esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Profile Antenna System for Cognitive Radio in IoST CubeSat Applications
## 3 Antenna Fabrication and Discussion of Results
### Comparison  
The proposed antenna features the benefits of sensing and communication integrated antenna, compatibility with unity CubeSat size, low profile, dual CP sensing, communication diversity, frequency reconfigurability, and UHF support. Table 1 lists a comparison between the proposed antenna and the referenced CubeSat antennas. It is easy to notice that pattern diversity has been achieved in [35; 44] based on complicated 3D structures. The dual polarization has been achieved in [8] as CP and in [46] as LP without pattern diversity and reconfigurability based on slot antenna and shared aperture antenna, respectively. On the other hand, the antennas in [3; 8; 38; 40; 47; 48] achieved CP with a complicated structure. Therefore, the proposed antenna is a good candidate for future CR-IOST applications.
## 4 Conclusions  
When uncorrupted signals are loaded, the coefficient of variation of the longitudinal grain spacing is 2.6% \(\pm\) 0.2%, indicating essentially uniform longitudinal grain spacing, regardless of whether the machine uses a position compensation function or not. However, when interference signals are added, the coefficient of variation in grain spacing increases,  
Figure 7: Test data of sowing straightness. (**a**) Random disturbance signal. (**b**) Sine wave disturbance signal. (**c**) Random disturbance signal plus sine wave disturbance signal.  
along with deviations in longitudinal grain spacing, whether the machine uses position compensation or not. With the dynamic compensation function, the variation coefficient in seeding distance decreases by an average of 6.94% as compared to not using it, after adding interference signals.  
As the strength of the interference signal increases, the lateral grain spacing will increase, and its dispersion will also increase, regardless of whether the machine uses a position compensation function or not. When the dynamic compensation function is used, the coefficient of variation of lateral grain spacing decreases by an average of 9.16% compared to not using it. Through bench testing, the stability of the longitudinal and lateral planting distances was analyzed, thereby verifying the stability of the proposed planting position dynamic compensation system.  
Overall, the successfully designed and implemented dynamic compensation system allows for real-time adjustment of planting positions based on feedback from the sowing variable measurement and control system. This system improves the precision and uniformity of individual plant placement and row spacing during corn sowing operations, thereby promoting optimal crop growth and yield. In summary, this study presents an effective solution to the issue of inconsistent plant and row spacing during corn sowing in the middle of a field. In the future, adding a set of equipment to existing seeding machines will certainly increase costs when applying this seeding technology. Therefore, in further research and planning, it is necessary to find a balance point among economic benefits, social benefits, and costs.  
Conceptualization, K.C.; data curation, B.Z.; formal analysis, C.W. and Y.Y.; funding acquisition, Y.Z.; investigation, Y.Y. and L.Z.; methodology, K.C.; project administration, Y.Z. and K.N.; resources, B.Z. and L.Z.; software, S.G.; supervision, H.W. and K.N.; validation, S.G.; writing--original draft, K.C.; writing--review and editing, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Key Research and Development Program] grant number [2022 YFD2001005-5] and The APC was funded by [2022 YFD2001005-5].  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
This work was financially supported by the National Key Research and Development Program of China Subproject (No. 2022 YFD2001005-5).  
The authors declare no conflict of interest.
## 6 Discussion
### Limitations and Challenges  
Despite its advantages, the proposed methodology has certain limitations. One challenge is the assumption of Gaussianity in the GMM clustering process. In some cases, real-world engine data may exhibit non-Gaussian characteristics, which could impact clustering accuracy. Future work could explore the integration of more flexible clustering methods, such as deep clustering or Gaussian Processes, to enhance adaptability.  
Another limitation is the variability in sequence lengths in real-world datasets. Since LSTM models require fixed-length sequences, padding was applied, which introduced some noise into the RUL prediction process. More sophisticated sequence alignment techniques, such as dynamic time warping, could be considered in future implementations.  
Real-world data introduced noise from sensor drift (e.g., \(\pm 2\%\) Pt2 readings) and intermittent gaps (15% missing cycles). This degraded RMSE by 7-26 vs. CMAPSS (Table 1). Mitigation strategies included robust autoencoders, in which denoising AEs [27] trained with 10% Gaussian noise improved reconstruction by 18%, and outlier handling by winsorizing (5 th-95 th percentiles).which reduced Score function error by 22%.  
Future work will integrate uncertainty quantification (e.g., Bayesian LSTMs [34]) to flag low-confidence predictions. Similarly, zonotopic observers and neuro-fuzzy systems [36; 37; 38] have proven effective for fault diagnosis in noisy wind turbine data, suggesting applicability to turbofan health monitoring.
# A 4.1 W/mm\({}^{2}\) Hybrid Inductive/Capacitive Converter for 2-140 mA-DVS Load under Inductor +
## 2 Architecture
### Inductive Converter
#### 2.3.4 Efficiency Improvement Techniques  
Two important techniques have been used for efficiency improvement, i.e., switch size scaling and adaptive clock frequency.  
_Switch size scaling:_ In order to reduce the switching losses the size of the power switches are scaled according to the load conditions. The PMOS switch consists of 6 equal sized cells each of width 2 mm and the corresponding drivers preceded by the MUX. Hence the switch size of the PMOS switches are varied from 2 mm to 12 mm in steps of 2 mm. Similarly, the NMOS switch is divided into 2 parallel cells of size 2 mm each, i.e, the size of the NMOS switches are varied from 2 mm to 4 mm in steps of 2 mm.  
_Adaptive clock frequency:_ The clock frequency of the ADC and the accumulator is reduced in steady state with the DPWM switching frequency remaining unaltered. This is possible because in steady state the reference voltage and the output voltage are not expected to change significantly. The steady state condition is detected by checking the ADC output which is the difference between the reference voltage and the output voltage. When the error is less than a certain value a clock whose frequency is 1/4 th the frequency of the reference clock is selected with the normal clock frequency being selected when there are larger transients.
###### Abstract  
Understanding the spatial variation law of soil respiration (Rs) and its influencing factors is very important when simulating and predicting the terrestrial carbon cycle process. However, there are still limitations in understanding how different sampling scales affect the spatial heterogeneity of Rs and whether the spatial scale effect will change with habitat types. Our objectives were to explore the effects of different sampling scales on the spatial variability of Rs and the relative importance of soil abiotic characteristics and plant traits in influencing the spatial variability of Rs. The Rs, soil properties, and plant traits were measured through field investigation and indoor analysis in the Tugai forest desert plant community in the Ebinur Lake Basin in northwest China. The Rs showed significant water gradient changes, with a coefficient of variation of 35.4%-58%. Plot types had significant effects on Rs, while the change of sampling scale did not lead to significant differences in Rs. At the plot scale, Rs spatial variation at the 5 m \(\times\) 5 m sampling scale mainly depended on plant traits (leaf length, leaf thickness, leaf dry matter content, and leaf phosphorus content, \(p<\) 0.05), while Rs spatial variation at the 10 m \(\times\) 10 m scale mainly depended on soil properties (soil total phosphorus, ammonium nitrogen, soil water content, and pH, \(p<\) 0.05). At the local scale, soil nutrients (soil available phosphorus and ammonium nitrogen) and plant traits (maximum plant height, leaf length, and phosphorus content) at the 5 m \(\times\) 5 m scale jointly explained 49% of the spatial change of Rs. In contrast, soil microclimate (soil water content), soil nutrients (soil pH, available phosphorus, and nitrate nitrogen), and plant traits (leaf thickness) jointly explained 51% of the spatial variation of Rs at the 10 m \(\times\) 10 m scale. These results demonstrate the potential to predict the spatial variability of Rs based on the combination of easily measured aboveground functional traits and soil properties, which provides new ideas and perspectives for further understanding the mechanism of Rs change in Tugai forests.  
desert ecosystem; ecosystem function; growing seasons; soil CO\({}_{2}\) emission; spatial variation +
Footnote †: journal: Physics Letters B
## 3 Results and Discussion
### Identification of Volatile and Semi-Volatile Compounds by GC-MS
The presence of these specific \(m/z\) values indicates that the compound retains the core cyclic structure of 1,6-Dioxacyclododecane-7,12-dione but with additional polymerization units, resulting in a more complex molecular structure. Figure 1: Chromatogram of (**a**) PBAT film and (**b**) TPS/PBAT blended film using GC-MS.  
For TPS/PBAT films, both with and without food preservatives, the compound with the highest intensity identified was glycerol (CAS 56-81-5), which serves as an internal additive substance (IAS) and plasticizer for forming TPS via extrusion. Glycerol is also listed in the EU regulation No. 10/2011 [26] for food contact applications, either as an additive, a monomer, or for use in polymer production. Additionally, the results revealed five compounds not previously detected, including butylolactone, 1,4-butanediol, 1,4-benzenedicarboxylic acid ethyl methyl ester, palmitic acid, and an unknown compound at a retention time of 9.10 min (fragments: \(m/z\) 61.0, 75.0, 108.0, 117.0, and 133.0). Palmitic acid is an additive used as a slip agent and it is also an approved additive and monomer for food contact material in the EU regulation [26]. The other compounds are likely degradation products formed during film manufacturing through reactions between ketones, aldehydes, and ethers. Compounds such as 1,6-dioxacyclododecane-7,12-dione and other cyclic esters have been previously reported in polyester-based materials and may lack comprehensive toxicological data [27; 30; 31; 32]. Although substances such as glycerol and BHT are listed as approved additives under EU Regulation No. 10/2011, their migration levels must remain below specific limits to avoid potential endocrine-disrupting or cytotoxic effects [28; 29]. The detection of palmitic acid and 2-ethyl salicate also warrants attention, as these may originate from additives or external contamination and contribute to cumulative dietary exposure. The incorporation of food preservatives to enhance the functionality of TPS/PBAT films also influenced the chemical structure of starch, PBAT, and TPS/PBAT matrix by acting as hydrolysis agents, plasticizers, and compatibilizers [3; 12; 33]. This structural modification may have promoted the breakdown of polymer chains and the formation of monomers and by-products.
# Real-Time State Evaluation System of Antenna Structures in Radio Telescopes Based on a Digital Twin
## 1 Introduction
It is demonstrated that DT technology has  
Figure 1: Nanshan 26 m radio telescope (self-made photo). great potential to optimize manufacturing production. [PERSON] et al. [12] proposed an aero-engine fault monitoring scheme based on DT technology, which realizes online fault detection based on a convolutional neural network. [PERSON] et al. [13] created a virtual DT method that can reconstruct the entire flight scenario and can predict signal and interference reception, which in turn dramatically improves the reliability and safety of navigation systems. The above examples prove that DT can play a huge role in the engineering field. At present, many scholars have introduced DT technology into the field of radio telescopes. Aiming at the problem of the low automation management of radio telescopes, [PERSON] et al. [14] designed a digital twin system (DTs) for the whole-lifecycle supervision of feed cabins by taking Tianyan as an example. [PERSON] et al. [15] created a virtual model of the radio telescope system based on DT technology. This was simulated and analyzed in order to optimize the maintenance schedule, improve the availability of the system, and guarantee the reliable operation of the radio telescope in remote and harsh environments. [PERSON] et al. [16] developed a set of FAST cable-net-operation quasi-real-time evaluation DTs for FAST cable-net structure. [PERSON] [17] used DT technology to create virtual models of radar antennas and platforms, using LyceanEM 0.07 to rapidly predict antenna radiation patterns and maximum directivity, enabling efficient virtual prototyping performance evaluation. Although the above research has realized the intelligent and digital monitoring of the radio telescope, the monitoring and evaluation of the overall structural state of the reflector antenna still needs further research. Therefore, based on DT technology, a real-time monitoring and evaluation system for the DT structure state of a radio telescope is established in this paper. Through mesh simplification and mesh reconstruction technology, a lightweight virtual model of a radio telescope is constructed. The kinematic model of the radio telescope and the data-driven high-fidelity random forest (RF) regression surrogate model are established to realize the rapid prediction and evaluation of the antenna structure state. At the same time, the intelligent algorithm is used to realize the virtual shape-performance mapping of the antenna. Finally, based on the visualization platform, through the communication, transmission, and storage of twin data, the construction of the DT visualization system of the radio telescope is completed, and the feasibility of the system is verified by an example.
# Study on Actuator Line Modeling of Two NREL 5-MW Wind Turbine Wakes
## 4 A Single NREL 5-MW Turbine Case
### Results in 8 m/s  
The computed power and thrust are 1.9026 MW and 433.47 kN, respectively, and the errors between FAST and these are 4.245% and 5.079%, correspondingly. For more details, the reader can refer to Table 9. Due to the tower's influence on the rotor, the changes in \(C_{p}\) and \(C_{T}\) are not unilateral but periodical (See Figure 16). It can also be seen that the wind turbine can obtain a relatively stable status. Furthermore, the interval between the two extreme points is about 2.2 s. That is when the blades rotate nearly 120 degrees, which is one-third of the cycle of the rotor revolving.  
Figure 16: The curves of power and thrust coefficient: (**a**) Power coefficient; (**b**) Thrust coefficient.  
Figure 15: Streamwise profile of the average axial velocity at hub height.  
Figure 17 shows the spanwise distribution of the time-averaged normalized axial velocity at the streamwise positions of \(-0.5D\), \(0.5D\), \(1D\), \(2D\), \(3D\), \(5D\), \(6D\), \(7D\), and \(8D\), which represent the development of the velocity in the wake. There is a clear phenomenon of the turbine extracting momentum from the incoming flow when the airflow passes through the wind turbine. That is the velocity deficit. The asymmetrical velocity profile is observed and the velocity value is not \(1.0\) at \(y/D=0\), when the effects of the tower and the hub are included. The velocity profile changes from a single peak line to double peaks when the wind flows past the rotor, and gradually becomes a single peak again. The double peak line represents that the velocity deficit is large in the middle of the blade and small in the hub, which is an indication that the loading is focused on the middle of the blade. The rate of velocity recovery becomes slower with the increasing distance, which means that the influence of the wake becomes weaker. More details about the wake development can be seen in the research of [PERSON] [31]. It seems that the wake effect is little from \(5D\) to \(8D\), and further study is presented in the next section.
# Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN +
## 4 Case Analysis
### Analysis of Network Data Augmentation and Pattern Recognition Effectiveness  
To comprehensively assess the effectiveness of the proposed data augmentation method across various classifiers and evaluate its influence on classification accuracy, a multi-class evaluation framework based on the confusion matrix was employed. This approach provides a thorough examination of the method's performance in enhancing classification outcomes. The formulas for calculating classification accuracy (\(\lambda_{accuracy}\%\)), F1 score (\(\lambda_{F1}\%\)), and G-mean (\(\lambda_{G-mean}\%\)) are provided in reference [31].
## 1 Introduction
The morphology of the internal structures was analyzed by scanning electron microscopy (SEM) measurements using a JEOL 5910 LV instrument (Tokyo, Japan), (secondary electron mode). SEM observations were made of NiO and ZnNb\({}_{2}\)O\({}_{6}\) to confirm the single-phase structure. Using a dispersive spectrometer (EDS) and an Oxford-Inca 7274 equipment, the chemical compositions were evaluated. Shielding effectiveness measurements of NiO/PANI/ZnNb\({}_{2}\)O\({}_{6}\) composites were measured in a wide frequency range (0-8 GHz) including certain frequencies with a dual-port vector network analyzer (VNA) (R&S FSH-K42).
## 6 Methodology
### Case (2): Constant Current Control
#### 6.2.1 Turn-Off Angle Control  
The current control of the turn-off angle is implemented using a PID controller. The PID parameters tuned by PSO are \(k_{p}=0.01\), \(k_{i}=40\), \(k_{d}=0.0032\). The system is tested with the mentioned load of 250 V battery in series with a 0.1 \(\Omega\) resistor at different current levels, and the setting turn-on angle is 0\({}^{\circ}\). The output response is shown in Figure 13(a,b).  
Figure 12: The transient response of turn-on angle control, (**a**) different reference output voltage (v), (**b**) the corresponding generated turn-on angle in deg.  
Figure 13: Maximum generated output voltage in volts at (**a**) different initial turn-off angles in deg, (**b**) different reference maximum input current.  
The time response of the constant (charging) current is shown in Figure 15 at a fixed 0\({}^{\circ}\) turn-on angle. The peak overshoot value is 10% for all cases, occurring at \(t=0.025\) s. The steady-state settling time is constant for all cases, which is 0.1 s, as is the current response. The battery voltage impacts the turn-off angle and the constant current, where the effect of battery voltage on the turn-off angle is illustrated in Figure 16. The maximum generated current from SRG depends on the battery voltage of the load, where at 250 V, the system can implement 250 A at \(\theta_{\text{off}}=45^{\circ}\), while at 350 V, the maximum current is 200 A at \(\theta_{\text{off}}=45^{\circ}\). The maximum generated current depends on the initial value of the turn-on angle, as shown in Figure 17, where the higher the initial turn-on angle, the less generated current. The system's response at the maximum generated current is overdamped and has a steady-state settling time of 0.05 s.
## 5 Dynamics of Jack-Up Platform and Seabed Foundation
### Displacement, Effective Stresses, and Pore Pressure
Figure 15 shows the wave-induced dynamic displacement distribution in the jack-up platform and its seabed foundation when a trough reaches below the platform under the condition of wave height \(H=5\) m. It is observed that when the wave trough arrives, the wave-induced horizontal displacement at the top of the jack-up platform is opposite to the direction of the wave, and the displacement magnitude is relatively significant. The specific magnitude has been illustrated in Figure 13. However, the wave-induced horizontal displacement in the seabed foundation is not significant. It can be seen in Figure 15b that the upper seabed foundation under the wave trough has an upward displacement, and the maximum value can exceed 2 mm. Meanwhile, the upper seabed foundation under the  
Figure 14: Wave surface, and the distribution of dynamic pore pressure, effective stress, and shear stress in the jack-up platform and its seabed foundation when a wave trough reaches the jack-up platform in Case 2 (\(t=54.9\) s). Figure 15: Distribution of the wave-induced dynamic displacement in the \(x\) and \(z\) directions in the jack-up platform and its seabed foundation when a wave trough reaches the platform at \(t=54.9\) s (\(H=5\) m, \(d=50\) m, \(T=15\) s). wave crests also has a maximum downward displacement of about 2 mm. In general, it is known that the horizontal displacement of a jack-up offshore platform under the action of a wave is greater than its vertical displacement, while the horizontal displacement in the seabed foundation is much smaller than its vertical component.
## 6 Simulation of Meshing
### Spur Gear Pair with In-Plane Misalignment
To showcase the different effects of each modification, the TE function (\(\Delta\varphi\)) for a misalignment error of 0.4\({}^{\circ}\) (7.0 mrad) and for every modification is presented in Figure 9. In this way, the \(S_{c,p,l}\) can be determined and is listed as follows:  
\[S_{c}=-0.001595;\;S_{p}=-0.001611;\;S_{l}=-0.001575.\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Angular**} & \multicolumn{2}{c}{**Circular**} & \multicolumn{2}{c}{**Parabolic**} & \multicolumn{2}{c}{**Logarithmic**} \\ \cline{2-7}
**Misalignment \(\boldsymbol{\gamma}\) (deg)** & \(\boldsymbol{C_{c}}\) **(mm)** & \(\boldsymbol{\Delta\varphi_{max}}\) **(arcsec)** & \(\boldsymbol{c_{1}}\) **(mm\({}^{-1}\))** & \(\boldsymbol{\Delta\varphi_{max}}\)**(arcsec)** & \(\boldsymbol{a_{1}}\) **(mm)** & \(\boldsymbol{\Delta\varphi_{max}}\)**(arcsec)** \\ \hline \multirow{4}{*}{0.01\({}^{\circ}\)} & 1.00 & \(-5.14156\;10^{-6}\) & 0.0064 & \(-5.13592\;10^{-6}\) & 1.12 & \(-5.12879\;10^{-6}\) \\  & 1.03 & \(-5.09568\;10^{-6}\) & 0. It can be seen that in this case, the integral of the logarithmic functions is smaller than the rest, and more specifically, less than the parabolic by 2.23% and about 1.25% smaller than the circular function. Further, Figure 10 displays the comparison of the contact paths of all three modifications, which shows that for every type of modification, the point of contact is close to the center of the tooth surface. However, upon more precise examination, the contact path for a logarithmic function is located closer to the center of the tooth surfaces compared to others. For instance, along the tooth width, contact at the pitch point produced by the logarithmic function is closer to the center than the parabola by about 0.027 mm and to the circular function by approximately 0.015 mm. Simulations for other misalignment angles also show that different modifications almost equally affect the path of contact on the tooth surface, demonstrating similar results to each other but with marginally lower \(\Delta\varphi\) for the logarithmic function.