esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
#### Communications Model for U2U and U2G Link  
A communication model is used to implement U2U and U2G communication links. The elevation angle and environmental factors determine the probability of line of sight (LoS) between two UAVs, or UAV and ground device. The probability of LoS, denoted as \(P_{LoS}\), can be modeled by  
\[P_{\text{LoS}}=\frac{1}{1+a\exp(-b(\theta-a))}, \tag{2}\]  
\[\theta=\frac{180}{\pi}\sin^{-1}\left(\frac{h}{d}\right), \tag{3}\]  
where environmental factors a and b are set to be 4.88 and 0.43, respectively, under the assumption of a rural environment with few obstacles and wide-open space [28]. \(\theta\) denotes the elevation angle between the UAV and the ground station or another UAV. In addition, \(d\) denotes the Euclidean distance between the UAV and the ground station or another UAV. The probability of non-line of sight (NLoS) \(P_{\text{NLoS}}\) is \(P_{\text{NLoS}}=1-P_{\text{LoS}}\). The path loss PL is calculated by  
\[PL=P_{\text{LoS}}\cdot PL_{\text{LoS}}+P_{\text{NLoS}}\cdot PL_{\text{NLoS}}, \tag{4}\]where \(PL_{\text{LoS}}\) and \(PL_{\text{NLLoS}}\) are the path losses under NoS and NLoS channel conditions, respectively. \(PL_{\text{LoS}}\) and \(PL_{\text{NLLoS}}\) are calculated by  
\[PL_{\text{LoS}}=20\log_{10}(d)+20\log_{10}(f)+20\log_{10}\biggl{(}\frac{4\pi}{c }\biggr{)}, \tag{5}\]  
\[PL_{\text{NLLoS}}=PL_{\text{LoS}}+\eta_{\text{NLoS}}, \tag{6}\]  
where \(f\) denotes the carrier frequency, \(c\) is the speed of light, and \(\
u_{\text{NLoS}}\) is an additional attenuation factor that is set to 20 dB, assuming a rural environment [28]. The path loss \(PL\) affects the quality of a communication link, and the data transmission rate \(\rho\) can be calculated by the Shannon formula,  
\[\rho=B\log_{2}\biggl{(}1+\frac{P_{t}}{PL\cdot N_{0}\cdot B}\biggr{)}, \tag{7}\]  
where \(P_{t}\) represents the transmission power, \(B\) is the bandwidth, and \(N_{0}\) is the noise power spectral density.
## 2 Theoretical Background
### Introduction to XAI and LIME  
Although deep learning methods provide significant advancements and yield positive outcomes in various fields, the lack of interpretability due to their black-box structure could hinder practitioners from adopting them. Henceforth, the literature in recent years has seen a significant number of studies proposing XAI techniques to elucidate the decision-making process of deep learning models. Although interpretability can be classified in various ways, it is generally analyzed through two categories: intrinsic interpretability and post-hoc explanations.  
Figure 3: Attention mechanism.  
Intrinsic interpretability approaches aim to create DL models that are transparent. However, post-hoc methods utilize techniques like feature attribution methods, textual explanations, and visualizations to explain the behavior of black-box models, which are opaque DL models. There are two ways to examine post-hoc explanations: model-agnostic and model-specific explanations. Model-specific explanations are limited to particular models, with the embedded feature importance function of tree-based models being the most well-known. Conversely, model-agnostic approaches are not dependent on a specific model and can be applied to any type of model. Post hoc model-agnostic approaches, which aim to develop an algorithm to explain the decision-making process of a DL model, are generally preferred over other approaches in practice. In terms of scope, XAI methods can provide either global or local explanations. For the purpose of this discussion, we will focus solely on local explanations. LIME, a notable model-agnostic algorithm, elucidates forecasts by replacing complex models with a locally interpretable surrogate model [46]. Locally interpretable surrogate models can be formulated as follows:  
\[\text{explanation(x)}=\text{argminL(f,g,\pi_{x})}+\Omega(\text{g});\text{g} \in\text{G} \tag{15}\]  
Here, G represents the overall potential linear regression model while g is the instance (x)-specific explanation model. The goal of model g is to minimize loss L which measures its proximity to the prediction of the actual f model. Moreover, \(\Omega(\text{g})\) is responsible for keeping the complexity of the model low in the above formula. \(\pi\)x represents a measure of proximity that describes the size of the neighborhood surrounding the instance x for explanation purposes. In practice, the user must specify the complexity of LIME by choosing the maximum number of features that can be used by the linear regression model, since LIME solely optimizes the loss.
###### Abstract
This study addresses the integrated scheduling problem of parallel imaging and data transmission for agile Earth observation satellites, considering sliding time windows and ground station resource constraints, establishes a mixed-integer linear programming model and designs various neighborhood operators and heuristic rules within a large neighborhood search algorithm framework. The remainder of this paper is organized as follows: Section 2 presents the problem description and model formulation. In Section 3, the CPLEX CP Solver Solution is shown. Section 4 provides the details of the Rule-based Large Neighborhood Search algorithm (RLNS) algorithm. Section 5 describes the simulation experiments, including the experimental settings and results. Finally, Section 6 presents conclusions and directions for future research. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Literature**} & \multirow{2}{*}{**Agile Satellite**} & \multicolumn{2}{c}{**Imaging and Transmission Are Performed in Parallel**} & \multicolumn{1}{c}{**Limitated Ground Station Resources**} & \multicolumn{1}{c}{**Time Window Discretized**} \\ \hline
[3,5] & ✓ & \(\times\) & \(\times\) & \(\times\) \\
[1,2,6,11,12] & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\
[7, 8, 9, 10] & ✓ & \(\times\) & ✓ & ✓ \\
[13, 14, 15] & ✓ & \(\times\) & ✓ & \(\times\) \\
[16, 17, 18] & ✓ & ✓ & \(\times\) & \(\times\) \\
[19, 20] & ✓ & ✓ & \(\times\) & \(\times\) \\
[21, 22] & ✓ & ✓ & ✓ & \(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Previous research on satellite imaging and data transmission scheduling.
# On Finding Optimal (Dynamic) Arborescences
## 5 Experimental Evaluation
### Dynamic Optimal Arborescences
However, the dynamic updating required three times more memory on average than the ab initio computation, which is expected given that a more complex data structure needed to be managed. Figure 23: Memory usage of Tarjan algorithm for _Yersinia.wgMLST_ dataset. Figure 22: Optimal arborescence updating versus ab initio computation for DELETE and ADD operations for _Yersinia.wgMLST_ dataset. Running time averaged over 10 random operations. (**a**) DELETE operations. (**b**) ADD operations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Ab Initio** & **Dynamic Updating** & **Memory Ratio** \\
**\%** & **(MB)** & **(MB)** & **(MB)** & **Memory Ratio** \\ \hline
10 & 20.96 & 85.10 & 4.06 \\
20 & 38.98 & 142.42 & 3.65 \\
30 & 78.79 & 240.36 & 3.05 \\
40 & 131.39 & 415.18 & 3.16 \\
50 & 195.35 & 565.25 & 2.89 \\
60 & 261.86 & 841.66 & 3.21 \\
70 & 376.26 & 1100.50 & 2.92 \\
80 & 465.27 & 1374.02 & 2.95 \\
90 & 612.67 & 1684.40 & 2.75 \\
100 & 724.01 & 1997.67 & 2.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Memory usage comparison for the dynamic updating and ab initio computation of an optimal arborescence for the _Yersinia.cgMLSTv1_ dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Ab Initio** & **Dynamic Updating** & **Memory Ratio** \\
**\%** & **(MB)** & **(MB)** & **Memory Ratio** \\ \hline
10 & 6.94 & 21.05 & 3.03 \\
20 & 7.87 & 24.14 & 3.07 \\
30 & 8.90 & 27.73 & 3.12 \\
40 & 10.54 & 32.52 & 3.0
# Si Photonics for Practical LiDAR Solutions
## 2 An Overview of LiDAR Technologies  
Before diving into the details of Si photonics light detection and ranging (LiDAR), we'd like to clarify some terminologies used in this article. The general application we discuss here is 3D sensing, which is about acquiring the three-dimensional information of a scene often with point cloud data constructed from a depth map of a two-dimensional image. Presently, 3D sensing can be realized by a variety of approaches including, but not limited to, interferometry, triangulation, stereo vision, structured light reconstruction, and LiDAR. LiDAR emphasizes the use of a light wave, laser light in many cases, to illuminate a scene in order to calculate depth information from the reflection. Many LiDAR solutions involve using direct or indirect time-of-flight (ToF) for distance calculation so these two terms sometimes are used interchangeably. The LiDAR term is coined from radar and its adoption began with long-range applications such as terrestrial mapping. The LiDAR term tends to be associated with relatively long-range applications, such as autonomous driving, while the ToF camera or the ToF sensor term is more commonly used for short-range applications such as facial recognition and augmented reality. The distinction between these terms may be vague and arbitrary. In this article, we refer LiDAR to a group of 3D sensing methods using light wave to generate depth maps while restrict ToF to a group of distance calculation methods.  
We coarsely categorize a LiDAR solution by two main design dimensions: the distance calculation method and the scene mapping method. Table 1 summarizes our knowledge on the maturity of some popular LiDAR solutions for autonomous driving, advanced driver-assistance systems (ADAS) and consumer 3D sensing. These distance calculation methods generally leverage decades of radar development with adjustment for optical wavelength operation.
# A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception
## 5 Optimization Algorithm
\[g\mathbf{X}_{r}=\{gX_{1}^{r},gX_{2}^{r},\cdots,gX_{u}^{r},\cdots,gX_{UL}^{r}\} \tag{41}\]  
\[g\mathbf{X}_{u}^{r}=gX_{u}^{*}+\omega\cdot\hat{c}_{r}\cdot\gamma_{u} \tag{42}\]  
Figure 3: SPM map, (**a**) Scatter plot, (**b**) frequency statistics. In the above equation, the value in the \(j\)-th dimensional variable of the \(k\)-th wolf obtained after chaotic perturbation is denoted by \(gX_{u}^{r}\), and the perturbation radius in the \(j\)-th dimensional variable space is denoted by \(\gamma_{u}\). The sign function \(\omega\) is obtained according to Equation (43). \[\omega=\left\{ \begin{array}{rl}1,&\text{if }rand\geq 1/2\\ -1,&\text{if }rand<1/2\end{array} \right. \tag{43}\]  
where \(rand\ \in(0,1)\). Each variable space has different ranges, i.e., each variable takes values in different ranges, and the perturbation radius needs to be discussed in dimensions. The perturbation radius of the \(j\)-th dimensional variable space can be calculated according to the following equation. \[\gamma_{u}=\left|\frac{1}{H}\sum\limits_{i=1}^{H}x_{hu}-gx_{u}^{*}\right| \tag{44}\]  
Step 3: The newly generated \(R\) chaotic wolves are ranked in fitness with the current \(H\) wolves, and greedy decisions are made to select the optimal \(H\) wolves among \(R+H\) wolves and proceed to the next iteration. Algorithm 1 is major steps of the proposed method. ```
Input: Observed signal \(x(t)\), threshold \(\sigma\), chaotic perturbations \(R\). Output: Mixing matrix of estimation \(\hat{\mathbf{A}}\), separated signal \(y(t)\), estimated DOA, BER, estimated target distance and velocity. Step 1: Wavelet denoising of the observed signal; Step 2: Calculate the time delay correlation matrix group \(\tilde{\mathbf{R}}_{x}(\tau)\) Step 3: Calculate optimal coefficient \(\beta^{*}\) according to Formulas (26)-(28), and get unitary matrix \(\mathbf{C}^{*}\); Step 4: Robust whitening according to Equations (14)-(17); robust whitening is performed according to Equations (29) and (30) to obtain whitening signal \(z(t)\); Step 5: Calculate diagonalizable target matrix group \(\mathrm{M}=\{\mathbf{M}^{\prime},\mathrm{l}=1,\ldots,\mathrm{L}\}\) of whitening signals; Step 6: According to the number of sources, determine the number and dimension of wolves, and initialize the positions of wolves; Step 7: Calculate the nonlinear convergence factor \(\rho\) according to Equation (39) and update the wolves' positions according to (40); Step 8: Chaos disturbance and greedy decision according to Equations (40)-(44); Step 9: Judge whether the maximum iteration is reached. If so, output the unmixing matrix and the estimated mixing matrix according to Equations (36)-(38), calculate the separated signal. If not, return to Step 7 for the next iteration; Step 10: Perform DOA estimation, BER detection, target distance and velocity estimation. ```  
**Algorithm 1**Flow of the proposed algorithm.
## 4 Study on the Blast Resistance of Ultra-Wide Concrete Twin-Edge Box Girders Coated with Polyurea
### Finite-Element Model of Ultra-Wide Concrete Twin-Edge Box Girder
#### 4.1.2 Refined Finite-Element Model  
The modeling approach, material characteristics, and blast load application for the concrete twin-edge box girder coated with polyurea follow the simulation method outlined for the blast response of the box-girder specimen as discussed in Section 2.3. Due to the 5 m spacing between hangers on the Hunan Road Bridge, connected to the lower part of each transverse diaphragm, a comparative analysis was performed to assess the blast-response distribution along the transverse direction of the box girder. Specifically, the analysis targets scenarios where the blast epicenter is positioned at the midpoint of each chamber, the web, and the central connection section (i.e., the T-beam section) to evaluate the non-uniformity of the response distribution. To minimize the influence of continuous transverse diaphragms along the girder's transverse direction, three 5 m-long segments of the box girder were chosen for the study. The full length of the modeled box girder prototype in the longitudinal axis is 15 m, with the detailed finite-element model shown in Figure 23.  
Solid164 elements were employed to represent both the concrete and polyurea materials, while BEAM188 elements were used for the rebars. The nodes of the concrete and polyurea elements were shared, and interfacial debonding was not considered. The relationship between the concrete and the rebars was defined using the \"CONSTRAINED_BEAM_IN_SOLID keyword in the LS-DYNA software, and the effects of prestressing tendons were not considered. Both longitudinal ends of the twin-edge box girder were fully constrained. Based on the model validation in Section 3.3 and the findings of other scholars [37; 38], a mesh size of 200 mm was adopted for computational accuracy and efficiency. The number of concrete elements totaled 127,416, and the number of rebar elements totaled 171,792.
# Research on Pure Hydrogen Production Using a Fuel-Processing System Combined with a PSA System
## 12 Analysis of Results  
This study attempted to understand improvements of the pure-hydrogen-production system by observing the performance and sensitivity of the system according to load change in the reformer, the water-gas shift reactor, and the PSA, and analyzing the operation efficiency. Simulations were conducted at a temperature between 600 and 1000 \({}^{\circ}\)C at a reformer pressure of 1 atm and 10 atm for the fuel-processing system and conducted at a temperature of 15 \({}^{\circ}\)C and a pressure 9 atm for the PSA system. In the fuel-processing system, efficiency varies from 35.95% to 82.40% depending on temperature and pressure conditions, and the maximum efficiency was about 82.40% with reformer conditions of 800 \({}^{\circ}\)C and 1 atm. In the PSA system, efficiency varied between 55% and 67%, and the maximum efficiency was 67% at reformer conditions of 10 atm and 600 \({}^{\circ}\)C. The efficiency of the overall system ranged from 21.28% to 43.17% and the highest efficiency was 43.17% with reformer conditions of 800 \({}^{\circ}\)C and 1 atm. The most critical variable in these simulation results is assumed to be the input methane amount, denoted as PNG. Based on this, using the commercial software MATLAB R2023b(r), 3D graphs depicting the overall yield according to temperature and pressure conditions of the reformer were generated at various temperature and pressure conditions (shown in Figure 14).  
Figure 13: Overall system yield.  
As is shown in the figure, the methane conversion increases as temperature increases and pressure decreases. However, at a higher temperature, there is no noticeable difference in methane conversion as the reformer pressure changes. The empirical model for calculating the conversion of methane at given temperatures and pressures of the fuel-processing system is listed at Equation (11).  
\[\begin{array}{l}Conversion(T,P)=-2.39-0.001365P+0.008121\times T+1.39\times 10^{-7 }P^{2}\\ +1.269\times 10^{-6}TP-4.747\times 10^{-6}T^{2}\end{array} \tag{11}\]  
where \(T\) is the temperature of the reformer reactor and \(P\) is the pressure of the fuel-processing system. The \(R^{2}\) value of the empirical equation is 0.9805.  
The overall yield of the whole system at various temperature and pressure conditions is summarized in Figure 15.  
Figure 14: 3D graph of the methane-conversion ratio according to temperature and pressure.  
Figure 15: 3D overall yield of the combined fuel-processing system and the PSA system according to temperature and pressure.  
The amount of each energy supplied at different overall yields is also shown in Figure 18.  
In Figure 18, the amount of PNG supplied, and the compression work decrease rapidly as overall yield increases, while the small amount of BNG supplied decreases as overall yield increases.
## 4 Results  
### Spatial Distribution Types  
ArcGIS 10.2 software was harnessed to apply the methodology of natural fracture classification in concert with Thiessen polygons for the cartographic depiction of the landscape adorned with China's smart healthcare facilities. The graphical manifestation of these endeavors is unveiled in Figure 2, an exquisite rendering of our findings. Within this visual tableau, the depth of hue in a given geographical region within the figure correlates with the sheer abundance of healthcare bases residing therein. Likewise, the intricacy of polygonal borders mirrors the compactness of the territory they demarcate, thereby elucidating the density of healthcare centers found within.  
Evidently, the smart health and geriatric care facilities in China exhibit a discernible spatial agglomeration. These agglomerative enclaves are predominantly situated in the central and eastern domains of the nation, with three provinces, namely Zhejiang, Sichuan, and Shandong, standing out as conspicuous exemplars of this geographic clustering.  
In pursuit of a more profound analysis and elucidation of the spatial distribution modalities inherent in intelligent health and geriatric care facilities, we computed the coefficient of variation and the nearest neighbor index for these establishments (Table 1). The calculation results show that by 2021, the coefficient of variation of the smart health retirement base is \(C_{V}=281.33\%\), the nearest neighbor index is \(R=0.506\), the Z score is \(-8.705\), and the \(p\) value is 0. Among them, \(R<1\) and \(C_{V}>64\%\) indicate that the spatial distribution type of the demonstration base is an agglomeration distribution; a \(Z\) score \(<-2.58\) indicates that the possibility of randomly generating this clustering pattern is less than \(1\%\), and the possibility of the demonstration base being an agglomeration distribution is greater than \(99\%\).  
Figure 2: Spatial distribution of the smart healthy aging center.  
From 2017 to 2021, the actual nearest neighbor distance of China's smart health care demonstration base was shortened by nearly 210 km.  
The nearest neighbor index of the smart health care base in 2017 was \(R=0.928\), and the spatial structure showed a random distribution state. In 2018, 2019, 2020, and 2021, the nearest neighbour index values were \(R=0.839\), \(R=0.674\), \(R=0.589\), and \(R=0.506\), respectively, indicating that after 2017, the spatial taxonomy of China's intelligent healthcare hubs has initiated a trajectory towards clustering. This transition reflects a shift in the spatial distribution pattern from a stochastic arrangement to a discernible clustering disposition.
## 4 Experimental Results and Analysis
### Modeling of Initial Health Assessment Model Based on MSET
#### 4.1.4 Data with Different Fault Degrees at Different Speeds  
Input data with fault 1 at the speed of 1797 r/min, data with fault 2 at the speed of 1772 r/min, and data with fault 3 at the speed of 1750 r/min into the health assessment model, and the output deviation distance is shown in Figure 27.  
Figure 28 shows that among the three types of fault data, data with fault has a fault diameter of 0.1778 mm, which is the smallest fault degree, and operates at the speed of 1797 rpm, which is the highest speed condition. It exhibits the smallest deviation distance. On the other hand, data with fault 3 has a fault diameter of 0.5334 mm, which is the largest fault degree, and operates at the speed of 1750 rpm, which is the lowest speed condition. It shows the largest deviation distance. When both the fault degree and the speed change, the deviation distance is primarily determined by the fault degree, thereby reducing the influence of the operating conditions. This verifies that MSET can establish a health baseline that adapts to changes in the operating conditions of the data being evaluated, minimizing the impact of operating conditions on health assessment.  
Figure 28: (**a**) Deviation distance of data with different faults at different speeds; (**b**) Health score of data with different faults at different speeds.  
Figure 29: (**a**) Deviation distance of data with fault 2 at different speeds; (**b**) Health score of data with fault 2 at different speeds.
## 5 Postfiltering Approaches for Dual-Microphone Smartphones
### Parametric Wiener Filtering  
Under the distortionless constraint of MVDR, if we assume an accurate estimation of the RTF, we can approximate the single-channel speech signal \(Z(t,f)\) at the beamformer output as  
\[Z(t,f)\simeq X_{1}(t,f)+V(t,f), \tag{62}\]  
where \(V(t,f)\) is the residual noise at the beamformer output,  
\[V(t,f)=\mathbf{F}^{H}(t,f)\mathbf{N}(t,f). \tag{63}\]  
The PSDs of the clean speech signal \(X_{1}(t,f)\) and the residual noise \(V(t,f)\) are given by \(\phi_{x_{1}}(t,f)\) and \(\phi_{\psi}(t,f)\), respectively. The corresponding Wiener filter (WF) for \(X_{1}(t,f)\) is then defined as  
\[G(t,f)=\frac{\xi(t,f)}{1+\xi(t,f)}, \tag{64}\]  
where  
\[\xi(t,f)=\frac{\phi_{x_{1}}(t,f)}{\phi_{\psi}(t,f)} \tag{65}\]  
is the _a priori_ SNR.  
The noise reduction performance of WF can be improved if we consider the _a posteriori_ SPP \(p_{x}(t,f)\) in the postfiltering design. Our goal is to decrease the gain factor for time-frequency bins where speech is absent. This yields the parametric WF (pWF) [34]  
\[G(t,f)=\frac{\xi(t,f)}{\mu(t,f)+\xi(t,f)}, \tag{66}\]where \(\mu(t,f)\) is an SPP-driven trade-off parameter. To obtain this parameter, we use the same mapping function as in Reference [5],  
\[\mu(t,f)=\mu_{min}+\left(\mu_{max}-\mu_{min}\right)\frac{10^{\frac{\phi}{10}}}{10 ^{\frac{\phi}{10}}+\left(\frac{p_{x}(t,f)}{1-p_{x}(t,f)}\right)^{\rho}}, \tag{67}\]  
which is similar to the mapping function of (59). We can see that \(\mu(t,f)\in[\mu_{min},\mu_{max}]\) and therefore, the lower the _a posteriori_ SPP, the higher \(\mu(t,f)\), thus improving the noise reduction performance.