esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
Due to the periodic nature of the triple-band light absorber, we only provide one unit cell with \(Px=70~{}\upmu\)m and \(Py=68~{}\upmu\)m, see Figure 1a. As shown in Figure 1a, three layers (two metallic layers as the first and third layers and an insulation dielectric layer as the second layer) are used in this absorber structure. We chose the high conductivity (\(\sigma=4.09~{}\times 10^{7}~{}\mathrm{S/m}\)) Au as the first and the third layers of the absorber. The first layer is a pattern Au strip, while the third layer is a bulk flat Au film, which can block the transmission of the light wave. The length and width of the Au strip are respective \(l=60~{}\upmu\)m and \(w=15~{}\upmu\)m. The second layer is an insulation dielectric film with the thickness of \(t=9.6~{}\upmu\)m, which is sandwiched by the two Au layers. The dielectric constant of the second layer (polyimide) is \(3(1+i0.06)\)[15; 16]. The reason why we chose a single metallic strip array as the research object is that it has the ability of simple structure design and easy construction. Finite-difference time-domain methods based on (FDTD Solutions, Lumerical Canada) are used to investigate and analyze the electromagnetic properties of the presented absorber structure. The limitations of the proposed are mainly reflected in occupying a large amount of computer memory, long calculation time, only calculating periodic structure, and difficult to calculate a single structure. The light incident to the three-dimension unit cell is assumed to be a plane wave, and its \(E\) field is parallel to the \(x\)-axis. The unit cell in the directions of the \(x\) and \(y\) is modeled by periodic boundary conditions, while perfectly matched layers are used in the propagation direction (\(z\)-axis) of the incident wave. Additionally, compared with previous work in Reference [51], the methods and materials used here are different as follows: (1) Non-dispersive materials are used here, including metallic and dielectric components; (2) different simulation software and calculation methods; (3) specific values of resonant structure are not analyzed, such as mutual inductance and mutual capacitance etc. (4) Different purposes of light regulation, only multi-band absorption is considered here.
## 1 Introduction
These models are sufficient for theoretical analysis of vegetation shadows and for drawing conclusions based on detailed and complete data collection in limited study areas. However, for large-scale satellite images with several spectral bands and various terrain backgrounds, these models may be not suitable due to a lack of necessary parameters. The vegetation shadow models mentioned above also rarely considered the connection between sunlight conditions and vegetation red edge, which has important roles in inversion and calculation when more detailed proportions of illumination and shadow are considered. Further study on vegetation red edge identified that red edge characteristics play an important role in vegetation growth conditions. Satellites equipped with red edge bands have been produced, such as ESA's Sentinel-2 and China's GF-6. It is important to study the relationship between vegetation shadows and red edge performance and to construct models that can differentiate vegetation shadow proportions using satellite multispectral images. In this study, we proposed a new vegetation index named the normalized difference canopy shadow index (NDCSI) to represent the proportion of shadows in forest canopies quantitatively. The approach was based on analyzing correlations between red edge parameters (curve slope and reflectance) and the normalized difference vegetation index (NDVI) of vegetation canopies in different proportions of illumination and shadow. Moreover, NDCSI was also utilized for linear spectral unmixing with the bare soil index (BSI) based on a Sentinel-2A multispectral image, obtaining the unmixing fractional cover results of illuminated vegetation, shaded vegetation and bare soil.
# A Joint Dual-Frequency GNSS/SINS Deep-Coupled Navigation System for Polar Navigation
## 2 Availability Analysis of GNSS in Polar Areas and Grid SINS Mechanization
### Grid Frame  
In order to avoid SINS navigation errors caused by the meridian convergence in polar areas, the grid frame (denoted \(G\)) is used and is introduced firstly in this subsection. In Figure 1, point \(P\) represents the position of the user's mass center. The grid planes are the planes that are parallel with the Greenwich plane. The grid plane of point \(P\) is the grid plane that passes through point \(P\).  
The grid plane and the tangent plane of the earth passing point \(P\) intersect into a line, and the grid north axis (\(oy_{G}\)) lies along the line. Besides this, the grid up axis (\(oz_{G}\)) is perpendicular to the local horizontal plane and lies along the geographic frame \(z\) axis. The grid east axis (\(ox_{G}\)), \(oy_{G}\), and \(oz_{G}\) constitute the right-handed coordinate system that is the grid frame.  
The angle between the geographic north and grid north axis is \(\sigma\). As the grid up axis coincides with the geographic up axis, the grid frame can be obtained from the geographic frame by rotating \(\sigma\) around the up axis. Therefore, the transformation matrix between the Earth-centered-Earth-fixed frame (denoted \(e\)), geographic frame (denoted \(g\)), and \(G\) frames can be described as  
\[\mathbf{C}_{g}^{G}=\left[\begin{array}{ccc}\cos\sigma&-\sin\sigma&0\\ \sin\sigma&\cos\sigma&0\\ 0&0&1\end{array}\right] \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **Minimum** & **Maximum** & **Mean** \\ \hline GDOP & 1.60 & 5.55 & 2.56 \\ PDOP & 1.45 & 4.77 & 2.27 \\ HDOP & 0.66 & 1.17 & 0.79 \\ VDOP & 1.29 & 4.63 & 2.13 \\ TDOP & 0.69 & 2.85 & 1.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dilution of precision (DOP) values of different schemes (mask angle 10\({}^{\circ}\)).  
Figure 1: Description of the grid frame.  
where \(\sigma\) can be obtained by  
\[\sin\sigma=\sin\lambda\sin\varphi/\sqrt{1-\cos^{2}\varphi\sin^{2}\lambda} \tag{2}\]  
\[\cos\sigma=\cos\lambda/\sqrt{1-\cos^{2}\varphi\sin^{2}\lambda}. \tag{3}\]  
Besides this, \(\mathbf{C}_{e}^{g}\) can be calculated by  
\[\mathbf{C}_{e}^{g}=\left[\begin{array}{ccc}-\sin\lambda&\cos\lambda&0\\ -\sin\varphi\cos\lambda&-\sin\varphi\sin\lambda&\cos\varphi\\ \cos\varphi\cos\lambda&\cos\varphi\sin\lambda&\sin\varphi\end{array}\right] \tag{4}\]  
where the latitude and longitude of point \(P\) are defined as \(\varphi\) and \(\lambda\), respectively, and \(\mathbf{C}_{e}^{G}=\mathbf{C}_{e}^{g}\mathbf{C}_{g}^{G}\).
## 3 ML for SatCom RRM: Architecture and Techniques
### Reinforcement Learning for RRM
#### 3.3.1 Q Learning  
In the QL algorithm, the \(Q_{b}\) value of a pair \((s_{t}^{b},a_{t}^{b})\) contains the sum of all possible rewards for each state-action of the \(b\)th agent at instant \(t\). Provided that the \(b\)th agent knows a priori the \(Q_{b}\) values of all possible pairs \((s_{t}^{b},a_{t}^{b})\), it could use this information to select the appropriate action for each state. Table 3 is generated for each agent (Figure 8a), representing a matrix of size \(J\times K\) where \(J\) is the number of possible states of the \(b\)th agent and \(K\) the number of possible actions, and in each position of the matrix, the \(Q_{b}\) value for each state-action pair is represented. The first objective of the agent is to approximate as much as possible the assignment of \(Q_{b}\) values, which depends on both future and current rewards, as shown in the following:  
\[Q_{b}(s_{t}^{b},a_{t}^{b})=r_{t}(s_{t}^{b},a_{t}^{b})+\gamma[\max_{a_{t}^{b}}Q _{b}(s_{t+1}^{b},a_{t}^{b})] \tag{11}\]  
Figure 8: The agents are trained from the acquired observation data using an RL algorithm to obtain the agents’ policies and update the resources. (**a**) Q-Learning (QL): Table \(Q\) is generated for each agent and will represent the \(Q_{b}\) value for each state–action pair. (**b**) Deep Q-Learning (DQL): DQL uses a neural network to approximate the function of the \(Q_{b}\) value. (**c**) Double Deep Q-Learning (DDQL: Each agent in DDQL uses two neural networks with the same architecture to learn and predict what action to perform at each step.  
where \(s^{b}_{t+1}\) represents the following state, and \(\gamma\) is the discount factor controlling future rewards' contribution. Since this is a recursive equation, it starts by making arbitrary assumptions for all \(Q_{b}\) values and is implemented as an update:  
\[Q_{b}(s^{b}_{t},a^{b}_{t})\gets Q_{b}(s^{b}_{t},a^{b}_{t})+\alpha[r_{t}(s^ {b}_{t},a^{b}_{t})+\gamma\max_{a^{b}_{t}}Q_{b}(s_{t}+1)^{b},a^{b}_{t})-Q_{b}(s^ {b}_{t},a^{b}_{t})] \tag{12}\]  
where \(\alpha\) is the learning rate or step size; this determines the extent to which newly acquired information overrides old information.
# Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Reschargeable Sensor Networks
## 7 Conclusions  
A mathematical optimization model for the energy efficiency problem in hybrid SDWRSNs is derived, which provides an outline and paradigm for energy saving problem in hybrid SDWRSNs. Then, we put forward an Energy-saving Traffic Scheduling (ETS) algorithm for hybrid SDWRSNs. The ETS algorithm takes charging losses and the new energy efficiency requirements into account, and schedules the flows through both SDN nodes (directly) and normal nodes (indirectly). The simulation results show that ETS can substantially improve the energy efficiency in hybrid SDWRSNs for up to 20-40% compared with traditional routes with feasible data delay. Therefore, ETS can make the network working more steadily and effectively for a much longer term.  
In this paper, we only use the first SDN node along a source-destination pair, so as to avoid too high algorithm complexity. In future work, some improved algorithm that can employ all SDN nodes  
Figure 8: Latency with different network data load.  
Figure 9: Energy consumption of different sink node location.  
along a source-destination pair with low complexity can be studied, which might further improve the energy efficiency while ensuring feasible data delay.
## 5 Deployment and Integrability
### In-Device Integration  
Local integration embedded directly in the X-ray acquisition device, where the DICOM object is directly present in-device after its generation and inference, is performed automatically using local resources, returning a pneumothorax probability and triggering an alarm for the technician even before the patient leaves the X-ray acquisition room. The technician could alert the referring physician or the radiologist, reducing the diagnosis time and providing faster care.  
As the data is managed inside the modality itself, it does not leave the hospital/clinic network. The drawback is that the model must be embedded inside the modality itself, something that is currently not supported out of the box for most of X-ray vendors.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 5 Modification Processes of Kaolin: Enhancing Industrial Applicability
### Acid-Base Chemical Treatment
#### 5.3.1 Acid Activation
This process results in metakaolin with both stable surface areas and significant Bronstedacidity. The properties of the treated metakaolin, including its surface area, stability, and pore size, are influenced by the calcination temperature of the clay and the amount and temperature of HCl treatment. Deviations from the optimal calcination temperature range can significantly reduce alumina solubility and surface area. Additionally, while excessive HCl treatment can enhance the surface area, it may also lead to the over-removal of aluminum, compromising the catalytic activity of the treated metakaolin [57]. [PERSON] et al. [58] investigated the acid activation of metakaolin by treating it with 6 M hydrochloric acid at room temperature and at 90 \({}^{\circ}\)C under reflux for 6 and 24 h. They found that at room temperature, the structure and properties of metakaolin remained unchanged. However, a six-hour reflux treatment removed most octahedral Al3+ cations, forming an amorphous silica phase with a surface area of up to 219 m\({}^{2}\)/g. Conversely, a more potent acid treatment (reflux for 24 h) resulted in further amorphization of the silica, reducing the surface area to 23 m\({}^{2}\)/g. These acid-treated solids have potential as substrates for catalysts and adsorbents.
# Fiber Orientation Reconstruction from SEM Images of Fiber-Reinforced Composites
## 3 Results and Discussion
### Single-Fiber Deflection Angle Measurement Results
#### 3.1.1 Prominent Fiber Orientation  
Figure 5 presents the prominent fiber orientation measurement process. Figure 5a,b illustrate the prominent fiber images captured from different tilt angles. Figure 5c,d show the GAC model results of Figure 5a,b. The highest and lowest contour points (L\({}_{\rm T}\)-R\({}_{\rm T}\), L\({}_{\rm B}\)-R\({}_{\rm B}\)) divide the boundary to be matched into two sections. The contour points (A-A\({}^{\prime}\)) in the boundary to be matched were searched only on the same side of the search boundary. The fiber contour point cloud shown in Figure 5e was fitted using the coordinate  
Figure 4: Microscopic images of composite plate specimens: (**a**) carbon fiber reinforced PLA plate; (**b**) single-fiber image acquired by image measuring instrument (the fiber marked in the orange circle); (**c**) prominent carbon fiber SEM image; (**d**) carbon fiber pull-out cavity SEM image.  
transformation-based cylindrical fitting method described in Section 2.4. The original points, shown in green, are the contour points that were collected to create the shape. The fitting surface was represented by a scatter of red points. The results of this fitting produced (x\({}_{0}\), y\({}_{0}\)) = (295.257, 278.773), \(\alpha\) = 4.740\({}^{\circ}\), \(\beta\) = 7.188\({}^{\circ}\), and r = 125.387. This demonstrate that the fiber contour reconstruction results were relatively satisfactory and that the 3D point cloud of the reconstructed contours accurately reflected the height difference between the fiber end boundary and the fiber-matrix bond line. Figure 5f depicts the column surface drawn according to the calculation results.  
Figure 5: Prominent fiber cylindrical fitting results: (**a**) SEM image when the sample stage tilted \(-5^{\circ}\); (**b**) SEM image when the sample stage tilted \(+5^{\circ}\); (**c**) contour segmentation tilted \(-5^{\circ}\); (**d**) contour segmentation tilted \(+5^{\circ}\); (**e**) fiber contour 3D point cloud; (**f**) cylindrical 3D shape.  
The unit vector of the fiber axial direction was determined from the unit vector \(t^{\prime}=(0,\,0,\,1)^{\mathrm{T}}\) in the Cartesian coordinate system with R, i.e., \(t=Rt^{\prime}=(-0.125,\,-0.082,0.989)^{\mathrm{T}}\). According to Equation (1), the deflection angle and azimuth angle of the fiber were \(8.605^{\circ}\) and \(-56.774^{\circ}\), respectively.
# Reliability Modelling and Evaluation for LTD System Based on Load-Sharing Model
## 3 Load-Sharing Based Voltage Redistribution Mechanism for Modules in Each Level
### Local-Equal Load-Sharing Rule  
Similar to the equal load-sharing rule but more complicated, the local-equal load-sharing rule assumes that the load on the failure module is equally distributed to the remaining normal modules within a limited distance. In this condition, we have:  
\[\begin{split}\Delta V_{p}(x)=\begin{cases}0&p\in S_{1}(k)\text{ \ or }D_{pq}>f\\ e&p\in S_{0}(x)\text{\ and }D_{pq}\leq f\\ \end{cases}\\ s.t.&\sum\Delta V_{p}(x)=\delta\cdot V_{q}(x-1)\\ &1\leq f\leq 30,\ x\geq 1\end{split} \tag{20}\]  
According to Equation (20), we can obtain:  
\[e=\frac{\delta\cdot V_{q}(x-1)}{Size\Big{(}S_{0}(x)\cap\big{\{}D_{pq}\big{|}D _{pq}\leq f\big{\}}\Big{)}} \tag{21}\]  
Thus, the voltage variation \(\Delta V_{p}(x)\) can be obtained by:  
\[\Delta V_{p}(x)=\begin{cases}0&p\in S_{1}(k)\big{|}D_{pq}>f\\ \frac{\delta\cdot V_{q}(x-1)}{Size\big{(}S_{0}(x)\cap\big{[}D_{pq}\big{|}D_{pq }\leq f\big{\}}\big{)}&p\in S_{0}(x)\&D_{pq}\leq f\end{cases} \tag{22}\]  
where \(1\leq f\leq 30\) represents the area influenced by the module failure.
## 4 Discussion
### Limitations and Future Research Directions  
We recognize that although the feature selection method showed advantages in this study, the robustness of the model in extreme environments or unconventional driving behaviors is yet to be further validated. Future research could explore how to further automate the feature selection and optimization process through machine learning techniques to enhance the model's adaptability and generalizability to different driving states.
# Dual-Band MIMO Antenna for 5G/WLAN Mobile Terminals
###### Abstract  
This paper presents a dual-band four-element multiple-input-multiple-output (MIMO) array for the fifth generation (5G) mobile communication. The proposed antenna is composed of an open-loop ring resonator feeding element and a T-shaped radiating element. The utilization of the open-loop ring resonator not only reduces the size of the antenna element, but also provides positive cross-coupling. The dimension of a single antenna element is 14.9 mm \(\times\) 7 mm (0.27x \(\times\) 0.13\(\lambda\), where \(\lambda\) is the wavelength of 5.5 GHz). The MIMO antenna exhibits a dual-band feature from 3.3 to 3.84 GHz and 4.61 to 5.91 GHz, which can cover 5G New Radio N78 (3.3-3.8 GHz), 5G China Band N79 (4.8-5 GHz), and IEEE 802.11 ac (5.15-5.35 GHz, 5.725-5.85 GHz). The measured total efficiency and isolation are better than 70% and 15 dB, respectively. The calculated envelope correlation coefficient (ECC) is less than 0.02. The measured results are in good agreement with the simulated results.  
5G antenna; sub-6 GHz; MIMO; dual-band antenna; open-loop ring resonator +
Footnote †: journal:  
0000-0001-6000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-000000-00000-000000-000000-000000-000000-0000000-0000000000000000000000000000000  
The third Generation Partnership Project (3 GPP) has proved that 5G New Radio (NR) involves three sub-6 GHz operating bands, i.e., N77 (3.3-4.2 GHz), N78 (3.3-3.8 GHz) and N79 (4.4-5.0 GHz). Diverse countries and areas can choose their own 5G operating bands from the above three bands. For example, the use of 3.3-3.6 GHz and 4.8-5.0 GHz bands has been announced officially by China, the 3.6-4.2 GHz and 4.4-4.9 GHz has been utilized globally by Japan, and the 3.4-3.8 GHz band has been adopted by European Union (EU). Designing dual-frequency compact antenna with broad bandwidth in both bands is often a challenging task for antenna designers. The loop antennas can significantly minimize the size. Usually, the 0.5\(\lambda\), 1.0\(\lambda\) and 1.5\(\lambda\) modes are the first three resonance modes of loop antennas. Besides, an open-slot, a tuning section and a parasitic section at the reverse side can further boost the bandwidth for both the lower and higher bands.  
In this paper, a novel ring antenna for the applications of 5G MIMO antenna system in mobile terminals is proposed. The antenna array consists of an open-loop ring resonator and a T-shape radiating element. The combination of the 0.27\(\lambda\) and 0.13\(\lambda\) modes forms an obvious dual-band operation that can cover 3.3-3.84 GHz in the lower band and 4.61-5.91 GHz in the higher band. Different radiators have been designed and analyzed for the proposed compact 4-antenna MIMO system. The capability of the proposed 4-antenna MIMO system is verified consistently by both simulation and measurement.
## 4 Proposed Metaheuristic Schemes
### Particle Swarm Optimization (PSO)
#### 4.1.2 Fitness  
The proposed particle encoding representation was evaluated to determine an optimal schedule of directional sensors with a fitness function. To determine the optimal solution for the problem, the evolutionary process of our proposed PSO should achieve two objectives: (1) maximizing the target coverage rate and (2) minimizing the active sensor rate. The second condition is equivalent to maximizing the number of inactive sensors. These two objectives suggest the following. If two schedules of directional sensors or particles have the same total number of covered targets, a chromosome with fewer active sensors is preferable because of its reduced energy consumption. To achieve these two objectives, an appropriate fitness function is proposed which is used in the metasearch algorithm for searching for a schedule that maximizes the fitness function. In general, a schedule of directional sensors with a high fitness value would satisfy all of the objectives well. Therefore, the fitness function for the target coverage problem is defined as follows:  
\[F=w\cdot R_{T}+(1-w)\cdot(1-R_{S}),\]  
where \(R_{T}\) is the target coverage rate and \(R_{S}\) is the active sensor rate. The target coverage rate is the ratio of the total number of covered targets to \(M\), and the active sensor rate is the ratio of the total number of active sensors to \(N\). In addition, \(w\) is a predefined weight where \(0\leq w\leq 1\).