esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Possibility of Communication with High Insertion Loss  
In this section, we discuss the feasibility of communication when transmission coefficients (or insertion loss (IL)) are lower than \(-40\) dB. We calculate the necessary power transmission and explore how to optimize the communication system. Let us define a few equations:  
\[InsertionLoss\ (IL_{dB})=10\times\log_{10}\biggl{(}\frac{P_{in}}{P_{out}} \biggr{)}, \tag{1}\]  
\[NosieFigure\ (NF_{dB})=10\times\log_{10}(F), \tag{2}\]  
where \(F\) is the noise factor and is given as \(F=\frac{SNR_{in}}{SNR_{out}}\).  
\[NoisePower(N_{0})=KTBF, \tag{3}\]  
where \(K\) is the Boltzmann constant (\(1.38\times 10^{-23}\) J/K), \(T\) is the absolute temperature (273.15 K for 0 \({}^{\circ}\)C plus 50 \({}^{\circ}\)C room temperature ), and \(F\) is the \(10^{\frac{NF}{M}}\) conversion of NF from dB to a linear value. Now converting \(N_{0}\) into dBm gives the following:  
\[N_{0}(dBm)=10\times\log_{10}\biggl{(}\frac{KT_{0}BF}{1 mW}\biggr{)}, \tag{4}\]  
Upon simplifying,  
\[N_{0}(dBm)=-173.5+10\times\log_{10}(B)+NF \tag{5}\]  
Now we can calculate the required transmission power that has an insertion loss \(IL_{dB}\) of 40 dB with a noise figure \(NF\) of around 10 dB (which is a typical value for THz communication); then the transmission power (\(P_{t(dBm)}\)) is as follows:  
\[P_{t(dBm)} =P_{r(dBm)}+IL_{dB} \tag{6}\] \[=N_{0(dBm)}+SNR_{dB}+IL_{dB}\] \[=-173.5+10\times\log_{10}(B)+NF+SNR_{dB}+IL_{dB}\] \[=-173.5+10\times\log_{10}(4\times 10^{9})+10+24+40\] \[=-3.479\text{ dBm}\]  
Hence, the calculated transmission power of \(-3.479\text{ dBm}\) is feasible for modern communication despite having high transmission loss.
# An Overview of Variants and Advancements of PSO Algorithm
## 3 Parameters of PSO
### Population Size  
Swarm size (n\({}_{\mathrm{s}}\)) is the number of particles in the whole swarm. A large number of the particles cover a larger part of the search space per iteration, but it also increases the computational complexity per iteration. It can also be possible that a larger number of particles may lead to a lesser number of iterations to obtain an optimal solution. From the heuristics found in the publications, the success rate of getting a good solution in the PSO algorithm is greater with a swarm size of 20 to 50 particles (Eberhart and Shi) [16]. However, it is also found that the optimal population size is usually problem dependent.
# Separable Data-Hiding Scheme for Encrypted Image to Protect Privacy of User in Cloud
## 2 Block Compressive Sensing (BCS)  
In the compressive sensing (CS) theory, assume a real-valued signal \(\mathbf{x}\) with length, \(N\), is \(k\)-sparse in an orthonormal basis matrix \(\mathbf{\Psi}\), sized \(N\times N\), i.e., \(\mathbf{x}=\mathbf{\Psi}\mathbf{\theta}\), where the coefficient vector \(\mathbf{\theta}\) only has \(k\) non-zero significant coefficients. The signal \(\mathbf{x}\) can be reconstructed with certain accuracy by \(m\) measurement values:  
\[\mathbf{y}\mathbf{=}\mathbf{\Phi}\mathbf{x}\mathbf{=}\mathbf{\Phi}\mathbf{\Psi}\mathbf{\theta}, \tag{1}\]  
where \(\mathbf{\Phi}\) is an \(m\times N\) random measurement matrix that is incoherent with \(\mathbf{\Psi}\), and \(\mathbf{y}\) is the measurement vector with length \(m\), where \(k<m<<N\). The reconstructed process is used to solve the optimization problem [26] under the constraint of \(\mathbf{y}\mathbf{=}\mathbf{\Phi}\mathbf{\Psi}\mathbf{\theta}\):  
\[\widehat{\theta}=\operatorname*{argmin}_{\mathbf{\theta}}\lVert\mathbf{\theta} \rVert_{I_{1}}\;s.t.\;\mathbf{y}\mathbf{=}\mathbf{\Phi}\mathbf{\Psi}\mathbf{\theta}. \tag{2}\]  
Finally, \(\mathbf{x}\) can be reconstructed by \(\widehat{\mathbf{x}}\mathbf{=}\mathbf{\Psi}\widehat{\mathbf{\theta}}\).  
In order to save storage space and speed-up image processing, a block-based CS (BCS) is proposed and applied. First, an image is divided into non-overlapping \(B\times B\) blocks, and then, each block \(\mathbf{x}_{i}\) is individually sampled based on the same measurement matrix, \(\mathbf{\Phi}_{B}\), with a constrained structure [27]. In other words, the measurement vector, \(\mathbf{y}_{i}\), with the size of \(m_{B}\times 1\) can be obtained from \(\mathbf{y}_{i}\mathbf{=}\mathbf{\Phi}_{B}\mathbf{x}_{i}\), where \(\mathbf{\Phi}_{B}\) is an \(m_{B}\times B^{2}\) measurement matrix with \(m_{B}=\left\lfloor n/NB^{2}\right\rfloor\).  
Figure 1: Three different cases for the receiver.  
Compressive sensing has features of compression and encryption. In 2008, [PERSON] et al. [28] discussed the security of the measurement values and had proved that compressed sensing-based encryption did not achieve [PERSON]'s definition of perfect secrecy, but can provide a computational guarantee of secrecy. [PERSON] et al. [29] also proved that the measurement values can achieve perfect security under certain conditions, and it provided the theory base for the encryption application of CS.
###### Abstract
Specifically, the method uses an autoencoder to extract features from the audio signals of electrical equipment, obtaining low-dimensional latent representations, with a parallel attention mechanism applied to weigh the latent representations, highlighting features related to anomalies. Finally, an anomaly scoring function is designed based on reconstruction error or feature distance to achieve anomaly detection for electrical equipment. Experimental validations show that the proposed method shows excellent performance in multiple anomaly detection tasks for electrical equipment, effectively identifying different types of abnormal conditions and demonstrating high practical value. The main contributions of this article are as follows:  
* An unsupervised anomaly detection method based on adversarial training is proposed, which can effectively extract key features from audio signals of electrical equipment;
* A novel attention block is proposed in this article, which improves the model's sensitivity to anomalous features through multi-channel attention weighting;
* In addition to testing on common public datasets, we also use real-world experiments to verify the effectiveness of proposed model. The structure of this paper is organized as follows: Section 2 introduces the relevant research background and existing methods. Section 3 details the proposed unsupervised anomaly detection method. Section 4 presents the experimental design and result analysis. Section 5 summarizes the work and outlines future research directions.
# Response of Seismically Damaged Steel Reduced Beam Section Joints under Fire
## 4 Results of Parametric FEAs
### Fire Behaviour of Seismically Damaged Joints  
The fire performances of seismically damaged internal joints are depicted in Figure 11 in terms of time-rotation and temperature-rotation curves. In Figure 12, the distributions of PEEQ, temperatures, and Von Misses stresses of the IJ-RBS-65-NC are pointed out for S1, S2, and S3. Additionally, in these cases, due to the symmetry of the connections' geometry and the applied loads, the results are solely shown for one side of the beam.  
It is worth noting that similar to the undamaged joints, the FE results for the damaged RBS in S1 and S2 are similar, while the joints in S3 exhibit greater deterioration due to a substantial increase in temperature. In particular, it can be observed that after 10 min of exposure, the IJ-RBS-65-NC-S1/S2 experience a rotation of 0.05 rad, while the IJ-RBS-65-NC-S3 joints exhibit a rotation of 0.13 rad. Figure 12 confirms that all plastic deformations develop in the beam, while the connection and the column remain in the elastic range (Figure 12a,d,g). In addition, large plastic deformations as well as out of plane deformations can be observed in the lower part of the beam for the S1 and S2; in contrast, small deformations in the same parts of the beam occur in the case of S3 due to a more homogeneous distribution of the temperatures (see Figure 12b,c,e,f,h,i).  
Figure 10: Results of the IJ-RBS-80 joints in terms of time–rotation curve (**a**), temperature–rotation curve (**b**) and PEEQ (**c**).  
Figure 11: Time–rotation (**a**,**c**) and temperature–rotation (**b**,**d**) of the RBS-65/85 internal joints subjected to the S1–S3 scenarios.  
Figure 12: PEEQ, temperature, and Von Misses stress distributions of internal joints subjected to S1 (**a**), S2 (**b**), and S3 (**c**) fire scenarios.  
Similar to the results depicted in Figure 9, it can be observed that the resistance of the RBS influences the fire performance of the joint. In fact, independently from the fire scenario (S1, S2, and S3), the RBS-80 joints reach a greater ultimate temperature and resist for a longer time than RBS-65 (see Figure 13).  
However, the difference between the RBS-80 and the RBS-65 is rather small. In fact, despite the seismic damage reducing the beam capacity, its entity marginally affects the fire performance of the joints since the seismic damage is mainly concentrated in the beam flanges, while the fire resistance is mainly influenced by the shear resistance of the beam, which is not impaired by the cyclic loading. This aspect is clarified in Figure 14, where the results of the undamaged joints are compared to those which are seismically damaged. It can be observed that the imposed cyclic damage reduces both the maximum resisting temperature and the resisting time, but such a reduction is not directly influenced by the cut of the RBS.
# Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
## 2 Materials and Methods
### Graph Construction and Classification
#### 2.3.2 Node Classification via a Graph Convolutional Network  
This study adopts the GraphSAGE framework, which is a kind of inductive learning model in the spatial domain [39]. The framework first aggregates the information of each node and its adjacent nodes through several graph convolution layers and then outputs the classification graph. The output classification graph is the graph data with the same structure as the input graph, but the feature of each point is the category of nodes, as shown in Figure 2.  
In the hidden layer, each layer first samples each node and its surrounding neighbors and then aggregates the sampled data of each node to obtain the new characteristics of the node, as shown in Figure 3. There are two key points in the hidden layer: sampling and information aggregation. Taking node V2 as an example, first, we can randomly sample the neighbor nodes of V2, set the number of samples as \(N\), and obtain the set \(\varkappa(N)\), while \(\ell\) is the dimension of node characteristics. If the number of vertex neighbors is less than \(k\), the sampling method with put-back is adopted until n nodes are sampled. We can aggregate the mean value of the set sampled from each node and obtain the new characteristics of the node after linear transformation and activation function, as shown in the following formula:  
\[H_{v}^{k}=\sigma(W\cdot MEAN(\varkappa(N)) \tag{3}\]  
Figure 3: Illustration of information aggregation of GCN.  
Figure 2: Illustration of the structure of the graph convolutional neural network, where c1 and c2 are the output classes of nodes. (**a**) Input layer; (**b**) hidden layer; (**c**) output layer.  
In the output layer, the cross entropy of the predicted label and the real label of each node is used as the loss function.  
\[L=-\sum_{i}\sum_{c=1}^{M}y_{ic}\log(p_{ic}) \tag{4}\]  
where \(L\) is the loss and \(M\) is the number of categories. If the real category of sample \(i\) is category \(c\), we take \(y_{ic}=1\); otherwise, \(y_{ic}=0\), \(p_{ic}\) is the prediction probability that sample \(i\) belongs to category \(c\).
# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 3 Materials and Methods
### Case Study and Definitions  
Nonhomogeneous haze is common in real-world hazy weather conditions. One example of hazy images taken in such scenes is shown in Figure 1a, where the haze density is nonuniform across the image. It can been that the image suffers from low contrast and detail loss. Additionally, the degree of image degradation is closely related to the haze density.  
It is well-established that such image degradation caused by haze can be formulated by the atmospheric scattering model [9]:  
\[I(x)=J(x)t(x)+A(1-t(x)), \tag{1}\]  
where \(I(x)\) is the observed hazy image, \(J(x)\) is the latent haze-free image, \(t(x)\) is the transmission map, and \(A\) is the global atmospheric light. In previous studies, the transmission map \(t\) is sometimes assumed to be attenuated exponentially with the scene depth \(d\), i.e., \(t(x)=e^{-\beta d(x)}\), where \(\beta\) is the scattering coefficient of the atmosphere. However, this relation is invalid under nonhomogeneous hazy scenes since the assumed transmission map is only distance-dependent, and hence inconsistent, with the randomness of nonhomogeneous haze.  
Given an observed hazy \(I(x)\), the task of single-image dehazing aims to recover the latent haze-free \(J(x)\). To this end, we take the deep learning approach and propose an end-to-end RRA-Net model to tackle the challenge of nonhomogeneous degradations. In the meantime, high dehazing efficiency is taken into consideration to make sure of its practicability in real-time applications.
# Design and Dynamic Analysis of the Wire-Line Coring Robot for Deep Lunar Rocks
## 1 Introduction
However, the sampling missions, especially the full profile sampling, are accomplished by multiple liftings of the drill and collecting samples from each drill. For example, the Autonomous Tethered Corer achieved the drilling and coring of 10 m of limestone by multiple liftings of the drill and the removal of core samples. The coring device weighs 7 kg and has 50-75 W of power [18]; Auto-Gopher (AG) II with an ultrasonic percussion drill obtained a core at a 7.52 m depth with gypsum as the drilling target and is expected to achieve more than 50 m drilling [19]. However, the current wire-line sampling device still cannot achieve fully autonomous deep coring, and all the above drilling approaches require human assistance to exchange the coring tubes. Therefore, the development of a deep coring robot with autonomous capability is necessary. In this paper, a wire-line robot for deep coring lunar rocks was designed with low energy consumption and a small space as an entry point. In addition, this robot provides a prototype solution for the deep coring of other celestial bodies. Firstly, the wire-line scheme was proposed, and the coring executor was designed. Secondly, a rotating impact composite coring robot with functions of subsurface anchoring, in-hole change in direction, etc., was designed for the requirements of subsurface self-excavation coring functions. Subsequently, the anchoring support force model with depth was established for the anchor module, which provided theoretical support for the drilling scheme at different depths. Finally, the structure parameters of the impact module were optimized with the objective of maximum impact energy. The optimized structure parameters were also simulated to verify the drilling and breaking effect of the impact module on the lunar rocks. The flow chart of the research methodology is shown in Figure 1.
# GNSS RTK Positioning Augmented with Large LEO Constellation
## 3 Situation and Strategy  
In this section, we will introduce the constellation configuration, simulation situation, and data processing strategy, respectively. Firstly, four kinds of LEO constellations with different satellite numbers, as well as nominal GPS constellation with full operational capability, are adopted in order to investigate different LEO constellations augmented performances. Then, the station distribution and its support constellation are described, together with length information of different baselines. Finally, in terms of the short and medium-to-long baselines, different data processing strategies of RTK solutions are set to make a preparation for the assessment of combined GPS/LEO RTK for different length baselines in detail.
## 2 Materials and Methods
### Testing Methodology and Demographics  
For system evaluation purposes, the parameters for the serverless platform, AWS Lambda, are shown in Table 1. To achieve a consistent result, each Lambda function was implemented using the same parameter values. The Python 3.8 development language was used with allocated memory of 10,240 MB.  
Initially, we met with VIB teachers from the Tainan City Life Rehabilitation Center for the Visually Impaired to let them feel the current TEM. This subjective evaluation compared the different methods of the tactile materials and the level of simplicity within the image. We provided the teachers with 20 types of images, and they commented on the aspect that was the easiest to understand. A summary of the demographics of the teachers that tested the tactile flashcards is shown in Table 2. One teacher was blind from birth and the other developed blindness at age 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Platform Used** \\ \hline Platform & AWS Lambda \\ Development Language & Python 3.8 \\ Framework & TensorFlow \\ Memory Allocation & 10,240 MB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Software and hardware platform used for development.  
Figure 7: Sample of the generated and 3D printed tactile peg puzzle.  
Next, we visited the Taichung Special Education School for the Visually Impaired, wherein six VIB students were given the educational aids to trial and provide comments. Their ages are between 13 and 20 years. Of the 6 students, 4 were VIB from birth, whereas two developed visual impairment; 1 is fully blind, 4 have light perception, and 1 has vision in one eye. Five of the students are able to read both English and Chinese Braille, although some are better able to use Chinese Braille, and one student uses books with big characters to read from one eye. A summary of the demographics of the students is found in Table 3. After the evaluation, the students were asked about their thoughts on the tactile materials in individual interviews.  
The usability test for the generation website was conducted with the counselling teachers. Their task was to generate a tactile material using a 2D image with the TacPic website. They were then interviewed about their experience and asked for comments on the system. Based on the ISO/IEC 25010:2011 Systems and software engineering--Systems and software Quality Requirements and Evaluation (SQuaRE) quality models, the usability of a system can be determined based on the following sub-characteristics: appropriateness, recognizability, learnability, operability, user error protection, user interface aesthetics, and accessibility. Regarding the aspect of usability evaluation, the most widely used method is the empirical method, consisting of either questionnaires or usability testing which has the advantage that the feedback of the users is directly provided.
## 2 System Model and Signal Design
### Integrated Signal Design  
Next, we perform a \(I\times N_{c\_sub}\) block interleaver and inverse fast Fourier transform (IFFT) on all transmit antennas. In addition, in order to eliminate intersymbol interference (ISI), the guard intervals (GI) sequence should be inserted before all OFDM signal blocks [42]. The traditional way is to directly add a cyclic prefix (CP), but the introduction of a cyclic prefix will produce a high CP sidelobe in radar signal processing, which will affect the target detection performance [43]. Therefore, according to the performance standard of the proposed system, this paper adopts the nonlinear optimization method in [44] to obtain the optimal GI sequences of all OFDM symbols.  
Then, after conventional OFDM operations such as serial-to-parallel conversion, the signal is simultaneously transmitted from \(N_{t}\) transmitting antennas via the frequency-selective Rayleigh fading MIMO channel.  
According to the above method, all OFDM signals of \(P\) pulses are generated, and the total number of activated subcarriers is \(K^{\prime}=K\cdot I\cdot P\). Then, the indexes of the activated subcarriers are arranged in the vector \(\mathbf{\zeta}\) in ascending order, which is expressed as  
\[\mathbf{\xi}=\left[\begin{array}{cccc}\xi_{1}&\xi_{2}&\cdots&\xi_{K^{\prime}} \end{array}\right] \tag{5}\]  
where \(\xi_{1}\), \(\xi_{2}\), \(\cdots\), \(\xi_{K^{\prime}}\) denote the first to \(K^{\prime}\) activated subcarrier indexes, respectively. According to the signal generation flow in Figure 2, the spectral efficiency (in bps/Hz) of the proposed system is given by the following formula:  
\[E=\frac{N_{t}(p_{1}+p_{2})I}{N_{c}+N_{cp}}=\frac{N_{t}\Big{(}\left\lfloor\log _{2}C_{N_{c\_sub}}^{K}\right\rfloor+K\log_{2}M\Big{)}I}{N_{c}+N_{cp}} \tag{6}\]  
It is worth mentioning that the balance among spectrum utilization, transmission reliability, and complexity can be achieved by adjusting relevant parameters in practical applications.
## 1 Introduction
The light use efficiency (LUE) model combines satellite vegetation products to widely generate regional GPP [27; 28; 29; 30]. After obtaining ET and GPP time series, different methods can be used to quantitatively rank environmental elements affecting ET or GPP under different conditions. Field control methods have also been used to identify the contributive mechanisms of environmental derivers in early research [31; 32]. Although the above methods are relatively reliable, their results are generally limited to small scales. Field experiments require lengthy observational data series, which are excessively time-, manpower-, and resource-intensive. Scenario settings combined with numerical simulation models can more quickly reveal the regional effects of various factors on changes in ET or GPP [8; 16]. However, the interactions between different factors when the setting scenarios change can make the factors difficult to separate for subsequent analysis. Statistical methods (e.g., multivariate regression) have also been increasingly applied to distinguish the contributions of drivers of ET or GPP [10; 33; 34; 35] as they are accurate and relatively easy to operate. Therefore, a remote sensing-based ecohydrological model derived by long-time-series, remotely sensed LAI and meteorological data was developed to simulate the spatiotemporal changes in ET and GPP over Mainland China between 1999 and 2018 in this study. Simulation and statistical results were combined to (1) visualize spatiotemporal ET and GPP patterns in China over the last 20 years and (2) comprehensively analyze the contributions of climatic factors and anthropogenic activities to the variations in ET and GPP in China.