esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Orbit Determination Tool  
The Goddard Trajectory Determination System (GTDS) is utilized for both user and TDRS satellites to perform state vector propagation, orbit determination, ephemeris generation, and tracking data evaluation [10]. GTDS uses a batch estimation technique based on a weighted least squares approach, which consists of five main steps.  
In the first step, the state vectors and state-transition matrix are propagated using a Cowell-type numerical integration method. This technique allows for the determination of the satellite's position and velocity as a function of time based on the forces acting on the spacecraft, such as gravity, solar radiation pressure, and atmospheric drag.  
In the second step, the observations and their partial derivatives are computed using integrated trajectory and measurement models. GTDS can process numerous types of measurements simultaneously, which can be broadly classified into three main categories: Range, Doppler, and Angles. The trajectory models account for the spacecraft's motion, and the measurement models represent the signals transmitted and received by the satellite, which are affected by various factors such as atmospheric conditions and equipment performance.  
The third step involves computing a series of observation residuals by taking the difference between real observations and computed observations over a selected period, known as the data arc. This comparison provides a measure of the accuracy of the computed trajectory and helps to identify any discrepancies.  
The fourth step is to form the error function by summing the weighted squares of these observation residuals. The weighting factors are determined based on the expected accuracy of the observations and the modeling errors. The error function provides a measure of the error between the real observations and the computed observations.  
Figure 4: A graphic representation of the Monte-Carlo analysis method employed to obtain DOWD orbits from TDRS orbits, which are determined solely using TT&C tracking.  
In the final step, the error function is minimized with respect to the selected subset of the estimation parameters. This process is repeated several times until convergence is achieved. Each iteration starts with a new trial trajectory generated using the estimation parameters obtained from the previous iteration. This approach allows for the refinement of the estimation parameters and the improvement of the accuracy of the computed trajectory.  
For several decades, GTDS has been an invaluable asset to the FDF. This tool has been an essential component of the FDF's operational procedures for navigating numerous satellites and computing DOWD-based user ephemerides.
## 2 Materials and Methods
### Model of Dislocation-Precipitate Interaction
The resolved shear stress \(\sigma\) in the system determines the forces \(F_{x}\) and \(F_{a}\) acting on the moving edge and screw segments in accordance with [57; 58] as:  
\[F_{x}=\left\{ \begin{array}{ll}b\sigma-\varepsilon_{2e}\frac{d}{ D}[\eta(x,L-l+x)+\eta(L-l+x,x)],&\text{stage (A);}\\ b\sigma-\frac{2\varepsilon_{p}}{D-d}\text{stage (B);}\\ b\sigma-\frac{d-2e}{D-2a}\varepsilon_{2e}[\eta(x-l,L-x)-\eta(L-x,x-l)]-\frac{ 2}{D-2a}\Big{[}\varepsilon_{1}+\varepsilon_{2s}\gamma\Big{(}a,\frac{D-2a}{2} \Big{)}\Big{]},&\text{stage (C);}\\ b\sigma-\varepsilon_{2e}\frac{d}{D}[\eta(x-l,L-x)-\eta(L-x,x-l)],&\text{stage (D).}\\ \end{array} \right. \tag{1}\] \[F_{a}=\left\{ \begin{array}{ll}b\sigma^{\prime}+\frac{1}{x-l} \big{[}\varepsilon_{p}+\varepsilon_{1}+\varepsilon_{2e}\gamma(x-l,L-x)\big{]}- \varepsilon_{2s}[\eta(a,D/2-a)-\eta(D/2-a,a)],&\text{stage (C);}\\ \text{undefined at stages (A), (B) and (D).}\end{array} \right. \tag{2}\]  
For briefness we introduce the following functions [57]:  
\[\gamma(x,y)=\log\biggl{(}\frac{\min(|x|,|y|)}{R_{0}}\biggr{)},\qquad\eta(x,y)= \frac{1}{x}\Theta(|y|-|x|) \tag{3}\]  
Figure 3: Stages of dislocation–precipitate interaction: (**A**) dislocation free slip in front of \(\theta^{\prime}\); (**B**) dislocation looping around \(\theta^{\prime}\); (**C**) closing the Orowan loop; (**D**) dislocation slip behind \(\theta^{\prime}\). where \(\Theta(x)\) is the Heaviside step function. The equations of motion for dislocation in aluminum matrix are used for calculations of the positions and velocities of moving dislocations [69]:  
\[m_{0}\tilde{x}=F_{x}\left(1-\dot{x}^{2}/c_{t}^{2}\right)^{3/2}-B_{0}\dot{x}, \hskip 28.452756 ptm_{0}\tilde{a}=F_{a}\left(1-\dot{a}^{2}/c_{t}^{2}\right)^{3/ 2}-B_{0}\dot{a}, \tag{4}\]  
where \(B_{0}\) is the coefficient of phonon friction at low dislocation velocity; \(c_{t}=\sqrt{G/\rho}\) is the transverse sound velocity where \(\rho\) is the mass density; and \(m_{0}\) is the rest dislocation mass. To determine the velocity of both edge and screw segments we use the same parameters of equation of motion found for the case of edge dislocation [69], because it was shown that the coefficient of phonon friction differs by about 15% for edge and screw segments [34]. The condition for the closure of the loop around the precipitate and the dislocation separation from the inclusion is chosen in the form \(a\leq R_{0}\).
## 4 Numerical Example
### Bridge System Reliability Estimation of the Single Failure Criterion  
According to the method proposed in this paper, the system reliability of the case bridge under different failure criteria could be evaluated. The system reliability evaluation under failure criteria I and II used the binary copula function. The ternary copula function was used for the system reliability evaluation under failure criterion III.
# Hybrid-Compliant System for Soft Capture of Uncooperative Space Debris
## 5 Simulation Study and Results
### Debris-Mass and Compliance Relation
Based on the diagrams, desired stiffness and damping coefficients of the hybrid system's equivalent compliance can be selected for a specific debris mass and minimum contact time required. In Figure 16, the relation of the variables is derived. In particular, when the equivalent stiffness increases for a specific minimum contact time, the range of the debris masses increases and the minimum debris mass to be targeted increases. One could say that small stiffnesses are appropriate for targeting a small range of small debris and larger stiffnesses are appropriate for targeting a wider range of debris masses of larger debris masses. Furthermore, when the minimum contact time required increases, the maximum debris mass to be targeted remains constant for a specific spring stiffness due to displacement limitations of the compliant parts and the minimum debris mass that can be targeted increases. Thus, when the minimum contact time required increases, the range of debris masses to be targeted decreases. Figure 16: Range of space debris masses to be targeted for different stiffness coefficients and minimum contact times achieved.
## 1 Introduction
The formal description in our paper will be given in terms of super-operators. The super-operator \(\overline{\mathbf{A}}\) is defined as a mapping of one area of quantum operators onto another one (in contrast to an operator, which defines the correspondence between two areas of functions). The formation of the mathematical theory of super-operators [40] began simultaneously with the emergence of quantum mechanics (the von Neumann equation and Lindblad master equation [41] are related to the typical class of super-operator equations for the matrix of density). In this paper, we introduce another concept of a linear super-operator, which allows us to apply the integral equations theory to the scattering of quantum light and quantum antennas. It is important to make a note on the role of losses in our concept of a super-operator. The losses in conventional quantum language mean the exchange of energy between the considered system (\"small\" system) and large external system described as a bath [42]. The \"small\" system is considered from the point of view of an open system. If we speak about thermal losses, the energy exchange means the energy transformation to heat. In this case, the bath takes the form of a thermal reservoir. Such an exchange is accompanied by the production of noise, without which the correct commutation relations become unattainable. A similar situation takes place for an active medium: the gain is necessarily accompanied by the noise [43,44,45]. The modeling of quantum light scattering and quantum antenna emission in this general language leads to the description of every partial region as an open quantum system. It continues the concept formulated previously for quantum antennas [7]: the emission of a quantum antenna looks like a loss from its point of view (in spite of the absence of any absorption). Therefore, the external space with the receiver and the environment is considered as a bath of non-thermal origin. This allows us to apply the general strongly developed theory of open systems [42] to quantum antennas. The subject of this paper is an application of the super-operator concept to the scattering of quantum light and quantum antennas. The paper is organized as follows: the general form of the super-operator integral equation will be given in Section 2. The noise term will be considered and proved to have the form that guarantees the required form of commutation relations in the general case. The application of the developed approach to the quantum antennas will be given in Section 3. The manifestations of the noise component in the transmission reflection and scattering by the circular cylinder will be considered as examples in Sections 4 and 5, respectively. The conclusion and future perspectives are discussed in Section 6.
## 3 Experiment and Analysis
### Experimental System and Vibration Data  
The experimental dataset was collected on the PRONOSTIA platform, provided by the FEMTO-ST Institute [31]. This dataset was used in the IEEE PHM 2012 Data Challenge for predicting the RUL of bearings. The PRONOSTIA platform is shown in Figure 4. The experimental platform conducted accelerated degradation tests to collect degradation data of ball bearings until their total failure. The  
Figure 3: Proposed CWTCNN-HI model for Remaining Useful Lifetime (RUL) estimation.  
vibration signals were measured by two accelerometers placed on the vertical and horizontal axes. Acceleration measures were sampled every 10 s for a sample period of 0.1 s and a frequency of 25.6 kHz.  
In the experiment, 17 bearings were tested under three different operating conditions (i.e., 1800 rpm and 4000 N, 1650 rpm and 4200 N, and 1500 rpm and 5000 N), as shown in Table 1. In the three operation groups, there were seven, seven, and three bearings. The first two bearings of each group were used for training the run-to-failure dataset, and the remaining 11 bearings were truncated to predict the RUL. The bearings were naturally degraded without initially assuming a type of failure. Therefore, the degradation patterns of the bearings differed from each other. Any failures of the balls, rings, or cage could occur simultaneously.
## Materials and Methods
### Data Acquisition  
#### 2.2.1 In Situ Data  
Four datasets comprising in situ SD data were collected: Dianchi Lake (from 2015 to 2020), Yilong Lake (from 2010 to 2019), Fuxian Lake (from 2012 to 2017), and Erhai Lake (from 2012 to 2017). Sampling sites were spaced at least 1 km apart, with 10 points in Dianchi Lake, 18 points in Erhai Lake, 13 points in Fuxian Lake, and 3 points in Yilong Lake, with a sampling frequency of once a month. Table 1 presents the statistics of SD measured in the four lakes.
### Background and Related Work
#### 1.1.1 Channel Models
Simulations and measurements in an office room for frequencies up to 30 GHz are presented in [39], and the indoor radio channel of a corridor and stairwell is characterized in [40] at 38 GHz. In [39], the authors draw the same conclusion as in [37], i.e., room furniture impacts the multipath characteristics of the channel. The indoor industrial channel at 28 GHz is characterized in [41], where a mean root-mean-square (RMS) angular spread value of 36\({}^{\circ}\) is reported for LOS scenarios, and 63.6\({}^{\circ}\) for NLOS.
# Enhanced Learning Behaviors and Ability Knowledge Tracing
## 6 Results and Analysis
### Ablation Experiments (**Rq2**)  
In this section, we propose three ablation models based on LBAKT to verify the effectiveness of each model item. The ablation model details are as follows:  
* Ablation 1: Compared to the proposed model, this ablation model does not have the difficulty of the exercise. It is used to test the influence of the difficulty of the exercise.
* Ablation 2: In this ablation, we do not have the forgetting layer. It is used for a performance comparison with the full model, considering the significance of forgetting in the learning process.
* Ablation 3: This model does not consider answer time and hint times. This is done to assess the impact of students' learning ability on the proposed model.  
Table 4 presents the results in terms of AUC and ACC with LBAKT serving as the baseline. In the table, we performed three ablation experiments, where LBAKT-D refers to LBAKT without the difficulty of the exercise, LBAKT-F is LBAKT without the forgetting layer, and LBAKT-T refers to LABKT with the answer time and hint times removed. From Table 4 we can infer that when the difficulty component was removed, the model's AUC slightly decreased to 0.80994, showing a marginal reduction of 0.43% compared to the  
Figure 3: Comparison results of the influence of learning sequence length of LBAKT and LPKT on ASSISTChall.  
Figure 2: Comparison of model prediction curves.  
baseline. Similarly, ACC dropped to 0.75259, indicating a minimal decline of 4.8%. This suggests that the difficulty component contributes positively to the model, but its impact is relatively small. Eliminating the forgetting component resulted in a more noticeable decline in performance. AUC dropped to 0.78919, which is a decrease of 2.551% from the baseline. ACC also fell to 0.73583, showing a reduction of 1.724%. These results highlight the importance of the forgetting component in maintaining the model's performance. The most significant performance drop was observed when both the attention and hint components were removed. AUC decreased to 0.77213, which is a reduction of 3.824%. ACC fell to 0.72122, indicating a decrease of 3.103%. This obvious decline demonstrates that both answer time and hint times are crucial components for the model's effectiveness. To enhance the clarity and impact of our ablation study results, we have illustrated them using a bar chart, as depicted in Figure 4. From Figure 4, for either the ACC or AUC values of the model, it can be seen that the most significant performance drop is observed when both the forgetting layer and the answer time and hint times were removed. This obvious decline demonstrates that all these components are crucial for the model's effectiveness. The ablation experiments confirm that these factors lead to the most effective student performance prediction.
## 3 Applications of the Tactile Internet  
In various scenarios, communication will be enhanced through the Tactile Internet (TI), which will lead to increasingly reasonable social interaction. A portion of the imagined 5G-enabled tactile applications requires competencies such as RTT (1 ms or below), ultra-high availability and ultra-high reliability, whose reliability should be significant orders higher than in the current radio access network. It is also seen that existing wireless or cellular networks do not yield anything to accomplish an ultra-low latency of 1 ms and ultra-high reliability of 99.999%, which are essential for TI applications. In this way, it is troublesome to grasp all the possible emerging tactile applications at the early stage of 5G development. It could play a crucial role in eradicating the socio-economic boundaries of society [1]. Once the challenges are fulfilled, numerous applications can be explored and achieved with 5G mobile communications. Hence, the taxonomy of possible applications of TI with 5G framework and their requirements are presented in Table 1. However, TI can support various applications as some of the ideas are shared as follows.
## 5 Performance Evaluation
We then measured the completion time for data collection from all member nodes within a cluster and calculated the mean recovery latency time after running the conditions 10 times. The  
Figure 10: Recovery latency comparison between checkpointing and non-checkpointing LEACH by obtained using the GloMoSim simulator. Figure 9: Comparing average energy consumption of selected node group sizes. implementation results (Figure 11) were similar trend to simulation result in Figure 10. As in the simulation results, the implementation results showed that recovery latency using our checkpointing scheme steadily increases, while that of the original LEACH increases exponentially. Therefore, our scheme is also more efficient than previous clustering routing protocols without checkpointing in terms of energy consumption and recovery latency.
## 3 Raman Laser Spectrometer  
### Excitation Source  
The scheme of the spectrometer is shown in Figure 1. As the excitation source, we employed a continuous-wave diode-pumped solid-state laser (MSL-R-532, CNI-Lasers) emitting \(\sim\)5 W of linearly polarized output at a wavelength of 532.123 nm with a full-angle divergence of 1.5 mrad, the beam quality parameter M\({}^{2}\)x\({}_{,y}\) <1.1 (TEM\({}_{00}\) mode) and a laser linewidth of <1 pm (\(\sim\)0.03 cm\({}^{-1}\)). The excitation beam was expanded using a \(\times\)7 Vega laser beam expander (\(\lambda\) = 532 nm, Edmund Optics) and focused into the gas cell using a specially designed doublet lens with a focal length f = 55.9 mm with both sides coated in antireflection (AR) coating at 532 nm. The measured spot size in the focus was less than 10 \(\upmu\)m (diameter, as measured by the optical knife method). The peak on-axis laser intensity was then about \(\sim\)10 MW/cm\({}^{2}\). More details about the focusing system can be found elsewhere [30].  
Prior to the beam expanding system, we installed a system based on a pair of crossed polarizers (P1 and P2) and a \(\lambda\)/2 waveplate and a Faraday rotator placed between them. It prevented back-scattered  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gas** & **CP, \%** & **\(\delta\)C, \%** \\ \hline \({}^{12}\)CO\({}_{2}\) & \(>\)99.987 & 99.992 \\ \({}^{13}\)CO\({}_{2}\) & \(>\)99.994 & 99.57 \\ \({}^{12}\)CH\({}_{4}\) & \(>\)99.5 & \(>\)99.99 \\ \({}^{13}\)CH\({}_{4}\) & \(>\)99 & \(>\)99.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical purity (CP) and carbon isotopic enrichment (\(\delta\)C) of the studied carbon dioxide and methane gases.  
Figure 1: (**a**) Scheme of the Raman gas spectrometer: 1—excitation laser, 2—lens expander, 3—focusing lens, 4—gas cell, 5—laser radiation absorber, 6—collecting lens, 7—notch filter, 8—focusing lens, 9—spectrometer, 10—computer; (**b**) system for attenuation and optical alignment of the laser beam: P1 and P2—polarizers, WP—half-wave plate, M1, M2—highly reflective (HR) mirrors.  
light from damaging the excitation laser. In addition, a pair of flat highly reflective (HR) mirrors was used to precisely align the focal spot position in the gas cell, Figure 1b.
## 4 Augmented Performance Analysis
### Tracking Sensitivity with LEO Assisted  
For comparison, the performance of the third-order PLL filter with second-order FLL assist [3] in a standalone receiver is analyzed by simulation firstly. The coherent integration time is set to be 20 ms due to the navigation bit reversal. The discriminator of the PLL is atan (\(Q_{P}\)/\(I_{P}\)), and that of the FLL is atan\(2(cross,dot)/\Delta t\). Figure 12 shows the phase jitters of the tracking loop under different coefficients. From the figure, we can see that the phase jitter and tracking sensitivity are basically dependent on the bandwidth of the PLL. When the PLL bandwidth is 10 Hz, it loses lock at 26 dB-Hz, regardless of the FLL bandwidth. In contrast, the PLL loop with 5 Hz has a smaller phase jitter and can track the 22-24 dB-Hz signal, which is almost consistent with the results of the classical reference [3]. Thus, in the 2 nd order FLL-assisted 3 rd-order PLL structure, the FLL can reduce the impact of the dynamic. However, when the dynamic is eliminated, the bandwidth of the PLL decides the tracking sensitivity. Moreover, a narrower PLL bandwidth induces a smaller phase jitter and a better tracking sensitivity.  
To solve the problem of the loss of lock under an ultra-low SNR condition, such as wideband jamming and signal blocked navigation, we propose the LEO-assisted GNSS tracking method in Figure 3. Since the dynamic is eliminated in the new method and the GNSS ephemeris can be received from the LEO, the receiver can extend the coherent integration time and reduce the loop bandwidth to track the ultra-weak signal. With the known ephemeris, a frequency-locked loop can be used instead of the PLL to improve the tracking sensitivity furtherly. In FLL tracking, the frequency jitter caused by mechanical vibration and Allan variance can be ignored [26]. The frequency jitter \(\sigma_{FLL}\) mainly comes from thermal noise.  
Figure 13 gives the simulation results of the frequency jitter caused by the thermal noise. It can be seen that frequency jitters keep decreasing as the C/N\({}_{0}\) increases. A long coherent integration time of 400 or 200 ms can greatly reduce the frequency jitter compared to that of the 20 ms. Nevertheless, the combination of 200 ms coherent integration and two non-coherent integrations can achieve almost the same performance as the 400 ms coherent integration. The result is coincident with the SNR analysis above. In addition, we can also  
Figure 11: Acquisition performance under different carrier-to-noise ratio (C/N\({}_{0}\)).  
figure out that the proposed method can track weak signals of 4 dB-Hz, just considering the thermal noise.
# Optimal Route Planning for Truck-Drone Delivery Using Variable Neighborhood Tabu Search Algorithm
## 5 Numerical Experiment
### Sensitivity Analysis
#### 5.3.1 Number of Truck-drones  
The number of truck-drones \(v\) is directly related to the total delivery time \(\tau\). It assumes that the \(v\) varies from 1 to 4, and the sensitivity analysis results of \(v\) are shown in Figure 6. With the increase in \(v\), \(\tau\) shows an obviously decreasing trend, but the decreasing speed of \(\tau\) gradually slows down, indicating that the marginal benefit generated by increasing \(v\) is gradually reduced; that is, after \(v\) reaches a certain amount, continuing to increase \(v\) will not significantly reduce \(\tau\).
## 2 Numerical Investigations
### Fatigue Simulation Analysis
#### 2.3.1 Fatigue Life of CFRP Shaft Tube under Different Load Coefficient  
The displacement-cycle number curve of the loading head when the output load coefficients are 0.7, 0.75, and 0.8 respectively, are shown in Figure 5.  
Taking the load factor of 0.7 as an example, it can be seen from Figure 4 that when the number of cycles N = 420,000, the displacement begins to decrease significantly, and the shaft tube has lost most of its bearing capacity. When N = 480,000, the downward trend of the curve disappears, indicating that the bearing capacity of the shaft tube has already been completely lost. At the same time, the shaft tube has been fatigued and damaged, and the fatigue life of the shaft tube is 480,000 cycles. In summary, the analysis shows that when the load factor is set to 0.7, 0.75, and 0.8, the fatigue life of the shaft tube is 480,000, 340,000, and 280,000 cycles, respectively. The smaller the load factor, the longer the fatigue life of the shaft tube.