esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Numerical Analysis
### Enhanced Frequency Regulation Capability with Optimization of \(H_{\text{DC}}\)  
The steady state power flows before and after the optimization of \(H_{\text{DC}}\) are given in Table 3. The increased inertia provided by the VSC-HVDC with the optimized \(H_{\text{DC}}\) based on the improved DPF model is validated as shown in Figure 9.  
With the optimized \(H_{\text{DC}}\), the inertia provided by the VSC-HVDC is further enhanced, thus the transient drop rate of the system frequency is reduced (see Figure 9a,c,e). The maximum \(U_{\text{DC}}\) approaches the constraint, showing that the stored energy in the DC capacitor is fully utilized to prevent frequency drop (see Figure 9b,d,f)). The larger deviation of the DC voltage leads to more output of the DFIG (see Table 3), and less deviation of the steady-state frequency. The above conclusions are validated under different ranges of the wind speed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Wind Speed** & **Parameter** & \(P_{\text{wt}}\) **(p.u.)** & \(\Sigma P_{\text{G}}\) **(p.u.)** & \(\Sigma P_{\text{L}}\) **(p.u.)** & \(\Delta P_{\text{wt}}\) **(p.u.)** \\ \hline \multirow{2}{*}{Low} & Before & 0.5257 & 3.6804 & 4.1900 & \multirow{2}{*}{0.0646} \\  & After & 0.5903 & 4.0725 & 4.6194 & \\ \hline \multirow{2}{*}{Medium} & Before & 0.7338 & 3.4678 & 4.1900 & \multirow{2}{*}{0.0645} \\  & After & 0.7983 & 3.8593 & 4.6195 & \\ \hline \multirow{2}{*}{High} & Before & 1.2963 & 2.9085 & 4.1900 & \multirow{2}{*}{0.0643} \\  & After & 1.3606 & 3.2987 & 4.1697 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Steady state power flows under different wind speeds before and after load disturbance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wind Speed** & **Parameter** & \(P_{\text{wt}}\) **(p.u.)** & \(\Sigma P_{\text{G}}\) **(p.u.)** & \(\Sigma P_{\text{L}}\) **(p.u.)** \\ \hline \multirow{2}{*}{Low} & Before & 0.5903 & 4.0725 & 4.6194 \\  & After & 0.6140 & 4.0522 & 4.6231 \\ \hline \multirow{2}{*}{Medium} & Before & 0.7983 & 3.8593 & 4.6195 \\  & After & 0.8219 & 3.8392 & 4.6232 \\ \hline \multirow{2}{*}{High} & Before & 1.3606 & 3.2987 & 4.1697 \\  & After & 1.3816 & 3.2812 & 4.6229 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Steady state power flows under different wind speeds before and after optimization of \(H_{\text{DC}}\).
# Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties
## 2 Problem Formulation
### MPC Control Structure
#### 2.2.1 Lyapunov Function-Based MPC  
The approach presented herein marks the pioneering use of matrix inequalities in model predictive control (MPC). Many subsequent MPC methods have drawn inspiration from the principles outlined in this method, and more detailed information can be found in the reference [5]. To convert a problem to a convex programming problem, we first find the upper limit of \(\mathbf{J_{0}^{\infty}}(\mathbf{k})\) on the convex set \(\Omega\), then lower this limit with a feedback control law to the following form.  
\[u(k+i|k)=Fx(k+i|k).i\geq 0 \tag{17}\]  
Consider the following square  
**Lemma 1:** **Consider the system described below.**_At each time step \(k\), the state feedback matrix \(F\) in the control law \(\mathbf{u}(\mathbf{k}+\mathbf{i}|\mathbf{k})=\mathbf{F}\mathbf{x}(\mathbf{k}+\mathbf{i}|\mathbf{k})\), which restricts the upper bound \(\mathbf{V}(\mathbf{x}(\mathbf{k}|\mathbf{k}))\), is determined based on the following equation:_  
\[F=\mathrm{Y}S^{-1} \tag{23}\]  
In which, \(\mathrm{S}>0\) and \(\mathrm{Y}\) are obtained by solving the following convex programming problem:  
\[\begin{array}{ll}\min\limits_{\gamma,s\cdot\gamma}\ \gamma\\ \begin{bmatrix}1&x(k|k)^{T}\\ x(k|k)&S\end{bmatrix}\geq 0\\ \begin{bmatrix}S&SA_{j}^{T}+Y^{T}B_{j}^{T}&SQ^{\frac{1}{2}}&Y^{T}R^{\frac{1}{ 2}}\\ A_{j}S+B_{j}Y&S&0&0\\ Q^{\frac{1}{2}}S&0&\gamma I&0\\ R^{\frac{1}{2}}Y^{T}&0&0&\gamma I\end{bmatrix}\geq 0\\ \begin{bmatrix}u_{max}^{2}I&Y\\ Y^{T}&S\end{bmatrix}\geq 0.\\ \begin{bmatrix}S&*\\ C\big{(}A_{j}S+B_{j}Y\big{)}&y_{max}^{2}\end{bmatrix}\geq 0.\ j=1.2.\ldots.L. \end{array} \tag{24}\]  
Unequal Equation (24) ensures the stability of the state variable in the elliptical area determined by the \(S\) matrix.
## 3 Methods
### Simulations
#### 3.2.3 Varying Temperature  
Time varying temperature was considered:  
\[\frac{\partial^{2}\varphi}{\partial x^{2}}=\frac{1}{a_{v}(t)}\frac{\partial \varphi}{\partial t}\quad a_{v}\left(t\right)=\frac{\delta_{v}^{0}v_{s}(T(t) )}{\xi} \tag{10}\]  
Here, the material temperature is equal to the boundary temperature. It is purely a function of time:  
\[\frac{\partial^{2}\varphi}{\partial x^{2}}=\frac{1}{a_{v}\left(t\right)} \frac{\partial\varphi}{\partial t} \tag{11}\]  
A variable substitution is introduced:  
\[\tau(t)=\int\limits_{0}^{t}a_{v}(t^{\prime})\,dt^{\prime} \tag{12}\]  
The moisture balance equation is transformed to:  
\[\frac{\partial^{2}\varphi}{\partial x^{2}}=\frac{\partial\varphi}{\partial\tau} \tag{13}\]Here, there is an equation that is similar to the one-dimensional moisture balance Equation (7) with the diffusivity \(a_{v}\) equal to 1. The equation is linear when using this transformed time variable and superposition techniques can be used. Therefore, it is necessary with a basic periodic solution to handle complex cyclic changes in the RH.  
The problem can be solved in the \(\tau\)-domain using superposition technique. First, the boundary values were transformed into this domain. The following general, even functions, for the boundary RH and temperature were assumed:  
\[\varphi_{b}(t)\ T_{b}(t) \tag{14}\]  
The maximum and minimum value of the boundary RH are:  
\[\varphi_{b,\max}\quad\varphi_{b,\min} \tag{15}\]  
The \(\tau\) is defined as:  
\[\tau(t)=\frac{\delta_{v}^{0}}{\xi}\int\limits_{0}^{t}v_{s}(T_{b}(t^{\prime})) \ dt^{\prime} \tag{16}\]  
Since the integrand is always positive an inverse function can be found:  
\[\tau^{-1}(\tau)=t \tag{17}\]  
The boundary RH becomes:  
\[\varphi_{b}(t)=\varphi_{b}(\tau^{-1}(\tau))=\varphi_{b,\min}+(\varphi_{b,\max} -\varphi_{b,\min})\cdot\varphi_{b}(\tau) \tag{18}\]  
Since a periodic and even function was assume, it becomes:  
\[\varphi_{b}(\tau)=\frac{a_{0}}{2}+\sum\limits_{n=1}^{\infty}a_{n}\cdot\cos \left(2n\pi\frac{\tau}{\tau_{p}}\right) \tag{19}\]  
where  
\[a_{n}=\frac{2}{\tau_{p}}\int\limits_{0}^{\tau_{p}}\varphi_{b}(\tau)\cdot\cos \left(2n\pi\frac{\tau}{\tau_{p}}\right)d\tau\quad\tau_{p}=\tau(t_{p}) \tag{20}\]  
This description suggested that the moisture problem, in the \(\tau\)-domain, can be solved by summing an infinite number of solutions, one for each cosinusoidal variation using Fourier series. This solution is found in [10]:  
\[\frac{\varphi(\tau,x)-\varphi_{b,\min}}{\varphi_{b,\max}-\varphi_{b,\min}}= \frac{a_{0}}{2}+\sum\limits_{n=1}^{\infty}a_{n}\cdot A_{n}e^{-x/d_{pv,n}}\cdot \cos\left(2n\pi\frac{\tau}{\tau_{p}}-\frac{x}{d_{pv,n}}-\phi_{n}\right) \tag{21}\]  
\[d_{pv,n}=\sqrt{\frac{\tau_{p}}{n\pi}}\quad\phi_{n}=\arctan\left(\frac{d_{v}}{ d_{pv,n}+d_{v}}\right) \tag{22}\]  
\[A_{n}=\frac{1}{\sqrt{\left(1+d_{v}/d_{pv,n}\right)^{2}+\left(d_{v}/d_{pv,n} \right)^{2}}} \tag{23}\]
## 4 In-Network Computing via ICN
### Compute Reuse  
Caching service outputs is a highly effective strategy that can significantly reduce the load on servers by avoiding redundant computations, decrease network traffic, and accelerate service delivery. While not all service results are suitable for caching and reuse, there are many scenarios where this approach is both feasible and advantageous. For instance, XR applications used by attendees at the same event, such as a music concert or a popular tourist attraction, can benefit from shared computation results. Similarly, in a museum setting, visitors capturing photographs of the same artwork from different angles may request similar computational tasks, such as retrieving historical information about the painting. From the client's perspective, the source of the result, i.e., whether retrieved from the cache or dynamically generated by the server, would be entirely transparent, ensuring a seamless user experience.  
Given these advantages, most existing research on INC via ICN inherently assumes the concept of compute reuse [4; 58; 63; 68]. The core principle is that services are computed only upon the first request. The resulting output is treated as newly generated content, uniquely named based on a combination of the function name, content name, and parameters. This result is encapsulated in one or more Data packets, which are signed by the executor to ensure authentication and integrity. Similar to static content, these Data packets can be cached at the executor or at intermediate nodes and they can be retrieved using the standard Interest-Data exchange mechanism in ICN. Subsequent requests for the same service can bypass re-execution by fetching the cached results, significantly improving efficiency and reducing computational overhead.  
In [50], the authors observe that reusable outputs can often be time-limited, meaning they have a finite period of validity after which they may become obsolete or inconsistent. Examples include tasks that process time-sensitive input data, such as environmental parameters or context-aware information collected by surveillance cameras. In such cases, the executor can specify a _freshness period_ in the header field, which acts as a time-to-live to automatically invalidate the result once it expires.  
In ICedge [48], partial and full compute reuse is considered. In the first case, the existing output is reused only partially, and additional computations are required. The authors define compute-aware naming conventions to cluster tasks and direct similar service requests to the same computing nodes to maximize the chances of reusability. Examples include clustering tasks by location (/<building_X/floor_y/Room_z>), geographic coordinates (/<X_coordinate/Y_coordinate>), or configuration parameters (/<config_X/Param_Y>). In parallel, naming conventions are associated with a compute-aware forwarding schemes. For example, a zone-based policy would cluster tasks by regions, such as ZIP codes, forwarding requests based on location metadata.
# Distributed Joint Source-Channel Coding Using Quasi-Uniform Systematic Polar Codes
## 2 System Model
There is no interaction between the source layer and the channel layer in the sense of encoding and decoding. Such a DSSCC scheme is asymptotically optimal when the code-length goes to infinity. However, with finite code-length, such source-channel separate schemes are generally sensitive to the residual errors of channel decoder, and thus are ineffective in some scenarios such as wireless sensor networks (WSNs). To address this problem, in this paper we propose a DJSCC scheme using polar-like codes to achieve the corner point in \(\mathcal{R}_{\text{rate}}\), i.e.,  
\[\tilde{R}_{i}=\frac{H(v_{i}^{j}|v_{1}^{j}v_{2}^{j}...v_{i-1}^{j})}{C_{i}} \tag{5}\]  
for \(i=1,2,...,s\). As shown in Figure 2, each source is equipped with only one encoder to encode the source message for both compression and error protection. Note that the order of the chain rule \(v_{1}^{j}v_{2}^{j}...v_{s}^{j}\) in (5) has no effect on the code construction and overall performance, hence the ascending order is considered in this paper. It is also obvious that multiple points in \(\mathcal{R}_{\text{rate}}\) can be achieved with time-sharing operations, provided that DJSCC schemes for corner points have been well constructed. Figure 1: Distributed separate source-channel coding (DSSCC) scheme.
# Terahertz Single-Pixel Imaging Improved by Using Silicon Wafer with SiO\({}_{2}\) Passivation
## 1 Introduction  
Terahertz (THz) imaging has potential applications in a variety of fields, such as non-destructive detection [1; 2], security inspection [3; 4], and biomedicine [5; 6]. Despite great progress, commercially available terahertz focal plane array detectors are still too expensive for most researchers. Therefore, terahertz imaging techniques based on single-pixel detectors have been the focus to date. Among these techniques, computational sampling methods are attractive due to their efficiency compared with the slow mechanical scanning method.  
As the key for THz single-pixel imaging based on computational sampling methods, terahertz modulation techniques have received increasing attention. In 2008, [PERSON] et al. [7] adopted standard printed circuit boards as the random matrix to modulate the terahertz transmission, and demonstrated a THz single-pixel imaging by further combining the compressed sensing (CS) algorithm. In order to avoid the use of moving masks, which are difficult to align, [PERSON] et al. [8] made use of a metamaterial spatial modulator, [PERSON] et al. [9] employed arrays of graphene electro-absorption modulators, and [PERSON] et al. [10] and [PERSON] et al. [11] demonstrated photo-induced coded-aperture imaging using a semiconductor illuminated by a commercial available digital light processing projector. Recently, silicon wafers have been widely used as THz photomodulators, based on which [PERSON] et al. [12; 13] demonstrated THz superresolution imaging. [PERSON] et al. [14] used graphene on silicon as the THz photomodulator and demonstrated THz single-pixel imaging based on the Fourier space spectrum. While various approaches have been adopted to boost photomodulation [15; 16; 17], there has been less focus on the semiconducting photoconductive materials themselves. Quite recently, [PERSON] et al. [18] demonstrated that a passivated silicon wafer cangreatly improve the efficiency of THz photomodulation. However, THz single-pixel imaging based on such a THz photomodulator has not been reported to date.  
In this work, we demonstrate that THz single-pixel imaging based on compressed sensing algorithm can be greatly enhanced by THz photomodulators based on silicon passivated with SiO\({}_{2}\). We will first show that THz photomodulators based on passivated silicon can have a much higher modulation factor than one based on bare silicon. The effects of the SiO\({}_{2}\) thickness on the photomodulation improvement and the underlying physics will be discussed. Imaging quality based on the as-fabricated THz photomodulators will be compared with the conventional one based on bare silicon.
# Experimental Study on the Effect of Different Parameters on Rotor Blade Icing in a Cold Chamber
## 4 Conclusions  
The effects of different parameters on the ice shapes of rotor blade were studied in this paper. An icing experimental platform for rotor blades was built in a cold chamber. The components of the experimental platform and the measurement method of related parameters were introduced. The icing experiments of rotor blade under different conditions were carried out, and the change rules of rotor blade icing under different parameters were analyzed. The main conclusions of the experiments are as follows:  
Figure 16: Comparison of ice shapes under different blade materials.  
Figure 14: Blade distribution of different materials.  
Figure 15: Ice shapes under different blade materials: (**a**) steel material; (**b**) composite material.  
1. The icing phenomenon of a rotor blade mainly occurred at the leading edge. It was observed that the amount of ice increased with the increase of blade spanwise length.
2. The icing temperature had a great influence on the ice type. When the temperature was high, glaze ice accumulated, and at a low temperature, rime ice accumulated.
3. With an increase of LWC, rotation speed, and icing time, the ice thickness of the leading edge of rotor blade increased.
4. Under the condition of low rotation speed and small rotor size used in this paper, the blade number had little effect on the ice shapes.
5. At the scale of the rotor model in this paper, the effect of blade material on ice accretion can be ignored.  
The research content of this paper can provide the basis for de-icing system design conditions and provide reference for the protection range of anti-icing and de-icing systems of rotating machinery such as helicopter and wind turbine.  
The experiments in this paper were limited by the experimental conditions, and thus the rotor size and the range of experimental parameters were small. In the future, icing experiments of different large-scale high-speed rotors can be carried out to verify the correctness of the expansion of conclusions.  
Methodology, C.Z.; experiment and analysis, Z.W.; writing--original draft preparation, Z.W.; writing--review and editing, N.Z.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (grant no. 51806105), the Natural Science Foundation of Jiangsu Province (grant no. BK20180442), and Open Fund of Rotor Aerodynamics Key Laboratory (grant no. RAI20190401). The authors declare no conflict of interest.
## 3 DST-LEO Satellite-Traffic Prediction Model
### Model Architecture
#### 3.4.1 Data Separation and Data Fusion Module  
By processing the input in the in_channels dimension, the traffic volume and link connectivity are split into tensors of shape (batch_size, num_nodes, in_channels//2). Multi-source data processing can simplify the difficulty of data handling. Data from different types of satellites usually have different characteristics, formats, and noise distributions. This approach avoids interference between different types of data and enhances the data specificity. After separation, it is possible to analyze and mine data from specific sources more precisely and explore the information contained in different data in-depth.  
The features of traffic volume and link connectivity are concatenated and fused, resulting in a feature channel number of 2*hidden_channels*2 (representing bidirectionality). This method makes full use of the unique information of different data and also discovers the potential relationships between them. Compared with directly processing mixed data, it can more effectively improve the performance of the model.
# Optimal Control of Passive Cascaded Liquid Crystal Polarization Gratings
## 5 Summary and Discussion  
The diffraction efficiency of the orthogonal cascaded LCPGs decreased with an increase in the deflection angle. To address this issue, many studies have been conducted on how to use light-controlled orientation technology and new light-controlled orientation materials to improve the diffraction efficiency of LCPGs. In this study, a liquid crystal tunable phase retarder was introduced in front of a tilted LCPG. By tuning the control voltage of the liquid crystal tunable phase retarder, the diffraction efficiency was increased in the case of large-angle deflection. The experimental results demonstrate that by adjusting the voltage of the liquid crystal phase retarder at various deflection angles, the diffraction efficiency can be increased. It has laid the foundation for realizing non mechanical beam deflection with large angle, high accuracy, and high diffraction efficiency. It is believed that the excellent characteristics of liquid crystal polarization grating will certainly shine in the future communication or display fields.  
Data curation, H.Q. and Z.L.; formal analysis, Z.L. and Q.W.; investigation, Q.F. and Q.W.; methodology, Z.L.; project administration, Z.L. and Q.F.; resources, Q.F.; software, Q.W.; supervision, Z.L.; validation, H.Q.; visualization, Z.L. and Q.W.; writing--original draft, H.Q.; writing--review and editing, H.Q. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (61890960).  
Figure 10: Diffraction efficiency diagram before and after optimization control.
# A Fabric Defect Segmentation Model Based on Improved Swin-Unet with Gabor Filter
## 2 Related Work
### Traditional Approaches  
Traditional image processing methods can be further subdivided into statistical methods [7; 8; 9], spectral approaches [10; 11; 12], and model-based methods [13; 14; 15; 16].  
The statistical methods typically include mathematical morphology, co-occurrence matrices, autocorrelation, local contrast enhancement, histogram feature analysis, and other algorithms [17; 18]. [PERSON] and [PERSON] [7] used independent component analysis and vector quantized principal component analysis to extract the texture and structure features from the template image, then compared these features with the input image to identify defect areas. [PERSON] et al. [8] proposed an automated fabric defect detection system using the Gray-Level Co-occurrence Matrix (GLCM); however, its universality is limited to constant background texture. On the other hand, [PERSON] et al. [9] introduced a universal defect detection method that combines the Integral Image concept with the Elo Rating algorithm (IIER), enabling quick detection of defects in various fabrics. However, this method may not be effective for segmenting large periodic texture fabrics.  
The spectral method is based on spatial frequency domain features, which are less sensitive to noise and intensity changes compared to features extracted from the spatial domain. It includes well-known techniques such as Fourier transform [19], wavelet transform [20], and Gabor transform [21], which have been extensively studied for fabric defect detection to eliminate image texture. In [10], the authors developed the Wavelet Preprocessing Gold Image Subtraction (WGIS) method for defect detection of patterned fabrics or repetitive pattern textures. In [11], the Optical Fourier Transform (OFT) method was applied to detect defects in plain cotton fabric. [PERSON] [12] presented a GIS algorithm based on the Gabor filter and used a genetic algorithm to optimize its parameters. The Gabor filter is a commonly used technique in fabric defect detection, offering more accurate separation of fabric features such as texture and background compared to wavelet transform, and appears in several learning-based methods. However, spectral methods require a high degree of periodicity, and are not suitable for fabrics with random textures.  
Model-based methods overcome the difficulty of fabric defect detection with random surface variations. This method constructs an image model and generates fabric textures matching the observed ones. In [13], the authors used Gaussian Markov Random Fields (GMRA) to model the texture image of a non-defective fabric. In [16], L0 gradient minimization was applied to eliminate background texture interference, then the processed image was clustered using fuzzy c-means. Finally, the clustering centers of fabric defects and non-defects were calculated and updated to segment the defect area. However, manually verifying and adjusting numerous parameters is often necessary due to the need to capture specific details in different textures.
# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
## 3 Model
### System Parameter  
Table 2 presents the parameters used in the simulation. Additionally, these parameters are also provided in tabular form for ease of reading and reference by others.