esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Batch Adsorption Experiments  
#### 2.2.1 Organic Dyes  
Methylene blue (MB, MM 319.86 g/mol), erythrosine B (EB, MM 879.84 g/mol), and bromothymol blue (BB, MM 624.40 g/mol) were purchased from Sigma-Aldrich and used as received. Tests were carried out in 100-mL flasks, sealed with a plastic tarp, by adding fixed amounts of PACs to aqueous solutions containing a known initial concentration of organic dyes (Table 1). The samples were continuously stirred at 800 rpm at ambient temperature (\(20\pm 2\)\({}^{\circ}\)C), keeping the pH between 5 and 6. The experimental conditions were chosen according to [PERSON] et al. (2007) [44]. Samples were collected at different time intervals, filtered with a PTFE 0.45-\(\upmu\)m syringe filter, and analyzed to calculate the adsorption capacity according to Equation (1), and the adsorption kinetics using a pseudo-first-order (Equation (2)) or pseudo-second-order kinetic models (Equation (3)) [45]. The tests, conducted in triplicate, were stopped when the measured concentration was constant after two consecutive measurements, or it was below the detection limits.  
\[\textit{Adsorption capacity}\ (\%)=\frac{C_{i}-C_{t}}{C_{i}}\ \times\ 100 \tag{1}\]  
where \(C_{i}\) (in mg/L) is the initial concentration and \(C_{t}\) (in mg/L) is the concentration at time \(t\).  
\[\ln(q_{e}-q_{t})=\ln(q_{e})-k_{1}t \tag{2}\]  
\[\frac{t}{q_{t}}=\frac{1}{k_{2}q_{e}^{2}}+\frac{t}{q_{e}} \tag{3}\]  
where \(q_{e}\) and \(q_{t}\) represent the amount of the adsorbed compounds (mg/g) at equilibrium and at time \(t\) (h), respectively; \(k_{1}\) (h\({}^{-1}\)) is the first-order kinetic constant, and \(k_{2}\) is the one for pseudo-second-order kinetic (g/mg\(\cdot\)h).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameters** & **MB** & **EB** & **BB** & **Ph** & **CP** & **BPA** \\ \hline Initial solutions & & & & & & \\ concentration (g/L) & & & & & & \\ PACs dosage (g/L) & 0.5 & 1 & 5 & 1 & 1 & 0.0125 \\ \hline \end{tabular}
\end{table}
Table 1: Batch adsorption tests experimental conditions for organic dyes and phenolic compounds.  
For a fair comparison of the adsorption capacity of each material and to leverage the effect of the experimental conditions (i.e., initial loading concentration) the partition coefficient (PC) was calculated according to the Equation (4) [46; 47; 48; 49]  
\[PC\;(in\frac{mg}{g}/\mu M)=\frac{\textit{Adsorption capacity }(in\;mg/g)}{\textit{Final adsorbate concentration }(\mu M)} \tag{4}\]
## 2 Theoretical Foundation  
### Optimization Design Process for Pulse-Controlled Correction Projectiles  
Figure 1 shows the optimized design process diagram, where the first step is the establishment of the sample design space using Latin hypercube design (LHD), a well-balanced sampling design that uniformly covers the entire experimental range and evenly divides the design space of each variable. The design variables in this study include aerodynamic shape parameters (such as fin count and fin cant angle) and correction mechanism parameters (such as axial eccentricity and number of pulse thrusters), which directly affect the projectile's flight trajectory performance and tactical indicators. The goal of the comprehensive optimization design is to achieve a well-performing controlled trajectory design by properly matching the aerodynamic shape parameters and correction mechanism parameters. The next step is to calculate the projectile's aerodynamic force and moment coefficients using engineering algorithms; Projectile Design Analysis System (PRODAS) software V3 is used in this study for solving the aerodynamic coefficients [25; 26]. Based on the obtained parameters, a six-degree-of-freedom (6 DOF) ballistic simulation is conducted, and a sample library is created with information on the design variables and the target performance.  
The Latin hypercube sampling and 6 DOF model will be used to generate a sample library, which will be used to construct the DK model and SK model. These models will allow for a quick evaluation of target performance at various design points. Verification of  
Figure 1: Flowchart of the correction projectile design optimization process.  
the model prediction accuracy will enable selection of several performance indicators as objective functions for the design scheme, along with corresponding constraint conditions. The design process will then be transformed into a constrained nonlinear programming problem with multiple design variables, objectives, and constraints. To obtain the optimal design scheme that meets the performance criteria, the MOSSA, a non-dominated sorting multi-objective optimization algorithm, will be utilized based on the optimization objective functions designed in this paper. For further details, please see Section 2.4. Given the existence of an unknown Pareto frontier in the design problem addressed in this article, the convergence criterion is established such that the degree of algorithm optimization will not exhibit further improvement with increasing iterations.
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 2 Preliminaries
### Dynamic Weight Design  
To better accommodate the increasingly complex demands of urban and intercity transportation, we propose a multi-objective dynamic weight adjustment mechanism grounded in real-world traffic scenarios. This mechanism dynamically modifies the weight distribution among multiple objective functions in the route planning process, based on environmental perception and scenario recognition outcomes. In doing so, it enables precise responsiveness to user requirements and adaptive optimization across diverse travel contexts.  
During the weight adjustment process, the system accounts for potential correlations among various objectives. For instance, total travel time, total distance, and expressway usage often exhibit positive correlation. Improper weight allocation in such cases may result in the overdominance of a single objective, thereby undermining the overall fairness of the optimization outcome. To address this, we introduce a correlation suppression module that automatically reduces the joint influence of strongly correlated objectives, promoting a more balanced optimization result. Furthermore, the newly introduced avoidance zone feature enhances the system's ability to circumvent high-risk or sensitive areas, thereby improving safety.The weight configurations corresponding to different travel scenarios are summarized in Table 1.  
In light of increasingly diverse urban travel demands, differentiated weight configurations for route planning are designed for specific scenarios to align better with real-world usage requirements:  
Daily commutes prioritize travel efficiency and smooth traffic flow, emphasizing time minimization and the avoidance of congested routes to ensure a stable and efficient commuting experience.  
Long-distance travel places greater emphasis on overall route economy and safety, focusing on path optimization and risk avoidance to ensure a smooth and secure journey.  
School pickup and drop-off prioritizes safety above all, with particular attention to avoiding complex road segments and potential hazard zones, thereby ensuring a secure and manageable environment for child transportation.  
Night-time deliveries emphasize driving safety and risk mitigation under low-light conditions, while also considering delivery efficiency to maintain operational stability and reliability during off-peak hours.
# Energy Regeneration Hydraulic System via a Relief Valve with Energy Regeneration Unit
## 2 Configuration of the PRV with HERU
### Working Principles of the PRV  
Figure 2 is the working principle diagram of a PRV. The rated pressure of the PRV is set by the pilot operated proportional valve. A and B denote the inlet and outlet of the PRV, respectively. Back pressure in port B is produced by the HERU 5, which is connected to port B of the PRV.  
When the adjusting spring of the pilot valve is pre-compressed to some extent, the relief pressure of the PRV is set. The system pressure is equal to the relief pressure of the PRV. The oil supplied to the PRV is divided into three parts: one acts on the bottom of the main valve spool, the second on the upper of the main valve spool and the third acts on the pilot valve. The latter two are supplied through orifice 2 in the main valve spool. The pilot spool remains closed and no oil flows through it  
Figure 1: Comparison of the pilot relief valve (PRV) with and without hydraulic energy regeneration unit (HERU).  
Figure 2: Working principles of the PRV (1—Main valve spool; 2—Orifice; 3—Reset spring; 4—Pilot valve; 5—HERU; 6—Spring chamber).  
when the force produced by the pressure \(p_{3}\) in the spring chamber 6 is less than the electromagnetic force. The main spool is also closed because the inlet pressure \(p_{1}\), which is equal to the pressure \(p_{3}\) in this case, is smaller than the resultant of the spring force and the pressure \(p_{3}\). The forces on the main spool satisfy the equation  
\[p_{3}A_{1}+F_{0}=p_{1}A_{1}+F_{0}>p_{1}A_{1} \tag{4}\]  
where \(F_{0}\) is the pre-tightening force of the reset spring; \(A_{1}\) is the effective cross-sectional area of the main spool; and \(p_{3}\) is the pressure of the spring chamber.  
The main spool continues closing until the inlet pressure is higher than the spring force of the pilot valve. At this time, the pilot valve is open and the pressure \(p_{3}\) is smaller than the inlet pressure \(p_{1}\) because there is a pressure drop across the orifice 2. Due to the small stiffness of the reset spring, the forces on the main spool satisfy the equation  
\[p_{1}A_{1}>p_{3}A_{1}+F_{0} \tag{5}\]  
Therefore, the main valve opens and the main valve spool moves upwards to allow the liquid to flow through port A to port B and then through HERU to the tank. The pressure in the HA of the HERU increases, while the pressure drop between the inlet and outlet of the main valve reduces. Following this, the overflow energy loss of the PRV decreases accordingly.  
When the pre-compression of the spring of the pilot valve changes, the relief pressure changes accordingly. Furthermore, when the pressure of the HA changes, the performance of the PRV may be influenced. The influence of the HERU should be discussed to ensure a stable performance of the PRV and obtain a comparable, better control characteristic.
## 5 Conclusions  
In this paper, the dual-stream transmission and power control for multiple satellites-assisted IoT networks are investigated to improve the data rate of IoT terminals. In order to resist the influence of high-moving LEO satellites, a multi-satellites synchronization scheme is proposed to compensate for the frequency offset caused by different satellites. Moreover, the downlink power control scheme is investigated to keep the data rate stable. In addition, in the hardware implementation, because the proposed synchronization scheme adopts FFT, it is necessary to select the appropriate number of FFT points to reduce the complexity of the algorithm and facilitate the hardware implementation. The simulation results show that  
Figure 11: Terminal communication rate with fixed transmit power and power control scheme.  
Figure 10: Dynamic characteristics of frequency offset along with time.  
the proposed scheme can effectively resist the frequency offset of LEO satellites, achieve good performance and the communication rate can reach more than 1 Gbps for a single IoT terminal.  
Conceptualization, B.X. and X.L.; methodology, B.X. and Y.M.; software, X.X.; validation, B.X., X.X. and M.K.; formal analysis, X.L.; investigation, X.X.; resources, Y.M.; data curation, B.X.; writing--original draft preparation, X.X.; writing--review and editing, B.X.; visualization, Y.M.; supervision, M.K.; project administration, B.X.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2020 YFB1808002.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.
# Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System
## 5 Conclusions  
Various approaches to optical navigation are compared in this article to deliver a lunar-based positioning system. Lunar exploration roadmaps from space agencies such as NASA and JAXA seek surface positioning performances below 40 m. This is difficult if the orbit determination of the ranging satellites is not below 30 m.  
Optical systems compared include lunar and Earth horizon-sensing systems, lunar craters, and celestial body triangulation by point sources. As stand-alone sources, they do not meet the performance requirements. The lunar limb approach presents the best possible performances.  
The article then considers sensor fusion by optical approach and inter-satellite links. This may deliver a performance around 100 m that might be applicable to an LCNS. The filter stability is not constant, however, and so the required user performance may not be achieved at all times. Additional sensor measurement might be required, and will need further study. Some of these methods will be implemented and tested using the University of Tokyo EQUULEUS spacecraft, which has been recently launched with successful initial operations on-board Artemis I.  
Conceptualization, methodology, software, J.J.R.C.-M.; supervision, X.W.; conceptualization, methodology, Y.K.; supervision, project administration, S.N. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the NSW Space Research Network Grant Number SRN-Pilot-2022-04.  
**Data Availability Statement:** Data are contained within the article.  
Figure 14: Inter-satellite link with lunar horizon velocity error for 10 distributed spacecraft in ELFO.  
**Acknowledgments:** The authors would like to thank The University of Tokyo for welcoming [PERSON] as a visiting researcher in the Intelligent Space Systems Laboratory (Nakasuka-Funase Laboratory) from October 2022 to March 2023, therefore enabling this study.
## 3 Grid-Connected PV/BESS System Description
### PV System  
The power generation method of the PV module is to convert the energy of sunlight into DC energy of power electricity through the PV effect of a solar cell. The rated capacity of the PV module in this study is the output power of 330 Wp by using module type of monocrystalline silicon solar cell, the conversion efficiency of 19.78%, temperature coefficient of \(-0.39\%/^{\circ}\)C and the lifetime of more than 25 years. The tilt angle of \(10^{\circ}\) in the factory roof is set by the installation angle of the PV module. The PV system is composed of 32 modules connected in series to form a string, and 24 strings are connected in parallel to form a subsystem with a capacity of 253.44 kWp. However, for the convenience of simulation setting and analysis, it is simplified to 0.25 MWp at each level. The parameters are set as the lifetime of 20 years and the PV derating factor of total system loss of 85%. The output power formula in the HOMER Grid software is as shown in Equation (12) [40; 41].  
\[P_{PV}=P_{pv,STC}f_{pv}\frac{G_{T}}{G_{T,STC}}\big{[}1+K_{p}(T_{C}-T_{STC}) \big{]}, \tag{12}\]  
where:  
\(P_{pv,STC}\) = PV system rated power (kWp);  
\(f_{pv}\) = PV derating factor (%);  
\(G_{T}\) = the solar irradiance on the surface of the PV module (kW/m\({}^{2}\));  
\(G_{T,STC}\) = the solar irradiance under the standard test conditions (1 kW/m\({}^{2}\));  
\(K_{p}\) = the temperature coefficient of PV module (%/\({}^{\circ}\)C);  
\(T_{C}\) = the operation temperature of the PV module (\({}^{\circ}\)C);  
\(T_{STC}\) = the temperature of the PV module under the standard test conditions (25 \({}^{\circ}\)C).  
Figure 8: Schematic diagram of the grid-connected PV/BESS system.
## 5 Results of Health Monitoring
### Internal Force Monitoring Results
#### 5.3.1 Cable Forces  
The measured change percentages of cable forces are shown in Table 4. The cable forces decreased when compared with the initial state, the reduction percentages are less than 1%, which reflects the stable performance of the cable system.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Tower Position**} & \multicolumn{3}{c}{**First Half Year Period**} & \multicolumn{3}{c}{**One-Year Period**} & \multicolumn{3}{c}{**Construction Period**} \\ \cline{2-10}  & **Transverse Direction** & **Longitudinal Direction** & **Vertical Direction** & **Transverse Direction** & **Longitudinal Direction** & **Vertical Direction** & **Transverse Direction** & **Longitudinal Direction** & **Vertical Direction** \\ \hline Southwest & \(-0.5\) & 0.4 & 0 & 0.3 & 0.4 & \(-0.6\) & 2.7 & \(-0.1\) & \(-1.0\) \\ Northwest & \(-1.0\) & 0.2 & 0 & \(-0.6\) & 0.4 & \(-0.6\) & \(-0.7\) & 0 & \(-0.7\) \\ Southeast & 0.4 & \(-0.1\) & 0 & 0.7 & \(-0.3\) & \(-0.1\) & 2.1 & \(-0.2\) & \(-1.6\) \\ Northeast & 0.1 & \(-0.1\) & 0 & \(-0.1\) & \(-0.9\) & \(-0.2\) & \(-1.3\) & 0.4 & \(-1.2\) \\ \hline \hline \end{tabular} Note: “\(\cdot\)”—Northward, eastward and upward displacement; “\(\cdot\)”— Southward, westward and downward displacement.  
\end{table}
Table 2: Measured tower deformation (cm).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Order**} & \multirow{2}{*}{**Calculated Initial Values**} & \multicolumn{2}{c}{**Measured Values**} \\ \cline{3-5}  & & **15 April 2015** & **15 April 2016** \\ \hline
1 & 0.757 & 0.756 & 0.756 \\
2 & 1.256 & 1.245 & 1.241 \\
3 & 1.274 & 1.318 & 1.312 \\
4 & 1.421 & 1.392 & 1.397 \\
5 & 1.457 & 1.447 & 1.447 \\
6 & 1.464 & 1.465 & 1.463 \\
7 & 1.474 & 1.489 & 1.481 \\
8 & 1.611 & 1.611 & 1.600 \\
9 & 1.649 & 1.660 & 1.658 \\
10 & 1.698 & 1.685 & 1.685 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculated and measured girder vibration frequency (Hz).
## 4 Discussion
### Customization and Modularity  
Customization within JAVIS is straightforward, facilitated by its stack of Vue.js for front-end updates and Django for back-end management. The integrated feedback loop enables departments to request specific functionalities, which can often be implemented swiftly. For example, front-end and back-end changes for multimodality support required approximately one week of development. Such flexibility illustrates JAVIS's capability to adapt to diverse departmental needs efficiently, although larger functionalities may necessitate additional development time. Once deployed, the containerization ensures seamless transitions from development to production environments, minimizing potential disruptions.
## 3 Results
### Impact of Tooth Type and Site  
When considering the different tooth types, the Kruskal-Wallis test revealed significance (\(p<0.001\)), and the post hoc test confirmed significant differences among the majority of teeth (Table 5). When grouping the tooth types, significantly higher thickness values at the central incisors were noted (\(p<0.001\), Figure 3).  
When grouping data for the type of reference point, significant differences in thickness values were also detected (\(p<0.001\)). Particularly, at the mesiodistal centre of the central fissure (FIS), the thickness values were significantly higher than in all the other locations, whereas at the central point of the facial surface (V) thickness values were significantly lower than in all the other locations except the mesiobuccal cusp tip (MB) (Figure 4 and Table 6).  
\begin{table}
\begin{tabular}{c c c c} \hline
**1.6** & **Without N2** & **With N2** & \(p\)**-Value** \\ \hline
2.4 & Without N2 & With N2 & \(p\)-Value \\ B & 0.61 [0.56, 0.61] & 0.61 [0.61, 0.61] & 0.345 \\ P & 0.61 [0.61, 0.66] & 0.66 [0.66, 0.66] & 0.429 \\ FIS & 0.75 [0.70, 0.80] & 0.70 [0.66, 0.80] & 0.915 \\ V & 0.51 [0.51, 0.56] & 0.51 [0.51, 0.51] & 0.134 \\
2.5 & Without N2 & With N2 & \(p\)-Value \\ B & 0.61 [0.56, 0.61] & 0.61 [0.61, 0.66] & 0.212 \\ P & 0.61 [0.61, 0.61] & 0.66 [0.61, 0.66] & 0.262 \\ FIS & 0.72 \(\pm\) 0.09 & 0.74 \(\pm\) 0.10 & 0.767 \\ V & 0.51 [0.51, 0.51] & 0.51 [0.51, 0.51] & 1.000 \\
2.6 & Without N2 & With N2 & \(p\)-Value \\ MB & 0.61 [0.61, 0.61] & 0.70 [0.70, 0.70] & 0.005 \\ MP & 0.61 [0.61, 0.61] & 0.70 [0.66, 0.80] & 0.019 \\ DB & 0.61 [0.61, 0.61] & 0.70 [0.66, 0.70] & 0.037 \\ DP & 0.66 [0.66, 0.66] & 0.66 [0.66, 0.70] & 0.736 \\ FIS & 0.71 \(\pm\) 0.06 & 0.78 \(\pm\) 0.10 & 0.237 \\ V & 0.56 [0.51, 0.56] & 0.56 [0.56, 0.56] & 0.343 \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Thickness** & \(p\)**-Value** & **Ce** & **L** & **Ca** & **1P** & **2P** & **M** \\ \hline Ce & 0.75 & & - & & & & \\  & [0.70, 0.80] & & - & & & & \\ L & 0.63 & & \(<\)0.001 & - & & & \\  & [0.61, 0.66] & & \(<\)0.001 & \(<\)0.001 & - & & & \\ Ca & 0.58 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 & - & & & \\  & [0.56, 0.60] & \(<\)0.001 & 0.45 & \(<\)0.001 & - & & \\
1P & 0.64 & & \(<\)0.001 & 0.45 & \(<\)0.001 & - & & \\  & [0.62, 0.67] & & \(<\)0.001 & 1.00 & \(<\)0.001 & 1.00 & - \\  & 0.64 & & \(<\)0.001 & 1.00 & \(<\)0.001 & 1.00 & 1.00 & - \\ M & 0.64 & & \(<\)0.001 & 1.00 & \(<\)0.001 & 1.00 & 1.00 & - \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of local thickness values (mm) aggregated for tooth type. \(p\)-value: Kruskal–Wallis test \(p\)-value. Pairwise comparisons were achieved using the Mann–Whitney U-test with [PERSON]’s method for \(p\)-value adjustment. Ce: Centrals; L: Laterals; Ca: Canines; 1P: First Premolars; 2P: Second Premolars; M: Molars.
## 3 Results and Discussion  
### Contact Heat  
For both composites \(A\) and \(B\), results of insulation against contact heat were achieved for both contact temperatures of 100 \({}^{\circ}\)C and 250 \({}^{\circ}\)C and are presented in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \begin{tabular}{c} **Physical** \\ **Parameter** \\ \end{tabular} & **Basalt** & \(\mathbf{ZrO_{2}}\) & \(\mathbf{TiO_{2}}\) & \(\mathbf{Al}\) & \(\mathbf{Parylene}\) & \(\mathbf{C}\) & \begin{tabular}{c} **HT Sealant** \\ **Silicone** \\ \end{tabular} & **Mylar\({}^{\text{\textregistered}}\)** & **Air** \\ \hline \begin{tabular}{c} Density \\ [kg\(\cdot\)m\({}^{-3}\)] \\ \end{tabular} & 2650 & 6150 & 4005 & 2730 & 1289 & 1250 & 1390 & 1.2 \\ \hline \
\begin{tabular}{c} Specific heat \\ [J\(\cdot\)kg\({}^{-1}\)\(\cdot\)K\({}^{-1}\)] \\ \end{tabular} & 860 & 540 & 697 & 982 & 712 & 1000 & 960 & 1005 \\ \hline
\begin{tabular}{c} Thermal \\ conductivity \\ [W\(\cdot\)m\({}^{-1}\)\(\cdot\)K\({}^{-1}\)] \\ \end{tabular} & 1.3 & 2.7 & 4.8 & 213 & 0.08 & 0.4 & 0.14 & 0.03 \\ \hline \end{tabular}
\end{table}
Table 4: Structural and thermal parameters of raw materials used in heat transfer simulations.  
Figure 9: Initial conditions of heat transfer simulations through both composite models inside the computational domain.  
The longer threshold time \(t_{\rm t}\) obtained for composite \(B\) (by 9 s for \(T_{\rm c}\) = 100 \({}^{\circ}\)C and by 7 s for \(T_{\rm c}\) = 250 \({}^{\circ}\)C) may be due to the fact that parylene C is characterized by the lowest thermal conductivity coefficient (0.08 W\(\cdot\)m\({}^{-1}\).K\({}^{-1}\)) among all the components that make up composite \(A\) and \(B\). Moreover, in the case of composite \(B\), the \(t_{\rm t}\) obtained for both contact temperatures is higher compared to composite \(A\), which may be caused by the double-sided deposition (as opposed to the PVD coating) of the parylene-C coating deposited on the surface of the basalt fabric. The deposition of the parylene C coating on both sides of the fabric provided it with a larger cooling surface compared to the ZrO\({}_{2}\)-TiO\({}_{2}\)-Al coating of the same thickness but deposited on one side.