esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Self-Demodulation-Based Visualization Method
### Computational Complexity of the Proposed Method  
From the above description, it is indicated that the simulation of the proposed method can be regarded as calculating Equation (31) or Equation (27) for \(M1\) times, where \(M\) denotes the number of initial acoustic rays and \(I\) denotes the total number of time frames until the end of simulation.  
In the case that the above approximation is not utilized, where Equation (27) is calculated instead of Equation (31), the computational complexity of the proposed method can be expressed as  
\[G_{\text{prop.w/oappr.}}\propto MI^{2}. \tag{32}\]  
In the case that the approximation as Equation (31) is utilized, the computational complexity of the proposed method can be expressed as  
\[G_{\text{prop.}}\propto MI. \tag{33}\]  
As mentioned, this approximation can significantly reduce the computational complexity of the proposed method.  
Considering an FDTD-based method in three-dimensional space as a comparison, the whole field needs to be updated for \(I\) times if the same destination condition is applied. In this case, the computational complexity of the FDTD-based method can be given as  
\[G_{\text{3 dFDTD}}\propto N_{x}N_{y}N_{z}I, \tag{34}\]  
where \(N_{x}\), \(N_{y}\), and \(N_{z}\) denote the number of meshes in the \(x\), \(y\), and \(z\)-axis, respectively. It can be obviously noticed that the proposed method with approximation can be regarded as an \(O(N)\)-algorithm, while FDTD-based methods are generally \(O(N^{3})\)-algorithms. Moreover, to simulate ultrasound in a large space, the number of meshes in the FDTD-based methods should be set large enough, while the number of rays in the proposed method can be set based on the desired resolution rather than physical constraints.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 3 Satellite Networks Vulnerability Risk Assessment Method
### Satellite Networks Vulnerability Risk Assessment Formula
#### 3.1.2 Dynamic Adjustment Factors  
Dynamic adjustment factors calibrate risk values over time and account for data reliability, ensuring adaptability to evolving threats.  
Time decay factor (\(\delta(t)\)): This factor models risk attenuation through an exponential decay function:  
\[\delta(t)=\begin{cases}1.2\cdot e^{-k(t-t_{0})}&Unpatched\\ \phantom{-}0.8&Patched\end{cases} \tag{2}\]  
where k defines the decay rate, and \(t_{0}\) is the vulnerability discovery time. The exponential decay model (2) reflects the urgency of patching. For unpatched vulnerabilities, k = 0.05 ensures a gradual risk reduction (e.g., \(\delta\)(t) decreases by 5% monthly), calibrated using vulnerability lifespan data from satellite operators. For patched cases, \(\delta\)(t) = 0.8 accounts for residual risks (e.g., delayed patch deployment). In different scenarios, the speed of vulnerability risk attenuation over time is different, and the k value needs to be accurately set according to the specific situation.  
Confidence (C): Confidence adjusts risk scores according to data source reliability, with values constrained to the interval \(C\in[0.7,1.3]\). Expert assessments (C = 1.3) and automated scans (C = 0.9) are weighted to balance subjective and objective inputs.
## 3 Analysis and Verification of Crushing Test Results
### Results of Multifactorial Experiments
#### 3.2.1 Significance Test and Regression Model Analysis  
Using Design-Expert software to fit a multiple regression to the results of the response surface test of the HCME crushing qualification rate, a quadratic regression equation for the corn ear crushing qualification rate was obtained:  
\[\text{S}=97.38+0.68\text{A}+0.7\text{B}-1.88\text{C}+2.79\text{AB}-0.66\text{AC }+2.26\text{BC}-3.72\text{A}^{2}-0.63\text{B}^{2}-1.92\text{C}^{2}. \tag{18}\]  
We used Design-Expert software to perform a Box-Behnken simulation test analysis of variance, as shown in Table 8. In the \(p\)-value analysis, it can be seen that A\({}^{2}\) on the crushing rate of conformity is extremely significant, A, B, C, AB, BC, and C\({}^{2}\) on the crushing rate of conformity of the impact are significant, and AC and B\({}^{2}\) on the crushing rate of conformity of the impact are not significant. According to the size of the F-value in statistics, the level of influence of the factor on the results can be determined: the larger the F-value, the greater the influence of the F-value. The F-value of the model represents the impact of the test factors on the crushing pass rate from large to small for the secondary crushing roll speed > secondary crushing roll gap > primary crushing roll speed. The \(p\) value of the regression model is less than 0.0001, which indicates that the regression model is highly significant. The coefficient of determination of the regression equation is R\({}^{2}\) = 0.9758, the corrected coefficient of determination is adjusted R\({}^{2}\) = 0.9448, which is very close to 1, and the coefficient of variation is C.V = 0.82. This summary shows that the regression model is extremely significant.  
The results of the ANOVA demonstrate that the three selected parameters represent the primary factors influencing the crushing effect in corn ears. Among the aforementioned parameters, the secondary crushing roller speed exerts a considerable influence on the quality of corn ear crushing. When the secondary crushing roller speed is excessive, the crushing roller exerts a disproportionate extrusion and shear effect on the material, resulting in an elevated proportion of impurities in the crushed material. The impact of the primary crushing roll speed and the secondary crushing roll gap on the quality of corn ear crushing is moderately lower than that for the secondary crushing roll gap.
## 2 Antenna Design
### Matching Network  
To obtain a good impedance matching, a matching network is used. It consists of an LC circuit, L1 and C1, Figure 2b. Figures 7 and 8 show the S11 response of the antenna with and without the matching network in the two states of the PIN diode.  
Figure 9 shows the current distribution on the antenna structure at the different resonant frequencies. We see that for the 868 MHz, the current distribution is high in the meandered line structure. For 401 and 466 MHz frequencies, both structures,  
Figure 5: Simulated S11 for varied parameter L4 when the PIN diode is ON (Ls = 0.6 nH and Rs = 1 \(\Omega\)).  
the meandered line and the structure reserved for a future Inverted F Antenna (for GPS and BLE/Wi-Fi), participate in the antenna radiation.  
Figure 10 shows the antenna radiation patterns at the different resonant frequencies. The antenna has omnidirectional radiation patterns at lower frequencies with a gain of \(-8.5\) and \(-5.2\) dB at 401 and 466 MHz, respectively. At 868 MHz, the antenna has a maximum gain of \(-2.5\) dB.  
Figure 8: Simulated S11 of the antenna in ON configuration with and without matching network.  
Figure 9: Current distributions: (**a**) at 401 MHz (OFF state); (**b**) at 868 MHz (OFF state); (**c**) at 466 MHz (ON state).
# Utility Analysis about Log Data Anomaly Detection Based on Federated Learning
## 5 Conclusions  
This paper analyzed the utility of log data anomaly detection based on federated learning. The results were compared with the existing centralized method under the same conditions for model verification analysis. As a result of the comparison through the weighted F1-score, the method using federated learning suggested the applicability of federated learning to logarithmic anomaly detection when compared to the existing learning method. Hybrid models perform better than single models for conventional learning methods and federated learning in log data anomaly detection.  
However, although federated learning ensures data anonymity, it is still in the early stages of research, and more in-depth research is needed regarding security. In particular, we are exposed to new risks due to the model's parameter sharing and communication for learning, allowing the parameters and outputs of the learning model to be manipulated [24]. In the real-world environment of federated learning, models are updated through the devices of various clients, so it is necessary to select trusted clients. In consideration of these situations, future federated learning requires further studies for improvement, such as introducing indicators [25] that measure client reliability.  
Conceptualization, T.-H.S.; Methodology, T.-H.S.; Software, T.-H.S.; Validation, T.-H.S.; Formal analysis, T.-H.S.; Investigation, T.-H.S.; Resources, T.-H.S.; Data curation, T.-H.S.; Writing--original draft, T.-H.S.; Writing--review & editing, T.-H.S.; Visualization, T.-H.S.; Supervision, S.-H.K.; Project administration, S.-H.K.; Funding acquisition, S.-H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by an Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No.2022-0-01203, Regional strategic Industry convergence security core talent training business).  
Not applicable.  
Figure 7: Performance comparison by deep learning models for datasets.
## 1 Introduction
[29] proposed a normalized photochemical reflectance index (PRI\({}_{\text{norm}}\)) using a combination of the re-normalized difference vegetation index (RDVI), which is sensitive to canopy structure, and a red edge index, sensitive to chlorophyll content (R700/R670), to detect xanthophyll pigment changes and normalize for leaf area and chlorophyll content level reduction induced by water stress. They found that PRI\({}_{\text{norm}}\) was highly correlated with midday \(g_{s}\) (\(r\) = 0.89) and leaf water potential (\(\Psi_{\text{Leaf}}\); \(r\) = 0.88) in an experimental pinot nor vineyard in California. Lower correlations were also obtained with original PRI (\(r\) = 0.72 and 0.7, respectively) and NDVI (\(r\) = 0.57 and 0.62, respectively) [29]. Using leaf-level spectroradiometer measurements, [PERSON] et al. [30] and [PERSON] et al. [31] showed that water status parameters such as equivalent water thickness (EWT), \(\Psi_{\text{Leaf}}\), \(\Psi_{\text{stem}}\) and \(g_{s}\) can be derived from spectral-based VIs in vineyards. Though promising, most of these studies (including some not mentioned here) are conducted at the leaf-level or make use of sensors mounted on small airplanes or drones, offering a limited monitoring technique in terms of coverage area. The financial cost of ground or aerial sensing can hardly allow their application on a daily basis. Also, historically, the satellite spatial and/or temporal resolution has been too coarse for in-field water status monitoring [32]. A constellation of nano-satellites (using a large number of small compact satellites at the same time) was recently suggested to overcome this spatio-temporal limitation [33]. Planet [34] is an example of such a company operating a large number of CubeSat 3U form (10 \(\times\) 10 \(\times\) 30 cm) nano-satellites, providing daily images with 4 wavebands (RGB + NIR) at a high spatial resolution of 3 m since early 2016. In this study, we make use of the high spatio-temporal resolution images provided by Planet to compare four different VIs with midday \(\Psi_{\text{stem}}\) measured weekly along the growing season of 2017 in 81 commercial vineyards and in one experimental vineyard (Mevo Beitar) in Israel. We examine the use of simple regression and multivariable linear models, with several VI-based metrics extracted from the VI time series, to predict spatio-temporal variability in \(\Psi_{\text{stem}}\) along the growing season. The main goal is to provide a high-resolution satellite remote sensing tool to support real-time deficit irrigation management in high-quality commercial vineyards.
## 5 Experiment and Application  
In this part, the improved FOA was tested and the proposed method IFOA-DTCWT-BF was used to remove noise of the images which were polluted by Gaussian noise and Speckle noise. Moreover, the denoising method was applied to denoise the real images from the surface experiment. To test the performance of improved fruit fly algorithm, a Schaffer function that has only one minimum point \(\min f(0,0)=1\) was used to test the search ability of algorithms. The Schaffer function is always used to test the algorithms' performance, since there are many local extremum points around the minimum point, forming two loop ditches. The optimization algorithms are easy to fall into the local minimum point. In addition, the typical image denoising methods, e.g., DTCWT, block-matching and 3D filtering (BM3D), median filtering, wiener filtering, wavelet decomposition filtering (DWF) and bilateral filtering (BF), were applied to compare with the proposed method. Experiments and algorithm implementation were performed using Matlab, the experiment configuration was showed in Table 1, and the testing gray images were showed in Figure 5.  
Some key parameters of IFOA are initialized: maximum number of generation is 30, population size is 20, location range is \([-10,10]\), etc.
# Comparative Study on Physicochemical and Nutritional Qualities of Kiwifruit Varieties
## 3 Results
### Sugar: Acid Ratio  
In this study, we classified different kiwifruit samples according to their flesh color and species to analyze the sugar:acid ratio (Figure 3Q,R). The results showed that there were significant differences in the sugar:acid ratio in different varieties. The sugar:acid ratio ranged from 5.40 (Y5, 'Jinmei') to 14.67 (R1, 'Hongyang'). The two highest sugar:acid ratios were detected in 'Hongyang' and 'Donghong' (10.90), which may account for their widespread acceptability and preferability among consumers worldwide. The content of total sugar in green-fleshed kiwifruits, with an average of 10.27, was similar to that in 'Donghong'; however, that of total acid was also higher than in 'Donghong'. Hence, the sugar:acid ratio in green-fleshed kiwifruits was lower than in red-fleshed ones, meaning that red-fleshed kiwifruits taste sweeter than green-fleshed ones. Moreover, yellow-fleshed 'Jintao' had a low total sugar content but it had a higher sugar:acid ratio, and so it had a better taste. A previous study showed that the sugar:acid ratio in mature 'Hongyang' kiwifruits from Shanxi (China) was 10.39 [34], which was lower than that in this study (14.67). The average temperature in Shaanxi is lower than that in Hubei, so it is reasonable to speculate that the cause of this trend might be the effect of temperature and cultivation management.
## 5 Simulation Debugging
### Simulation Waveform of Star Point Coordinate Transformation  
The simulation of the star point space coordinate transformation waveform is shown in Figure 18. The star point space coordinate transformation consists of two parts. The first part is the transformation of the star point coordinates from the celestial coordinate system to the star sensor coordinate system, and the second part is the transformation of the star points in the star sensor coordinate system to the 2D planar coordinate system. \(\begin{bmatrix}x_{1}&y_{1}&z_{1}\end{bmatrix}^{T}\) are the star point coordinates in the celestial coordinate system, \(\begin{bmatrix}x_{2}&y_{2}&z_{2}\end{bmatrix}^{T}\) are the star point coordinates in the star sensor coordinates, and pix_x and pix_y are the star point coordinates in the 2D planar coordinate system. Sum_xyz_C are two 32-bit-wide registers that store the star point coordinates and attitude transformation matrix C in the celestial coordinate system. Registers that host the computed results of the star point coordinates and attitude transformation matrix C in the celestial sphere coordinate system. At 6395 ns, the two registers complete the computation simultaneously. At 7085 ns, two adders completed the calculation. The computation process in the figure conforms to the representation of Equations (8), (13) and (14). The FPGA consumes 730 ns for parallel computation here and 770 ns if a fully serial computation method is used.
## 1 Introduction
The morphology and composition of magnetic nanoparticles have a great influence on their magnetic properties not only in their own particles but also in bulk samples after sintering. When the saturation magnetization of magnetic nanoparticles increases, the initial permeability generally also increases according to the relationship of \(\mu_{i}\)' = M\({}_{\text{s}}\)D/K\({}_{1}\), where K\({}_{1}\) = M\({}_{\text{s}}\)H\({}_{\text{c}}\)/0.96 is the crystalline anisotropy constant, M\({}_{\text{s}}\) is the saturation magnetization, H\({}_{\text{c}}\) is the coercivity and D is the average grain size [26; 27]. However, there are few studies available in the literature showing the relationship that the permeabilities of sintered magnetic samples are inversely proportional to their magnetization  
Figure 1: The spinel structure of ferrites is shown indicating the tetrahedral and octahedral sites. value, and explaining the relationship in detail. Therefore, it seems to be beneficial studying NiZn ferrite systems showing these magnetic behavioral relationships in understanding and improving magnetic properties. In this paper, we synthesized Ni\({}_{1-x}\)Zn\({}_{x}\)Fe\({}_{2}\)O\({}_{4}\) (x = 0.5, 0.6, 0.7) ferrite nanoparticles by the thermal decomposition method and examined the influence of the Ni/Zn ratio on the crystal structures, microstructures and magnetic properties of the prepared NiZn ferrites using an X-ray diffractometer (XRD), a field emission scanning electron microscope (FE-SEM), an impedance analyzer and a vibrating sample magnetometer (VSM).
## 4 Results
### Direct Validation with In Situ Datasets
Table 3 shows the classical statistical validation metrics, including correlation coefficient (R), bias, and root-mean-square error (RMSE), as well as unbiased root-mean-square error (ubRMSE). Compared with GM-derived SM, the corresponding OWCM-derived SM shows a significant improvement via higher R and lower RMSE values against in situ SM measurements. Taking the three Naqu measurement sites as examples, the R and RMSE values between RF-OWCM SM and in situ SM range from 0.720 to 0.970, and from 0.039 m\({}^{3}\)/m\({}^{3}\) to 0.070 m\({}^{3}\)/m\({}^{3}\), respectively. By contrast, the R and RMSE values between RF-GM SM and in situ SM range from 0.203 to 0.437, and from 0.106 m\({}^{3}\)/m\({}^{3}\) to 0.138 m\({}^{3}\)/m\({}^{3}\), respectively. This indicates that the correlation between RF-GM SM and the in situ SM is weak, and the corresponding RMSE is generally above an acceptable accuracy threshold of 0.040 m\({}^{3}\)/m\({}^{3}\) (as defined by the NASA SMAP mission). The same is true for other GM-derived SMs. In contrast, OWCM-derived SM estimates meet this threshold at most in situ measurement sites. Compared to in situ SM, the ubRMSE of OWCM-derived SM over the selected in situ measurement sites ranges between 0.0165 m\({}^{3}\)/m\({}^{3}\) and 0.0895 m\({}^{3}\)/m\({}^{3}\)--better than the corresponding ubRMSE of GM-derived SM, which ranges between 0.0300 m\({}^{3}\)/m\({}^{3}\) and 0.1209 m\({}^{3}\)/m\({}^{3}\). This significant improvement has two likely sources, associated with both the application of OWCMs--which implement the fitting procedures in the WT space--as well as the selection of grid cells with low spatial heterogeneity: First, the WT applied in the
## 2 Materials and Methods
### GPP Data
The updated GLASS GPP product incorporates additional environmental variables, including atmospheric CO\({}_{2}\) concentration, direct and scattered radiation fluxes, and atmospheric water vapor pressure deficit [26], to better capture long-term trends in GPP. Additionally, to mitigate the uncertainty arising from individual remote sensing datasets, we employed the Multisource Data Synergized Quantitative (MuSyQ) algorithm for GPP assessment [27]. Unlike GLASS GPP, MuSyQ GPP incorporates a clear-sky index, substantially enhancing GPP accuracy for remote sensing observations [28]. MuSyQ GPP data (0.05\({}^{\circ}\), 8 days) are accessible at [[https://zenodo.org/records/3996814#.Y5V46](https://zenodo.org/records/3996814#.Y5V46) FFBxD-]([https://zenodo.org/records/3996814#.Y5V46](https://zenodo.org/records/3996814#.Y5V46) FFBxD-), accessed on 25 January 2024. To verify the accuracy of the model and remote sensing data in estimating GPP, this study utilized observation data from flux towers in China, comparing them with the model and remote sensing data for validation. The flux tower data can be accessed at [[https://fluxnet.org/](https://fluxnet.org/)]([https://fluxnet.org/](https://fluxnet.org/)), accessed on 23 June 2024. The methodology involved obtaining GPP observation data from the flux towers and determining their corresponding longitude and latitude. These coordinates were then used to locate the matching grids in the model and remote sensing data, from which data for the same years were extracted for comparison and analysis. The results (Figure 3) indicated that the GPP values from the flux tower data closely match those from the model and remote sensing data, exhibiting similar variation trends. This concordance verified the accuracy and reliability of the model and remote sensing data used in this study.
## 3 Results and Discussion  
### Characterization of Prepared Zeolite H-Beta  
The obtained zeolite H-Beta was characterized by XRD, N\({}_{2}\) adsorption-desorption, SEM, and ICP-OES (Figure 2). Compared with the XRD pattern of zeolite H-Beta reported by [PERSON] [39] and [PERSON] et al. [40], the well-resolved diffraction peaks in the XRD patterns of the prepared zeolite H-Beta demonstrated representative Bragg reflections corresponding to typical BEA zeolite H-Beta topologies, without any impure crystalline phase (Figure 2A), indicating the successful synthesis of zeolite H-Beta. A typical IV isotherm was observed, with a steep N\({}_{2}\) uptake in the relative pressure p/p\({}_{0}\) below 0.1 and a slight rise in the p/p\({}_{0}\) range between 0.1 and 0.9, corresponding to micropore filling and capillary condensation of N\({}_{2}\) among the interparticle voids, respectively. A large micropore surface area (S\({}_{\text{mic}}\)) of 530.6 m\({}^{2}\)/g and micropore pore volume (V\({}_{\text{mic}}\)) of 0.21 cm\({}^{3}\)/g were obtained. The existence of interparticle mesopores can be indicated from the high specific surface area (S\({}_{\text{BET}}\)) of 600 m\({}^{2}\)/g and external surface area (S\({}_{\text{ext}}\)) of 69.4 m\({}^{2}\)/g, total pore volume (V\({}_{\text{t}}\)) of 0.36 cm\({}^{3}\)/g, and mesoporous volume (V\({}_{\text{meso}}\)) of 0.15 cm\({}^{3}\)/g, which can be ascribed to the small crystal size of the prepared H-Beta. The SEM images showed a uniform ellipsoid morphology of zeolite H-Beta, with a particle size of -200 nm (Figure 2C,D). The ICP-OES result displayed the molar ratio of 36 for theSiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\)in zeolite H-Beta.