esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Stability Parameters for Wide Body Aircraft Using Computational Techniques
## 3 Xflr
### XFLR Methodology  
The design process in XFLR starts with airfoil analyses. To design wing and tail surfaces of Boeing 747-200, two airfoils (BACxxx and NACA 0012) were used. The former was imported from the airfoil database available online and NACA airfoils are available in XFLR's in-built library. When airfoils were successfully imported in direct foil design, the number of panels was adjusted to 120. The number of panels had to be selected to get sufficient data points of leading and trailing edges. The flap deflection is generally set between \(-10\) to \(10\) degrees. Once settings were done, foil analysis could then be performed. A multithread analyses option studies multiple foil simultaneously. The range of Reynolds number was adjusted to between 100,000 and 500,000. In order to study the aerodynamic characteristics, the airfoil analyses are recommended before creating the aircraft model. Airfoil characteristics are obtained graphically in the form of lift, drag, longitudinal moment, and pressure distribution over the length of airfoil. Once the stability parameters are achieved (Cm\(\alpha\) < 0, Cm0 > 0), the complete aircraft design process can then be completed. During the initial design phase, the wing surface is generated and divided into a number of sections. Each section is assigned a chord length, a twist, and dihedral angles according to the design requirements. Once wing design is completed, the remaining surfaces, i.e., fin and elevator (vertical and horizontal tails, respectively), are created by following the same method. In XFLR, to design the fuselage surface, the body option is selected because it is not part of primary aircraft components. Once the surfaces are generated, the masses for each component of the airplane and some additional masses--passenger, avionics, etc.--are added. The mass distribution must be carried out carefully because it affects the moments of inertia and center of gravity. Aircraft analyses are performed in two phases: aerodynamics and stability. XFLR uses VLM, the panel method, and interactive boundary layer methodology for complete wing analyses. The results are obtained in the form of stability and control coefficients. The flow chart showing the detailed methodology of XFLR is shown in Figure 9.  
Figure 9: The XFLR methodology—the roadmap to designing and analyzing an aircraft using XFLR software.
## 4 Results: Design and Evaluation of LSHT Machines
### The 10-Pole LSHT Designs Obtained Through Multi-Objective Optimization  
The optimization problem previously described in Table 1 was solved for the 10-pole LSHT machines. Figure 6 illustrates the evolution of the optimization process, displaying the mean torque and efficiency of the machine specimens. The geometry of stator and rotor structures of the best 10-pole LSHT machines is shown in Figure 7, while the mesh used for the fault assignments in the 2D FE models is illustrated in Figure 8. The main dimensions and specifications of the machines are presented in Appendix A, Table A1.  
The performance of the best designs obtained from the optimization process is summarized in Table 2, with a focus on rated performance. From the results, it is evident that the PMSM outperforms the SynRM and the IM in terms of torque capacity, efficiency, and power factor, largely due to the adoption of permanent magnets and the superior performance provided by fractional-slot concentrated windings. This is in agreement with most research related to LSHT designs. Nonetheless, among rare-earth-free topologies, the SynRM and IM exhibit comparable torque capacities, with the SynRM having a slight margin over the IM. In turn, the IM offers a notably lower torque ripple and a higher power factor. Although the performance of the SynRM could be improved through step skewing or adopting an asymmetric design, this is outside the scope of this paper, which focuses on fault evaluation [55]. Additionally, the SynRM suffers from a very low power factor, which  
Figure 5: Flowchart of the assessment of selected machines using FE simulations.  
Figure 6: Optimized machines for 10-pole LSHT applications: (a) PMSM; (b) SynRM; (c) SICIM.  
can be associated to the saliency effects at high pole counts. It is important to note that neither the PMSM nor the SynRM has self-starting capability.
## 2 Brief Background of USV Technology
### Evolution of USVs  
The development of USVs has progressed significantly, evolving from early remote-controlled vessels to highly autonomous systems [12]. Initially adapted from manned vessels, USVs have advanced to purpose-built platforms with specialized capabilities [18]. Technological advancements, including artificial intelligence, advanced sensors, and improved communication systems, have enabled modern USVs to conduct complex missions with minimal human intervention [19]. These developments have expanded USV applications in environmental monitoring, marine production, territorial surveillance, and offshore operations [20]. Key demonstrations, such as the Wave Glider and Sea Hunter USV, have showcased long-endurance capabilities and naval applications. The integration of machine learning, situational awareness sensors, and telemetry tools has further enhanced USV autonomy and control [19].  
As research and innovation continue, USVs are poised to play an increasingly significant role in marine operations, improving efficiency, safety, and sustainability [20]. Recent research highlights significant advancements in Unmanned Surface Vehicle (USV) technology, focusing on enhancing autonomy and multi-mission capabilities. The integration of artificial intelligence and machine learning in adaptive control systems is improving USV decision-making and navigation [21]. Embodied intelligence is playing a crucial role in developing intelligent navigation, collision avoidance, and swarm behavior systems [22], where multiple USVs coordinate their activities [23]. Ongoing research aims to further improve USV autonomy by incorporating the _International Regulations for Avoiding Collisions at Sea_ into obstacle avoidance protocols [9]. As AI-driven navigation continues to evolve, USVs are becoming more reliable, efficient, and versatile. However, challenges remain, including navigational uncertainties, energy constraints, and the need for standardized regulatory frameworks [22]. Future developments in USV technology are expected to revolutionize maritime operations and data collection efforts.
# A Lidar-Inertial Navigation System for UAVs in GNSS-Denied Environment with Spatial Grid Structures
## 2 Lidar-Inertial Navigation System for UAVs in GNSS-Denied Environment with Spatial Grid Structures  
### System Overview  
The overall framework of the UAV lidar-inertial navigation system proposed in this paper is shown in Figure 2. The system can be divided into three modules: point cloud extraction and matching, lidar-inertial fusion, and mapping.  
The point cloud extraction and matching module receive the point cloud data from the surrounding lidar. Then, the point cloud distortion is removed using the high-frequency pose prediction data of the lidar-inertial fusion module. After the removal of distortion, the features are extracted from the point cloud. Finally, the extracted features are used to match with the local map generated by the mapping module; the high-precision pose is output at a low frequency of 10 Hz.  
Figure 1: The spatial grid structures in industrial and living scenes.  
Figure 2: The framework of the proposed navigation system.  
The lidar-inertial fusion module receives the angular velocity and acceleration data of the IMU and the low-frequency and high-precision pose output by the point cloud extraction and matching module. It pre-integrates the IMU measurement [22] and fuses the above data to achieve loose coupling of lidar-inertial and outputs high-frequency pose prediction. The predicted pose is used for point cloud distortion removal and point cloud matching.  
The mapping module receives the matching results from the point cloud matching module and the feature points from the corresponding keyframes. This module uses the above data to build a global map and generates a local map for the point cloud matching module.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 6 RAN Domain  
In the RAN domain, the technology radar puts the focus on three different dimensions: functionality, radio resource allocation and penetration. The _functionality_ dimension provides a deep dive on the applicability of open RAN principles on NR protocol stack functions to design and configure RAN slices, going from monolithic solutions towards more flexible, service-tailored composition patterns. The _radio resource allocation_ dimension discusses the availability of solutions to segregate and dispatch cell radio resources to competing RAN slices, so that their targeted KPIs are met. Finally, the _penetration_ dimension refers to the penetration of RAN slicing technology within the operator's footprint.
## 1 Introduction
* Review of the structure, operation, and characteristics of existing excellent QCA-based RFAS circuits
* Design of a reversible quantum gate (RQG)-based RFAS after presenting the problems of implementing the existing QCA circuits
* Best performance in all aspects compared with the existing circuits and significant improvements of at least 67% and 54% in delay and energy dissipation, respectively. * Remarkable improvements of 180% and 562% in two representative design costs, \(\mathit{Cost}_{AD}\) and \(\mathit{Cost}_{ED}\), respectively, compared to the best existing circuit
* Noise-free and high and stable output polarization, especially a significant improvement of 3.8% in the average output polarization
* Proposal of an efficient QCA-based RFAS that minimizes information loss and provides reliable information transmission through the best performance of the proposed reversible circuit  
The structure of the paper is as follows. Section 2 explains the basic operation principle of QCA and reviews existing QCA reversible full adder-subtractors. Section 3 explains the operation and structure of the proposed reversible full adder-subtractor. Section 4 analyzes and compares the results through simulations. Section 5 concludes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Year** & **Structure** & **Major Contributions** \\ \hline
[32] & 2017 & RFAS &
\begin{tabular}{c} 3 \(\times\) 3 QCA Reversible (QR) gate using Toffoli and \\ Fredkin gates \\ \end{tabular} \\
[33] & 2018 & RFA &
\begin{tabular}{c} 4 \(\times\) 4 N1 and 3 \(\times\) 3 N2 gates based on a 5-input \\ majority gate \\ \end{tabular} \\
[34] & 2019 & RFA & Area optimization using a 5-input majority gate \\
[35] & 2017 & RFAS & 3 \(\times\) 3 Reversible Quantum Gate (RQG) \\
[36] & 2018 & RFAS &
\begin{tabular}{c} 3 \(\times\) 3-New Reversible Gate (NRG) and Modified \\ Feynman Gate (MFG) \\ \end{tabular} \\
[37] & 2023 & RFAS & Optimization of RQG \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Major contributions to the development of RFA(S).
# Performance Estimate of a Spin-Stabilized Drag Sail for Spacecraft Deorbiting
## 1 Introduction
The first proposed spinning sail concept is the heliogyro [49], which consists of multiple reflective blades deployed and stabilized by centrifugal force. A further development of the heliogyro concept, in which the blades are equipped with tip-satellites to provide stabilization and spin axis precession, has been proposed more recently [50; 51; 52]. However, such concepts have never been tested in orbit, while spin-deployment and stabilization of a square solar sail was achieved by JAXA's IKAROS mission. In particular, IKAROS successfully performed a two-stage deployment [53; 54] but the strategy was flexible enough to overcome a possible failure of the deployment mechanism by means of a single-stage deployment [55]. A similar strategy has also been proposed for a deployment from an LEO, and some experimental data should be provided by the Gama Alpha mission [40]. In the analysis conducted in this paper, it is assumed that, after its deployment, the sail still has a residual angular velocity that provides gyroscopic stiffness, so that its axis of rotation is fixed with respect to an inertial reference frame. It is worth noting that in a realistic scenario the spin axis of the drag sail may be subject to a precession motion, which would be very small (and therefore negligible) if the spin rate provided adequate gyroscopic stiffness to the satellite. Accordingly, the spin rate should be selected for a specific deorbiting scenario to provide sufficient gyroscopic stiffness to minimize precession of the spin axis without causing structural failure of the membrane. To quantify the performance of a spinning drag sail, this is compared with that corresponding to the best and worst situations, i.e., the case of a sail stabilized on three axes and that of a spacecraft with no control (that is, subject to tumbling motion), respectively. The remainder of the manuscript is structured as follows. Section 2 describes the mathematical model used to analyze the spacecraft dynamics in LEO, with a detailed discussion of the main sources of perturbation. Section 3 specializes the model to the case of a spinning drag sail, whose axis of rotation is inertially fixed. Section 4 estimates the performance of a spin-stabilized drag sail in a realistic deorbiting scenario and makes a performance comparison (in terms of decay times) with a three-axis stabilized sail and an uncontrolled case. Finally, the main results of this analysis are summarized in Section 5.
# Vehicle Collision Prediction under Reduced Visibility Conditions
## 3 Visibility-Based Collision Warning System Design
### Prediction Horizon Estimation Model  
[PERSON] et al. proposed a quantitative model to estimate the PRT under a target visibility [19]. When target visibility becomes low, driver's PRT is expected to become long. Some limits exist in the quantitative model. First, the experiments did not consider the data with detection rate below 50%. A 50% detection rate means the target has 50% chance to be seen when we look at it. Second, PRT longer than 2.5 s were not considered. With a camera sensor, the system monitors the environment in real time. Thus, target visibility can be computed via visibility distance estimation employed in image processing model [21]. Then, the quantitative model computes driver's PRT under the current target visibility.  
Given the estimated PRT, our proposed prediction horizon estimation model transforms the PRT into a prediction horizon for velocity prediction, as follows:  
\[T_{ph}=\left\{\begin{array}{c}0.932T_{pr}^{3}-4.6822T_{pr}^{2}+10.48T_{pr}+13.16\text{ if }V_{LV}\geq 30\\ -0.0207T_{pr}^{3}+0.3642T_{pr}^{2}+0.2078T_{pr}+0.6447\text{ if }V_{LV}<30\end{array}\right. \tag{4}\]  
where \(T_{ph}\) is the prediction horizon, \(T_{pr}\) is the SV driver's PRT in seconds, and \(V_{LV}\) is the LV speed in feets per second. A third order polynomial quantitative function is used to fit the data. The round function is used to make \(T_{ph}\) an integer. It can be observed that the longer the PRT is, the longer the prediction horizon is.
## 2 Materials and Methods
### Spectral Measurement and Preprocessing  
Soil spectra were measured with an ASD Field Spec(r)3 field spectrometer (Analytical Spectral Devices, Inc., Colorado, USA), which covers a spectral range of 350-2500 nm. Spectral measurements were collected in a dark room. A 50 W halogen lamp was installed on a tripod as the light source and placed 30 cm from each soil sample at a 15\({}^{\circ}\) angle to the vertical. An optical fiber probe was located directly above each sample and 10 cm from it to obtain the spectral information of the soil. Before spectral measurement, the instrument was preheated for 30 min and calibrated with a white spectral reference panel. The soil samples were shaken evenly after drying and grinding to make the soil more homogeneous. Then the soil sample was uniformly tiled in glassware with a diameter of 10 cm to make its surface smooth. Each sample was tested five times. ViewSpecPro, a type of spectral processing software, was used to view and eliminate the spectral curves with obvious errors, and the arithmetic average value was obtained as the final spectral reflectance data.  
Two edge bands of 350-399 nm and 2400-2500 nm with obvious noise were removed from all sample spectral data. During the acquisition process, spectral data will be affected by random factors, resulting in noise, making it necessary to process the signal and smooth the reflectance spectrum. In this study, multiplicative scatter correction (MSC) was used to eliminate the scattering [41] and then spectra were subjected to the Savitzky-Golay smoothing method with a second-order polynomial fit and a window size of 7 data points. The first derivative (FD), the second derivative (SD), and the reciprocal logarithm (RL) were used to transform the original spectrum after pretreatment with SG smoothing and MSC. Derivative transformations are less sensitive to noise effects, eliminate interference from baselines and other backgrounds, and minimize the baseline drift of the original soil spectrum [42]. The reciprocal logarithm not only enhances the difference in the visible spectrum, but also reduces the influence of multiplication factors under illumination conditions [36]. The spectral preprocessing was performed using ViewSpecPro and MATLAB.
# Conditional Generative Adversarial Networks for Domain Transfer: A Survey
## 4 The Applications of CGAN
### Image Inpainting  
In the process of image storage, some pixels may be lost due to various reasons. In order to restore the original appearance of the image as far as possible, certain methods are needed for image inpainting. Intuitively, pixels in an image have a great correlation with their neighboring pixels but less correlation with distant pixels. Therefore, traditional methods similar to interpolation have been widely used [93; 94; 95]. However, if there are continuously missing pixels in the image, the significant effect of the interpolation method will be greatly limited, especially when the missing part contains the whole object, which will render the interpolation method to be completely unpredictable. Under this circumstance, it is a great alternative to employ CGAN [96; 97; 98; 75; 99].  
However, using CGAN to solve image inpainting also faces many new problems. For example, reference [96] can only fill in the center of the image, not the area of variable sizes. When the missing pixels form a connected region, the boundary of the missing region can still be seen on the inpainted image [97]. In addition, problems such as model collapse and overfitting are also quite serious. In order to solve these problems, reference [100] optimized the overall framework and super parameters of CGAN. A multi-stage generation method is introduced to create high-quality images by gradually adding layers to the generative network and discriminative network in [55]. Following this work, reference [101] uses the adaptive normalization layer [102] to control the visual features of images at different scales on the basis of [55]. Some studies also attempt to improve from the perspective of loss functions, such as Wasserstein distance [20], least squares [20] and energy-based GANs [103]. In addition, similar to the SR, here we can also adapt the curriculum learning method [104].
## 2 Description of the Mathematical Model
### Sliding Mode Control  
Sliding mode control can be used to reject uncertainties and external disturbance. Generally, it assumes that \(\overline{F}(\mathbf{z})\) and \(\overline{G}(\mathbf{z})\) are given. Here, we define a sliding mode control law for the quarter-car with a PM-ASS while the system contains external disturbances. The output tracking error is written as:  
\[e=y-y_{d} \tag{22}\]  
where \(y_{d}(t)\) is a reference signal, which represents road conditions in this paper. A switch surface for an nth-order system is defined as:  
\[s=c_{1}e+c_{2}\dot{e}+\ddot{e} \tag{23}\]  
where \(c_{i}\) are chosen such that \(\dot{\sum\limits_{i=1}^{3}c_{i}i^{l-1}}\) is a Hurwitz polynomial in which \(l\) is a Laplace operator. Equation (23) shows that:  
\[\ddot{e}=-c_{1}e-c_{2}\dot{e}+s \tag{24}\]  
If \(\mathbf{e}=[e,\dot{e}]^{T}=[e_{1},e_{2}]^{T}\), the error dynamics are:  
\[\dot{\mathbf{e}}=\mathbf{A}_{1}\mathbf{e}+[0,s]^{T} \tag{25}\]  
where \(\mathbf{A}_{1}=\left[\begin{array}{cc}0&1\\ -c_{1}&-c_{2}\end{array}\right]\).  
Figure 3: Illustration of a quarter-car with a PM- active suspension system (ASS).  
**Lemma 1**.: _For a nonlinear system (21) with the given nonlinear functions \(\overline{F}(\mathbf{z})\) and \(\overline{G}(\mathbf{z})\), if the control input is:_  
\[u=\frac{-\overline{F}(\mathbf{z})-\sum\limits_{i=1}^{2}c_{i}e_{i+1}-\sum\limits_{i= 1}^{2}p_{2i}e_{i}+y_{d}^{(3)}-k\mathrm{sgn}(s)}{\overline{G}(\mathbf{z})} \tag{26}\]  
_and \(\mathbf{P}>0\), \(\mathbf{P}\in\mathbf{R}^{2\times 2}\) satisfies the Lyapunov matrix equation:_  
\[\mathbf{A}_{1}^{T}\mathbf{P}+\mathbf{P}\mathbf{A}_{1}=-\mathbf{Q}_{1} \tag{27}\]  
_where \(s\) is the sliding surface defined in Equation (23), \(p_{2i}\) are elements of \(\mathbf{P}\), \(k>0\), and \(\mathbf{Q}_{I}>0\) is given, then \(s\to 0\) and \(e\to 0\) as \(t\to\infty\)._  
Proof.: See Appendix A.  
However, \(\overline{F}(\mathbf{z})\) and \(\overline{G}(\mathbf{z})\) for a quarter-car with a PM-ASS are generally uncertain rather than given, thus, there is no control input for Equation (26). The constant \(k\) in Equation (26) is designed to attenuate the uncertainties. However, a larger value for \(k\) produces increased chatter. To relax these two constraints, a Fourier Neural Network (Fourier-NN) is used to approximate the unknown functions \(\overline{F}(\mathbf{z})\) and \(\overline{G}(\mathbf{z})\) to eliminate model dependency. \(H_{\infty}\) tracking design is also used to compensate for the approximation error, which gives improved control performance and reduced chatter.
# Degree-Constrained Minimum Spanning Hierarchies in Graphs
## 7 Results, Evaluation of the Solutions
### Discussion
#### 7.2.3 Varying the Size of the Graphs  
A third series allows us to see the effect of the size of Albert-Barabasi random graphs on the minimum spanning structures. The experiment is summarized in Table 4. The number of nodes varied from 80 to 170 nodes. \(D_{max}\) was equal to 3 and \(C_{max}\) to 5. It can be seen that the observed values increase almost linearly with the size of the graphs. For instance, let us compare the lines with 80 and 160 nodes. The average number of edges, the number of desired leaves in \(V_{1}\), and the cost are almost double. The number of DCMSHs and DCMSTs are mainly the same. Note that trees are 5-10% more expensive than hierarchies.  
\begin{table}
\begin{tabular}{r r r r r r r r r} \hline \(C_{max}\) & 1\(E_{G^{\prime}_{A}}\) & _dupl_ & _nb(H)_ & _nb(T)_ & _c(MSTSL)_ & _c(Heur)_ & _c(DCMSH)_ & _c(DCMST)_ \\ \hline
1 & 100.725 & 1.242 & 91 & 57 & 99 & 144.176 & 101.143 & 99 \\
2 & 102.349 & 1.542 & 83 & 57 & 104.675 & 141.265 & 111.458 & 109.088 \\
3 & 105.556 & 2.044 & 90 & 63 & 116.5 & 154.589 & 132.644 & 132.746 \\
4 & 107.536 & 2.369 & 84 & 53 & 132.417 & 174.774 & 155.643 & 158.585 \\
5 & 108.837 & 2.5 & 92 & 55 & 147.804 & 194.859 & 176.652 & 181.709 \\
6 & 109.209 & 2.813 & 91 & 61 & 165.934 & 220.824 & 197.593 & 206.541 \\
7 & 108.965 & 3.012 & 85 & 63 & 180.365 & 240.624 & 215.153 & 228.381 \\
8 & 110.022 & 3.198 & 91 & 61 & 199.56 & 264.835 & 240.495 & 259.279 \\
9 & 109.683 & 3.598 & 82 & 54 & 219.646 & 290.707 & 263.268 & 283.13 \\ \hline \end{tabular}
\end{table}
Table 3: Results by varying the edge costs.