esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Design of a Novel 5-DOF AC-DC Hybrid Magnetic Bearing
## 2 Structure and Working Principle  
The configuration of the 5-DOF AC-DC HMB is shown in Figure 1. The HMB includes an axial control core, two axial control coils, two radial control cores, radial control coils, a rotor core, and two axially polarized permanent magnet rings. To reduce the axial length, the two ends of the rotor are designed as inclined structures. Three radial stator magnetic poles are evenly arranged around the circumference of the left and right radial control cores. Three radial control coils are, respectively, wound on the radial stator magnetic poles. For high-speed mechanical systems, high-speed electric machines can be installed between the two radial control cores and integrated with the HMB to improve the axial utilization ratio, critical speed, and power density.  
Figure 2 shows the magnetic flux paths of the 5-DOF AC-DC HMB. The bias flux path (solid lines with arrows) starts from the N-pole of the permanent magnet, passing through the axial control core, axial air gap, rotor core, radial air gap, and radial control core, and finally returns to the S-pole of the permanent magnet. The axial control fluxes flow through the axial control core, the axial air gap, and the rotor core. The loop of the radial control fluxes is formed by the radial control core, the radial air gap, and the rotor core. The air-gap fluxes are composed of bias fluxes and control fluxes. Since the magnetic resistance of the permanent magnet is large, the control fluxes do not pass through the permanent magnet pole, and the demagnetization of the permanent magnet by the control fluxes can be avoided.  
In the common state, the rotor is in the equilibrium position. At this moment, there is no control current, and the rotor is suspended under the magnetic attractive force generated by the bias fluxes. If the rotor is offset, the displacement sensors detect the radial and axial displacements. The axial DC power amplifier adjusts the axial control current to generate axial control fluxes, while the radial AC inverters adjust the radial control fluxes generated by the three-phase symmetrical AC current in the radial control coils. In the direction of  
Figure 1: A 3D model of the 5-DOF AC-DC HMB.  
Figure 2: Flux paths of the 5-DOF AC–DC HMB.  
the rotor offset, due to the superposition of the control fluxes and bias fluxes, air-gap fluxes are weakened. In the opposite direction of the rotor offset, air-gap fluxes are enhanced. The rotor is pulled back to the balanced position by the suspension forces in the opposite direction to the rotor offset direction.
# Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems
## 5 Information Receiver for TDS-OFDM-NOMA
### Doppler-Shift Compensation Using Two-Step Approach  
In this paper, the two-step approach is used to compensate for the Doppler shift as in [25]. The doppler shift compensations based on the two-step approach are based on two points; (1) resampling for nonuniform Doppler compensation by converting the wideband into narrowband; (2) high-resolution uniform compensation of residual Doppler to avoid intercarrier interference. For the resampling method in the passband signals, the received signal is resampled using a resampling factor \(\epsilon\) as \(\widetilde{z}(t)=\widetilde{y}\left(\frac{t}{1+\delta}\right)\). The effect of the resampling will be in two directions, rescaling the waveform and providing a frequency-dependent Doppler compensation. After resampling the received baseband signal, it can be written as:  
\[\begin{array}{l}z(t)=e^{/2\pi\frac{\epsilon-\delta}{1+\delta}\ell\tau} \times\left\{\frac{1}{\sqrt{N}}\sum_{k=0}^{N-1}\left[s(k)e^{/2\pi k\Delta f \tau k}\times\left[\sum_{p}A_{p}e^{-2\pi f\kappa\tau p}g\left(\frac{1+\alpha} {1+\delta}t-\right.\right.\right.\\ \left.\left.\left.\tau_{p}\right)\right]\right\}+\frac{1}{\sqrt{N}}\sum_{k=N}^ {N+M}\left\{c(k)e^{/2\pi k\Delta f\tau k}\times\left[\sum_{p}A_{p}e^{-2\pi f \kappa\tau p}g\left(\frac{1+\alpha}{1+\delta}t-\tau_{p}\right)\right]+v(t) \right\}\end{array} \tag{37}\]  
where \(v(t)\) is the ambient and additive noise. If \(\frac{1+\alpha}{1+\delta}\) is close to one, then the received signal can be as:  
\[\begin{array}{l}z(t)\approx e^{/2\pi\frac{\epsilon-\delta}{1+\delta}\ell\tau} \times\left\{\frac{1}{\sqrt{N}}\sum_{k=0}^{N-1}\left[s(k)e^{/2\pi k\Delta f \tau k}\times\left[\sum_{p}A_{p}e^{-2\pi f\kappa\tau p}g\left(t-\tau_{p}\right) \right]\right]+\\ \frac{1}{\sqrt{N}}\sum_{k=N}^{N+M}\left[c(k)e^{/2\pi k\Delta f\tau}\times \left[\sum_{p}A_{p}e^{-2\pi f\kappa\tau p}g\left(t-\tau_{p}\right)\right]\right] +v(t)\right\}\end{array} \tag{38}\]The residual Doppler effect is the same over all the subcarriers; hence, the wideband OFDM system is converted into a narrowband OFDM system with a frequency-independent Doppler shift, representing the carrier frequency offset (CFO), \(\epsilon=\frac{\alpha-\delta}{1+\epsilon}f_{c}\). By estimating the CFO, the received signal can be compensated. The Doppler scaling factor estimation and frame synchronization can be based on the preamble and the post-amble of a data packet as in [24; 25; 27; 28].
# Connectivity Analysis in VANETs with Dynamic Ranges
## 4 Discussion
### Distribution of Ranges
#### 4.1.1. Lognormal Distributed Ranges  
Firstly, the distributions are plotted in Figure 2 with \(\sigma=1\), \(P_{t}=0.01\) W, for varying inter-vehicle distances. Then, the communication range is distributed below the 500 m distance mark as stipulated and recommended for short-ranges of DSRC standard. Generally, for all range distribution plots, varying impacts of the path-loss fading are seen around \(\alpha=2\) (Figure 2a), which is a value that differentiates typically between non-fading and fading environments. As the path-loss exponent value increases, the communication range is exponentially distributed with relative expectations ranging between 50 m and 200 m. For example, with \(\alpha=(1,2]\) for a fading environment on a highway, urban and suburban areas, short-range communications are expected with average ranges around 50 m with big-vehicle obstructions. Moreover, the probability of connectivity drops beyond \(\alpha=2\) as can be seen in Figure 2b).
## 2 System Description  
### System Model  
We assume that the synchronization signal is transmitted without DFT precoding [11]. The system of interest uses OFDM symbols with \(N\) subcarriers for the data transmission. At the transmitter, the quadrature amplitude modulation (QAM) signal is converted into the time domain using an \(N\)-point inverse fast Fourier transform (IFFT) operation, which is equivalent to modulate every \(N\) symbols onto \(N\) orthogonal subcarriers. Then, a CP of \(N_{g}\) samples is appended at the front of OFDM symbol in order to avoid both the inter-symbol interference (ISI) and inter-carrier interference (ICI). Accordingly, the effective OFDM signal during the \(l\)-th period is generated as  
\[x_{l}(m)=\sum_{k=-N/2}^{N/2-1}X_{l}(k)e^{l2\pi km/N},\ m=-N_{g},-N_{g}+1,\cdots,N-1, \tag{1}\]  
where \(l\) denotes the index of the OFDM symbol and \(X_{l}(k)\) is the frequency-domain QAM symbol at the \(k\)-th subcarrier over the \(l\)-th symbol duration. The remaining \(X_{l}(k)\)'s except for the synchronization signal are DFT-precoded signals.  
The signal is transmitted over frequency-selective fading channels and corrupted by additive white Gaussian noise (AWGN). Because the performance of the conventional IFO estimation methods developed in [25; 26; 27] is marginally affected by the presence of the FFO, we assume the FFO is completely estimated. The accuracy of STO estimate may be considerably deteriorated in the presence of multipath channels. For this reason, we consider the scenario where there is a residual STO \(\tau\) after initial STO estimation is performed. The received signal is sampled at a sampling frequency of \(f_{s}=N\Delta_{f}\) with \(\Delta_{f}\) being the subcarrier spacing. Consequently, the \(m\)-th time-domain sample over the \(l\)-th symbol duration \(y_{l}(m)\), \(m=-N_{g},-N_{g}+1,\cdots,N-1\), is written as [20]  
\[y_{l}(m)=e^{l\epsilon\rho}e^{l2\pi\epsilon(m-\tau)/N}\sum_{p=1}^{L}c_{l}(p)x_{ l}(m-\tau_{p}-\tau)+z_{l}(m), \tag{2}\]  
where \(\rho=2\pi N_{g}/N\), \(\epsilon\) is the normalized IFO by the subcarrier spacing \(\Delta_{f}\), \(c_{l}(p)\) denotes the channel impulse response with \(L\) multipaths, \(\tau_{p}\) is the delay of the \(p\)-th path, and \(z_{l}(m)\) is the \(m\)-th AWGN sample. It is assumed that the IFO is uniformly distributed in range \([-M,M]\) with \(M\) being the maximum value of the IFO that determined by the local oscillator's stability. Given the assumption that the CP is longer than the maximum excess delay of the channel, QAM symbols can be demodulated taking a FFT on the CP-removed time-domain sample \(y_{l}(m)\) as follows  
\[Y_{l}(k)=C_{l}(k-\epsilon)X_{l}(k-\epsilon)e^{-2\pi(k-\epsilon)\tau/N}e^{l \epsilon\rho}+Z_{l}(k), \tag{3}\]  
where \(k\) represents the subcarrier index, \(X_{l}(k)\) is the frequency-domain modulated QAM symbol at the \(k\)-th subcarrier, \(C_{l}(k)\) is the channel frequency response (CFR) over the \(l\)-th period, and \(Z_{l}(k)\) is a complex zero-mean AWGN with variance \(\sigma_{Z}^{2}\).
## 4 Full Bridge Aeroelastic Model
### Simulation of Wind Field  
An atmospheric boundary layer wind field (turbulent wind) with a scaling ratio of 1:150 was simulated in the TJ-3 wind tunnel of Tongji University using the roughness and spirelets, as indicated in Figure 14, for a Class B landform based on an investigation of the wind environment at the bridge location. The exponent of mean wind profile is set at 0.16, and the gradient wind height is set at 350 m. The mean wind speed profile measured in the wind tunnel at the bridge's intended location is shown in Figure 15a. The targeted profile with a power law exponent of 0.16 is also presented on the same graph. This demonstrates that the provided mean wind profile is appropriate. Figure 15b compares the measured turbulence intensities to the target values, which were taken at the bridge's intended location in the wind tunnel. Figure 16 shows the measured turbulence spectra at deck level, as well as several empirical spectra [33; 34; 35].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Nodes**} & \multicolumn{2}{c}{**Computed Frequency**} & \multirow{2}{*}{**Design Values**} & \multicolumn{2}{c}{**Measured**} & \multicolumn{2}{c}{**Discrepancy**} & \multirow{2}{*}{**Limit**} & \multirow{2}{*}{**Damping**} \\  & **of Prototype** & & & & **Values** & **(\%)** & **Ratio (\%)** \\ \hline
1V-S & 0.2809 & 3.440 & 3.312 & 3.73 & 5.00 & 0.75 \\
2L-S & 0.3613 & 4.425 & 4.396 & 0.66 & 5.00 & — \\
3V-A & 0.4008 & 4.909 & 4.940 & \(-\)0.64 & 5.00 & 0.90 \\
4T-S & 0.6268 & 7.677 & 8.063 & \(-\)5.02 & 5.00 & 0.83 \\
5T-A & 0.9378 & 11.486 & — & — & 5.00 & — \\ \hline \hline \end{tabular}
\end{table}
Table 6: Scaling parameters for Froude number similitude.  
Figure 13: Full bridge aeroelastic model in TJ-3 wind tunnel.  
Figure 14: Atmospheric boundary layer turbulent wind field simulator.  
Figure 15: Wind profiles measured in wind tunnel. (**a**) Mean wind speed profiles; (**b**) turbulence intensities profiles.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## Key Research Findings
### SUHI Variations among Cities and Their Drivers
[50] showed that ISA explained more than 60% of the total LST variance for cities surrounded by forests at mid-high latitudes. However, a recent study by [PERSON] et al. [120] found an insignificant correlation between SUHI and ISA in a highly populated urban agglomeration area. Urban area size was also found positively and significantly related to SUHI [2; 52; 177; 178; 52; 184] and even to be a dominant factor of SUHI [3; 50]. For example, [PERSON] et al. [3] demonstrated that nearly 87% of the SUHI variations among cities in the United States could be explained by the urban area size, especially in the regions with relatively homogeneous land covers surrounding urban areas. At the same time, some studies have indicated a minor or insignificant effect of urban area size on SUHI [4; 6; 139]. Similarly, both significant positive [114; 120; 184; 187; 184] and insignificant [2; 4; 178; 179] correlations have been found between SUHI and the urban population. Some studies have stressed the importance  
Figure 5: Potential impacts of the studied factors on SUHI’s variability among cities. The red solid and gray dashed arrows represent the direct and indirect effects, respectively. Energy components influenced by these factors directly were listed above the arrows. of the urban landscape structure [5; 115; 177], while others showed that it made a weak contribution to SUHI variations among cities [2; 184]. Other factors such as surface albedo [4; 6; 51; 125], energy consumption or anthropogenic heat release [4; 6; 75; 112; 178], and atmospheric aerosols [126; 181] have been suggested as major drivers for SUHIs. The contrasting data sources, methods, geographic regions, scales, and periods of past SUHI research caused these diverse and/or controversial results. This is discussed below in Sections 6 and 7.
# BER Minimization by User Pairing in Downlink NOMA Using Laser Chaos Decision-Maker
## 4 User Pairing in NOMA System with Laser Chaos Decision-Maker (LCDM-NOMA)
### Operating Principle
In these rules, the threshold value comparison finishes when all \(M\) bits information of the specified option are decided. The update formula of the threshold adjuster value \(TA\) can be expressed as follows [30; 31]:  
\[TA_{L,I_{1},I_{2},...,I_{(l-1)}}(t+1)= \begin{cases}\pm\Delta+\alpha TA_{(L,I_{1 },I_{2},...,I_{(l-1)})}(t),\text{if selected slot machine wins.}\\ \mp\Omega_{L,I_{1},I_{2},...,I_{(l-1)}}+\alpha TA_{L,I_{1},I_{2},...,I_{(l-1)} }(t),\text{if selected slot machine loses.}\end{cases}  \tag{9}\]  
Figure 5: User pairing using the laser chaos decision-maker. Figure 6: User pairing using the laser chaos decision-maker in the NOMA system in the case of 6 users. where \(\Delta\) is a constant and \(\Omega_{L,I_{1},I_{2},\ldots,I_{(L-1)}}\) can be calculated using Equation (8). \(L\) corresponds to the \(L\)th MSB. \(L\) is \(1\leq L\leq M\) and \(I_{0}\) is not described. After determining the pairing based on the above algorithm, BS transmits data to users based on the determined pairing. The users check bit errors after decoding the received data. The users send back ACK or NACK signals to BS as the following. If bit errors do not occur, the users send ACK to BS. Otherwise, bit errors occur and the users transmit NACK to BS. Finally, BS gets a reward or non-reward according to the number of ACKs received. If the total amount of ACKs received at the base station is greater than or equal to \(X\), we assume that BS gets a reward (win), otherwise gets a no-reward (lose). Here, \(X\) is defined as the reward judgment factor. The threshold value is updated by updating the threshold adjuster value in Equation (10). After the update, BS generates the next user pairing and transmits data to users again. Receiving and decoding data, users check bit errors and respond ACK or NACK to BS. BS updates the threshold of the laser chaos decision-maker. By repeating these processes, the BS searches for the optimal pairing.
## 2 Airborne Instruments and Interface with the ATR-42
### CAROLS Radiometer
#### 2.1.1.2 Technical Specifications  
The main technical specifications are presented in the following table (Table 1) where AFE and DFE refer to the 'Analog Front End' and 'Digital Front End' electronics respectively:  
\begin{table}
\begin{tabular}{|c|c|} \hline CAROLS: correlation radiometer with direct sampling \\ \hline Receiver type & Total power receiver \\ Operating frequency & [1.4–1.427 GHz] \\ Bandwidth & 24 MHz@ \(-\)3 dB \\ Polarization & Polarimetric (4 stockes parameters: I, Q, U, V) \\ \hline \end{tabular}
\end{table}
Table 1: Main technical specifications of the CAROLS radiometer.
# Optimization Design of the Spaceborne Connecting Structure for a Lightweight Space Camera
## 1 Introduction
By using their self-developed Tcl script, an optimum design of mirrors was achieved [9]. These studies show the practicability of the kinematic structures without fiction in the optical machinery and the development of the multi-objective optimization design of optomechanical structures. However, these are designs based on the known structures. It is still necessary to explore the configuration design methods of optomechanical structures and further structural optimization design methods integrating optical metrics. Topology optimization is one of the most effective methods in the structural conceptual design stage, which has attracted extensive attention in aerospace, automotive, and civil engineering. Some researchers have applied the topology optimization method to the design of optomechanical structures. [PERSON] et al. studied the topology optimization technology of a multispectral space camera, calculated the mass of its mirror and the sensitivity of the camera's Strehl ratio by using the direct differentiation method, and obtained topology optimization results of mirrors under different mass constraints by an optimality criteria method [10]. [PERSON] et al. established a topology optimization model with the mirror shape error caused by gravity and thermal effects as an objective function and the first-order natural frequency of mirror assembly as a constraint condition. The results showed that this optimization method significantly improves the optical performance of the mirror assembly [11]. These studies demonstrated the feasibility of topology optimization in the design of optomechanical structures. In this paper, we present an optimization method considering the LOS drift for designing a connecting structure. Qualitative analysis for the camera LOS drift is conducted, and the LOS drift is decomposed into quantities that are easy to analyze. The initial configuration of the connecting structure is obtained by topology optimization considering the relative displacements between mirrors. The optimal size parameters are obtained with detailed parameters optimization. Finally, performance analysis and evaluation results verify the effectiveness of the optimization method.
## 2 Literature Review
### FEM in Residual Stress Simulation  
Finite element modeling (FEM) has been extensively used in mechanical engineering to simulate various manufacturing processes and predict material behavior under different loading conditions. FEM is particularly useful for predicting residual stresses because it allows for a detailed, layer-by-layer analysis of how stresses develop during processes like deep rolling [21]. The primary advantage of FEM over experimental techniques is its ability to simulate different parameter settings without the need for costly physical experiments, making it a valuable tool for process optimization. Several studies have attempted to model the residual stresses in railway rails using FEM. [PERSON], [PERSON] et al. [22] developed a FEM model to simulate the deep rolling process on steel components and validated it with experimental data. Their results showed good agreement between the simulated and measured residual stresses, though the authors noted that discrepancies could still arise due to the difficulty of accurately capturing material properties and boundary conditions. Similarly, [PERSON], [PERSON] et al. [23] used FEM to predict residual stresses in railway rails subjected to rolling contact fatigue. Their study highlighted the importance of accurately defining the meshing, material properties, and boundary conditions in FEM to achieve realistic results. Despite the growing application of FEM in this field, challenges remain. One major issue is the difficulty of validating FEM results with experimental data, as minor discrepancies in model assumptions can lead to significant variations in predicted residual stresses. For instance, [PERSON], [PERSON] et al. [24] found that while their FEM model accurately predicted stress distributions near the rail surface, the deeper layers exhibited discrepancies when compared to experimental data. These findings emphasize the need for robust experimental validation in any FEM-based study [25].
# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
## 6 Future Research Directions
### Frame-Based Precoding  
Nowadays, satellite communication standards embed more than one user in each frame in order to increase the channel coding gain, exploiting the super-framing structure of the latest physical layer evolutions in satellite communications. Due to the framing constraints, the same precoder needs to apply to different data of multiple receivers, which entails a modification of the conventional precoding scheme and leads to the consideration of precoding on a frame-by-frame basis. Specifically, major concerns are how to select the users that are grouped in the same frame as well as how to calculate the precoding matrix. To further improve the system performance, it is imperative to design an advanced frame-based precoding scheme while maintaining a low computational complexity.
## 3 Results
### The Recognition Performance of Transhumeral Subjects  
Table 5 shows the recognition performance of DAIDA for transhumeral amputees under different training schemes. The recognition accuracy tested directly after pretraining the model using only sEMG signals under all arm positions is 42.08% \(\pm\) 8.21%. The recognition accuracies are 43.61% \(\pm\) 8.77%, 94.93% \(\pm\) 3.21% and 40.60% \(\pm\) 9.68% when using data from a single arm position to pretrain and test the performance of the domain adaptation in the source and target domain data as well as the generalization performance of the models corresponding to scenarios 2 to 4, respectively. When pretraining using data from all arm positions to detect the performance of domain adaptation effects on the total dataset, the recognition accuracies were 94.06% \(\pm\) 4.18% and 58.43% \(\pm\) 8.38%, corresponding to schemes 5 and 6, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Labeled Source** & **Unlabeled Target** & \multirow{2}{*}{**Testing Data**} & \multirow{2}{*}{**Accuracy (\%)**} \\  & **Domain Data** & **Domain Data** & & \\ \hline
1 & x, y, z & \(\backslash\) & x & 42.08 \(\pm\) 8.21 \\
2 & x & y & x & 43.61 \(\pm\) 8.77 \\
**3** & x & **y** & **y** & **94.93\(\pm\)3.21** \\
4 & x & y & z & 40.60 \(\pm\) 9.68 \\
**5** & **x, y, z** & **y** & **y** & **94.06\(\pm\)4.18** \\
6 & x, y, z & y & NOT y & 58.43 \(\pm\) 8.38 \\ \hline \hline \end{tabular}  
* The characters x, y, z each represent one arm position, respectively (x, y, z \(\in\) (1,2,3) and x \(\
eq\) y \(\
eq\) z). Bold indicates the best results.  
\end{table}
Table 5: Recognition results of different training programs for the transhumeral amputees.  
Figure 7: Performance of position separability. Each confusion matrix represents the classification performance of three arm positions for one motion.
## 4 Simulation Results
### Case Study 1  
In this case, the PV and WT will supply their maximum available generating power according to the hourly forecast. The proposed EMS controls and sets the output power of the other DG units to decrease the generating cost and according to their corresponding power limits and constraints.  
The results of the proposed algorithm are tabulated in Table 3. When observing the behavior of the proposed EMS during the first and last eight hours of the day, results show that during these periods the proposed EMS depends on the FC and the utility grid for generating the largest portion of output power. This occurs because the energy price during these periods is low which means that generating electricity from the utility grid is cheaper than the DEGs. This is not the case at midday, when the EMS mainly depends on the PV, WT and DEG units as the energy price of the utility grid is expensive.