esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Experiments  
### Simulation Process  
To study the effects of different sampling rates, sparse transform bases, measurement matrices, and reconstruction algorithms on the effect of 3D imaging reconstruction in compressed sensing, simulation experiments were performed in the MATLAB software environment. Figure 2 is the simulation target used in the simulation experiment. In the figure, the distance \(d_{1}\) of the grayscale value 255 (white) of the pixel is 1 m, and the distance \(d_{2}\) of the grayscale value 0 (black) is 6 m.  
The laser modulation frequency was f = \(5\times 10^{6}\) Hz, and the sampling frequency was fs = \(1\times 10^{9}\) Hz. Comparative experiments were conducted using the following compressed sensing parameters:  
1. Sampling rate: 0.2, 0.4, 0.6, and 0.8.
2. Sparse transformation basis: Discrete cosine transform (DCT) and fast Fourier transform (FFT).
3. Measurement matrix: Partial Hadamard matrix (HA) [16] and Bernoulli matrix (BE) [17].
4. Reconstruction algorithm: Basis pursuit (BP) [18], orthogonal matching pursuit (OMP) [19], and minimum total variation (TV) [10; 20].  
Because the DMD micro-mirror can only represent 1 or 0, we select as the measurement matrix the partial Hadamard matrix and Bernoulli matrix, and we replace -1 in the matrix by 0 [21]. TV is an image restoration method based on the discrete gradient of an object. The discrete gradient of most natural images is sparse, so this algorithm does not need to perform sparse transformation. The simulation uses the root mean square error (RMSE) of the delay phase obtained after reconstruction, at each point, as a metric to evaluate the reconstruction effect.
## 3 Efficient Modal Modification Analysis Method
### Neumann Series Expansion- and Reduced Basis-Based Preconditioner Method
#### 3.1.2 Convergence Condition and Relative Computational Effort of the Neumann Series Expansion  
As stated before, the convergence of a Neumann series expansion depends on the eigenvalues of the non-identity term. For the convergent Neumann series expansion in Equation (18), one obtains the necessary and sufficient condition  
\[\rho(\xi_{i}\mathbf{B}_{m}^{-1}\Delta\mathbf{A})<1 \tag{26}\]  
Here, \(\rho(\bullet)\) denotes the spectral radius of the matrix \((\bullet)\). This means that all the eigenvalues of the matrix have absolute values less than one. The maximum eigenvalues of the matrix \(\mathbf{B}_{m}^{-1}\Delta\mathbf{A}\) can be found by solving the minimum eigenvalues of the following eigenproblem:  
\[\mathbf{B}_{m}\mathbf{y}_{i}=s_{i}\Delta\mathbf{A}\mathbf{y}_{i} \tag{27}\]  
Once the minimum eigenvalue \(s_{\text{min}}\) is solved, the convergence condition can be given by  
\[|s_{\text{min}}\tilde{\xi}_{i}|<1 \tag{28}\]  
By considering the relationship in Equation (17), we obtain  
\[|s_{\text{min}}|<|\lambda_{mi}| \tag{29}\]  
which can be approximately determined using the known undamped (real) frequencies:  
\[|s_{\text{min}}|<|\lambda_{i}| \tag{30}\]  
For structures modeled by the lumped mass matrix \(\mathbf{M}\) and heat generation matrix \(\mathbf{H}\), the system matrix \(\mathbf{B}\) is a diagonal matrix, and the operation count for the matrix decomposition of \(\mathbf{B}\) is trivial. The process of forward and backward substitutions for these basis vectors is \(LrN_{s}b\) for real \(\mathbf{K}\) matrices and \(4 LrN_{s}b\) for complex \(\mathbf{A}\) matrices, where \(b\) is the semi-bandwidth of the combined matrix and \(L\) is the number of the modes of interest. Since the number of the basis vectors \(r\ll N\) and the number of the calculated modes \(L\ll N\), the other operation count is trivial. Since the half-bandwidth \(b\) is roughly proportional to \(N_{s}^{0.5}\)[56], the operation count is simplified as \(O(LrN_{s}^{1.5})\) in the case of real modal analysis and \(O(4 LrN_{s}^{1.5})\) in the case of complex modal analysis. Therefore, the proposed repeated analysis method shows a clear advantage in the eigenvalue analysis step and thus results in an improvement over the full analysis in engineering and optimization applications.  
It is worth noting that solving the complex eigenvalue problem is not an end in itself, as the final goal of the modal analysis of the optimization process is to obtain the sensitivity number with respect to the design variable. Thus, the repeated analysis method is implemented in an optimization process along with the parallel sensitivity analysis technique.
# Modeling and Performance Analysis of Satellite Network Moving Target Defense System with Petri Nets
## 6 Discussion  
With the gradual application of active defense technologies on satellite networks, it is particularly necessary to study their impact on network performance. Since it is too costly to study satellite networks by physical experiments, a mathematical approach to modeling and evaluating their performance is a proven research method. In this paper, a new scheme is provided for evaluating the performance of satellite network moving target defense system using SPN. Based on the advantages of SPN's powerful mathematical model simulation and graphical modeling, the network model is established visually and intuitively, and some instructive conclusions are drawn through theoretical reasoning and experimental analysis. Nevertheless, there are still some issues and limitations that need to be considered and understood:  
1. MTD technology has a more complex and extensive defense scheme, and the scheme studied in the paper based only on the change of the network attack surface, which is still simple and general, and cannot fully reflect the impact of the application of moving target defense technology on the network.
2. When the problems studied and the network structure involved are more complex, modeling with Stochastic Petri Nets is prone to state space explosion, low efficiency, and complex calculation.  
In future work, we can further model and analyze the satellite network moving target defense system based on the change of three other attack surfaces (i.e., data, software, platform), and optimize the configuration of defense techniques based on the research results, so as to provide more comprehensive guidance for satellite network system security protection.
# Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
## 5 Proposed Model
### Preprocessing  
In this phase, the images of our proposed dataset went through some preprocessing steps to prepare the data for further processing. First, the images were processed in order to remove noise using the algorithms reported in the noteworthy work [60]. Then, we converted the images of our dataset gray-scale and then resized to \(28\times 28\) pixels by keeping the aspect ratio locked.
# Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data
## 5 Conclusions  
We applied a satellite data-driven LUE model framework to produce 30-m resolution daily GPP and annual crop yield estimates for the seven major crop types in Montana from 2008 to 2015. A fused 30-m eight-day NDVI record was developed using an empirical regression model combining similar spectral information from overlapping MODIS and Landsat imagery. The fused NDVI record was used with 4-km resolution gridded daily surface meteorology as primary inputs to the LUE model to derive _fPAR_ and GPP at 30-m resolution and an eight-day time step over MT cropland areas. The fused NDVI record overcomes many of the limitations of the contributing Landsat and MODIS sensor records, including Landsat data gaps and relatively coarse (250-m) MODIS spatial resolution (Figure 2). The fused record also shows generally consistent NDVI magnitudes and seasonal variations as the original Landsat and MODIS data (Figure 3), while providing complete spatial coverage, eight-day temporal fidelity and 30-m spatial resolution. The resulting 30-m GPP simulations over MT croplands are produced benefiting from the finer resolution (30-m, eight-day) NDVI and the refined model cropland calibration. Estimated annual crop production and yields are derived from the 30-m eight-day GPP simulations over MT agricultural areas defined from the annual USDA NASS cropland data layer (CDL) and calibrated HI\({}_{\text{GPP}}\) values for the seven major MT crop types. The model results corresponded favorably to NASS county-level crop production data (Table 5), indicating that the modeling approach captures regional patterns and annual variations in annual crop yield across MT and that obtaining regional-scale crop production assessments using existing global production models is possible.  
This work was conducted at the University of Montana with funding provided by the USDA NIFA (National Institute of Food and Agriculture) contract 658 2016-67026-25067, USDA 365063, NASA EPSCoR (Established Program to Stimulate Competitive Research) contract 80 NSSC18M0025 and NASA (NNX14 AI50G, NNX14A169G, NNX08 AG87A). The field-level crop yield observations were funded by the Montana Research and Economic Development Initiative.  
[PERSON], [PERSON] and [PERSON] developed this study. [PERSON] wrote the manuscript, and all authors discussed and reviewed the manuscript. B.D.M. provided field scale crop yield data.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Econometric Model Specification  
We will specify the following model to check the association between dependent and independent variables:  
\[CO2e_{t}=f(CA_{t},EN_{t},FO_{t},GDPPC_{t},ISD_{t},TF_{t},WA_{t}) \tag{1}\]  
In the above Equation (1), \(CO2e_{t}\) = carbon dioxide emission in (kt); \(CA_{t}\) = cropped area in million hectares; \(EN_{t}\) = energy use in kg of oil equivalent per capita; \(FO_{t}\) = fertilizer offtake in N/T; \(GDPPC_{t}\) = gross domestic product per capita in current USD; \(ISD_{t}\) = improved seed distribution in tons; \(TF_{t}\) = total food grains in tons and \(WA_{t}\) = water availability in MAF. We can also write Equation (1) as;  
\[\begin{split} CO2e_{t}=\delta_{0}+\delta_{1}CA_{t}+\delta_{2}EN_{t }+\delta_{3}FO_{t}+\delta_{4}GDPPC_{t}+\delta_{5}ISD_{t}\\ +\delta_{6}TF_{t}+\delta_{7}WA_{t}+\mu t\end{split} \tag{2}\]  
By employing the logarithm to Equation (2), the model follows a log-linear form:  
\[\begin{split} lnCO2e_{t}=\delta_{0}+\delta_{1}lnCA_{t}+\delta_{2} lnEN_{t}+\delta_{3}lnFO_{t}+\delta_{4}lnGDPPC_{t}\\ +\delta_{5}lnISD_{t}+\delta_{6}lnTF_{t}+\delta_{7}lnWA_{t}+\mu t \end{split} \tag{3}\]  
Equation (3) is the log-linear form of the variables. \(lnCO2e_{t}\) displays the natural logarithm of CO\({}_{2}\) emission. \(lnCA_{t}\) displays the natural logarithm of the cropped area. \(lnEN_{t}\) displays the natural logarithm of energy use. \(lnFO_{t}\) shows the natural logarithm of fertilizer offtake. \(lnGDPPC_{t}\) shows the natural logarithm of GDP per capita. \(lnSD_{t}\) displays the natural logarithm of improved seed distribution. \(lnTF_{t}\) shows the natural logarithm of total food grains. \(lnWA_{t}\) displays the natural logarithm of water availability. t demonstrates the dimension of time. \(\mu t\) indicates the error term. The model coefficients \(\delta_{1}\) to \(\delta_{7}\) demonstrate long-run elasticity.  
The stationarity of the variables was checked by employing the Augmented Dickey-Fuller [46] and Phillips-Perron [47] unit root tests. The null hypothesis is the drift of the unit root, and the two substitute hypotheses are that the series does not have a unit root with a linear time trend, and that the series has a non-zero mean and a stable trend, with no time trend. Equation (4), below, presents the unit root test.  
\[\Delta U_{t}=\alpha_{\circ}+\partial_{\circ}T+\partial_{1}U_{t-1}+\sum_{i=1} ^{m}\alpha_{1}\Delta U_{t-1}+\mu_{t} \tag{4}\]  
In Equation (4), the variable U demonstrates the unit root test. \(\Delta\) show the first difference. T is the linear trend. t indicates the time. m represents the white noise residuals to achieve.
## 2 Materials and Methods
### Soil Treatments with Cd and/or Nanoparticles  
Ninety grams of soil were placed in glass jars, and HANPs were added at 1% (_w/w_). The decision to use 1% (_w/w_) HANPs in the assays was based on both previous studies and preliminary experiments conducted by our research team. In earlier studies, doses with 2% and 3% of HANPs were tested in different experimental settings, demonstrating that higher concentrations may offer increased metal immobilization but could also pose potential risks to plant development due to their high reactivity [13; 33]. For untreated soils, this step was made without nanoparticles. An aqueous Cd(NO\({}_{3}\))\({}_{2}\) solution was added to achieve final Cd concentrations of 0, 5, 10, 25, and 50 mg kg\({}^{-1}\) (Table 1). The selected Cd concentrations were determined based on the established Generic Reference Levels (NGRs) for Galicia and other international guidelines [34]. These levels represent increasing degrees of contamination, from uncontaminated soils (0 mg/kg) to highly contaminated scenarios (50 mg/kg). The inclusion of these concentrations simulates realistic contamination scenarios that may occur due to agricultural or industrial activities. The samples were adjusted to field capacity and incubated at room temperature on a rotary shaker for 96 h. No fertilizers were applied during the experiments. The only amendments used were HANPs and Cd(NO\({}_{3}\))\({}_{2}\), added before planting quinoa seeds. Treated and untreated soils were used for phytotoxicity assays with quinoa.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Cd Concentration Added** & **Nanoparticles** \\ \hline
0 mg kg\({}^{-1}\) & Without nanoparticles \\
0 mg kg\({}^{-1}\) & 1\% HANPs \\ \hline
5 mg kg\({}^{-1}\) & Without nanoparticles \\
5 mg kg\({}^{-1}\) & 1\% HANPs \\ \hline \hline \end{tabular}
\end{table}
Table 1: Treatments applied for the different soil analyses with Cd and/or HANPs.  
* ApIndex: values between 90 and 110% are classified as non-toxic to the species for shoot growth under Cd exposure.
* ApIndex: values < 90% are classified as having an inhibitory effect on shoot growth.
* ApIndex: values > 110% are classified as having a stimulatory effect on shoot growth.
## 5 Simulation Analysis
### Simulation Results of S11 Reflection Coefficient for Without Block  
The graph compares the observed and simulated S11 reflection coefficients (in dB) throughout a frequency range of 1 GHz to 15 GHz. The Y-axis denotes the reflection coefficient, from \(-5\) dB to \(-55\) dB, signifying the quantity of signal reflected from the device input. The X-axis represents the frequency in GHz. Two curves are depicted: the green dashed line indicates the measured S11 values, whilst the red dashed line denotes the simulated S11 values. Both curves show analogous tendencies, with identical peaks and troughs across the frequency spectrum, while some discrepancies between the measured and simulated data are apparent (Figure 12). The observed S11 exhibits pronounced dips, especially at 4 GHz and 14 GHz, where the reflection coefficient decreases markedly to about \(-45\) dB. The simulated results show a similar general pattern but have less dramatic dips, suggesting somewhat stronger reflection in certain areas. Between 5 GHz and 12 GHz, the two curves exhibit a closer correlation, while discrepancies in magnitude remain at some intervals. The graph demonstrates a significant connection between observed and simulated S11 values, especially at crucial frequencies like 14 GHz, where both curves intersect strongly. The disparities at specific peaks and troughs indicate possible areas for enhancing the simulation model to reflect real-world performance accurately.
## 2 System Model and Formulation
### Complex Load Model  
At present, many load models, such as the constant power model, constant current model, and constant impedance model, are used in power load flow, transient stability studies, and system control. An excellent formulation can predict the system performance more accurately.  
Figure 4: Classical gas turbine governor and prime mover (The parameters are defined in the literature [27]).  
Figure 3: Typical model of the Hydro Turbine-Governor system (The parameters are defined in the literature [27]).  
Figure 5 shows the composite load model proposed by the Western Electricity Coordinating Council (WECC) [28].
# Surface Ripple Formation by Bombardment with Clusters: Influence of Mass
## 3 Results and Discussion
### Temperature  
Temperature plays a key role in the process, as, if kept low, diffusion processes can be considered negligible compared to collisional processes [7]. In a bombardment event with an Xe cluster and an incidence angle of 50\({}^{\circ}\), the system temperature does not exceed 600 K, but in half a ps, it drops to 400 K. Therefore, diffusive processes do not have time to modify the surface structure of the solid appreciably. However, the collisional transfer of kinetic energy from the cluster to the substrate is different if the ion is light or heavy. To observe this circumstance, Figure 3 shows the local temperature of the atoms at a time instant of a bombardment event for a Ne cluster (a) and another Xe cluster (b). Specifically, cluster number 168 is launched at an angle of incidence of 50\({}^{\circ}\). Only a strip which is parallel to the \(xz\) plane and centered around the point of impact is represented. The conservation of linear momentum in a collision with a more massive atom results in the exchange of a greater amount of linear momentum and, therefore, greater kinetic energy. The number of Co atoms that locally reach the melting temperature at that instant is of the order of 75 for Ne and 155 for Xe. It is verified that the quotient of both quantities is approximately proportional to the quotient of their square roots. This rule also holds for the rest of the atomic species. The zone of hot atoms is located on the surface and below the groove and runs up the wall of the right ridge. The maintenance of the temperature below the melting point avoids its influence on the processes studied, especially in the sputtering of light cluster atoms [23]. In the case of He, the number of atoms that reach the melting temperature (in the order of 25) is lower than that of Ne. However, the essential difference between both species is that the channeling effect can occur in He, which could increase said number in some cases, since the projectile He atoms would not be sputtered and would be stopped in the substrate.
# A Hierarchical Generative Embedding Model for Influence Maximization in Attributed Social Networks
## 6 Results and Analysis
### Performance of Link Prediction of the HGE Model  
Link prediction aims to predict the future interactions of nodes in a network. In this subsection, we compare the proposed HGE model with the baselines on the link prediction task. As in [26], we create a validation/test set that contains 5%/10% randomly selected edges, respectively, and an equal number of randomly selected non-edges. To measure the performance of the link prediction task, we report the area under the ROC curve (AUC) and the average precision (AP) scores for each method.  
Table 4 shows the link prediction performance of the proposed HGE model and the baselines on the five datasets mentioned in Section 5.2. Our HGE model significantly outperforms the baselines across all datasets, which demonstrates that modelling the hierarchical network structure, general network structure as well as node attributes in latent space is effective to learn better node embeddings.
# Global Time-Varying Path Planning Method Based on Tunable Bezier Curves
## 3 Transient Path under the Single-Obstacle Environment
### Generating Curve
The final improvement curve \(p(\kappa)\) and its first and second derivatives can be obtained by adjusting the weight matrix according to expectations and the curve is the final Bezier curve at this moment. The curve \(p(\kappa)\) in two-dimensional space changes along the \(x\)-axis and \(y\)-axis directions and the corresponding change functions can be written as \(p_{x}(\kappa)\) and \(p_{y}(\kappa)\). The radius of curvature \(R\) corresponding to this curve is calculated as follows. \[P_{i}^{j}=(1-\kappa)P_{i}^{j-1}+\kappa P_{i+1}^{j-1} \tag{14}\]4  
By controlling the parameters in the equation, it is guaranteed that the radius of curvature \(R\) of the Bezier curve is always within the specified range. So that the deflection angle of the joint is always within the limit when the moving object (such as a snake manipulator) moves along this path.
## 5 Discussion
Combined with the actual construction situation and the relevant research content in this paper, we give several basic suggestions for the configuration of disc cutters in different strata:  
* In soft and medium-hardness rock formations, the cutter penetrates easily, so the rock-breaking efficiency of the disc cutter should be improved as much as possible. This means that the rock-breaking influence range of the disc cutter should be increased. Therefore, it is recommended to use a disc cutter with a large blade width (25-28 mm) and a small blade fillet (0-5 mm);
* In hard rock formations, it is difficult for the disc cutter to penetrate, so the ability of the disc cutter to penetrate the rock should be ensured. This will result in a reduction in the penetration force. Therefore, a disc cutter with a medium or small blade width (19-22 mm) and a medium blade fillet (9-10 mm) should be selected;
* In extremely hard rock formations and soft-hard mixed formations, it is hard to penetrate the rock and the damage to the cutter is serious, so a balance between the life of the disc cutter and penetration ability should be found. Therefore, it is appropriate to use a disc cutter with a large blade width (28 mm) and a large blade fillet (15 mm).