esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Vibration Control of an Isolated Structure
### RBF Based Adaptive Sliding Mode Control  
The biggest advantage of RBF adaptive control is that the unknown nonlinearity of the system can be approximated by an RBF neural network whose weight value parameters are adjusted according to the adaptive law for the purpose of controlling the nonlinear system to track a given trajectory. Let's consider a system derived from Equation (20).  
\[\dot{z}(t)=f[z(t)]+Bu(t)+Ew(t) \tag{33}\]  
Matrix \(Az(t)\) is replaced by a term \(f[z(t)]\) that considers both changing stiffness and damping properties of the system. The magnetic circuit has been studied to conclude that the \"smart\" base isolator can be adjusted when the system is subjected to an external uncertainty such as an earthquake [54; 55; 56]. Thus, an adaptive control law can be designed to meet the system uncertainty and the requirement of system adaptability. Defining the sliding mode function as:  
\[s(z)=re+\dot{e},\ \text{Then} \tag{34}\]  
\[\dot{s}(z)=r\dot{e}+\ddot{e}=r\dot{e}+f[z(t)]+u-\ddot{z}_{d} \tag{35}\]  
RBF neural network has its superiority in its good approximation capability to any unknown function. The algorithms of RBF networks are:  
\[h_{j}=exp\left(\frac{\|z-c_{j}\|^{2}}{2b_{j}^{2}}\right) \tag{36}\]  
\[f=W^{*T}h(z)+\varepsilon \tag{37}\]  
where \(z\) is the input signal of the network, \(j\) is the number of hidden layer nodes in the network, \(h=[h_{1},h_{2},\cdots,h_{n}]^{T}\) is the output of the Gaussian activation function, \(W^{*}\) is ideal neural network weight value, and \(\varepsilon\) is the approximation error that satisfies \(\varepsilon\leq\varepsilon_{N}\cdot f[z(t)]\) can be approximated by an RBF neural network defined as:  
\[\hat{f}(z)=\widehat{W}^{T}h(z) \tag{38}\]  
Thus, the control law can be expressed as:  
\[u=\frac{1}{B}\Big{[}\hat{f}(z)+\ddot{z}_{d}+mE\Big{]} \tag{39}\]  
where \(m\) is a parameter with respect to system errors, \(E=\big{(}e,\ \dot{e}\ \big{)}^{T}\).  
The adaptive law is expressed as:  
\[\widehat{W}=-\gamma E^{T}Pbh(x) \tag{40}\]  
where \(P\) is a parameter with respect to Lyapunov stability analysis.
## 2 Materials and Methods  
### Simulation Model  
A cantilever beam with elastic support shown in Figure 1 is taken as a simulation example. The cantilever beam considering elastic support is a simplified model of large tall structures such as offshore platforms and offshore wind turbines. Therefore, we only consider the bending mode of the cantilever beam, while the torsion and bending modes are ignored here. The material is aluminum. The beam is assumed to have dimensions of 1 \(\times\) 0.1 \(\times\) 0.02 m, and it is under a semi-rigid connected boundary conditions, i.e., the elastic support is neither simple nor fixed, but in between; \(k_{t}\) and \(k_{r}\) are the tensile stiffness and torsional stiffness of the elastic support, respectively. The concentrated mass \(m\) is 0.4 m away from the elastic support. The beam's mechanical properties are considered to be as follows: the elastic modulus is \(E\), Poisson's ratio is 0.33, and density is 2700 kg/m\({}^{3}\). Here, \(E\), \(m\), \(k_{t}\) and \(k_{r}\) are selected as updating parameters. The specific details are shown in Table 1. The target value is assumed to be the measured value (i.e., truth value), which is used to update the initial parameters.  
As can be seen from Table 1, the initial errors between the initial value and target value of the updating parameters are very large, especially the initial errors of \(k_{t}\) and \(k_{r}\) which are both more than 100%. The error is calculated by Equation (1).  
\[\text{Error}=\frac{\text{Initial}-\text{Target}}{\text{Target}}\times 100\% \tag{1}\]
## 4 Frequency Estimation of Midspan Deflection Signal Acquisition
### Determination of the Acquisition Frequency Estimation Method  
Accurate selection of the appropriate cutoff frequency is a fundamental aspect within the extended discretization of structural response signals and analytical mode decomposition (AMD). Currently, the procedure for determining the cutoff frequency involves performing a Fourier transform on the original time series to obtain its spectrum. Subsequently, a position is identified between two poles in the spectrum, and the frequency value corresponding to this position is considered as the selected cutoff frequency [9].  
The estimation of the acquisition frequency may be subject to subjective influences when exclusively depending on the amplitude spectrum. The selection of a certain cutoff frequency is crucial, since it should be determined in accordance with the distribution features of the probability density function. In order to attain a signal curve reconstruction reliability of approximately 95%, it is necessary to conduct experiments with different cutoff frequencies, utilizing the structural acquisition frequency. The accuracy of determining the cutoff frequency can be achieved through power spectrum estimation. However, it is observed that the fitting error of the power spectrum probability density function is higher compared to that of the amplitude spectrum probability density function. This discrepancy significantly impacts the reliability calculation of the acquisition frequency.  
Based on the aforementioned information, the following notions are proposed: This study has two primary objectives. Our primary objective is to ascertain the cutoff frequency of the signal by an analysis of the amplitude-frequency curve of the power spectrum. Additionally, our objective is to determine the probability density function of the initial time-history curve data by utilizing the amplitude spectrum. Furthermore, we will compute the digital characteristic errors for both the low-pass signal with a cutoff frequency and the quasi-static signal. The paper demonstrates the reliability of the suggested method by measuring the ratio between the amplitude spectrum area of the acquired signal at an appropriate acquisition frequency and the area of the amplitude spectrum function of the original signal.
## 3 Methods
### Calculations of Drought Indices
#### 3.1.5 The Normalized Difference Water Index  
NDWI is strongly associated with plant water content [85]. Shortwave infrared (SWIR) reflectance captures changes in both spongy mesophyll composition and plant water content. In contrast, NIR (near-infrared) reflectance is determined by the internal structure of the leaf and its dry matter content rather than its water content. The combination of NIR and SWIR is designed to improve the accuracy of SM determination by eliminating variations due to internal leaf composition and leaf dry matter quality.  
\[NDWI_{i}=\frac{NIR_{i}-SWIR_{i}}{NIR_{i}+SWIR_{i}} \tag{5}\]  
NDWI values range between \(+1\) and \(-1\). An NDWI value close to \(+1\) indicates higher plant water content, while a value close to \(-1\) indicates the absence of plant water content. In this study, the NDWI was normalized using the same min-max normalization applied to the other indices, ensuring they fall within the same range (0-1). NDWI values close to 1 are associated with wet periods, whereas values close to 0 are associated with dry periods.
## 3 System Design
### Multicast Member Registration Procedure  
In CCSM, the multicast source and multicast receiver have to register to MMC at first, and multicast source is responsible for multicast receiver management, and MMC is responsible for multicast service, multicast membership and multicast delivery tree management.  
If a user in UIN wants to provide a multicast service, it has to register in MMC at first. Then MMC replies to source a multicast address (here called MSI) and a token for secure authentication. If the multicast receivers want to subscribe this service, they have to request source for permission. The source replies to the multicast receivers and transfers the multicast address and tokens. After that, multicast receivers can register to MMC with multicast address and token. MMC maintains the token corresponding to certain multicast address so it can tell whether one multicast receiver is legitimate. Figure 4 shows the registration procedure.  
The multicast source in a given multicast group can be multiple. In single-source multicast, the only multicast source executes all the privileges. It registers in MMC, authorizes the receivers and sends multicast packets. While in multi-source multicast, only one multicast source can execute all the privileges, which is called as primary source. The other sources' privileges are authorized by the primary source.  
In this way, CCSM can have full control over the group members and multi-source group could be organized centrally. The token is used in Join/Leave procedure to assure that legitimate users are allowed while illegitimate users will be denied.
## 5 Effects of Magnetic Fields on Quenching Characteristics of Superconducting Tapes
### Effects of Magnetic Fields on the Quenched Characteristics of Superconducting Tapes
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**The Value of Quenched Resistance (m\(\Omega\)/m)**} \\ \cline{2-3}
**Magnet Field (mT)** & **SC\_SH** & **SC\_8602** \\ \cline{2-3}  & **1200(A)** & **1200(A)** \\ \hline
0 & 53.28 & 43.14 \\
42.4 & 56.92 & 41.60 \\
75.9 & 59.87 & 43.14 \\
122.9 & 61.81 & 43.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Value of Quenched Resistance. Figure 7: The relationship between the prospective current and the quenched resistance per meter of the testing tapes. SC_SH increased with the increase of magnetic fields. The effects of the magnetic fields on SC_8602 were smaller than the effects on SC_SH. Figure 8b shows the relationship between the transport current and voltage of the superconducting tapes under the magnetic field of 42.4 mT. The waveforms of SC_SH and SC_8602 can be described as Equations (1) and (2). In Equations (1) and (2), \(I_{\text{SC\_SH}}\) is the transport current through SC_SH, and \(I_{\text{SC\_8602}}\) is the transport current through SC_8602. \(I_{\text{SC\_SH}}\) is the voltage of SC_SH, and \(I_{\text{SC\_8602}}\) is the voltage of SC_SH:  
\[U_{\text{SC\_SH}} =5.099\;\text{exp}\;(I_{\text{SC\_SH}}/1261)-5.7\;(0<\text{I}_{ \text{SC\_SH}}<1500\;\text{A}) \tag{1}\] \[U_{\text{SC\_8602}} =1.67\;\text{exp}\;(I_{\text{SC\_8602}}/982)-2.05\;(0<\text{I}_{ \text{SC\_8602}}<2000\;\text{A}). \tag{2}\]  
Figure 9 shows the relationship between the peak value of the prospective current and the peak value of the limited current of the two tapes under the magnetic field of 122.9 mT. The dots in Figure 9 are taken from experimental results, and the lines are linearly fit lines of the dots. The limited current increased almost linearly between SC_SH and SC_8602 when the prospective current increased. The limited current of SC_8602 was higher than SC_SH due to its smaller quenched resistance. The relationship between the prospective current and the limited current was similar in other testing magnetic fields such as 42.4 and 75.9 mT.
## 2 Materials and Methods
### Algorithm for Dynamic Weighting Between Estimates of Kalman Filter and GERT Model
The system partially reduces the value of \(\alpha(t)\), keeping it at the level of \(\approx\)0.65, which means a partial switch to the GERT forecast with the preservation of the influence of the Kalman filter. Scenario 3 (green dash-dotted line) demonstrates a situation when the Kalman filter loses stability while other parameters remain unchanged. In this case, the coefficient \(\alpha(t)\) decreases slightly to \(\approx\)0.87, which indicates the preservation of confidence in the Kalman filter under the condition of stable input measurements and GERT forecasts. All scenarios indicate an adequate response of the intelligent module to changes in the parameters of the situation such as a decrease in stability and the loss of measurements or tracking probability. Thus, the dynamic weighting model provides a flexible adaptive combination of estimates, which is critical for the stable tracking of targets in variable and hostile conditions. The key advantage of this approach is the ability to maintain objectivity and stability of the estimates even in cases of partial or complete data loss, when the Kalman filter begins to 'drift' at short intervals due to the lack of new measurements. In this case, the GERT model plays the role of a probabilistic compass that orients the assessment within the permissible scenarios of the target's behavior. The intelligent module, implemented on the basis of a combination of fuzzy logic and a neural network block, is responsible for generating weight coefficients at each moment in time, taking into account not only the instantaneous state of the signal, but also its history. For example, an increase in the variation of innovations, an increase in the number of lost measurements, or unstable filter dynamics automatically lead to a change in the fusion mode with an emphasis on the GERT assessment. In the case of a reverse trend, the system gradually returns to the dominance of the Kalman filter.
## 2 Factors Affecting the IOP Response to Exercise
### Fitness Level  
The IOP responsiveness to physical efforts seems to be mediated by fitness level, with physically fitter individuals showing a more stable IOP behavior during exercise performance. Indeed, this finding has been observed for different physical activities, including maximal treadmill running, all-out cycling sprints, strength exercises and low-intensity endurance exercises [26; 29; 75; 76; 77]. These observations suggest that a higher baseline fitness level may confer a protective effect against exercise-induced IOP fluctuations.  
This evidence aligns with the gradual progression models recommended by the American College of Sports Medicine for exercise prescription [78; 79]. These guidelines emphasize the importance of tailoring exercise programs to an individual's initial training status to minimize exercise-related complications, such as significant IOP fluctuations. For example, [PERSON] and colleagues [76] found that less fit individuals (relative maximal oxygen uptake: 43.4 \(\pm\) 1.2 mL min\({}^{-1}\) kg\({}^{-1}\)) exhibited a greater IOP rise (effect size = 0.95) after completing a treadmill test in comparison to individuals with higher fitness levels (relative maximal oxygen uptake: \(57.1\pm 1.7\) mL min\({}^{-1}\) kg\({}^{-1}\)). Such fluctuations have been identified as a critical risk factor for glaucoma onset and progression [61; 80]. This underscores the necessity of a measured and progressive approach to physical training in glaucoma patients or those at risk of developing the condition.  
For individuals with lower baseline fitness levels, initial exercise programs should prioritize low-intensity activities with gradual increments in intensity and duration. This approach can help build endurance and strength without inducing abrupt changes in IOP. For example, unfit individuals might begin with light walking or stretching exercises before progressing to moderate-intensity activities such as brisk walking or cycling. Fitness professionals and healthcare providers should collaborate to ensure that exercise plans for glaucoma patients or at-risk individuals are both safe and effective. Regular assessments of physical fitness and IOP behavior during exercise can provide valuable insights for tailoring these programs. Additionally, promoting adherence to these tailored plans through education and consistent support is crucial for maximizing long-term benefits. Future research should aim to further clarify the relationship between fitness levels, IOP responsiveness, and glaucoma progression. Studies exploring the optimal rate of progression in exercise programs for individuals with varying fitness levels could provide critical insights, enhancing the precision of exercise prescriptions for this population. Moreover, randomized controlled trials could help establish definitive guidelines for integrating fitness considerations into glaucoma prevention and management strategies.
## 4 Discussion
The potential of forests to store and sink atmospheric carbon for the long-term is highly affected by altitudinal gradients [62]. Growth conditions, species structure and composition, and other disturbance factors may all play a role in the carbon storing capacity of these forests [17]. Estimation of total biomass of forests in the current climate change scenario has received attention as the world is facing an increase in temperature; the forests are the only source to cope with this tension. The tree biomass and carbon in a forest is highly influenced by tree type, forest structure, tree diameter, tree age, precipitation, stand condition, and different topographic and edaphic variables [68]. [PERSON] et al. [13] recorded a positive but weak relationship (R\({}^{2}\) = 0.02) between aboveground biomass carbon and elevation (m, asl). In a similar study, [PERSON] et al. [19] found a strong positive relation (R\({}^{2}\) = 0.57) of biomass carbon with elevation. Moreover, [PERSON] and [PERSON] [4] also reported the direct dependency of carbon stock and altitude across three forests of Loess Plateau (China). The possible reasons for such relations are the variation in temperature along the altitudinal gradient [13,19]. However, in contrast, lower biomass at high elevation ranges was also documented by several workers [69, 70, 71]. [PERSON] et al. [72] reported a strong positive relation (R\({}^{2}\) = 0.67) of ABG biomass with elevation in the central highland, Vietnam. Furthermore, the findings of [PERSON] et al. [19] exposes no significant interaction of altitude with vegetation carbon stock in Chitteri reserve forest. Several studies reported a reduction in the amount of aboveground biomass with an increasing trend in elevation [73, 74, 75], while some of the workers reported the opposite scenario, i.e., an increase in biomass and carbon with an increase in altitude [76,77]. The current findings exposed an uneven trend in the amount of vegetation biomass and carbon along the altitudinal gradient; however, soil carbon was reported with an increasing pattern along the elevation. It could possibly be linked to the geographical aspect of the area, where the nearly-steep slope has isolated itself from continued interaction. These results are matched with the findings of [PERSON] et al. [28]; however, the reports of [PERSON] et al. [45] offer a visible challenge. One of the possible reasons for such a pattern is the presence of fewer but larger diameter trees at higher elevation. However, these findings disagree with previous results, which highlighted that mid altitude had the highest AGC [78,79]. The current study reported a variation in major carbon-storing tree species along the differed altitudinal zones. Tree species with wood density and greater DBH have the potential to contribute more biomass and can store more atmospheric carbon.
## 7 Future Directions
### Direction-of-Arrival (DoA) Estimation  
Direction-of-arrival (DoA) estimation is critical to improving HO and mobility performance in 5G and B5G HetNets. Accurate DoA estimation helps identify the best serving cell for mobile devices, ensuring a smooth HO process, reducing HOF, and minimizing interference. There are several methods to improve DoA estimation, including massive MIMO, adaptive beamforming techniques, and ML techniques.  
* Multiple antennas (massive MIMO) can improve the received signal power and reduce noise and interference.  
* Advanced signal processing techniques such as adaptive beamforming can enhance the accuracy of DoA estimation, reduce interference from other directions, and provide a more reliable communication link.
* ML techniques can be used to analyze large datasets of signal measurements, improving the accuracy of DoA estimation.  
By implementing these schemes, DoA estimation can be made more efficient, reducing congestion and improving load balancing and leading to better network performance in terms of HO and mobility.
## 1 Introduction
### The Importance of Analysis of Hydrological and Meteorological Conditions  
Familiarity with the geological history of the Baltic Sea is essential for proper understanding of the present hydrological and meteorological conditions of the area. The sea was formed in a succession of multiple glacial and interglacial periods, which determined its topography and depth and the composition of the waters. The modern stage of the Baltic Sea's development, referred to as the Mya Sea, is characterized by variable salinity, which is lower than that of other seas. This fact has a significant impact on the operational conditions of LNG vessels [14].  
The hydrological and meteorological conditions in the southern Baltic include a number of key aspects, including the following:  
* The hydrodynamics and structure of waters--flows of sea currents, variability of the temperature, and the salinity profile, affected by, inter alia, the inflow of fresh water from large rivers, such as the Vistula and the Oda;
* The atmospheric conditions--mean and extreme wind speeds, precipitation, and variability of the barometric pressure, which affect waves and water level [15];
* Ice--although the Baltic Sea does not freeze entirely, ice may form locally in winter, especially in the Gdansk Bay, which may restrict the availability of ports and navigational routes [16];
* The impact of hydrological and meteorological conditions on the LNG infrastructure--an analysis of how vessels, quays, and cargo handling systems withstand variable and/or severe weather, such as storms or surges and falls in water level [17].
# Research on Nonlinear Vibration Characteristics of Internal Beveloid Gear Transmission System
## 3 Numerical Solution of Internal Beveloid Gear with Parallel Axes
### Influence of Excitation Parameters on Nonlinear Vibration Characteristics of Transmission System  
#### 3.2.1 Effect of Bearing Backlash on Vibration  
Both the bearing backlash parameters and tooth backlash parameters are piecewise functions. This work does not differentiate between radial and axial bearing backlash when analyzing the bearing backlash function; both are assigned the same value. Considered is the vibration response when the bearing backlash \(b_{b}=20\) um, and bearing backlash \(b_{b}=0\) um, input speed \(n=1000\) r/min, excitation \(T_{at}=10\) N \(\cdot\) m, torque fluctuation \(T_{f}=0.05\)\(T_{at}\)N \(\cdot\) m, transmission error \(E=20\) um, tooth backlash \(2\)\(b=10\) um, the vibration\(-\)displacement response and frequency domain response curves are obtained for the external gear. As seen in Figure 3 below.  
Comparing Figure 2a,e and Figure 3a,c, it can be observed that when the bearing backlash \(b_{b}=0\) um is used, the radial time domain response of the gear exhibits improved periodicity, and the harmonic characteristics of the frequency domain response are weakened. The nonlinear vibration characteristics are also weakened, and the harmonic characteristics of the frequency at which the peak value is located do not change. Evidently,  
Figure 3: Time domain response and spectrogram of vibration displacement with bearing backlash \(b_{b}=0\) μm (**a,b**) the radial and axial time responses of external gear; (**c,d**) the radial and axial spectrograms of external gear.  
the bearing backlash has a significant impact on the degree of radial nonlinear vibration of the gear and enhances the nonlinear vibration of the gear pair.  
Comparing Figure 2c,f and Figure 3b,e, it is evident that when the impact of bearing backlash is disregarded, the axial vibration periodicity is more pronounced, the frequency domain response harmonic characteristics vary less, and the vibration amplitude varies significantly. It can also be observed that the axial displacement equilibrium point varies with the harmonic function. When analyzing the effect of excitation, it will be indicated later that the mean change is connected to the excitation torque fluctuations \(T_{f}\).