esa-sceva/satcom-chunk-collection huggingface dataset:
# A Combined High and Low Cycle Fatigue Life Prediction Model for Wind Turbine Blades
## 1 Introduction
This model integrates a nonlinear damage accumulation approach and considers the effects of load interactions and sequence on fatigue damage, aiming to address the limitations of traditional life prediction methods and provide a more reliable prediction framework. Additionally, this study validates the proposed method using both experimental data and finite element analysis (FEA) on critical blade sections, ensuring its practical applicability and accuracy in engineering scenarios. The remainder of this paper is organized as follows. In Section 2, we describe the CCF load spectrum and the accumulation of fatigue damage under variable amplitude cyclic loading, along with the introduction of a nonlinear fatigue damage accumulation model. Based on this model, a nonlinear damage accumulation model considering load sequence and load interactions is established, and the CCF damage curve is modified using fatigue damage curves and equivalent damage theory. In Section 3, a new CCF fatigue life prediction model is proposed, which incorporates a coupling damage module while considering load interactions. This model establishes a connection between HCF and LCF, enabling more accurate life predictions under CCF cyclic loading. In Section 4, we briefly discuss Miner's rule, the M-H model, and the T-K model and validate the proposed model using existing experimental data, demonstrating superior prediction accuracy. In Section 5, finite element analysis (FEA) is performed on the critical sections of the wind turbine blades, and the proposed method is applied to assess fatigue life predictions. Section 6 summarizes the main conclusions and suggests directions for further research.
# Pockels Effect at the Interface between Water and Ti Electrode
## Appendix C Reflectance in a Bilayer Film  
Let us derive formulas to calculate the reflectance due to the EDL and oxide film sandwiched between a semi-infinite transparent bulk medium layer and a bulk metal layer. The assumed interface conditions are shown in Figure 12.  
Defining the complex amplitude reflection coefficient \(r_{ij}\,:i\to j\) at the incident angle \(\theta_{0}=0\) from layer \(i\) to layer \(j\), the following equation holds.  
\[r_{01}=\frac{n_{0}-n_{1}}{n_{0}+n_{1}} \tag{11}\]  
\[r_{12}=\frac{n_{1}-n_{2}}{n_{1}+n_{2}}=\frac{n_{1}-(m+i\gamma)}{n_{1}+(m+i\gamma )}=\frac{(n_{1}-m)-i\gamma}{(n_{1}+m)+i\gamma}=x_{2}+iy_{2} \tag{12}\]  
\[r_{23}=\frac{n_{2}-n_{3}}{n_{2}+n_{3}}=\frac{(m+i\gamma)-(n+i\kappa)}{(m+i \gamma)+(n+i\kappa)}=\frac{(m-n)+i(\gamma-\kappa)}{(m+n)+i(\gamma+\kappa)}=x_ {3}+iy_{3} \tag{13}\]  
Focusing on the layer highlighted in beige on the left of Figure 10, the reflection coefficient considering multiple reflections within a monolayer (layer 2) is given by the formula [40; 41]:  
\[r_{2}=\frac{r_{12}+r_{23}e^{2i\phi_{2}}}{1+r_{12}r_{23}e^{2i\phi_{2}}}=\frac{ x_{2}+iy_{2}+(x_{3}+iy_{3})e^{-\delta}(\cos\psi+i\sin\psi)}{1+(x_{2}+iy_{2})(x_{3}+ iy_{3})e^{-\delta}(\cos\psi+i\sin\psi)}=X+iY \tag{14}\]  
where \(\phi_{2}=k_{0}n_{2}d_{2}=k_{0}(m+i\gamma)d_{2}\) and \(e^{2i\phi_{2}}=e^{-2\gamma k_{0}d_{2}}e^{2 ik_{0}md_{2}}=e^{-\delta}e^{i\phi}= e^{-\delta}\)\((\cos\psi+i\sin\psi)\). Using this, we can calculate the reflection coefficient in a bilayer (layer 1 + layer 2) by focusing on the layer highlighted in beige on the right of Figure 10. With \(\phi=2k_{0}n_{1}d_{1}\),  
\[r_{1+2}=\frac{r_{01}+r_{2}e^{i\phi}}{1+r_{01}r_{2}e^{i\phi}}=\frac{r_{01}+(X+ iY)e^{i\phi}}{1+r_{01}(X+iY)e^{i\phi}}. \tag{15}\]  
From the above, the reflectance in the bilayer is  
\[R=\frac{(r_{01}+X\cos\phi-Y\sin\phi)^{2}+(X\sin\phi+Y\cos\phi)^{2}}{\left[1+r_ {01}(X\cos\phi-Y\sin\phi)\right]^{2}+r_{01}^{2}(X\sin\phi+Y\cos\phi)^{2}}. \tag{16}\]  
The reflectance change \(\Delta R/R\) is obtained as \((R^{\prime}-R)/R\) from the reflectance \(R\) calculated with EDL thickness \(d_{1}=0\) and no change in complex refractive index of the oxide film, and \(R^{\prime}\) with EDL thickness \(d_{1}>0\) and energy shift in the complex refractive index of the oxide film.
# The Computability of the Channel Reliability Function and Related Bounds
## 2 Definitions and Basic Results
### Basic Concepts of Information Theory
The channel reliability function is therefore known for this interval. The channel reliability function is generally not known for the interval \([0,R_{crit}]\). For the interval \([0,R_{crit}]\), there are also better lower bounds than the random coding lower bound. \(R_{\infty}(W)\) is the infimum of all rates \(\underline{R}\) such that \(E_{SP}(W,\underline{R})\) is finite on the open interval \((\underline{R},C(W))\). \(C_{0}(W)\leq R_{\infty}(W)\) applies if \(C_{0}(W)>0\). The following representation of \(R_{\infty}\) exists (see [9]):  
\[P_{e}(\mathcal{C}(n),W,m)=\Pr\{d(Y^{n})\
eq m|X^{n}=e(m)\}.\]8  
There exist alphabets \(\mathcal{X},\mathcal{Y}\) and channels \(W\in\mathcal{CH}\) such that \(C_{0}(W)=0\) while \(R_{\infty}(W)>0\). Moreover, for the zero-error feedback capacity \(C_{0}^{FB}\), it holds that \(C_{0}^{FB}(W)=R_{\infty}(W)\) whenever \(C_{0}(W)>0\). However, if \(C_{0}(W)=0\), there exists a channel \(W\) for which \(C_{0}^{FB}(W)=0\) while \(R_{\infty}(W)>0\) (see [9]). For the zero-error feedback capacity, the following is known. **Theorem 4** ([PERSON] 1956, [40]). : _Let \(W\in\mathcal{CH}(\mathcal{X},\mathcal{Y})\); then,_  
\[C_{0}^{FB}(W)=\left\{ \begin{array}{ll}0&\text{if }C_{0}(W)=0\\ \max_{P\in\mathcal{P}(\mathcal{X})}\min_{y}\log_{2}\frac{1}{\sum_{x:W(y|x)>0} P(x)}&\text{otherwise.}\end{array} \right. \tag{11}\]
## 2 Materials and Methods
### Data Processing and Analysis  
The input for AEFEA and further analyses were 60 facial video recordings, with a mean length of 1455.862 s (SD = 167.831 s; range: 508.535-1688.733 s) (the mean length of the video recordings was somewhat lower than that of total play times presented above (and the range and SD were greater); this was due to one video recording stopping earlier than the end of the game). These were analyzed by FR8 for gaining EFE intensity data. No preprocessing was performed on the recordings before FR8 analyses. FR8 provides expression intensity values between 0 and 1 on 8 emotions: neutral, happy, sad, angry, surprised, scared, disgusted, and (optionally) contempt. FR8 attempts to assign such intensity values to each frame of the video recording. If it is unable to find the face in any given frame, it logs a \"FIND FAILED\" entry for the frame, for each emotion. If it is able to find the face, but unable to fit the face model onto it, it creates a \"FIT_FAILED\" entry for all emotions for the frame. These data were exported with their time stamps and served as raw data for further analyses.  
FR8 provides several settings options to customize EFE analysis. Appendix A Table A1 presents and explains the settings used in this study.  
For test-retest reliability analysis, each recorded facial video was analyzed by FR8, repeatedly, a maximum of three times. If the first two subsequent analyses yielded exactly the same EFE intensity values, then no further analysis was run on that specific recording. If, however, no complete agreement was found, then a third analysis was performed.  
This repeated analysis was performed by repeatedly sweeping along the set of video recordings in the same order. That is, firstly, all recordings were analyzed once, from subject #1's recording to subject #60's recording, and then the second analysis was carried out in the same order. After checking the level of match between corresponding individual data series, only those recordings were analyzed again (in the same order) where total test-retestidentity was not reached with the second sweep. Those with perfect test-retest identity were omitted from reanalysis. One dataset (file) was gained from each analysis of each recording, and these datasets served as input for further analyses on test-retest reliability. See Results, Section 3, for descriptive data on the repeated analyses and their outcomes.
# Stochastic-Based Spin-Programmable Gate Array with Emerging MTJ Device Technology
## 6 Performance Analysis and Comparison
In comparison, the second architecture [39] MTJ-based SPGA implementation can significantly save power because of using low power MTJ devices for both memory bits and logic computation. However, replacement of SRAM cell by MTJ cell still remain on high usage of routing resource. The third and fourth architecture employ 3D layout to minimize routing resource. Our proposed stochastic-based SPGA achieves the highest energy efficiency. There are several contributing factors. First, our proposed Taylor series approximation method fundamentally reduces the quantity of connections in system level. Second, MTJ devices can be constructed in a 3D metallic structure, therefore drastically reducing the interconnection energy dissipation. Third, MTJ devices can be operated with ultra-low currents for reading and writing procedure (few \(\upmu\)As). Moreover, static power dissipation almost vanishes due to its non-volatile character. Fourth, the analog comparison circuit with the help of ultra-low power MTJ operations can result in very high energy and area efficiency. Fifth, its latch-free architecture does not require any CMOS latch to improve its delay performance. In fact, its delay is mostly determined by the MTJ switching speed and the sample size. Finally, the proposed high output number SLB can drastically reduce the nodes number and delay. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **STT-MRAM** & **Proposed** \\ \hline transistor count & 154 MOS + 32 MTJ & 6 MOS + 32 MTJ \\ active power (\(\upmu\)W) & 13.4 & 3.22 \\ standby power (\(\upmu\)W) & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Delay of SPGA architecture with 45 nm CMOS technology node. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Conv: 2D Arch. **} & \multicolumn{4}{c}{**RRAM FFGA [90]**} & \multicolumn{4}{c}{**3D CMOS FFGA [10,40]**} & \multicolumn{4}{c}{**3D \#FREG [41]**} & \multicolumn{4}{c}{**Proposed SPGA Arch. **} \\ \cline{2-13}
**Design** &
# Fragility Analysis of Transmission Towers Subjected to Downburst Winds
## 3 Typical Engineering Case Study
### Nonlinear Buckling Analysis  
As mentioned previously, the nonlinear buckling analysis of the transmission tower under a downburst is conducted by finite element analysis. First, the eigenvalue buckling analysis of the transmission tower structure is carried out. According to the results of the eigenvalue buckling analysis, the initial defects are imposed by the deformation of the eigenvalue buckling mode. The nonlinear buckling analysis of the transmission tower is then carried out, and the force-deformation curves of the top displacement of transmission towers under different wind attack angles are obtained, as shown in Figure 12. It can be found that 45\({}^{\circ}\) and 60\({}^{\circ}\) directional angles are more prone to buckling failure, and 0\({}^{\circ}\) and 90\({}^{\circ}\) directional angles have better ductility. The threshold displacement values of different damage states of transmission towers can be obtained. Subsequently, the uncertain collapse analysis result and fragility analysis result can be obtained.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Point Number** & \(\mathbf{z/m}\) & \(\overline{\mathbf{U}}_{3}(z)\) (m/s) & \(\overline{\mathbf{U}}(z)\) (m/s) \\ \hline
1 & 5 & 19.0 & 13.4 \\
2 & 10 & 33.9 & 23.8 \\
3 & 15 & 45.5 & 32.0 \\
4 & 20 & 54.6 & 38.4 \\
5 & 25 & 61.6 & 43.3 \\
6 & 28.2 & 64.9 & 45.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Wind velocity at the centroid points of each wind pressure section.  
Figure 11: Comparison of estimated auto-/cross correlation with targets (2000 samples).
## 4 Discussion
### Limitations  
The main limitations of this work are represented by the approximations in the ankle joint motion patterns that were provided as input for the multi-body model. In particular, the three rotational DoFs of the bushing joint do not accurately match the actual physiological ones. Unlike the anatomical interpretation of joint axes (antero-posterior, longitudinal, medio-lateral), the modeling of the joint in the multi-body approach considers three mutually orthogonal axes. Moreover, the DoFs associated with any joint axes are independent by mathematical formulation of the bushing. This feature leads to a simplified representation of the joint DoFs, which result to be decoupled in the model, whereas they cannot be isolated in the real scenario. For these reasons, the simulated flexion/extension, adduction/abduction, and inversion/eversion motion patterns can only approximate the real joint DoFs and have limited meaning from the anatomical/physiological point of view. However, this approximation was considered reasonable for the aim of the current work, which focused on the objective evaluation of the helical axis and SARA techniques in a realistic scenario, rather than on the accurate representation of ankle joint motion through multi-body modeling. Moreover, this simplification greatly facilitates the formulation of the model.
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 5 Simulations and Evaluation
### Measure
Figure 7a shows the GE for azimuth against \(RT_{60}\), Figure 7b shows the GE for elevation against \(RT_{60}\). Figure 8 shows the relationship between the results and SNR with the threshold \(\lambda=20\). Figure 8a shows the GE for azimuth against SNR, and Figure 8b shows the GE for elevation against SNR. Figure 9 indicates the relationship between the results and parameters, which is given in with the threshold \(\lambda=10\). Figure 9a shows the GE against \(RT_{60}\), and Figure 9b shows the GE against SNR. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(\lambda\) **= 20** & \(\lambda\) **= 15** & \(\lambda\) **= 10** \\ \hline MUSIC & 51.56\% & 63.46\% & 72.41\% \\ DPD-MUSIC & 36.41\% & 38.55\% & 45.06\% \\ FOA & 18.24\% & 24.01\% & 28.96\% \\ \hline TFD & 22.01\% & 27.78\% & 31.30\% \\ TFD-S & 19.33\% & 26.45\% & 29.41\% \\ \hline SHD & 18.02\% & 23.88\% & 28.46\% \\ SHD-S & 16.63\% & 20.31\% & 27.13\% \\ \hline AED & **13.43\%** & **19.27\%** & **26.11\%** \\ AED-S & **11.33\%** & **17.31\%** & **25.05\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: GE for the elevation estimation. Figure 7: GE for estimation against \(RT_{60}\) with the threshold \(\lambda=20\). (**a**) is the GE for azimuth against \(RT_{60}\). (**b**) is the GE for elevation against \(RT_{60}\). From Figure 7, it can be obtained that the proposed input feature, the AED matrix, is the best input feature for improving accuracy. Furthermore, the segmentation strategy is also effective. Thus, the combination of the AED matrix and the segmentation strategy can help to resist reverberation effectively. From Figure 8, it can be found that the proposed input feature, the AED matrix, is also the best input feature for reducing the error rate. Furthermore, the segmentation strategy is still effective. Thus, the combination of the AED matrix and the segmentation strategy can help to effectively resist noise. From Figure 9, it can be seen that the proposed input feature still performs best even with the lower threshold. Thus, it can be concluded that the proposed method can improve accuracy in all the simulated conditions.
## 2 Analysis of Forming Process
### Finite Element Process Simulation and Analysis
#### 2.3.5 Post-Processing Result Analysis  
The forming load, equivalent stress during the upsetting process, and damage value during the spinning process were analyzed separately, as seen in Figure 4. Figure 4a illustrates that the forming load initially increases rapidly to a peak value with increasing downward displacement, followed by a brief decrease and a subsequent continuous rise. The final forming load reaches \(4.99\times 10^{7}\) MN. The main reason is that in the initial stage, the material begins to undergo plastic deformation. Due to the low yield strength of the material at this time, the load rises rapidly. With the increase in the amount of pressure, the internal stress of the material changes, the fluidity of the local area increases, and the load decreases briefly. Subsequently, with the increase in the contact area between the material and the mold, the temperature decreases, the deformation resistance increases, and the load continues to grow. The maximum load is within the output load range of the hydraulic press. Figure 4b shows the equivalent stress distribution cloud map of the pre-upsetting process. It can be seen from Figure 4b that the equivalent stress is mainly concentrated at the edge of the forging circle, and the equivalent stress in the central area is relatively small and evenly distributed. This is because the material in the central area is easier to flow and the stress is smaller in the process of pier thickening. The maximum equivalent stress of the forging is 39.3 MPa. Figure 4c illustrates the damage value distribution during the first spinning pass. As shown in Figure 4c, the damage values are primarily concentrated at the junction of the two anvils and the edge of the biscuit blank, while the central region exhibits near-zero damage. The damage values exhibit a gradual increase from the center towards the surface of the forging, with a maximum value of 0.592. This is due to the existence of stress concentration, friction, wear, and uneven temperature distribution on the surface area of the forging, resulting in a higher damage value.  
Figure 4: Analysis of simulation results: (**a**) forming load–time curve for preform block upsetting, (**b**) equivalent stress dispersion contour diagram, and (**c**) contour chart for distribution of damage values.
# Integration of Deep Learning and Collaborative Robot for Assembly Tasks
## 5 Results
### Voice Recognition  
The voice recognition system obtained an average accuracy of 94.5% from the 200 tests conducted. Figure 12 displays the obtained predictions. The command \"five\" had an 89.4% accuracy; in most of the mistaken predictions, the command was confused with \"one\". The prediction mistakes are attributed to the low sample rate used for data acquisition and command recording, variations in word pronunciation, and voice volume. It was observed that better results were obtained under isolated conditions. The system presented a high sensitivity to background noise and better performance to low-pitched voices. This is attributed to the training dataset used.  
Misclassification of voice commands may lead to the execution of incorrect routines. However, the \"cancel\" command serves to halt misidentified routines, as depicted in Figure 4. Moreover, our tests revealed that the \"cancel\" command demonstrated a prediction accuracy of 99.7%, as observed in Figure 12. For safety measures, an emergency stop button is available for immediate use when necessary.  
Figure 11: Box plot showing the confidence rate from the CNN classification of the objects within the 210 object classification system tests performed.
###### Abstract  
Chronic ankle instability (CAI) patients often exhibit postural control deficits and rely on visual information to maintain static balance to compensate for decreased proprioception. Fatigue impairs neuromuscular control, in addition to postural control, in CAI patients. However, whether functional fatiguing exercises alter postural control and sensory organization strategies during single-leg balance tests in CAI patients remains unclear. This study involved a controlled trial on 28 CAI patients in a laboratory setting. Each participant performed a single-leg balance test with eyes open (EO) and eyes closed (EC) before and after a functional fatigue protocol. Two-way repeated-measures ANOVA evaluated fatigue (pre- vs. post-fatigue) \(\times\) vision (EO vs. EC) interactions for outcome variables. Additionally, paired-sample \(t\)-tests examined differences between two conditions (pre- vs. post-fatigue) for time-to-boundary (TTB) minima (%modulation). We found significant interactions between fatigue and vision conditions in ML and AP TTBmeans and AP TTBds. %Modulations were significantly decreased after fatigue in AP TTBmean, ML TTBsd, and AP TTBsd. In conclusion, static postural control ability decreased after the functional fatigue protocol with EO, but was unchanged with EC. This suggests that decreased balance ability is more pronounced with EO under fatigue due to less visual dependence. This may increase ankle strain incidence under fatigue.  
chronic ankle instability; postural control; functional fatigue; sensory organization
# Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme in Intrabody Nanonetworks
## 2 Related Work  
Over the past decade, the need for realizing IBNNs has gained considerable attention. The existing work emphasized on the development of novel low-complexity communication protocols for IBNNs. In this section, we briefly discuss the feasibility and scope of FLBDS and bio-inspired firefly algorithms for IBNNs as our proposed protocol combines these concepts for realizing low-complexity routing protocol. Further, we have discussed the advantages and limitations of existing routing protocols in the rest of this section.
# Shooting Prediction Based on Vision Sensors and Trajectory Learning
## 4 Shooting Results Prediction
### Shot Prediction Based on Time and Motion Trajectory  
Firstly, we used time t and basketball trajectory coordinates (x, y) as a combination of sample features to predict the shooting result, and the obtained prediction accuracy is shown in Figure 7a. We can see that the prediction accuracy of the four classification algorithms all show an upward trend when the number of trajectory points increases. Among the four classifiers, the CART decision tree algorithm exhibited the best stability and a higher accuracy. When the number of trajectory points reaches 20 (one-third of the entire trajectory), we can get the best prediction results. As the number of trajectory points increases, the accuracy remains around 85%. The prediction accuracy of the four classifiers  
Figure 6: Basketball trajectory in shooting video.  
is higher than 70%, and the highest is 85%. Secondly, the results shown in Figure 7b are based on the feature combination [D, t], and the prediction accuracy after stabilization is 60%. Lastly, the feature combination used in the prediction result shown in Figure 7c is time t and height Y. When the number of trajectory points is greater than 12, we can see that the prediction accuracy of the CART decision tree algorithm is basically stable at about 85%, the prediction accuracy of the LDA algorithm is higher than 80%, and the prediction accuracy of the KNN and NB classifiers is higher than 70%. The overall prediction effect is similar to the prediction results based on time and trajectory features. According to the above three experiments, the CART algorithm has a good effect on the shot prediction. We conclude that the shooting prediction results based on time and trajectory features and time t or height Y features are better, and the overall accuracy can reach 85%. The prediction accuracy of experiment based on time and distance D is low, so it is not suitable for basketball shoot prediction. In addition, it can be seen that the accuracy of shot prediction based on time and trajectory features stabilizes after the 17 th trajectory point, accounting for 39.53% of the entire basketball trajectory. The shot prediction based on time and distance D features can reach the steady state after the 12 th trajectory point, accounting for 7.91% of the entire basketball trajectory. Therefore, the best feature combination scheme for shot prediction is the shot prediction based on time and height. At the same time, we can find that the method of this paper can be used to predict the shot result with high accuracy by obtaining the data of the previous period during the shooting process of the athlete.