esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Preparation of REFeO\({}_{3}\) Precursor  
To prepare precursors for the R-D process, the CeFeO\({}_{3}\) phase was synthesized by a solid-state reaction at 850 \({}^{\circ}\)C for 6 h in an Ar atmosphere using CeO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\) and Fe powders (Ce:Fe = 1:1). Figure 2 shows the XRD results of the samples with different raw material ratios. The XRD results show that a CeFeO\({}_{3}\) phase was successfully formed when Fe powder was substituted for two-thirds of the Fe\({}_{2}\)O\({}_{3}\) in the precursor mixture, as shown in Figure 2, but residual CeO\({}_{2}\) and Fe\({}_{2}\)O\({}_{3}\) phases were also present. To eliminate these unreacted phases, the Fe content was increased to a Ce:Fe ratio of 1:7, corresponding to the stoichiometric composition of the 2-14-1 phase. Although the XRD pattern of the sample with 1:7 of Ce:Fe exhibited distinct peaks of CeFeO\({}_{3}\), minor amounts of CeO\({}_{2}\) and Fe\({}_{2}\)O\({}_{3}\) phases remained. Interestingly, Fe peaks were also observed, which can be potentially utilized in the subsequent synthesis of the 2-14-1 phase through a reduction-diffusion process.  
To promote the formation of CeFeO\({}_{3}\) and eliminate residual CeO\({}_{2}\) and Fe\({}_{2}\)O\({}_{3}\) phases, the heat-treatment temperature was progressively increased. As shown in Figure 3, as the heat-treatment temperature was increased, the amount of residual CeO\({}_{2}\) and Fe\({}_{2}\)O\({}_{3}\) was gradually decreased. At 1050 \({}^{\circ}\)C, the precursor composed of pure CeFeO\({}_{3}\) and Fe phases was achieved. This result demonstrates that a CeFeO\({}_{3}\)-Fe precursor with a Ce:Fe ratio of 1:7 can be successfully synthesized by the heat treatment of a mixture of CeO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\) and Fe at 1050 \({}^{\circ}\)C for 6 h under an Ar atmosphere.  
Figure 1: Flowchart of preparation of \(RE_{2}\)Fe\({}_{14}\)B magnetic powder by the modified reduction-diffusion process.  
Figure 2: XRD analysis results of precursor samples prepared by solid-state reaction (850 \({}^{\circ}\)C, 6 h, Ar).
## 6 Experimental Results and Discussion
### 1-D CNN and 2-D CNN Comparisons  
The 2-D CNN model proposed in this section was compared with the first two models. The test set was used to test under different SNR and the comparison results are shown in Table 5 above. As can be seen, the performance of the 2-D CNN multi-measurement point model proposed in this section was significantly improved in the 10 dB SNR over the 1-D CNN multi-measuring point model. However, when the SNR was 5 dB, the accuracy of the 2-D CNN multi-measuring point model was only 49.67%, which is lower than the accuracy of the 1-D CNN single-measurement point model. However, when the SNR was 10 dB, the accuracy was significantly improved compared to the 1-D CNN single-measuring point model, and when the SNR was higher, the two types of models behaved similarly. Therefore, the greater the number of fusion points, the accuracy of the model is not necessarily higher. The next chapter will study the influence of the number of measurement points and the selection of the measuring points on the accuracy of the model.  
The test set data with different SNR were extracted at the output of the dense layer 1 and PCA was applied to obtain Figure 15. Though the model accuracy was not high when the SNR was 5 dB, the output of the noise-added signal of the dense layer 1 was separable in the high-dimensional space. This result indicates that the model can effectively extract the features of the signal. Therefore, adding fully connected layers can improve the accuracy of the model. From Figure 15, there are a small number of spring damage-type samples the valve disc notch type samples' output at the fully connected layer 1 is too close, which is also the main reason for the small number of model identification errors.  
Figure 15: PCA of the output of the test set samples in the dense 1 of the 2-D CNN multi-measurement point model under different noise strengths (**a**) SNR is 5 dB; (**b**) SNR is 10 dB; (**c**) SNR is 20 dB; (**d**)SNR is 50 dB.
###### Abstract  
In order to reduce the complexity of the receiving system for wideband signals, a novel space-borne high-resolution synthetic aperture radar (SAR) system with the non-uniform hybrid sampling technology is proposed in this paper. The non-uniform hybrid sampling technology is firstly applied in the SAR imaging system for the detection of space targets. The non-uniform hybrid sampling technology is able to optimize the transmitted and receiving timing of SAR signals, reducing the requirement of the sampling rate of the analog-to-digital converter (ADC) for the reception of the wideband echoes from space targets. Meanwhile, according to the oversampling requirement of SAR imaging in the azimuth direction, a theoretical model of non-uniform hybrid sampling parameters and relative velocity between the SAR system and the space targets is established. A series of simulation experiments with different targets and different non-uniform hybrid parameters are performed, and an X-band SAR imaging experimental system is constructed to verify the effectiveness of the proposed non-uniform hybrid sampling technology. The experimental results show that the imaging resolution is better than 8 cm. When the non-uniform hybrid sampling interval is 15 us, the imaging quality is consistent with imaging results of the Nyquist real-time sampling, and it is easier to implement in the high-resolution imaging for space targets.  
Keywords:3 March 2022  
Accepted: 9 May 2022  
Published: 11 May 2022
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 3 5G Security Architecture: A Systematic Approach
### Radio Access Network
#### 3.2.6 Network Slicing
Network slicing changed the landscape for operators from a network-as-an-infrastructure to a network-as-a-service paradigm. It has also reduced the need for a multitude of necessities within a network [78]. However, this switch brings new challenges and considerations related to network security. The authors of [74] created an in-depth survey of 5G network slicing with a focus on AI and ML. Their work states that AI is another potential weakness in a 5G network due to the rise of adversarial machine learning attacks. Adversaries attempt to fool the ML model by adding data samples with disrupting factors to force the appearance of otherwise hard-to-find vulnerabilities. To combat this, models can be trained with adversarial methods in mind, eventually developing a secure environment using adversarial machine learning [79]. Other discussions in [80] share the implementation of a network slice for military and government use. The authors share that key services of the slice would facilitate push-to-talk, fixed mobile convergence, prioritized access in case of disaster or war, autonomous edge, coverage on demand, satellite backhaul, many-to-many communication service, and the use of the RAN to sense and detect drones and perform jamming and anti-jamming operations.
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
## 2 Materials and Methods
### G-Code and Physical Part Watermarking  
After being watermarked, the digital model is converted into a G-code program by using a specially designed _slicer_. This slicer is capable of translating voxel models into G-code programs. Its algorithms, data structures, and operational procedures can be found in [26]. During the G-code generation procedure, the space occupied by watermarked voxels is treated as void spaces or filled with different hatch patterns or materials, depending on the characteristics of the underlying 3D-printing platforms and the applications of the model. Hence, the watermark is implicitly embedded in the G-code program. By using this G-code program to layered-manufacture a physical part, the resultant object will contain the watermark and is under protection too.
# Application Layer Protocol Identification Method Based on ResNet
## 2 Related Work
### Deep Learning  
[PERSON] [21] proposed a novel method that utilizes natural language processing (NLP) techniques for semantic analysis of network data packets to extract protocol features. Additionally, transforming data into images for learning and identification is also a viable approach.  
[PERSON] [22] converted data into grayscale images and employed convolutional neural networks (CNNs) for feature extraction. [PERSON] [23] introduced a protocol reverse engineering method based on convolutional neural networks, termed CNNPRE. This method utilizes a pre-trained DenseNet-169 model to extract features from network traffic data and employs UMAP for dimensionality reduction and traffic feature calculation. Finally, it combines the HDBSCAN clustering algorithm to identify message types. Experimental results demonstrate that the CNNPRE method achieves high uniformity and V-Measure scores in identifying message types across multiple datasets. [PERSON] [24] combined multiple packet header data during communication into grayscale images. They utilized a dual-path autoencoder model to extract and aggregate payload features and statistical features. Further, they employed a correlation-adjusted clustering module to identify the fused features.  
[PERSON] [25] proposed a method that combines deep neural networks and clustering algorithms. First, an autoencoder (based on Network-in-Network, Channel Attention, and Spatial Attention) is used to reduce the dimensionality of protocol data. Then, improved clustering techniques, such as the K-Means algorithm, are applied for clustering to identify unknown protocols.  
[PERSON] [26] proposed the Complex Application Recognition (CAR) method, and then to address the issue of low protocol recognition accuracy in large-scale network environments, he proposed the SAIMM method based on a MinMax principle classifier ensemble to effectively improve the accuracy of CAR technology in large-scale network environments.  
However, the methods proposed in the above deep-learning-based papers do not strike a balance between recognition efficiency and accuracy. Therefore, the aim of this study is to find a method that balances real-time recognition efficiency and resource consumption and can be applied in real-time network interactions, namely, the compound feature-based RGB image generation algorithm.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 3 Factors Influencing Sticking in MXene Electrodes
### Electrode Processing Techniques
#### 3.3.1 Dispersion Method  
The dispersion method used for MXene flakes is a crucial factor in preventing sticking. Traditional methods include sonication, mechanical stirring, or high-shear mixing to disperse MXene flakes in a solvent. The intensity and duration of sonication or mixing play a role in achieving optimal dispersion. Over-sonication or excessive mixing can lead to increased flake damage and agglomeration, resulting in sticking issues. Hence, careful optimization of the dispersion process is necessary to achieve a homogeneous and stable MXene dispersion.
# A Study of Forest Phenology Prediction Based on GRU Models
## 2 Method
### Experimental Design  
At the experimental site of the forest farm, three groups of different irrigation methods and amounts were designed, including rain-fed (no irrigation), sufficient drip irrigation, and sufficient furrow irrigation, to cultivate the poplar B301. The irrigation was conductedfrom 25 March when the poplar buds began to swell until the arrival of the rainy season on 26 June. Time-series images were acquired using near-ground remote sensing equipment, and the three irrigation methods were calibrated as preset points 1, 3, and 5.  
The fully drip-irrigated (DIFI) system employed drip irrigation tubes produced by Netafim, Israel, with a drip head flow rate of 1.6 L\(\cdot\)h\({}^{-1}\) and a spacing of 50 cm between drip heads, placed on the ground surface in a row-and-two-belt layout along the tree row direction (with drip irrigation pipes placed 30 cm away from the tree on each side). During the growing season, based on the quantitative relationship between the growth of the white poplar and the soil moisture effectiveness [31], the fully drip-irrigated system was set to begin irrigation when the soil water potential at a depth of 20 cm directly below the drip head reached around \(-\)18 kPa (i.e., 79% of the field water holding capacity \(\theta\)_f_ and 73% of the soil moisture effectiveness \(r\theta\)) and to stop when the soil moisture content within the soil wetting zone was raised to the field water holding capacity [29; 32].  
The system of full furrow irrigation (BIFI) uses PVC pipes to ensure synchronous water supply to each canal and installs tension sensors directly below the canals to record soil water potential. Groundwater is used for irrigation, and the water flow is automatically controlled by a water meter installed at the top of the valve. Irrigation is stopped when the average soil moisture content in the shallow soil layer (50 cm) reaches the field capacity \(\theta\)_f_, and the water meter reading is recorded. In contrast, the control group (CK) receives no irrigation treatment and is left unwatered throughout the year.
## 3 Federated Kalman Filter Design
### Kalman Filtering Algorithm for Inertial/Satellite/Odometer Integrated Navigation  
Based on the error equation of the strapdown inertial navigation system presented in Section 3.1, the satellite position and velocity information were selected as the observations, and the inertial/satellite integrated navigation Kalman filter was constructed. Regardless of the satellite positioning system error information, a Kalman filter based on a loose coupling model was designed. The filter state variable was selected to have 16 dimensions including attitude angle error, velocity error and position error, gyroscope zero bias, accelerometer bias, and odometer scale factor error. The state variables for the Kalman filter were \(\mathrm{x=}\big{[}\mathrm{\varphi_{E}}\;\mathrm{\varphi_{N}}\;\mathrm{\varphi_{U }}\;\mathrm{\varphi_{E}}\mathrm{\delta\mathrm{V_{E}}}\;\mathrm{\delta\mathrm{V _{N}}}\;\mathrm{\delta\mathrm{V_{U}}}\;\mathrm{\delta\mathrm{L}}\;\mathrm{ \delta\mathrm{\delta\mathrm{\delta\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm  
\[\mathrm{H=}\left[\begin{array}{cc}H_{V}&H_{P}\end{array}\right]^{\mathrm{T}} \tag{20}\]  
In Equation (20), \(H_{V}\) and \(H_{P}\) can be defined as follows:  
\[H_{P}\mathrm{=}\left[0_{3\times 6}\ \mathrm{diag}(R_{M}+H,\ (R_{N}+H)\cos L,\ 1) \ \ \ 0_{3\times 6}\ \right] \tag{21}\]  
\[H_{V}=\left[0_{3\times 3}\ \ \mathrm{diag}(1,\ 1,\ 1)\ \ \ 0_{3\times 1}\ \right] \tag{22}\]  
The measured noise of the satellite positioning system was \(\mathrm{v=}\left[\begin{array}{cc}v_{8V_{P}^{\prime}}&v_{8V_{N}^{\prime}} &v_{8V_{U}^{\prime}}&v_{8L}&v_{8A^{\prime}}&v_{8H^{\prime}}\end{array}\right]^{ \mathrm{T}}\), and the variance matrix of the measured noise of the filter was set according to the actual position and speed error levels of the satellite positioning system.  
The inertial/GNSS/odometer integrated navigation system used the satellite positioning system time as the reference and synchronized the time of the inertial measurement unit and the odometer to the time system of the satellite positioning system. The integrated navigation and data fusion in Sections 3.5 and 3.6 are also based on GPS time as the time synchronization reference.
# Image Transmission through Scattering Media Using Ptychographic Iterative Engine
## 4 Discussion and Conclusions
However, in a practical application, small probe apertures as well as a high overlap ratio might not be suggested, as some more factors should be taken into consideration, such as the imaging speed, illumination power, aberrations, and noise. Therefore, the specific choice of these parameters should be adjusted according to the specific motivation. In conclusion, we have demonstrated the use of the PIE algorithm for transmitting images through scattering media. We proposed to use a small probe aperture to scan the object with multiple shots and use the PIE algorithm to reconstruct the object behind the scattering media. The FOV of the reconstructed image was then enlarged. Additionally, the advantage of the PIE algorithm makes it possible to overcome the problems of conventional phase retrieval techniques and get outstanding image performance. The PIE algorithm we adopted in our study needs accurate knowledge of the shape and positions of the probe apertures, which are controllable in our proof-of-concept experiments, as the probe was implemented by a DMD. In practical application, however, these parameters may not be accurately known. As a result, the reconstructed image will suffer from noise and reduction of resolution. One should bear in mind that many studies have been carried out to improve the PIE algorithm [27; 28; 29]. The proposed method can be applied in particular to situations where the target can be illuminated with desired patterns--for example, in the application of aircraft landing in cloudy days [30]. In cases  
Figure 7: Effect of the overlap ratio of the probe beam to the reconstruction performance. (**a**) The target, (**b**) 90%, (**c**) 88%, (**d**) 86%, (**e**) 82%, (**f**) 75%, (**g**) 58%, and (**h**) 39%. (i) The correlation coefficient against a different overlap ratio. where signal lights on the runway can be switched on and off in sequence, it analogously performs a scan of the runway with a 'probe' beam whose aperture equals to the size of the signal light. Conceptualization, G.S. and G.L. ; methodology, W.Y. and H.W. ; software, G.L. and H.W. ; validation, W.Y. ; formal analysis, G.S. ; investigation, G.L. ; resources, G.L. ; writing--original draft preparation, G.L. ; writing--review and editing, G.S., W.Y. and H.W. ; visualization, G.L. ; supervision, G.S. ; project administration, G.S. ; funding acquisition, G.S. This work was supported by the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (QYZDB-SSW-JSC002), Sino-German Center for Sino-German Cooperation Group (Grant No. GZ1391), National Natural Science Foundation of China (Nos. 61705241) and natural Science Foundation of Shanghai (No. 17 ZR1433800). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} DMD & digital micro-mirror device \\ FOV & field-of-view \\ HIO & hybrid-input-output \\ PIE & ptychographical iterative engine \\ \end{tabular}
# Optimizing Physics-Informed Neural Network in Dynamic System Simulation and Learning of Parameters
## 5 Computational Simulations of Dynamical Systems
### Lotka-Volterra Model
Figure 18 displays the Lotka-Volterra model's learned time-varying parameter values for various inputs of \(P_{NN}\) and \(Q_{NN}\). The learned time-varying parameters are \(a(t)\), \(b(t)\), \(c(t)\), and \(d(t)\). It was observed that the functional form of the curve of \(a(t)\) is linear, while the functional forms of \(b(t)\), \(c(t)\), and \(d(t)\) are  
\[b(t) =B\ sin(\lambda t+\alpha)+k\] \[c(t) =C\ sin(\lambda t+\alpha)+k\] \[d(t) =D\ sin(\lambda t+\alpha)+k\]  
where \(B\), \(C\) and \(D\) are the amplitude, \(\lambda\) determines the frequency, \(\alpha\) is the phase shift, and \(K\) is the vertical shift (which should be close to the initial values, given the curve's behavior). Figure 19 plots the absolute error between the target and the Lotka-Volterra model's predictions. Tables 14 and 15 show the initial and the obtained parameters for the Lotka-Volterra model and the error metrics through the approaches described in Scenario 2. Figure 16: The Lotka-Volterra model solution for the real output of species P and Q against time data and the predicted output of species P and Q against time data. Figure 17: The phase space plot of the actual output of species P against species Q and the predicted output of species P against species Q of the Lotka-Volterra model. Figure 19: Absolute error plot between the data and PINN solution of the Lotka-Volterra model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(a\) & \(b\) & \(c\) & \(d\) \\ \hline True Values & 1.0 & 2.0 & 1.0 & 0.3 \\ Approach 2 & 0.999136 & 1.9990387 & 1.000002 & 0.3 \\ Error of Approach 2 & 0.000864 & 0.0009619 & 0.000002 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: The parameter estimation of the Lotka-Volterra model. Figure 18: The learned time-varying parameter values of the Lotka-Volterra model. **(a)** Time-varying parameter \(a\). **(b)** Time-varying parameter \(b\). **(c)** Time-varying parameter \(c\). **(d)** Time-varying parameter \(d\). Furthermore, our findings, obtained through the utilization of physics-informed neural networks (PINN), were compared with those reported in scholarly literature employing deep neural networks (DNN) [33]. As illustrated in Table 16, this comparison involved using a single hidden layer encompassing 20 neurons and a sigmoid activation function over a training span of 50,000 epochs. The comparison shows that PINNs perform better than DNNs regarding parameter estimation for the Lotka-Volterra model.