esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Section
### Typical Procedure for the Synthesis of the Perfluorobis(alkanesulfonyl) Monomer (Scheme 4)
#### 2.3.6 Perfluoro[6-Fluorosulfonyl-5-(Fluorosulfonylmethyl)-4-Oxahexanoyl] Fluoride (**18**)  
In a 3 L autoclave made of stainless steel, equipped with a condenser maintained at 25 \({}^{\circ}\)C, an NaF pellet packed layer and a condenser maintained at \(-\)10 \({}^{\circ}\)C in series at its gas outlet, as well as a liquid returning line in order to return the condensed liquid from the condenser maintained at \(-\)10 \({}^{\circ}\)C, **6** (4,520 g) was stirred at 25 \({}^{\circ}\)C. Nitrogen was blown into the system for 1 hr, and then 20% F\({}_{2}\)/N\({}_{2}\) for 0.5 h at a flow rate of 93.4 L/h. While blowing 20% F\({}_{2}\)/N\({}_{2}\) at the same rate, the internal pressure of the reactor was raised to 0.1 MPa, and a solution of **16** (404 g, 532 mmol) in R113 (1,640 mL) was injected over a period of 31.5 h. After the injection, 20% F\({}_{2}\)/N\({}_{2}\) was further blown at the same rate for 8 h. The reactor was cooled to room temperature and purged with nitrogen. The removal of the solvent afforded the crude mixture (643 g), including the perfluorinated ester **17**; \({}^{19}\)F NMR (282.7 MHz, CDCl\({}_{3}\)) \(\delta\) (ppm): 46.59 (2F), \(-\)78.62 \(\sim-\)80.46 (4F), \(-\)82.15 (8F), \(-\)84.23 \(\sim-\)87.36 (5F), \(-\)104.54 (4F), \(-\)128.40 (2F), \(-\)130.16 (2F), \(-\)132.26 (1F), \(-\)133.78 (1F), \(-\)145.96 (1F).  
A flask was charged 623 g of the mixture above and KF (3.16 g, 54.3 mmol). The mixture was heated at 90 \({}^{\circ}\)C for 1.5 h. The distillation _in vacuo_ provided **18** (99.5 g, 216 mmol, 36% from **16**); bp 77 \(\sim\) 78 \({}^{\circ}\)C/40 mmHg ; \({}^{19}\)F NMR (282.7 MHz, CDCl\({}_{3}\)) \(\delta\) (ppm): 46.65 (s, 2F), 25.29 (tt, \(J\) = 4.6, 9.2 Hz, 1F), \(-\)81.37 (d, \(J\) =13.7 Hz, 2F), \(-\)104.60 (m, 4F), \(-\)120.53 (m, 2F), \(-\)133.72 (t, \(J\) =19.8 Hz, 1F).  
3.7 Perfluoro[9-Fluorosulfonyl-8-(Fluorosulfonylmethyl)-2-Methyl-3,7-Dioxanonaosyl] Fluoride (**19**)  
**18** (82.7 g, 180 mmol), CsF (2.33 g, 15.3 mmol) and monoglyme (18.0 g) were charged into a 200 mL vacuumed autoclave. The mixture was stirred and hexafluoropropene oxide (HFPO, 33.0 g, 199 mmol) was introduced at 5 \({}^{\circ}\)C. Internal pressure was raised to 1.4 atm. The mixture was further stirred for 1 h. The distillation _in vacuo_ provided **19** (38.9 g, 62.0 mmol) and recovered **18** (35.9 g, 78.0 mmol) (conversion = 57%, yield of **19**: 60%); bp 79-80 \({}^{\circ}\)C/5 mmHg ; \({}^{19}\)F NMR (282.7 MHz, CDCl\({}_{3}\)) \(\delta\) (ppm): 46.65 (s, 2F), 26.66 (q, \(J\) = 4.6 Hz, 1F), \(-\)78.95 (ddt, \(J\) = 18.3, 149.5, 7.6 Hz, 1F), \(-\)79.32 (m, 2F), \(-\)82.08 (s, 3F), \(-\)86.35 (dm, \(J\) =149.5 Hz, 1F), \(-\)104.46 (m, 4F), \(-\)128.20 (s, 2F), \(-\)130.87 (d, \(J\) = 16.8 Hz, 1F), \(-\)133.73 (m, 1F).
## 4 Numerical Examples  
In this section, several numerical examples are given to verify the performance of the designed waveform. We demonstrate the performance of the designed waveforms through three aspects, namely waveform parameter impacts, detection performance, and estimation performance. In the following experiments, we consider a 5G BS mounted on a gantry (with height denoted as \(h\)) to detect and estimate the parameters of a vehicle that is underneath. The simulation of traffic scenarios is shown in Figure 6, where \(v\) denotes the forward velocity of the vehicle, \(v_{t}\) denotes the radial velocity of the vehicle, and \(r_{t}\) denotes the radial range of the vehicle. Therefore, the clutter is modeled as a Gaussian clutter model [42], and the thermal noise is complex Gaussian noise with zero mean. The mounting height of 5G BS is \(h=7\) m, the wind velocity in the clutter model is \(v_{w}=2\) m/s, the 5G system parameter is \(\mu=3\), the subcarrier interval is \(\Delta f=120\) KHz, the bandwidth is \(B=400\) MHz, and the carrier frequency is \(f_{0}=26\) GHz. Therefore, \(T=8.33\)\(\upmu\)s, \(T_{cp}=0.58\)\(\upmu\)s, and the maximum range of detection is \(R_{max}=T_{cp}c/2=87.5\) m.  
5G base station  
Figure 6: Simulation of traffic scenarios.
## 3 Experimental Results
### Permanent Damage Characteristics
#### 3.1.1 105 Internal Angle V-Structures  
Figure 7 shows the deformed 3D profile plots for blast tested V-structures (105\({}^{\circ}\) internal angle, 32 mm tip radius) for different charge masses. As expected, higher charge masses  
Figure 6: Photographs showing the top view a blast-tested 105\({}^{\circ}\) V-structure, 32 mm tip radius, with localised permanent deformation along the central ridge: (**a**) tested at 10 g, (**b**) tested at 40 g.  
Figure 7: 3D profiles of blast tested V-structures (105\({}^{\circ}\) internal angle, 32 mm tip radius) tested at (**a**) 10 g, (**b**) 15 g, (**c**) 20 g and (**d**) 40 g.  
caused greater amounts of damage. There was an increase in the total deformed area and larger permanent displacements. The permanently deformed profiles along the ridge and the cross-cut planes in Figure 8 for the same tests show this more clearly. Along the ridge, the deformed region at 10 g covered one third of the width, whereas at 40 g, the deformed region was approximately two thirds of the width. However, in the crosscut (transverse) direction, the deformed area was more localized. The magnitude of the displacement increased with charge mass in both directions. At the highest charge masses, the central deformation caused some rotation of the inclined sheets at the outer bend radii (near the clamped edges). Complete inversion of the ridge is evident in the cross-cut profiles in Figure 8b.  
For the 105\({}^{\circ}\) V-structures with the larger tip radius of 62 mm, the deformation is less localized in both the crosscut and ridge directions, as shown in Figure 9. These specimens exhibited larger displacements for the given charge mass when compared to their 32 mm tip radius counterparts. As the ridge area has lower plastic stiffness due to the increased bend radius, there is no visible angular rotation at the boundary edges in the cross-cut profiles, despite the larger permanent mid-point displacement.  
Figure 8: Permanently deformed profiles of blast tested V-structures (105\({}^{\circ}\) internal angle, 32 mm tip radius) of the (**a**) ridge plane, (**b**) cross-cut plane.
## 6 Conclusions and Future Work  
In this paper, we proposed an end-to-end deep reinforcement learning framework incorporating search-based approaches for air-ground unmanned system navigation named SO-DRL. Compared to traditional methods, instead of using a map building and planning process, SO-DRL used reinforcement learning to solve the path planning obstacle avoidance problem end-to-end in an unknown environment. In addition to reinforcement learning, SO-DRL adopted the advantage of search-based algorithms. Compared to general reinforcement learning, it had the benefit of quick convergence and superior early realization for target action. Specifically, SO-DRL used the search-based approach and employed prioritized experience replaying, with experiments using a two-stage training process. Experimentally, SO-DRL could improve the training speed and made the training effect reach the expectation well. SO-DRL had an obvious effect improvement over the original  
Figure 12: Realistic air–ground unmanned systems. (**a**) UGV. (**b**) UAV.  
Figure 13: Real experimental scenes. (**a**) Experimental scenario of UGV. (**b**) Experimental scenario of UAV.  
DDPG and had a different performance effect under different search-based algorithm optimization, which could effectively solve the path planning problem. SO-DRL worked on real UGV/UAV in the air-ground unmanned system without any parameter adjustment, which has practical implications for real systems.  
This paper is an exploration of the intelligence of unmanned systems, but the work in this paper falls short. In our next study, we will use more different algorithms to verify the selection of the algorithm (including more search-based algorithms and reinforcement learning algorithms) and test the adaptation of SO-DRL in different scenarios. We will improve SO-DRL and make it a more sophisticated algorithm. In future work, we intend to incorporate 3D radar, depth maps, and other 3D sensing data to enable path planning and obstacle avoidance in the 3D environment for the UAV. Additionally, we will expand towards increasing the cluster work of UGVs and UAVs and the synergy of both.  
Conceptualization, X.C., Y.Q. and H.C.; Methodology, X.C.; Software, X.C., Y.Y., Y.C. and L.L.; Validation, X.C.; Investigation, X.C. and Y.Y.; Resources, X.C.; Data curation, X.C., Y.Q. and Y.C.; Writing--original draft, X.C. and Y.Q.; Writing--review and editing, Y.Q., Y.Y. and H.C.; Supervision, L.L. and H.C. All authors have read and agreed to the published version of the manuscript. This research was funded by Research on Path Planning Algorithm of Swarm Unmanned System Based on Deep Reinforcement Learning of China University Industry, Education and Research Innovation Fund No. 2021 ZYA11010.  
Not applicable  
The authors declare no conflict of interest.
## 4 Optimization Problem Modeling
### Dynamic Task Offloading and Computation Scheduling Problem Modeling and Solving
#### 5.2.1 Markov Decision Process Modeling  
When we closely examine Equation (32), it becomes apparent that we are dealing with a long-term optimization challenge that incorporates randomness through its variables. To effectively address this problem, it is structured as a Markov decision process, which is mathematically characterized by the tuple \((S,A,r_{t})\). Here, \(S\) signifies the array of system states, \(A=\{a_{t}\}\) signifies the suite of potential actions, and \(r_{t}\) stands for the immediate reward function associated with the process [51]. The constituents of the state space, action space, and reward function within this context are further detailed as follows. Let \(S=\{s_{t}\}\) denote the array of system states at a given time \(t\), with each state \(s_{t}\) conceptualized as follows:  
\[s_{t}=\{Q_{t},Q^{i}_{t},L^{p}_{t},x^{i}_{t}\} \tag{33}\]  
In this expression, \(Q_{t}=\{Q_{1,t},Q_{2,t},\ldots,Q_{k,t},\ldots,Q_{k,t}\}\) captures the buffer queue conditions for each relay satellite at time \(t\); \(Q^{i}_{t}=\{Q^{i}_{1,t^{\prime}},Q^{i}_{2,t},\ldots,Q^{i}_{m,t^{\prime}},\ldots,Q^{i}_{M,t^{\prime}}\}\) reflects the buffer queue status for each IoT device during the same period; \(L^{p}_{t}=\{L^{p}_{m,k,t^{\prime}},1\leq m\leq M,1\leq k\leq K\}\) indicates the rain attenuation coefficient relevant to the communication link connecting each IoT device and each relay satellite at time \(t\); and \(x^{i}_{t}=\{x^{i}_{m,k,t^{\prime}},1\leq m\leq M,1\leq k\leq K\}\) depicts the status of logical link existence between each IoT device and each relay satellite at this time instance. At time \(t\), IoT devices have the capability to transfer computation tasks stored within their buffer queues to relay satellites. Accordingly, let \(A=\{a_{t}\}\) denote the comprehensive set of actions available at time \(t\), where each action \(a_{t}\) encapsulates the strategies for selecting task offloading modes by IoT devices at that specific moment and can be expressed as follows:  
\[a_{t}=\{z_{m,t^{\prime},k,t},\lambda^{g}_{m,t^{\prime},k,t^{\prime}},y_{m,t^ {\prime},k,t},1\leq m\leq M,1\leq t^{\prime}<t,1\leq k\leq K\} \tag{34}\]  
Under the scenario wherein IoT devices occupy state \(s_{t}\) and undertake action \(a_{t}\), the resulting immediate reward \(r_{t}\) is meticulously designed as follows:  
\[r_{t}=-\mu_{1}\left(\sum_{k=1}^{K}Q_{k,t}+\sum_{m=1}^{M}Q^{i}_{m,t}\right)-\mu _{2}E_{t} \tag{35}\]
## 4 Vibration and Acoustic Sensors in Non-Destructive Testing of Port Cranes
### Integration with IoT for Remote Monitoring of Port Cranes  
The role of the IoT in remote monitoring has revolutionized monitoring and management of various systems, including port cranes. By connecting devices and sensors to the Internet, the IoT enables seamless collection, transmission, and analysis of data in real time. In the context of port cranes, IoT provides a centralized platform for data storage and processing, allowing engineers and technicians to remotely access and analyze sensor data. This capability is particularly valuable, given the remote and inaccessible locations of port cranes. IoT facilitates the implementation of automated monitoring systems that can trigger alerts when abnormal conditions are detected, significantly enhancing the efficiency and effectiveness of maintenance operations.
# A Novel CDMA-Based Secret Message Multiplexing Scheme by Image Data Hiding +
## 4 Experimental Results and Discussion  
In this section, several experiments are carried out to demonstrate the performance of the proposed algorithm. Performance evaluation based on the visual quality of marked images and robustness against noise and cropping attacks are given. Four commonly used grayscale images sized \(512\times 512\) are chosen as the test images: Lena, Baboon, Airplane, and Peppers, respectively. These images are shown in Figure 4. All experiments are simulated with MATLAB software on PC Windows operating system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(N\) = 2 & \(N\) = 4 & \(N\) = 8 & \(N\) = 16 & \(N\) = 32 \\ \hline PSNR (dB) & 45.12 & 39.89 & 34.33 & 28.56 & 22.67 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The expected PSNR value (dB) versus number of users \(N\).
## 3 System Implementation and Test
### Format of Communication Protocol and Data Analysis
After receiving the data, the main system performs formalization of the data with the data base software as Figure 9.  
Normally in livestock industrial field, the farmer needs an estrus [31] time of targets. The activity graph shows the most important information about the state of the target, so it could be possible to estimate insemination time [32]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Packet Buffer**} & \multirow{2}{*}{**Index No.**} & \multirow{2}{*}{**Meaning**} & \multicolumn{2}{c}{**Value**} \\ \cline{3-4}  & & & **Min.** & **Max.** \\ \hline \multirow{8}{*}{RF TX Buffer} & 0 & 0 xFF (Start-Sync.-First check bit) & 252 & 252 \\  & 1 & 0 xFF (Start-Sync.-Mid. check bit) & 1 & 250 \\  & 2 & 0 xFF (Start-Sync.-Final check bit) & 252 & 252 \\  & 3 & Sensor ID & 1 & 250 \\  & 4 & Flag Bits (DATA) & 0 & 127 \\  & 5 & Number of activities (present) & 0 & 250 \\  & 6 & Number of activities (before 10 Min.) & 0 & 250 \\  & 7 & Number of activities (before 20 Min.) & 0 & 250 \\  & 8 & Packet Sequence Number & 0 & 250 \\  & 9 & Check Sum & 0 & 250 \\  & 10 & Time data bit—MSB (counting the time) & & \\  & 11 & Time data bit—LSB (counting the time) & & \\  & 12 & 0 xFF (End-Sync. check bit) & & \\  & 13 & 0 xFF (End-Sync. check bit) & & It can be extension. \\  & 14 & 0 xFF (End-Sync. check bit) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Definition of the packet units. Figure 9: Analyzed information from the data in the Main system. Figure 10 is an activity graph about one target. The red line in the graph represents the average activity every seven days and the blue line is activity number within one day. Farmers can estimate the insemination time about the target with graph analysis. After formalizing the data, it is processed into information and analyzed by the software that can provide a customized service by the needs from the famers, researchers, government officers, supporters and etc. [33; 34]. On a specific basis, the continuous collection of data regarding the amount of activity of the animals of the object has a role in distinguishing the animal from the object, such as fertilization, estrus, disease, fertility and other biological changes (water medicine). Therefore, one months of activity (band curve) and average activity (fast curve) Change has the most important meaning. For more specific judgment, one day of dramatic changes in the amount of activity, the change of activity (the Bohr icon) and the change of the average activity (red chart) are used as important information to determine the biological state of the animal, if the results of the analysis are used to isolate livestock by isolation, germination, disease and so on. Measures of protection and so on to enlargement the effect of animal husbandry.
## 4 Residual Properties Analysis
### Peak Deformation  
The law of peak deformation of specimens is shown in Figure 15. The peak deformation is the axial deformation corresponding to the peak load for test blocks and columns or the mid span deflection corresponding to the peak load for beams. As seen from Figure 15, peak deformation is insignificant affected by high temperature when \(T\) is less than or equal to 400 degrees centigrade. The peak deformation of the test block, column and shear beam increases rapidly with temperature increases when \(T\) is higher than 400 degrees centigrade. The peak deformation of the test block is about 2.26 times that of the room temperature. But the peak deformation of bending beams is less affected by temperature. Its peak deformation is approximately 1.24 times that of the room temperature when \(T\) is equal to 600 degrees centigrade. The increase of peak deformation is related to the increase of porosity inside concrete after exposure to elevated temperature.  
According to Figure 15f, the effect of replacement percentage on peak deformation of different components is different. The peak deformation linearly increases as the replacement percentage increases for a test block. And when the replacement percentage is 100%, the peak deformation is about 1.22 times that of replacement percentage at 0%. The peak deformation of the bending beams and shear beams increases first and then decreases. And those maximum increment is 39% and 32%, respectively. However, the replacement percentage has an insignificant effect on the peak deformation of axial compression column. And its peak deformation only decreases by 4% when the replacement percentage is 70%.
# Satellite Edge Computing for the Internet of Things in Aerospace
## 4 System Model  
In this section, we introduce the system model of space edge computing, which includes the computation model, the communication model, and the satellite execution model.
# Analytical Model of Gear Thermal Stiffness Based on the Potential Energy Method
## 3 Gear Thermal Stiffness Based on Potential Energy Method
### Effect of Temperature on Contact Stiffness  
#### 3.2.1 Contact Model Considering Temperature Influence  
The tooth surface contact of spur gears is usually approximated as the contact problem of two elastic cylinders with parallel axes, as shown in Figure 3. The straight line N\({}_{1}\)N\({}_{2}\) is the line of action, the contact point K is the origin of the coordinate, and the line of action is the \(y\)-axis used to construct the coordinate system. The pressure distribution, \(p(x)\), and the half contact width, \(s\), determined by the Hertz contact model, are given by Equations (9) and (10) [30], respectively.  
\[p(x)=\frac{2F_{\rm n}}{\pi s}\sqrt{1-\frac{x^{2}}{s}} \tag{9}\]  
\[s=\sqrt{\frac{4F_{\rm n}R}{\pi E^{*}}} \tag{10}\]  
where \(F_{\rm n}\) is the normal load per unit length; \(R\) is the equivalent radius, and \(1/R=1/\rho_{k1}+1/\rho_{k2}\), \(\rho_{k1}\) and \(\rho_{k2}\) are the radius curvatures of the pinion and gear at contact point K, respectively, as shown in Figure 3, \(\rho_{k1}=\overline{\rm N_{1}K}\), \(\rho_{k2}=\overline{\rm N_{2}K}\); \(E^{*}\) is the equivalent [PERSON]'s modulus, and \(1/E^{*}=(1-v_{1}^{2})/E_{1}+(1-v_{2}^{2})/E_{2}\), \(E_{1}\), \(E_{2}\), \(v_{1}\) and \(v_{2}\) are [PERSON]'s modulus and [PERSON]'s ratio of the pinion and gear materials, respectively.  
Based on plane strain theory, the relationship between stress and strain after considering the effect of temperature is determined by Equation (11).  
\[\varepsilon_{y}=\frac{1-v^{2}}{E}\bigg{[}\sigma_{y}-\frac{v}{1-v}\sigma_{x} \bigg{]}+(1+v)\alpha_{T}\Delta T \tag{11}\]  
where \(\sigma_{x}\) and \(\sigma_{y}\) are the stresses on the \(x\)-axis and \(y\)-axis, respectively, and they are functions of the variable \(y\)[30]; \(\alpha_{T}\) is the linear expansion coefficient of the gear material; and \(\Delta T\) is the temperature rise of the gear under the influence of friction heat. The compression deformation of the two cylinders on the \(y\)-axis can be obtained by integrating \(\varepsilon_{y}\) on the interval (0, \(R\)) to determine the gear thermal contact stiffness \(K^{\rm hT}\). The key is to determine the distribution function of \(\sigma_{x}\) and \(\sigma_{y}\).  
Figure 3: Tooth surface contact model.
## 6 Improvement of Atom Trapping Lifetime in Optical Tweezers  
In the experiment, the power of the 852-nm cooling laser is 105 \(\upmu\)w, the power of the 894-nm repumping laser is 50 \(\upmu\)W, the quadrupole magnetic field intensity is 254 Gauss/cm, and the power of the 1064-nm optical tweezers is 50 mW. Firstly, we capture a single-atom in the MOT and adjust the spatial overlap of the MOT and the optical tweezer. We change the experimental sequence to control the time overlap of the optical tweezer and the MOT for 25 ms, which maximizes the atomic transfer efficiency between the MOT and the optical tweezer. Next, we measure the single-atom transfer efficiency under different time duration of the optical tweezer. As the duration of optical tweezer  
Figure 8: Intensity fluctuation of laser in the time domain in the free-running case and the feedback-on case. The light intensity is suppressed from \(\pm\)3.360% to \(\pm\)0.064%: 1.6 W for a total power of the 1064-nm laser, and the sampling power is 4.1 mW (**b**), zooming of the feedback-on case in (**a**).  
Figure 9: Comparison of the intensity fluctuation of laser in the frequency domain when feedback loop is on and off. The feedback bandwidth in the frequency domain is 33 kHz. Several noise peaks are occurring from the external environment or the YDFA’s cooling system.  
increases, atom transfer efficiency goes down exponentially. Finally, the single-atom trapping lifetime in the 1064-nm optical tweezer can be obtained by fitting measured data.  
As shown in Figure 10, the trapping lifetime of single-atom in the 1064-nm optical tweezer without the feedback loop on is only about \(4.04\pm 0.92\) s, while the trapping lifetime is \(6.34\pm 0.41\) s after the feedback loop is on. We can see that the trapping lifetime of single-atom in the optical tweezers is extended after the suppression of the intensity fluctuation of the optical tweezer.  
The typical trapping lifetime here we achieved seems shorter than that of our previous work [19] because the background Cs density in the ultra-high vacuum (UHV) glass cell has increased a lot in this experiment. Now the trapping lifetime of single-Cs atom in the optical tweezer is mainly limited by the atomic collisions under the typical pressure of approximately \(6\times 10^{-7}\) Pa.
# Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression
## 1 Introduction
The sliding window approach provides better estimation accuracy and robustness against the possible errors of the degradation model with respect to the ones using Bayesian approaches. This paper is organized as follows. Section 3 outlines the overall process of the RUL estimation of bearings as a predictive maintenance mechanism. Section 4 presents the feature extraction and postprocessing to build the health indicator. Section 5 describes the parameter estimation methods of the adaptive sliding window method. Sections 6 and 7 describe the experimental setup and performance evaluation, respectively. Finally, we summarize the proposed method and future works in Section 8.