esa-sceva/satcom-chunk-collection huggingface dataset:
# Design Methodology and Application Dynamics of Compact Quasi-Zero Stiffness Isolators
## 1 Introduction
QZS isolation provides an excellent vibration isolation effect in the low-frequency range close to zero frequency, and it can effectively inhibit the propagation of low-frequency vibration. The key to the design of QZS isolation is how to choose a reasonable negative stiffness structure, and in the actual engineering, there are many components with negative stiff  
Figure 1: Amplitude-frequency characteristic curves under different damping. ness characteristics. The review [17] systematically summarizes the structure of negative stiffness, mainly including mechanical spring structure, flexural beam structure, geometrically nonlinear structure, magnetic structure, metamaterials, and bionic structure. Each method has its own advantages, disadvantages, and scope of application. In contrast, this review focuses mainly on space-limited engineering applications and provides a systematic summary of existing compact designs of QZS isolators. In many engineering application scenarios, the available space is often very limited. In the aerospace field, for example, the space left for vibration isolation systems is extremely scarce because of the need to carry all kinds of complex instruments and equipment inside the aircraft. The traditional QZS isolation is not compact enough and occupies a large space, which may limit the layout of other equipment. Therefore, the compact design of QZS isolation is particularly important, which can make the space layout more reasonable and provide the possibility of optimizing the layout of the vibration isolator. Compact design is not simply to reduce the volume but requires comprehensive consideration of space volume, load-carrying capacity, vibration isolation frequency band, dynamic travel, and other multi-objective parameters. Its essence is to solve the engineering contradiction of \"small volume, large load, wide frequency vibration isolation\" through the innovation of nonlinear mechanics and multidisciplinary integration. In this paper, we systematically sort out the design mechanism of compact design, comprehensively analyze its research status, and elaborate on the applicable occasions. Section 1 introduces the engineering significance of the compact design of QZS isolators, and Section 2 describes the basic principles, design process, and nonlinear dynamic properties of the QZS system. Section 3 analyzes and discusses the compact design methods, vibration isolation performance, application scenarios, and current research bottlenecks. Section 4 summarizes the whole paper and gives an outlook and further proposes prospective and feasible research directions for the reference of researchers in related fields.
## 2 Methodology
### CFD Modeling
#### 2.3.2 Mathematical Model  
Modeling flows in centrifugal pumps requires careful consideration of complex turbulent phenomena, which play a key role in the formation of the hydrodynamic characteristics of the pump. Models based on the averaged Navier-Stokes equations (RANS) are widely used to describe turbulence in hydrodynamic problems. RANS models are based on averaging the Navier-Stokes equations, which allows one to take into account the influence of turbulent fluctuations on the flow without having to directly model all scales of turbulence. This is achieved by decomposing instantaneous quantities such as velocity and pressure into average and pulsating components. The averaging process leads to the appearance of additional terms, known as Reynolds stresses, which describe turbulent stresses in the flow:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(}\rho U_ {j}\big{)}=0 \tag{28}\]  
\[\frac{\partial\rho U_{i}}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(} \rho U_{i}U_{j}\big{)}=-\frac{\partial p^{\prime}}{\partial x_{i}}+\frac{ \partial}{\partial x_{j}}\Bigg{[}\mu_{eff}\Bigg{(}\frac{\partial U_{i}}{ \partial x_{j}}+\frac{\partial U_{j}}{\partial x_{i}}\Bigg{)}\Bigg{]}+S_{M}, \tag{29}\]  
where \(S_{M}\) is the sum of body forces and \(\mu_{eff}\) is the effective viscosity defined by  
\[\mu_{eff}=\mu+\mu_{t}, \tag{30}\]  
and \(p^{\prime}\) is a modified pressure, defined by  
\[p^{\prime}=p+\frac{2}{3}\rho k. \tag{31}\]  
Modeling flows in centrifugal pumps requires accurate consideration of not only turbulent processes but also the interaction of the flow with the impeller blades and the features of the flow around the pump channels. One of the key turbulence models used to solve such problems is the Shear Stress Transport (SST) model, implemented in the Ansys CFX software package (v.23.2). The SST model combines the advantages of two widely used turbulence models: the k-\(\omega\) model, which is effective near the walls, and the \(k\)-\(\epsilon\) model, which is more suitable for modeling the main part of the flow. Thanks to this approach, the SST allows for more accurate modeling of the flow separation, which often occurs in centrifugal pumps, especially in the area of the impeller blades and volute. One of the important aspects of using the SST model is its ability to take into account the effects of low Reynolds numbers, which is especially important for modeling the boundary layer, which significantly affectsthe flow distribution and, therefore, the pump characteristics such as the pressure and efficiency. The turbulent viscosity \(\mu_{t}\) in the SST model is calculated as follows [29; 30]:  
\[\mu_{t}=\frac{\rho a_{1}k}{\max(a_{1}\omega,SF_{2})}, \tag{32}\]  
where \(S\) is the invariant measure of the strain rate, and \(F_{2}\) is a second blending function.
# Design, Experiment, and Improvement of a Quasi-Zero-Stiffness Vibration Isolation System
## 4 Experimental Setup and Results
### Experimental Results  
From the experiment, the results of the acceleration of the QZS system and the linear system were obtained, as shown in Figure 11. The black line represents the acceleration of the excitation, the blue line is the acceleration of the mass of the QZS system, while the red line is that of the linear system.  
As shown in Figure 11, the acceleration of the mass of the QZS system was always lower than that of the linear system, especially for the peak of these two different systems. The acceleration of the QZS system was lower than that of excitation in both the high-frequency and low-frequency ranges. In Figure 11, the frequency for the beginning of isolation of the linear system was about 24 Hz, while that of QZS system was 16 Hz, which shows the QZS vibration isolation platform had a wider frequency range for vibration isolation than its corresponding linear system and could achieve the low frequency for the initial vibration isolation.  
Using the experimental data of the linear and QZS systems, the acceleration transmissibility in the frequency range 5-40 Hz was obtained, as shown in Figure 12. It can be seen that the peak of the transmissibility of the QZS system was 13.5 Hz, while that of the linear system was 16.5 Hz. Furthermore, the transmissibility of the QZS system was lower than that of the linear system. Therefore, it was concluded from the experiment that the QZS vibration isolation system had a better isolation performance than its corresponding linear system in the low-frequency range.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline The inner diameter of the inner magnetic ring \(D_{1}\) & 10 mm \\ The outer diameter of the inner magnetic ring \(D_{2}\) & 30 mm \\ The inner diameter of the outer magnetic ring \(D_{3}\) & 60 mm \\ The outer diameter of the outer magnetic ring \(D_{4}\) & 80 mm \\ Remanence \(B_{r}\) & 1.24 T \\ The stiffness of the linear spring & 7500 N/m \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main geometric parameters of the vibration isolation platform.  
Figure 11: The comparison between the linear system and the QZS system.  
The QZS vibration isolator backward sweep experiment was carried out to verify the jump phenomena, as shown in Figure 13. It was found that the starting vibration isolation frequency of the forward sweep experiment was about 15 Hz, while the starting vibration isolation frequency of the backward sweep experiment was about 13.8 Hz. Therefore, the jump range was about 1.2 Hz.
###### Abstract  
The finite element method (FEM) has been applied to extract the coupling-of-modes (COM) parameters of surface acoustic wave (SAW) devices for a long time. It always involves calculating the dispersive curves or harmonic admittance, which makes the extraction process and results sophisticated, time-consuming and inaccurate. Therefore, a simple method is proposed to extract all COM parameters of the SAW devices rapidly and accurately in this paper. It is based on the FEM and combines the stationary analysis with modal analysis. We have described in detail the basic principles and procedures of the proposed method, and made a comprehensive comparison between the proposed method and the other two existing methods. We have also examined the proposed method by extracting COM parameters of some common SAW substrate, and compared our extracted results with those reported in the other literatures. Results show that our proposed method holds higher accuracy and more efficiency (\(\sim\)s order) than the others (-h order). Moreover, our extracted COM parameters are in an excellent agreement with those reported in the other literatures.  
SAW; COM parameters extraction; FEM +
Footnote †: journal: Applied sciences  
Article  
Fast Extraction of Coupling of Modes Parameters for Surface Acoustic Wave Devices Using Finite Element Method Based Simulation
# A Review of Electrochemical Sensors for the Detection of Glyated Hemoglobin
## 3 Indirect Type Electrochemical HbA1c Sensors
### FAO Type
The sensor had a low detection limit of 0.05 mM, and its linear detection range for FV was 0.1-3 mM. The modified electrodes could be stored for 160 days and be used to analyze human whole blood samples. Magnetic nanoparticles have drawn considerable research attention as special biomolecular immobilized carriers [97; 98]. [PERSON] et al. introduced amino groups onto the surface of core-shell magnetic nanoparticles [99]. The sensor was built by immobilizing FAO on the modified nanoparticle surface to achieve high sensitivity. The linear detection range for FV was 0-2 mM, and the detection limit was as low as 0.1 mM. Moreover, this enzyme electrode could be stored for three months and used up to 250 times. Recently, some semiconductor nanomaterials have been applied in the research of nanometer sensors due to their good stability [100]. [PERSON] et al. [101] established a direct, rapid, and sensitive HbA1c sensor, which was constructed by immobilizing FAO onto a ZnO nanorod-modified indium tin oxide (ITO)-coated glass plate electrode. The sensor presented significant sensitivity and detection limit advantages (0.1 uM), a fast response time (4 s), and a wide linear range (0.1-2000 uM). The working electrode was stable for approximately 4 months at 4 \({}^{\circ}\)C. This sensor could be used to distinguish HbA1c in blood samples from healthy and diabetic patients. In addition, [PERSON] et al. fabricated an amperometric biosensor by drop-coating an FAO enzyme onto an SPE surface [102]. The biosensor showed high current output, high linearity, and effectiveness for FV (0-8000 uM), as well as human blood samples. The drawback of the Prussian blue (PB) electrode for indirect HbA1c sensors is that Fe\({}^{3+}\) in PB easily reacts with OH\({}^{-}\) in solution [103]. [PERSON] et al. utilized tris(hydroxymethyl) aminomethane to modify PB on the SPE surface [103] because OH and NH\({}_{2}\) functional groups in tris can be complexed with Fe\({}^{3+}\) in PB to avoid reactions of Fe\({}^{3+}\) and OH\({}^{-}\) in solution. The modified Tris-PB/SPE was applied in the detection of H\({}_{2}\)O\({}_{2}\), presenting a linear range of 0-2000 uM FV.
## 5 Discussion, Implications, and Recommendations
### Price Value  
The findings of this study indicated that the price of newer technologies was a significant predictor of technology acceptance. Participants in the study indicated that the average cost of USD80 to USD95 a month for Internet, smartphone, and data package was reasonable and provided value to them. The findings in this study validated that UTAUT2 is a useful model for predicting the behavioral intention to adopt technology in a consumer context.
# TSCH and RPL Joining Time Model for Industrial Wireless Sensor Networks
## 4 TSCH Analytical Model
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(T_{ES}\) & Represents the beacon transmission period. \\ \(SF_{SIZE}\) & Represents the size of the slotframe dedicated to the TSCH traffic plane, counted as the number of periodically recurring timeslots. \\ \(C\) & It represents the total number of channel used. \\ \(N\) & Represents the number of neighbors sending beacons within the coverage area of the node that wants to join the network. \\ \(T_{SCAN}\) & Scanning period. In TSCH, the channel is changed randomly every \(T_{SCAN}\) seconds. \\ \(SF\) & Represents the average number of slotframes needed to receive an EB correctly. \\ \(PDR\) & The Packet Delivery Rate parameter represents the message reception success rate. \\ \(T_{STNC}\) & Represents the time needed for a new node to be synchronized, taking into account the number of neighbors and the beacon transmission period. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: TSCH analytical model variables. used in each slotframe. To get the same slotframe to use different channels, it is necessary to configure a \(SF_{SIZE}\) with a prime value [27]. This average number of slotframes needed to receive a beacon correctly (\(SF\)) can be modeled as a uniform discrete random variable between 1 and \(C\). This will mean that, at best, the first beacon sent matches the scan channel, requiring only one slotframe. In the worst case, \(C\) slotframes will be needed to receive a beacon on any of the configured channels. All intermediate cases can occur with the same probability. If it is also taken into account that it is possible to have losses due to the channel, the average number of slotframes would be represented by the following equation:  
\[SF=\frac{C+1}{2}\cdot\frac{1}{PDR} \tag{2}\]  
Assuming that a beacon is sent with a transmission period of \(T_{EB}\), this factor would multiply the average number of slotframes. In the case that there are \(N\) nodes sending beacons within the coverage area of the node trying to join, the average value of beacon transmission period can be understood as \(T_{EB}/N\), since \(N\) beacons are sent within the same slotframe. The average time needed for a node to synchronize, when sending a message with a fixed transmission period of \(T_{EB}\) would be represented by the following equation:  
\[T_{SYNC}=\frac{T_{EB}}{N}\cdot\frac{C+1}{2}\cdot\frac{1}{PDR} \tag{3}\]
## 3 Simulation Experiment
### Results Analysis
#### 3.2.1 Analysis of the Application Effects of the RSGM
The distribution function of the RSGM calculation results conforms to a normal distribution, with a maximum standard deviation of 586 N and a minimum standard deviation of 275 N. Under the same conditions, the maximum difference in the mean results of the two methods is 1, and the standard deviation of the RSGM calculation results is at its minimum 17.1% and at its maximum 69.4% of the SGM calculation results.
## 6 Example Analysis
### Model Evaluation Metrics  
This paper employs five evaluation metrics to assess the model's prediction accuracy: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (\(R^{2}\)).  
\[MAE=\frac{\sum\limits_{i=1}^{n}{\left|{{\hat{y}}_{i}-{y}_{i}}\right|}}{n} \tag{32}\]  
\[MSE=\frac{\sum\limits_{i=1}^{n}{\left({{\hat{y}}_{i}-{y}_{i}}\right)^{2}}}{n} \tag{33}\]  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}{\left|{{\hat{y}}_{i}-{y}_{i}}\right|^{ 2}}}{n}} \tag{34}\]  
\[MAPE=\frac{1}{n}{\sum\limits_{i=1}^{n}{\left|{\frac{{{\hat{y}}_{i}-{y}_{i}}}{ y_{i}}}\right|}} \tag{35}\]  
\[R^{2}=1-\frac{{{\left({{\hat{y}}_{i}-{y}_{i}}\right)^{2}}}}{\sum\limits_{i=1} ^{n}{\left({\overline{y}-{y}_{i}}\right)^{2}}} \tag{36}\]  
Here, \(y_{i}\) is the actual photovoltaic output value, \({\hat{y}}_{i}\) is the predicted photovoltaic output value, \({\overline{y}}\) is the mean of the actual photovoltaic output samples, and \(n\) is the number of samples. The smaller the values of the MAE, MSE, RMSE, and MAPE, the closer \(R^{2}\) is to 1, indicating higher model prediction accuracy. If \(R^{2}>0.4\), the model fitting is considered to be good.
# A New Approach to Quantify Soccer Players' Readiness through Machine Learning Techniques
## 3 Data Analysis
### Analysis of the Relationship between LEI and Weekly Training Load  
One of the aims of the current study was to understand whether the locomotor index was sensitive to variations in the weekly training load. For this purpose, the weekly training load was calculated as the rolling sum of the 7 previous days for the following external load variables: total distance, distance \(>19.8\) km/h, distance \(>25.2\) km/h, number of accelerations \(>3.5\) m/s\({}^{2}\), number of decelerations \(<-3.5\) m/s\({}^{2}\). Since weekly training load can be influenced by a player's position and profile, weekly load was also individualized in relation to the players' season average and standard deviation during the season by adopting z-score transformation. Considering the z-score variations, the following cutoffs were identified [24], and different classifications were made:  
* Decrease in weekly training load (D-WL): the player registered a z-score \(<-1\).
* Stability in the weekly training load (S-WL): the player registered a z-score between \(-1\) and \(1\).
* Increase in weekly training load (I-WL): the player registered a z-score \(>1\).  
All of the steps previously performed are summarized in Figure 1.
# Effects of Equatorial Plasma Bubbles on Multi-GNSS Signals: A Case Study over South China
In the present work, we directly use all-sky airglow images to visually determine the EPB structure and depletion degree, and use GNSS measurements to characterize the corresponding signal quality effects, to investigate the direct correspondence between EPBs and GNSS signals. The responses of multi-constellation and multi-frequency signals to EPBs are comprehensively compared and discussed.
## 3 Modeling Approach for Tray Column
### Tray Column Simulation  
For simulating the tray absorber column, ASPEN PLUS software was used due to its extensive property databanks and rigorous equation solvers. The rate-based model was applied due to its empirical correlations for the estimation of mass transfers. For building the rate-based model by using Aspen PLUS, the column was divided into five stages or trays. The inlet gas consists of air and CO\({}_{2}\); the mole fraction of CO\({}_{2}\) was 0.3. The water was used as an absorbent; the inlet volumetric flow of water was 0.17 m\({}^{3}\)/h. Two series of the simulation were run: one at 15 Nm\({}^{3}\)/h of inlet gas flow rate and the other at 20 Nm\({}^{3}\)/h. The pressure was increased in the range of 0.2-0.3 MPa. The [PERSON] (1958) correlation [21] was applied for the prediction of the binary mass transfer coefficient for the liquid. [PERSON]'s (1984) correlation [22] was used to predict the binary mass transfer coefficient for the gas. The correlation of Chilton-Colburn was applied to estimate the heat transfer coefficient. The nonrandom two-liquid model (NRTL) was applied for the prediction of phase behavior properties.  
Assumptions for the rate-based model approach are summarised below:  
* The absorption column is assumed to be adiabatic.
* There is diffusion resistance in film.
* There are no reactions in the film.
* The liquid is well mixed, and the vapor is a plug flow; changes in concentration and temperature in the radial direction are negligible.
* The interfacial surface area is identical for both heat and mass transfer.
* The interface temperature is identical to the bulk liquid temperature since the liquid-side heat transfer resistance is small and balanced to the gas phase ([PERSON] and [PERSON] 2013) [19].
* Both liquid and gas phases are formally handled as non-ideal mixtures.
# Implementation of MS Circle Map in Digital Image Encryption
## 2 Materials and Methods
### Mathematical Features of MS Circle Map  
The MS Circle Map used in our image encryption algorithm has good chaotic properties for image encryption. Whether a keystream sequence is random or not can be seen from the selection of initial values and control parameters. It can be seen in the bifurcation diagram, where the periodic points of a dense map indicate that the resulting keystream is likely to be very random, as shown in Figure 1. In addition to randomness, sensitivity to initial values and control parameters is a property of chaotic maps that is highly expected in the MS Circle Map. Sensitivity to initial values can be seen from the positive Lyapunov exponent value, as shown in Figure 2. By using the MS Circle Map to design a digital image encryption algorithm, it not only produces highly random encrypted images but can also effectively resist numerous attacks targeting secret keys [30].  
The parameters \(x_{0}\), \(r\), \(\lambda\), \(K\), and \(\Omega\) were selected within specific ranges as they produce highly random keystream values compared to other ranges. These selections were based on the presence of dense bifurcation values and a positive Lyapunov Exponent, ensuring a high level of chaotic behavior for the encryption process. The chaotic nature of the MS Circle Map makes it suitable for encryption, as a small deviation in these parameters leads to significant differences in the keystream, which enhances security against cryptographic attacks.