esa-sceva/satcom-chunk-collection huggingface dataset:
# Continuous Latent Spaces Sampling for Graph Autoencoder
## 7 Ablation Study
### Effect of Embedding Size  
The impact of varying the embedding size on the performance of our proposed model is illustrated in Figure 3. The embedding size is a critical factor in graph representation learning, as it reflects the efficacy of information compression. Our results indicate that our proposed model benefits significantly from a larger embedding dimension, with its performance generally improving as the embedding size increases in most cases. This is  
Figure 2: t-SNE analysis, showing a visualization comparison of the node embeddings on the Cora and Citeseer datasets. (**a**) GCN on Cora; (**b**) colaGAE on Cora; (**c**) GCN on Citeseer; (**d**) colaGAE on Citeseer.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Cora**} & \multicolumn{2}{c}{**CiteSeer**} & \multicolumn{2}{c}{**PubMed**} \\ \cline{2-7}  & **AUC** & **AP** & **AUC** & **AP** & **AUC** & **AP** \\ \hline GAE & 91.09 \(\pm\) 0.01 & 92.83 \(\pm\) 0.03 & 90.52 \(\pm\) 0.04 & 91.68 \(\pm\) 0.05 & 96.40 \(\pm\) 0.01 & 96.50 \(\pm\) 0.02 \\ VGAE & 91.40 \(\pm\) 0.01 & 92.60 \(\pm\) 0.01 & 90.80 \(\pm\) 0.02 & 92.00 \(\pm\) 0.02 & 94.40 \(\pm\) 0.02 & 94.70 \(\pm\) 0.02 \\ ARGA & 92.40 \(\pm\) 0.00 & 93.23 \(\pm\) 0.00 & 91.94 \(\pm\) 0.00 & 93.03 \(\pm\) 0.00 & 96.81 \(\pm\) 0.00 & 97.11 \(\pm\) 0.00 \\ ARVGGA & 92.40 \(\pm\) 0.00 & 92.60 \(\pm\) 0.00 & 92.40 \(\pm\) 0.00 & 93.00 \(\pm\) 0.00 & 96.50 \(\pm\) 0.00 & 96.80 \(\pm\) 0.00 \\ SAGE & 86.33 \(\pm\) 1.06 & 88.24 \(\pm\) 0.87 & 85.65 \(\pm\) 2.56 & 87.90 \(\pm\) 2.54 & 89.22 \(\pm\) 0.87 & 89.44 \(\pm\) 0.82 \\ MGAE & 95.05 \(\pm\) 0.76 & 94.50 \(\pm\) 0.86 & 94.85 \(\pm\) 0.49 & 94.68 \(\pm\) 0.34 & 98.45 \(\pm\) 0.03 & 98.22 \(\pm\) 0.05 \\ \hline colaGAE & 96.37 \(\pm\) 0.51 & 96.24 \(\pm\) 0.46 & 98.01 \(\pm\) 0.52 & 97.92 \(\pm\) 0.30 & 98.46 \(\pm\) 0.27 & 98.19 \(\pm\) 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Link prediction results (%) on the three citation networks.  
consistent with the methods reported in [40], which typically require larger dimensions (e.g, 512) to achieve their best performance.  
For instance, considering the Cora dataset, we observed that our proposed model achieved its best performance at a 300-dimensional embedding size, whereas DGI and GIM achieved their best performance at a 512-dimensional embedding size. This suggests that even though 300-dimensional embeddings are relatively small compared to 512-dimensional embeddings, they are nearly as effective. This may indicate that information compression plays a crucial role in graph autoencoder models, and that higher information density leads to higher performance.
# Calibration of Magnetometers with GNSS Receivers and Magnetometer-Aided GNSS Ambiguity Fixing
## 3 Calibration of Magnetic Flux Sensors  
The calibration of the magnetic flux sensor requires the estimation of the magnitude \(m^{\mathrm{g}}\) of the magnetic flux and of the sensor bias \(b_{m^{\mathrm{s}}}\). We consider the rotation matrix \(R_{\mathrm{g}}^{\mathrm{s}}\) as known. It is derived from GNSS in this section.
###### Abstract  
Faults in bearings and gearboxes, which are widely used in rotating machines, can lead to heavy investment and productivity loss. Accordingly, a fault diagnosis system is necessary to ensure a high-performance transmission. However, as mechanical fault diagnosis enters the era of big data, it can be difficult to apply traditional fault diagnosis methods because of the massive computation cost and excessive reliance on human labor. Meanwhile, unsupervised learning has been shown to have excellent performance in processing mechanical big data. In this paper, an unsupervised learning method known as sparse filtering is applied, the multi-correlation of a weight matrix is investigated, and a method that is more suitable for the feature extraction of signals is proposed. The main contribution of our work is the modification of original method. First, to understand the non-monotonicity testing accuracies of the original method, the physical interpretation of input dimensions is studied. Second, using the physical interpretation, an overfitting phenomenon is discovered and examined. Third, to reduce the overfitting phenomenon, a method which eliminates the multi-correlation of the weight matrix is proposed. Finally, bearing and gear datasets are employed to verify the effectiveness of the proposed method; experimental results show that the proposed method can achieve a superior performance in comparison to the original sparse filtering model.  
fault diagnosis; multi-correlation; overfitting; sparse filtering; big data +
Footnote †: journal: Applied Sciences
# Parallel Computation of CRC-Code on an FPGA Platform for High Data Throughput
## 5 Experimental Results  
Prior to the implementation on FPGA, we simulate the algorithm by using multi-threaded C++ program and verify the results by comparing it with the serial CRC algorithm. The hybrid algorithm passes all the test cases where data are from 128 KB to 2 MB with 256 KB increasement. In addition, we also measure the performance of the simulation. A linear congruential generator is used to generate input for various data lengths. Each test configuration is repeated 20 times, each time with different seeds for random testing. To cancel the noise in the performance measurement, we average 20 test cases of a given configuration. Figure 8 shows performance results. Figure 9 shows the comparison between Sarwate algorithm, Matrix Multiplication method, and the hybrid method to validate the effectiveness of parallel computing.  
Figure 8: Performance results of a multi-threaded C++ program.  
Figure 7: (**a**) FSM of the architecture and (**b**) Implementation of the Slicing-by-N computation unit.  
As expected, the CPU version could not achieve a speed factor equal to the number of threads used due to thread management overhead and other CPU resources sharing (e.g., data transfer, cache memory). Figure 10 below shows speed up achieved with the number of threads used in normalized time. Unlike a CPU, an FPGA is parallel in nature; therefore, it would be able to achieve speed-up factors approaching theoretical results.  
To verify the functionality of the parallel CRC computational system, we synthesized and implemented the architecture on the ZEM5310 FPGA evaluation board from Opal Kelly Inc. (Portland, OR, USA). This board is a commercial, off-the-shelf FPGA board that contains a USB 3.0 driver and supports C++ packages. A test program has been written in C++ to generate the data, transfer the data to the FPGA via USB protocol, obtain the CRC results, and compare it with software computed CRC results. The experiment has  
Figure 10: Measured normalized operating time vs. threads for 2 MB data.  
Figure 9: Comparison between three methods, hybrid methods running with two threads.  
successfully passed all the test cases. The test cases are similar to what we described in the experiment with CPU. The test system is described in Figure 11.  
To analyze the effectiveness of the parallel CRC computational system, we measured the number of cycles against the number of logic elements needed to implement the algorithm on an FPGA platform for various data lengths. The logic element is defined as the smallest logic block of the FPGA family architecture which consists of a 4-input lookup table and a D-flipflop. All of the experiments are synthesized and measured by using Quartus compiler. Figure 12 below shows the measurement results with the system clock at 100 MHz. It is interesting to note that resource investment is only useful when the data length is sufficient long. For example, the number of cycles to compute the CRC does not reduce much in the case of 2 KB data length.
# Metal-Coordinated Polymer-Inorganic Hybrids: Synthesis, Properties, and Application
## 2 Methods of Synthesizing Coordinated Polymer-Inorganic Hybrids
### General Synthetic Route for Supramolecular Coordination Complexes (SCCs)
#### 2.2.1 Synthesizing Metallacycle/Metallacage-Cored Star Polymers (MSPs)  
Metallacycle/metallacage-cored star polymers (MSPs) are branched polymers characterized by several linear chains connected to a central core, offering properties like compact structures, lower internal viscosity, and high arm density [51]. These characteristics make MSPs highly useful in applications such as drug delivery, thermoplastics, and nanoelectronics. Two synthetic approaches for MSPs are widely used: post-assembly polymerization and post-polymerization assembly. As shown in Figure 1A, in post-assembly polymerization (A), metallacycles or metallacages are first constructed through coordination-driven self-assembly, followed by the polymerization of multiple chains from these cores. In the post-polymerization assembly approach (B) (Figure 1B), macroligands with polymer chains and coordination sites are first synthesized, and then coordination-driven self-assembly forms the final MSPs [52].
# A Controller Switching Mechanism for Resilient Wireless Sensor-Actuator Networks
## 4 Controller Switching Mechanism
### Madb  
The MADB is the maximal sampling period that guarantees stability even with low system performance. Numerous methods have been developed to analyze the stability of network control systems. Most of these methods employ Lyapunov stability analysis and provide conditions for the global asymptotic stability of a system [42] Furthermore, a majority of the reported methods are typically overcomplicated for practical applications. By contrast, a simple stability analysis method was presented in [43] that allowed the determination of the MADB in a continuous network control system derived using the finite difference approximation of the delay term and the Lyapunov system stability theorem. More detailed proofs and details can be found in [43]. We considered a continuous state-space representation that is given by  
\[\begin{split}\dot{x}(t)&=Ax(t)+Bu(t),\\ y(t)&=Cx(t)+Du(t),\end{split} \tag{6}\]  
Figure 5: Implementation detail of scheduling with controller switching.  
where \(x(t)\in R^{n}\), \(u(t)\in R^{m}\) and \(y(t)\in R^{p}\) are system state vector, system control input, and system output, respectively. Furthermore, \(A,B,C,\) and \(D\) are the constant matrices with appropriate size. The state feedback controller is expressed as  
\[u(t)=Kx(t-\tau), \tag{7}\]  
where \(K\) is feedback controller gain matrix. The following were assumed:  
1. Time-driven sensor and event-driven actuator transmit data as a single packet and reject old packets.
2. All system states are measurable and ready to be sent. The time delay is sufficiently small to be less than 1 unit of measurement.  
**Theorem 1**.: _Considering that the above-mentioned assumptions hold, for the system of (6) with the feedback control of (7), the closed-loop system is globally asymptotically stable if \(\lambda_{i}(\Psi)\in C^{-}\), for \(i=1\), \(2\), \(\ldots\), \(n\) and all the second-order reminders of the state variables are sufficiently small for the given value of \(\tau\), where \(\Psi\) is given by_  
\[\mathbf{\Psi}=[(I+\tau BK)^{-1}(A+BK)]. \tag{8}\]  
_Here, \(\lambda_{i}\) are the eigenvalues of \(\Psi\) that must lie on the left-hand side of the complex plane. The second-order reminders refer to Taylor series expansion of state variables \(x(t-\tau)\)._  
**Corollary 1**.: _For Control System (6) with control law (7), the closed-loop system is globally asymptotically stable if_  
\[\lambda<\frac{1}{\left\|BK\right\|}, \tag{9}\]  
_where \(\left\|\cdot\right\|\) is the spectral matrix norm._  
**Corollary 2**.: _System (6) with Control Law (7) is asymptotically stable if_  
\[\lambda<\frac{1}{\left|\lambda_{min}(BK)\right|}, \tag{10}\]  
_where \(\lambda_{min}\) is the minimal eigenvalue of \(BK\)._  
**Corollary 3**.: _For System (6) with Control Law (7), the closed-loop system is globally asymptotically stable if_  
\[\lambda<\frac{1}{\left|KB\right|}, \tag{11}\]  
_where, \(\left|\ \cdot\ \right|\) is the absolute value._
# Toward Efficient Mobile Electric Vehicle Charging under Heterogeneous Battery Switching Technology
## 4 Performance Evaluation
### Evaluation Parameters
#### 4.2.2 Total Waiting Time for Selection of BSS  
Assigning EV to the appropriate battery switching station (BSS).  
* The BSS within the EV reach-ability according to CSOC of EV, that upon charging request to the GC, guarantees the lowest switching delay.
* The foreseen switching delay for EV is computed as the sum of time to complete the switching of EVs currently present at the BSS, switching time of single vehicle and battery recharging time (if required battery type is available on charge).
* If two BSS have the same switching delay, select BSS which has minimum traveled distance and nearest among to \(R_{V}\).
## 4 Experimental Results and Analysis  
We evaluated and verified the U2-Net using a Google Colab Pro with 32 GB of RAM, an NVIDIA P100 Tensor GPU, and two virtual CPUs. Figure 3 shows the flowchart of the recommended methodology. It shows the development and evaluation processes we follow to reach the results. It shows four general steps: the Datasets step focuses on the dataset we have selected to evaluate the U2-net model; more details will be presented in Section 4.1. The Data Pre-processing step shows our operations to prepare the data as an input to the U2-net mode. It is designated in Section 4.2. Section 4.3 depicts the Model Training step in which we describe how we use the datasets to train the U2-net model along with the model structure, layers, input, and output. The final step is the Model Evaluation step, in which we describe the evaluation metrics we used, and this step is described more in Section 4.4.
[PERSON]  
1 Imer Mongolia Regulatorycapacity, long service life, wide temperature adaptation range, and high specific energy compared to other flexible energy devices [3]. Flexible electrode materials are the main factor determining the electrochemical performance of flexible lithium-ion batteries [4; 5]. Therefore, they have become a research hotspot for scholars to develop flexible electrode materials with excellent electrochemical properties. Carbon fibers are used extensively as flexible lithium-ion anode materials due to their good conductivity and mechanical strength [6]. However, carbon fiber anodes face the problems of poor flexibility, low reversible capacity, and insufficient rate performance when used directly. To improve their electrochemical performance, an effective measure is to fabricate holes of different sizes in the carbon fibers or dope the carbon fibers with some active substances. [PERSON] et al. reported that micropores could provide a larger surface area, which is beneficial to the distribution of reaction sites [7]. Mesopores were reported to greatly enhance the rate capability and facilitate the transport of ions [8]. Additionally, [PERSON] et al. reported that macropores could accommodate great volume variation during cycling [9]. At present, N-doping is also widely considered due to the advantages of low cost, stable combination of carbon and nitrogen, and improved performance [10; 11]. However, it is difficult for a single mending strategy to fully satisfy the requirements of high electrochemical performances. Therefore, it may be an effective strategy to coordinate various modification methods for improving the performance of carbon nanofiber anodes. Based on the above analysis, we produced a flexible N-doped carbon nanofiber anode with microporous, mesoporous, and macroporous structures using gas-electric co-spinning technology in this work. Compared to commonly used electrospinning, this technology can not only greatly improve the production efficiency but can also reduce the power consumption caused by a high-voltage power supply. In addition, this technology has a series of advantages such as device simplicity, easy operation, and low cost. The hierarchical and heterogeneous porosity structure can increase the number of reactive active sites of lithium ions and facilitate the transport of ions to effectively improve the reversible capacity of the anode, and N-doping can improve the conductivity. The N-doped carbon nanofiber anode with hierarchical and heterogeneous porosity structure can be directly used as the anode of lithium-ion batteries without adding adhesives and any substrate. Moreover, it has a high first reversible capacity of 1108.9 mAh g\({}^{-1}\), capacity retention of 86.1% after 100 cycles, and excellent rate performance, which is much higher than the results reported for other carbon fiber anodes for lithium-ion batteries. This work provides a new way to manufacture flexible carbon nanofiber anodes with high performance for lithium-ion batteries.
## 4 Discussion
### Limitations of Work and Future Work  
For ecological sensitivity evaluation, there is no unified evaluation rule system in academia. In this paper, 11 ecological factors closely related to the natural environment, human disturbance, and soil erosion in the study area are selected as evaluation indicators, which can effectively reflect the most significant ecological and environmental problems in the study area and quickly evaluate the ecological sensitivity of the study area. Due to the limitation of data acquisition, the influence of geological disasters, environmental pollution, and other factors on the ecological environment are not included, so the ecological sensitivity evaluation factor system of the study area can still be further improved.  
In addition, the classification rules of the ecological sensitivity factors (Table 2) are determined according to the actual situation of the Tumen River Basin, relevant national standards, and previous research experience, so there is necessarily an artificial and subjective element [2; 7; 24; 38]. If the classification rules for each indicator change, the evaluation results will also vary. Therefore, the rigid classification rules of ecological sensitivity evaluation factors also need to be studied in depth.  
The AHP method relies heavily on the subjective decisions of experts when calculating the weights of each evaluation factor, and the number of experts or subjective experiences will affect the evaluation results. Therefore, in further research, we can combine the AHP method with objective assignment methods, such as the variation coefficient method and the entropy method, to weaken the influence of subjectivity and make the evaluation results more scientific.
## 3 Properties and Structure of Chlorine Dioxide and Ozone  
### Similarities  
The spatial structure and electron dot diagram of chlorine dioxide are shown in Figure 2a,b. Chlorine dioxide is a symmetric nonlinear triatomic molecule. The chlorine-oxygen bond is formed with a bond angle of 117.5\({}^{\circ}\) and a bond length of 1.47 A [40]. The chlorine-oxygen bond shows obvious double-bond characteristics. Chlorine dioxide has a conjugated system as the 3d orbital of chlorine is conjugated to the p-orbital of oxygen to form a \(\pi\) bond [41]. Chlorine dioxide possesses an odd number of electrons and selectively reacts with electron-rich groups (aniline, phenols, etc.) [42]. In an aqueous solution, chlorine dioxide reacts with organic substances by electron transfer [43]. In the reaction, chlorine dioxide gains electrons to be reduced to the chlorite anion (Equation (1)) [34].  
\[ClO_{2}+e^{-}\to ClO_{2}^{-} \tag{1}\]  
Figure 2: (**a**) Structure of chlorine dioxide; (**b**) electron dot diagram of chlorine dioxide; (**c**) structure of ozone; and (**d**) electron dot diagram of ozone in double radical mode. The red balls represent chlorine atoms and the blue balls represent oxygen atoms.  
Figure 1: Preferred reporting items for systematic review and meta-analyses recommendations flowchart.  
Ozone is a symmetric nonlinear triatomic molecule. The spatial structure of ozone and the electron dot diagram of the two-radical mode are shown in Figure 2c,d. One oxygen atom is the central atom, and the other two oxygen atoms are connected to the central atom through covalent bonds. The bond angle of ozone is \(116^{\circ}49^{\prime}\), and the bond length is 1.278 A [44]. The three oxygen atoms are in sp\({}^{2}\) hybridization. The electrons of the central oxygen atom form a \(\pi\) bond with the electrons of the two terminal oxygen atoms [45; 46]. Due to the molecular structure of ozone, there are three types of reactions when reacting with aromatic compounds: ozone-oxygen substitution, electron transfer, and Criegee ozonation [47]. In the reaction of ozone with phenol, electron transfer may have occurred to produce a superoxide anion radical (Equation (2)) [48].  
\[C_{6}H_{5}O^{-}\overset{O_{3}}{\rightarrow}C_{6}H_{5}O^{\bullet}+O_{3}^{ \bullet-} \tag{2}\]  
Chlorine dioxide and ozone have a similar spatial structure and chemical properties. In their spatial structure, both chlorine dioxide and ozone show symmetrical and nonlinear triatomic molecules with \(\pi\) bonds in the molecular structure. During the reaction with pesticides, chlorine dioxide and ozone react with some organic compounds by electron transfer.
# Research on Backstepping Linear Active Disturbance Rejection Control of Hypersonic Vehicle
###### Abstract  
In this paper, the velocity and altitude control problem of hypersonic vehicles is studied. Aiming at the nonlinear parameter uncertainties, external disturbances and coupling of the hypersonic vehicle system, a control method combining backstepping control with linear active disturbance rejection control is proposed. The backstepping control solves the coupling of the system and transforms the longitudinal dynamic model of the hypersonic vehicle into the form of strict feedback, which is divided into the altitude subsystem and velocity subsystem. The linear extension state observer (LESO) can observe parameter uncertainty disturbance and external disturbance. At the same time, the stability of the system is proved by Lyapunov theory. Finally, the effectiveness of the designed controller is verified by numerical simulation and comparison with classical PID control.  
velocity and altitude control; hypersonic vehicle; backstepping control; classical PID control; linear extension state observer
# Robust Image Watermarking in Spatial Domain Utilizing Features Equivalent to SVD Transform
## 2 Review of the Algorithmic Concepts
### Matrix Norm  
The Frobenius norm of a matrix \(A\), denoted \(\left\|A\right\|_{F}\), is the square root of the sum of the square of the singular values and is defined as follows [52]:  
\[\left\|A\right\|_{F}=\sqrt{\sum_{i=1}^{m}\sum_{j=1}^{m}a_{i,j}^{2}}=\sqrt{trace (A^{T}A)}=\sqrt{\sum_{i=1}^{\min\{m,n\}}\sigma_{i}^{2}} \tag{3}\]  
The matrix 2-norm (or spectral norm) of matrix \(A\) is the maximal singular value of \(A\) or the square root of the maximum eigenvalue \(\lambda_{max}\) of \(A^{T}A\). When \(F=2\), the matrix 2-norm is identical to the maximum singular value and is provided by the following formula:  
\[\left\|A\right\|_{2}=\sqrt{\lambda_{\max}(A^{T}A)}=\sigma_{\max} \tag{4}\]  
To obtain the maximum singular value of an image, SVD is a widely used technique in digital watermarking because the maximum singular value of an image does not change considerably when it is subjected to conventional image processing attacks. However, the matrix product in SVD calculations will enhance its complexity and the cost of computation. On the other hand, the largest singular value of a matrix is equivalent to the matrix 2-norm [49]. [PERSON] et al. [52] compared the complexity of the SVD transform and matrix 2-norm and found that matrix 2-norm is faster. Due to the relationship between the matrix 2-norm and the maximum singular value, the proposed watermarking scheme uses the matrix 2-norm instead of the singular values from the SVD transform.