Jinan Municipal Government in 2023. The bus route information of the five districts of Jinan City obtained in this paper was imported into ArcGIS software for spatial correction and then output into a.shp file in different layers to obtain the bus route map of the five districts of Jinan City, as shown in Figure 1.  
As places with dense bus lines have more developed traffic, the charging stations near the bus lines in the five districts of Jinan City are extracted, and the location data of remote infrastructure are excluded with the distance of 100 m. Finally, 75 effective charging stations are obtained. The charging stations are numbered as 1, 2, 3...  
The five districts of Jinan City are imported into the ArcGIS layer to obtain the regional map of the five districts of Jinan City. Vector data of bus lines and bus stations in the five districts of Jinan City are captured through the open platform of AmAP and imported into ArcGIS, and then 75 charging stations near the bus lines are imported together, as shown in Figure 2. As can be seen from the figure, the charging stations are more distributed in areas with more developed traffic. The charging stations are distributed in all five regions, among which Lixia District, Shizhong District, Tianqiao District, and Pagoda Shady District, and their junctions and bus line interchanges are most distributed.
# Influence of Multiple Factors on the Wettability and Surface Free Energy of Leaf Surface
## 3 Results and Discussion
### The Influence of Droplet Falling Height on Wettability  
Figure 5 shows six partial screenshots of droplets falling from 1 cm altitude. Among them, (a) and (d) refer to the first frame that the droplet (water and organosilicon adjuvant) appears in the lens field of vision; (b) and (e) refer to the moment immediately before the droplet touches the solid surface; (c) and (f) refer to stable status of droplets on the solid surface. Based on the number of shooting frames of the high-speed camera and the corresponding time difference, it is possible to calculate the instantaneous velocity of the droplet touching the wax surface.  
Figure 6 shows the variation of contact angle of droplet on the wax surface with the increase of droplet falling height. When the water droplets were falling, the contact angle fluctuated between 97\({}^{\circ}\)  
Figure 4: Surface free energy and its components of rice leaf and rape leaf on both adaxial and abaxial surfaces (% represents the ratio of dispersive component to the SFE).  
Figure 5: Partial screenshots of droplets falling from 1 cm altitude: water droplet (**a**,**b**,**c**) and organosilicon adjuvant droplet (**d**,**e**,**f**).  
and 103\({}^{\circ}\) with the increase of droplet falling height. It indicates that there is an obvious hydrophobic relationship between the wax and water droplet, and the increase of the droplet falling height as well as the instantons velocity of the droplet contacting wax surface does not change the hydrophilicity between the droplet and the wax surface. However, when the organosilicon droplets were falling, the contact angle between droplet and wax surface was only 34.04\({}^{\circ}\) at 1 cm droplet falling height. The contact angle between organosilicon droplet and wax was obviously better than that of the water droplet, which indicates that the organosilicon adjuvant could improve the wettability to a large extent. Besides this, the contact angle was only 17.38\({}^{\circ}\) when the droplet falling height was increased to 2 cm. It tended to be stable with the increase of droplet falling height, and finally fluctuated between 12\({}^{\circ}\) and 13\({}^{\circ}\). Therefore, it can be seen that the use of organosilicon adjuvant can greatly increase the surface tension of droplets. Since the organosilicon droplets were already extremely hydrophilic at low falling altitudes, the variation of contact angle was small as the falling height rose continuously, where the contact angle tended to be stable gradually.