esa-sceva/satcom-chunk-collection huggingface dataset:
# Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
## 2 Data and Proposed Model
### Data Preprocessing  
TEC data are typical time series data, and only stable non-random time series can be predicted. Therefore, a series of preprocessing is required before TEC prediction. The preprocessing of the selected ionospheric TEC data in this paper includes a TEC data stationarity test, difference processing, a pure random stationarity test, and TEC data normalization.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Region** & **Lat** & **Lon** \\ \hline Beijing & 40\({}^{\circ}\) N & 115\({}^{\circ}\) E \\ Guangzhou & 22.5\({}^{\circ}\) N & 115\({}^{\circ}\) E \\ Harbin & 45\({}^{\circ}\) N & 125\({}^{\circ}\) E \\ Tibet & 30\({}^{\circ}\) N & 90\({}^{\circ}\) E \\ Yushu & 35\({}^{\circ}\) N & 95\({}^{\circ}\) E \\ Ziyang & 30\({}^{\circ}\) N & 105\({}^{\circ}\) E \\ Hangzhou & 30\({}^{\circ}\) N & 120\({}^{\circ}\) E \\ Heze & 35\({}^{\circ}\) N & 115\({}^{\circ}\) E \\ \hline \hline \end{tabular}
\end{table}
Table 1: The coordinates of the research areas in this paper.  
Figure 1: The information on the location of the eight regions on the map.  
2.2.1. The TEC Data Stationary Test and Difference Processing  
The stationarity of time series is the basic assumption of time series analysis. Therefore, the stationarity of time series needs to be tested first before TEC prediction. In this paper, we use the ADF test to determine the stationarity of TEC sequences. The stationarity test results of the eight regions are all non-stationary time series. In order to transform the data into stationary time series for prediction, first-order difference processing is required. The calculation formula of the first-order difference is as follows:  
\[\triangle\ x_{t}=x_{t}-x_{t-1} \tag{1}\]  
where \(\triangle\) is the first-order difference operator and \(x_{t}\) is the observation data at the time \(t\). Figure 3 shows the results of the first-order difference of the TEC data of the eight regions in Table 1. After the first-order difference processing, the ADF (Augmented Dickey-Fuller) test is performed again. This time, all eight regions pass the ADF test, that is, the first-order difference data of the eight regions are stable time series.
# Numerical Assessment of Side-Wind Effects on a Bus in Urban Conditions
## 3 Results
### Flow Formations  
Since multiple magnitudes of flow velocities had been examined, only the 20 km/h velocity distribution was assessed horizontally and vertically. The horizontal velocity distribution at y = 1.5 m high was assessed in the two extreme side-wind cases: the symmetrical flow (Figure 9a) and the 2 m/s side-wind case (Figure 9b).  
Figure 8: Asymmetric \(F_{X}\) forces on the side of the bus at 20 km/h for (**a**) 1 m/s, (**b**) 1.5 m/s, (**c**) and 2 m/s side winds.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Side-Wind Condition** & \(\mathbf{F_{XLeft}\ (N)}\) & \(\mathbf{F_{XRight}\ (N)}\) & \(\mathbf{F_{XLeft}\ \text{-}F_{XRight}\ (N)}\) \\ \hline Symmetric & 0.457 \(\pm\) 0.014 & 0.461 \(\pm\) 0.017 & \(-\)0.004 \\
0 m/s & 0.388 \(\pm\)  
Even where the flow did not have any external influence, it showed an unsteady trait, as the vortex shedding can be seen in Figure 9a. When side wind was present, the average velocities were increased, most notably on the windward side (positive z region). When the bus was affected by the perpendicular wind load, the rear wake region was reduced. On the windward side of the bus, the vortex region was larger, which can be attributed to the fact that the surface of the bus altered the flow direction.  
From the vertical plane (Figure 10a), it can be seen that the wake flow pattern is similar to what [PERSON] et al. [15] described. The stagnation point shifted to the lower section of the rear side of the bus. Over the range of x = 16-21 m, a small separated flow can be seen, which also strengthens the claim that the low-velocity flow makes force distribution uneven over time. It is also visible in Figure 10b that the wake region was reduced both horizontally and vertically. To assess the vortices, \(Q\)-criterion [23] was used to depict them, where \(Q\) is the second invariant of the velocity gradient tensor, and \(S\) and \(\Omega\) are the symmetric and antisymmetric parts of the tensor and are expressed with Equation (2).  
\[Q=0.5\cdot\left(\parallel S^{2}\parallel+\parallel\Omega^{2}\parallel\right) \tag{1}\]  
Figure 9: Horizontal velocity distributions at 1.5 m height for 20 km/h (**a**) symmetrical flow and (**b**) 2 m/s side wind.  
In Figure 11, large numbers of vortices were noted at the rear region and the wheel of the bus, at a \(Q\)-criterion value of 330 s\({}^{-2}\).  
It can be seen that due to the rounded corner at the front the corner, vortices were minimal. The bulk of the top air-condition unit generated most of the wakes beside the wheels and the rear region. The horseshoe vortices were not detected, which is a common flow structure at blunt bodies [24]. The \(Q\)-criterion values also indicate the asymmetrical pattern, mostly behind the air-conditioning box. On the windward side, large vortices were generated.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 4 Methodology
### Proposed Stacked Convolutional Autoencoder for Data Reconstruction  
To train a conventional CAE, the weights of the convolution and dense layers must be randomly initialized before training. However, randomly initializing the weights leads to an optimized loss function \(\mathcal{L}\) that is prone to falling into weak local optima after finishing training. Consequently, the performance of the CAE is significantly degraded.  
To address this problem, we propose an SCAE model for efficiently reconstructing problems caused by external and internal events. Specifically, our proposed model is composed of two AE subnets, such as subnets AE 1 and AE 2, which are trained to obtain the optimal weights. After each subnet is trained, the complete initial AE network uses alternative weights from the AE subnets for the training process. The architecture of the proposed model is illustrated in Figure 6.  
The structure of subnet AE 1 is composed of the encoder and decoder kernels from the initial CAE network; they contain weights \(W_{\text{conv}}\) and \(W_{\text{deconv}}\), respectively. The structure of subnet AE 2 is composed of the dense layers of the initial CAE network, which contain weights \(W_{12},W_{23}\), \(W_{34}\), and \(W_{45}\), respectively. Based on the stacked AE framework, the first step is to train subnet AE 1 to obtain the weights \(W_{\text{conv}}\) and \(W_{\text{deconv}}\). Subsequently, subnet AE 2 is trained using the trained subnet AE 1 to obtain \(W_{12},W_{23},W_{34}\), and \(W_{45}\). Finally, the entire SCAE network is trained using the pretrained weights as the initial weights. The training process is presented in detail below.
# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 3 Problem Modeling
### Security Trust Model  
Since the satellite network is subject to internal attacks during the routing process, to cope with such malicious attacks, this paper proposes to build a security trust model on the satellite to reinforce the routing. The structure is shown in Figure 2. To identify the malicious attack behavior, the interaction information between nodes should be collected and should be used as trustworthy evidence by the quantity of interaction information, then the direct trust and indirect trust vectors can be established. When the VAE network is trained, it needs a clean trust vector as a dataset, so this paper first uses D-S evidence theory to clean the trust vector. After the VAE network is trained successfully, the VAE network is used to identify the collected trust evidence for malicious purposes. Finally, the ant colony algorithm can use the security factor to combine with its pheromone in the routing process, so as to identify malicious nodes and establish a secure route.
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 3 An Introduction to Mainstream Consensus Algorithms
### The Mainstream Consensus Algorithms of Private Blockchains
#### 3.2.1 Paxos  
The Paxos consensus mechanism, proposed by [PERSON] in 1990, is a classic solution to the distributed consistency problem [40], whose core goal is to synchronize the state of nodes in asynchronous network environments, so that the system can reach a majority decision even if some of the nodes fail. The protocol adopts a two-stage interaction process to achieve a fault-tolerant consensus, i.e., the \"propose-accept\" model. In the first phase (preparation phase), the proposer submits an incrementally numbered proposal to the acceptor to obtain the commitment of the majority of the nodes and to lock the priority. In the second phase (acceptance phase), the proposer submits a final proposal based on the numbering, and a decision is reached if a majority of the acceptors agree. The Paxos mechanism maintains a balance between security and activity, and can theoretically tolerate up to half of the node failures. However, its implementation complexity makes it difficult to apply directly in practice, prompting the development of optimized versions such as Multi-Paxos and Raft, which improve utility by simplifying the process of role partitioning and log replication. Consensus algorithms are a key infrastructure for achieving reliability in distributed computing environments. Recent academic research has focused on refining and expanding the theoretical foundation of the Paxos protocol, which serves as the cornerstone of distributed consensus. [PERSON] et al. [41] established a comparison framework between Paxos and Raft, emphasizing their shared commitment to fault tolerance while contrasting their methods for leader election dynamics. To improve the accessibility of teaching, [PERSON] et al. [42] structured a lecture system around the principles of leader-based sequential Paxos, bridging the gap between abstract theory and practical implementation for learners. [PERSON] et al. [43] introduced Heterogeneous Paxos, showcasing an innovative architecture that achieves optimal consensus in three message exchanges, addressing heterogeneity in node roles, network assumptions, and fault models. At the same time, significant progress has been made in formalizing the security guarantees of Paxos. Aman
## 3 Research Conditions and Methods
### Main Factors of the Experiment
#### 3.1.1 Spray Pressure  
Spray pressure is a critical factor influencing dust adhesion to and removal from the sensor surface. Higher pressures can cause dust to adhere more strongly or dislodge pre-existing dust, which is valuable for analyzing performance degradation. In the primary experiment, a pressure range of 1-3 bar was established, considering the equipment's operational capabilities.
# Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection
## 5 Conclusions  
This paper established a crowd-sensing-based system for bridge structural crack detection aiming at facilitating the participation of pedestrians to help structural crack detection tasks. A deep neural network was utilized to deal with the collected structural crack images to evaluate whether those crack images were fitted for structural crack detection. Based on the investigation, some conclusions can be drawn as follows:  
1. The crowd-sensing-based system for bridge structural crack detection was consisted of the cloud-based management platform and the mobile-based APP for crack information collection. The cloud platform was designed to conduct user management, crack information management and record query tasks. Meanwhile, the mobile-based APP could capture the crack images, location information and allow for short description. The location accuracy was around 5-10 m, and the crack description function can be used to provide assistance for crack location.
2. The preliminary tests showed that the system could realize the intended crack information acquisition purposes. However, since these images were not obtained by trained engineers with uniform equipment, the quality, picturing condition and angles, and the resolution of the crack images varied.
3. The crack detection results based on the deep neural network and the smartphone-based images showed that the deep neural network could detect most of the crack regions on these images when the background was clear and bright. Meanwhile, when the background was less contrast and complex, noise motifs could cheat the deep neural network.  
Figure 14: Crack detection result.  
**Author Contributions:** Conceptualization, [PERSON] and X.Y.; methodology, [PERSON] and H.Y.; validation, X.Y. and H.Y.; formal analysis, H.Y.; investigation, [PERSON] and X.Y.; resources, [PERSON] and H.Y.; data curation, H.Y.; writing--original draft preparation, [PERSON] and T.J.; writing--review and editing, [PERSON] and T.J.; visualization, [PERSON]; supervision, T.J.; project administration, [PERSON]; funding acquisition, [PERSON] and X.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work described in this paper was jointly supported by the China Postdoctoral Science Foundation (Grant No. 2022M712787), and the National Natural Science Foundation of China (Grant No. 52178306).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
However, in [13], the rate outage performance was mathematically analyzed when there exist only two cell-edge users and each user is equipped with a single receive antenna. In this paper, we extend the diversity-controlled MUST technique to the case where multiple cell-edge users are multiplexed with a single cell-center user and each user is equipped with multiple antennas. Henceforth, we define the user with a higher signal-to-noise ratio (SNR) as _MUST-based user equipment_ (UE) and the user with a lower SNR as _MUST-enhanced UE_ since the received SNR depends on not only the location of users but also the allocated power in the diversity-controlled MUST technique. In general, cell-center users become the MUST-based UEs and cell-edge users become the MUST-enhanced UEs. In particular, we derive the closed-form expressions on the bit error rate (BER) of both MUST-based UEs and MUST-enhanced UEs in Rayleigh fading channels for a given power allocation ratio. Based on the mathematical analysis, we also optimize the power allocation ratio on the component constellation to minimize the power consumption at the NOMA transmitter for given BER requirements. The mathematical analysis is validated via link-level simulations. The remainder of the paper is organized as follows. In Section 2, the system model is described; in Section 3, we explain the overall procedures of both the transmitter and receiver with the proposed diversity-controlled MUST technique in detail and derive the BER of the proposed technique; in Section 4, we formulate an optimization problem of the power allocation ratio to minimize the power \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Power Ratio of Signal** & **Bit-Labeling** \\ \hline MUST Category 1 & adaptive & non-Gray mapping \\ MUST Category 2 & adaptive & Gray mapping \\ MUST Category 3 & N/A & Gray mapping \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of MUST Techniques in 3 GPP LTE Systems. consumption at the NOMA transmitter; simulation results are shown in Section 5; finally, conclusions are drawn in Section 6.
###### Abstract  
Cycle slip detection and repair are crucial for precise GPS-derived orbit determination of the low-Earth orbit (LEO) satellites. We present a new approach to detect and repair cycle slips for dual-frequency LEO satellite GPS observations. According to [PERSON]'s equation of motion, the second-order time difference of the LEO satellite's position (STP) is only related to the sampling interval and the satellite's acceleration, which can be precisely obtained from the known orbit dynamic models. Then, several kinds of second-order time-difference geometry-free (STG) phase combinations, taking full advantage of the correlation between the satellite orbit variations and the dynamic model, with different level of ionospheric residuals, are proposed and adopted together to detect and fix cycle slips. The STG approach is tested with some LEO satellite GPS datasets. Results show that it is an effective cycle slip detection and repair method for LEO satellite GPS observations. This method also has some important features. Firstly, the STG combination is almost independent of the pseudorange. Secondly, this method is effective for LEO satellites, even in real-time application. Thirdly, this method is suitable for ground-based GPS receivers if we know the acceleration of the receivers.  
cycle slip detection; second-order time difference; ionospheric-free liner combination; geometry-free liner combination; LEO satellite +
Footnote †: journal:  
Article
## 2 Experimental System  
### Monitoring Program  
The network topology of the integrated monitoring system for the mining process is shown in Figure 1. The specific arrangement is shown in Figure 2. In the center of the first mining area on the surface (the location of 2# slip shaft) and the surrounding area, two hollow monitoring holes were set up; around the center of the first mining area, three TDR monitoring points were arranged through surface drilling, and one monitoring coaxial cable was laid in each TDR monitoring hole; finally, 20 microvibration sensors (18 single-component sensors and 2 three-component sensors) were laid in the N4, N1, S3, and S6 penetration veins of the 3720 mine level downhole. At the same time, four boreholes were constructed around the first mining area through the surface downward, two single-component microseismic sensors were arranged in each borehole, and 12 sets of stress displacement sensors were installed in the S2 penetration vein at the 3720 exit level.  
The monitoring program was expanded based on the original comprehensive online monitoring system. After the collapse of the surface monitoring area, the monitoring  
Figure 1: Network topology diagram of the integrated monitoring system for the mining process.  
Figure 2: Monitoring point layout. N1–N4, S1–S7 are the label of transverse drift.  
equipment, such as the empty holes, TDR, and microvibration installed at the surface were removed, and the monitoring focus was shifted to the bottom structure. Based on the preliminary monitoring results and experience, the impact of the revealed fault was fully considered in the later stage, and the bottom structure stability monitoring points were arranged in a targeted manner. Considering the impact of the blasting vibration through the vein, the digital mining substation was installed in the east and west along the outer channel of the vein to reduce the amount of maintenance of the system.  
The microseismic monitoring program was designed to move the microseismic digital mining substation on the surface to the 3660 m transport level, and on the basis of the previous monitoring program (there were already four microseismic sensors), to supplement four microseismic sensors and detachable devices to form a spatial three-dimensional envelope with the microseismic sensors arranged at 3720 m to improve the positioning accuracy of the microseismic events. The stress monitoring system was designed to be installed in the pull bottom advance line of the bottom structure at 3720 m, where 24 stress gauges were installed on the key ore pillars, excluding the 12 stress gauges in the pre-S2 penetration vein. The specific monitoring scheme is shown in Figures 3 and 4.
# An Eight-Channel C-Band Demux Based on Multicore Photonic Crystal Fiber
## 2 Materials and Methods
The conditions for divide eight different wavelengths in the proposed design are given by:  
\[\mathrm{L}_{\mathrm{main}}\approx\mathrm{P}_{1}\mathrm{L}_{ \mathrm{Coupling}}^{\lambda_{1}\lambda_{3}\lambda_{5}\lambda_{7}}\ =\ (\mathrm{p}_{1}+\mathrm{q}_{1})\mathrm{L}_{ \mathrm{Coupling}}^{\lambda_{2}\lambda_{4}\lambda_{6}\lambda_{8}} \tag{2}\] \[\mathrm{L}_{\mathrm{right,1}}\approx\mathrm{P}_{2}\mathrm{L}_{ \mathrm{Coupling}}^{\lambda_{8}\lambda_{4}}\ =\ (\mathrm{p}_{2}+\mathrm{q}_{2})\mathrm{L}_{ \mathrm{Coupling}}^{\lambda_{2}\lambda_{6}}\] \[\mathrm{L}_{\mathrm{left,1}}\approx\mathrm{P}_{3}\mathrm{L}_{ \mathrm{Coupling}}^{\lambda_{3}\lambda_{7}}\ =\ (\mathrm{p}_{3}+\mathrm{q}_{3})\mathrm{L}_{ \mathrm{Coupling}}^{\lambda_{5}\lambda_{1}}\] \[\mathrm{L}_{\mathrm{right,2}}\approx\mathrm{P}_{4}\mathrm{L}_{ \mathrm{Coupling}}^{\lambda_{2}}\ =\ (\mathrm{p}_{4}+\mathrm{q}_{4})\mathrm{L}_{ \mathrm{Coupling}}^{\lambda_{6}}\] \[\mathrm{L}_{\mathrm{left,2}}\approx\mathrm{P}_{5}\mathrm{L}_{ \mathrm{Coupling}}^{\lambda_{3}}\ =\ (\mathrm{p}_{5}+\mathrm{q}_{5})\mathrm{L}_{ \mathrm{Coupling}}^{\lambda_{7}}\] \[\mathrm{L}_{\mathrm{right,3}}\approx\mathrm{P}_{6}\mathrm{L}_{ \mathrm{Coupling}}^{\lambda_{8}}\ =\ (\mathrm{p}_{6}+\mathrm{q}_{6})\mathrm{L}_{ \mathrm{Coupling}}^{\lambda_{4}}\] \[\mathrm{L}_{\mathrm{left,3}}\approx\mathrm{P}_{7}\mathrm{L}_{ \mathrm{Coupling}}^{\lambda_{5}}\ =\ (\mathrm{p}_{7}+\mathrm{q}_{7})\mathrm{L}_{ \mathrm{Coupling}}^{\lambda_{1}}\]  
where \(\mathrm{L}_{\mathrm{main}}\) is the fiber length located between core 1 and core 8 (silica areas), \(\mathrm{L}_{\mathrm{right,1}}\) is the fiber length located between core 2 and core 4 (silica areas), \(\mathrm{L}_{\mathrm{left,1}}\) is the fiber length located between core 7 and core 5 (silica areas), \(\mathrm{L}_{\mathrm{right,2}}\) is the fiber length located between core 2 and core 6 (silica areas), \(\mathrm{L}_{\mathrm{left,2}}\) is the fiber length located between core 3 and core 7 (silica areas), \(\mathrm{L}_{\mathrm{right,3}}\) is the fiber length located between core 8 and core 4 (silica areas), \(\mathrm{L}_{\mathrm{left,3}}\) is the fiber length located between core 5 and core 1 (silica areas), \(\mathrm{P}_{1/2/3/4/5/6/7}\) is a natural number, and \(\mathrm{q}_{1/2/3/4/5/6/7}\) is an odd number. For analyzing the performances of our proposed \(1\times 8\) wavelength demultiplexer multicore PCF, cross-talk (Equation (3)) and loss (Equation (4)) were calculated to observe the ratio between a desirable and undesirable wavelength in a given port and power losses, respectively. Figure 1:0  
where \(\mathrm{P}_{\mathrm{n}}\) is the power transmission for the suitable port, and \(\mathrm{P}_{\mathrm{m}}\) is the interference power transmission from the other ports. The insertion loss is given by:  
Figure 1:1  
where \(\mathrm{P}_{\mathrm{out}}\) is the power at the output port, and \(\mathrm{P}_{\mathrm{in}}\) is the power in the input port 1.