esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 4 Deep-Learning-Based Cooperative Spectrum Sensing
### Applications of Deep Neural Networks in Cooperative Spectrum Sensing
The experimental results show that the nn-ResNet proposed in this paper has a higher accuracy of SU detection than the other two deep neural networks at noise power densities ranging from \(-140\) to \(-120\), and with an increase in the number of SUs, the accuracy of this model is also better than the CNN and RNN, with a detection accuracy of \(97.1\%\) for an SU number of \(20\). In ICECAA 2020, [PERSON] et al. [100] combined deep residual network and data-cleansing algorithms to design DRN test statistic refinement for a crowd sensor CSS. That is, the fused data in the CSS system is fed into the ResNet as a matrix together with the sensing information, and the data-cleansing algorithm senses the spectral availability of crowd sensors along the data fusion center to complete the statistical refinement of the DRN test. The simulation results show that the proposed model achieves better detection probability than data-cleaning algorithms and hypothesis-thresholding methods. Graph neural networks (GNNs), deep learning models capable of learning and reasoning about graph-structured data [101], can help sensing devices model node characteristics and support information dissemination and cooperative decision making among nodes in cooperative spectrum sensing due to their ability to represent nodes and edges as vectors or matrices. These advantages provide a new approach and tool for performance improvement in cooperative spectrum sensing. Scholars are beginning to apply GNNs to cooperative spectrum sensing. In 2023, in order to adapt to the dynamically changing radio environment and the problem of modeling hidden node scenes, [PERSON] et al. proposed a model of GCN-CSS for modeling dynamic real-world scenes under multiple antennas [102]. The GCN-CSS method has a lower computational complexity than the CNN method and still improves perceptual performance even with imperfect reporting channels. It is found that the proposed scheme provides the best performance improvement over different algorithms when the number of hidden nodes increases, and the proposed method outperforms traditional algorithms such as CNN, ANN, SVM, and K-means clustering in terms of perceptual performance. The results show that GCN-CSS has significant advantages in complex wireless environments that require dynamic adaptation and strong performance. In this section, we present applications of deep neural networks such as convolutional neural networks, residual networks, and graph neural networks to cooperative spectrum sensing. We summarize the literature described above in Table 8. These studies make certain enhancements to the performance of cooperative spectrum sensing in dynamic environments, and by combining them with suitable algorithms, they are able to achieve improvements in specific aspects compared to traditional spectrum-sensing algorithms.
## 4 Software Defined Networks
### SD-VANET Architecture: Advantages and Disadvantages  
Traditional VANET networks are struggling to meet the increasing demands for flexibility, security, and scalability, leading to the development of SD-VANET structures integrated with SDN technology to meet these needs. However, this new technology brings some disadvantages along with its advantages [5].  
Figure 3: Software Defined Vehicular Ad Hoc Network Architecture.  
Advantages:  
_Flexibility and Vendor Independence:_ The flexible and programmable nature of SDN eliminates vendor dependency by isolating network applications from hardware. This makes it easier to integrate new technologies.  
_Performance Optimization:_ With its central controller, SDN can effectively address optimization issues such as traffic scheduling, congestion control, and packet routing by enabling efficient information exchange between network layers.  
_Ease of Configuration and Innovation:_ The central controller simplifies network configuration and facilitates the addition of innovations, which is a significant advantage in complex and heterogeneous network environments [32].  
_Security and Defense Mechanisms:_ SDN enables centralized monitoring and analysis of network traffic, enabling the development of effective defenses against attacks. In addition, its self-healing capabilities help detect malicious traffic and prevent attacks.  
_Dynamic Channel Management:_ SD-VANET can dynamically select channels and frequencies based on the communication needs of vehicles, optimizing communication according to traffic density and road conditions.  
_Efficient Routing:_ The central controller of SDN optimizes data flow, reducing message delay and network bandwidth consumption.  
Disadvantages:  
_Security Threats:_ As a critical part of the network, SDN's central controller becomes a target for attack. Failure of the controller could render the entire network inoperable [33].  
_Scalability and Latency:_ Efficient management of centralized control in large-scale networks is challenging, leading to potential latency and scalability issues. As network traffic increases, the controller may become a bottleneck, adversely affecting network performance [7].  
_Dynamic and Rapidly Changing Structure:_ The high mobility of VANET can cause communication disruptions between the controller and data plane devices, reducing overall network performance.  
_Security Challenges:_ The dynamic nature of SD-VANET requires new security solutions to protect network devices. Moreover, controlling malicious nodes can lead to an overload on the controller.  
_Need for AI-Based Solutions:_ Increasing vehicle density in traffic can complicate the controller's ability to manage traffic. This problem should be addressed by integrating AI-based traffic prediction algorithms.  
While the SD-VANET architecture offers innovative solutions to the challenges faced by traditional VANET structures, it also presents new challenges that require careful management, particularly in areas such as security and scalability [34].
## 4 Discussion
This research received financial support from the National Key Research and Development Program of China from MOST (2016 YFB0501501; 2017 YFB0504000; 2017 YFC0503803), the National Natural Science Foundation of China (41401110, 31400393) and Open Fund of State Key Laboratory of Remote Sensing Science (OFSLRSS201405) and State key laboratory of Resources and Environmental Information System. We obtained the soil data from Beijing Normal University, China ([[http://globalchange.bnu.edu.cn/](http://globalchange.bnu.edu.cn/)]([http://globalchange.bnu.edu.cn/](http://globalchange.bnu.edu.cn/))), meteorological data from China Meteorological Administration ([[http://cdc.cma.gov.cn/](http://cdc.cma.gov.cn/)]([http://cdc.cma.gov.cn/](http://cdc.cma.gov.cn/))), and ESA CCI soil moisture from the CCI SM project (http://wwwseas-soil moisture-cci.org). Special thanks go to the data provider from ChinaFLUX networks. We would like to acknowledge [PERSON] for the assistance in producing LAI datasets. We would also like to thank [PERSON] for the assistance in scaling up the ET flux data. The authors declare no conflict of interest.
## 1 Introduction
### Literature Survey  
The potential in the field of aircraft morphing is evident as hundreds of research groups worldwide have been dedicated to studying the different aspects of morphing. Two of the earliest groups were launched by NASA, and they worked within the \"Active Flexible Wing program\" and the \"Mission Adaptive Wing program\" [18,19]. In these programs, flutter suppression, load alleviation and load control during rapid roll maneuvers were investigated. One of the potential consequences of flexible morphing structures is that both dynamic properties and aerodynamic loads of the wing are affected. Hence, the aeroelastic effects arising from interactions between the configuration-varying aerodynamics and the morphing structure are significant. [PERSON] et al. have presented important morphing studies in terms of aeroelastic, control and optimization aspects [20]. The nonlinear aeroelastic behaviour of a composite wing with morphing trailing-edge has been studied [21]. Moreover, the nonlinear aeroelastic behaviour of a composite wing with morphing trailing-edge has been studied. Aero-elastically stable configurations of a morphing wing trailing edge driven by electromechanical actuators have been investigated [22]. The effects induced by trailing-edge actuators stiffnesses on the aeroelastic behaviour of the wing were simulated using different approaches. The results revealed that flutter could be avoided if sufficient stiffness was provided by these actuators. An analytical sensitivity calculation platform for flexible wings has been studied [23,24]. This platform has been used to perform wing aeroelastic optimization and stability analysis. In another study, a low-fidelity model of an active camber aero-elastic morphing wing was developed to investigate the critical speed values by varying its chord-wise dimension [25]. A wide range of configurations was explored to predict the dynamic behaviour of these active camber morphing wings. The combined flutter behaviour and gust response of a series of flexible airfoils has been investigated [26]. [PERSON]'s beam equation was used to model this series of flexible airfoils. The effect of chorwise flexibility of a compliant airfoil was investigated numerically to demonstrate its dynamical stability [27,28]. An actuated two-dimensional membrane airfoil was investigated experimentally and numerically, and it was found that membrane flexibility decreased the drag and delayed the stall. These aeroelastic studies are significant in understanding the flexible morphing wing and its potentially relevant effects. However, our study does not consider the structural effects of morphing, and therefore the optimization is aimed to improve the aerodynamic drag performance and endurance in the given range of angles of attack.
## 2 Materials and Methods
### Duration of Data Collection  
For a relative field calibration approach, it is necessary to determine an appropriate data collection duration that is long enough to collect the required GNSS observations. The premise of a reliable antenna calibration is to collect GNSS observations that are distributed as densely and uniformly as possible over the antenna hemisphere [34]. However, since there were no attitude control robots in the relative field calibration setup, both antennas could only remain fixed or undergo a simple 180\({}^{\circ}\) horizontal rotation, waiting for the slow change of satellite distributions.  
Most existing studies on the relative field calibration are related to GPS, which is a Medium Earth Orbit (MEO) constellation. A mean sidereal day is the duration taken for the Earth to rotate exactly 360\({}^{\circ}\), which is about 23 h 56 min 4.1 s. For a static receiver antenna, the position of every GPS satellite is periodic with a period of a mean sidereal day [36] (pp. 12-19). So, we cannot densify the satellite distribution by extending the data collection duration to more than a mean sidereal day.  
However, it is different for GNSS systems other than GPS. For example, due to a different design of the orbit altitude, the MEO satellites of BDS hold a ground trace repeat period of seven mean sidereal days [37]. We can see from Figure 4 that the trajectory of a BDS MEO satellite does not repeat within seven consecutive days. This can be exploited to improve the satellite distribution by continuously collecting data for seven days. Furthermore, because the construction of the third phase of BDS was not completed until 2020, many off-the-shelf receivers may not be able to capture all BDS satellites signals, making it much more valuable to extend the duration of data collection in a relative field antenna calibration.  
The north hole (or south hole) also affects the satellite distribution, resulting in a region of sky with no visible satellites. A widely used method is to arrange two data collection sessions, between which the antenna under test is horizontally rotated 180\({}^{\circ}\)[16]. In our contribution, however, both antennas are supposed to be rotated, since we tend to obtain a relative phase pattern. Consequently, the GPS data could be collected for up to two days, with a horizontal rotation for both antennas at the beginning of the second day; the BDS data could be collected for up to 14 days, with a horizontal rotation for both antennas at the beginning of the eighth day.
## 4 Case Study
### The Influence of the Number of Harmonics  
Given the previous results, it is now intended to compare the influence of the number of harmonics used for the generation of the synthetic unevenness profile. Thus, Figure 9 presents the mean curves of the previous cases in the same plot for comparison purposes.  
In general, there is a high similarity between both curves, either in the time or frequency domain. The differences observed when considering 25, 50, and 75 harmonics are lower than those inferred by the randomness of the generation process of the rail unevenness profile. It should be highlighted that the randomness effect is also considered here, once the curves represented correspond to the mean curves of the previous one hundred cases. Thus, it is possible to conclude that, and for the conditions assumed, the effect of the number of harmonics has a small influence on the vibration levels evaluated.  
Figure 9: Influence of the number of sinusoidal functions used to generate the rail unevenness profile on vertical velocity: (**a**) point at the symmetry plane of the track (observation point A); (**b**) point B at 30 m away from the track (left: running RMS mean curves; right: one-third octave mean bands). dB ref. \(1\times 10^{-8}\) m/s.  
This finding is particularly relevant to the computational efficiency of the numerical tools. In fact, the computation CPU time is directly proportional with the number of harmonics, as can be seen in Figure 10. This figure illustrates the performance of the different cases using a bar chart. The calculation times are normalized in relation to the case of a number of sinusoidal functions equal to 25.
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 2 TCS Design and Requirements
### Analysis of the External Thermal Loads  
Given the structure, orbit parameters and flight attitude, the external thermal loads on the satellite's external surface in long-term mode, the external thermal loads are calculated as orbital average heat flux density for all the surfaces, including direct solar radiation, albedo of the Earth and infrared radiation of Earth, as shown in Table 7, the variation curve of heat flux density is shown in Figure 4, parameters used in calculation as follows:  
* Solar constant: \(\mathrm{S}=1412\ \mathrm{W/m^{2}}\ \mathrm{(December\ solstice)},\mathrm{S}=1322\ \mathrm{W/m^{2}}\mathrm{(June\ solstice)}\);
* Earth infrared radiation: \(\mathrm{E}_{e}=0.25(1-\rho)S,\ \ \rho=0.30\);
* Earth albedo: \(\mathrm{E}_{r}=\rho S,\ \ \rho=0.30\);
* Earth radius: 6378.14 km;  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Subjects** & **Parameters** \\ \hline Temperature inside the cabin & \(-10\)\(\sim\)\(45\)\({}^{\circ}\)C \\ Temperature of the battery & \(10\)\(\sim\)\(30\)\({}^{\circ}\)C \\ Temperature of star trackers & \(-30\)\(\sim\)\(30\)\({}^{\circ}\)C \\ Temperature of camera body & \(0\)\(\sim\)\(30\)\({}^{\circ}\)C \\ Temperature of solar panel & \(-85\)\(\sim\)\(135\)\({}^{\circ}\)C \\ Temperature of antennas & \(-90\)\(\sim\)\(\
eq\)\(90\)\({}^{\circ}\)C \\ Mass allocated to thermal control & \(\leq\)\(1\) kg \\ Power allocated to thermal control & \(\leq\)\(2\) W \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters of TCS design requirements in general.  
Figure 3: Shock absorbers in the Luojia 1-01 satellite (without the side solar panels).  
* Space temperature: 4 K.
## 1 Introduction
This study utilized the Google Earth Engine platform to investigate the Huinong District of Shizuishan City located in the Ningxia Hui Autonomous Region. The focus of the study was to analyze crop data pertaining to the Sentinel-2 image data, which had a resolution of 10 m. Machine learning algorithms were employed to accurately identify and extract the maize planting areas within the Huinong District from 2017 to 2021. Simultaneously, the integration of MODIS image data was employed to observe and assess the growth of maize in Huinong District between April and September 2021. This approach capitalizes on the temporal resolution and frequent revisit rate of MODIS, which enable the accurate recording of crop growth stages. The methodology presented in this research study demonstrates a high level of precision in extracting maize in Huinong District. Additionally, it enables the monitoring of maize development at a high frequency. Consequently, this methodology can serve as a valuable scientific foundation for local governments and agricultural departments, aiding in the organization and guidance of agricultural activities. Using remote sensing monitoring data on crop growth and development at the local level, agricultural production managers can change how crops are managed in their own areas. This enables agricultural decision-makers and extension workers to enhance their communication with local farmers by utilizing remote sensing monitoring maps of crops.
# Design of Multi-Cell Cooperative Control Algorithm Based on Fuzzy Brain Emotional Learning
## 4 Multiple Cell Manipulation Control
### Multi-Cell Controller Design  
There is a chance that cells could collide when holographic optical tweezers are in action. The control model's effectiveness is significantly diminished in the event of a collision. As a result, it is important to keep the space between each cell reasonable. In this study, the problem of cell formation is transformed into a problem of tracking ideal trajectories by adjusting each captured cell to maintain a reasonable distance from other cells. It is necessary to establish a virtual location as the origin of reference for the multicellularity. Since each cell maintains a specific positional relationship with the virtual position, the position information of the reference position can be used to create a cell formation, as shown in Figure 5:  
In this paper, it is assumed that there is a virtual cell with a reference origin and its position can be expressed as \(\mathbf{X_{R}}\). The virtual cell moves in strict accordance with the set ideal trajectory. During the uniform movement of the formation, other captured cells need to keep a certain distance from the virtual cells. Therefore, the \(\mathbf{\chi}\) formation matrix is introduced to effectively control the movement of the formation.  
\[\mathbf{X_{R}}-\mathbf{X}_{i}=\chi \tag{33}\]  
where \(\chi\) represents the expected distance between each captured cell and the virtual cell, which can be obtained by setting the ideal formation matrix.  
According to (33), the error between the actual position of each cell and the expected formation trajectory in multi-cell manipulation can be obtained:  
\[\mathbf{e_{i}}=\mathbf{X}^{d}-\mathbf{X}_{i}-\chi \tag{34}\]  
where \(\mathbf{e_{i}}\) is the error vector of each captured cell.  
The structure of the multicellular cooperative controller based on the FBEL neural network is shown in Figure 6.  
By changing the control input \(e\) of the cell manipulation controller in Figure 4 to the multi-cell error \(e_{i}\) redesigned in this section, a new multi-cell cooperative controller is constructed. At this time, the output U by the controller is the position of the optical trap that can manipulate each cell to the desired trajectory.  
Figure 5: Reference diagram of multicellular formation position.
## 3 Multi-Strategy Improved Chimpanzee Optimization Algorithm
### Benchmark Weight Strategy and Gaussian Modulated Cosine Factor
#### 3.2.2 Gaussian-Modulated Cosine Factor  
In the Chimp Optimization Algorithm (ChOA), the convergence factor \(f\) is primarily used to balance global exploration and local exploitation during the search process, playing a crucial role in the algorithm's performance.  
However, in Equation (5), the linear decay of \(f\) does not align with the actual convergence behavior during the algorithm's execution. To enhance the global search capability and local exploitation ability of the Chimp Optimization Algorithm, this paper proposes a Gaussian-modulated cosine factor, as shown in Equation (14):  
\[f=f_{start}+(f_{end}-f_{start})\cdot\exp(-\frac{(t-T/2)^{2}}{2\sigma^{2}}) \cdot(1-\cos(\frac{\pi t}{2\cdot T})) \tag{14}\]  
In the above equation, \(f_{start}\) and \(f_{end}\) are the initial and final values of the convergence factor, respectively. \(t\) is the number of current iterations, \(\sigma\) is the standard deviation of the Gaussian function, which controls the width of the Gaussian function, and \(T\) is the maximum number of iterations. The Gaussian distribution function enables a smooth adjustment of the factor throughout the entire search process. In the later stages of iteration, the exponential decay of the Gaussian function suppresses the oscillation of the cosine term, allowing the population to focus on the vicinity of the optimal solution and enhancing local exploitation. The cosine modulation term introduces periodic oscillations, which improve the algorithm's randomness and diversity during the global search phase. \(f_{start}\) and \(f_{end}\) control the dynamic range of the factor, enabling the algorithm to flexibly adapt to the requirements of different search stages. The Gaussian-modulated cosine factor combines the advantages of the Gaussian function and the cosine function, enabling dynamic and smooth adjustments to the convergence factor while providing a balanced effect that other factors cannot achieve simultaneously. Additionally, it does not require frequent parameter adjustments for specific problems, making it suitable for various scenarios, including high-dimensional, multimodal, and dynamic problems. In practical experiments, the Gaussian-modulated cosine factor demonstrated excellent convergence speed, precision, and robustness, particularly outperforming other factors in complex optimization problems.
## 3 Materials and Methods
### Headspace Solid-Phase Microextraction (HS-SPME)  
Volatile compounds from the rice samples were extracted using a dynamic headspace solid-phase microextraction (HS-SPME) attached with divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) StableFlex fibre of 50/30 \(\upmu\)m thickness and 2 cm length (Sigma-Aldrich, Saint Louis, MO, USA). According to the experimental design, rice grains were accurately weighed at either 0.5, 1.5 or 2.5 g and Milli-Q water was loaded at either 0, 2.5 or 5.0 mL into a 15 mL screw top vial, then 100 \(\upmu\)L of aqueous solution containing 5 ng of 2,4,6-trimethylpyridine (TMP) (Sigma-Aldrich, Saint Louis, MO, USA) as the internal standard was added and the vial was sealed with PTFE/silicone septa. The HS-SPME was carried out according to the design of experiment (DOE), varying the extraction factors of equilibration time (5-15 min), adsorption temperature (40-100 \({}^{\circ}\)C) and adsorption time (10-50 min). Thermal desorption of the analytes from the SPME fibre was done at 250 \({}^{\circ}\)C. Before starting the extraction, 0.1 mL of TMP standard solution was added into the sample. Every peak area in the chromatograms were standardized by the resulting area for the TMP peak.