esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussion
### Summary of OECS Performance  
Table 4 presents a summary of OECS performance for different flight maneuvers and flight conditions. The simulation results for aircraft level flight, descending, and climbing maneuvers show that the errors of airspeed and altitude are very small in windless condition. Besides that, the \(\Delta q\) and \(\dot{L}_{\rm s}\) are successfully regulated to a value close to zero. The errors with the presence of wind are acceptable for aircraft level flight, descending, and climbing maneuvers. Moreover, the settling time for climbing and descending maneuvers is the same for tracking a similar altitude difference. This shows that the linearity of the total energy model is satisfied. Furthermore, by comparing the simulation results of OECS and FLC, it shows that the proposed OECS has better performance and fuel efficiency than that of FLC.
## 1 Introduction
Measurements of lineal energy are typically limited to simulated tissue volumes larger than tens of nanometres, i.e., they exclude volumes relevant to DNA-sized critical structures [13; 15]. Theoretical calculations of lineal energy often employ Monte-Carlo track structure (MCTS) codes, such as GEANT4-DNA [26], KURBUC [27], PARTRAC [28] and PHITS [29], among others [30; 31]. Many such efforts for determining the microdosimetric spectra for protons and heavier ions are available in literature [32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45]. MCTS codes simulate, collision-by-collision, all the interactions between the primary (and secondary) particles and the atoms/molecules of the medium (commonly liquid water) until all particles (primaries and secondaries) become non-ionizing (i.e., fall below about 10 eV). Thus, MCTS codes enable simulations with nanometre resolution, including the explicit simulation of radiation-induced DNA damage at various levels of sophistication [46; 47]. However, MCTS simulations are well known for being time-intensive while requiring a fair amount of computer expertise. Analytical models for calculating lineal energy spectra can overcome many of the above difficulties as they are based on simple mathematical expressions that approximate the energy-loss process. Such models have been developed in a series of papers by [PERSON] [48; 49; 50; 51] and [PERSON] [52; 53]. The main idea is to analytically calculate the dependence of lineal energy upon LET while adding suitable correction terms for the effect of energy-loss straggling and the finite range of \(\delta\)-rays. The aim of this paper was to: (i) compare and combine various analytical models for calculating the microdosimetric parameter \(y_{D}\) for protons in the energy range of 1-250 MeV for different sphere diameters (10, 100, 1000, 3000 nm); (ii) use the calculated \(y\) spectra to determine the variation of the quality factor (\(Q\)) with proton energy according to both the TDRA and ICRU Report 40 microdosimetric methodologies and compare against the LET-based ICRP Report 60 recommendations; (iii) compare the analytical model calculations against published MCTS data of \(y_{D}\) and corresponding \(\overline{Q}\); (iv) examine the sensitivity of the results to the choice of target sphere size and water-medium transport parameters (gas vs. liquid).
## 4 Results
### Failure Analysis and Failure Detection
#### 4.3.1 Failure Analysis-Occlusion  
One of the method's biggest weaknesses compared to a deep learning method like BAAM [13] is its occlusion resistance. Firstly, if the keypoints are not visible in the image timeseries, their corresponding _Semantic Vertices_ cannot be reconstructed (unless their symmetrical pair is detected). Secondly, our pose estimation is the solution to a multi-view pose estimation problem from 2D-3D keypoint correspondences. This has well-studied properties [14; 27; 30], showing its performance is best when the most possible keypoints are visible and they do not lie on the same plane. In the single-view case, at least four non-coplanar points need to be detected, and in the multi-view case, at least three corresponding points (or their mirrored point in the Semantic Mesh Model) must be detected for the pose estimation to be theoretically solvable [27].  
Qualitatively, we show a failed pose estimation under partial occlusion in Figure 9. This is a typical case where the detected _Semantic Keypoints_ are _almost coplanar_, resulting in a strong local minimum that the algorithm becomes stuck in. On the right, as soon as point 33 is also detected, the points are no longer near coplanar; therefore, the algorithm finds the global optimum.  
Unfortunately, the Argoverse 1 dataset does not currently contain actual \"occlusion\" data even though it's present in the API, its set to 0 everywhere. Therefore, we cannot quantitatively verify this, although future work will include research on occlusion-resistant pose estimation.  
Although the quantitative evaluation of occlusion resistance is not possible here, similar research in the literature [14] indicates that the likelihood of failure for occluded single-view cases is quite high for our algorithm. BAAM handles occlusion especially well; that is a large part of its contribution.  
However, we would also like to point out that in a Smart Parking garage application, the reduction in implementation costs versus LiDAR-based alternatives or 3D annotation costs for each sensor may be so high that an arbitrary number of cameras may be installed. This enables the construction of such a system where occlusion is scarce.
# Investigation of the Machining Stability of a Milling Machine with Hybrid Guideway Systems
## 1 Introduction
The milling machine discussed in this study was driven on hybrid guideway systems which were assembled with a linear roller guide and a sliding guide with coated antifriction layers. In creating the finite element models of the milling machine, apart from mechanical frame structure, the sliding guides and roller guides were also modeled by different interface elements with appropriate interface stiffness. Besides, the motorized built-in spindle model was created and implemented in the whole machine model. As a dynamic evaluation of the machine tool, the vibration modes, dynamic stiffness, and machining stability of the spindle tool system were predicted. The results were used to compare the influence of the use of linear roller guides with different preload.
## 5 Conclusions  
The drone surveying was carried out for long-term soft ground settlement measurement at a wide land reclamation area, and it was assessed for its accuracy and reliability. The following conclusions can be derived from the long-term experiments. The drone surveying showed \(13\) cm, \(42.9\) cm, and \(6.23\%\) differences in RMSE for vertical accuracy, long-term settlement prediction, and consolidation, respectively. The drone LiDAR accuracy thus seemed very useful for monitoring the settlement over the large and remote land reclamation site of soft ground, showing the settlement up to several meters where, without a surface measurement, installment is limited. Moreover, frequent drone data acquisition can produce redundant settlement data for the reliable prediction of settlement and consolidation through outlier removal. Drone survey data enabled settlement measurement and prediction for any point where no surface settlement is installed. Reliable settlement measurement using a drone survey requires consideration of undergoing embankment as, otherwise, it is not challenging to separate the settlement from the embankment as well as PBD and DCM operations. For future study, the use of a stable surface target with ground control data is recommended in order to achieve better vertical accuracy, such as 5-10 cm.  
Conceptualization, J.L. and J.O.; methodology, J.L. and J.O.; software, J.L. and J.O.; validation, J.L. and J.O.; formal analysis, J.L. and J.O.; investigation, J.L. and J.O.; resources, J.L.; data curation, J.L.; writing--original draft preparation, J.L. and J.O.; writing--review and editing, J.O. and H.J.; visualization, J.L. and J.O.; supervision, J.O.; project administration, J.L. and J.O.; funding acquisition, J.L. and J.O. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Research Foundation of Korea, grant number 2019R11A3A01062109 and the Ministry of Oceans and Fisheries of Republic of Korea for its the research on Advanced Port Construction technology.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 3 Study Case and Results
### Bus Faults
#### 3.1.2. Undervoltage Algorithm  
The pre-selected threshold is UV\({}_{\text{THR}}\) = 0.1 pu, which corresponds to 2.7 kV. This threshold has been selected based on the nominal voltage levels and the acceptable voltage drop during faults, ensuring the algorithm reacts promptly to significant deviations indicative of faults.  
One of the greatest advantages of this algorithm is its simplicity in terms of calculation. Once the measured voltage is below the selected value, it automatically detects the fault. In contrast to the overcurrent algorithm, when a fault occurs, the measured voltage drops rapidly as the current increases more slowly. The highest fault detection time is observed in ground faults with the highest tested fault resistance value, 20 \(\Omega\). As the fault resistance increases, the fault current is lower, so the voltage drop will be slower too, as seen in Figure 4.  
Table 2 shows the detection time for the UV algorithm, under pole-to-pole and pole-to-ground faults, varying the fault resistance. The detection time increases, as the fault resistance is higher. As can be seen, for solid faults in both pole-to-pole and pole-to-ground faults, the detection time is 0.01 ms. As there is almost no fault resistance, the voltage drops instantaneously. The registered longest time is 0.14 ms for high-resistance pole-to-ground faults. In spite of this, it is still a very low detection time, which would allow the fast operation of the circuit breakers. The main difference between different fault types' detection times appears in the 20 \(\Omega\) fault resistance case, as the pole-to-pole faults take less than half the time (0.065 ms) the pole-to-ground faults take (0.14 ms). Referring to Table 2, all the simulated scenarios meet the DC protection requirements.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fault at Bus 2**} & \multicolumn{2}{c}{**Detection Time at R21 (ms)**} & \multicolumn{2}{c}{**Detection Time at R24 (ms)**} \\ \cline{2-6}  & **P2P** & **P2G+** & **P2P** & **P2G+** \\ \hline \multirow{3}{*}{OC} & R = 0.01 \(\Omega\) & 1.690 & 3.175 & 3.565 & 8.115 \\ \cline{2-6}  & R = 2 \(\Omega\) & 1.730 & 3.270 & 3.755 & 8.230 \\ \cline{2-6}  & R = 20 \(\Omega\) & 3.000 & - & 6.295 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fault at bus 2, detection times at R21 and R24, for the OC algorithm.  
Figure 4: Fault at bus 2, DC voltage at R21 and R24: (a) pole-to-pole fault, and (b) pole-to-ground fault.
## 3 Results and Discussion
### Influence of Adsorbent Dosage  
The percentage of Pb(II) uptake with respect to the non- and acid-modified clinoptilolite-rich tuffs dosages (mg L\({}^{-1}\)) is shown in Figure 9. It can observed that the dosage of all of the sorbents used is a very important parameter for Pb(II) uptake. The increment in sorption capacity was attributed to a higher number of available sorption sites [12]. In the case of sorbents with Si/Al ratios of 4.37, 5.31, and 7.91, sorption efficiency was highly dependent on increase in the sorbents dosage of solution up to 6 mg L\({}^{-1}\) when the maximum Pb(II) uptake percentage (99.99%) was achieved. For sorbents with a Si/Al ratio of 9.05 and 9.52, Pb(II) removal increases gradually with dosage, reaching 92.36% and 35.99%, respectively. One explanation of this behavior is the few available sorption sites in the zeolite networks of HCLI-0.5 M and HCLI-1.0 M to be occupied by the Pb(II) chemical species. The H-CLI1.0 M has the lowest amount of Al in the zeolitic material diminishing the ion exchange capacity. The amount of Ca\({}^{2+}\) in the zeolitic material also drastically diminished, limiting the ion exchenge: Pb\({}^{2+}\)(s) + Ca\({}^{2+}\)(z) \(\rightarrow\) Pb\({}^{2+}\)(z) + Ca\({}^{2+}\)(s), where \(s\) represents the solution and \(z\) the zeolitic material. The sites where the K\({}^{+}\) are located into the zeolite network [47] do not favor the ion exchange with Pb\({}^{2+}\). Another explanation of this behavior is by the change of the nature of the zeolitic material from hydrophilic to hydrophobic character [36].  
Figure 8: Comparison between the log PC for Nat-CLI and HCLI-1.0M as a function of log Ci of Pb(II) in solution.
## 2 Materials and Methods
### Quantitative Evaluation and Comparison  
The out-painted projections were generated by applying the trained model to the test projections, and the image reconstruction was subsequently performed using the filtered back-projection algorithm with a Ram-Lak filter. The reconstructed s-IGDT image had a size of 150.48 \(\times\) 150.48 \(\times\) 168.96 cm\({}^{3}\) with a 228 \(\times\) 228 \(\times\) 512 array. In order to assess the performance of the proposed out-painting method, we measured voxel value profile, signal-to-noise ratio (SNR), normalized root-mean-square error (NRMSE), peak SNR (PSNR), and structural similarity (SSIM) for the reconstructed s-IGDT images. The ROI for the SNR measurement was manually selected from a homogeneous area within the s-IGDT images. The NRMSE, PSNR, and SSIM were calculated as follows [25; 26; 27]:  
\[NRMSE(R,O)=\frac{1}{MAX_{O}-MIN_{O}}\sqrt{\sum_{i=1}^{N}{(R_{i}-O_{i})^{2}/N}} \tag{3}\]  
\[PSNR(R,O)=10\log\left(\frac{MAX_{O}^{2}}{MSE(R,O)}\right) \tag{4}\]  
\[SSIM(R,O)=\frac{(2m_{R}m_{O}+c_{1})(2\sigma_{R,O}+c_{2})}{(m_{R}^{2}+m_{O}^{2} +c_{1})(\sigma_{R}^{2}+\sigma_{O}^{2}+c_{2})} \tag{5}\]  
where \(MAX_{O}\) and \(MIN_{O}\) are the maximum and minimum voxel values of the out-painted s-IGDT image, respectively, and \(N\) is the number of voxels used for calculating the NRMSE. \(m_{R}\) and \(m_{O}\) are the mean voxel values of the reference and out-painted s-IGDT images, respectively, \(\sigma_{j}\) is the standard deviation of the corresponding image \(j\), and \(\sigma_{R,O}\) is the covariance between the two images. The positive constants \(c_{1}\) and \(c_{2}\) were used to avoid a null denominator.  
In the previous study, we reported the projection data correction (PDC) method for alleviating the truncation effects, which are caused by an s-IGDT system [3]. The PDC method can be simply implemented by using the geometric parameters of an imaging system, and the results showed that the PDC method outperformed the conventional method in reducing truncation effects. For comparison, the truncated projections were also restored by the PDC method, and the restored projections were reconstructed under the identical conditions used for acquiring the out-painted s-IGDT image.  
Figure 5: Training workflow of the proposed out-painting network.
## 1 Introduction
Uncertainty quantification using MC simulation would, in this case, become computationally very expensive. To achieve better computational efficiency, various non-sampling-based methods, such as generalized polynomial chaos (gPC) expansion, have received much attention in recent years [38; 39; 40]. In the gPC expansion method, the uncertain parameters are represented by a series of orthogonal polynomials with unknown coefficients [41; 42]. Recently, [PERSON] et al. [43] demonstrated the application of the gPC expansion in a stochastic dynamic response analysis of a nonlinear rotor-bearing system, in which the nonlinear bearing forces are obtained using the IS model. The literature review reveals that researchers have, for the most part, conducted deterministic stability analyses of rotor-bearing systems. However, a few studies are available that investigate the issue of uncertain stability on the basis of a simplified HDB model using a sampling-based method, such as MC simulation, but without considering the misalignment effect. This study reports an uncertain stability analysis for a rotor system supported by finite-length HDBs while taking into account the misalignment effect. We develop a stability analysis framework guided by the Routh-Hurwitz criterion. The characteristics of the HDB used for stability analysis are evaluated by solving the Reynolds equation numerically. Collocation-based gPC expansion is employed to determine the impact of uncertainties. The paper is organized as follows: The next section introduces the theoretical background on characteristics of the general finite-length HDB. The process of determining the stability threshold is presented in Section 3, and the uncertain stability analysis based on the gPC expansion is presented in Section 4. Section 5 presents the numerical results of a rotor-bearing system under uncertainty. The paper's conclusion then follows.
## 4 Stability Evaluation
### Result Analysis
#### 4.3.4 Dilatancy Safety Factor  
The expansion safety factor mainly reflects the damage situation of the salt cavern's surrounding rock. Figure 15 presents the distribution map of the expansion safety factor of the salt cavern's surrounding rock under different internal pressures. Seen from the figure, it is evident that areas with a safety factor below 6 are predominantly situated around the cavity. By contrast, the safety factor of the salt cavern covered with residue is generally above 6, which demonstrates the residue's inhibitory effect on the damage and deformation of the salt cavern. At low pressure, the figure reveals that the safety factor of the interlayer at the shoulder of the deep cavity drops below 1, which indicates that this interlayer is damaged during operation, and the interlayer is the weak point of damage in the deep cavity. Moreover, under the same pressure, the area with a low safety factor at the top of the deep cavity is significantly larger than that at the top of the shallow cavity, which reflects that the stability of the deep cavity is relatively weaker than that of the shallow one. As the pressure increases, for example, from 12 MPa to 25.5 MPa, the expansion safety factor of the cavity's surrounding rock increases significantly, thereby enhancing the cavity's stability.  
Figure 15: Distribution diagrams of expansion safety factor in surrounding rock under different internal pressures. (**a**) 10.5 MPa; (**b**) 12 MPa; (**c**) 25.5 MPa; (**d**) 29 MPa.
## 5 Modulation of Intelligent Techniques for Power System Stability, Control and Protection  
Inverters and controlled converters are switched on and operated using modulation techniques to create output voltages and currents of superior quality for various loads. We can regulate the switching electronic device by applying these modulation approaches to obtain the desired amplitude, frequency, and quality. Numerous modulation approaches have been previously examined. A single-phase matrix converter's use as a cycloconverter and cycloinverter was described in [103]. The matrix converter switches were controlled by sinusoidal pulse width modulation (SPWM) pulses, which produced output. [PERSON] et al. [103] described the creation of a microcontroller-based single-phase sinusoidal pulse width modulator (SPWM) inverter. This configuration's usage of a microprocessor to produce sinusoidal pulse width modulation (SPWM) pulses makes it appealing. Reference [104] used MATLAB-Simulink tools to compare the space vector pulse width modulation (SVPWM) and sinusoidal pulse width modulation (SPWM) approaches, and they concluded that the SVPWM method is the most trustworthy one. To address this gap, it is essential to clarify that [PERSON] and [PERSON] utilized MATLAB's SimPowerSystems toolbox, which is particularly suited for simulating and modeling power systems in the time domain. This choice was driven by the toolbox's robust capabilities in handling complex calculations required for accurately comparing the performance of SVPWM and SPWM methods. This toolbox allows for a realistic simulation of electrical circuits and power system dynamics, providing a trustworthy basis for their conclusion that SVPWM
## 3 Finite Element Models
### Boundary Element Models  
In order to model the surrounding air volume, a closed external surface of the structure is defined and meshed as illustrated in Figure 7, left. The external volume of this surface is defined as a Boundary Element Method (BEM) subsystem. A volume diffuse acoustic field excitation is defined in this subsystem. This excitation is implemented using the reciprocity relationship between direct field radiation and diffuse reverberant loading [30]. This BEM subsystem is then coupled to the FEM subsystem through a surface junction. The boundary condition of the BEM subsystem becomes a structural coupling, and the pressure loading on the FEM subsystem is equal to the acoustic pressure at the BEM boundary. This strong coupling creates a classical FEM-BEM model [14]. The boundary element method in Wave6 is based on a mixed direct/indirect variational formulation and uses acceleration methods for large problems along with a direct rather than iterative linear solver (in order to avoid the convergence issues often encountered with iterative solvers and boundary integral equations).  
Another BEM subsystem is defined to model both the external and internal air volumes. The external surface of the structure is defined and meshed excluding the six circular surfaces corresponding to the holes in the bottom of the structure (see Figure 7, right). This open surface makes it possible to create a BEM subsystem that includes the internal and external air volumes of the structure.  
In this subsystem, the meshed surface is \"double-sided wetted\" with a discontinuity in the acoustic pressure field from one side of the surface to the other. The coupling with the FEM subsystem is then performed as a surface junction. This coupling associates the external faces of the FEM subsystem with the external side of the BEM surface and, reciprocally, the internal faces of the FEM subsystem with the internal surface of the BEM subsystem.
# A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems
###### Abstract  
With the internet developing rapidly, mobile edge computing (MEC) has been proposed to offer computational capabilities to tackle the high latency caused by innumerable data and applications. Due to limited computing resources, the innovation of computation offloading technology for an MEC system remains challenging, and can lead to transmission delays and energy consumption. This paper focuses on a task-offloading scheme for an MEC-based system where each mobile device is an independent agent and responsible for making a schedule based on delay-sensitive tasks. Nevertheless, the time-varying network dynamics and the heterogeneous features of real-time data tasks make it difficult to find an optimal solution for task offloading. Existing centralized-based or distributed-based algorithms require huge computational resources for complex problems. To address the above problem, we design a novel deep reinforcement learning (DRL)-based approach by using a parameterized indexed value function for value estimation. Additionally, the task-offloading problem is simulated as a Markov decision process (MDP) and our aim is to reduce the total delay of data processing. Experimental results have shown that our algorithm significantly promotes the users' offloading performance over traditional methods.  
mobile edge computing; task offloading; delay sensitive; Markov decision process; deep reinforcement learning Article  
A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems  
2022