esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
To sum up, we proposed and experimentally demonstrated a simple scheme for the generation of full-duty-cycle square and triangular pulse trains using a DDMZM. No additional assistant optical spectral processing was needed in our scheme. Full-duty-cycle square and triangular pulse trains with repetition rates of 5, 8, and 10 GHz were successfully generated. The RMSEs between the generated triangular waveform and the theoretical waveform with repetition rates of 5 GHz, 8 GHz, and 10 GHz are 0.042, 0.046, and 0.078, respectively. The degradation of RMSE may be due to the phase error between the first- and third-order harmonics which originate from the electrical amplifier connected to the PD and the hybrid coupler. This scheme has good practicality in engineering with the utilization of a bias controller.  
[PERSON] designed and set up the experiment; [PERSON] and [PERSON] carried out the measurements under the supervision of [PERSON], [PERSON] and [PERSON] provided the theoretical support for modelling the experiment. [PERSON] wrote the manuscript and all authors suggested improvements. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Key Research and Development Project (2019 YFB2203201), National Natural Science Foundation of China (61605023, 61805032, 61875028, 61520106013, and 61727816), National Research Foundation of China (6140450010305 and 61404130403), Dalian Science and Technology Innovation Foundation (2018J11 CY006), and Fundamental Research Funds for the Central Universities.  
The authors declare no conflict of interest.
## 3 Results
### Control of RWC on GPP MODIS Anomalies  
Before testing our hypothesis that RWC would be a better GPP control than f(VPD), we checked if f(VPD) used in MOD17A2 GPP is affected by potentially biased climate inputs from CRU NCEP or a biome type misclassification as already pointed out by [21,27,28] (Figure A3A,B). We obtained enhanced f(VPD) effects with the tower climate data when compared to NCEP, leading to lower root mean square error (RMSE) between MODIS/EC GPP anomalies and f(VPD) (RMSE = 0.247 for NCEP and RMSE = 0.202 for tower climate). Using the mixed forest vegetation type (MF) parameters also increased the performance of f(VPD) for GPP estimates with RMSE = 0.214 with NCEP and RMSE = 0.179 for tower data (Figure A3B). However, the temporal matching between MODIS/EC GPP anomalies and f(VPD) remained low (year 2008 Figure A3A for example). We then tested for the relationship between soil RWC and GPP errors observed between MODIS and EC (Figure 4). We obtained a significant relationship following a non-linear decay (Equation (3)):  
\[RE=(aRWC\,/\,(RWC-b))+c \tag{3}\]  
with a = 36.7, b = 0.39, c = -70.2, R\({}^{2}\) = 0.77 and \(p<0.0001\). The curve exhibits a vertical asymptote for RWC \(\cong\) 0.4, and the RE starts to deviate from 0 when RWC = 0.7. The larger anomalies of GPP were mainly concentrate in the 0.4-0.5 interval of RWC. When comparing RE with GPP anomalies between MODIS and EC, we obtained RMSE = 0.162 (Figure A3A), and RMSE = 0.149 when combined with f(VPD) calculated with the tower climate, both outperforming the f(VPD) correction alone.  
We finally used the yearly WSI, derived from daily simulation of soil RWC and ecosystem water budget, to characterize the dry season for each year in terms of both the length and intensity of drought.  
Figure 3: Meteorological data collected at the flux tower site from 2001–2010. Eight-day averages for vapor pressure deficit (VPD, in hPa), minimum temperature (T\({}_{\text{min}}\), in \({}^{\circ}\)C) and photosynthetically active radiation (PAR, in mol\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)). The dashed lines represent the seasons: spring (DOY 65–152), summer (DO 153–248), autumn (DOY 249–335) and winter (DOY 0–64 and 336–366).  
We investigated this annual drought index as an empirical driver controlling the anomaly between MODIS GPP and the actual tower GPP cumulated for each year, by plotting these two variables in Figure A4. We obtained a significant linear relationship (\(\mathrm{RE}=-26.65-0.4\mathrm{WSI}\), \(\mathrm{R}^{2}=0.71\) and \(p=0.0021\)) between the relative errors of annual GPP (between MODIS and EC), and WSI. We will note that the years 2003 and 2006 were the most out-of-the-trend years, both years respectively corresponding to an extreme heat wave where high temperatures might have abnormally modified carbon assimilation rates, and the driest year of the period with very low RWC for which water potential calculations are very sensitive and potentially biased.
## 4 Results and Discussion
### Mean Wind Characteristics
#### 4.1.1 Wind Profiles and Wind Speed Amplification Factor
Owing to the wide river channel and the lower terrain in front of the bridge, the two towers are less affected by the interference from the terrain. Thus, the distribution of wind profiles at the northeast tower is very similar to that at the southwest bridge tower. When the oncoming wind direction is 30\({}^{\circ}\), although the wind is obstructed by the mountains, the mountains are relatively low, and  
Figure 6: Wind profiles of the southwest tower under uniform inflow with different wind speeds and directions. Figure 7: Wind profiles of the southwest tower under ABL inflow with different wind speeds and directions. the obstruction effect is not as significant as when the wind direction is 180\({}^{\circ}\). Therefore, the mean wind speed at the 30\({}^{\circ}\) wind direction is lower than at 0\({}^{\circ}\) but higher than at 180\({}^{\circ}\). When the oncoming wind is uniform, the northeast bridge tower is less affected by the wind speed, and the wind speed profiles essentially overlap. When the oncoming wind is ABL, the mean wind speed at different locations on the northeast tower decreases with increasing oncoming wind speed. This distribution pattern is consistent with the southwest bridge tower. The wind speed amplification factors on the main beam are shown in Figures 10 and 11. When the oncoming wind is uniform, the wind speed amplification factors on the main beam at the 0\({}^{\circ}\) and 30\({}^{\circ}\) wind directions are both greater than 1. This indicates that the wind accelerates as it passes over the terrain and reaches the main beam. Additionally, the wind speed amplification factors on the main beam remain largely consistent regardless of the oncoming wind speed. When the oncoming wind is ABL inflow and the wind directions are 0\({}^{\circ}\) and 30\({}^{\circ}\), the relatively flat terrain of the river channel also leads to acceleration at the main beam, with the wind speed amplification factors being greater than 1 at the entire main beam. When the wind direction is 180\({}^{\circ}\), the wind speed amplification factor on the main beam decreases in the direction of the northeast, which is influenced by the presence of mountains in the northeast. As the oncoming wind speed increases, the wind speed amplification factor on the main beam gradually decreases. When the wind speed reaches 12 m/s or above, the wind speed amplification factor remains unchanged. Figure 8: Wind profiles of the northeast tower under uniform inflow with different wind speeds and directions. Figure 9: Wind profiles of the northeast tower under ABL inflow with different wind speeds and directions.
# Continuous-Thrust Circular Orbit Phasing Optimization of Deep Space CubeSats
###### Abstract  
The recent technology advancements in miniaturizing the primary components of spacecraft allow the classic CubeSats to be considered as a valid option in the design of a deep space scientific mission, not just to support a main typical interplanetary spacecraft. In this context, the proposed ESA M-ARGO mission, whose launch is currently planned in 2026, will use the electric thruster installed onboard of a 12U CubeSat to transfer the small satellite from the Sun-Earth second Lagrangian point to the orbit of a small and rapidly spinning asteroid. Starting from the surrogate model of the M-ARGO propulsion system proposed in the recent literature, this paper analyzes a simplified thrust vector model that can be used to study the heliocentric optimal transfer trajectory with a classical indirect approach. This simplified thrust model is a variation of the surrogate one used to complete the preliminary design of the trajectory of the M-ARGO mission, and it allows to calculate, in an analytical form, the typical Euler-Lagrange equations without singularities. The thrust model is then used to study the performance of a M-ARGO-type CubeSat (MTC) in a different scenario (compared to that of the real mission), in which the small satellite moves along a circular heliocentric orbit in the context of a classic phasing maneuver. In this regard, the work discusses a simplified study of the optimal constrained MTC transfer towards one of the two Sun-Earth triangular Lagrangian points. Therefore, the contributions of this paper are essentially two: the first is the simplified thrust model that can be used to analyze the heliocentric trajectory of a MTC; the second is a novel mission application of a CubeSat, equipped with an electric thruster, moving along a circular heliocentric orbit in a phasing maneuver.  
M-ARGO CubeSat; circular orbit phasing; optimal control law; two-dimensional trajectory optimization
# The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities
## 3 Calibration of SMAP-R Signals
\[\mathrm{P}_{\mathrm{coh}}(\tau,f_{\mathrm{d}})=\mathrm{T}_{\rm i}^{2}\frac{ \mathrm{P}_{\rm tx}G_{\rm tx}G_{\rm rx}(\overrightarrow{\rho}_{\rm sp}) \lambda^{2}\Lambda^{2}(\tau-\tau_{s}(\overrightarrow{\rho}_{\rm sp}))}{(4\pi )^{3}(\mathrm{R}_{\rm rx}(\overrightarrow{\rho}_{\rm sp})+\mathrm{R}_{\rm tx }(\overrightarrow{\rho}_{\rm sp}))^{2}}\times\big{|}S(f_{\mathrm{d}}( \overrightarrow{\rho}_{\rm sp})-f)\big{|}^{2}\widetilde{\mathcal{R}}( \overrightarrow{\rho}_{\rm sp})\big{|}^{2} \tag{2}\]  
where:  
* \(\overrightarrow{\rho}_{\rm sp}\) corresponds to the specular point on the surface;
* \(\overrightarrow{\mathcal{R}}(\overrightarrow{\rho}_{\rm sp})\) is an average reflection coefficient at the specular point, obtained from the average reflection coefficient of the scattering surface \(\overrightarrow{\mathcal{R}}(\overrightarrow{\rho})\) evaluated at the specular direction, i.e., \(\overrightarrow{\mathcal{R}}(\overrightarrow{\rho}_{\rm sp})=\overrightarrow {\mathcal{R}}(\overrightarrow{\rho})\ \delta(\overrightarrow{\rho}- \overrightarrow{\rho}_{\rm sp})\). SMAP-R data presents calibration challenges since the GPS direct power (\(\mathrm{P}_{\rm tx}\)) is not measured and there is a lack of knowledge about GPS antenna gain (\(\mathrm{G}_{\rm tx}\)). In addition, the SMAP high antenna gain results in a footprint of 40 km diameter at a 40\({}^{\circ}\) incidence angle that limits the number of specular bistatic observations. Therefore, to obtain \(\sigma_{0}\) (\(\overrightarrow{\rho}\)) or \(\overrightarrow{\mathcal{R}}(\overrightarrow{\rho}_{\rm sp})\) from Equations (1) and (2), there are a number of calibration corrections that need to be applied to the received power in watts (\(\mathrm{P}_{\mathrm{incoh}}\) or \(\mathrm{P}_{\mathrm{coh}}\), depending on the type of scattering surface). The next two subsections provide the calibration methodology, which uses external information from CYGNSS to account for the direct Equivalent Radiated Isotropically Power (EIRP) information (\(\mathrm{P}_{\rm tx}G_{\rm tx}\)) and the calibration methodology to account for the filtering effect of the SMAP antenna. The two calibration methodologies are analyzed under two different scenarios:  
1. A surface dominated by incoherent scattering, i.e., Equation (1), either the ocean surface or land areas either covered or not by vegetation with no presence of river, lakes, wetlands, flooding, or any kind of coherently dominated reflection surface;A surface dominated by coherent scattering, i.e., Equation (2), any type of land area containing mirror-like surfaces (such as rivers, lakes, wetlands, flooded surfaces, or sea ice surfaces). Note that this work employs CYGNSS version 2.1 data. EIRP values provided in version 2.1 are estimated coarsely and contain errors. Those inaccuracies are a source of error in the calibration depicted in this study. New CYGNSS version 3.0 data will provide more accurate EIRP information.
## 5 Simulation Results
### Simulation Results
#### 5.2.3 Performance Comparisons on Different Topologies  
Furthermore, to verify the universality of the MODECP, we evaluate the algorithm in three different topologies to obtain efficient solutions for distributed controller placement.  
Figure 6 shows the performance of the proposed algorithm in three topological situations: Xspedius, Bellcanada, and Uninett2010. From Figure 6a, it can be seen that the performance of the proposed algorithm generally shows an increasing trend with the  
Figure 4: The performance of different algorithms under the Bellcanada topology. (**a**) Performance regarding end-to-end delay; (**b**) performance regarding node security; (**c**) performance regarding controlling node load rate; (**d**) performance regarding link cost.  
Figure 5: The running time of the algorithm.  
increase in the number of controllers regarding the end-to-end delay. When the number of controllers increases, the control scope of the controllers is reduced, resulting in a reduction in the switch-controller transmission delay, but the synchronization delay of the inter-controller will increase. Since the data packets synchronized by the controller are usually larger than the data packets transmitted by the switch controller, the performance of controller synchronization delay is more obvious. At the same time, the greater the number of nodes in the network, the greater the end-to-end delay. Figure 6b shows that node security generally shows a downward trend. When the number of controllers increases, the probability of the controllers being placed on switches with greater degree centrality will increase, so the security of the network decreases. That is, the more controllers deployed in the network, the worse the security performance. At the same time, when the number of nodes in the network is small, the search space for placing the controller becomes smaller, resulting in lower security. Therefore, the number of controllers deployed should not be too high to improve security. As can be seen from Figure 6c, the difference in topology has little effect on the load rate. Although the time interval of controller synchronization remains the same, the number of synchronizations between controllers will rise as the number of controllers grows. The link cost caused by synchronization increases, so Figure 6d shows that the link cost generally shows an increasing trend.  
Figure 6: The performance of the proposed algorithm under different topologies. (**a**) performance regarding end-to-end delay; (**b**) performance regarding node security; (**c**) performance regarding controlling node load rate; (**d**) performance regarding link cost.
# Secure Aviation Control through a Streamlined ADS-B Perception System
## 4 Methodology
### The Dataset  
This sub-section will discuss the dataset regarding features, records, and content; the dataset is publicly available on Mendeley Data and includes authentic ADS-B messages samples acquired from the OpenSky Network and injected messages simulated using PyCharm (version 2021.3.1). There are no personally identifiable information violations in this dataset. The adopted dataset by [PERSON] et al. has two files totaling 3 MB. This dataset contains authentic ADS-B message samples acquired from the OpenSky Network and injected messages for path modification, ghost aircraft injection, and velocity drift attacks.  
The dataset authors used PyCharm, a development environment for programming integrated within Python, to simulate malicious messages with the required attack intentions. The dataset is balanced and contains 22,316 messages, of which 11,158 are authentic, and 11,158 were injected within the dataset files. The CSV files were specifically chosen because they typically provide data distribution information.  
After removing authentic messages from the dataset, we noticed that the injected message numbers were unequal, implying that the dataset was imbalanced. This required us to use different over- and under-sampling techniques to balance it and improve the model's quality to help classify imbalanced data and eliminate bias. The following over- and under-sampling techniques were utilized in this study:  
* **Random oversampling**: performing random oversampling involves selecting random data from the class that is considered the minority and then replacing or adding that data to the training dataset.
* **Random undersampling**: the opposite of random oversampling, performing random undersampling involves selecting random data from the class that is considered the majority and replacing or adding that data to the training dataset.
* **Synthetic minority oversampling technique (SMOTE)**: an oversampling technique used with imbalanced datasets to help classification using machine learning, performed by generating new data from the existing minority data and then using it to supplement the dataset [34].
## 2 The FSI Model of Reciprocating Compressors
### Convergence Judgment and Grid Independence Verification  
Since the simulation of the compressor is an unsteady state issue, the simulation of full working cycles (including compression, discharge, expansion, and suction) based on the complete fluid-structure interaction model of the reciprocating compressor was executed to ensure the convergence of simulation results. The simulation results showed that the pressure variation of the third and fourth cycles were almost identical, as shown in Figure 7. Therefore, the dynamic convergence of the grid can be guaranteed by simulating the compressor for three working cycles [23; 24; 28].  
In order to verify grid independence, four groups of flow field grids and four groups of structure field grids were selected [29; 30]. The mesh numbers of the flow field were 8838, 25,416, 35,352, and 70,704. The mesh numbers of the structure field were 1056, 1568, 2646, and 3376. The maximum pressure in the cylinder was used as an index to characterize the grid independence. The results are shown in Table 3.  
Figure 6: Decoupling method of the FSI model.  
Figure 7: The pressure variation in the cylinder by the FSI model in four cycles.  
The results indicate that when the mesh numbers of the flow field were 35,352 and 70,704, the difference of the simulation results was less than 0.32%. When the mesh numbers of structure field were 2645 and 5290, the difference of the simulation results was less than 0.64%. Considering the calculation speed and accuracy, the mesh numbers of the flow field and structure field were 35,352 and 2645, respectively.  
By comparing the simulation results of different time steps, the independence of the time step was verified. Three kinds of time steps were selected in this paper, such as \(10^{-6}\) s, \(10^{-5}\) s, and \(10^{-4}\) s. As shown in Figure 8, there was little difference in the pressure variation of the cylinder under three kinds of time steps, and the trend of the pressure variation was identical. Therefore, the time step of \(10^{-4}\) s could be selected to achieve better accuracy and a higher computing speed.
## 4 Result and Discussion
### Feature Analysis  
In this section, the MIC-FCBF combined feature selection method is employed to analyze the input features for IMF1-10 and the residuals, exploring the correlation between input features and IMF1-10 as well as the residuals. Figure 10 shows the MIC (Maximal Information Coefficient) values between input features and IMF1-10, as well as the residuals. MIC values range between 0 and 1, with higher values indicating stronger correlations between input features and predicted values.  
As depicted in Figure 10, since the residuals are obtained after the CEEMDAN signal decomposition, exhibiting stable long-term variations, other features such as WS, CC, and ECP display a certain degree of correlation. Particularly, the feature ECP contains a significant amount of valid information related to the residuals. MIC values for Day, DT, BP, WS, and CC with respect to IMF1-10 are relatively low, indicating a lower correlation, which aligns with the actual scenario. Therefore, input features with MIC values below 0.2 are excluded, and the remaining features undergo further screening using the FCBF method.  
Figure 11 presents the SU (Symmetrical Uncertainty) values calculated through the FCBF (Fast Correlation Based Filter) method for the correlation between input features and the predicted values. These SU values are used to measure the association between features and the target variable, aiding in the selection of features that are crucial for regression prediction, thereby optimizing model performance and reducing redundant features. For different IMF (Intrinsic Mode Function) components, as well as the residuals, with respect to TIME, WT, and T, the SU values are generally high. This aligns with the real-world relationship between energy consumption and features. However, due to the impact of  
Figure 9: CEEMDAN energy consumption data decomposition spectrum.  
noise and nonlinear relationships, there is a slight decrease in the SU values for TIME in IMF5 and IMF7. This observation underscores the practical value of the FCBF method.  
Therefore, in the subsequent FCBF filtering process, features with SU values greater than 0.3 are selected as the final input features for IMF1-10 and the residuals in the prediction model training. Through this combined feature selection approach, which ensures the relevance between features and predicted values, the training and prediction of the energy consumption forecasting model are conducted.  
Figure 10: MIC values between features and decomposition results.
## 2 Materials and Methods
### Discrete Fourier Transform  
Another instrument used in this research is the Discrete Fourier Transform (DFT), which is a hypothesis test that measures the randomness in a bit string. The goal is to analyze that there is no repeating pattern in the binary string [39]. Additionally, it is part of the NIST 800-22 standard. Finally, the variables involved in the test are expressed in Equations (23)-(25), which are written below.  
\[N_{0}=\frac{(0.95)\times n}{0.05} \tag{23}\]  
In Equation (23), \(n\) is the length of the string. Moreover, using this data, a bound \(l\) is calculated according to Equation (24).  
\[l=\sqrt{\mathrm{Ln}\frac{1}{0.05}(n)} \tag{24}\]  
Additionally, the \(g_{j}\) functions of the procedure are calculated according to Equation (25). Where \(x_{k}=\{-1,1\}\), \(i=\sqrt{-1}\) and \(j=1,2,\ldots,\frac{n}{2}-1\), in our case the value \(n\) is always even. Another key point in Equation (25) is that \(g_{j}\) is a complex function. The variable \(N_{1}\) in Equation (26) starts equal to zero. Subsequently, \(\|g_{j}\|\) is determined. In case of a value less than \(l\), then one is added to \(N_{1}\). Otherwise, the \(N_{1}\) value is not modified.  
\[g_{j}=\sum_{k=1}^{n}x_{k}e^{\frac{2\pi(i)(k-1)j}{k}} \tag{25}\]  
Once \(N_{1}\) has been calculated, it is possible to obtain \(d\) using Equation (26). From here, the decision rule is: if the \(P\)-value, (defined in Equation (27)), is less than 0.01, then the hypothesis that the string is random is rejected. Otherwise, it is accepted.  
\[d=\frac{N_{1}-N_{0}}{\sqrt{\frac{n(0.95)(0.05)}{4}}} \tag{26}\]  
\[P\text{-}value=\mathrm{erfc}\frac{\mid d\mid}{\sqrt{2}} \tag{27}\]  
The erfc function is calculated according to Equation (28).  
\[\mathrm{erfc}\frac{\mid d\mid}{\sqrt{2}}=2(1-\Phi(\mid d\mid)) \tag{28}\]