esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results  
### Simulation Setup  
In the simulation, a HetNet with three SBSs and five UEs uniformly located in a macrocell with radius 500 m is considered. Backhaul transmission model considered in [35] is adopted in the simulations. The MBS is equipped with 100 antennas and has 20 beamforming groups [35]. Slow Rayleigh fading channels are adopted for simulations where the channel remains unchanged throughout each episode. The Rayleigh channel coefficient is modeled as \(h\sim\mathcal{CN}(0,1)\). We also adopt the non-line-of-sight path-loss model for urban MBSs and SBSs [36]. Each subchannel is randomly allocated to a user, and the subchannel allocation is assumed known for the agent. The other settings of the simulation are summarized in Table 3. The Adam optimizer is employed for all DNNs that are embedded in P-DQN. The \(\epsilon\)-greedy algorithm and Ornstein-Uhlenbeck noise is used for explorations of discrete actions and continuous parameters, respectively. We set the threshold \(r_{th}=0.1\), discount factor \(\gamma=0.95\), batch size \(N=128\), the maximum number of episodes as 2000, and the maximum steps per episode as 100. Other parameter settings used in the P-DQN are given in Table 4. The simulation codes used in this research can be found in [37].
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 5 Advancements in Optical Wireless Communications
### Optical Camera Communication (OCC)
Some of the applications for hybrid RF-optical, optical-optical, and convergent RF-optical systems are summarized in Table 2. The integration of both technologies is possible with a wireless RF-optical hybrid network. If the user demands NLoS communication or more mobility, RF wireless technology is utilized. If a user needs a higher throughput, an optical wireless network is recommended. Indoor and outdoor applications, as well as automotive and underwater communications, can benefit from the use of RF-optical wireless hybrid systems. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Context** & **Type of Application** & **Ref.** & **Description** & **Hybrid Network Type** \\ \hline \multirow{8}{*}{Indoor} & Link reliability improvement, energy efficiency, secrecy performance, energy & [390, 391, 392, 393, 394, 395, 396, 397] & Convergence of two or more different networks improves link reliability. & WiFi/VLC, PFLC/VLC/RF, WiFi/LiFi, \\  & harvesting, and the IoT & & networks improves link reliability. & VLC/Femto-cell, \\  & & & Femto-cell/LiFi, and \\  & Localization/ positioning/ navigation & [398, 399, 400, 401] & A hybrid system is used to improve accuracy in indoor CATV, mobile robots, and smart phones. & SMF/PCF/GL-POF/VLC, Infrared/VLC, LiFi/OCC, Macro-cell/OCC, and Macro-cell/VLC \\  & & By providing network-sharing capabilities, hybrid systems ensure high-data-rate services. From & Macro-cell/VLC/WiFi, VLC/LTE, VLC/Femto-cell, PLC/VLC, and VLC/RF \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Synopsis of hybrid and convergent systems with their applications. Figure 38: Various modulation techniques being used in WOC.
# Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
## 2 Theorem and Methods
### Station Clock Estimation  
#### 2.3.1 Station Clock Description  
Station clock error is one of the main contributing sources in VLBI, while DeltaDOR could eliminate station clock drift by quasar-spacecraft-quasar sequence observation and linear interpolation, and highly stable hydrogen maser clocks in VLBI station keeps nonlinear error source small. However, a priority station clock value is part of aprioriity delay model, which is essential to correlator. Without aprioriity model with proper accuracy, fringe of quasar may not be obtained, and it becomes even worse in the case of low SNR, such as lower flux of quasar.  
CLTC uses GPS common view technique measurement system (see Figure 4) to determine station clock information, which is expressed with two parameters to model the clock, an offset and a rate. It is noticed that this method can measure the approximate clock value of the VLBI system because of different signal propagation path, but it is enough for  
Figure 3: Flow scheme of the algorithm.  
correlation. The clock model value could be used directly to refresh the aprioriity model for correlator within CLTC deep space stations. Meanwhile, when it comes to correlation raw data from different agencies, such as in cross support, clock information may not be provided or the accuracy of aprioriity clock information from different agencies may be limited, and sometimes it has to perform correlation without proper clock information.
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 2 Likelihood-Based Modulation Recognition Methods
### Signal Model  
The complex baseband form of the received signal in an additive white Gaussian noise (AWGN) channel can be expressed as:  
\[r(\mathbf{t})=s(\mathbf{t})+n(\mathbf{t}), \tag{1}\]  
where \(s(t)\) is the modulated signal, and \(n(t)\sim\mathcal{CN}\big{(}0,\sigma^{2}\big{)}\) represents the noise. After discretization, the received signal for the \(n\)-th symbol is:  
\[r_{n}=s_{n}(\mathbf{u}_{i})+n_{n}, \tag{2}\]  
here, \(\mathbf{u}_{i}\) represents the set of parameters under the modulation hypothesis \(H_{i}\), including amplitude, phase, symbol timing, etc. Under the hypothesis \(H_{i}\), the conditional probability density function (PDF) of the received signal is:  
\[p(r_{n}|H_{i},\mathbf{u}_{i})=\frac{1}{\pi\sigma^{2}}\text{exp}\left(-\frac{|r _{n}-s_{n}(\mathbf{u}_{i})|^{2}}{\sigma^{2}}\right), \tag{3}\]  
where \(\sigma^{2}\) is the noise power, and \(|r_{n}-s_{n}(\mathbf{u}_{i})|^{2}\) represents the squared Euclidean distance between the received symbol \(r_{n}\) and the hypothesized signal \(s_{n}(\mathbf{u}_{i})\). For \(N\) independent observed samples, the joint likelihood function is:  
\[\Gamma(\mathbf{R}|H_{i})=\prod\
olimits_{n=0}^{N-1}p(r_{n}|H_{i},\mathbf{u}_ {i}), \tag{4}\]  
where \(\mathbf{R}\) denotes the set of received signal samples, i.e., \(\mathbf{R}=\{r_{0},r_{1},\ldots,r_{N-1}\}\).
## 4 Fatigue Life Analysis of Suspension Control Arm
### Fatigue Life Prediction of Control Arm  
The number of fatigue cycles of the suspension control arm is usually greater than \(10^{5}\), which belongs to the high cycle fatigue failure. Thus, the nominal stress method was adopted to predict its fatigue life. It was firstly necessary to determine the S-N curve, in which one axis denotes stress and the other axis is the number of cycles. The relationship between fatigue life and stress can be expressed by S-N curve, and its mathematical expression is:  
\[\sigma_{a}=\sigma_{f}^{\prime}\left(2N_{f}\right)^{b} \tag{18}\]  
where \(\sigma_{a}\) represents the stress amplitude; \(\sigma_{f}^{\prime}\) represents the fatigue strength coefficient; \(N_{f}\) represents the number of load cycles; and \(b\) is fatigue strength index. The material of the control arm was QSTE420 TM, its yield strength was 420 MPa, and its ultimate strength was 520 MPa. Then, the S-N curve was estimated using the material mapping, as shown in Figure 8.  
Figure 8: S-N curve of control arm material.  
Figure 7: Load spectrum of control arm.  
The S-N curve is generally measured under the stress ratio of \(-1\), which means the compressive stress equals the tensile stress. However, the stress ratio of control under load spectrum usually may not be \(-1\). In a certain range, the compressive stress increases the fatigue limit while the tensile stress decreases the fatigue limit. Therefore, it is necessary to correct the effect of stress ratio on fatigue damage when the mean stress is not zero. Goodman theory can be used to correct the mean stress, that is:  
\[\frac{S_{n}}{S_{e}(\mathbb{R}=-1)}+\frac{S_{m}}{\text{UTS}}=1 \tag{19}\]  
where \(S_{a}\) and \(S_{m}\) are the stress amplitude and mean stress of the material under load spectrum, respectively; UTS is the ultimate tensile strength of the material; and \(S_{e}\) is the stress amplitude when the stress ratio is \(-1\).  
The load spectrum of the above nine cases were integrated with stress analysis results of the control arm under unit load to calculate the dynamic stress time histories. The Goodman method and the survival rate of 95% were adopted to conduct the S-N curve correction. The nominal stress method was then used to predict the fatigue life of the control arm, as shown in Figure 9.  
It can be seen from Figure 9 that the minimum number of fatigue cycles of the control arm was 1.393 \(\times\) 10\({}^{6}\), which appeared near the front point connection. According to the length of established road model, the fatigue life durability mileage of the control arm was 1.393 \(\times\) 10\({}^{6}\)\(\times\) 0.16 = 222,880 km. By considering the enhancement coefficients of the pebble road, washboard road, and Belgian road, which took the value of 5.8, 4.9, and 12.6, respectively [35], the enhancement coefficient of the durability road model was calculated as 5.8 \(\times\) 0.25 + 4.9 \(\times\) 0.25 + 12.6 \(\times\) 0.5 = 8.975. Thus, the minimum life mileage of the control arm was 222,880 \(\times\) 8.975 = 2,000,348 km. The life mileage had a large margin, and there is potential for lighter weight.
# Mechanical Fault Prognosis through Spectral Analysis of Vibration Signals
## 2 Key Algorithms Based on Spectral Analysis
### Envelope Spectrum
Therefore, Equation (11) is sampled at a sampling rate \(f_{s}\) to yield the discretized analytic sequence \(\mathbf{z}=\{z_{0},\cdots,z_{N-1}\}\),  
\[z_{n}=\frac{1}{N}\sum_{k=0}^{N-1}Z_{k}W_{N}^{-kn}=\mathbb{F}^{-1}[X_{k}H_{k}]\; \Rightarrow\;\mathbf{z}=\mathbb{F}^{-1}[\mathbf{X}\mathbf{H}]. \tag{19}\]  
Obviously, the envelope sequence, \(\mathbf{b}=\{b_{0},\cdots,b_{N-1}\}\), is given as  
\[\mathbf{b}=|\mathbf{z}|\;, \tag{20}\]  
and the envelope spectrum sequence, \(\mathbf{B}=\{B_{0},\cdots,B_{N-1}\}\), can be immediately obtained as  
\[B_{k}=\left|\sum_{n=0}^{N-1}|z_{n}|W_{N}^{nk}\right|=|\mathbb{F}[|z_{n}||]|\; \Rightarrow\;\mathbf{B}=|\mathbb{F}[|\mathbf{z}||]. \tag{21}\]  
Correspondingly, the instantaneous frequency \(f_{n}\) is given by  
\[f_{n}=\frac{\varphi_{n}-\varphi_{n-1}}{2\pi T}=\frac{f_{s}}{2\pi}\angle \left(z_{n}z_{n-1}^{*}\right)\;, \tag{22}\]  
where the sampling interval \(T=1/f_{s}\), and the symbols \((\cdot)^{*}\), \(\angle(\cdot)\) denote the conjugate and angle of a complex number, respectively.
# WaveConv-sLSTM-KET: A Novel Framework for the Multi-Task Analysis of Oil Spill Fluorescence Spectra
## 1 Introduction
First, a new data acquisition approach enhances reliability and diversity by using a custom-designed detection system combined with an environmental simulation setup to collect spectra from five engine oils at various thicknesses. Second, a lightweight multi-task framework, named the Wavelet CNN-sLSTM-Kolmogorov-Arnold Network-Enhanced Transformer (WaveConv-sLSTM-KET), hereafter referred to as the Hybrid framework, is developed. This framework integrates noise-adaptive multi-scale local feature extraction, sequential dependency modeling, and global dependency representation with nonlinear feature mapping to enable simultaneous qualitative and quantitative analyses. Experimental results show that the proposed method achieves accurate oil-type identification and thickness prediction without preprocessing. It outperforms traditional methods in thickness prediction accuracy and demonstrates robust performance against variations in detection distance and environmental conditions. Furthermore, by eliminating fully connected layers, the framework reduces the parameter count, facilitating deployment on resource-limited systems.
# Knee Loading Asymmetries During Descent and Ascent Phases of Squatting After ACL Reconstruction
## 4 Discussion
#### Strengths and Limitations  
This is the first study to compare loading asymmetries between the injured and uninjured limbs during the descent and ascent phases of bilateral squats after ACLR. By breaking down the movement into its directional components, we gain novel insight into the specific biomechanical strategies patients use after ACLR, which could inform more targeted rehabilitation protocols to address persistent deficits. Limitations of our study include a relatively small sample size of athletic participants with a young average age (19 years). Therefore, our findings may not be generalized to older populations. In addition, the differences in vGRFi, KFMi and HKRi interlimb ratios between the descent phase and ascent phase were relatively small; it is unknown if these differences are clinically meaningful. Finally, not controlling squat depth across participants aided in the generalizability of our study findings to real-world movements but could have impacted findings.
## 4 Discussion
In conclusion, the results obtained from the roughness analysis suggest that 1064-nm Nd:YAG treatment can be considered the most invasive, while both the 1064-nm Q-switch Nd:YAG nanosecond pulse and 980-nm diode do not significantly affect surface topography. Our 1064-nm Nd:YAG effects meet well with recent work on the erbium-doped yttrium aluminum garnet laser (Er: YAG) at 4 W and 400 mJ/pulse, which induced melting, micro-cracks, signs of coagulation, and microfractures on grade-4 titanium [31]. Conversely, the Er: YAG at 1 W and 100 mJ/pulse affected the titanium surface, which decreased its porosity and micro-roughness; both represent a positive alteration in terms of prevention of bacterial adhesion [31]. Lastly, considering that the defined thermic rise begins at 5 \({}^{\circ}\)C [17] for biological tissue, such as the bone around implants during the osseointegration, we observed that only the 1064-nm Q-switch Nd:YAG nanosecond pulse laser was able to perform its action, without increasing the temperature of the metal beyond that threshold. The 980 nm diode lasers and 1064 nm Nd:YAG instead created a very high-temperature difference and were not compatible with the health of the bone cells around the implant.
## 5 Performance and Simulations of Instantaneous Frequency Rate Tracking Approach
### Tracking Threshold Evaluation
#### 5.4.1 Aerobraking Flight  
Three groups of processing parameters are used in the simulation to evaluate tracking thresholds with different integration times. The detailed simulation parameters are shown in Table 2.  
The simulated probability of loss of lock for different integration times is shown in Figure 17. The tracking threshold SNR for 0.15 s integration is about 25.2 dB/Hz, while for 0.2 s and 0.25 s integration, the threshold SNR is 23.6 dB/Hz and 22.6 dB/Hz, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Group** & **Group 1** & **Group 2** & **Group 3** \\ \hline \(F_{S}\) & & 200 kHz \\ \(f_{s}\) & & 4 kHz \\ Integration time & 0.15 s & 0.20 s \\ Simulation trials & & 100 times \\ \hline \hline \end{tabular}
\end{table}
Table 2: Processing parameters for tracking threshold simulation evaluation.  
Figure 17: Probability of loss of lock versus carrier-to-noise density ratio (CNR).  
As a comparison, the predicted RMSE (root of mean squared error) performance of the frequency and frequency rate for 0.15 s, 0.2 s, and 0.25 s of integration is computed by Equations (61) and (62), and plotted in Figure 18, which seems to be consistent with the simulation results shown in Figure 17. RCRLB is short for Root of Cramer-Rao Lower Bound.  
The simulation results of 100 trials for aerobraking flight with 22.6 dBHz and 21.8 dBHz CNR are separately shown in Figures 19 and 20, respectively.
# Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation
## 2 System Model  
Consider a \(N\times M\) uplink large MIMO model, where each one of the \(M\) single antenna devices transmit a symbol from a complex \(m_{k}\)-QAM alphabet, denoted by \(\mathcal{A}^{(c)}\), and where \(m_{k}\) corresponds to the constellation size of the \(k\)th user. The possible transmit symbols by the \(k\)th user are taken from an \(\mathcal{M}_{k}\)-QAM alphabet \(\mathcal{A}^{(c)}\), such that \(\mathcal{A}^{(c)}=\mathcal{A}+j\mathcal{A}\), where \(\mathcal{A}=\{-\log_{2}\sqrt{\mathcal{M}_{k}}+1,...,-1,1,\log_{2}\sqrt{ \mathcal{M}_{k}}-1\}\). Let \(x_{m}\in\mathcal{A}^{(c)}\) denote the transmitted symbol from user \(k\) and \(\mathbf{x}=[x_{1},...,x_{M}]^{T}\) the vector containing the \(M\) complex symbols. The transmitted symbols from each user (or antenna) are assumed to have normalized energy equal to one, that is \(\mathbb{E}\{\mathbf{x}^{H}\mathbf{x}\}=M\), where \(\mathbf{x}^{H}\) is the Hermitian transpose of the virtual vector transmitted by the terminals. The uncoded transmitted signal \(\mathbf{x}\) passes through a channel (known at the receiving BS), characterized by the matrix \(\mathbf{H}\in\mathbb{C}^{N\times M}\) and is received by a BS with \(N\) antenna elements. Thus, the received signal at the base station \(\mathbf{y}\) can be expressed as:  
\[\mathbf{y}=g\mathbf{H}^{(c)}\mathbf{x}+\mathbf{n}, \tag{1}\]  
where \(g=\sqrt{\frac{\mathrm{SNR}}{M}}\) corresponds to the average gain of the channel and \(\mathbf{n}\) represents the additive noise at the receive BS. Both entries of \(\mathbf{H}\) and \(\mathbf{n}\) are i.i.d. random variables taken from a complex normal distribution \(\mathcal{CN}(0,1)\). For implementation purposes, the complex-valued system model is converted into a real-valued one (as in [11] or [23])  
\[\left[\begin{array}{c}\mathfrak{R}\{\mathbf{y}\}\\ \mathfrak{I}\{\mathbf{y}\}\end{array}\right]=\left[\begin{array}{cc} \mathfrak{R}\{\mathbf{H}^{(c)}\}&-\mathfrak{I}\{\mathbf{H}^{(c)}\}\\ \mathfrak{I}\{\mathbf{H}^{(c)}\}&\mathfrak{R}\{\mathbf{H}^{(c)}\}\end{array} \right]\left[\begin{array}{c}\mathfrak{R}\{\mathbf{x}\}\\ \mathfrak{I}\{\mathbf{x}\}\end{array}\right]+\left[\begin{array}{c} \mathfrak{R}\{\mathbf{n}\}\\ \mathfrak{I}\{\mathbf{n}\}\end{array}\right], \tag{2}\]  
therefore, designating \(n=2M=2N\), the _real_ channel matrix \(\mathbf{H}\in\mathbb{R}^{n\times n}\), while both vectors \(\mathbf{x},\mathbf{n}\in\mathbb{R}^{n\times 1}\) and the complex constellation \(\mathcal{A}^{(c)}\) is now a real-valued set of symbols denoted by \(\mathcal{A}\).
## 3 Results and Discussions
### Storey Drift Responses  
Storey-drifts refer to the difference of displacement of two consecutive stories. Peak storey drift responses for both near- and far-field stations of the Chi-Chi earthquake are presented in Figure 11. The sme legends have been adopted as discussed previously with green, blue, and red representing fixed base, passively isolated, and MRE BIS, respectively, and a box marker against near-fault and a star marker against far-field earthquakes are used. Firstly, it is evident that storey drift responses decrease as we move from the bottom to the top storeys. It can also be observed that for both the near-field and far-field time histories, MRE BIS has the lowest response, while FBS shows the highest storey drift response. This validates the applicability of MRE BIS to earthquakes of both types, i.e., near-field and far-field. Similarly, it is evident that response against near-field earthquake time histories is higher than far-field earthquake time histories for all the three structures under consideration.  
A comparison of peak storey drift response for all the adopted earthquake time histories is presented in Figure 12. Greater response for passively isolated structure and greater subsequent reduction in response can be observed for near-field earthquakes compared to far-field ones. It can also be observed that storey drift response of passively isolated structure against Kocaeli far-field station is very low compared to that of other responses, thus its response reduction of MRE BIS compared to a passively isolated structure is also lower.  
Figure 11: Peak storey drifts, Chi-Chi earthquake.  
Figure 12: Peak storey drifts response at storey 1.  
The average storey drift response improvement of MRE BIS compared to passively IS is presented in Figure 13. It is evident that response improvement is greater for the near-fault stations compared to the far-field ones. However, no reduction in average response for Kocaeli far-field station earthquake case can be observed due to the discussed reasons [21; 35; 36] in the literature which states that this behavior is typical for hybrid base isolation systems where the supplementary force may forcefully confine the base displacement of the passive base isolation system at the expense of larger accelerations, as well as increased inter-storey drifts to the superstructure.
## 3 Results and Discussion
### Effect on the Physical Characteristics  
In these tests, the porosity, P-wave velocity, dry density, and SEM images were used to assess the physical changes in the tested samples. For the samples that were subjected to 30 F-T cycles, a few cracks appeared on the surface with a small number of mineral particlesspalling (Figure 5a). Besides, the failure modes of the samples varied after different F-T cycles, showing from a shear failure to the typical tensile-shear failure (Figure 5b). As the number of F-T cycles increased, the F-T damage promoted the formation of loose structures within the sample, leading to a reduction in the internal friction angle [17]. Therefore, the angle between the failure surface and the maximum principal stress (axial direction) gradually increased, and the sample gradually changed from shear to tensile-shear failure. Furthermore, the F-T action leads to the formation of microcracks in the rock, so that the number of macroscopic cracks increases with the F-T action under load.