esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Evaluation
### Performance of WS-UDA in Single-Source vs. Multi-Source Comparison  
Table 4 compares the performance of WS-UDA in a single-source and multi-source setting. As previously outlined, there is a distinction between these two settings, that can be seen in the accuracy of the single-source scenario, which ranges from 70.9 to 80.6%, compared to the multi-source case, which spans from 76.8 to 87.5% across the two data sets. This is substantiated since using multiple sources enhances the variety of shared features, which thus improves the possibility of discovering features that are more relevant to the target domain. In other words, in the single-source/single-target scenario, the divergence between the labeled domain (one source domain) and the target domain is greater than the divergence between (many sources) and the target domain in the multi-source/single-target scenario. As a result, a target's accuracy from several sources is higher than the accuracy of the same target from a single-source configuration. As was already mentioned, the further apart the two domains are from one another, WS-UDA can enhance adaption performance. It is obvious that this is the case here because the divergence is more in the single-source/single-target setting than it is in the multi-source/single-target setting. As a result, we can observe that our weighted sequential learning strategy is enhanced more in this situation, as shown in Table 4, than it is in the multi-source/single-target setting. With contemporary UDA techniques, which depend on the similarity between the two domains to prevent instability, WS-UDA overcomes the problem.  
Figure 11: The average performance improvement achieved by the WS-UDA architecture, along with the the normalized average cosine similarity for the three data sets.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Target Domain** & **Improvement\%** & **Avg. Similarity** \\ \hline \multirow{4}{*}{Amazon} & Books & 2.94\% & 0.20 \\  & DVD & 2.71\% & 0.29 \\  & Electronics & 2.24\% & 0.74 \\  & Kitchen & 1.46\% & 0.90 \\ \hline \multirow{2}{*}{FDU-MTL} & MR & 3.64\% & 0.470 \\  & Baby & 2.74\% & 0.525 \\  & Apparel & 2.27\% & 0.545 \\ \hline \multirow{2}{*}{Spam} & Enron & 2.34\% & 0.462 \\  & Spam & 1.03\% & 0.632 \\ \hline \end{tabular}
\end{table}
Table 3: WS-UDA performance gains vs. average similarity.
## 2 Conventional Channel Estimation Methods
### Blind Channel Estimation Method
#### 2.2.2 HOS-Based Blind Channel Estimation  
The utilization of HOS information on the signal can yield additional insights into both the signal and noise details, which has the potential to enhance the channel estimation accuracy. The HOS-based blind channel estimation mainly estimates the CSI through the HOS characteristics of received signals. [PERSON] et al. [36], faced with channels with constant scaling phases and identical alignments, proposed to compute channel estimation values in a recursive scheme using the parallel factor (PARAFAC) decomposition and the HOS tensor of the system output. The proposed method eliminates pre-whitening and has a lower estimation error than conventional methods. In [37], [PERSON] et al. investigated a blind estimation algorithm for space-time block coding (STBC) systems based on HOS. Leveraging the statistical independence of sources before space-time encoding, this algorithm estimates the channel matrix by minimizing a kurtosis-based cost function following zero-forcing (ZF) equalization. In simulations, the proposed algorithm exhibits superior generalization ability over other methods, and it applies to the whole linear STBC class.
## 2 Mathematical Model and Assumptions  
Figure 1 shows the computational model for the horizontal transient vibration of a pipe pile in saturated soil and the pile inner soil is assumed to be flush with the pile top. A horizontal transient load, _p(t)_, is applied at the pile head in the _roz_ plane. The following assumptions are made in the analysis:  
Both the surrounding soil and the pile core soil are homogeneous, isotropic, and modeled as a saturated biphasic elastic medium.
2. The pile is modeled as an elastic, uniform cross-section annular shaft, using the Timoshenko beam theory to simulate its behavior. Pile shear deformation is considered, and the pile base is fixed.
3. The pile-soil vibrations are assumed to involve small deformations, with perfect contact between the pile and the surrounding soil. The displacement and force at the pile-soil interface are continuous, and the interface is impermeable.
4. During the horizontal vibration, the vertical displacement of the soil skeleton and pore fluid is much smaller than the lateral displacement of the pile. The effect of lateral friction along the pile surface is also small and thus neglected in the analysis. This study is limited to the scope of elastic dynamics and does not explore dynamic strength considerations. For simplicity, the effects of strain rate caused by impact loads are excluded from the analysis [25].
# Comparison of Multi-Tracked Running Gears in Terms of Obstacle Negotiation Capabilities
## 4 Simulation Results
### Log Negotiation  
The results of the first test--maximum diameters for each solution--are shown in Figure 15. The critical moment in this obstacle was when the rear module began to be pulled in. The kinematics of the single-joint solution are not conducive to driving over individual objects. Lifting one of the tracked modules transfers the vertical force component to the other module, causing it to become unloaded (Figure 16a) and eventually lose contact with the ground. For this reason, solutions C and B overcome obstacles that are 23% smaller than the reference solution A. Double-jointed solutions D and E provide the ability to overcome the 57% largest obstacle. The rotary joint placed in the middle of the rear track module on solution E allows it to be towed (by the front module) while pressing it to the ground. (Figure 16b). An additional limitation may be the platform clearance, which was not taken into account in the simulations.  
The second test indicated high track slip values at maximum drive load moments (Table 5). It visibly affected the time required to overcome the obstacle. Solution C overcame the log, taking 15% longer than solution E. Momentary slip values equal to 1 were also obtained, caused by the dynamic impact with the obstacle and the momentary complete loss of contact between the track and the ground. Solution B, as with the curb, turned out to be the least advantageous in terms of the pitch angle, while solution E was the most advantageous. Compared to the reference solution, it allowed for a reduction in the maximum pitching angle and maximum pitching angle speed by 59% and 86%, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Solution**} & **Obstacle Overcoming** & **Track Slip at a** & **Maximum Pitching Angle of the** & **Maximum Pitching Angle Speed of** \\  & **Time [s]** & **Critical Moment** & **Platform Frame [Degrees]** & **the Platform Frame [Degrees/s]** \\ \hline A & 3.27 & 0.64 & 32 & 164 \\ B & 3.69 & 1 & 66 & 171 \\ C & 3.17 & 0.57 & 28 & 89 \\ D & 3.23 & 0.91 & 51 & 265 \\ E & 3.22 & 0.55 & 10 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of second test for curb negotiation.  
Figure 14: Comparison of the tracked module positions at the moment of reaching the highest pitching angle for solutions A to E (**a**–**e**).
# Dynamic Multi-Task Graph Isomorphism Network for Classification of Alzheimer's Disease
## 4 Experiments
### Experimental Settings  
#### 4.2.1 Baseline  
In our experiments, the proposed DMT-GIN is compared with four classic models in Alzheimer's disease fMRI image classification: Support Vector Machine (SVM) [45], K-Nearest Neighbors (KNN) [46], Weighted K-Nearest Neighbors (WKNN) [47], CNN [48], GCN [49], Graph Attention Networks (GAT) [50], Graph Wavelet Neural Network (GWNN) [51], and Topology Adaptive Graph Convolutional Networks (TAGCN) [52]. SVM, KNN and WKNN are machine learning methods, with SVM using the RBF kernel. CNN is a deep learning model with powerful representation learning capabilities, while the GNN-based methods include GCN and GAT, GWNN, and TAGCN.
## 5 Improved NSGA-II Algorithm
### Good Point Set Population Initialization  
The random initialization of populations in NSGA-II may result in several shortcomings, as outlined in reference [34]. Firstly, randomly generated populations may not adequately cover the search space, particularly in high-dimensional optimization problems. This may result in the algorithm prematurely converging to a locally optimal solution. Furthermore, an unequal distribution of the initial population may impact the efficacy of the non-dominated ordering and congestion distance calculations, which in turn influences the diversity and distribution of the Pareto front. Furthermore, randomly generated individuals may not satisfy the constraints of the problem, necessitating additional correction steps that increase the computational complexity and time cost. These factors may interact to affect the overall performance and efficiency of NSGA-II. Therefore, the use of the good point set for population initialization can significantly ameliorate these problems, enhance population diversity by uniformly distributing individuals, improve the convergence speed of the algorithm, and reduce the complexity of constraint processing, thereby improving the overall effectiveness of the multi-objective optimization algorithm [35; 36].  
The good point set [37] was first proposed by mathematician [PERSON]. Let \(G_{s}\) be the unit cube of s-dimensional Euclidean space, then \(r\in G_{s}P_{n}(i)=(r_{1}i_{1},r_{2}i_{2},r_{3}i_{3},\ldots r_{n}i_{n})\), \(i=1,2,3,\ldots n\). \(n\) denotes the number of samples, \(P_{n}(i)\) denotes the set of good points, and \(r\) denotes the good points, which is generally taken as \(r=\left\{2\cos\left(\frac{2\pi i}{k}i\right),1\leq i\leq n;1\leq i\leq s\right\}\), or \(r=\left\{e^{j}i\right\}\), where \(k\) is the smallest prime number that satisfies \((k-3)/2\geq s\).  
Figure 5 demonstrates that the good point set initialization exhibits a more uniform data distribution within the 0-1 interval, which is less random than the traditional random initialization method. A comparison of scatterplots demonstrates that the good point set markedly enhances the uniformity of the distribution in multidimensional space while partially compromising the randomness inherent to the process. This approach effectively circumvents the data aggregation issue commonly observed in traditional random sam  
Figure 4: Joint3 trajectory planning results by 5-7-5 polynomial interpolation.  
pling, thereby ensuring comprehensive coverage of the search space. Consequently, the good point set initialization not only facilitates a more comprehensive global search for the NSGA-II algorithm but also markedly reduces the probability of falling into a local optimum.
# A Sparse Algorithm for Computing the DFT Using Its Real Eigenvectors
## 7 Number of Operations
### Example  
Suppose we must determine the 2D DFT for a high-definition image with resolution \(1080\times 1920\). When using the row-column approach for finding the 2D DFT, first the transform is taken along the columns, followed by a transform along the rows. Version R2020b of MATLAB(r) was not sufficiently precise enough to determine the Matveev eigenvectors to a high enough accuracy for any \(N\geq 20\). As mentioned before, this cap of around 20 is from the limitation of the machine that was used to compute the eigenvectors. In practice, this cap will be higher or can be eliminated by pre-computing the eigenvectors with high accuracy. It is also possible that a more numerically sound version of the Matveev algorithm can be found. This numerical accuracy issue actually occurs in every DFT computational algorithm to varying degrees and impacts. Typically, however, the DFT is calculated by decomposing the larger computation into smaller DFTs in a divide-and-conquer strategy.  
Since both 1080 and 1920 have many factors, it is possible to decompose the DFT into smaller DFTs of length less than or equal to 20. For instance, consider first taking the 1D DFT along the columns. We know that \(1080=2^{3}\times 3^{3}\times 5\). Therefore, first we can use the prime-factor algorithm [3] to divide the 1080-point DFT into separate DFTs of length \(2^{3}\), \(3^{3}\) and 5. The \(2^{3}\) and \(3^{3}\) DFTs can be recursively divided into smaller lengths via decimation-in-time. The final 9-point and 5-point DFT butterflies are computed using the proposed method which result in 24,800 real multiplications. Computing the 2-point DFTs are trivial and require 1,620 real multiplications by twiddle factors. Thus, using this method to find a 1080-point DFT requires a total of 26,420 multiplications. For the 2D DFT, this has to be repeated 1920 times, for a total of around 50.7 million multiplications. Now, take the DFT along the rows. All the rows together need around 49.1 million multiplications. Thus, in total, the 2D DFT uses approximately 99.8 million multiplications.
# Multi-View Network Representation Learning Algorithm Research
## 4 Experiments and Evaluations
### Parameter Sensitivity  
MVNR has two parameters needed to be adjusted, they are the representation length (vector dimension) and \(K\). Here, \(K\) is the pre-selected constant for the \(k\)-step network, where \(1\leq k\leq K\). We let \(K\) value range from 1 to 6 and the representation length of \(k\)-step network varies within 25, 50, 100 and 200 for Citeseer, Cora and Wiki. The last network representation size 75, 150, 300 and 600. We fix the training ratio to 0.5 and we then test classification accuracy with diverse representation lengths and \(K\) values in Figure 2.  
As shown in Figure 2, the accuracy of network classification shows an increasingly trend with the increase of \(K\) and representation length. Importantly, the growth of accuracy becomes slower with the growth of the representation vector length. In addition, the average degree of Cora is bigger than that of Citeseer and Wiki, so that the accuracy fluctuation of Cora within a small range compared with Citeseer and Wiki.
## 3 Results and Discussion
### NO Formation Characteristics of NH\({}_{3}\)/CO Combustion
#### 3.3.5 Effect of Gas Concentration on NO Production  
The influence of NH\({}_{3}\)/CO doped gas concentration changes on the amount of NO under different excess air coefficients and combustion temperatures was investigated. The experimental results are shown in Figure 11. The proportion of ammonia in the mixed combustion gas was 50%; the excess air coefficients were set at 0.9, 1.0, and 1.1; and the combustion temperature was set at 1500 \({}^{\circ}\)C. As shown in Figure 11, when the excess air coefficient \(\alpha\geq 1\), the amount of NO produced by combustion also increased with the  
Figure 9: The content of NO in combustion blue gas varies with temperature.  
increase in the fuel concentration, and the influence of the concentration was also enhanced by an increase in the excess air coefficient. This is because the OH group and HNO group produced by the doubling of the fuel concentration in the lean combustion state increased, and they were further converted into NO. However, when the excess air coefficient \(\alpha<1\) increased the fuel concentration, the production of NO decreased instead, because the incomplete combustion of CO and NH\({}_{3}\) will participate in the reduction reaction of NO in the case of rich combustion, so the production of NO will decline.
## 5 Prototype Development for Wireless PLC
### Hardware Architecture  
The industrial wireless control system includes three parts: the PLC controller, the 5G-based protocol adapter, and the actuator. The PLC controller transmits and receives the Modbus messages. The protocol adapter resolves and converts the protocol between 5G and Modbus. The actuator receives the instruction of the PLC controller and realizes the corresponding operation through the instruction.  
As Modbus RTU and Modbus TCP utilize different transmission media, we choose the PLC controller containing the RS-485 serial port and the Ethernet port. Fully considering the advantages of smaller size, lower power consumption, lower cost and higher performance,  
Figure 7: The encapsulation and decapsulation process of 5G data.  
we use ARM Cortex-A72 architecture as the system on a chip (SoC), where the Linux operating system is utilized as the underlying driver. Additionally, the 5G module in the protocol adapter is an industrial-level module. In other words, we have a 5G module that uses the sub-6 GH band with strong signal penetration, which also supports LTE-A and WCDMA bands at the same time. It has two modes of non-standalone (NSA) networking and standalone (SA) networking. Theoretically, the maximum uplink and downlink transmission rates in 5G SA mode are 1 Gbps and 2 Gbps, respectively. In contrast, the maximum uplink and downlink transmission rates in 5G NSA mode are 575 Mbps and 2.2 Gbps, respectively. We also choose equipment with small volume, large power, and low noise as the actuator.
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 3 Reflector Antenna Configurations in Spaceborne SAR Systems
### Deployable Antenna  
Spaceborne SAR systems require large-aperture antennas. Therefore, the application of deployable antennas is more in line with the trends of high gain and lightweight design [26, 28, 29]. When the satellite is launched, the antenna is folded up and then deployed in orbit. Figure 6 illustrates the deployable antenna of HJ-1-C [30], where Figure 6a is the antenna folded state and Figure 6b is the antenna deployed state. According to the way the antenna is folded up and deployed, deployable antennas can be classified into the following three categories: framework deployable antennas, perimeter truss deployable antennas, and radial rib deployable antennas [31, 32, 33]. In the following sections, we will present an overview of the three types of deployable antennas.
###### Abstract
However, such deep-learning approaches impose significant computational demands, which limits their practical deployment in energy- and resource-constrained devices. More recently, an approximate ML (AML) detection scheme tailored for cooperative DF MIMO systems with one-bit ADCs was introduced [26]; it focuses on alleviating error propagation effects that arise in such configurations. The AML scheme is sufficiently versatile to be extended to one-way and two-way DF relay systems and has been shown to outperform direct communication in terms of error probability. Despite these advances, a rigorous analytical characterization of DF MIMO systems with one-bit ADCs, especially in the presence of fading channels, remains lacking. In the absence of such an analysis, it is difficult to gain theoretical insights into the error performance and achievable diversity gains of the system. Therefore, there is a clear need for analytical tools that can quantify the performance limits of cooperative MIMO systems with one-bit ADCs. This paper presents a detailed performance analysis that focuses on the PEP of the AML detection over Rayleigh fading channels for addressing the performance limitations of cooperative DF MIMO systems with one-bit ADCs. Obtaining a closed-form expression for the exact PEP is analytically intractable because of the severe nonlinearity induced by one-bit quantization. To overcome this challenge, a tractable upper bound on the PEP is derived by employing [PERSON]'s inequality and the Chernoff bound. Building on the derived upper bound, the asymptotic behavior of the PEP is further investigated in the high signal-to-noise ratio (SNR) regime. These asymptotic results yield key theoretical insights into the diversity gain achievable in cooperative MIMO systems with one-bit ADCs. A primary finding is that the diversity gain can be enhanced through the DF relay protocol when symbol pairs considered in the PEP analysis form sign-reversed pairs and the relay correctly detects the source symbol. In contrast, the diversity contribution from the source-to-relay link is lost when the relay fails to decode the source symbol correctly and the gain is limited to the source-to-destination link. Extensive Monte Carlo simulations were conducted on Rayleigh fading channels to validate the analytical findings and evaluate their practical relevance. The simulation results closely match the derived upper bounds, demonstrating the accuracy of the analysis. Finally, the proposed analysis provides fundamental insights and design guidelines for cooperative MIMO systems with one-bit ADCs in energy-efficient wireless networks. The remainder of this paper is organized as follows: Section 2 presents the system model for MIMO communications with one-bit ADCs and introduces the JML detection scheme along with its low-complexity approximation. The AML detection performance is analyzed in Section 3. Section 4 provides simulation results to validate the effectiveness of the proposed method. Finally, Section 5 concludes the paper.