esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 4 Experimental Setup and Performance Evaluation
### Hardware Implementation  
To effectively utilise edge computing, a processing powerhouse was required, ideally in a small form factor. The prototype was developed with the intention to be deployed in remote and rural areas. Therefore, the hardware setup was designed to achieve the utmost unsupervised automated operation in order to minimise physical site visits to perform maintenance or modification. As for the powerhouse for processing, a compact yet powerful NVIDIA Jetson Nano was used as the computing platform. For the RGB-D sensor, the Intel RealSense D455 was chosen as it could reliably provide both RGB and depth information through its on-board processing capabilities. The entire setup was connected to the Internet through a Teltonika industrial router, which provided a connection fail-over redundancy through a Long-Term Evolution (LTE) mobile network in case of disconnection from the main wired broadband network. Figure 6 displays the hardware topology.  
Figure 5: Satellite view of the physical measurement site.  
The Intel RealSense D455 contains an in-body IR projector that allows depth perception in low-light situations, thus allowing the extraction of depth at night or in low-light conditions without additional hardware required. The camera was placed vertically below the platform and the electronics devices were housed in a weatherproof enclosure mounted on the platform. The physical setup of the computing platform, communication device and the RGB-D sensor are shown in Figure 7. The surface of the camera was parallel with the water surface as it provided the best viewing option of the water from the platform. Furthermore, whether the water level increased steadily or at a steep gradient towards the camera, the measurement accuracy increased, as it effectively shortened the distance between the camera sensors and the measuring object. The water gate itself housed a monitoring system which comprised ultrasonic sensors and a logging device. The data collected from this pre-existing system were used to verify the accuracy and reliability of the depth readings from the proposed system.
# Quad-Port Multiservice Diversity Antenna for Automotive Applications
## 5 On-Car Scenario  
The various locations in a vehicle where the proposed antenna can be installed are depicted in Figure 20. The antenna can be mounted on the bumper, roof, rear window, or side mirrors. The distance between the antenna and the ground should be larger to avoid ground losses. Therefore, the roof of the vehicle is the best place for antenna mounting. The antenna can be mounted on the roof using a shark-fin mounting system or in the chassis cavity. Figure 19: S-parameters of the antenna considering the housing effect. Furthermore, the antenna is imported into an open-source CAD model to evaluate its directivity for on-vehicle conditions. Additionally, the far-field performance of the proposed antenna is investigated for an on-car scenario. The results showed that the antenna has omnidirectional characteristics over the desired frequencies. The proposed antenna has directivity greater than 7 dBi, as shown in Figure 21.  
Figure 21: On-car performance of the proposed antenna: (**a**) 1.5 GHz, (**b**) 2.45 GHz, (**c**) 5 GHz. Figure 20: Different antenna mounting locations in a car. A comparison of the proposed antenna to previously reported antennas is shown in Table 3. The salient features of the proposed antenna are as follows:  
1. The proposed antenna covers multiple bands, including UWB, whereas other antennas reported in the literature [38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53] operate on a single wideband frequency. 2. The proposed antenna requires less space than the antenna configurations reported in [39; 40; 41; 42; 44; 45; 46; 47; 48; 49; 50; 51; 52; 54; 55]. 3. The multiband operation is achieved in the proposed prototype without any need for reconfigurability. 4. The orthogonal configuration of the antenna provides additional polarization and covers signals from all directions. 5. The MIMO antenna diversity performance is investigated using parameters such as ECC, DG, TARC, and CCL, and the calculated values are found in the acceptable limits. 6. The proposed antenna is tested in an automotive environment, and the results show that the performance of the proposed antenna is stable.
# Study on the Effect Mechanism of MRD to Rotor System
## 2 System Model and Basic Theory
### The Investigated Rotor System  
Figure 3 shows a schematic diagram of a general coupling system model of MRD-bearing-rotor with supporting of squirrel cage spring. The rotor shaft is considered to be a Timoshenko beam and  
divided into ten elements. Each node contains two translational and two rotational degrees of freedom along \(x\) and \(y\) directions.  
According to the Lagrange equation, the dynamic equation of the rotor system [25] can be given as:  
\[\left[\begin{array}{cc}M+J/2&0\\ 0&M+J/2\end{array}\right]\left[\begin{array}{c}\ddot{q}_{1}\\ \ddot{q}_{2}\end{array}\right]+\omega\left[\begin{array}{cc}0&J\\ -J&0\end{array}\right]\left[\begin{array}{c}\dot{q}_{1}\\ \dot{q}_{2}\end{array}\right]+\left[\begin{array}{cc}K&0\\ 0&K\end{array}\right]\left[\begin{array}{c}q_{1}\\ q_{2}\end{array}\right]=\left[\begin{array}{c}Q_{1}+Q_{u1}\\ Q_{2}+Q_{u2}\end{array}\right] \tag{12}\]  
\[k_{e}=\frac{EI}{\beta(1+\phi)}\left[\begin{array}{cccc}12&sym.\\ 6l&(4+\phi)l^{2}&\\ -12&-6l&l^{2}&\\ 6l&l^{2}(2-\phi)&-6l&(4+\phi)l^{2}\end{array}\right] \tag{13}\]  
\[m_{e}=\frac{\rho Al}{(1+\phi)^{2}}\left[\begin{array}{cccc}m_{1}&&&sym.\\ m_{2}&m_{5}&&&\\ m_{3}&-m_{4}&m_{1}&&\\ m_{4}&m_{6}&-m_{2}&m_{5}\end{array}\right] \tag{14}\]  
\[j_{e}=\frac{2\rho l}{(1+\phi)^{2}l}\left[\begin{array}{cccc}m_{7}&&&sym.\\ m_{8}&m_{9}&&&\\ -m_{7}&-m_{8}&m_{7}&\\ m_{8}&m_{10}&-m_{8}&m_{9}\end{array}\right] \tag{15}\]  
where  
\(m_{1}=\frac{13}{35}+\frac{7\phi}{10}+\frac{\phi^{2}}{3}\), \(m_{2}=l(\frac{11}{210}+\frac{11\phi}{120}+\frac{\phi^{2}}{24})\), \(m_{3}=\frac{9}{70}+\frac{3\phi}{10}+\frac{\phi^{2}}{6}\), \(m_{5}=l^{2}(\frac{1}{105}+\frac{\phi}{60}+\frac{\phi^{2}}{120})\), \(m_{6}=-l^{2}(\frac{1}{140}+\frac{\phi}{60}+\frac{\phi^{2}}{120})\), \(m_{7}=\frac{5}{6}\), \(m_{8}=l(\frac{1}{10}-\frac{\phi}{2})\), \(m_{9}=l^{2}(\frac{2}{5}+\frac{\phi}{6}+\frac{\phi^{2}}{3})\), \(m_{10}=l^{2}(-\frac{1}{30}-\frac{\phi}{6}+\frac{\phi^{2}}{6})\), \(\phi=\frac{12\pi L^{2}}{\mu AG^{2}}\), \(G=\frac{E}{2(1+\
u)}\), \({q_{1}}^{T}=[x_{1},\sigma_{A},x_{2},\sigma_{x2},\cdots,x_{n},\sigma_{\Delta m}]\) and \({q_{2}}^{T}=[y_{1},\partial_{1}y_{1},y_{2},\partial_{y2},\cdots,y_{n},\partial _{yn}]\). \(\omega\) is the rotating speed of the rotor shaft. The mass matrix \(M\), gyroscopic matrix \(J\), and stiffness matrix \(K\) of the rotor system are given in the Appendix A. \(E\) is the elastic modulus. \(G\) is the shear modulus. \(\
u\) is the Poisson ratio. \(I\) is the moment of inertia. \(\rho\) is the material density. \(A\) is the cross-sectional area. \(l\) is the length of the beam unit. \(\phi\) is the shear coefficient. \(Q_{1}\) and \(Q_{2}\) are the generalized forces which contain unbalance excitations \(F_{ux}=m_{d}e\omega^{2}\cos(\omega t)\) and \(F_{uy}=m_{d}e\omega^{2}\sin(\omega t)\). \(m_{\rm d}\) is the equivalent lump mass at disc of the rotor system.
## 5 Conclusions  
The HS-SPME with GC-MS analysis for the volatiles described the differences between the infested and uninfested cabbage plants and their role in attracting natural enemies of aphids. Collection of volatiles from cabbage occurred by using HS-SPME to detect volatiles compounds between uninfested and plants infested with _M. persicae_ and examined the attraction of natural enemies. A total of 28 VOCs were identified in cabbage plant treatments, by using HS-SPME combined with GC-MS. The parasitoids _A. colemani_ and _A. abdominalis_ laid eggs within the body of _M. persicae_ and immature stages completed development inside the hosts, eventually killing them by feeding the wasp larva inside the aphids; the parasitoid pupates inside the aphid mummy and they emerges as an adult. To detect and locate hosts, it is believed that _A. colemani_ and _A. abdominalis_, as with many parasitoids, rely on odors released from infested plants as a response to aphids feeding. The results indicated that the preferences of _A. colemani_ and _A. abdominalis_ to infested plants with _M. persicae_ compared with uninfested plants and clean air by using an olfactometer. The results showed that parasitoids can discriminate the infested cabbage and significantly respond to the plant odor. Thus, we believe that aphid parasitoids can find damaged plants and then detect aphids on the plant-by-plant odor. It is likely that the natural enemies' search for aphid infestation may start before landing on the uninfested plant, because parasitoids will first find a damaged plant and then begin searching for aphids. For this reason, many aphid parasitoids efficiently search for damaged plants where aphids will be present, as explained by [20].  
Conceptualization, Q.A., M.A., R.A., H.Z. and Y.R.; data curation, Q.A.; investigation, Q.A.; methodology, Q.A., H.Z. and R.A.; project administration, Q.A. and Y.R.; resources, Q.A. and Y.R.; supervision, M.A. and Y.R.; validation, Q.A., M.A. and Y.R.; writing--original draft: Q.A.; writing--review and editing: Q.A., M.A. and Y.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data are contained within the article.  
**Acknowledgments:** We would like to thank the Iraqi government and the University of Baghdad for supporting to the first author and sponsoring a Ph.D. scholarship. We also appreciate the support of Murdoch University Postharvest Biosecurity and Food Safety Laboratory, and thanks to the technician team in the laboratory.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 8 Spin Nernst Effect in Topological Materials  
The relationship between AHE and ANE can also be transferred to spin current generation. In addition to SHE where the driving force is an electric field, a spin current can also be generated by a temperature gradient, which is known as the spin Nernst effect (SNE) [112; 113; 114]. In the SNE, a transverse spin current \(\overrightarrow{J}_{s-i}^{k}\) can be generated by an applied temperature gradient \(\
abla\overrightarrow{T}_{j}\) via the linear response \(\overrightarrow{J}_{s-i}^{k}=\alpha_{ij}^{k}\
abla\overrightarrow{T}_{j}\), where \(\alpha_{ij}^{k}\) is the linear response tensor of the spin Nernst conductivity (SNC). Very recently, the SNE was observed in heavy transition metals in thermoelectric transport measurements [112; 113; 114].  
Because the SNE shares similar mechanisms with the ANE, only with different symmetry requirements, a strong SNE is also expected in topological materials. Figure 7 gives a general understanding of the relation between SNE and topological band structures. For a symmetry model of a TI with a global band gap, a constant SHC exists in the inverted band gap. However, because the SNE is dependent on the first derivative of the SHE with respect to the chemical potential, the SNE vanishes at the charge neutral point, as presented in Figure 7a-c. To obtain a non-zero finite SNE, breaking the balance of the SHC distribution in energy space is necessary. An effective way to achieve this is to decrease the symmetry and tilt the inverted band gap with a finite Fermi surface, as the schematically shown in Figure 7d. With the tilted inverted band gap, the distribution of SHC is no longer symmetric in energy space and therefore a finite SNC appears at the Fermi energy (see Figure 7e,f). From the model analysis, one can find that a topological semimetal or a doped topological insulator is expected to host a strong SNE, which is similar to the ANE in magnetic topological semimetals.  
Figure 6: SHE in topological materials. (**a**) Schematic of the SHE. A transverse pure spin current is generated by an applied longitudinal electric field. (**b**) The table for some typical SHE materials, with data collected from Refs. [100; 101; 102; 103; 104; 105]. The unit of SHC is \(\frac{e}{2\hbar}\frac{S}{cm}\).
# INS/Vision Integrated Navigation System Based on a Navigation Cell Model of the [PERSON]
## 3 Results Construction of INS/Vision Brain-like Navigation Hardware System
### Construction of the INS Hardware System
#### 3.1.1 Analog Signal Collection  
The 1521L-010 accelerometer is powered by \(\pm\) 5 V with low noise and high stability, and the sensitivity is 200 mV/g. It is noted that the output signal is an analog value which can be converted into a digital signal by an analog-to-digital converter (ADC) chip. The ADS131a04 is an ADC that has low power, four channels, simultaneous sampling, is 24-bit, has delta-sigma modulation and the output rate is 128 K samples per second (SPS). In addition, synchronous acquisition of data depends on the output protocol of the serial peripheral interface (SPI). Compared with other transmission modes, such as the inter-integrated circuit (IIC) or the universal asynchronous receiver/transmitter (UART), the SPI transmission rate is faster, which makes it more suitable for real-time calculation of navigation parameters.
## 3 Results and Discussion  
### Characteristics of Au Nanomaterials by TEM and XRD  
TEM images of a series of round nanodots, short nanobars, long nanorods, and ultralong nanowires prepared with different molar ratios of [TPS]:[Au]:[OA]:[OAm] are shown in Figure 1. Obviously, the morphologies of the gold nanomaterials could be controlled by changing the ratio of [OA]:[OAm]:[Au]. The aspect ratio of the gold nanoparticles increased with an increase in the ratio of OAm and OA to chloroauric acid. For example, at a low molar ratio of OAm and OA to chloroauric acid ([OA]:[OAm]:[Au] = 0.8:3.8:1), round nanodots with an average diameter of 18.8 nm were obtained (Figure 1a). Increasing the ratio of [OA]:[OAm]:[Au] to 48:45.6:1 resulted in the formation of short nanobars with an aspect ratio of about 1.5-3 (Figure 1b). Long nanorods with a length in the range of tenths of nanometers to several hundred nanometers appeared when the ratio of oleic acid was decreased ([OA]:[OAm]:[Au] = 24:45.6:1), as shown in Figure 1c. When the ratio was further increased to [OA]:[OAm]:[Au] = 24:114:1, silk-like nanowires with ultralong lengths (>2 \(\upmu\)m) and ultrathin diameters (<2 nm) were produced (Figure 1d). These silk-like ultralong and ultrathin nanowires may offer significant potential applications in microelectronics and biosensors. From the high resolution transmission electron microscope (HRTEM) image in Figure 1, the interfringe distances of the Au nanodots, Au nanobars, Au nanorods, and Au nanowires were measured to be 0.234 nm, 0.235 nm, 0.233 nm, and 0.239 nm, respectively, corresponding to (111) lattice spacing (0.235 nm) of face-centered cubic (fcc) Au (JCPDS89-3697), and the diameter of the nanowires was determined to be 1.82 nm. The crystallographic structure of the ultralong nanowires was further confirmed by the X-ray diffraction pattern in Figure 2. The diffraction peaks at 20 = 38.2\({}^{\circ}\), 44.0\({}^{\circ}\), and 64.7\({}^{\circ}\) were respectively ascribed to (111), (200), and (220) planes of fcc gold lattice, which agrees well with the diffraction standard of fcc gold.  
Figure 1: _Cont._  
The solutions of round nanodots, short nanobars, long nanorods, and ultralong nanowires in chloroform were clear and transparent, displaying pink, wine red, light brown, and deep brown colors, respectively, at room temperature (Supplementary Material Figure S1). These solutions showed no perceptible color change and aggregation or precipitation of gold nanoparticles even after they had been stored in a fridge at 4 \({}^{\circ}\)C for two months, indicating that these gold nanomaterials have excellent long-term stability.
# Drug-Target Affinity Prediction Based on Cross-Modal Fusion of Text and Graph
## 4 Discussion  
### Comparison Between Existing Methods and Cross-Modal Fusion of Text and Graph Methods  
Cross-modal methods offer significant advantages over traditional experimental methods and computational methods. Traditional experimental methods rely on experimental data, have low computational efficiency, are suitable for small-scale datasets, and face  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Model** & **Drug** & **Target** & \multicolumn{3}{c}{**Davis**} & \multicolumn{3}{c}{**KIBA**} \\ \cline{4-9}  & & & & **MSE** & **CI** & \(\mathbf{r_{m}^{2}}\) & **MSE** & **CI** & \(\mathbf{r_{m}^{2}}\) \\ \hline
1 & GRU & Text & Text & 0.279 & 0.848 & 0.659 & 0.231 & 0.843 & 0.663 \\
2 & GIN & Graph & Graph & 0.241 & 0.880 & 0.681 & 0.185 & 0.860 & 0.684 \\
3 & GRU+MC & Text & Text & 0.244 & 0.867 & 0.660 & 0.216 & 0.849 & 0.691 \\
4 & GIN+MC & Graph & Graph & 0.233 & 0.889 & 0.702 & 0.174 & 0.871 & 0.684 \\
5 & MA & Graph & Graph & 0.227 & 0.898 & 0.712 & 0.166 & 0.875 & 0.705 \\
6 & GRU+GIN & T+G & T+G & 0.235 & 0.899 & 0.684 & 0.173 & 0.863 & 0.688 \\
7 & GRU+MA & T+G & T+G & 0.221 & 0.905 & 0.759 & 0.148 & 0.890 & 0.774 \\
8 & GRU+GIN+MB & T+G & T+G & 0.223 & 0.902 & 0.736 & 0.159 & 0.875 & 0.702 \\
9 & GRU+MA+SMB1 & T+G & T+G & 0.224 & 0.901 & 0.744 & 0.151 & 0.885 & 0.772 \\
10 & GRU+MA+SMB2 & T+G & T+G & 0.221 & 0.901 & 0.746 & 0.148 & 0.889 & 0.782 \\
11 & GRU+MA+MB & T+G & T+G & 0.219 & 0.912 & 0.767 & 0.144 & 0.897 & 0.785 \\
12 & GRU+MA+MB+RE & T+G & T+G & 0.198 & 0.923 & 0.784 & 0.121 & 0.902 & 0.798 \\
**13** & **GRU+MA+MB+RE+MC** & **T+G** & **T+G** & **0.182** & **0.929** & **0.786** & **0.114** & **0.917** & **0.806** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation study of each module on Davis and KIBA datasets.  
high costs and long timeframes. While computational methods improve computational efficiency, they depend on manual annotation and feature extraction, which limits their applicability. In contrast, cross-modal deep learning methods can handle large-scale datasets, and, by integrating text and graph features, capture complex structural relationships and interactions. These methods offer greater flexibility and prediction accuracy, achieving better performance in a wider range of tasks, as shown in Table 8.
## 5 Results
### Experiment 1--The Output-Oriented CCR Model with Two Input and One Output Parameters  
Next, we build a basic output-oriented CCR model. The goal for this experiment is to increase the supply of thermal energy to heat networks.  
The results obtained are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \(\boldsymbol{\mathscr{N}}\)**CHPP** & **O (y1)** & **I (x1)** & **I (x2)** \\ \hline
1 & 3635 & 1554 & 642 \\ \hline
2 & 4519 & 1530 & 790 \\ \hline
3 & 1077 & 752 & 401 \\ \hline
4 & 3626 & 1554 & 641 \\ \hline
5 & 4380 & 1405 & 767 \\ \hline
6 & 1001 & 752 & 386 \\ \hline \end{tabular} O—output; I—input.  
\end{table}
Table 2: Initial data on two input indicators and one output indicator of the studied CHPP sample.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\boldsymbol{\mathscr{N}}\)**CHPP** & **Eff** & **O (y1)** & **I (x1)** & **I (x2)** \\ \hline
1 & 0.873 & 4161.898 & 1554 & 642 \\ \hline
2 & 0.978 & 4620.025 & 1530 & 790 \\ \hline
3 & 0.466 & 2312.812 & 752 & 401 \\ \hline
4 & 0.872 & 4158.590 & 1554 & 641 \\ \hline
5 & 1.000 & 4380.000 & 1405 & 767 \\ \hline
6 & 0.442 & 2263.190 & 752 & 386 \\ \hline \end{tabular}
\end{table}
Table 3: Efficiency calculation based on the output-oriented CCR model with two input and one output parameters.  
It can be seen that only CHPP 5 has an efficiency indicator of 1 and is, thus, effective. It does not require setting the output indicator. Therefore, in the table, the output indicator y1 remains unchanged for CHPP 5. For all other CHPPs, the y1 indicator changes. Table 3 shows the y1 values required to achieve the effectiveness of this CHPP. For example, the efficiency indicator calculated by the DEA method for CHP1 is 0.873 with the maximum possible being 1. The input indicator output (y1) is initially equal to 3635.  
In order to increase the efficiency of this CHPP1 to 1, and so make the enterprise fall on the efficiency frontier built on this sample, it is necessary to increase output (y1) to 4161,898. The efficiency coefficient calculated by the DEA method will be equal to 1, and, accordingly, the enterprise will become efficient.  
Input indicators input (x1) and input (x2) remain unchanged when the model is oriented to the output of the CCR model; only the output indicator changes.
## 3 Proposed Method
### Weibull Cycle Family Estimation and Its Random Behavior
#### 3.3.2 Determination of the Weibull Cycle Parameters  
By using the cycle to failure \(N\) value and the _Weibull element_\(t_{0\alpha\beta}\) value that corresponds to the equivalent stress value in Equation (20), the corresponding _Weibull scale cycle to failure_\(\eta_{t}\) parameter is set.  
\[\eta_{t}=\frac{N}{t_{\alpha\alpha\beta}} \tag{33}\]  
At this point, by using the Weibull cycle \(\eta_{t}\) parameter, the corresponding expected cycle to failure values that correspond to each one of the \(Y_{i}\) elements from Equation (15) are determined.  
\[N_{i}=\eta_{t}*t_{0i} \tag{34}\]  
Thus, by using the Weibull stress \(\beta_{s}\) value, the Weibull cycle to failure family is W (\(\beta_{s}\), \(\eta_{t}\)).  
Figure 1: Goodman, Gerber and ASME failure criteria.  
**Note 3.** The \(\beta_{s}\) value is used on the assumption that the failure mode remains constant.  
Finally, the \(N_{i}\) value that corresponds to the used \(S_{y}\) value is determined based on the \(t_{0 sy}\) element given as:  
\[t_{0 sy}=\eta_{s}/S_{y} \tag{35}\]  
Then by using the \(t_{0 sy}\) value in Equation (34), the \(N_{i}\) value that corresponds to the \(S_{y}\) value is determined. The numerical application is as follows.  
**4. Mechanical Application with Weibull/Finite Element Analysis (FEA)**  
The numerical application is performed by using a flat spring undergoing cyclic variable stress that causes tensile deflection. The load is considered as a cantilever and is the direct cause of failure affecting a product's reliability. By way of initial conditions, the following data are established: the minimal operational deflection of the flat spring is 3.0 mm, and the maximal operational deflection of the spring is 8.0 mm. It is also necessary to perform the spring stress analysis at point A. At this point, the convex side of the base of the spring experiences the varying tensile stresses [5]. The relevant data to determine the main applied stresses are, length \(L=65\) mm, thickness \(t=0.80\) mm, width \(b=6\) mm, and the used material is a steel AISI 4340 OQT 1300 with elasticity modulus \(E=207\) GPa. The spring is joint to the base by using the double-sided longitudinal welded joint that corresponds to the category B of joints in AWS D1.1 [28]. The structure geometry (see Figures 2 and 3) is subjected to variable cyclic load, within the elastic range, of frequency and magnitude sufficient to initiate cracking and progressive failure due to fatigue.  
The relation between stress range \(F_{SR}\) and the allowable cycle number \(N\) under variable amplitude load condition [29] is:  
\[F_{SR}=\left(\frac{C_{f}\;X\;329}{N}\right)^{0.333} \tag{36}\]  
\[F_{SR}=\sigma_{max}-\sigma_{min} \tag{37}\]  
in which:  
\(F_{SR}=\) Allowable stress range, \(C_{f}=\) Fatigue constant load, \(N=\) Number of cycles of stress range.  
With the relevant data provided, the \(\sigma_{1}\) and \(\sigma_{2}\) principal stress can be determined to perform the Weibull FEA stress data analysis as follows.  
Figure 3: Cross section of spring.  
Figure 2: Free-Body diagram of flat spring.