esa-sceva/satcom-chunk-collection huggingface dataset:
# Centimeter-Level Precise Orbit Determination for the Luojia-1A Satellite Using BeiDou Observations
## 6 Conclusions  
This study focuses on the precise orbit determination of the Luojia-1A low earth orbit satellite with onboard BDS observations. The onboard BDS observations are analyzed firstly and demonstrated that the visibility and continuity of the Luojia-1A are good enough for precise orbit determination. Moreover, the multipath error of onboard BDS code observations is modeled, which shows evident elevation-dependent code bias for the multipath. An elevation-dependent code bias model using LEO data is established, which is capable of modeling the elevation-dependent code bias for the Beidou GEO satellites. The daily boundary of the BeiDou orbit product is calculated and demonstrates that the precision of the GEO satellite orbit is not as good as IGSO and MEO satellites. To overcome the poor precision of the GEO satellite orbit, the strategy that reduces the weight of GEO satellite observations is adopted. The precise orbit result is obtained by adopting the reduced weight strategy, which can achieve 2.3 cm precision by reducing the weight of GEO satellites down to 1/900, whereas the normal equal weight strategy only achieves the 3D precision of 6.3 cm. Finally, the effect of pseudo-stochastic parameters is explored, and the optimal 3-dimensional orbit precision of the Luojia-1A can achieve 2 cm. The strategy that reduces the weight of GEO satellite observations is effective for the precise orbit determination of the Luojia-1A Satellite.  
[PERSON] created the idea and contributed to writing the manuscript. [PERSON] wrote the first draft and carried out the experiment. [PERSON], [PERSON], and [PERSON] helped to carry out the data analysis. [PERSON] and [PERSON] helped to carry out the experiments. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No. 41704002, 91638203 and 41904038) and the China Postdoctoral Science Foundation (No. 2017M620337 and 2019M662713). This work is also partially funded by the Fundamental Research Funds for the Central Universities.  
The authors declare no conflict of interest.  
Figure 13: The error of Luojia-1A satellite orbit with different piecewise constant acceleration parameter strategies. (**a**) Without piecewise constant accelerations. (**b**) 30 min interval. (**c**) 15 min interval. (**d**) 6 min interval.
# Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach
## 4 Methodology Based on Cooperative Game Model for Platoon
### String Stability and Traffic Flow Stability
The influence of headways policy on traffic flow can be generally shown in fundamental diagram, which reveals the relationship between traffic flow rate and traffic density [36]. A traffic flow is always stable when the traffic flow rate \(Q\) increase as the traffic density \(\rho\) increase [37]. The highest traffic density can be achieved by letting \(\frac{\partial Q}{\partial\rho}=0\). In the CTH policy, the traffic density at stead state is given in Equation (33). \[\rho=\frac{1}{d_{cth}+t_{\tau}v} \tag{33}\]  
The equation between \(Q\) and \(\rho\) can obtained in Equation (34). \[Q=\rho v=\frac{1-\rho d_{cth}}{t_{\tau}} \tag{34}\]  
where \(d_{cth}\) is always positive and the slope \(\frac{\partial Q}{\partial\rho}\) is negative which means that the traffic flow is unstable in the CTH policy. Assuming that the platoon is driving in a stable state, where \(v_{i-1}\) is equal to \(v_{i}\). Then, the traffic density at steady state is \(\rho=\frac{1}{(d+L)}\), which is equal to the CD policy. Idealized CD policy has optimal traffic capacity and traffic flow stability. The desired spacing in proposed method and CD policy is independent of speed, which means that the flow rate is linear with velocity. Proposed spacing policy is same with CD policy in the form of mathematic expression so that it has similar traffic characteristics.
## 3 Theory  
### Forward problem  
In this study, the stress-velocity equation of the wave equation in the time domain in an anisotropic viscoelastic medium with rheology described by a GSLS [25; 26] is taken to solve the forward problem [27; 28]:  
\[\rho\frac{\partial v_{i}}{\partial t}=\frac{\partial\sigma_{ij}}{\partial x_{j}}+ f_{i} \tag{1}\]  
\[\dot{\sigma}_{ij}=\frac{\partial v_{k}}{\partial x_{k}}\{M(1+\tau^{p})-2\mu(1+ \tau^{s})\}+2\frac{\partial v_{i}}{\partial x_{j}}\mu(1+\tau^{s})+\sum_{l=1}^{ L}r_{ijl}\quad if\quad i=j, \tag{2}\]  
\[\dot{\sigma}_{ij}=\left(\frac{\partial v_{i}}{\partial x_{j}}+\frac{\partial v _{j}}{\partial x_{i}}\right)\mu(1+\tau^{s})+\sum_{l=1}^{L}r_{ijl}\quad if \quad i\
eq j \tag{3}\]  
\[\dot{r}_{ijl}=-\frac{1}{\tau_{el}}\Bigg{\{}(M\tau^{p}-2\mu\tau^{s})\frac{ \partial v_{k}}{\partial x_{k}}+2\frac{\partial v_{i}}{\partial x_{j}}\mu \tau^{s}+r_{ijl}\Bigg{\}}\quad if\quad i=j, \tag{4}\]  
\[\dot{r}_{ijl}=-\frac{1}{\tau_{el}}\Bigg{\{}(M\tau^{s}\Bigg{[}\frac{\partial v _{i}}{\partial x_{j}}+\frac{\partial v_{j}}{\partial x_{i}}\Bigg{]}+r_{ijl} \Bigg{\}}\quad if\quad i\
eq j. \tag{5}\]  
where \(\sigma_{ij}\) denotes the \(i\)\(j\)th component of the stress tensor, \(v_{i}\) denotes the components of particle velocity, \(f_{i}\) is the components of external body force, \(\rho\) is density, \(M\) is the P- wave modulus, and \(\mu\) is the S- wave modulus. \(r_{ijl}\) denotes the \(L\) memory variables (\(l=1,\ldots,L\)) which correspond to the stress tensor \(\sigma_{ij}\), \(\tau_{el}\), are the \(L\) stress relaxation times for P- and S- waves and \(\tau^{p}\), \(\tau^{s}\) are the level of attenuation for P- and S- waves respectively. It is necessary to mention that the dot over symbols indicates partial differentiation with respect to time. The attenuation of rocks is defined by the seismic quality factor (Q):  
\[Q(\omega,\tau_{el},\tau)=\frac{1+\sum_{l=1}^{L}\frac{\omega^{2}\tau_{el}^{2}} {1+\omega^{2}\tau_{el}^{2}}\tau}{\sum_{l=1}^{L}\frac{\omega^{2}\tau_{el}}{1+ \omega^{2}\tau_{el}^{2}}\tau} \tag{6}\]  
where \(\omega\) is the angular frequency, and the variable \(\tau\) denotes  
\[\tau=\frac{\tau_{el}}{\tau_{el}} \tag{7}\]  
where \(\tau_{el}\) is the stress relaxation time, and \(\tau_{el}\) is the strain retardation time for the \(l\)th Maxwell body of the GSLS. With Equation (6), \(L+1\) parameters \(\tau_{el},\tau\) are obtained that describe a constant Q-spectrum within a limited frequency range by a limited number of Maxwell bodies [27]. The forward problem is solved by using a time-domain two-dimensional second order FD operator in time and space on a staggered grid [27]. To reduce the edge effects and reflections at the boundaries the CPMLs are implemented [29; 30].
# An Improved Hilbert-Huang Transform for Vibration-Based Damage Detection of Utility Timber Poles
## 5 Results and Discussion  
### Raw Data  
Time domain signal of Serviceable Pole 1 from the hammer impact and its corresponding frequency spectrum after performing Fast Fourier Transform (FFT) are demonstrated in Figure 9a,b, respectively. From the frequency spectrum of impulse, it is observed that the data tends to lose energy when the frequency exceeds 1500 Hz. That means the main frequency range of hammer hit lies between 0 and 1500 Hz. It is also obvious that the low frequency components are dominant in the signal.  
Figure 8: (a) Schematic diagram of experimental test set up (b) Horizontal cross section.  
Raw acceleration data (time domain) of layer 1 and layer 2 of Pole 1 at 0\({}^{\circ}\), 90\({}^{\circ}\), and 180\({}^{\circ}\) position and their corresponding frequency spectrums are shown in Figures 10-12, respectively. Frequency response function (FRF) is implemented here to normalise the effect of hammer impact. From the results, it can be observed that the time acceleration signals from all 12 sensors are non-stationary and exhibit nonlinear characteristics. Since all the time information is lost after performing FRF, it is impossible to predict how the frequency components present in the raw acceleration signals and how it changes over time. It is also very difficult to identify which peaks in the frequency spectrum are the dominant frequency.  
Figure 10: Captured time domain acceleration data and their frequency spectra at 0\({}^{\circ}\) position.  
Figure 9: Hammer impact in (**a**) time domain (**b**) frequency spectrum.  
From the comparison of Figures 10 and 12, it can be found that longitudinal components at 0\({}^{\circ}\) and 180\({}^{\circ}\) position are not in same phase, and they are not out of phase as well. It is also observed that radial components at layer 2 are almost in same phase whereas layer 1 radial components possess very small amplitude. From Figure 11, it can be witnessed that circumferential component is dominant at 90\({}^{\circ}\) position and contains more energy than that of longitudinal component at the same position. Behaviour of these three displacement components can be found in details in [41; 42].  
Figure 11: Captured time domain acceleration data and their frequency spectra at 90\({}^{\circ}\) position.  
Figure 12: Captured time domain acceleration data and their frequency spectra at 180\({}^{\circ}\) position.
## 7 Conclusions  
In this study, the energy consumption and recovery performance of six different powertrain architectures, including transmission and nontransmission layouts, were analyzed for HFCEVs. The stability control performance of RESC was tested with respect to the desired yaw rate and sideslip angle of the reference model, and the results were satisfactory. The conclusions of the study can be summarized as follows:  
* A transmission system can reduce the energy consumption of HFCEVs tremendously as compared to other powertrain layouts, as it provides the optimum operating conditions for electric motors. If high torque capacity is required at low velocities of the vehicle, an automated transmission system must be used for improved energy consumption.
* An in-wheel motor layout is able to recover more energy if an RESC system is added to it. Overall, it spends less energy, except in the case of a downsized electric motor and two-stage automatic transmission. In addition, if the vehicle is used on a curved road, the in-wheel architecture with an RESC system can save a significant amount of energy.
* Using a CVT transmission, the required peak torque of the electric motor can be decreased; therefore, it is possible to downsize the electric motor. Downsizing helps to achieve a reduction in the weight of not only the motor but also its driver and other power units, which results in the lowest energy consumption rate as compared to the other layouts.  
The simulation results can be used for designing different types of vehicles, such as electric buses and trucks, as the findings will be easy to implement. Moreover, the simulation results can be further tested in future studies for exploring more innovations.  
Conceptualization, A.Y.; methodology, A.Y.; software, M.A.O.; validation, A.Y. and M.A.O.; formal analysis, A.Y. and M.A.O.; investigation, A.Y. and M.A.O.; resources, A.Y. and M.A.O.; data curation, A.Y. and M.A.O.; writing--original draft preparation, A.Y. and M.A.O.; writing--review and editing, A.Y.; visualization, A.Y. and M.A.O.; supervision, A.Y.; project administration, A.Y. and M.A.O.; funding acquisition, A.Y. and M.A.O. Both authors have read and agreed to the published version of the manuscript.  
Figure 16: Comparison of energy recovery/consumption rates.
## 3 Analysis of Shield Tunneling Parameters
### Total Thrust  
The correlation between the total thrust and the shield tunnel ring number is illustrated in Figure 10. Table 6 presents the statistical characteristics of the total thrust across three distinct stratum conditions.  
Analysis of Figure 10 and Table 6 indicates that the total thrust exerted by the shield in strata II and III is greater than that in stratum I. It is crucial to rigorously control the tunneling thrust during shield excavation to prevent tool damage and segment rupture.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stratum Serial** & **Mean** & **Standard** & **Median** & **Minimum** & **Maximum** & **Maximum** & **Skewness** \\
**Number** & **Value** & **Deviation** & **Median** & **Value** & **Value** & **Value** & \\ \hline I & 17,934.2 & 2953.6 & 17,487.0 & 11,368.0 & 24,005.0 & 0.023 \\ II & 31,881.4 & 8243.1 & 31,927.0 & 12,245.0 & 59,617.0 & 0.322 \\ III & 31,927.6 & 5511.5 & 32,837.0 & 17,555.0 & 42,533.0 & \(-\)0.736 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical characteristics of different strata with the total thrust.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stratum Serial** & **Mean** & **Standard** & **Median** & **Minimum** & **Maximum** & **Maximum** & **Skewness** \\
**Number** & **Value** & **Deviation** & **Median** & **Value** & **Value** & **Value** & \\ \hline I & 4.157 & 0.489 & 4.054 & 3.135 & 5.175 & 0.164 \\ II & 1.752 & 2.227 & 2.004 & \(-4.811\) & 5.887 & \(-1.036\) \\ III & \(-2.631\) & 2.965 & \(-3.168\) & \(-8.577\) & 5.010 & 0.579 \\ \hlineIn stratum II tunneling, the total thrust experiences significant fluctuations, displaying an overall increasing trend with an amplitude as high as 19,125 kN. The excavation parameters in this stratum fluctuate considerably, necessitating real-time monitoring and prompt adjustments. During excavation in stratum III, the shield's total thrust mainly falls within the range of 30,268 to 37,780 kN. Compared to stratum II, the amplitude is smaller, making it easier to control during the excavation process. Stratum I, characterized by a soil layer of \"volcanic ash debris + round gravel\", facilitates with smoother advancement. In stratum II, where the soil layer is \"clay + volcanic ash debris \", the soil has substantial cementation and higher propulsion resistance. Stratum III features a soil layer of \"clay + volcanic ash debris + round gravel\", exhibiting good particle gradation, minimal propulsion resistance, and a downward trend in the total thrust.  
In contrast to other strata [32; 33], the volcanic ash strata exhibits smaller particle density, larger interparticle gaps, and higher friction resistance during shield tunneling. Consequently, at the same excavation speed, the shield in the volcanic ash strata requires a relatively larger total thrust.
## 5 Types of UAV Deployments
### UAV Deployment with Fixed BSs (UAV-Assisted Cellular Networks)  
This involves the deployment of either single or multiple UAV-BSs on top of existing fixed cellular network infrastructure (terrestrial BSs) to provide enhanced capacity and coverage in different scenarios [105], as shown in Figure 5. This deployment scenario can be used to enable many of the application scenarios highlighted in Section 4, such as load balancing, pop-up networks, backhauling, capacity enhancement, etc. For example, UAVs can be deployed as pop-up networks at the edge of existing fixed networks to extend coverage to disaster zones, or at entertainment venues such as football stadia and concert halls to improve data streaming speeds for fans, as an on-demand MEC server to improve data processing when required, etc. In such scenarios, UAV-BSs can be deployed to provide additional capacity in order to reduce network congestion and to enhance the data rate of the users in hotspot regions and at the cell edges [107]. In addition, UAV-BSs can help to provide communications with highly mobile user equipment (UE), thereby preventing frequent handovers, which can negatively affect their QoS [111]. One of the main advantages of deploying UAVs in existing cellular networks is that they can change their locations depending on the network conditions, which helps in increasing the QoS, restoring the network at failed BS sites, achieving load balancing, offloading traffic from macro BSs, and extending the coverage. Hence, UAV-BS deployment in cellular networks is a key design consideration in future heterogeneous wireless networks that can enable applications such as smart cities, mobile computing, autonomous vehicle networks, etc. [112].  
Figure 5: Illustration of the two major types of UAV deployments: UAV stand-alone deployment comprising single and multiple UAV networks, and UAV-assisted cellular networks comprising both UAV-BSs and terrestrial BSs.
# Experimental Study of LoRa Transmission over Seawater
## 1 Introduction
In addition, we explored different LoRa parameters, covering all characteristic cases: (1) minimum sensitivity of the transceiver by setting \(SF=7\) and \(BW=125\) kHz; (2) mid-range value as a trade-off between sensitivity and data rate by setting \(SF=10\) and \(BW=125\) kHz; and (3) maximum sensitivity by setting \(SF=12\) and \(BW=125\) kHz. To make experimental data more reliable, we deployed three different transceivers on the receive side, where one was own-developed and optimized in terms of sensitivity [10], and two were commercial transceivers. Finally, to provide an accurate interpretation of experimental data, we performed laboratory measurements and provided scaling factors for RSS indicators of all three transceivers, and determined linear and saturation regions of operation for SNR measurement chain of each transceiver used in the experiments. The results of this study show that LoRa links over seawater are fully feasible in LOS propagation condition at both frequencies, namely 868 MHz and 434 MHz up to at least 22 km. The link margin, which yields more than 15 dB in each experimental scenario suggests that the link length can be even longer. Furthermore, due to use of the characteristic sets of LoRa parameters (lowest, mid-range and highest value for SFs, and lowest for BW standardized by LoRaWAN) it can be concluded that links are feasible for virtually any set of LoRa parameters. Deployment of low-cost off-the-shelf rubber duck antennas on both transmitting and receiving side extends the usability of the experimental results also to commercial deployment arena. Finally, this study shows that links in obstructed LOS propagation mode are only possible at 434 MHz using costly and bulky antennas on both sides. The paper is laid out as follows: Section 2 briefly introduces LoRa technology, while in Section 3 materials, experimental setup, and RSSI correction factors are given. In addition, SNR measurement chain linearity is investigated. Section 4 presents and analyzes experimental results, while in Section 5 conclusion remarks are given.
## 4 Results and Discussions
### QAM Scattering Plots  
From Figures 10 and 11, the QAM symbols can be seen at the receiver before and after applying the FFB algorithm. We are able to understand how good the algorithm works after analyzing both scattered plots. In Figure 10, the 64 QAM symbols are received at 20 dB SNR, and the algorithm is not applied. From Figure 11, the QAM received symbols are shown after a noisy underwater acoustic channel with the application of the FFB algorithm. It is found that if the FFB algorithm is not applied,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Algorithm** & **Data Required** & **Training Time** & **Fit Goodness** \\ \hline \multirow{3}{*}{Leverberg–Marquardt} & 25,800 x 6 * & MSE: 0.00138 at 93 frequentatives. \\  & Number of OFDM & 16.59 s & Data Division \\ \cline{1-1}  & symbols = 300 & Random \\ \hline \hline \end{tabular}
* it relates to the column “25800”6 Number of OFDM symbols = 300.  
\end{table}
Table 3: Learning algorithm.  
Figure 9: Bit error rate (BER) vs. SNR for the FFB algorithm.  
many of the received symbols are out of the QAM coordinates. The proposed algorithm reduces the unnecessary noise to a more significant extent, as can be observed in Figure 11. Here all QAM symbols are within the decision coordinates for a specific symbol's alphabets. It is proved that the QAM symbols with no PAPR reduction algorithm (FFB) are noisier than the ones with FFB applied. After comparing both pictures, it is noticeable that many symbols in Figure 10 are scattered out of the 64 quadrants. In the case of FFB, almost all the QAM symbols are near the quadrature. Thus, it can be demodulated with less error and results in a decrease in BERs.  
Next, the variation in BERs is analyzed at different PA saturation levels when the distortion is due to PA nonlinearity, which is the same as the BER variation with clipping and PA nonlinearity. If the PA saturation levels are increased, the BER decreases. It can be justified as an increase in the saturation level leads to an enhancement in the linear operation range of the PA. Figure 12 exhibits the change in BER performance with PA saturation levels of 10, 7, and 5 dB. It is clear from Figure 12 that the BER is lowest at the 10 dB saturation level compared with other levels.  
Figure 11: QAM scatter plot received symbols with FFB. Distortion due to clipping with PA model.  
Figure 10: QAM scatter plot received symbols without FFB. Distortion due to clipping with PA model.
# Denoising Directional Room Impulse Responses with Spatially Anisotropic Late Reverberation Tails
## 4 Denoising by Tail Resynthesis  
With a maximum independent coverage spatial domain determined, the main EDR analysis and tail resynthesis procedure presented in [7] can be applied to each look direction individually. However, certain aspects of the process originally relying on the assumption of an isotropic field must be adjusted. In this section, we will review the main steps of the denoising procedure and highlight the particularities of the anisotropic case.