esa-sceva/satcom-chunk-collection huggingface dataset:
# Adversarial Optimization-Based Knowledge Transfer
## 1 Introduction
Notably, the aforementioned methods are adopted by the Euclidean distance-based similarity measure to calculate the cost function of transferring distilled knowledge. There have been other studies in terms of knowledge transfer [30; 31], attempting to transfer knowledge extracted by the pre-trained DNN to other target DNNs using a network structure similar to the generative adversarial network (GAN) [32; 33]. In their studies, a target DNN was modeled as a generator, and a discriminator tried to distinguish the output results created by the pre-trained DNN from the result provided by the target DNN. The aforementioned knowledge transfer methods adopting the GAN structure experimentally showed that the target DNN using the adversarial optimization-based approach would be better captured into the pre-trained knowledge distributions than the \(l^{2}\)-norm-based knowledge transfer methods, as mentioned above. Therefore, it is crucial to extract useful knowledge from the pre-trained DNN and efficiently transfer the extracted knowledge to the target DNN. In this study, we propose a layer-wise dense flow (LDF)-based knowledge transfer technique coupled with an adversarial network to generate low complexity DNN models with high accuracy performance that can be adaptively applied to target domains with limited computing resources. First, the proposed method introduces densely overlapped flow using FSP matrices as distilled knowledge of the pre-trained DNN. To rephrase, the multiple-overlapped flow-based knowledge is densely distilled, such that each piece of flow-based knowledge extracted between two different corresponding layers is superimposed. Second, KTF using the adversarial network transfers densely extracted knowledge with layer-wise concurrent training between the pre-trained and target DNNs. In this stage, we designed multiple independent discriminators for adversarial optimization-based knowledge transfer using multiple pairs of dense flows, where each discriminator is assigned to compare a pair of flow-based features between the pre-trained and target DNNs. Owing to the proposed LDF and its concurrent transfer for the adversarial optimization process, the target DNN can efficiently and accurately learn a plethora of information from the pre-trained DNN. In contrast to previous work on the dense flow-based knowledge transfer [34], there are major differences between [34] and the proposed method. First, flow-based knowledge in [34] is transferred based on \(l^{2}\)-norm-based training when the dense flow extracted from layers is transferred and trained to another target model. However, knowledge in our study is transferred in the GAN structure-based adversarial optimization manner. Second, the densely extracted flow-based knowledge in [34] is sequentially transferred step-by-step to a target model, but this study deals with layer-wise concurrent training when transferring the dense flow.
# Dynamic Routings in Satellite Networks: An Overview
## 6 Conclusions  
This paper provides a comprehensive overview of the existing dynamic routing schemes for satellite networks in academia. First, satellite network characteristics are introduced, and essential dynamic routing technologies and challenges are evaluated. Due to the constraints of single-layer satellite dynamic routing, this paper focuses on SDN-based, QoS-based, and traffic-balancing multi-layer satellite dynamic routings. In addition, to better optimize the performance of future dynamic routing schemes for satellite networks, especially for multi-layer networks, potential technologies such as ML, MEC, and Digital Twin are also discussed in this paper. Finally, this paper predicts the future development trends of dynamic routing methods in satellite networks, such as multi-scale information sensing and processing in complicated settings, intelligent satellites, and intelligent routing.  
**Author Contributions:** Conceptualization, X.C. and Y.L.; validation, J.W. and X.X.; investigation, X.C., Y.L. and J.W.; resources, X.C. and Y.L.; data curation, X.X.; writing--review and editing, Y.L. and X.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant 61801319; in part by the Sichuan Science and Technology Program under Grant 2020 JJQ0061, Grant 2020 JJQ0075, and Grant 2021 YYG0099; in part by the Sichuan University of Science and Engineering Talent Introduction Project under Grant 2020 RC33; in part by the Innovation Fund of Chinese Universities under Grant 2020 HYA04001; in part by the Artificial Intelligence Key Laboratory of Sichuan Province Project under Grants 2021 RZJ03 and 2021 RZJ04; in part by the open research fund of the Artificial Intelligence Key Laboratory of Sichuan Province under Grant 2021 RZJ01; and in part by the 2021 Graduate Innovation Fund of Sichuan University of Science and Engineering under Grant y2021058.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 5 Results and Discussion
### Performance Analysis of the Proposed Algorithm
Figure 3: Best Pareto front determined by NSGA-II, MSSA, and EMSSA on ZDT1. Figure 6: Best optimal front determined by NSGA-II, MSSA, and EMSSA on DTLZ7 with 3 objectives. For problems with three objectives, the Pareto-optimal solutions obtained from Figure 6 indicate that the EMSSA algorithm exhibits the best convergence and coverage, while NSGA-II shows the poorest convergence and coverage. These results suggest that the EMSSA algorithm is also capable of approximating the true Pareto front of three-objective optimization problems.
# A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images
## 2 Proposed Method Framework
### Position Correction  
#### 2.2.1 RPCs Model  
RPCs model is the most popular method for geometry calibration of satellite images. It is a simple and generalized model that can obtain approximately the same accuracy as the strict geometric imaging model. The image coordinate is represented as ratios of the cubic polynomials in the ground coordinates and its form is as follows:  
\[\begin{array}{l}y=\frac{\text{Num}_{\text{t}}(u,v,w)}{\text{Den}_{\text{t}} (u,v,w)}\\ x=\frac{\text{Num}_{\text{s}}(u,v,w)}{\text{Den}_{\text{s}}(u,v,w)}\end{array} \tag{3}\]  
with  
\[u=(\varphi-\varphi_{0})/\varphi_{\text{s}},v=(\lambda-\lambda_{0})/\lambda_{ \text{s}},w=(h-h_{0})/h_{\text{s}},y=(l-l_{0})/l_{\text{s}},x=(s-s_{0})/s_{ \text{s}} \tag{4}\]  
where \((u,v,w)\) and \((x,y)\) are the normalized ground and image coordinates respectively. \((\varphi,\lambda,h)\) is geodetic latitude, longitude, and height, \((l,s)\) are the line and sample in image coordinates. \(\varphi_{0},\lambda_{0},h_{0},l_{0},s_{0}\) and \(\varphi_{\text{s}},\lambda_{\text{s}},h_{\text{s}},l_{\text{s}},s_{\text{s}}\) are the offset and scale factors for latitude, longitude, height, line, and sample, separately. The Num and Den are rational functions whose form as follows:  
\[\begin{array}{l}p(u,v,w)=a_{1}+a_{2}v+a_{3  
where \(a_{1}\sim a_{20}\) are the coefficients of RPCs model. As ships are at sea, the elevation \(h\) approximates to 0. The corresponding ground coordinates can be calculated using the inverse transformation of Equation (3) which can be solved by Taylor series expansions using a certain number of iterative solutions.  
After ship detection, the image coordinates of ships can be obtained, and the ground coordinates of ships can be estimated using the RPCs model. However, the initial ground coordinates are inaccurate for direct geo-referencing of images because of the systematic error of RPCs. To correct the system error, the image error compensation model is introduced. The commonly used bias-compensated RPCs model is an affine transformation function, which is expressed as  
\[\begin{array}{l}l^{\prime}=e_{0}+e_{1}l+e_{2}s\\ s^{\prime}=f_{0}+f_{1}l+f_{2}s\end{array} \tag{6}\]  
where \((l^{\prime},s^{\prime})\) and \((l,s)\) are image coordinates obtained from RPCs and tie points respectively, \((e_{i},f_{i}),(i=0,1,2)\) are the transformation parameters. The key to get transformation parameters is to get the point pairs. In this paper, we use the AIS data which are associated with GF-4 detections as the control points.
# A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models +
## 3 Extended State Observer
### ADRC2 Controller Design  
When considering integrative model \(S_{2}(s)\) with a time constant \(T_{1}\), ESO (Figure 3) may be described as  
\[\begin{array}{ll}\dot{x}_{1}=&x_{2}+p_{1}(y-\overline{y})\\ \dot{x}_{2}=-x_{2}/T_{1}+K_{s}(x_{3}+u(t-\overline{T_{d}}))/T_{1}+p_{2}(y- \overline{y})\\ \dot{x}_{3}=&0+p_{3}(y-\overline{y})\\ \overline{y}=x_{1}\end{array} \tag{33}\]  
This model offers both the input disturbance estimate \(\overline{d}=x_{3}\) and the output derivative estimate \(\dot{\overline{y}}\approx x_{2}\) useful for the PD controller (12) (alternatively, it might be calculated from the first row in (33).  
When neglecting differences in delays of the plant and model, ESO dynamics may be described by  
\[\begin{array}{ll}\dot{\mathbf{x}}=\left[\begin{array}{ccc}-p_{1}&1&0\\ -p_{2}&-1/T_{1}&K_{s}/T_{1}\\ -p_{3}&0&0\end{array}\right]\mathbf{x}+\\ +\left[\begin{array}{c}0\\ K_{s}/T_{1}\\ 0\end{array}\right]u(t-\overline{T}_{d})+\left[\begin{array}{c}p_{1}\\ p_{2}\\ p_{3}\end{array}\right]y;\\ \overline{y}=[1\ 0\ 0]\mathbf{x};\ \dot{\overline{y}}\approx[0\ 1\ 0]\mathbf{x};\ \dot{ \overline{d}}=[0\ 0\ 1]\mathbf{x}\end{array} \tag{34}\]  
Its dynamics is then characterized by  
\[\begin{array}{ll}det\big{(}s\mathbf{I}-\overline{\mathbf{A}}\big{)}=\left[ \begin{array}{ccc}s+p_{1}&-1&0\\ p_{2}&s+1/T_{1}&-K_{s}/T_{1}\\ p_{3}&0&s\end{array}\right]=\\ =s^{3}+s^{2}(p_{1}+1/T_{1})+s(p_{1}/T_{1}+p_{2})+p_{3}K_{s}/T_{1}\end{array} \tag{35}\]  
By choosing three poles at \(s=-1/T_{c}\), the ESO parameters become  
\[p_{1}=\frac{3}{T_{c}}-\frac{1}{T_{1}};\ p_{2}=\frac{3}{T_{c}^{2}}-\frac{p_{1}} {T_{1}};\ p_{3}=\frac{T_{1}}{K_{s}T_{c}^{3}} \tag{36}\]  
With respect to accelerated transients with the closed loop time constant (16), one may expect as a reasonable choice \(T_{c}\approx T_{d}/2\). Thereby, according to the \"half-rule\" [13], dead-time \(T_{d}\) of the IPDT plant should equal to the modified loop delay \(T_{d}=\overline{T}_{d}+T_{1}/2\). However, experiments show that such equivalence is appropriate just for \(T_{1}<<T_{m}\). For longer \(T_{1}>T_{m}/2\), it is more appropriate to work with \"average residence time\" equivalence yielding \(T_{d}\approx\overline{T}_{d}+T_{1}\). Moreover, since \"half rule\" holds only for significantly small \(T_{1}\), we do not make large error by choosing \(T_{d}=\overline{T}_{d}+T_{1}\) in all cases.
# A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images
## 3 Solving Hierarchical Localization By Means of a Classification CNN and Regression CNNs
### Fine Localization Stage Using a Regression CNN  
After a coarse estimation of the pose of the robot (area of location), a fine estimation was performed. The objective then was to use the CNN to estimate the robot coordinates, i.e., the precise position of the robot. In order to achieve this, we propose addressing it as a regression problem. To this purpose, the space is divided in different zones according to their similarity from the visual point of view. Then, a CNN is created for each one of these zones. At this point, given an input image (test image), the objective is to estimate, as output, the coordinates of the point where that image was taken (localization). In this case, since the CNN was designed for classification, some modifications should be made. First of all, now the output is not a hot vector but two values: X and Y coordinates. Therefore, the output layers should be modified. Regarding the transfer learning technique, we continued taking advantage of an existing CNN and therefore we obtained the coordinates of the robot using the feature information. This is explained in more detail in Section 5.
# Thermal Fatigue Life Prediction of Thermal Barrier Coat on Nozzle Guide Vane via Master-Slave Model
## 2 Modeling and Simulation Methods
### Physical Model and Meshing Method  
The focus of this paper was on the first stage NGV of an axial flow turbojet engine [26]. The flow cascade was comprised of 24 vanes. The internal and external radii of the link rings were 95 mm and 135 mm, respectively. In this study, the fan-shaped cascade was simplified to the square cascade with a vane space of 38 mm, while both cooling holes and link rings were ignored. The geometrical  
Figure 2: High-integrity life predication approach for the thermal barrier coat (TBC) on NGV.  
size of the NGV is shown in Figure 3. As illustrated in Figure 3, the geometry of the master model consisted of three layers (i.e., TC (material mode no. 8 YSZ with thickness 0.25 mm), BC (material mode no. NiCrAIY with thickness 0.125 mm), and Ni-based alloy substrate (material mode no. GH3030)). The TGO layer only existed in the slave model to calculate the strain range for different thicknesses. The master-slave model is shown in Figure 4a.  
Based on the morphological characteristics of the TGO layer [27], a sinusoidal interface, as indicated in Figure 4b, was adopted to approximate the TGO and mixed layer with the wavelength of 0.04 mm and amplitude of 0.01 mm. Three kinds of slave models were established, as seen in Figure 4b, for the TBC with a flat surface, TBC with a sinusoidal interface, and TBC with a TGO layer. As high-temperature regions are usually at the center of the leading edge and trailing edge, two pieces of the TBC layer  
Figure 4: Meshing of the NGV and master–slave models. (**a**) Master–slave model; (**b**) Typical slave models; (**c**) Flow domain.  
Figure 3: Section view (mm).  
were defined as the subdomain of the master model in the positions. To prevent a large volume ratio of grid elements in the master model, the subdomain was slightly larger than the slave model.  
According to the structure of the master model, the gas and cooling air models were meshed. The thickness of the first layer near the wall grids was 1 \(\upmu\)m and the expansion ratio was 1:2. The grids of gas, cooling air, and master model were selected by mesh independence tests with respect to the sizes of the cell of 520,000, 40,000, and 330,000, respectively, and the grid numbers of the subdomain and slave model were 10,000 and 580,000, respectively. All grid models are shown in Figure 4. A fully coupling element, ANSYS-Solid 226 (Provided by workbench 18.0 software, ANSYS, Pittsburgh, PA, USA), was adopted in the master model. The fully coupling technology can improve the accuracy of thermo-elastic coupling by 2% to 3% with regard to temperature displacement coupling theory [28]. As the temperature was already solved in the master model, a weak coupling element, ANSYS-Solid 186, was adopted in the slave model. As for the master-slave model, it is unnecessary to repeatedly build and simulate the FE model with the increase in the TGO thickness. Thus, it is necessary to improve the work efficiency of the TFL prediction.
## 4 Discussion
### Center of Pressure Irregularity  
A very clear pattern emerged with ApEn analysis. All participants displayed an increase in ApEn while under increasingly unstable conditions when compared to baseline EOFT. All participants, except one, showed the highest ApEn in the two ECFTan trials. Motor control theory suggests that an increasing ApEn is related to the postural control system choosing a non-optimal movement to maintain balance and, therefore, having to try another movement [48]. As standing with eyes closed and feet in a tandem orientation is a less natural position than having the eyes open or the feet together, one's postural control system will be less practiced in making accurate movement decisions. Therefore, a greater ApEn under those circumstances could be expected as the postural control system will be forced to change between numerous states [49]. The participant for which this did not apply differed from the rest due to increased variability among trials of the same condition. Due to this variability, it is not unexpected that the patterns of that participant may look different than those of other, more consistent participants.  
Furthermore, in all participants aside from the same one with increased variability, all stability conditions except for ECFT showed a significant difference from the baseline EOFT condition based on [PERSON]'s post hoc testing. Half of the time, even the ECFT condition showed a significant increase in ApEn from the baseline condition. The ability to identify significant differences between positions with relatively small changes in stability level shows that ApEn could be a very sensitive and useful metric for analyzing postural control. Changes in ApEn did not appear to differ between the AP direction and ML direction.  
Studies that have compared COP ApEn in healthy participants and participants with concussion using more drastic techniques to adjust stability level found that in healthy participants, ApEn increases with decreased stability, supporting the results of this study. Alternatively, ApEn tends to decrease when stability decreases in subjects with a concussion. In addition to decreasing with lowered stability, participants with concussion also displayed a decreased ApEn in the same standing position between pre-injury testing and testing post-concussion [32; 34; 50; 51]. This decrease in ApEn may be caused by the decrease in cognitive function following concussion, leading the postural control system to respond in less complex ways.
###### Abstract  
In this study, a new concept for a Ka-band 5G communication tunable reflector metasurface (MS) for beam steering at 28 GHz is proposed. Varactor diodes are used as the tunability component of each unit cell of this MS. Significant improvements in beam steering and bandwidth performance were achieved using this new concept referred to as the stripes configuration. Several different geometries of unit cells arranged in stripes were designed to achieve better performance in directionality, gain, sidelobe level (SLL), and bandwidth in the stripes configuration. Simulation results for a three-stripe MS with different unit cells in each stripe showed better performance in the phase dynamic range and reduced reflectance losses compared to a typical one-stripe MS. The simulation results showed a significant improvement of 3 dB, depending on the steering angle in reflectance gain, compared to a uniform MS (one stripe). Furthermore, a significant improvement of approximately 50% in the accuracy of the steering angle for different operating frequencies was demonstrated. Manufacturing considerations are discussed in this study.  
beam steering; Ka-band; metasurface; varactor diode +
Footnote †: journal: Applied Sciences
# Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts
## 4 Results and Evaluation
### Qualitative Analysis
#### 4.2.1 Bundling Evaluation
However, the resulting bundles repeat the pattern existent in the H-Tree layout's fractal structure, making the edges patterns harder to be identified and less visually pleasant. Despite that, we can recognize the edges patterns by analyzing bundles' density. These comparisons show that all the selected state-of-the-art techniques could produce edge bundling layouts that highlight significant patterns. FDEB and MINGLE produce better results when the vertices are spread over the visual space (H-Tree layout) but tend to fail to produce meaningful results for the radial layout, resulting in heavily cluttered representations. CUBu and SDEB present a better clutter-reduction, but CUBu concentrates the bundling close to the source/target edges, resulting in misleading bundlings. SDEB produces better layouts reducing ambiguities and creating more compact bundles.
## 6 Simulation
### Capacity Estimation of Model 1 Using MIMO System  
In this section, the proposed CR system model 1 is equipped with \(2\times 2\) MIMO antenna systems. Thus, simulations are carried out to estimate the capacity versus SINR of OSA, DSCA, OC-DSA, and DSCA-MC-CR techniques using the single-input single-output (SISO) antenna system and the proposed DSCA-MC-CR system using the MIMO smart antenna system. Also, the simulation assumes that the CSI is not available at the transmitter side, and the bandwidth is equal to \(1\) when applying the Shannon law given in [10,33] and applying Equations (17) and (18). In addition, the channel gain of the desired signal is assumed to be normalized, \(K=0.25\), \(\gamma=0.15\), and the SNR of the interferers at the desired  
Figure 11: OMNeT proposed network for model 1 with practical medium parameters. (**a**) OMNeT network of model 1. (**b**) PU system and SU system in OSA mode.  
user is equal to \(10^{-3}\). Furthermore, the channel \(H\) matrix is a complex one, where singular value decomposition (SVD) is applied to the term \(HH^{H}\) of Equation (24) to obtain the maximum eigenvalue that represents the channel gain of the desired signal at the MIMO receiver. Figure 13 shows that the MIMO systems achieve a noticeable improvement in the capacity in bits of the PU and the SU systems compared to the SISO antenna systems, i.e., when the SNR is 2 dB, the PU system achieves 1.4 bits by using OSA, 1.6 bits by using DSCA, 2.1 bits by using OC-DSA, 2.4 bits with the proposal using the SISO system, and 3.3 bits with the proposal using MIMO systems. Moreover, the SU system achieves 1.35 bits by using OSA, 1.2 bits by using DSCA, 1.2 bits by using OC-DSA, 1.9 bits with the proposal using the SISO system, and 3.4 bits with the proposal using MIMO systems.
# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## Methods
### LAI Data (In Situ LAI and Upscaling, MODIS LAI, EPIC LAI)
#### 2.2.1 In Situ LAI Measurements and Upscaling
This algorithm ingests either a L5 TM or L7 ETM+ derivative ban--the vegetation index, SR, to predict LAI. Managed _P. taeda_ LAI was predicted with approximately 14% error when comparing SR LAI with field-measured LAI [39]. Finally, in situ LAI mean and standard deviation was weighted by LC class over the 1.0 km resolution to arrive at an overall mean and standard deviation for the LAI-RM. LAI from young forest regeneration estimated solely using the SR LAI algorithm due to a more dominant presence of loblolly pine (Appomattx site only). Figure 3: Example of quadrat and sub-plot distribution within 1.0 km LAI research site (Fairystone research site only). Note: One quadrat sites (Hertford, Umstead, Appomattx).