esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 VDES and Maritime Wireless Channel  
### VHF Data Exchange System  
The VDES contains four sub-systems: AIS, ASM, VDE-TER, and VDE-SAT. Each system has an identified purpose, and is designed based on its target applications. The AIS provides broadcasting communication for the vessel position, course, and speed to avoid collisions. The AIS has the highest priority in the VDES, and other subsystems are meticulously structured to prevent adverse impacts on the AIS operation. The ASM offers robust message delivery reliability and comprehensive message acknowledgement support, including satellite up-link capability. Two dedicated 25 kHz simplex channels are allocated for both existing and defined automatic systems for maritime safety, outlined by the International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA) and the IMO. The VDE-TER encompasses a 100 kHz duplex channel designed to accommodate data exchange needs that require a greater capacity than that provided by the ASM.  
Figure 1 depicts a VDES functions and the frequency use case [19]. In Figure 1, the numbers 75, 76, 1024, etc., on the \(x\)-axis represent channel numbers in the VDES, and there is a 4.6 MHz gap between channels 1086 and 2024. The vessels can fully harness the potential of this duplex channel when communicating with coastal stations. VDE-SAT incorporates a satellite designed to facilitate bidirectional communication in high seas and remote areas where shore station coverage is lacking. The VDES concept was originally proposed to address emerging indications of overload of the VHF data link (VDL) of the AIS and simultaneously enable wider seamless data exchange for e-navigation, potentially supporting the modernization of the global maritime distress and safety system (GMDSS). The VDES can support the increasing communication requirements identified through the development of e-navigation, as documented in the e-navigation strategic implementation plan (SIP) IMO MSC.1/Circ.1595 [20]. E-navigation aims to enhance pier navigation and related services for safety and security at sea and to protect the marine environment. E-navigation seeks to enhance maritime safety by simplifying and harmonizing information. Additionally, e-navigation seeks to facilitate and increase the efficiency of maritime trade and transport by improving information exchange. The VDES concept recognizes the parallel work being carried out related to maritime services in the context of e-navigation, formerly known as the maritime service portfolio (MSP).
# Engine Speed Control System for Improving the Fuel Efficiency of Agricultural Tractors for
## 4 Throttle Control System
### Microcontroller Unit and Measuring System  
The AT90 CAN128 (Atmel Co., San Jose, California, USA) microprocessor was used to control the throttle actuator with the engine speed control algorithm. Because it is an 8-bit microprocessor, it has 128 K flash memory, 8 channels for analogue-to-digital conversion within 10-bit resolution, and 53 programmable I/O ports. In addition, it includes a Controller Area Network (CAN) controller for a CAN network.  
The strain gages were attached to the input shaft of the transmission to measure the load applied to the tractor engine. A full bridge circuit was constructed to improve the sensitivity of the torque effect. In order to collect the signal from the rotating shaft, the cable from the strain gages must be prevented from twisting. In this study, a hollow-type slip ring was used to prevent the cables from twisting. The engine speed was measured using a magnetic pick-up sensor attached to the slip ring of input shaft. Figure 15 shows the strain gages and the slip ring for the transmission input shaft.  
To determine the fuel consumption of the engine, inlet flow and drain flow of the fuel supply system were measured using two flow rate sensors [13]. A pulse-output flow meter was used for the measurements. Specifications of the flow meter are listed in Table 2.  
Figure 14: Schematic diagram of throttle control system.  
Figure 15: Strain gages and pick-up sensor installed on input shaft.  
DEWE-3010 (DEWETRON GmbH, Austria) was used as the data acquisition system for receiving signals from a sensor attached to the tractor. DEWE-3010 has 15 digital channels and 17 analogue channels with 16-bit resolution and 100 kHz maximum sampling rate. In this study, the throttle potentiometer and input shaft torque signal were collected through a 10 Hz low-pass filter within a 100 Hz sampling rate.
## 3 Results and Discussion
### Influence of Damping Loss Factor on the Sound Power Level of the P-FGM Constituents  
Figure 12 illustrates the SPL levels radiated by four distinct P-FGM plate constituents with varying damping loss factors for \(k=1\). It is found that with the P-FGM material Ti6 Al4V/ZrO\({}_{2}\), the decrease in sound power peak is more pronounced. The material Ti6 Al4V/ZrO\({}_{2}\) is less rigid than Al/Al\({}_{2}\)O\({}_{3}\) SuS304/Si\({}_{3}\)N\({}_{4}\), and Al/ZrO\({}_{2}\), which reduces sound power level. The sound power level peaks are also lowered when the damping loss factor increases. The variation is shown only for the power-law index, \(k=1\), for the sake of brevity.  
Figure 11: Influence on the sound power level peaks for different power-law indexes, \(k\), and modulus ratio \(E_{rat}\) (**a**) first peak, (**b**) second peak, (**c**) third peak, and (**d**) fourth peak.  
Radiation Efficiencies Variation with Power-Law Indexes, Varied P-FGM Constituents, and Effects of Damping Loss Factor  
The radiation efficiency is a metric that measures how well a structure radiates sound in an acoustic medium. Figure 13 shows how the variable, \(k\), affects the radiation efficiency of a P-FGM plate. With an increase in the power-law index, the first radiation efficiency peak rises. It is also worth noting that the radiation efficiency for a wide range of forcing frequencies (say, 500-1500 Hz) is lower for the power-law index, \(k=1\). This decrease in radiation efficiency suggests that choosing the right variable '\(k\)' and the frequency range of excitation can be well utilized as a design variable to control the sound radiation of vibrating P-FGM plates. In this case, the radiation efficiency peak is highest for the power-law index, \(k=10\), near 1900-2000 Hz.  
Figure 14 compares the radiation efficiencies of four P-FGM plates on a normal scale with different damping loss factors. It is clear that different damping loss factors have minute effects on the radiation efficiency, while P-FGM constituents affect radiation efficiency. It is also found that sound radiation efficiency increases with an increase in frequency until it approaches its maximum amplitude and then tries to approach towards unity. The lowest radiation efficiency is possessed by the Ti6 Al4V/ZrO2 FGM plate signifying it to be a better sound radiator as it has lower stiffness than other FGM plates for \(k=1\). Thus, the order of sound radiation efficiency by P-FGM plates is Ti6 Al4V/ZrO2 \(<\) Al/ZrO2 \(<\) SuS304/Si3N4 \(<\) Al/Al2O3.  
Figure 12: Sound power level for (**a**) Al/Al2O3, (**b**) Al/ZrO2, (**c**) SuS304/Si3N4, and (**d**) Ti6 Al4V/ZrO2 FGM plates under damping loss factor of 0.01, 0.02, and 0.04.
# Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks
## 2 System Model  
In this section, we first give a model of the LEO satellite constellation and its characteristics, based on which research scenario is depicted. After that, we describe the traditional Q learning algorithm and its application in routing problems. The definition of the routing problem is given in the last sub-section.
## 3 Experimental Verification
### Experimental Setup  
The experimental testing system, as shown in Figure 2, consisted of a dual channel arbitrary/function generator (Textronix AFG 3022C), a power amplifier (RITEC GA 2500), a mixed domain oscilloscope (Textronix MDO 3012), and a personal computer (PC) connected with the oscilloscope. The specimen was a 6061-T6 aluminum plate with a dimension of 450 mm \(\times\) 400 mm \(\times\) 2 mm. Four PZT piezoelectric transducers, 10 mm in diameter and 1 mm in thickness each, were permanently bonded to the surface of the aluminum plate by means of a thin cyanoacrylate adhesive layer. All the PZT transducers used for exciting or sensing Lamb wave signals had the same polarization direction.  
A 10-cycle Hanning-windowed sine wave, generated by the arbitrary generator, was applied as the excitation tone burst. The burst was amplified by means of the power amplifier before it was sent to one of the PZT transducers. The rest of the PZT transducers were used to receive Lamb wave signals. The signals were captured by the oscilloscope at a sample rate of 50 MHz with 256-time averaging, and then transmitted to the computer. The excitation-acquisition procedure was in turn implemented on each PZT transducer, such that six received signals were collected from these sensing paths. In order to reduce  
Figure 2: Schematic of the experimental testing system.  
the influence of stochastic error of the experimental measurements, the received signals were averaged with three measurements on each sensing path.  
A global cartesian coordinate was introduced, the bottom-left corner of a rectangle with an in-plane dimension of 200 mm \(\times\) 100 mm was at the origin, and the abscissa axis was parallel to the lower edge of the plate. Based on this reference coordinate system, the PZT transducers labeled as A, B, C, and D were bonded at coordinates (25, 10), (25, 90), (175, 90), (175, 10), respectively.  
Lamb wave signals in the aluminum plate were first received in an intact (crack-free) state. Then, a small crack at coordinates (100, 50) was produced by a milling cutter at the center of the aluminum plate. The physical length of the crack was about 1.025 mm, and the width was about 0.425 mm, as observed by means of an optical microscope (Figure 2). Afterward, Lamb wave signals in the aluminum plate with the small crack were measured in the current state.
# Failure Type Prediction Using Physical Indices and Data Features for Solenoid Valve +
## 2 Failure Mechanism of High-Speed Solenoid Valve and Modeling  
### Structure Analysis  
The high-speed solenoid valve can be divided into electromagnetic and mechanical components. The electromagnetic part is mainly composed of electromagnetic armature, coil, and movable pole block. The mechanical components usually include the valve body, valve stem, lifting spool, spring, and O-sealing ring. The structure of high-speed solenoid valve is shown as Figure 1.  
When the high-speed solenoid valve is not energized, the air inlet and outlet are disconnected by the valve element. After it is powered, the electromagnetic armature moves downward due to the magnetic force, driving the push rod to press down the valve core, and the air path is connected.
## 3 Results and Discussion
### MATLAB Simulation Results  
The MATLAB simulation results are illustrated in Figure 6 for the four cells in parallel (A) and series (B) configurations. It can be observed that in both graphs, the RSS presents a relatively large variation between a 0 and \(-50\) dBm interval. This variation, alongside the spectrum analyzer settings, may be accounted for by the discrete components and simulation solver tolerances, the floor function used in the FSK modulation threshold switching, the short simulation time and limited discrete filtering points used in the digital Gaussian filtering. The reception average values show a good reception above \(-40\) dBm in the cell parallel setup and above \(-30\) dBm for the series setup. This indicates a slightly better transmission channel when there is less of current and a higher voltage, as opposite to the case of a low voltage and high current. Furthermore, a better reception is suggested for both frequencies of 868 MHz and 2.4 GHz when the coupling capacitance is on the picofarads order for the high-current scenario, while for the high-voltage setup, the capacitance influence looks similar with an average variation of up to 5 dB for all three capacitive ranges. To summarize, the MATLAB simulation suggests a good high-frequency DC PLC, supporting the decision of empirical scenario testing.
## 3 Results and Discussion
### Flow around the NACA0021 Airfoil
#### 3.3.1 Local Model Training and Testing  
As in the SD7003 test case, we also use data optimized for the CL in the NACA0021 test case for testing the classical FIML paradigm. In this section, we present the results obtained by using the FFNN. The architecture and input variables are consistent with those used for the SD7003 test case. As shown previously, we have more angles of attack available for training and additional angles of attack to test the model outside the training set.  
Figure 23 illustrates the train and validation loss function through the epochs.  
By including more angles of attack in the training set, we aim to improve the robustness and generalizability of the model. This expanded dataset should allow the FFNN better to capture the complex aerodynamic behaviors across different flow conditions.  
For this test case, we did not apply any mapping function to the correction field. Avoiding negative values of \(\beta\) reduces the ability to capture the experimental results effectively during optimization. Unlike the SD7003 test case, the trained model did not perform well in prediction, despite achieving a high \(R^{2}\) value of 0.758. This high value led us to expect good results at least at the training points.  
As mentioned in Section 2.3.1, the model might find a relationship that does not correctly represent the physical behavior, potentially introducing instability during numerical transients. This issue was evident in this case, even when starting from a converged solution of the original SA model. Although reducing the CFL number can mitigate instability,  
Figure 21: Results of field inversion optimization for CD for the NACA0021 test case. (**a**) Velocity magnitude and (**b**) turbulent viscosity at \(\alpha=8^{\circ}\). (**c**) Velocity magnitude and (**d**) turbulent viscosity at \(\alpha=12^{\circ}\). (**e**) Velocity magnitude and (**f**) turbulent viscosity at \(\alpha=16^{\circ}\).  
Figure 22: Lift and drag coefficients predicted for NACA0021 test case after field inversion optimization for CD at various angles of attack.  
the solver could not find a new converged solution that accurately predicts the corrected turbulent viscosity field.  
Figure 24 presents the results obtained at different angles of attack using the FFNN. There is an improvement in CL at higher angles of attack after incipient stall (\(\alpha=12^{\circ}\)), but a significant discrepancy still exists with the experimental results. We did not observe any improvement for angles of attack before stall, and at \(\alpha=12^{\circ}\), the model performs worse than the original SA model. We tested two additional angles of attack, one before the incipient stall (\(\alpha=11^{\circ}\)) and one in the post-stall region (\(\alpha=17^{\circ}\)). For these angles, which were not part of the training set, we observed the same behavior.  
Figure 23: Training and validation loss for the FFNN model trained on NACA0021 data.  
Figure 24: Lift and drag coefficients predicted with FFNN-augmented Spalart–Allmaras model for the NACA0021 test case at various angles of attack.
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 5 Hydrogen Applications in End-Use Sectors
### Overview of Hydrogen Use  
According to the Global Hydrogen Review from IEA [8], hydrogen use in 2022 reached 95 Mt, representing an increase of 3% from the value estimated by the same organization for 2021. This increase is not a consequence of hydrogen policies but rather of global energy trends [8]. Refining and industry were the two major sectors that contributed to hydrogen use in 2022 (with 41 Mt and 53 Mt of hydrogen, respectively) [8]. Beyond its traditional use, the adoption of hydrogen in new applications, such as in heavy industry, transport, the production of hydrogen-based fuels, or the production and storage of electricity, is still minimal, accounting for less than 0.1% of the global demand [8]. This trend will have to change to meet the climate and energy goals set for 2050. According to the IEA, hydrogen use by 2030 should be more than 150 Mt, with nearly 40% coming from the new applicationspreviously explored and 70 Mt corresponding to low-carbon hydrogen. Figure 15 presents a schematic representation of hydrogen use by sector in 2022 and the projected hydrogen use for 2030, according to the Net Zero Emissions by 2050 scenario, based on the values presented in [8].
## 3 Results
### Repositioning Task
#### 3.2.2 Repositioning Error (Target Position 60 Degrees)  
All 12 participants were included in the analysis. The results revealed that the effects of the tape type (F [1,100] = 1.66, \(p\) = 0.21), measurement time (F [1,100] = 0.12, \(p\) = 0.72), and repetition (F [4,100] = 1.86, \(p\) = 0.12), as well as the interaction between the measurement time and group (F [1,100] = 0.60, \(p\) = 0.44), were statistically non-significant.  
Within the groups, a significant interaction effect of the measurement time and repetition was found (F [10,100] = 4.32, \(p\)! 0.01). However, the post hoc tests revealed no significant differences between measurement times at different repetitions. The estimation of the effect sizes revealed a small reduction from pre-lifting to post-lifting in the leukocyte group at the 60-degree target position (d = 0.30), while no such effect was found in the kinesio tape group (d = 0.17) (Table 2, Figure 4).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Confidence Interval (95\%)**} \\  & **Mean** &
\begin{tabular}{c} **Standard** \\ **Error** \\ \end{tabular} & **Lower Limit** & **Upper Limit** & **d** \\ \hline
**30-degree target** & & & & & 0.10 \\
**Leukotape** & & & & & 0.47 \\
**Pre-lifting** & 8.4 & 1.4 & 1.9 & 9.0 & \\
**Post-lifting** & 3.9 & 1.4 & 0.4 & 7.3 & \\ \hline
**60-degree target** & & & & & 0.30 \\
**Leukotape** & & & & & 0.17 \\
**Pre-lifting** & 3.4 & 1.0 & 1.0 & 5.9 & \\
**Post-lifting** & 3.8 & 0.8 & 1.1 & 5.9 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics for torso angle repositioning errors and estimation of effect sizes (pre vs. post; d).
## 6 Simulative Experiments and Analysis
### Fusion DWA Algorithm Simulation Experiment  
In order to assess the performance of the algorithm introduced in this study, experiments were carried out using a 20 m \(\times\) 20 m grid map. In the experiment, four unidentified static obstacles were placed on the map, and dynamic obstacles were incorporated to simulate a complex dynamic environment for further examination of the algorithm's obstacle avoidance capability and path optimization proficiency. Gray squares represent obstacles that are static and unknown, while yellow squares indicate dynamic obstacles. The blue triangle represents the robot's starting point, the blue circle represents the robot's endpoint, and the red * represents the local goal points. After conducting numerous tests, the robot's parameters are summarized in Table 2 below.  
The results of the simulation experiment are presented in Figure 10. The black grid denotes the static obstacles that were previously identified, the yellow grid indicates the dynamic obstacles, and the gray grid represents the unknown static obstacles, mimicking the circumstance where unknown obstacles emerge on a known map within a chemical laboratory. When t was the specified time, the trajectories of the robot and the dynamic obstacle converged at point \((6,7)\) and drew closer to each other. Figure 10a reveals that the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Map Size** & **Algorithm** & **Path Length (m)** & **Time (s)** \\ \hline \multirow{3}{*}{20 \(\times\) 20} & \multirow{3}{*}{Traditional A*} & 30.97 & 0.52 \\  & & 29.03 & 0.32 \\  & & 29.02 & 0.27 \\ \hline \multirow{3}{*}{30 \(\times\) 30} & \multirow{3}{*}{Algorithm from [25]} & 48.63 & 0.99 \\  & & 45.73 & 0.58 \\  & & 45.34 & 0.41 \\ \hline \multirow{3}{*}{50 \(\times\) 50} & \multirow{3}{*}{Algorithm in this paper} & 77.08 & 1.71 \\  & & 72.62 & 0.86 \\ \cline{1-1}  & & 72.05 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation results of three map sizes with different algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline Maximum line velocity & 1 m/s & Maximum angular velocity & 20 \({}^{\circ}\)/s \\ \hline Maximum linear acceleration & 0.2 m/s\({}^{2}\) & Maximum angular acceleration & 50 \({}^{\circ}\)/s \\ \hline Linear velocity resolution & 0.02 m/s & Angular velocity resolution & 1 \({}^{\circ}\)/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Robot motion parameters.  
Figure 9: Comparison of simulation results for 50 \(\times\) 50 raster maps. (**a**) Traditional A* algorithm. (**b**) Algorithm from [25]. (**c**) Algorithm in this paper.  
traditional fusion algorithm strayed from the initial route to evade the dynamic obstacle but was trapped in a local optimum and failed to reach the destination. Figure 10b demonstrates the algorithm proposed in this work. The final statistical results are shown in Table 3. Through the adoption of the stop-and-wait strategy, replanning strategy, and adaptive heading strategy, it reached the destination safely.
# UAV Cluster Behavior Modeling Based on Spatial-Temporal Hybrid Petri Net
## 2 The Modeling Method of UAV Cluster
### The Modeling Method of Heterogeneous UAV Cluster System Behavior  
A heterogeneous UAV cluster system is a hybrid system with discrete behavior and continuous behavior. There are two kinds of modeling methods for hybrid system. The non-formal method is incomplete because it cannot detect all possible states of the system exhaustively. Formal modeling methods include hybrid automata, hybrid behavior systems, hybrid communication sequence processes, and hybrid Petri nets. Compared with other methods, the hybrid Petri net has the advantages of finite state description, description simplification, and widening application range. Therefore, spatial-temporal factors are introduced into the spatial-temporal hybrid Petri net as a new model to describe the behavior of a heterogeneous UAV cluster system.  
**Definition 5**.: _Formal model of heterogeneous UAV cluster system behavior:_  
\[STHPN=\langle P,T,F,W,M_{0},H,E,V,I,A\rangle \tag{8}\]  
_where \(P\) is the finite set of places represented by circular nodes; \(T\) is the finite set of transitions represented by square nodes; \(F\) is the set of flow relations represented by directed arcs; \(W\) is the weight function; \(M_{0}\) is the initial identification vector; \(H\) is a hybrid function; \(E\) is the set of conditions; \(V\) is the excitation velocity of continuous transition; \(I\) represents the time function; \(A\) is the spatial position._  
**Definition 6**.: _There is a one-to-one correspondence between events and discrete transitions. The excitation of discrete transitions indicates the occurrence of corresponding events._  
There are both discrete state nodes and continuous state nodes in a spatial-temporal hybrid Petri net, and their interactions are represented by directed arcs. The excitation of transitions indicates the occurrence of corresponding events.