esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation and Validation
### Roll Behavior without Road Excitation  
Here, using vehicle dynamics model from Section 4, performance of vehicle roll behavior is simulated without presence of road input. Initial conditions used in the simulation are listed in Tables 1 and 2. Corresponding performance of roll behavior result parameters are shown in Figure 9. It can be seen from Figure 9 that without road excitation, half-car and full-car model are approximately equivalent. The _E_-Class (SUV) level car model in CARSIM(r) was used as benchmark results. Calculations carried out in MATLAB were found to be consistent with the CARSIM data. This suggests that vehicle dynamics model presented in Section 4 can be considered accurate to depict system dynamics under this condition.  
Figure 8: CARSIM simulation of roll behavior under various road excitation conditions. (**a**) Animation Screen. (**b**) Steering wheel input.  
Figure 9: Results without road excitation (steering wheel angle was 10\({}^{\circ}\) at 80 km/h). (**a**) Roll angle; (**b**) Roll rate; (**c**) Yaw rate.
# Analysis of Cascading Failures and Recovery in Freeway Network Under the Impact of Incidents
## 3 Methodology
### Modeling the Cascading Failures of the Freeway Network  
Cascading failure refers to the process whereby the failure of a particular road segment or node, triggered by a traffic incident, leads to the redistribution of traffic flows across the network [11]. From the perspective of road capacity and operational management, this study defines freeway traffic incidents as events such as traffic crashes and temporary road maintenance that result in the temporary closure of specific road segments and a sudden reduction in their capacity. In the study of cascading failures in transportation networks, real-world traffic incidents are often modeled from a network science perspective and are regarded as \"attacks\" within the network. Specifically, these incidents manifest as the failure of edges or a reduction in their capacities.  
When the network is attacked (as indicated by the black dashed line in Figure 2), it repeatedly suffers from the following: (1) the spread of failures triggered by overload conditions, as represented by the red links in Figure 2; (2) dynamic changes in the travel cost function, which impact both red and gray links; and (3) adjustments in route selection based on origin-destination (OD) travel demand. This process continues until the network reaches a new stable state. In the cascading failure model that we have developed for a freeway network, we used SUE as the model to describe travelers' route choices. The  
Figure 1: The overall architecture of our proposed method.  
details will be outlined for the following four aspects: the initial load, the edge capacity, the principle of load redistribution, and the process of cascading failures.
# The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities
## 2 Capabilities  
Compared to other GNSS-R datasets currently available from CYGNSS and TDS-1 missions, the SMAP-R dataset offers a number of unique features. We aim to utilize SMAP-R data for estimates of geophysical parameters that are not possible using other GNSS-R missions' data. In the following subsections, we discuss two key aspects that enable different capabilities to sense the Earth surface--the SMAP-R characteristics, as compared to other GNSS-R missions, and the spatial resolution of SMAP-R measurements.
## 2 Background
### Temporal Convolutional Network  
Researchers introduced the TCN into their networks to address the inefficiency of CNNs in handling sequential data. This neural network not only inherits CNN's powerful feature extraction capabilities but also leverages recurrent neural networks' ability to store historical information. The TCN network can efficiently process input sequences of any length, effectively improving the efficiency and accuracy of prediction models.  
Causal convolution predicts the output data \(x_{t+1}\) at time \(t+1\) by learning input information \(\{x_{1},x_{2},\cdots,x_{t}\}\) up to time \(t\), effectively preventing future information leakage and addressing differences in input and output time steps during model training. When processing long-sequence input data, stacking more layers of networks is necessary, increasing the network's complexity. To tackle these issues, dilated convolution is introduced to improve operational efficiency. Dilated convolution expands the receptive field by inserting zero elements between convolutional kernel elements. The dilated convolution of input data \(X=\{x_{1},x_{2},\cdots,x_{t}\}\) at time \(t\) is formulated as [21]  
\[(F*_{d}X)x_{t}=\sum_{k=1}^{K}f_{k}x_{t-(K-k)d} \tag{4}\]  
In the formula, \(F(f_{1},f_{2},\cdots,f_{k})\) represents the filter; \(K\) is the size of the convolution kernel; \(d\) represents the dilation factor.  
To effectively mitigate issues such as gradient explosion and vanishing gradients in deep networks, the residual neural network structure was introduced to learn residual errors of feature mappings across layers, thereby enhancing neural network stability. Residual connections combine with identity mapping functions to transmit information across layers in the network. The computational formula is  
\[o=Activation(x+F(x)) \tag{5}\]  
In the formula, \(x\) represents the input information; Activation represents the activation function; \(F(x)\) represents the output after residual connection.  
The structure of the temporal convolutional network is shown in Figure 2[22]. The output from the dilated causal convolutions is added to the input using \(1\times 1\) convolution operations. Weight normalization accelerates neural network convergence and improves training speed.  
Figure 1: Structure diagram of self-attention mechanism principle.
## 5 Conclusions  
We have studied the power control and resource allocation problem in the energy harvesting cognitive two-way relay network using the PS protocol and DF relay forwarding protocol. By considering transmission fairness, a joint resource allocation problem under power control is established, which aims at maximizing the minimum transmission rates of the cognitive users. To solve the optimization problem, a stepped alternative optimization algorithm, named the JPCRA scheme, is proposed to obtain the optimal parameter values. Simulation results have shown that the proposed scheme can improve the transmission performance of the cognitive users with poor channel quality and have verified its superiority compared with three other traditional schemes.  
Further, we intend to expand the proposed scheme in an indoor relay environment or unmanned aerial vehicle cognitive relay circumstance. Another interesting further research direction could be exploring deep learning for cognitive relay selection.  
The main contributions of [PERSON] and [PERSON] were to create the main ideas and execute performance evaluations by theoretical analysis and simulation, while [PERSON] worked as the cooperator to discuss, create and advise on the main ideas and performance evaluations. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the Science and Technology Research Project of Chongqing Education Commission under Grant KJQN201901103 and KJQN202003106 and KJQN202103111, in part by the innovation research group of universities in Chongqing under Grant CXQT21035, in part by the innovation research group of Chongqing University of Technology under Grant 2023 TDZ014, in part by the Research Start-up Fund of Chongqing University of Technology under Grant 2019 ZD127 and in part by the Incubation Program of National Natural Science Foundation of China under Grant 2021 PYZ10.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data is unavailable due to privacy or ethical restrictions.  
The authors would like to thank [PERSON], a lecture in Chongqing College of Electronic Engineering, who took part in the discussion of mathematical methodology and helped to carry out the simulation debugging and analysis.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
SWIPT Simultaneous wireless information and power transfer  
JPCRA Joint power control and resource allocation  
PU Primary user  
NOMA Non-orthogonal multiple access  
AF Amplify-and-forward  
DF Decoded-and-forward  
RF Radio frequency  
PS Power splitting
EH Energy harvesting
# An Observer-Based Current Sensor-Less Control Scheme for Grid-Following Converters
## 3 Conventional Control of Grid-Following Inverters
### Reactive Power Control Loop  
The reactive power control loop determines \(i_{q}^{ref}\) using the reactive current setpoint of \(Q_{out}\) based on (21):  
\[i_{q}^{ref}=\frac{Q^{ref}}{1.5v_{gd}}+\Delta i_{q}^{ref} \tag{21}\]  
According to (7), using (21) and even without \(\Delta i_{d}^{ref}\), \(Q_{out}\) is driven to \(Q^{ref}\). However, uncertainty in the system, the delays in the current control loop, and the simplification in the system model may lead to a steady state error in controlling \(Q_{out}\). To guarantee a zero steady-state error, a PI controller is also used, which provides the \(\Delta i_{q}^{ref}\) by (22):  
\[\Delta i_{d}^{ref}=\frac{K_{p-q}\delta+K_{i-q}}{s}\left(Q^{ref}-Q_{out}\right) \tag{22}\]  
The bandwidth of the reactive power control loop is chosen at least four times lower than that of the \(i_{q}\) control loop.
## 4 Discussion and Results
### Validation  
Table 4 compares the numerical findings obtained from prior simulation research [55] and the current investigation conducted on a gas turbine unit located in Kirkuk. The differences between the literature and the current investigations are minimal, suggesting that the present study's findings are consistent and dependable compared to the prior simulation study. The exhaust temperature exhibits the most notable variance, with a magnitude of 2.1%, whilst the other measurements show negligible disparities. The literature study reports a fuel flow rate of 8.8 kg/s, while the present study shows a slightly higher rate of 8.84 kg/s, resulting in a deviation of 0.45%. The outlet compressor temperature in the literature study is 315.7 \({}^{\circ}\)C, compared to 318 \({}^{\circ}\)C in the present study, yielding a deviation of 0.723%. The maximum temperature in the literature study is 1100 \({}^{\circ}\)C, while the present study records 1111 \({}^{\circ}\)C, leading to a deviation of 1%. The exhaust temperature is 500 \({}^{\circ}\)C in the literature study and 510.6 \({}^{\circ}\)C in the present study, showing a deviation of 2.1%. The literature study indicates a power output of 150 MW, whereas the present study shows 148.7 MW, resulting in a deviation of 0.87%. Thermal efficiency is 34% in the literature study and 33.8% in the present study, with a deviation of 0.59%.
## 4 Results and Discussion
### Euler Deconvolution  
Many authors [27; 28; 29; 30; 31] have used [PERSON]'s differential equation to interpret gravity and magnetic data in terms of simple sources characterized by an integer value of the degree of homogeneity. One major advantage that Euler deconvolution has over other magnetic source location methods is that it is to the orientation of the ambient magnetic field, meaning that it can be used effectively on total-field magnetic anomaly data [28]. The Euler deconvolution technique is carried out by using Oasis montaj, with structural indices (SI) = 0 according to [28]. The solution of the magnetic anomaly maps with SI = 0 for probable subsurface contacts are presented in Figure 12 for the study area. In this figure, there is absolute matching between the Euler clusters and the boundaries of different magnetic anomalies. These boundaries may represent contacts between rock units with different magnetic susceptibilities.  
Figure 11: Analytical signal technique for aero magnetic profile (P3) of Homrit Waggat area, Central Eastern Desert, Egypt.  
Figure 10: Analytical signal technique for aeromagnetic profile (P2) of Homrit Waggat area, Central Eastern Desert, Egypt.  
In addition, depth estimation by the 3D Euler deconvolution technique was used for rapid estimation of geologic contacts at different levels in the area under consideration. The zero value structural indexes are used mainly to detect the geological contacts between main lithological units with different magnetic susceptibilities. The reduced to pole map was used as a background to show the matching between the calculated plotted contacts and the boundaries of the magnetic anomalies. The following represents a brief description of the obtained Euler deconvolution map with the value of zero structure index, which is used mainly to detect the geological contacts between central lithological units. The solution contacts map (Figure 12) shows that the subsurface contacts are located at depths ranging between 0 and 1.2 km with major trends of NNW-SSE, NNE-SSW, NE-SW, and N-S. This means that these contacts are located at shallow depths from the surface.
# BFE-Net: Bidirectional Multi-Scale Feature Enhancement for Small Object Detection
## 4 Experiments
### Ablation Study
By analyzing the experimental data, it can be found that the accuracy of small target detection (AP\({}_{\text{S}}\)) using H-C6 is 0.3% higher than that of R-C6. At the same time, R-C6 also achieved the best results in the recognition of large targets and medium-sized targets. This result shows that the high-resolution features guided by C5 can supplement the lost high-resolution information in the bottom-up propagation process, which is very helpful for object detection at different scales. To verify the effectiveness of connecting dilated convolutions with different dilation rates in a residual manner in DFE, the following ablation experiments were performed. Feature extraction is performed in the following three ways: P-Dilated (Figure 4a) represents three dilated convolution layers for feature extraction in parallel connection; S-Dilated (Figure 4b) represents three dilated convolution layers in series for feature extraction; R-Dilated (Figure 3b) represents the feature extraction that is finally used in this paper in a residual manner. The results are shown in Table 5.  
By analyzing the experimental results, it can be found that the use of the R-dilated method has achieved the best effect, with an average accuracy (AP) of 34.6% and an improvement of 0.6%. In particular, the accuracy of small targets (AP\({}_{\text{S}}\)) has increased from 15.1% to 16.7%, and the improvement effect is as high as one percentage point. In addition, the effect of P-Dilated is better than that of S-Dilated, especially in small target recognition. We analyze that the reason for this phenomenon may be serial connection expansion convolution. Due to the large expansion rate, information will be lost during the convolution process, and the extracted features will be incoherent.
# Automatic Word Spacing of Korean Using Syllable and Morpheme
## 3 Word Spacing Correction Model
### Bidirectional Long Short-Term Memory  
Long Short-Term Memory (LSTM) is a network that complements the gradient vanishing problem and long-term dependency problem in RNN. Figure 4 describes Bi-LSTM structure with LSTM operations forward and backward. Bi-LSTM has the advantage of being able to specify sentence information by training the forward and backward information of the sequence [25]. In the model proposed in this study, C, the output result of multiple filter 1D-CNN, was used as the input of Bi-LSTM. As the forward sequence, \(\left[C^{a},\ C^{b},\ \ldots,\ C^{k}\right]\) was used as the input of the forward LSTM, and backward, the reverse of the forward sequence, \(\left[C^{k},\ \ldots,\ C^{b},\ C^{a}\right]\) was used as the input of the backward LSTM. \(h^{f}\) and \(h^{b}\) represent the forward and backward output values of LSTM respectively. Final outputs of Bi-LSTMs were \(\left[h^{f},\ h^{b}\right]\), which concatenate \(h^{f}\) and \(h^{b}\). Equations (5)-(10) are an equation that derive the output of the LSTM. \(x_{t}\), \(h_{t}\), \(c_{t}\), \(f_{t}\), \(i_{t}\), \(o_{t}\) mean input, output, cell state, forget gate, input gate, and output gate, respectively. Equation (5) shows what information is to be discarded from the cell state, and the weight is determined through the sigmoid layer. Equation (6) represents obtaining new information by deriving the input gate. The hidden state is derived through Equation (7). The cell state is derived through Equation (8), and the output gate is derived through Equation (9). Finally, the output is derived from Equation (10).  
\[f_{t}=\sigma\Big{(}W_{f}\cdot\left[h_{t-1},\ x_{t}\ \right]+b_{f}\Big{)} \tag{5}\]  
\[i_{t}=\ \sigma(W_{i}\cdot\left[h_{t-1},\ x_{t}\right]+b_{i}) \tag{6}\]  
\[\widetilde{C}_{t}=tanh(W_{c}\cdot\left[h_{t-1},\ x_{t}\right]+b_{c}) \tag{7}\]  
\[C_{t}=f_{t}\odot C_{t-1}+i_{t}\odot\widetilde{C}_{t} \tag{8}\]  
\[o_{t}=\sigma(W_{o}\cdot\left[h_{t-1},\ x_{t}\right]+b_{o}) \tag{9}\]  
\[h_{t}=o_{t}\odot tanh(C_{t}) \tag{10}\]  
In the proposed model, an integer sequence in levels of syllables and morphemes had passed through the embedding layer, multiple filter 1D-CNN and Bi-LSTM. Then, \(\left[h_{s}^{f},\ h_{s}^{b}\right]\) and \(\left[h_{m}^{f},\ h_{m}^{b}\right]\), the outputs of Bi-LSTM, are concatenates to combine syllables and morphemes weights to create \(\left[h_{s}^{f},\ h_{s}^{b},\ h_{m}^{f},\ h_{m}^{b}\right]\). \(h\) is the output value of LSTM, and \(s,\ m\) is syllable and morpheme. The combined information passes through the fully connected layer and is finally output through the last output layer. The  
Figure 4: Bidirectional long short-term memory.  
output layer is softmax to indicate the probability value. There are three probability values: Spacing, non-spacing, and padding.
## 3 Results
### Pairwise Meta-Analysis  
Based on studies assessing frequent interruptions, we calculated SMDs and 95%CIs for different types of PA breaks and different times of PA breaks and their effects on glucose, insulin, and triacylglycerolglycerol. An inconsistency test based on the network analysis found significant global inconsistency for glucose (\(p<0.001\)), insulin (\(p<0.001\)) and triacylglycerolglycerol (\(p<0.05\)). We conducted pairwise meta-analysis. A pooled analysis of MVPA showed a significant decrease in both glucose (SMD = \(-0.6\), 95%CI: \(-0.83\), \(-0.37\)) and insulin (SMD = \(-0.53\), 95%CI: \(-0.73\), \(-0.32\)) compared to prolonged sitting. Although the SMDs of LPA were larger, \(-1.04\) (95%CI \(-1.53\)\(-0.55\)) for insulin and \(-1.45\) (95%CI \(-2.32\)\(-0.57\)) for glucose, significant heterogeneity was present (\(\Omega^{2}>75\)%, \(p<0.05\)) (Table 2). In the case of LPA interruption, neither [PERSON]'s rank correlation ([PERSON] \(\tau=-5.00\), \(p\geq 0.84\)) nor [PERSON]'s regression (intercept = \(-5.69\), SE = \(5.074\), \(p\geq 0.26\)) indicated a bias in the insulin results. Although [PERSON]'s rank correlation ([PERSON] \(\tau=-27.00\), \(p>0.32\)) lacked statistical significance, [PERSON]'s regression (intercept = \(-9.98\), SE = \(1.56\), \(p\leq 0.001\)) hinted at a potential publication bias related to glucose. For triacylglycerolglycerol, both [PERSON]'s ([PERSON] \(\tau=-31.00\), \(p<0.05\)) and [PERSON]'s regression (intercept = \(-10.65\), SE = \(4.1\), \(p<0.05\)) were suggestive of publication bias. Subgroup analysis of frequent standing interruptions demonstrated a significant moderate decrease in glucose, but triacylglycerol exceeded expectations and was significantly negative, with SMDs being \(0.42\) and the 95%CI being \(0.17\) to \(0.68\) (Table 2). Finally, although a comprehensive analysis of RE showed significant declines regarding all three outcomes, the number of included studies and participants was small. \begin{table}
# Development and Simulation of Motion Control System for Small Satellites Formation
## Abbreviations  
The following abbreviations are used in this manuscript:  
ATO Along-Track Orbit  
GCO General Circular Orbit  
HCW Hill-Clohessy-Wiltshire  
LVLH Local-Vertical Local-Horizontal  
PCO Projected Circular Orbit  
PD Proportional-Differential  
PDC Proportional-Differential-Consensus  
PB Passification-Based  
PBC Passification-Based Consensus  
SDRE State-Dependent Riccati Equation
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 3 Domain Adaptation Tasks for EO Mission
### Post-Deployment Domain Adaptation  
Due to the domain gap, it is expected that \(f^{d}(\cdot\,;\,\theta^{s})\) will not be accurate when applied to the data collected in orbit, i.e., data in the **target domain**. Thus, it is necessary to perform domain adaptation on \(f^{d}(\cdot\,;\,\theta^{s})\) to obtain a **target cloud detector**\(f^{d}(\cdot\,;\,\theta^{d})\) parameterised by **target weights**\(\theta^{t}\). To this end, a new unlabelled dataset \(\hat{\mathcal{D}}^{t}=\{x_{j}^{t}\}_{j=1}^{N^{t}}\) (called the **unlabelled target dataset**) is collected onboard.
# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 2 Collaborative Channel Perception Algorithm
### Analysis of Characteristic Parameters of Node Devices
By comparing the antenna VSWR \(\rho_{i}^{t}\) at time \(t\) with the antenna VSWR corresponding to a reflection coefficient of 0 and the protection threshold value, it is converted into a normalized value ranging from 0 to 1. The normalization formula for the antenna VSWR \(z_{i}\) of node \(i\) is expressed as:  
\[z_{i}= \begin{cases}1&\rho_{i}^{t}\leq\rho_{i}^{u}\\ \frac{\rho_{i}^{max}-\rho_{i}^{t}}{\rho_{i}^{max}-\rho_{i}^{t}}&\rho_{i}^{u}< \rho_{i}^{t}<\rho_{i}^{max}\\ 0&\rho_{i}^{t}\geq\rho_{i}^{max}\end{cases}  \tag{2}\]  
where \(\rho_{i}^{t}\) represents the detected value of the antenna VSWR \(z_{i}\) at time \(t\), \(\rho_{i}^{u}\) represents the antenna VSWR value corresponding to a reflection coefficient of 0, and \(\rho_{i}^{max}\) is the protection threshold value for the antenna VSWR. By comparing the SNR of the node \(SNR_{i}^{t}\) at time \(t\) with the SNR value corresponding to the minimum bit error rate and the nominal value, it is converted into a normalized value ranging from 0 to 1. The normalization formula for the SNR of the node \(i\) is expressed as:  
\[s_{i}= \begin{cases}1&SNR_{i}^{t}\geq SNR_{i}^{max}\\ \frac{SNR_{i}^{max}-10\log\frac{\sigma_{i}^{u}}{\sigma_{i}^{t}}}{SNR_{i}^{max} -10\log\frac{\sigma_{i}^{u}}{\sigma_{i}^{t}}}&10\log\frac{\sigma_{i}^{u}}{ \sigma_{i}^{t}}\leq SNR_{i}^{max}\\ 0&SNR_{i}^{t}\leq 10\log\frac{\sigma_{i}^{u}}{\sigma_{i}^{t}}\end{cases}  \tag{3}\]  
where \(SNR_{i}^{t}\) represents the detected value of the SNR at time \(t\), \(SNR_{i}^{max}\) corresponds the \(SNR\) value that ensures the minimum bit error rate, and \(n_{i}^{t}\) is the received noise power at time \(t\). Using the above method, the normalization of the number of operating months and the MTBF can be completed. It will not be elaborated here.