esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Satellite communication is proposed to fulfill the ubiquitous coverage for next-generation wireless networks. Considering the propagation delay and path loss, low-earth orbit (LEO) satellites are widely adopted. However, since the beam boresight directions become close in quasi-earth-fixed cells (QEFC) scenarios at low elevation angles, the interference increases and causes low communication quality. This paper introduces the optimal beamwidth maximizing uplink coverage probability scheme for quasi-earth-fixed cells in LEO satellite communication systems. The proposed scheme dynamically adjusts the beamwidth to achieve max uplink coverage probability at different elevation angles. The simulation results show that the proposed scheme matches the exhaustive search method in different scenarios and target signal-to-interference-plus-noise ratios. Furthermore, the proposed scheme significantly mitigates interference and improves the uplink coverage probability. Compared with the 3 GPP setting, the proposed scheme improves the uplink coverage probability by 0.93 at time 100 s, and compared with the three-color frequency reuse, the proposed scheme improves the uplink coverage probability by 0.195 at time 100 s. The dynamic beamwidth and boresight direction adjustments enable the satellite to maintain seamless and reliable communication services across a wide range of operating conditions, ultimately realizing the goal of ubiquitous communications in the QEFC LEO satellite system.  
satellite communications; quasi-earth-fixed cells; low-earth orbit satellite; optimal beamwidth; uplink coverage enhancement
## 5 Sentinel-1: Opportunities  
The simulator presented in the previous section allows for a quantitative estimation of the performances of Sentinel-1 mission in water resource management applications. In the following, some applications will be discussed and the quality of the expected products compared with those obtained in the framework of the WARM-SAR project (see Section 2).  
\begin{table}
\begin{tabular}{c c c} \hline
**Simulation Parameter** & **Sentinel-1** & **COSMO-SkyMed** \\ \hline \(h\) [km] & 693 & 619.6 \\ \(v\) [m/s] & 7 & 7.5 \\ \(\theta\) [\({}^{\circ}\)] & 30 & 33.6 \\ \(f_{0}\) [GHz] & 5.405 & 9.3 \\ \(\tau\) [\(\mu\)s] & 35 & 35 \\ \(B\) [MHz] & 60 & 65.64 \\ \(f_{s}\) [MHz] & 60 & 116.25 \\ PRF [Hz] & 1500 & 3612.7 \\ \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 2: Block diagram of the SAR raw signal simulation. Radar (RD) and orbital (OD) data, along with the geometric (\(z\)) and electromagnetic (\(\varepsilon\) and \(\sigma\)) parameters of the surface are the required inputs.
# Lateral Control Calibration and Testing in a Co-Simulation Framework for Automated Vehicles
## 4 Discussion  
Figure 7 shows the steering angle of the vehicle in four cases using manual calibration, ABC, GA, and PSO algorithms. In the interval spanning from the 4 th to the 10 th second, alterations in the steering angle were observed for the manual calibration, GA, and ABC algorithms, registering an approximate variation of 1.9 rad. Conversely, the steering angle adjustment remained negligible when employing the PSO algorithm. Moreover, at the 24 th second, a sudden soar in the steering angle occurred within a span of 3 s, for the Manual calibration, GA, and ABC algorithms. This value then stabilized thereafter at approximately 11 rad. In contrast, the steering angle change generated by the PSO algorithm exhibited a gradual increment, culminating at 8 rad by the conclusion of the aforementioned timeframe. Therefore, the PSO algorithms can prevent the centrifugal acceleration experienced in the vehicle. Consequently, this incremental approach has the capacity to enhance both the precision of lateral displacement and the lateral velocity measurements.  
Table 1 compares the lateral error and lateral velocity using three algorithms as well as manual calibration. Regarding the root mean square values for lateral displacement, the PSO algorithm exhibits a value of 0.2274 m, outperforming the manual calibration (1.124 m), ABC (1.5092 m), and GA (1.0747 m). Moreover, the results across various values  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Compare**} & **Absolute Max** & **Absolute Mean** & **Absolute Max** & **Absolute Mean** \\  & **Values dL** & **Values dL** & **Values L** & **Values L** \\ \hline MiL & 0.0576 & 0.0061 & 0.7532 & 0.1582 \\ HiL & 0.2894 & 0.0098 & 2.5857 & 0.5339 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Lateral error comparison between MiL and HiLin the PSO algorithm consistently demonstrate a superior performance compared to the other algorithms under consideration.  
The lateral displacement and lateral velocity differences between the MiL and HiL are evident in Table 2. Notably, simulation values obtained from the HiL are larger than those generated by the MiL. Similarly, there are errors between the results obtained from the MiL and the HiL, particularly up to 53.23%, with lateral displacement and 78.89% lateral velocity when we use the REC method to validate. Because the value of the lateral velocity is too small, this REC value is excessive. When we use the maximum absolute value method to validate the difference between the results obtained from the MiL and the HiL, the figures for lateral displacement and lateral velocity are 2.35 m and 0.28 m/s, respectively. This divergence can be caused by the cyclic delay existing between Xpack and ADAS and the computer.
## 2 Materials and Methods
### Antioxidant Activity  
To assess antioxidant activity of _M. stylosa_, _M. Serrulata_ herb extracts and dominant compounds (lycopsamine _N_-oxide, 5-O-caffeoylquinic acid, rutin) microplate spectrophotometric assays against 2,2-diphenyl-1-picrylhydrazyl radicals (DPPH\({}^{\bullet}\)) [20], 2,2\({}^{\prime}\)-azinobis(3-ethylbenzothazoline-6-sulfonic acid) cation radicals (ABTS\({}^{\bullet}\)) [21], \(N\),_N_-dimethyl-p-phenylenediamine radicals (DMPD\({}^{\bullet+}\)) [22] were used. Trolox was applied as a reference standard in concentrations 1-100 ug/mL.
# Experimental Study on the Heat Transfer Performance of Pump-Assisted Capillary Phase-Change Loop
## 2 Experimental System
### Experimental Apparatus
It was made by sintering spherical nickel powder particles with a particle size of 20 \(\upmu\)m using the process of cold press sintering at the sintering temperature of 840 \({}^{\circ}\)C. In order to improve the porosity of the capillary wick, Na\({}_{2}\)CO\({}_{3}\) particles with a mass fraction of 15% were added as pore formers to the nickel powder particles. After sintering, the capillary wick was obtained through ultrasonic cleaning with deionized water. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Components** & **Parts** & **Material** & **Dimensions** \\ \hline \multirow{6}{*}{Evaporator} & \multirow{2}{*}{Baseplate} & \multirow{2}{*}{Copper} & Overall size: 36 \(\times\) 30 \(\times\) 2 mm\({}^{3}\) \\  & & & Vapor channel size: 1 \(\times\) 1 \(\times\) 23 mm\({}^{3}\) \\  & & & Vapor channel number: 11 \\ \hline \multirow{6}{*}{Evaporator} & \multirow{6}{*}{Wick} & \multirow{6}{*}{Sintered nickel powder} & Overall size: 23 \(\times\) 23 \(\times\) 5 mm\({}^{3}\) \\  & & & Particle size: 20 \(\upmu\)m \\ \cline{1-1}  & & & Pore-forming agent content: 15 wt\% \\ \cline{1-1}  & & & Porosity: 33\% \\ \cline{1-1}  & & & Permeability: 2.99 \(\times\) 10\({}^{-11}\) \\ \cline{1-1}  & & & Average pore size: 2 \(\upmu\)m \\ \cline{1-1} \cline{2-5}  & \multirow{2}{*}{Compensation chamber} & \multirow{2}{*}{Polycarbonate} & \multirow{2}{*}{60 \(\times\) 20 \(\times\) 12 mm\({}^{3}\) (inner)} \\ \cline{1-1} \cline{3-3} \cline{5-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main dimensions and test conditions in the experiments. Figure 2: Three-dimensional diagram of the evaporator: (**a**) front view of cross-section drawn; (**b**) side view of cross-section drawn. The baseplate was composed of red copper. Square grooves were formed on the upper surface by machining for vapor flow. There were a total of 11 square grooves with dimensions of 1 \(\times\) 1 \(\times\) 23 mm\({}^{3}\). A vapor collecting region was set at the end of the grooves and connected to a vapor collector on the upper cover plate to form a complete vapor flow path. The back of the copper baseplate was connected to the heating plate using thermally conductive silicone grease. The heating plate was a phosphorus-doped silicon plate with a resistance of about 50 \(\Omega\) and dimensions of 20 \(\times\) 20 \(\times\) 0.5 mm\({}^{3}\) The electrodes were affixed to both ends of the heating plate by ultrasonic welding, and connected to a DC power supply to generate joule heat. The voltage at both ends of the electrode could be controlled to limit heating power. See Table 3 for the range of experimental conditions in this study. The liquid inlet temperature of the compensation chamber was maintained at 10 \({}^{\circ}\)C. The heating power was 10-81 W, and the liquid-vapor pressure difference was 0-22 kPa.
# Autonomous Controller-Aware Scheduling of Intra-Platoon V2V Communications
## 8 Conclusions
\begin{tabular}{l l} \(\mathbf{u}(k)\) & controller actions vector at time instant \(k\Delta t\) \\ \(\mathbf{z}(k)\) & disturbances vector at time instant \(k\Delta t\) \\ \(x_{i}(k)\) & longitudinal position of vehicle \(i\) at time instant \(k\Delta t\) \\ \(v_{i}(k)\) & velocity of vehicle \(i\) at time instant \(k\Delta t\) \\ \(a_{i}(k)\) & acceleration of vehicle \(i\) at time instant \(k\Delta t\) \\ \(\dot{a}_{i}(k)\) & desired (controller-calculated) acceleration of vehicle \(i\) at time instant \(k\Delta t\) \\ \(a_{max}\) & maximum acceleration of vehicles (upper bound) \\ \(a_{min}\) & minimum acceleration of vehicles (lower bound) \\ \(a_{l}\) & autonomous controller coefficient (\(l\in\{1,2,3,4,5\}\)) \\ \(d_{d}\) & desired inter-vehicle distance in platoon (distance gap) \\ \(\tau\) & initial latency of the first transmitted message \\ \(\Delta t_{Tx}\) & time interval between subsequent transmissions of messages by a platoon vehicle \\ \(q_{i}^{(s)}(k)\) & transmission indicator of vehicle \(i\) at time instant \(k\Delta t\) \\ \(d_{Th}\) & emergency braking activation distance threshold \\ \(\epsilon\) & maximum acceptable fraction of time with emergency braking activated \\ \(e_{i}(t)\) & distance error between vehicles \(i-1\) and \(i\) at time \(t\) \\ \(T\) & observation interval for emergency braking activation analysis \\ \(I(.)\) & indicator function (equals 1 if condition is true, and 0 otherwise) \\ \(\Delta v_{i-1j}(k)\) & velocity difference between vehicles \(i-1\) and \(i\) at time instant \(k\Delta t\) \\ \(\theta_{m,n}^{(e)}(k)\) & recursive coefficient related to the distance error at time instant \(k\Delta t\) \\ \(\theta_{m,n}^{(e)}(k)\) & recursive coefficient related to velocity difference at time instant \(k\Delta t\) \\ \(\Delta t_{Tx}^{sel}\) & time interval between subsequent transmissions of messages, selected in adaptation using the heuristic approach \\ \(\tau\) & initial latency of the first transmitted message selected in adaptation using the heuristic approach \\ \(T_{d}\) & set of possible intervals between subsequent transmissions of a message in the heuristic approach \\ \(T_{tau}\) & set of possible initial latencies of the first transmitted message in the heuristic approach \\ \(t_{Th}\) & prediction time span with the heuristic approach \\ \(f(.)\) & controller function used with the heuristic approach \\ \(x_{i}^{*}(k)\) & predicted longitudinal position of vehicle \(i\) at time instant \(k\Delta t\) with the heuristic approach \\ \(v_{i}^{*}(k)\) & predicted velocity of vehicle \(i\) at time instant \(k\Delta t\) with the heuristic approach \\ \(a_{i}^{*}(k)\) & predicted acceleration of vehicle \(i\) at time instant \(k\Delta t\) with the heuristic approach \\ \(r\) & memory depth (time span) when using the heuristic approach with hysteresis \\ \(z(t)\) & leader acceleration disturbance process (continuous time representation of \(z_{3}(k)\)) \\ \(z_{min}\) & minimum acceleration change \\ \(z_{max}\) & maximum acceleration change \\ \end{tabular}
## 3 Factors Influencing and Mechanistic Pathways in Egg Yolk Emulsification
### Thermal Treatment
#### 3.2.2 Improving Thermal Tolerance of Egg Yolk to Maintain Emulsifying Properties  
[PERSON] et al. [80] showed that, depending on the quantity of sugar and salt, egg yolk proteins can tolerate severe thermal treatment up to 80 \({}^{\circ}\)C/2 min when coupled with sucrose and NaCl. Without impairing the egg yolk proteins' emulsifying qualities, sucrose and, to a greater extent, NaCl helped to delay the denaturation of yolk proteins. In line with these results, [PERSON] et al. [81] recently demonstrated that adding 4% compound sugar or 4% compound sugar-salt to liquid egg yolk significantly increased its heat stability and raised the denaturation temperature above 77 \({}^{\circ}\)C.  
Furthermore, [PERSON] et al. [12] reported that saccharides such as xylitol, erythroid, and arabinose (at 5%) were able to maintain the emulsifying properties of egg yolk during thermal treatment at 76 \({}^{\circ}\)C, compared to conventional sucrose. Because saccharides slowed down the process of turning unbound water into bound water during heat treatment, they had an inhibitory effect on the thickening of egg yolks. Additionally, it was discovered by Raman spectroscopy analysis that the saccharides inhibited the close interaction between the intact or denatured protein molecules in the egg yolk. Additionally, the addition of saccharides was able to prevent the formation of intermolecular hydrogen bonds, suggesting that hydrogen bonds may have a function in the heat-induced aggregation of proteins [12]. During thermal treatment, plasma with abundant lipids and proteins (LDL, \(\alpha\), \(\beta\), and \(\gamma\)-livetin) aggregated to form precipitates and transferred to the granule component to create insoluble components together. The amount of transfer increased with temperature, and concurrently, the egg yolks' release of free lipids rises as well. The addition of saccharide (xylitol) could inhibit this transfer and aggregation of soluble proteins such as \(\gamma\)-livetin and apo-LDL to insoluble granule components during thermal treatment [82]. These findings provide a greater selection of additives to increase the thermal stability of egg yolk proteins.  
According to [PERSON] et al. [13], the inclusion of proline and betaine enhanced egg yolk thermal stability and preserved its emulsifying qualities under heat treatment. The comparison between betaine and proline showed that egg yolk added with proline had excellent emulsifying activity, while betaine had superior emulsion stability. Analysis of water distribution and protein structure revealed that egg yolk supplemented with betaine or proline exhibited looser, more porous formations compared to the control. Betaine primarily altered water molecular arrangement, enhancing water mobility and overall egg yolk fluidity. Conversely, proline primarily improved egg yolk heat resistance by constructing an organized framework that promoted stability.
# Three-Dimensional Formation Control for Robot Swarms
## 7 Conclusions  
This article addresses a distributed 3D algorithm for coordinating a swarm of ARs to spatially self-aggregate into an arbitrary shape, utilizing local communication only. We make all ARs form a user-specified 3D formation such that the connection of all ARs is preserved during the maneuver. Our control scheme leads to a 3D formation for covering the given shape. Moreover, our approach only requires the global localization of the leader among all ARs.  
To the best of our knowledge, this study is novel in building a 3D formation for covering a given shape such that network connection is maintained while ARs maneuver  
Figure 8: The path of each maneuvering AR until the user-specified complicated shape is entirely covered (\(r_{sc}=50\) m). The path of a maneuvering AR is depicted with red line segments. The final position of a maneuvering AR is depicted as a blue diamond. See that blue diamonds are marked at the end points of each line segment.  
Figure 9: The ARs’ final locations under our formation controls (Algorithm 1) for covering the user-specified complicated shape (\(r_{sc}=50\) m).  
based on local communication only. We show that the proposed formation control yields reliable accuracy in handling significant AR failures and movement error. Once a swarm of ARs is aggregated into a user-specified 3D shape, the leader can maneuver synchronized to all other ARs by sharing its velocity command with all other ARs.  
Utilizing MATLAB simulations, we demonstrate the outperformance of the proposed formation controls. The proposed formation controls can be used in the case where the user-specified 3D shape varies as time goes on. Whenever the shape varies, we apply distributed rendezvous controls in Section 4.2, while not moving the leader. Once all ARs achieve rendezvous, they are redistributed by applying Algorithm 1 again. In the future, we plan to perform experiments to demonstrate the proposed formation controls using real ARs.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 1 Introduction
### Motivation and Contribution  
Although the ROS and RL have significantly enhanced robotic capabilities, facilitating efficient and adaptable performance, the integration of the ROS and RL will overcome several significant challenges that remain, including handling complex and unpredictable real-world settings [24]. In addition, it will resolve difficulties with data accuracy and correctness [25], maximize computing efficiency to minimize processing time [26], and maintain flawless hardware connectivity for continued successful task execution [15].  
The literature reviews published in recent years [5; 6; 7; 8; 9; 10; 27; 28; 29]--shown in Table 1-- highlight the independent contributions of either the ROS or RL but not both. These reviews do not address the important aspects that relate to the advantages and challenges of integrating the ROS as middleware and RL-based control/actuation of robotic systems. To bridge this gap, our work focuses on reviewing a significant body of work that integrates the ROS and RL for a varied use case in real-life robotic applications.  
Since this is an exponentially growing area of research, this review pays specific importance to research in the last 5 years. Our objective is to improve the understanding of ROS-based RL applications and bridge the gap between ROS and RL by providing a detailed review, discussing the implementation and highlighting the advantages and limitations. This review serves as a comprehensive resource for robotics researchers guiding future efforts to improve adaptability and robustness in learned behaviors and can guide further advancements in the area of RL-based robotics.  
The rest of this paper is organized as follows: Section 1 presents the introduction, which is followed by related background in Section 2, where we further subdivide Section 2 with Section 2.1 presenting an overview of RL, including model types, RL algorithms, mathematical principles, and a comparison of popular RL algorithms. Section 2.2 provides an overview of the ROS. Section 3 evaluates each application, highlighting the RL techniques used, and discusses the integration of RL with the ROS. Section 4 provides application-specific analysis and also briefly touches upon the simulation framework, hardware utilized, and provides relevant GitHub repositories and online resources. Section 5 provides recommendations for RL algorithms in robotics, and lastly, Section 6 presents conclusions.
# Study on Seismic Performance of Steel Fiber Reinforced Concrete Pier Under Bending-Torsion Coupling
## 6 Conclusions  
In this study, three piers with an SFRC height of 300 mm at the bottom were designed, and quasi-static tests under different torsion-bending ratios were carried out. On this basis, a parameter analysis was conducted using the ABAQUS software to study the influence of the torsion-bending ratio and SFRC height on the seismic performance of piers. The conclusions are as follows.  
1. The bending-torsion coupling effect leads to a decrease in the bending and torsion capacities of the pier, and the greater the torsion effect, the more rapidly the bearing capacity of the pier decreases. The presence of torque causes the plastic hinge position to move up and the plastic hinge area to expand.
2. Adding SFRC at the bottom of the pier can reduce the attenuation rate of the bearing capacity, delay stiffness degradation, control the cracking of concrete, and effectively improve the bearing capacity of the pier under earthquake action.
3. For the pier model considered in this paper, the optimal height of SFRC is half of the clear height of the pier under the common torsion-bending ratio. This type of pier can not only improve the seismic performance of the structure but also avoid material waste.  
The research results in this paper can provide a reference for the design of bridge piers. The seismic performance of SFRC piers under higher torsion-bending ratios, as well as the design capacity and bearing capacity of SFRC piers, need to be further studied.  
Conceptualization, Z.Z. and C.S.; methodology, C.S.; software, J.M.; validation, Z.Z. and J.F.; investigation, J.F.; resources, Z.Z.; data curation, J.M.; writing--original draft preparation, Z.Z. and J.M.; writing--review and editing, C.S.; visualization, C.S.; supervision, Z.Z.; project administration, Z.Z. and C.S.; funding acquisition, Z.Z. and C.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shandong Provincial Natural Science Foundation, China, grant numbers ZR2024 ME056 and ZR2024 ME092; the National Natural Science Foundation, grant number 51908338; and the Shandong Provincial Youth Innovation Team Plan, grant number 2023 KJ123.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors greatly acknowledge these fundings by Shandong Provincial Natural Science Foundation, National Natural Science Foundation and Shandong Provincial Youth Innovation Team Plan. The numerical calculations have been done on the supercomputer system in Shandong Jianzhu University, and the authors appreciate this help.  
The authors declare no conflicts of interest.