esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The First-Order Resonant Periodic Families  
### The Unperturbed Two-Body Model: \(\mu=0\)  
In the unperturbed two-body model, two different kinds of symmetric periodic orbits exist in the synodic frame [4; 13; 29; 30]. The periodic orbits related to the circular orbits of  
Figure 1: The planar Lyapunov families in the Sun–Earth system: (**a**) the [PERSON] family around \(L_{1}\); (**b**) the [PERSON] family around \(L_{2}\).  
Figure 2: Example resonant periodic orbits in the Sun–Earth system (\(\mu_{SE}\)): (**a**) 3:2 resonance; (**b**) 4:3 resonance; (**c**) 5:4 resonance; (**d**) 2:3 resonance; (**e**) 3:4 resonance; (**f**) 4:5 resonance.  
the small body are called _periodic orbits of the first kind_, which belong to the circular family. The periodic orbits related to the elliptic orbits of the small body are called _periodic orbits of the second kind_, which belong to the resonant periodic family. In Figure 3, the relationships between the circular family and the interior/exterior first-order resonant periodic families are displayed in the \(T-C\) plane; these are called _characteristic curves_ and are used in previous literature [31; 32; 33]. For each first-order resonant periodic family, the characteristic curve in Figure 3 represents two branches: one corresponding to the perigee on the positive \(x\)-axis and the other to the apogee on the positive \(x\)-axis; i.e., they differ only in phase.
## 3 Results and Discussion  
### Part I: Comparison of PosEst and dGNSS Data  
Figure 5a shows the average trajectory, vertical and horizontal components of velocity and acceleration, together with the SPM analysis. The average paired difference between each of the 16 jumps measured with the PosEst and dGNSS, together with the SPM analysis are presented in Figure 5b. The SPM t-test plots for all variables can be found in Appendix A. Mean absolute error was calculated for phases of 5 m for a better understanding of the magnitude of the difference, presented in Table 2.  
Figure 5: Comparison of data of the PosEst system and the dGNSS. (**a**) show the average trajectory (**a1**), vertical and horizontal velocity (**a2**) and acceleration (**a3**). The shaded error bands indicate the standard deviation with \(n=16\). PosEst data are indicated in red and dGNSS in blue. (**b**) shows the average paired difference of the same variables between the systems. The shaded error bands indicate the standard deviation with \(n=16\) and the dashed line zero difference. The paired differences in the x-direction are indicated in red and in the y-direction in blue. The SPM statistical information is represented by the horizontal bars for the paired difference. The black bar represents the statistical difference between the paired difference and zero in (**b1**). In (**b2**,**b3**), the light gray color represents statistical difference in the x-direction, dark gray in the y-direction and black in both directions, where ** denotes \(p<0.001\).  
A significant difference between the methods was observed for all variables from \(-5\)-\(0\,\mathrm{m}\), with an MAE of around twice the accuracy of the dGNSS (\(\pm 0.05\,\mathrm{m}\)). Regions of significant trajectory difference from zero between the two methods were also observed after \(0\,\mathrm{m}\); however, MAE was smaller than \(0.05\,\mathrm{m}\), i.e., within the expected accuracy for the dGNSS [39]. As for the trajectory, the MAEs of both the velocity and acceleration components were large for the first phase and seen to decrease. Regions where the paired difference deviated from zero in the vertical direction were observed in the flight phase, with an MAE of \(\pm 0.15\,\mathrm{m}\,\mathrm{s}^{-1}\) and \(\pm 0.97\,\mathrm{m}\,\mathrm{s}^{-2}\) as a maximum for the velocity and acceleration respectively. The largest difference was observed in the horizontal direction, with an MAE of \(\pm 0.41\,\mathrm{m}\,\mathrm{s}^{-1}\) and \(\pm 1.56\,\mathrm{m}\,\mathrm{s}^{-2}\) as a maximum for the region \(5\)-\(20\,\mathrm{m}\). Altogether, the uncertainty in the measurements was high the first \(5\,\mathrm{m}\) and deviated from the edge of the in-run. All measurements showed good agreement from \(5\,\mathrm{m}\) after the in-run. The consistency and agreement \(5\,\mathrm{m}\) indicates that these methods are reliable for use in such analyses.
## 4 Case Study
### Integration of Grid Elements into the Same Data Network  
After identifying, characterizing, and establishing the form of communication of all the elements of the microgrid, the next step is the incorporation of each of the elements into the same data network.  
In these cases, we have to connect the following elements to the data network: photovoltaic, wind turbine, and battery inverters, and a grid analyzer (PQube) to measure the loads.  
Photovoltaic inverter and grid analyzer (PQube) have a TCP/IP Ethernet communication card. Battery control system allows local communication (RS232), so, as seen previously, we have to install a converter RS232 to TCP/IP with Modbus. Finally, the wind turbine inverter is quite old and does not allow any communication, so we have installed a data acquisition system (National Instrument Compact FieldPoint, which allows TCP/IP communication with Modbus) to measure different parameters and communicate with Home Assistant control system.
# A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
## 4 Conclusions  
The traditional health indicator-based methods need the long-term tracking of the signals, which are laborious and time-consuming. A novel CNN based on the time window was proposed to tackle this issue. Additionally, the kernel module was constructed, which can let the network learn to select the kernels automatically. The effectiveness of the proposed network was demonstrated using the C-MAPSS dataset. The main contributions of this paper are as follows:  
(1) The raw industrial sensor signals were standardized and transmitted to the network for RUL prediction without long-term tracking and constructing HIs that relied on signal processing expertise.  
(2) A novel kernel module composed of one-dimensional convolutional kernels of different sizes was proposed for feature extraction in time series.  
(3) The 1\(\times\)1 convolutional kernel was introduced to handle the multiple channels in the kernel module.  
However, some limitations still existed in the presented methods. For example, the networks for each sub-dataset were trained and evaluated separately. Besides, the C-MAPSS dataset was created by simulation models in controlled environments. The real world has many uncontrollable events, which should be considered further.  
Methodology, H.Y., Z.S., G.J., F.Z. and X.M.; validation: H.Y., G.J. and Z.S.; writing--original draft preparation, H.Y., Z.S., G.J., F.Z. and X.M.; funding acquisition, F.Z.  
This research was funded by the National Key Research and Development Program of China, grant number 2018 AAA0101800.  
The authors declare no conflict of interest.
## 3 System Model and Problem Description
### Offloading Model  
Due to the relatively small amount of data offloading from the UAV to user devices, the delay can be ignored. We consider offloading transmission delay in the UAV-assisted MEC system. We adopt the TDMA protocol for the data offloading of the active user devices. Each user device can transmit data in its own time slot, and thus the interference among different devices can be ignored. The UAV flight service period is \(T_{f}\) divided into \(N\) equally long time slots, with each slot being longer \(\delta_{t}\). For simplicity, suppose \(N=k\), and the \(t\)-th time slot is allocated to the \(u\)-th user device for data offloading.  
The amount of data that the user device \(u\) needs to process in time slot \(t\) is defined as \(l_{u}(t)\). Due to local computing limitations, user devices need to offload some tasks to the UAV for processing. The task offloading rate is denoted as \(\eta_{u}(t)\) and \(\eta_{u}(t)\in(0,1)\). The amount of task data requested for processing by user device \(u\) in time slot \(t\) is  
\[I_{u}(t)=l_{u}(t)\eta_{u}(t) \tag{13}\]  
The offloading bandwidths obtained by the user device \(u\) at time slot \(t\) is denoted as \(b_{u}(t)\). Let \(B_{u}(t)\) denote the total offloading bandwidth capacity in the system. The allocation of bandwidth needs to meet the constraints denoted by (14):  
\[\sum_{t=1}^{N}b_{u}(t)\leq B_{u}(t) \tag{14}\]
## 4 PFAC Calibration Results
### Jason-3 Cal/Val Results
#### 4.3.2 Jason-3 Cal/Val Results (No. 109)  
The ascending Pass No. 3 of Jason-3 has been calibrated over the sea surface using the Gavdos Cal/Val site. The latest calibration results for Jason-3, along the sea surface of Pass No. 109, with GDR-D data and for about 150 cycles, are given in Figure 24.  
Figure 23: The latest calibration results for Jason-3 along its ascending pass No. 18 with transponder (upper) and sea-surface (lower) calibrations.  
To recap, Sections 4.1 and 4.2 presented the sea-surface and transponder calibration results per altimeter mission and with respect to the Permanent Facility for Altimetry Calibration in west Crete, Greece. Table 6 summarizes the contribution of each PFAC Cal/Val site to the determination of the altimeter bias for Sentinel-3A, Sentinel-3B and Jason-3. Interpretation of these Cal/Val results per satellite follows at the end of Section 4.  
The Gavdos sea-surface Cal/Val site appears to produce consistent results per satellite altimeter. Two different passes in ascending and descending mode of Sentinel-3A and of Jason-3 are supported by the Gavdos Cal/Val facility. It seems that there is no significant difference for ascending and descending passes of Sentinel-3A and Jason-3. Moreover, the difference in bias results between transponder and sea-surface calibrations using the same orbit and with calibration taken place a few seconds apart, agree within \(\pm\)1 cm for Sentinel-3A (Pass No. 14, ascending) and for Jason-3 (Pass No. 18, descending). This setting clearly advocates the FRM requirements for having diverse, redundant and independent methodologies to assess performance of satellite altimeters.
# Aging Performance and an Improved Evaluation Method for PE80 and PE100 Pipelines for Urban Gas
## 2 Accelerated Aging Experiment
### Aging Experimental Process  
The improved aging experimental platform is shown in Figure 2. In order to prevent the oxidation of the inner wall during the accelerating aging experiment, nitrogen gas was used for the inner pressure control. The experimental pressure can be automatically controlled to be constant through the pressure regulator and solenoid valve. During the experiment, the experimental pressure was controlled constantly by the pressure-raising and pressure-reducing solenoid valves. Considering the melting point of the materials, the design pressure, and the operating pressure of the pipes, the experimental conditions were designed as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Temp/\({}^{\circ}\)C**} & \multirow{2}{*}{**Pressure/MPa**} & \multicolumn{6}{c}{**Aging Times**} \\ \cline{3-6}  & & **1 st/h** & **2 nd/h** & **3 rd/h** & **4 th/h** & **5 th/h** \\ \hline \multirow{3}{*}{80} & 0 & 0 & 144 & 288 & 576 & 864 \\  & 0.2 & 0 & 144 & 288 & 576 & 864 \\  & 0.4 & 0 & 144 & 288 & 576 & 864 \\ \hline \multirow{3}{*}{90} & 0 & 0 & 96 & 192 & 288 & 384 \\  & 0.2 & 0 & 96 & 192 & 288 & 384 \\  & 0.4 & 0 & 96 & 192 & 288 & 384 \\ \hline \multirow{3}{*}{100} & 0 & 0 & 24 & 48 & 96 & 192 \\  & 0.2 & 0 & 24 & 48 & 96 & 192 \\  & 0.4 & 0 & 24 & 48 & 96 & 192 \\ \hline \multirow{3}{*}{110} & 0 & 0 & 8 & 16 & 32 & 72 \\  & 0.2 & 0 & 8 & 16 & 32 & 72 \\ \cline{1-1}  & 0.4 & 0 & 8 & 16 & 32 & 72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Aging conditions for polyethylene pipelines.  
Figure 2: Aging experimental platform: 1—nitrogen cylinder; 2—cylinder regulator; 3—pressure-raising solenoid valve; 4—regulator; 5—pressure-raising ball valve; 6—pressure-reducing ball valve; 7—pressure-reducing solenoid valve; 8—electronic barometer; 9—thermo-oxidative aging chamber, and 10—polyethylene pipe.
## 4 Discussion
### Surface Displacement  
#### 4.2.1 Settlement Trough Width  
According to previous studies, the transverse ground surface settlement trough over a tunnel can be described by a normal probability curve [27], which is widely accepted and modified by many researchers [45; 46], shown as the following equation:  
\[S_{v}=S_{\mathrm{vmax}}\exp(-\frac{x^{2}}{2i^{2}}) \tag{3}\]  
in which \(S_{v}\) is the vertical settlement at the distance x from the tunnel axis line, \(S_{\mathrm{vmax}}\) is the maximum vertical settlement generally located directly above the tunnel axis. \(i\) alone to the points of inflection of the settlement trough, which is also named settlement trough width and can be described as [47]:  
\[i=Kz \tag{4}\]  
where \(z\) is the depth of the tunnel and \(K\) is an empirical constant that is equal to 0.5-0.6 for cohesive soils, 0.2-0.3 for granular soils, 0.4-0.5 for stiff clay soils, and finally 0.6-0.7 for silty clay soils.  
For the sandy soil in water-rich, the observed sinkhole induced by insufficient support pressure is shown in Figure 17, which demonstrates a narrow settlement trough width. Surface settlement in the transverse cross-section of the tunnel, derived from the laser sensor is shown in Figure 18. By fitting test data, a value of 0.25 for the settlement trough width is obtained.  
Figure 17: The sinkhole induced by insufficient support pressure.  
Figure 18: Settlement trough under different buried depths.
# Numerical and Experimental Investigations of Particle Dampers Attached to a Pipeline System
## 3 Design and Simulation of Particle Damper
### Analysis of the Effect of External Excitation Parameters of a Particle Damper  
#### 3.5.1 Excitation Frequency  
A particle damper is capable of energy dissipation in a wide frequency domain. A sinusoidal excitation was applied in the x-direction of the damper. The excitation frequencies of 50, 300, 600, 900, 1200, 1500, 1800, and 2000 Hz were tested.  
Figure 15 shows that the total energy dissipated by the system increased with the increase in the excitation frequency. The total energy dissipated by the particle system at higher excitation frequencies increased much more than at lower excitation frequencies. The higher the excitation frequency, the greater the number of vibrations per cavity of time for a particle damper. The number of particle-particle and particle-container wall interactions increased, and the total energy dissipated through collisions and friction increased. At the same time, at different filling rates, the number of particles also affected the total energy dissipated by the particle system.  
Figure 14: Total energy dissipated with different numbers of damper cavities.
## 3 Results and Discussion
### Parametric Analysis
#### 3.2.4 Carbon Emission Reduction  
To further demonstrate the benefits of the system, the carbon footprint reduction introduced by its use was evaluated using emission data from literature [44; 45]. Assuming the system substitutes natural gas as a heating source, which has carbon emissions of approximately 510 tons of carbon dioxide equivalent (CO\({}_{2}\)eq) per GWh, the total amount of carbon footprint reduction was estimated. It was found that for the base properties, the proposed TES (with a volume of 1830 m\({}^{3}\), corresponding to 5490 tons of rock) would be able to reduce emissions in about 156 tons of CO\({}_{2}\)eq per year, for all the aspect ratios. This is equivalent to 85 tons of CO\({}_{2}\)eq/m\({}^{3}\) of rock per year, or 28 tons of CO\({}_{2}\)eq/ton of rock per year. It is important to note that for a system replacing coal (\(\sim\)905 tons of CO\({}_{2}\)eq per GWh) or heating oil (\(\sim\)730 tons of CO\({}_{2}\)eq per GWh), the decrease in carbon footprint could be even more significant, these being displayed in Table 3.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Simulation Environment and Parameter Setup  
The efficiency of V2I and V2V connections may be improved by using efficient routing techniques and propagation algorithms. Routing methods and propagation designs are evaluated in terms of throughput, average delay, packet delivery ratio, average routing throughput, and MAC/PHY overhead using large numbers of nodes and simulated time frames. The parameter settings for the simulation scenario are shown in Table 3.
### Motivation behind the Adoption of DSM  
The necessities of the load-grid from the perspectives of synchronization, stability of operation, security and data protection from external attacks, reliability issues, and profit maximization requirements have prompted attention in various areas of DSM research. The following are motivations for the rising interest in the application of DSM techniques:  
* To reduce consumer annoyance during the adoption of DSM by incorporating demand reduction bidding during peak hours, incentive DSM, and demand response (DR) programs.
* To create an interactive load management market, which is a prosumer-based market in which each customer plays a part in achieving low-cost energy usage.
* To match energy supplies and dispatch additional available sources within the current system and regulate the required demand.
* To enable proper demand and supply balance by either reducing or shifting energy use from critical loading periods to fewer off-peak times, factoring in economic standards and active control methods.
* To consider electricity generation and trading tariffs, environmental considerations, demand-based usage patterns, and prosumer convenience levels when creating optimal load dispatch and usage scheduling.
* To adapt to changes brought about by erratic consumption and a lack of understanding of the operational state of daily-use devices and machines [6].  
To conduct forecasting based on weather data assessing client comfort levels and convenience.
* To achieve the lowest possible electricity cost from an economic standpoint, maximizing energy consumption from geographically nearby renewable energy sources (RES) from an environmental perspective and preventing power quality issues.
* To raise consumer awareness of DSM's benefits, which can stimulate adoption or improve electricity usage patterns.
* To combine operational flexibility for an individual home with the flexibility of other residential customers in the neighborhood to achieve operational flexibility for a unique family.
* To improve grid efficiency and reliability by minimizing the peak-to-average ratio (PAR) by offloading optional loads during peak periods [7].
# First Evaluation of Topography on GNSS-R: An Empirical Study Based on a Digital Elevation Model
In the last part, the following question was addressed: Can topographic features determine the main spatial pattern of Earth's surface reflectivity as it is measured by CyGNSS? The remainder of this paper is organized as follows. Section 2 provides a theoretical background on the effects of topography on GNSS-R. Section 3 describes the methodology. Section 4 describes the effects of topography on CyGNSS-derived DDMs based on several parameters derived form a DEM. Additionally, it investigates the relationship between DDMs and a topography-based surface wetness index. Section 5 includes final discussions, and the main conclusions are discussed in Section 6.
# In-Band OSNR Measurement Method for All-Optical Regenerators in Optical Domain
## 5 Conclusions  
We put forward an in-band OSNR measurement method for all-optical regenerators in the optical domain and present the output OSNR formula in terms of the signal gain, total gain, and input OSNR. The proposed method is verified by simulating the OSNR of regenerated QPSK signals, in comparison to the EVM calculation. For the QPSK regenerator composed of the PSA and amplitude shaping units as given in [6], the OSNR can be improved by more than 3 dB in the input OSNR range of 10 to 26 dB of interest in the communication system. Moreover, this method can be applied to the various QPSK regenerators at different data rates.  
[PERSON]. ([PERSON]) conceived the theory and designed the simulation; [PERSON] put forward the idea and conceived the theory; [PERSON]. ([PERSON]) performed the simulation; all authors analyzed the data; [PERSON]. ([PERSON]) and [PERSON] wrote the paper; [PERSON]. ([PERSON]) and [PERSON] reviewed the manuscript.  
This research received no external funding.  
Figure 6: The output OSNR and optical shaping figure (OSF) curves of the QPSK regenerator.  
Figure 7: The OSNR differences between the two methods for QPSK signals at different data rates.  
**Acknowledgments:** This work is supported by the National Natural Science Foundation of China NSFC (No. 61671108).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Multi-Sensor Fusion  
### Hardware System Description  
The configuration of our system is shown in Figure 1, with the left model obtained through Unigraphics NX and the designed equipment in our paper shown on the right. The designed equipment is a scanning arm composed of various sensors that can be installed on a backpack or a vehicle. The sensor suite used in the scanning arm includes two 10 HZ Velodyne VLP-16 lidars, a 200 HZ YIS510A IMU, and a 5 HZ HY-269 GPS. The panoramic camera prepares for the follow-up research. The lidar has a horizontal FOV of 360\({}^{\circ}\) with 0.4\({}^{\circ}\) resolution, a vertical FOV 15\({}^{\circ}\) with 2\({}^{\circ}\) resolution, and an accuracy of \(\pm\)3 cm. The Roll/Pitch of IMU accuracy is 0.25\({}^{\circ}\), HY-269 GPS is a small-volume positioning and directional receiver, and the positioning accuracy of GPS is 1.2 m.