esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Comprehensive Analysis of Space Debris  
### Observability Analysis of Space Debris  
The observation of space debris by the star tracker should consider the influence of the Earth and the Sun: when the space debris is blocked by the Earth, the satellite cannot detect the space debris; when the space debris is within the Earth's shadow region, the satellite cannot observe it either; when the satellite observes the debris against the Sun's light, the observation cannot be achieved. The analysis of the visible situation can be divided into the following three cases:  
1. Consider the effect of ground shadow  
Space debris is captured by the CCD camera of the tracking satellite only when it is illuminated by the Sun. As space debris moves around the Earth, the Earth is irradiated by daylight and creates a ground shadow (as shown in Figure 1). Whether the space debris is within the ground shadow can be expressed by the irradiation factor \(F\)[22], which is defined as  
\[F\ =\ \left\{\begin{array}{ll}1&\quad\mbox{\it Space debris outside the ground shadow}\\ 0&\quad\mbox{\it Space debris within the ground shadow}\\ \end{array}\right.\]  
Assuming that the sunlight is parallel (\(\stackrel{{\rightarrow}}{{R}}_{\it{SOL}}\) is the sunlight unit vector), i.e., the ground shadow is a cylindrical ground shadow model, \(\stackrel{{\rightarrow}}{{r}}_{D}\) is the vector of space debris relative to the center of the earth, we can obtain \(D\) and \(H\)  
\[D\ =\ \stackrel{{\rightarrow}}{{r}}_{D}\cdot\stackrel{{ \rightarrow}}{{R}}_{\it{SOL}} \tag{1}\]  
\[H^{2}\ =\ \left|\stackrel{{\rightarrow}}{{r}}_{D}\right|^{2}-D^{2} \tag{2}\]  
When \(D\leq 0\), the space debris is illuminated by sunlight and \(F\ =\ 1\).  
When \(D>0\), if \(H^{2}\geq R^{2}\), the space debris is in the sunlight, \(F=1\); if \(H^{2}<R^{2}\), the space debris is in the earth's shadow, \(F\ =\ 0\).  
2. Considering the effect along and against the sunlight  
When the satellite is tracked against the Sun to observe the debris, the background light is so strong that the observation results will be blurred and invalid. Considering such factors, we define the tracking sun angle \(\theta\) as the angle between the tracking satellite and the line connecting the space debris and the sun (as shown in Figure 2), and according to the performance index of the CCD camera, \(\theta\) should be greater than a certain value (e.g., 80\({}^{\circ}\)) for effective observation.  
Figure 1: Ground shadow diagram.  
(3) Considering the effect of earth-atmosphere radiation  
Since weak energy is available for the star point and space debris acquired by the star tracker, the star tracker cannot work properly when there are too many stray lights in the field of view after the local gas light enters the optical system, so the space debris detection based on the star tracker can only function from low orbit to high orbit or in the same orbit direction, as shown in Figure 3.
# Multi-Scale Aggregation Stereo Matching Network Based on Dense Grouping Atrous Convolution
## 2 Related Work and Methods
### Methods  
#### 2.2.1 Network Architecture  
End-to-end binocular stereo matching consists of feature extraction, cost volume construction, cost aggregation, and disparity regression stages. We propose a network model, which is composed of a DenseGASPP and multi-scale cost aggregation module for feature extraction and cost aggregation, respectively. The architecture is shown in Figure 2.  
DenseGASPP utilizes dense grouping atrous convolution to extract features of the left and right images and acquire feature maps with different resolutions.  
For the cost volume part, feature correlation is used to correlate the left and right features of feature maps with different resolutions to construct differently scaled cost volumes.  
Multi-scale cost aggregation utilizes intra-scale cost aggregation and cross-scale cost aggregation to aggregate features between the cost volumes of different scales.  
Disparity regression utilizes the soft-argmin function [13].  
In the above four parts, our work focused on feature extraction and cost aggregation.
# First Evaluation of Topography on GNSS-R: An Empirical Study Based on a Digital Elevation Model
## 4 Understanding the Effects of Rough Topography on GNSS-R Space-Borne Data
### Regional Scale
#### 4.2.1 Introduction
Analysis of CyGNSS-derived TE at \(\theta_{\mathrm{e}}\)\(\sim\)[80, 90] \({}^{\circ}\) (**a–c**) and \(\theta_{\mathrm{e}}\)\(\sim\)[20, 30] \({}^{\circ}\) (**d–f**). Analysis of CyGNSS-derived \(\Gamma\) at \(\theta_{\mathrm{e}}\)\(\sim\)[80, 90] \({}^{\circ}\) (**g–i**) and \(\theta_{\mathrm{e}}\)\(\sim\)[20, 30] \({}^{\circ}\) (**j–l**). over areas above Latitude \(\sim\) 60 \({}^{\circ}\)[26]. However, this study was focused on areas within CyGNSS coverage Latitude \(\sim\) [-40, 40] \({}^{\circ}\).
# In-Plane Structural Analysis of Coated Masonry Walls via a Homogenized Model
## 3 Stress Distribution in the Three-Layer Model
### Normal Stress Distribution  
The _averaged_ stresses of the homogeneous cell, denoted as \(\sigma_{x},\bar{\sigma}_{y}\), involve the resulting normal forces \(N_{x}=\bar{\sigma}_{x}(h+2t)A\) and \(N_{y}=\bar{\sigma}_{y}(h+2t)B\), respectively.  
Let us consider \(N_{x}\); it distributes itself between the two parts 1 and 2 of the non-homogeneous cell, through the spring system in Figure 3a. For equilibrium \(N_{x}=N_{x1}+2N_{x2}\), and since the elements are in parallel, \(\dfrac{N_{x1}}{k_{x1}}=\dfrac{N_{x2}}{k_{x2}}\). Therefore, by letting \(\sigma_{x1}=\dfrac{N_{x1}}{h}\) and \(\sigma_{x2}=\dfrac{N_{x2}}{t}\), it follows:  
\[\begin{split}\sigma_{x1}&=\dfrac{2\beta+1}{1+2 \alpha_{Ex}\beta}\bar{\sigma}_{x},\\ \sigma_{x2}&=\dfrac{\alpha_{Ex}(2\beta+1)}{1+2\alpha _{Ex}\beta}\bar{\sigma}_{x}\end{split} \tag{6}\]  
where the definitions (5) have been used.  
In the same way:  
\[\begin{split}\sigma_{y1}&=\dfrac{2\beta+1}{1+2 \alpha_{Ey}\beta}\bar{\sigma}_{y},\\ \sigma_{y2}&=\dfrac{\alpha_{Ey}(2\beta+1)}{1+2 \alpha_{Ey}\beta}\bar{\sigma}_{y}\end{split} \tag{7}\]  
These expressions enable the assessment of (local) stress within the components of the non-homogeneous medium (specifically in the masonry and reinforcement), starting from  
Figure 3: Spring systems for the in-plane cell behavior: (**a**) extension along \(x\); (**b**) transverse dilatation along \(x\); (**c**) shear.  
the average normal stress acting on the homogenized medium (\(\bar{\sigma}_{x}\) or \(\bar{\sigma}_{y}\)). Furthermore, for small values of \(\beta\), the preceding local stress expressions can be approximated as:  
\[\begin{array}{l}\sigma_{x1}\simeq\bar{\sigma}_{x},\\ \sigma_{x2}\simeq\bar{\alpha}_{Ex}\bar{\sigma}_{x}\end{array} \tag{8}\]  
\[\begin{array}{l}\sigma_{y1}\simeq\bar{\sigma}_{y},\\ \sigma_{y2}\simeq\bar{\alpha}_{Ey}\bar{\sigma}_{y}\end{array} \tag{9}\]  
It means that the masonry normal stress is about equal to the global stress of the homogenized model, while the reinforcement normal stress is proportional to the former via the reinforced-to-unreinforced masonry modulus ratio \(\alpha_{Ex}\) or \(\alpha_{Ey}\).
# Contact Analysis and Friction Prediction of Non-Gaussian Random Surfaces
## 3 Validation of the Current Method  
The generated non-Gaussian surface with \(Sk=-1,Ku=5\), and \(\sigma=0.2\) um is shown in Figure 2(a). This is to be expected as the negatively skewed surface shows blunt asperity tip and more shape valleys. The comparison of ACF between the generated surface and the expected surface is displayed in Figure 2(b). It can be observed that the ACF of the generated surface matches excellently with the prescribed values. The results demonstrate the validity of the mothed for generating a prescribed non-Gaussian surface.  
A commercial twin-disc test implemented by [PERSON] et al. [38] is used to validate the prediction model of friction coefficient proposed in Section 2.2. The validation experiment was performed under different surface conditions and applied loads. The two surface roughness conditions are Rq surface roughness of 0.28 and 0.47 um, which are shown in Figure 4. Friction coefficients were acquired at two linear loads of \(2\times 10^{5}\) and \(1.5\times 10^{6}\) N/m for each set of surface roughness conditions. The detailed test parameters are summarized in Table 1. The contact pressure and gap under four sets of operating conditions are illustrated in Figure 5. As can be seen in Figure 6, the numerical results of current method demonstrate good agreement with the results of the experiment.  
Figure 3: The simulated rough surface (**a**) surface topography for \(Sk=-1\), \(Ku=5\), and \(\sigma=0.2\) μm; (**b**) comparison between the generated and prescribed ACF.  
\begin{table}
\begin{tabular}{c c} \hline \hline Applied load \(W\) (N/m) & \(2\times 10^{5}\) and \(1.5\times 10^{6}\) \\ Equivalent radius (m) & 0.0053 \\ Surface roughness \(\sigma\) (\(\upmu\)m) & 0.28 and 0.47 \\ Equivalent elastic modulus \(E^{\prime}\) (Pa) & \(2.28\times 10^{11}\) \\ Hardness of rollers \(H\) (Gpa) & 4.04 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment operating parameters for validation.  
Figure 6: Comparison of experiment results and current predictions.
## 2 Principle of Multiple Antenna Arrays based DM Transmission
### The Principle of Secure and Precise DM Transmission  
Without loss of generality, three antenna arrays at the transmitter side are taking as an example to elaborate the principle of secure and precise DM transmission, as shown in Figure 2.  
Assume that three antenna arrays are utilized to transmit symbol streams \(s_{1}\), \(s_{2}\), and \(s_{3}\) with AN-aided DM technique simultaneously. The green (solid line), purple (dashed line), and blue (dot dash line) areas represent the main information beam of the three antenna arrays, respectively.  
As we know, in traditional AN-aided DM schemes using a single antenna array model, only signals in the main beam region can be demodulated perfectly, while signals outside the main beam region will be seriously polluted by inserted AN.  
In our multiple antenna arrays based DM schemes, at first, we consider the symbol stream transmitted by the first antenna array. Obviously, the radiation region of the first antenna array can be divided into three parts. The first is the region outside the main beam region. The second is the main beam except the controlled region for the LU, as shown in Figure 2. The last is the controlled region for the LU, which is the overlap of main beams of the three transmit antenna arrays. Different from a single antenna array based AN-aided DM schemes, in our proposed DM schemes, signals in the previous two parts are both seriously polluted by the inserted AN and the AWGN (additive white Gaussian noise), while observations for the LU are a simple summation of useful signals and the AWGN. Thus, the LU can easily recover the useful signals transmitted from the first antenna array. Likewise, the symbol streams transmitted by the second and the third antenna array except the controlled area for the LU are all seriously polluted by the inserted AN. Therefore, we can employ multiple antenna arrays to transmit useful signals to a specified small neighborhood without any pollution. That is the principle to achieve secure and precise wireless transmission using a multiple antenna arrays model with the help of AN-aided DM technique.  
Figure 2: The principle of secure and precise wireless transmission for three antenna arrays with the help of AN-aided DM technique.  
Owing to the introduction of the multiple antenna arrays model, the useful signals are mixed together at the receiver side. Therefore, two secure and precise DM transmission schemes based on a single-carrier multiple antenna arrays model in Section 3 and a multi-carrier multiple antenna arrays model in Section 4 are proposed to recover the mixed useful signals, respectively.
# Construction Project Cost Prediction Method Based on Improved BiLSTM
###### Abstract  
In construction project management, accurate cost forecasting is critical for ensuring informed decision making. In this article, a construction cost prediction method based on an improved bidirectional long- and short-term memory (BiLSTM) network is proposed to address the high interactivity among construction cost data and difficulty in feature extraction. Firstly, the correlation between cost-influencing factors and the unilateral cost is calculated via grey correlation analysis to select the characteristic index. Secondly, a BiLSTM network is used to capture the temporal interactions in the cost data at a deep level, and the hybrid attention mechanism is incorporated to enhance the model's feature extraction capability to comprehensively capture the interactions among the features in the cost data. Finally, a hyperparameter optimisation method based on the improved particle swarm optimisation algorithm is proposed using the prediction accuracy as the fitness function of the algorithm. The MAE, RMSE, MPE, MAPE, and coefficient of determination of the simulated prediction results of the proposed method on the dataset are 7.487, 8.936, 0.236, 0.393, and 0.996%, respectively, where MPE is a positive coefficient. This avoids the serious consequences of underestimating the cost. Compared with the unimproved BiLSTM, the MAE, RMSE, and MAPE are reduced by 15.271, 18.193, and 0.784%, respectively, which reflects the superiority and effectiveness of the method and can provide technical support for project cost estimation in the construction field.  
construction cost prediction; particle swarm optimisation algorithm; attention mechanism; bidirectional extended short-term memory network +
Footnote †: journal: Applied Sciences  
1
Footnote 1: institutetext: School of Information and Control Engineering, Xi’an University of Architecture and Technology,  
Xi’an 710055, China; [EMAIL_ADDRESS]  
2
Footnote 2: institutetext: Correspondence: [EMAIL_ADDRESS]; Tel.: +86-150-9384-0963
###### Abstract  
In this paper, we propose a three-dimensional (3-D) sound image reproduction method based on spherical harmonic (SH) expansion for 22.2 multichannel audio. 22.2 multichannel audio is a 3-D sound field reproduction system that has been developed for ultra-high definition television (UHDTV). This system can reproduce 3-D sound images by simultaneously driving 22 loudspeakers and two sub-woofers. To control the 3-D sound image, vector base amplitude panning (VBAP) is conventionally used. VBAP can control the direction of 3-D sound image by weighting the input signal and emitting it from three loudspeakers. However, VBAP cannot control the distance of the 3-D sound image because it calculates the weight by only considering the image's direction. To solve this problem, we propose a novel 3-D sound image reconstruction method based on SH expansion. The proposed method can control both the direction and distance of the 3-D sound image by controlling the sound directivity on the basis of spherical harmonics (SHs) and mode matching. The directivity of the 3-D sound image is obtained in the SH domain. In addition, the distance of the 3-D sound image is represented by the mode strength. The signal obtained by the proposed method is then emitted from loudspeakers and the 3-D sound image can be reproduced accurately with consideration of not only the direction but also the distance. A number of experimental results show that the proposed method can control both the direction and distance of 3-D sound images.  
22.2 multichannel audio; spherical harmonic expansion; 3-D sound image reproduction; mode matching +
Footnote †: journal: 2022, 12, 1994. [[https://doi.org/10.3390/app12041994](https://doi.org/10.3390/app12041994)]([https://doi.org/10.3390/app12041994](https://doi.org/10.3390/app12041994))
## 2 Theory  
The in-line interferometer (IL) studied in this work uses a convergent lens to create the magnified image of the superimposed wavefronts on the recording medium. First, the transverse \(\mathrm{M_{T}}\) and longitudinal \(\mathrm{M_{L}}\) magnifications are calculated in terms of the focal length of the convergent lens, \(f\), and the image distance, \(s_{i}\). Afterwards, the magnifications \(\mathrm{M_{T}}\) and \(\mathrm{M_{L}}\) of the digitally reconstructed images obtained with the in-line interferometer are examined. In the following, we summarize the derivation of the main equations required in the presented method, but a complete derivation of such relations is presented in the Supplementary Materials.
# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 5 Experimental Design
### Image Formats  
In the context of image decolorization, different image formats exhibit different properties. Image formats vary in their compression methods and how they handle color  
Figure 3: Images from database [6].  
information, which in turn affects both image quality and file size. Image formats can be categorized into those that use compression and those that do not. Formats with compression include:  
* JPG/JPEG: Uses lossy compression, which reduces both color and luminance information by separating the image into luminance (Y) and chrominance (Cb and Cr) channels. This often results in a loss of fine details in both color and grayscale representation.
* GIF: Employs lossy compression with a limited color palette of 256 colors, significantly reducing the color depth and making it unsuitable for accurate luminance or RGB preservation, particularly in complex images.
* PNG: Uses lossless compression, preserving both luminance and RGB values without any degradation, making it ideal for images where color accuracy is essential.
* TIFF: Can use either lossy or lossless compression, with the lossless method preserving both RGB and luminance values, while lossy compression may reduce quality.  
Compressed formats balance image quality and file size, but compression artifacts from lossy compression can negatively affect the decolorization process.  
Formats without compression include BMP and PPM which store full RGB values for each pixel, preserving the original image's luminance and color without any loss or modification. In the dataset we used, the images are stored in PPM format while the images generated for the second part of our research are in PNG format. Both formats preserve color and luminance information without loss, but they typically result in larger file sizes compared to lossy compressed formats.
## Materials and Methods
### Data Analysis
#### 2.4.3 Vertical Velocity  
As stated before, one of the advantages of using ADCP instruments for zooplankton detection rather than other acoustic devices is that it can measure the speed and direction of the movement of zooplankton's vertical migration [26]. The ADCP instrument is designed to measure mass water velocity based on the Doppler principle by using an object's displacement in the water column [26]. Therefore, this instrument is also able to measure the velocity and direction of zooplankton's diurnal vertical migration. Since we only have time series data from moored ADCP, we cannot use mobile ADCP data due to the lack of continual observation data. Vertical velocity profiles from every single ping of moored ADCP data were used for zooplankton displacement speed [12; 30; 31]. The vertical velocity determines the descend and ascend speed at the range of time with specified zooplankton movement. Thus, frequency shift (\(\Delta f\)) based on the Doppler principle can be applied to identify zooplankton movements which can be written as follows:  
\[\frac{\Delta f}{f_{0}}=\frac{-xv}{c\sqrt{x^{2}+H^{2}}}, \tag{4}\]where \(c\) is the sound speed propagating through the water column (ms\({}^{-1}\)), \(x\) is the distance between the initial and final location of the zooplankton movement (mm), \(v\) is the zooplankton movement speed (mm s\({}^{-1}\)), and \(H\) is the distance between the transducer and object (mm). The mean vertical velocity speed of the sound scattering layer (SSL) is in average vertical current and can be calculated based on the average of Equation (4) for each bin.
# Multi-Layer Feature Fusion-Based Community Evolution Prediction
## 3 Multi-Layer Feature Fusion-Based Community Evolution Prediction
### Community Evolution Prediction  
This section will explain the feature set construction method used for prediction, and the overall process of MCEP.  
Suppose that, for the community \(i\) at time \(t\), the overall community features obtained by MCEP are represented by \(TF_{t,i}\), and the community role features are represented by \(RF_{t,i,1}\), \(RF_{t,i,2}\), \(RF_{t,i,3}\) (assuming that the number of roles is 3), the community mesoscopic feature is represented by \(MF_{t,i}\). The final community feature is obtained by splicing the above features:  
\[\mathbb{F}=[TF_{t,i},RF_{t,i,1},RF_{t,i,2},RF_{t,i,3},MF_{t,i}] \tag{8}\]  
Because the future evolution of the community will be affected by its historical evolution, to take time information into account, MCEP obtains the evolution sequence by tracking the evolution process of the community. Then MCEP splices the corresponding community features and their evolution events to obtain the features for training and prediction. The feature structure of community i at time t is as follows:  
\[T_{t,i}=[\mathbb{F}_{t-2,i},E_{t-2,i},\mathbb{F}_{t-1,i},E_{t-1,i},\mathbb{F} _{t,i}] \tag{9}\]  
where \(T_{t,i}\) represents the total feature of community \(i\) at time \(t\), \(\mathbb{F}_{t,i}\) is the feature of community \(i\) at time \(t\), and \(E_{t,i}\) is the evolution event of the community \(i\) at time \(t\). Because MCEP uses binary classification in evolution prediction, i.e., for each evolution event, MCEP trains a separate classifier to predict. Therefore, \(E_{t,i}\) has only two values: \(E_{t,i}\) is 1 when the corresponding evolution event occurs, and \(E_{t,i}\) is 0 when it does not occur.  
After the feature set is constructed, MCEP uses the classifier for training and prediction. For each community evolution event, MCEP constructs a corresponding training set. Then MCEP uses logistic regression for training and prediction. Although MCEP uses logistic regression as the classifier, and the main focus of MCEP is the obtaining of community features and the construction of the training set, so MCEP can also use other classifiers.  
The process of the MCEP algorithm is shown in Algorithm 2.  
```
0: Network structure \(G\)
1: Split the network to obtain \(G_{1}\), \(G_{2}\),..., \(G_{t}\)
2: Perform community discovery on different snapshots, and get the community structure of each snapshot \(C_{1}\), \(C_{2}\),..., \(C_{t}\)
3: Use the GED algorithm to obtain the evolutionary relationship between communities
4: For the community \(C_{i,j}\), calculate the overall community features \(TF_{i,j}\)
5: Use the GLRE to obtain the role features of the community \(RF_{i,i,1}\), \(RF_{i,j,2}\),..., \(RF_{i,j,r}\)
6: Obtain the community mesoscopic feature \(MF_{i,j}\) according to the algorithm described in Section 4.3
7: Combine the obtained features to obtain community features \(\mathbb{F}_{t,i}\)
8: Construct feature set according to Formula (9)
9: Train the classifier based on the constructed feature set
```  
**Algorithm 2** MCEP