esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Variable-Step-Size Adaptive Filter Algorithm with a Momentum Term
###### Abstract  
To address the contradiction between the convergence error and convergence rate in the LMS algorithm, this study proposes a variable-step-size adaptive filter algorithm with a momentum term based on the logistic function. First, the normalization LMS algorithm is obtained by seeking the extremum under the Lagrange function constraint. Second, to reduce the convergence error of the algorithm, the logistic model is used as a function model of step size variation with error, resulting in a variable-step normalization LMS algorithm. Our experimental results demonstrate that this algorithm achieves smaller convergence errors compared to those of the traditional LMS algorithm. Finally, to further improve the convergence rate of the algorithm, a momentum term is introduced into the weight coefficient update process of the LMS algorithm. This leads to the development of a variable-step adaptive filter algorithm with a momentum term based on the logistic function. The impact of different parameters on the algorithm performance is also investigated. In order to verify the rationality of the proposed algorithm, a dynamic system mathematical model was identified using the proposed algorithm. The results showed that the proposed algorithm had an identification accuracy of over 97% for the mathematical model parameters and a suppression of over 99% for noise. In order to verify the engineering application value of the proposed algorithm, real-time vibration data fitting experiments were conducted in the Aeroelasticity Laboratory of the China Aerodynamics Research and Development Center, and their results were compared with three algorithms: ARMAX, N4 SID, and LMS. The results showed that the proposed algorithm had a higher fitting accuracy than the three others. Through simulations and experiments, it is demonstrated that this study has value both theoretically and in engineering applications, promoting engineering applications of adaptive filtering algorithms and providing strong support for the research of adaptive control.  
m _applied sciences_
## 3 Case Analysis
### Prediction Accuracy with All Factors
#### 3.1.2 Performance of Single Flux Site  
As shown in Figure 1, the distribution map of the nine flux sites indicates that different sites have different vegetation and climate conditions. Therefore, it is necessary to test the GPP prediction accuracy of the GeoMAN model at different flux sites.  
1. Test site GPP against remaining sites  
We took the target flux site as the test data and the remaining sites as the training data, for a total of nine sites being tested. As shown in Figure 5, there is a large difference in performance among the different flux sites. The possible reasons for this difference are (1) some sites have different vegetation types from the target site, and (2) some sites have different climate conditions from the target site due to their long distance.  
2. Test site GPP against the other sites at distance of 500 km and 100 km  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **RF** & **SVR** & **DBN** & **GeoMAN** \\ \hline RMSE & 0.954 & 0.910 & 0.912 & 0.788 \\ MAE & 0.553 & 0.571 & 0.559 & 0.440 \\ R\({}^{2}\) & 0.810 & 0.827 & 0.827 & 0.870 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the four models with the use of all data.  
Figure 5: Predicted GPP vs. labeled GPP at a single site.  
The distances between each flux site were calculated using the Haversine formula based on their latitude and longitude. The calculation results are shown in Table 3.  
Based on the results in Table 3, each target flux site was predicted using the flux sites within 500 km and 100 km as the training data. There are no flux sites within 100 km of the GL and ZF sites, so they were not included in the prediction results for sites within 100 km. The prediction results are shown in Figures 6 and 7. According to the results, the accuracy of the AR site increases as the range of selected sites decreases, while the DXST, NMC, and ZF sites show a decreasing trend in accuracy as the range of selected sites decreases. The overall accuracy of the DXSW site is lower than when using all sites for prediction, but it shows a rebound trend as the range decreases. According to Table 1, the AR site with an increasing trend has alpine Kobresia meadow as its vegetation type, while the DXST, NMC, and ZF sites with a decreasing trend have alpine meadow steppe as their vegetation type. It can be speculated that the prediction effect of each site is related to the vegetation type of the other selected sites.  
Figure 6: Predicted GPP vs. the labeled GPP at a single site (500 km).  
Figure 7: _Cont._  
Figure 8: Predicted GPP vs. labeled GPP at a single site based on vegetation type.  
Figure 7: Predicted GPP vs. labeled GPP at a single site (100 km).
###### Abstract  
Offshore wind turbine structures (OMTs) commonly use monopile foundations for support, and long-term exposure to wind-wave cyclic loads may induce changes in foundation stiffness. Variations in foundation stiffness can significantly alter the inherent vibration characteristics of OMTs, potentially leading to amplified vibrations or resonant conditions. In this study, a numerical model considering soil-pile interaction was developed on the FLAC3D platform to analyze the natural frequency of OMTs under long-term cyclic loading. The study first validated the numerical model's effectiveness through comparison with measured data; a degradation stiffness model (DSM) was then embedded to assess how prolonged cyclic loading affects the degradation of foundation stiffness. A series of parametric studies were conducted in medium-dense and dense sand layers to investigate natural frequency alterations induced by prolonged cyclic loading. Finally, a simplified method for evaluating long-term natural frequency changes was established, and a 3.6 MW offshore wind turbine case was used to reveal the evolution characteristics of its natural frequency under long-term cyclic loads. The data reveal that the natural frequency of the structure undergoes a downward tendency as cyclic loading and frequency increase. To ensure long-term safe operation, the designed natural frequency should preferably shift toward 3P (where P is the blade rotation frequency).  
offshore wind turbine (OWT); soil-pile interaction; degradation stiffness model; natural frequency
# Conceptual Design of a Semi-Dual Polarized Monopulse Antenna by Computer Simulation
###### Abstract  
Presented in this paper is a conceptual design by computer simulation of a monopulse reflector antenna with dual-circularly polarized sum patterns and linearly polarized azimuth and elevation difference patterns, which can be called a semi-dual polarized antenna. The proposed antenna consists of a five-element monopulse feed and a prime-focus parabolic reflector. The novelty of the proposed antenna is a monopulse feed consisting of a dual-circularly polarized square waveguide sum channel radiator and linearly polarized rectangular waveguide azimuth and elevation difference channel radiators. The separation of dual circular polarization is realized by a septum polarizer. The difference pattern is obtained by feeding two rectangular waveguides in opposite directions using a coaxial probe. The proposed monopulse feed geometry requires only two power combiners for a monopulse comparator network while providing dual-polarized performance comparable to the full dual-polarized sum and difference channel monopulse scheme. The concept of the proposed antenna is shown in a conceptual design by computer simulation. The monopulse feed is designed first, and then combined with a parabolic reflector. The designed monopulse reflector antenna operates at 14.5-16.0 GHz, and shows excellent sum and difference pattern characteristics: 36.1-36.7 dBc sum channel directivity with 0.65 dB boresight axial ratio and 32.6-32.9 dBi difference channel directivity with 1.56-1.66\({}^{\circ}\) crossover angle.  
monopulse antenna; parabolic reflector; dual-circular polarization; linear polarization; monopulse comparator simplification; septum polarizer; coaxial probe
## 3 Model Test Verification
### Analysis of Test Results
The errors between the calculation results and the measured results are less than 15%, so the pile top rotation angle calculated by theory can be considered reliable. (3) Analysis of pile bending moment  
Figure 7 shows the measured and theoretical results of the pile bending moment of the ZK pile, the ZPJ-35 pile, and the ZPW-25 pile under the eighth load (50 N). According to Figure 7, the maximum bending moment points of the three test piles all appear in the observed section with a buried depth of 0.05 m, and the setting of the bearing  
Figure 6: Pile top rotation angle between ZK pile, ZPJ-35 pile, and ZPW-25 pile. (**a**) Comparison between the ZK pile and the ZPJ-35 pile by testing, theory and fitting curves about pile top rotation angle. (**b**) Comparison between the ZK pile and the ZPW-25 pile by testing, theory and fitting curves about pile top rotation angle. Figure 7: Bending moment between straight pile, ZPJ-35 pile, and ZPW-25 pile. (**a**) Comparison of bending moments of the ZK pile and the ZPJ-35 pile under the eighth load. (**b**) Comparison of bending moments of the ZK pile and the ZPW-25 pile under the eighth load. plate significantly reduces the maximum bending moment values of the piles. According to the measured results, it can be seen that the maximum bending moment of the pile body of the ZK pile is 8.032 N-m, while that of the ZPJ-35 pile and the ZPW-25 pile is only 5.609 N-m and 5.247 N-m, which are reduced by 43% and 53% compared with the straight pile, respectively. At the same time, the reduction in pile bending moment at the section on the plate further leads to the reduction in bending moment at each point of the pile body under the plate. It can be seen from Figure 7 that the maximum pile bending moment obtained by the actual measurement and theoretical calculation both occurs at the position where the pile body buried depth is close to 0.05 m, in which the calculated maximum bending moment of the ZPJ-35 pile is 5.504 N-m, which is 2% smaller than the measured value of 5.609 N-m. The calculated maximum bending moment of the ZPW-25 pile is 5.247 N-m, which is 3% larger than the measured value of 5.085 N-m.  
On the whole, the horizontal displacement of the pile top, the rotation angle of the pile top, and the bending moment of the pile body calculated by using the horizontal bearing capacity theory established in the paper are in good agreement with the test results, which proves the rationality and accuracy of the calculation method in the paper. 4 Analysis of the Influencing Factors of the Horizontal Bearing Capacity of the Squeezed Branch Pile  
In order to explore the bearing mechanism of squeezed branch piles under horizontal load, based on the above theoretical derivation, the changes in pile displacement and pile bending moment under horizontal load with the depth of the model pile are discussed. The influencing factors include the diameter of the bearing plate, the position of the bearing plate, the constraint conditions of the pile top, etc. The theoretical calculation parameters are consistent with the model test parameters.
# Cellulose as a Delivery System of Raspberry Juice Volatiles and Their Stability
## 2 Results
### Evaluation of Volatiles
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Juice** & **Complex \({}^{*}\)** & **RT (min) \({}^{1}\)** & **RI \({}^{2}\)** & **MW \({}^{3}\)** & **log P (w) \({}^{4}\)** & **Vapor Pressure (mm/Hg)** & **Odor Description** \\ \hline \(\alpha\)-ionol & + & 37.4621 & 1376 & 194.317 & 4.492 & 0.001 & berry \\ \(\beta\)-damascenone & + & 37.6571 & 1380 & 190.286 & 4.04 & 0.02 & woody \\ ethyl decanoate & + & 38.0311 & 1389 & 200.322 & 4.861 & 0.034 & fruity \\ \(trans\)-caryophyllene & - & 38.5426 & 1402 & 204.356 & 6.777 & 0.013 & woody \\ \(\alpha\)-ionone & + & 38.9487 & 1417 & 192.302 & 3.995 & 0.014 & berry \\ dihydro \(\beta\)-ionone & + & 39.2249 & 1432 & 194.317 & 3.990 & 0.01 & berry \\ dihydro-p-ionol & + & 39.4117 & 1440 & 196.33 & 4.634 & 0.001 & floral \\ geranyl acetone & + & 39.6066 & 1448 & 194.318 & 3.834 & 0.016 & floral \\ \(\beta\)-ionone & + & 4.3458 & 1477 & 192.302 & 3.995 & 0.017 & berry \\ myristicin & - & 41.0120 & 1511 & 192.214 & 2.586 & 0.008 & woody \\ liliai & + & 41.0091 & 1514 & 204.313 & 4.216 & 0.005 & floral \\ - & \(\alpha\)-cordol & 42.4579 & 1592 & 222.372 & 4.33 & 0.001 & woody \\ myristi aldehyde & - & 42.4825 & 1601 & 212.376 & 6.008 & 0.006 & woody \\ - & benzophenone & 42.2799 & 1618 & 182.222 & 3.1 & 0.001 & floral \\ methyl & + & 43.1727 & 1647 & 226.316 & 2.653 & 0.001 & floral \\ dihydroasionate & - & hexyl salicate & 43.4814 & 1667 & 222.284 & 5.07 & 0.00049 & green \\ hexyl & + & 44.3590 & 1737 & 216.324 & 4.866 & 0.001 & floral \\ \hline \end{tabular} \({}^{1}\) RT—attention time of volatiles: \({}^{2}\) RI—attention index of volatiles: \({}^{3}\) MW—molecular weight: \({}^{4}\) Log P—logarithm of octanol water coefficient that indicates the relative hydrophobicity of compound; data were obtained from [[http://www.thegoodsontscompany.com](http://www.thegoodsontscompany.com)]([http://www.thegoodsontscompany.com](http://www.thegoodsontscompany.com)) and [[http://www.chemicalbook.com](http://www.chemicalbook.com)]([http://www.chemicalbook.com](http://www.chemicalbook.com)); \({}^{4}\) detected \({}^{+}\)\({}^{*}\) and not detected \({}^{-}\)–volatile compounds in juice and cellulose/aspberry complexes.  
\end{table}
## 2 Materials and Methods
### Multifractal Detrended Fluctuation Analysis (MFDFA) Method
The width of the multifractal spectrum \(\Delta\alpha\) is calculated by Equation (17). \[\Delta\alpha=\alpha_{\max}-\alpha_{\min}\] (16)
* In addition, in the field of earthquakes, vibration velocity is often used as an evaluation criterion for vibration intensity. The three types of mine shock waves can be distinguished by the particle peak particle vibration velocity (PPV) attenuation coefficient. PPV propagates in the formation with a power exponential attenuation, and its relationship with the propagation distance can be expressed as Equation (17). \[v_{pp}=v_{0}.e^{-\alpha d} \tag{17}\]  
where \(v_{pp}\) is the peak particle velocity at a distance of \(d\) m from the source; \(v_{0}\) is the peak particle velocity at the source; and \(\alpha\) is the velocity attenuation coefficient.
# Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change +
## 1 Introduction
To overcome these limitations, the lane change maneuver algorithm proposed in this paper considers both the longitudinal and the lateral planning in a dynamic traffic. The LC algorithm is expected to have robustness to deal with the unexpected road events, while not inducing too heavy calculated load. The availability of lane change manoeuvres is increased by reducing the required margins to ensure a safe manoeuvre [19]. With these in mind, an algorithm with condition-triggered safety margin function is proposed for the trajectory re-planning control of lane change. The trajectory planning is designed with cost function to balance the safety, comfort and efficiency according to traffic condition. The time-variant safety margins is designed with the longitudinal and lateral constraints to avoid collision. This method can improve the success rate of lane change, and also reduce the computational complexity. Furthermore, by the prediction of traffic states, an evaluation model to select an appropriate inter-vehicle traffic gap and time instance for lane change is proposed. In the final step, by the comparison with the existing methods in a traffic simulation system, the results verify the effectiveness and robustness of algorithm under various traffic scenarios. The remainder of this paper is organized as follows. The detailed methodology of the lane change control is described in Section 2, including the algorithm design of gap evaluation, trajectory planning and time-variant safety margin. Simulation analysis with random traffic scenarios and specified traffics are given in Section 3. The conclusions of this paper are presented in Section 4.
###### Abstract
Atmospheric water vapor content is also a critical factor in studying climate change and volcanic activity through latent atmospheric heat and surface evaporation, and small changes in water vapor content can amplify temperature changes caused by other greenhouse gases [21; 22; 23]. PWV can also determine atmospheric temperature [24]. Both climate models and observations confirm that regional increases (decreases) in PWV are strongly correlated with increases (decreases) in surface temperature [25; 26; 27]. Volcanic eruptions release large amounts of gas into the atmosphere, where the volcanic gases undergo a series of chemical reactions to form volcanic aerosols [28]. Aerosols reduce the direct solar radiation and increase the scattering amplitude, ultimately reducing the total solar emissivity, possibly lowering surface temperatures [29; 30; 31]. [PERSON] et al. [32] observed negative surface temperature anomalies after eruptions in a volcanic eruption. [PERSON] et al. [33] found similar temperature decreases between \(\sim\)10.5 and \(\sim\)11.5 km altitude for the April 2010 Ejafjallajokull eruption and the June 2011 Puyehue eruption. The advantages and effectiveness of satellite remote sensing for volcanoes are demonstrated by a large number of thermal infrared images for the time-series monitoring of surface temperatures [34; 35; 36; 37]. In response to the preliminary and incomplete study of the environmental response of the lower atmosphere during the volcanic eruption, this paper describes the environmental response characteristics of the lower atmosphere of the eruption in terms of PWV, water vapor flux, surface temperature, low-pressure center, and precipitation. The analysis found that the volcanic eruption could have significantly triggered the response between the low-pressure center, PWV, precipitation, and surface temperature in the lower atmosphere, which influenced the environmental characteristics of this eruption.
## 3 Energy Storage
### Battery  
Dry cell and wet cell batteries, depicted in Figure 8, are two fundamental types of electrochemicals widely used in various applications [41]. Dry cell batteries, often found in portable electronic devices, feature immobilized electrodes and electrolytes in the form of paste or gel, preventing the flow of electrolytes [41]. This design enhances the convenience of dry cell batteries by allowing them to operate in any orientation without the risk of leakage, making them ideal for portable and handheld devices. Their compact and lightweight nature, coupled with low maintenance requirements, further solidifies their status as the preferred choice for portability-focused applications. On the other hand, wet cell batteries, commonly utilized in automotive, marine, and renewable energy sectors, contain liquid electrolytes, typically a mixture of sulfuric acid and water [41]. The liquid electrolyte enables efficient ion transfer between electrodes, resulting in higher energy output compared to dry cell batteries. However, the presence of liquid electrolytes introduces the risk of leakage, requiring careful handling and regular maintenance, such as monitoring electrolyte levels and ensuring adequate ventilation to prevent gas accumulation. Despite these maintenance challenges, wet cell batteries are favored in applications demanding high power output and reliability, such as in automotive and backup power systems, where their robust energy capacity is a critical advantage. The distinction between these battery types highlights their specific strengths, making each suitable for different operational environments and energy needs.
## 4 Results and Discussion  
### Comparison between the Actual Ambient Light Intensity and the Measured Value  
In Figure 16, we used photometer LM-81 LX to test the reliability of our ambient light sensor under normal condition. We therefore proved that our model was highly reliable unless it was performed in an extremely dim environment, where negative deviation became increasingly significant.
## 3 Results and Discussion
### Surface Structure Analysis by FT-IR  
Figure 4 shows the FT-IR analysis graph of the three kinds of Lewis acid surface modifications--AlCl\({}_{3}\), CaCl\({}_{2}\) and ZnCl\({}_{2}\). At wavenumbers 1637 cm\({}^{-1}\) and 3310 cm\({}^{-1}\), we can see the expansion of the vibration peak of C=O (amide I band) and N-H bonds. The chemical composition of the aramid surface was significantly changed after treatment with a Lewis acid ethanol solution. Among three kinds of Lewis acid AlCl\({}_{3}\), CaCl\({}_{2}\) and ZnCl\({}_{2}\), the content of N-H and C=O bonds with ZnCl\({}_{2}\) treatment was 3-4% and 5-10% higher than AlCl\({}_{3}\) and CaCl\({}_{2}\), respectively. The lower transmittance means the higher content of chemical bonds. The N-H and C=O bonds obtained the lowest transmittance under the  
Figure 3: Schematic illustration of hydrogen-bonded sheet structure of Kevlar fiber and ZnCl\({}_{2}\)-Kevlar complex.  
Figure 2: Mechanical properties test and specimens. (**a**) Tensile test, (**b**) Bending test, (**c**) Impact test and (**d**) Specimens picture.  
ZnCl\({}_{2}\) treatment condition. It proves that the ZnCl\({}_{2}\) treatment was successful in improving the aramid fiber surface modification because of the high content of N-H and C=O bonds were obtained. Therefore, the ZnCl\({}_{2}\) treatment is the best condition in three kinds of Lewis acid surface modifications.  
The modification of the ZnCl\({}_{2}\) treatment to aramid fibers is determined by the content of active groups, such as N-H and C=O. The concentration and treatment time of the ZnCl\({}_{2}\) treatment are important conditions that affect the content of active groups, such as N-H and C=O. Figures 5 and 6 show the results of FT-IR analysis under various concentration (weight ratio) conditions of 2, 4, 6, 8 and 10 wt% and processing conditions of 0, 2 h, 4 h, 6 h, 8 h, 10 h and 12 h. As a result of observing the peak values of the N-H and C=O bonds under the conditions of 2-10 wt% concentration at both wavenumbers 3310 cm\({}^{-1}\) and 1637 cm\({}^{-1}\), the transmittance showed a waveform shape as the concentration increased. Among them, the peak values appeared at 6 wt% and 10 wt%, respectively. Under the treatment of 0-12 h treatment time, the peak value obtained the lowest content of 72.5% by 6 h treatment conditions. The transmittance is 90.82% of the N-H bond and 72.50% of the C=O bond. This is 6% and 18% lower than the 4 h treatment condition, respectively. The content of N-H bonds increased with concentration, but the C=O bonds did show an increasing trend up to 6 wt% and a decreasing trend thereafter. At different treatment times, it also showed an increase in the content of N-H and C=O bonds as the treatment time approached 6 h. This phenomenon may indicate that the contents of N-H and C=O bonds do not increase with time and concentration but rather in a waveform growth manner. So that peak of the maximum increase was found by FT-IR. Additionally, this peak is the position described earlier. Therefore, two concentrations of 6 wt% and 10 wt% and 6 h treatment time were the most beneficial conditions.
## 5 Experimental Results
### Noise Attacks
#### 5.2.2 Gaussian Noise Attack  
Gaussian noise is a measurable common with a feasible density equivalent to an ordinary state, otherwise called Gaussian dissemination. Gaussian noise values with a variance of 0.01 are tested on various images.