esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Challenges Supporting School-Aged Children with Vision Problems: A Rapid Review
## 6 School-Aged Children's Vision Screening (RQ2)
### Vision Screening Programs for Schools  
There are several countries with school vision screening programs, such as parts of the US, South Africa [39], and a county in Norway [18]. Across different countries, there is a substantial variation in screening programs focusing on vision and hearing. Studies show that only 21-36% of children younger than six years of age have received vision screening in the US [108].  
A literature review [39] examining vision screening in 18 countries shows that many countries only perform partial vision screening; 88% are missing necessary tests, such as those indicating a need for eyeglasses (e.g., tests with Snellen or Tumbling \"E\" charts). In another study, [PERSON] et al. [109] highlighted the potential pitfalls in the screening process  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Studies** & **Stakeholders** & **Roles** \\ \hline
[96; 97] & Ophthalmologists, Optometrists, Orthoptists, Opticians, Nurses & Vision screening in clinical setting. \\
[14; 44; 104; 105; 106; 107] & Educationalist, Vision Teachers & Vision screening in schools. \\
[14; 96; 98] & Volunteers, Parents & Non-health professionals supporting vision screening directly or indirectly. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Papers grouped based on stakeholders and their roles.  
for preschool children, such as the agreement of effective methods of screening and the variability of test batteries in US states. Some studies reported the lack of professionals for vision screening in countries such as Turkey and Tanzania, and documented in reports from European countries [110; 111; 112]. [PERSON] et al. [101] also argued that refractive error, strabismus, and amblyopia are most likely considered for screening in preschool and school entry children, whereas there is no agreed policy for the screening of binocular vision problems, functional vision, and other eye conditions in Australia and worldwide. While the work of non-professionals can be supportive and complimentary, their duties and roles are unclear at the moment. They supplement professionals in different ways and with different, often limited tests [39; 42; 112]. [PERSON] et al. [98] found that the accuracy of non-healthcare workers is 75% compared to professionals. However, non-professional volunteers can be trained to detect vision problems in children, especially if they have the right tools to support them.
# Fast 3D Content Update for Wide-Angle Holographic Near-Eye Display
[25; 28] have proposed a content generation solution where a new viewpoint hologram is created by reusing part of information from a previous view. Thus, there is no need to generate an entire hologram for two consecutive views when existing data can be used to generate a part of the next view. In this solution, a ray-tracing method was used to process the CGHs and free movement of the viewpoint position was achieved as a superposition of three separate operations: parallel shift, rotation, and front-back movement. It is worth noting that front and back movement solutions are implemented by using scaling of the hologram information [29]. Unfortunately, the FoV of the proposed solutions is limited to \(10^{\circ}\), which is a case of paraxial approximation. Moreover, high degradation of the 3D content is present when large distances or angles are employed [28]. In this work, we address the hologram update problem for wide viewing angle near-eye holographic display, where the hologram plane is related to the eye position. As described in [25; 28], the generation of holographic content from a previous viewpoint significantly accelerates the processing time of CGHs. However, unlike those works, we propose accurate and non-paraxial tools for numerical manipulation of the holographic content. In the presented solution, an overlap region is found as a projection of original holographic data on the tilted and shifted plane of a new viewpoint hologram. It is shown that reusing the overlap region is possible by shifting, rotating, and repropagating part of the previous CGH. In our approach, these operations are done in space and frequency domains by using the AS concept. We show that for wide viewing angle holograms, rotation operation can be replaced by frequency shift with optimized coefficient, which minimizes computation errors. This improves the processing time of reused information several times over. Subsequently, the missing region of the hologram is generated with the PAS algorithm [23]. The combination of these two tools enables calculation of the next viewpoint. Performed numerical tests show fast and accurate calculations. Furthermore, it is shown that there is no degradation in image quality when a large number of viewpoints are generated. Thus, for continuous viewpoints change, it is sufficient to count only the first hologram frame. This is an advantage over the solution presented in [28], where the recalculation of the full frame is necessary after several updates. The update method of this work is demonstrated by using numerical and optical experiments for a display of FoV of 62\({}^{\circ}\). The paper is structured as follows: the next section presents the principles of the proposed hologram update algorithm. Sections 3 and 4 explain filling the blank part and reusing the overlap part in detail, respectively. Section 5 presents numerical tests of the updated hologram quality and execution time. Section 6 illustrates numerical and optical reconstructions of complex objects in the near-eye display. Finally, the conclusions of this work are presented in Section 7.
## 3 Methodology
### Multi-Layer Graph Attention Module (MGAT)  
To utilize rich hierarchical syntactic information, we use the MGAT block stacked by several designed graph attention layers [28]. GAT is a new graph neural network architecture, including an attention mechanism, which enables one to assign different attention weights to the information provided by the feature aggregation of the central node according to different nodes and propagate the sentiment information of node to its neighboring nodes.  
The set of input and output in the graph attention layer is \(h=\left\{\overset{\rightarrow}{h_{1}},\overset{\rightarrow}{h_{2}},\ldots, \overset{\rightarrow}{h_{N}}\right\}\) and \(h^{\prime}=\left\{\overset{\rightarrow}{h_{1}^{\prime}},\overset{ \rightarrow}{h_{2}^{\prime}},\ldots,\overset{\rightarrow}{h_{N}}\right\}\), from which the attention coefficient between the central node and neighboring nodes is obtained:  
\[e_{ij}=a([W\overset{\rightarrow}{h_{i}}||W\overset{\rightarrow}{h_{j}^{ \prime}}]) \tag{8}\]  
where \(a\) is attention mechanism and \(W\) is the weight matrix.  
GAT adopts a masked attention mechanism to prevent the dropping of all structural information and changes the previous situation where the self-attention mechanism will allocate attention to all nodes to allocate attention to neighboring nodes. In addition, the attention coefficient is normalized using the _softmax_ function, so the attention coefficient after the update is:  
\[\alpha_{ij}=softmax(e_{ij})=\frac{\exp\left(e_{ij}\right)}{\sum_{k=1}^{N}\exp(e _{ik})} \tag{9}\]  
The multi-head attention mechanism is used to obtain the influence of adjacent nodes on the central node, and the node features extracted by \(K\) heads are represented to complete the splicing operation, and finally, the \(K\) average is used to replace the connection operation to obtain the final node representation:  
\[\overset{\rightarrow}{h_{i}^{\prime}}=\sigma\left(\frac{1}{K}\sum\sum\
olimits _{j\in N}\alpha_{ij}^{k}W^{k}\overset{\rightarrow}{h_{j}}\right) \tag{10}\]  
where \(\alpha_{ij}^{k}\) is the normalized attention coefficients and \(W^{k}\) is the linear transformation correlation weight matrix.  
By stacking the above update process multiple times, node updates in a multi-layer attention graph can be represented as follows:  
\[H_{A}=MGAT(A) \tag{11}\]  
The syntactic matrix \(A_{syn}\) and semantic matrix \(A_{sem}\) are fed to the _MGAT_, respectively, to obtain the syntactic feature \(H_{syn}\) and semantic feature \(H_{sem}\):  
\[H_{syn}=MGAT\big{(}A_{syn}\big{)} \tag{12}\]  
\[H_{sem}=MGAT(A_{sem}) \tag{13}\]
# Flexible 5G New Radio LDPC Encoder Optimized for High Hardware Usage Efficiency
## 2 Encoding of LDPC Codes
### Partitioned PCM Two-Step Encoding  
In cases when the LDPC code is systematic, the PCM can be partitioned into two submatrices (\(\mathbf{H}=\left[\begin{array}{cc}\mathbf{H}_{1}&\mathbf{H}_{2}\end{array} \right]\)), where the first submatrix is of size \(m\times k\), and the second submatrix is of size \(m\times m\). By transposing both the left-hand side and the right-hand side of the equation, Equation (1) becomes  
\[\mathbf{H}\mathbf{x}^{\mathrm{T}}=0, \tag{5}\]  
which can further be written as  
\[\left[\begin{array}{cc}\mathbf{H}_{1}&\mathbf{H}_{2}\end{array}\right] \left[\begin{array}{cc}\mathbf{i}&\mathbf{p}\end{array}\right]^{\mathrm{T}}= \mathbf{H}_{1}\mathbf{i}^{\mathrm{T}}+\mathbf{H}_{2}\mathbf{p}^{\mathrm{T}}=0. \tag{6}\]  
Parity bits can now be calculated as follows:  
\[\mathbf{p}^{\mathrm{T}}=\mathbf{H}_{2}^{-1}\mathbf{H}_{1}\mathbf{i}^{\mathrm{ T}}=0. \tag{7}\]  
This way, the calculation is simplified since the submatrix \(\mathbf{H}_{1}\) is sparse, and the vector \(\mathbf{H}_{1}\mathbf{i}^{\mathrm{T}}\) can be calculated with linear complexity (\(O(n)\)). However, the matrix \(\mathbf{H}_{2}^{-1}\) is dense and therefore leads to significantly high hardware cost in terms of computational complexity and memory requirements. Nevertheless, when properly exploiting the matrix structure, it is possible to achieve significant hardware cost reduction for certain codes [17; 18; 19].
## 3 Experiment Setup and Results
In addition to this, a \(\sim\)12 mm long and 20 \(\upmu\)m wide heater was placed on top of the longer arm of the MZI filter to find the P\({}_{\pi}\) (input power required at the heater to achieve \(\pi\) phase shift in the CW signal) of the heater. All the heaters in the 1 \(\times\) 4 channel wavelength demultiplexer have similar geometry. It should be mentioned that the test MZI structure is similar to the MZI filters of the 1 \(\times\) 4 channel wavelength demultiplexer shown in Figure 4. However, as mentioned in Section 2.3, the extra delay lengths in the MZI filters of the 1 \(\times\) 4 channel wavelength demultiplexer is much smaller (_i.e._, _L\({}_{l}\)_ = 249.5 \(\upmu\)m, _L\({}_{2}\)_ = 124.96 \(\upmu\)m and _L\({}_{3}\)_ = 124.7 \(\upmu\)m) compared to the extra delay length (4.4 mm) of the test MZI filter. Therefore, in the test MZI filter we expect to have smaller FSR, which will facilitate the experimental measurement of the group index of the waveguide within a small wavelength range. In both the test MZI and demultiplexer, P\({}_{\pi}\) of the heaters will be similar, as the heaters have same length (\(\sim\)12 mm) in all the MZI filters presented in this article. Figure 9a shows the zoomed optical transmission response of the test MZI structure, where the input CW signal from the laser with an optical power of 10 dBm is tuned over the wavelength range from 1547 nm to 1552 nm. We found that at a heater bias voltage of 25 V (6 mA), the optical transmission response of the filter shifts in wavelength by an amount equal to 1/4 th of its FSR  
Figure 8: Optical transmission response of the continuous wave (CW) signal from the laser, 10 cm long Si\({}_{3}\)N\({}_{4}\) waveguide and 1.5 m long Si\({}_{3}\)N\({}_{4}\) waveguide. (0.09 nm). Therefore, the measured resistance and P\({}_{\pi}\)/2 is 4.17 k\(\Omega\) and 150 mW, respectively, for the MZI delay arm's heater. From this result, the approximate value of P\({}_{\pi}\) can be interpolated to be 300 mW. Next, we used the method provided in [17] to measure group index (n\({}_{\text{g}}\)) of the waveguide from the transmission response of the MZI. In this method, the measured FSR at different wavelengths is used to calculate the group index. Figure 9b shows the simulated and experimental value of the group index at wavelength near 1550 nm. From this figure, it is clear that the experimental value of the group index is within \(\sim\)0.001 error limit from the simulation value. We calculated that within this error limit of the group index, the FSR of the filter would change by only \(\sim\)0.003 nm. As the wavelength separation between the adjacent channels of the filter is 3.2 nm, this FSR variation due to this group index variation does not affect the filter performance.
## 3 Types of Supplementary Dampers and Their Characteristics
### Viscous Dampers
#### 3.1.2 Mathematical Model  
Viscous dampers operate on the principle of fluid dynamics. They typically consist of a piston moving through a cylinder filled with a highly viscous fluid, usually a silicone-based compound. As the piston moves, it forces the fluid through orifices, creating a resistance force that dissipates energy. The force-velocity relationship for a viscous damper is generally expressed as [51]:  
\[\text{F}=\text{C}\cdot\text{v}^{\text{-}}\text{a} \tag{5}\]  
where:  
F = Damping force.  
C = Damping coefficient.  
v = Velocity.  
a = Velocity exponent (typically between 0.3 and 1.0).  
For linear viscous dampers (a = 1), the equation simplifies to [52]:  
\[\text{F}=\text{C}\cdot\text{v} \tag{6}\]
## 7 Research Challenges, Open Issues, and Future Directions
### Near Real-Time Decisions  
Controlling and managing network connections between cars and infrastructure is a significant task. Vehicles that are connected should maintain a constant and highly reliable connection with fog devices to prevent communication system transmission failures and make decisions in time. In automotive networks, intermittent connections owing to vehicle movement or excessive packet loss must be avoided.
# WAMS-Based Online Disturbance Estimation in Interconnected Power Systems Using Disturbance Observer
## 3 Proposed Disturbance Observer-Based Disturbance Estimation  
The disturbance observer can be used to accurately estimate the disturbance magnitude. Figure 2 shows the structure of the proposed set up for disturbance estimation. For a disturbance which occurs at any time (\(t\)) in a power system. The input and the states of the system obtained via a state observer are required to estimate the disturbance magnitude. The design of both the power system state observer and the disturbance observer are described in what follows.
# FCN-Based 3D Reconstruction with Multi-Source Photometric Stereo
## 2 Materials and Methods
### Dataset  
#### 2.4.1 The Real-World Dataset  
In order to verify the effectiveness of our method, we hoped to use a real-world sample database to train and test our model. At the beginning, we hoped to match our needs to the currently available datasets. However, due to the practical difficulties in 3D data collection, many datasets are based on synthesis or rendering [27; 28] and some of them even have no corresponding GT, and so could not be used to train the neural network [33; 34]. We think that there are still great differences between  
Figure 1: Overview of the proposed network architecture. The network outputted a depth map when given a set of images from the light source direction with different angles as inputs. The kernel sizes for all the convolutional layers were \(3\times 3\) and for all up-sampling layers \(2\times 2\). Values above the layers indicate the number of feature channels.  
real scene data and rendered simulation data. Therefore, we made a batch of samples by hand and established a real Photometric Stereo experiment platform to collect the dataset we needed.  
Our sample database consisted of 100 equal-sized corrugated boards with different degrees of surface damage on them, as shown in Figure 2. The damage on each cardboard was caused by human random. Because the middle part of the corrugated board was partly hollow, the image of the damaged part was very complex under different angles of illumination. The surface features of our samples did not conform to the standard Lambert model, and there were fractures on the surface of the defects which were not a uniform transition. This was not friendly to the classic multi-source Photometric Stereo, as shown in the experimental results.
# Deep Watermarking Based on Swin Transformer for Deep Model Protection
## 4 Results
### Visual Quality of Watermarked Image and Extracted Watermark
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Task** & **Watermark** & **PSNR** & **SSIM** & **NC** & \(SR_{NC}/SR_{C}\) \\ \hline \multirow{8}{*}{Dearing} & Flower & [PERSON] [25] & 41.21 & 0.99 & 0.9999 & 100\%/100\% \\  & & Ours & 46.17 \(\pm\) 3.55 & 0.99 & 0.9999 & 100\%/100\% \\ \cline{2-6}  & Lena & [PERSON] [25] & 42.50 & 0.98 & 0.9999 & 100\%/100\% \\  & Ours & 47.38 \(\pm\) 3.49 & 0.99 & 0.9999 & 100\%/100\% \\ \cline{2-6}  & Peppers & [PERSON] [25] & 40.91 & 0.98 & 0.9999 & 100\%/100\% \\  & Ours & 45.35 \(\pm\) 3.33 & 0.99 & 0.9999 & 100\%/100\% \\ \cline{2-6}  & QR & Zhang [25] & 40.24 & 0.98 & 0.9999 & 100\%/100\% \\  & Ours & 44.42 \(\pm\) 3.81 & 0.99 & 0.9999 & 100\%/100\% \\ \cline{2-6}  & IEEE & Zhang [25] & 41.76 & 0.98 & 0.9999 & 100\%/100\% \\  & Ours & 46.28 \(\pm\) 3.68 & 0.99 & 0.9999 & 100\%/100\% \\ \hline \multirow{8}{*}{Denoise} & Flower & Zhang [25] & 38.90 & 0.98 & 0.9999 & 100\%/100\% \\  & Ours & 57.52 \(\pm\) 2.93 & 0.99 & 0.9999 & 100\%/100\% \\ \cline{2-6}  & Lena & Zhang [25] & 39.19 & 0.98 & 0.9999 & 100\%/100\% \\  & Ours & 59.66 \(\pm\) 2.82 & 0.99 & 0.9999 & 100\%/100\% \\ \cline{2-6}  & Peppers & Zhang [25] & 38.70 & 0.98 & 0.9999 & 100\%/100\% \\  & Ours & 56.39 \(\pm\) 2.73 & 0.99 & 0.9999 & 100\%/100\% \\ \cline{2-6}  & QR & Zhang [25] & 38.91 & 0.98 & 0.9999 & 100\%/100\% \\  & Ours & 53.73 \(\pm\) 3.19 & 0.99 & 0.9999 & 100\%/100\% \\ \cline{2-6}  & IEEE & Zhang [25] & 40.12 & 0.99 & 0.9999 & 100\%/100\% \\  & Ours & 57.50 \(\pm\) 3.22 & 0.99 & 0.9999 & 100\%/100\% \\ \hline \end{tabular}
\end{table} 
Table 3: Quantitative results of the proposed deep image-based invisible watermarking algorithm. in performance across different test images. From Table 3, it is evident that, even when embedding different watermark images, the overall visual quality in the deraining task remains significantly improved. On average, the PSNR values increased by approximately 4.5 dB. Despite the already high quality of [PERSON]'s methods [25], our method further minimized the visual impact of the watermark while enhancing overall image fidelity. In the denoising task, the PSNR average exceeded 53 dB, indicating that our method effectively maintains the integrity of the original image. Additionally, the SSIM values improved by approximately 0.01, further confirming the superior visual quality of the proposed method. The NC values, along with the subsequent \(SR_{C}\) and \(SR_{NC}\) results, remained consistent with [PERSON] [25]. With NC values close to 1, the extracted watermarks were nearly identical to the originals. Moreover, both \(SR_{C}\) and \(SR_{NC}\) achieved 100% accuracy, demonstrating that, under noise-free conditions, the proposed watermarking approach not only ensures flawless extraction, but also maintains better visual quality after embedding.
# Trajectory Tracking Control Method Based on Adaptive Higher Order Sliding Mode
###### Abstract  
To resolve the problem of high-precision trajectory tracking control under interference conditions in a missile's mid-guidance phase, according to the constructed nominal trajectory, an improved adaptive high-order sliding mode trajectory tracking controller (AHSTC) is proposed. In this method, the open-loop nominal trajectories are established according to the nonlinear programming and Gaussian pseudospectra method. A high-precision trajectory tracking controller is developed by designing a nonlinear sliding mode surface and an adaptive high-order sliding mode approaching law combined with the trajectory tracking nonlinear error model. To verify the effectiveness and superiority of the proposed method, analysis and simulation are carried out through the example of a missile mid-guidance phase tracking control. Compared to the linear quadratic regulator (LQR) and active disturbance rejection controller (ADRC) method, the simulation results show that the proposed AHSTC method shows faster convergence and improved tracking effect. Therefore, the proposed AHSTC method has a good results and engineering application value.  
trajectory tracking; high-order sliding mode; nonlinear; adaptive control +
Footnote †: journal: Applied Sciences  
1
Footnote 1: School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China 1  
[PERSON]  
1 School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China 1  
[PERSON]  
1 School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China 1  
[PERSON]  
1 School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China 1  
[PERSON]  
1 School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China 1  
[PERSON]  
2 School of Automation, Xi'an University of Posts and Telecommunications, Xi'an 710121, China 2  
###### Abstract  
To resolve the problem of high-precision trajectory tracking control under interference conditions in a missile's mid-guidance phase, according to the constructed nominal trajectory, an improved adaptive high-order sliding mode trajectory tracking controller (AHSTC) is proposed. In this method, the open-loop nominal trajectories are established according to the nonlinear programming and Gaussian pseudospectra method. A high-precision trajectory tracking controller is developed by designing a nonlinear sliding mode surface and an adaptive high-order sliding mode approaching law combined with the trajectory tracking nonlinear error model. To verify the effectiveness and superiority of the proposed method, analysis and simulation are carried out through the example of a missile mid-guidance phase tracking control. Compared to the linear quadratic regulator (LQR) and active disturbance rejection controller (ADRC) method, the simulation results show that the proposed AHSTC method shows faster convergence and improved tracking effect. Therefore, the proposed AHSTC method has a good results and engineering application value.