esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Rotary Transformer for Installations on Large Shafts
## 2 Rotary Transformer: Model and Parameters  
Different from power transformers installed in power plants or stations [16; 17], primary and secondary windings in rotary transformers do not share the same magnetic core. Indeed, two cores are required to allow relative movement between the two parts of the system. As a consequence, each winding is wound around the corresponding core. Due to the particular structure of this device, the flux should cross the air gap at least twice to close the magnetic path. The presence of any air gap is not foreseen in transformers installed in power plants (which do not allow any relative movement between primary and secondary due to their static application [18]) and it strongly impacts on the magnetic characteristics of the device. On one hand, this contributes to linearizing the behaviour of the core, but, on the other hand, the magnetic coupling drops dramatically with respect to transformers with a bulk core. As a matter of fact, the magnetic flux leakage is much higher than static, single-core solutions. Among the possible topologies which are available in the literature for rotary transformers [13; 19], the co-axial topology was selected (see Figure 1) because it allows for an easy installation and it ensures robustness from a magnetic coupling point of view. Indeed, the power transfer proves to be less sensible to misalignment and eccentricity than other arrangements. It can be noted that the literature typically reports primary on the outermost part of the structure and secondary on the innermost part. Actually, this choice depends only on the particular configuration of the considered system, and nothing prevents the system from adopting the arrangement proposed in Figure 1.
## 4 Analysis of Computational Complexity
### Analysis of Computational Complexity under Single Channel ANC System Configuration  
In Tables 1 and 2, the computational complexity of the proposed filtering scheme in terms of number of multiplications and additions, respectively, is shown. We calculate the number of addition and multiplication operations required per iteration by taking into account a typical case (\(M=128\)  
Figure 2: Block diagram of the proposed scheme with switching filter selection.  
\(N=100\) and \(L=5\)). As can be observed in Tables 1 and 2, the FXA-AP algorithm presents the highest computational cost. However, it offers the fastest convergence speed. As shown in Tables 1 and 2, the proposed filtering scheme adds a few computations per iteration when compared with conventional FXAPL-I and FXLMS algorithms because of the time required to select the appropriate switching filter. Nevertheless, the proposed filtering scheme converges rapidly with a low MSE by enabling one filter or the other at different moments of time, i.e., not both at the same time. The proposed filtering scheme improves these aspects by reducing the computational complexity of the FXAPL-I once the steady-state MSE is reached, i.e., at this moment, the filtering process is only performed by the FXLMS algorithm. However, if the dynamic adjustment of the scaling factor is enabled, the proposed  
we demonstrate that the proposed filtering scheme reduces the complexity of FXAPL-I also in the case of multichannel ANC systems. The proposed filtering scheme guarantees a low computational cost; the computational cost of the proposed algorithm decreases rapidly once the steady-state MSE is reached and the FXLMS algorithm is computed. Therefore, we achieve a significant improvement in terms of convergence speed and MSE level by paying a tolerable penalty in terms of computational complexity. The proposed filtering scheme with dynamic adjustment of the scaling factor extends \(4 NIJK+2\) more multiplications and \(2 NIJK+1\) more additions than the FXAP-L algorithm with a fixed scaling factor at each iteration. In addition, Table 6 shows the memory requirements of the FXLMS, FXAPL-I, FXA-AP and the proposed algorithm.  
We must consider two different types of signals \(x(n)\) to evaluate a single channel and a 1:2:2 multichannel as follows:  
1. A multitonal input with frequencies 200, 300, and 400 Hz. We added white Gaussian noise with an SNR of 30 dB to \(x(n)\) to validate the robustness of the proposed filtering scheme.
2. A white Gaussian noise signal with variance \(\sigma_{\mathbf{v}}^{2}=0.1\).  
To obtain the learning curves, we performed 50 experiments with 40,000 iterations each. We also induced an abrupt change at iteration 20,000, which was generated by multiplying the coefficients of the primary path by \(-1\), to evaluate the tracking skills of the proposed filtering scheme.
# A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
## 4 Materials and Methods
### Our Security Approach
#### 4.3.1 Privacy-Preserving Scheme Algorithms  
We develop two algorithms that involve quantizing, encrypting, inferring, decrypting, and de-quantizing tasks. Algorithm 1 is designed for the client side, while Algorithm 2 is designed for the server side. Algorithm 1 starts with generating secret and evaluation keys through the KeyGen method. Then, the evaluation key is sent to the server through the SendKey method. Once the client receives the confirmation from the server, the input data are quantized (Quantization transforms values from a continuous domain \(\mathbb{R}\) (Real numbers) to a discrete domain \(\mathbb{I}\) (Integers)) through the QuantizeInput method and then the input data are encrypted through Encrypt method. Encrypted input is sent to the server to predict the output through MakeInference method. Finally, the client must wait until the server returns the encrypted output to decrypt and de-quantize it through Decrypt and DequantizeInput methods. As for Algorithm 2, it consists of two methods: SaveKey and MakeInference. SaveKey is responsible for storing the evaluation key sent from the client, while MakeInference predicts the output using the evaluation key and sends back the encrypted message containing the result to the client.  
Figure 3: Privacy-preserving scheme for the learning model.  
```
1:Message \(input\), Message \(output\)
2:int riskLevel
3:Initialize: boolean newKeys \(\Leftr\)\(Tue\), boolean received \(\Leftr\)\(False\), riskLevel \(\Leftr\) 0;
4:(secretKey, evalKey) \(\Leftr\) KeyGen(newKeys);
5:received \(\Leftr\) SaveKey(evalKey); \(\triangleright\) Waiting for server response
6:quantized \(\Leftr\) QuantizeInput(input);
7:encrypted \(\Leftr\) Encrypt(secretKey, quantized); \(\triangleright\) It uses FHE
8:if received = \(Tue\)then
9:output \(\Leftr\) MakeInference(encrypted); \(\triangleright\) Waiting for server response
10:if output \(\
eq\)\(None\)then
11:decrypted \(\Leftr\) Decrypt(secretKey, result);
12:riskLevel \(\Leftr\) DequantizeInput(decrypted);
13:endif
14:endif
```  
**Algorithm 1** Tasks on the client side.
# Initialisation Approaches for Population-Based Metaheuristic Algorithms: A Comprehensive Review
## 5 Experiment, Result, and Discussion
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multirow{2}{*}{**Value**} & \multicolumn{8}{c}{**Butterfly Optimization Algorithm**} \\ \cline{3-10}  & & **Pop = 10** & **Pop = 20** & **Pop = 30** & **Pop = 50** & **Pop = 100** & **Pop = 300** & **Pop = 500** & **Pop = 1000** \\ \cline{3-10}  & & **Iter = 1000** & **Iter = 900** & **Iter = 800** & **Iter = 600** & **Iter = 500** & **Iter = 300** & **Iter = 100** & **Iter = 10** \\ \hline \multirow{4}{*}{Sphere} & Mean & 0 & 0 & 0 & 0 & 0 & 1.6922E-08 & 4.9084E-05 & 7.6957E-06 \\ \cline{2-10}  & Stand.Div & 0 & 0 & 0 & 0 & 0 & 1.7860E-08 & 5.0361E-05 & 1.3852E-05 \\ \cline{2-10}  & Best & 0 & 0 & 0 & 0 & 1.0376E-08 & 3.0436E-05 & 3.0197E-07 \\ \cline{2-10}  & Worst & 0 & 0 & 0 & 0 & 0 & 3.2348E-08 & 7.5007E-05 & 5.0646E-05 \\ \cline{2-10}  & MeanRunTimes & 1.06E+00 & 2.71E+00 & 7.82E+00 & 1.11E+01 & 2.68E+01 & 2.2878E+01 & 29.6371 & 22.1779 \\ \hline \multirow{4}{*}{Rastrigin} & Mean & 0 & 0 & 0 & 0 & 0 & 4.7966E-06 & 0.0028 & 1.5485 \\ \cline{2-10}  & Stand.Div & 0 & 0 & 0 & 0 & 0 & 5.3872E-06 & 0.0029 & 1.5518 \\ \cline{2-10}  & Best & 0 & 0 & 0 & 0 & 0 & 1.5685E-06 & 0.0013 & 1.3340 \\ \cline{2-10}  & Worst & 0 & 0 & 0 & 0 & 0 & 1.1829E-05 & 0.0041 & 1.7193 \\ \cline{2-10}  & MeanRunTimes & 1.33E+00 & 3.02E+00 & 5.16E+00 & 7.31E+00 & 1.78E+01 & 2.3616E+01 & 29.8871 & 22.4889 \\ \hline \multirow{4}{*}{Rosenbrock} & Mean & 2.89E+01 & 2.89E+01 & 2.88E+01 & 2.88E+01 & 2.88E+01 & 2.8787E+01 & 28.7735 & 28.9139 \\ \cline{2-10}  & Stand.Div & 2.89E+01 & 2.89E+01 & 2.88E+01 & 2.88E+01 & 2.88E+01 & 2.8787E+01 & 28.7735 & 28.9139 \\ \cline{2-10}  & Best & 2.88E+01 & 2.88E+01 & 2.88E+01 & 2.88E+01 & 2.8750E+01 & 28.7340 & 28.8728 \\ \cline{2-10}  & Worst & 2.89E+01 & 2.89E+01 & 2.89E+01 & 2.89E+01 & 2.89E+01 & 2.8832E+01 & 28.8062 & 28.9504 \\ \cline{2-10}  & MeanRunTimes & 9.82E-01 & 2.85E+00 & 6.86E+00 & 6.82E+00 & 1.91E+01 & 2.3152E+01 & 31.1730 & 22.1920 \\ \hline \multirow{4}{*}{Griewank} & Mean & 0 & 0 & 0 & 0 & 0 & 5.7050E-06 & 0.0068 & 1.1715 \\ \cline{2-10}  & Stand.Div & 0 & 0 & 0 & 0 & 0 & 5.7511E-06 & 0.0068 & 1.1715 \\ \cline{2-10}  & Best & 0 & 0 & 0 & 0 & 0 & 4.0656E-06 & 0.0059 & 1.1497 \\ \cline{2-10}  & Worst & 0 & 0 & 0 & 0 & 0 & 6.9312E-06 & 0.0079 & 1.1881 \\ \cline{2-10}  & MeanRunTimes & 1.29E+00 & 3.06E+00 & 7.07E+00 & 1.09E+01 & 2.58E+01 & 3.8891E+01 & 31.5429 & 22.5601 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Result for Butterfly Optimization Algorithm.
## 3 Results and Discussion
### Non-Isothermal Cold Crystallization
#### 3.1.1 Ozawa Theory  
The Ozawa theory is very popular amongst the methods for analyzing non-isothermal crystallization data [43]. Accordingly, the relative crystallinity, \(X_{t}\), at temperature, \(T\), is given by the equation:  
\[X(t)=1-\exp\left(\frac{-K(T)}{\alpha^{m}}\right) \tag{4}\]  
where \(m\) is the Ozawa exponent and \(K(T)\) is the heating rate function. By fitting the double logarithmic form of Equation (4), the values of \(m\) and \(K(T)\) can be obtained. This model presumes that non-isothermal crystallization is composed of many infinitesimal isothermal steps. It can provide insights on non-isothermal crystallization kinetics as it is substantiated theoretically, but the plots can often be non-linear and prevent the estimation of the exponent [44]. The results of the Ozawa analysis are presented in Figure S3 and Table 1. The Ozawa plots of neat PLA show good linearity, but m is not constant and therefore the Ozawa model cannot accurately describe the non-isothermal crystallization of PLA.  
Figure 2: Effect of filler and heating rate on the crystallization half-time of poly(lactic acid) (PLA) during non-isothermal cold crystallization.  
Additionally, no linearity appears for the nanocomposites where, in most cases, insufficient R\({}^{2}\) values were obtained, or not enough points were available to draw safe conclusions. The inadequacy of the Ozawa theory, when applied to the data of the nanocomposites, suggests that secondary crystallization might occur and that the fold length of the polymer chain depends on the temperature [45; 46; 47; 48; 49].
# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
## 4 Results and Discussion
### Comparison of the ELM Classification with Burned Area Products  
This section presents a comparison with methods of estimation of burned areas operatively used, namely, the method used by CONAF [62] and the FireCCI algorithm [54].  
The best indicators in the validation area were reached by ELM, followed by CONAF and FIRECCI (Table 7). The higher spatial coincidence (DICE = 0.857) occurred between the classification carried out by ELM and the reference one, resulting in low omission (0.080) and commission (0.132) errors. The spatial coincidence of ELM classification with the reference data is observed in the detailed description that the ELM carries out over the burned-area borders and in the capacity to describe the internal islands that were not affected by fire (Figure 6D). This was explained by the proper training of the network, its generalization capacity, and the correspondence with the spatial resolution of the images used. On the other hand, estimates by CONAF showed lower spatial coincidence with respect to the classification of reference, having a DICE of 0.799, with omission and commission errors of 0.064 and 0.301, respectively. Despite showing a good spatial coincidence with its reference, and using Landsat 7 and 8 images [62], the estimate by CONAF showed a higher number of commission errors. They can be presumably caused by a general visual interpretation of the areas affected by fire, making it hard to account for the complexity of the geometry of the burned polygons and the unburned islands (Figure 6B). Moreover, FIRECCI estimations showed the worst results for the validation metrics. The low spatial resolution of the FIRECCI product (250 m) prevents it from detecting internal unburned islands, which are located to the interior of the big burned patches (Figure 6C). Furthermore, it prevents it from detecting low-intensity fires, mainly of agricultural origin [13].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline - & **Ground Truth** & **ELM** & **CONAF** & **FIRECCI** \\ \hline Burned area (ha) & 37,472.28 & 39,702.96 & 50,216.84 & 43,868.44 \\ Unburned area (ha) & 209,928.72 & 207,698.04 & 197,184.16 & 203,532.56 \\ DICE & 1 & 0.857 & 0.799 & 0.773 \\ Omission & 0 & 0.080 & 0.064 & 0.160 \\ Commission & 0 & 0.132 & 0.301 & 0.282 \\ \hline \hline \end{tabular}
\end{table}
Table 7: ELM-CONAF-FIRECCI comparison.  
Figure 5: Training time for classification algorithms applied to POST and DELTA datasets.
## 2 Refined Numerical Modelling Approach
### Element Selection and Mesh Division  
Generally speaking, beam elements and solid elements are usually used for the numerical simulation of concrete box girder bridges, and using beam elements for calculations often produces higher computational efficiency. However, the use of beam element models presents challenges in simulating certain behaviors: (1) Since the main beam is composed of beam elements, it becomes challenging to consider the crack condition even with certain section properties and geometric configurations. Typically, the attenuation method of cross-section concrete's elastic modulus is employed to account for the impact of concrete cracking on section stiffness and mechanical properties. However, this approach fails to directly reflect crucial crack property parameters such as location, length, width, depth, and other relevant information. Additionally, it does not capture local stress changes resulting from stress concentration effects caused by cracks. (2) If the joint connection between beams is taken into consideration, the primary approach is to employ spring connections between nodes. However, due to the indeterminate value of spring stiffness, its mechanical characteristics cannot be accurately reflected in this model. Furthermore, the node-spring stiffness connection fails to precisely depict the joint state information. The beam element modeling method exhibits significant limitations in characterizing, extracting, and analyzing the mechanical properties of related joints and their impact on structural mechanics. Based on the above, the C3D8R solid element, available in ABAQUS, was selected to simulate the beams and piers of the CPCR bridge.  
The hexahedron shape was chosen as the basic element, and FE analysis utilized sweep technology combined with an advanced front algorithm [22]. The prestress tendons were simulated with the two-node linear 3D truss element T3D2. For optimal computational efficiency, a mesh sensitive study was conducted to determine grid number and layout, the results of which are listed in Table 2. Based on the optimal trade-off of calculation accuracy and time efficiency, the layout scheme of non-uniform 3D discretization along the span direction was determined, i.e., more mesh seeds were laid out in the unfavorable stress locations and a lower mesh density was set in the other area. The mesh division of the half main span of \"Bridge A\" is presented in Figure 3.  
Figure 2: Constitutive model used for concrete and prestress tendons. (**a**) Linear elastic model for concrete. (**b**) Ideal elastic–plastic model for prestress tendons.
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 1 Introduction
\(\text{PCE}\) = Path Cost Estimation
7. \(\text{RT}\) = Routing Table
```
1 Input: Records in Neighbor Table
8 Start
9. \(\text{If}\)\(\text{N}_{\text{i}}\) is at one hop to CNC then
10. \(\text{send data directly to CNC}\)
11. \(\text{LE}_{\text{i,j}}\) (NT) \(\longleftarrow\) \(\text{LE}_{\text{i,j}}\) (HP)
12. \(\text{\bf Else}\)
13. \(\text{for each record in Neighbor Table}\)
14. \(\text{Calculate PCE}\) = i=0N\(\times\) 1 RE+ \(\beta\)\(\times\) 1 LE+ \(\gamma\)\(\times\)HC+ \(\delta\)\(\times\)d
15. \(\text{\bf If}\) (\(\text{N}_{\text{i}}\) PCE bit = 1) then // PCE previously checked = 1 not checked = 0
16. \(\text{Select other neighbor node N}_{\text{i}}\) from NT
17. \(\text{\bf else}\)
18. \(\text{Compute SNR and ensure that it is heigheir than 1}\)
19. \(\text{Compute Neighbor distance d}\), \(\text{HC}\), \(\text{LE}\), and \(\text{TE}\)
20. \(\text{TE}\) = \(\text{E}_{\text{Current}}\) + \(\text{E}_{\text{Harvested}}\)
21. \(\text{If}\) (\(\text{N}_{\text{i}}\)\(\text{E}_{\text{total}}\) < \(\text{TH}\)) then select other node from NT
22. \(\text{\bf Else}\)
23. \(\text{calculate PCE of N}_{\text{i}}\)
24. \(\text{record PCE and update NT}\)
25. \(\text{Ensure all neighbors are checked}\)
26. \(\text{else send the data in single hop
27. \(\text{\bf else}\) skip sending and wait for another time
28. \(\text{\bf endif}\)
29. \(\text{\bf endif}\)
30. \(\text{\bf endif}\)
31. \(\text{\bf End}\) ```
**Algorithm 2**Next Hop/Forwarder Node Selection phase
```
1. \(\text{A}_{\text{Data}}\) = Normal Data
2. \(\text{B}_{\text{Data}}\) = Emergency Data
3. \(\text{D}_{\text{packet}}\) = Data Packet
4 Input: Data Packets from any node 'i'
4. \(\text{\bf Start}\)
5. \(\text{\bf If}\)\(\text{B}_{\text{data}}\) is emergent
6. \(\text{Send it directly to CNC}\)
7. \(\text{\bf else}\)
8. \(\text{send the data as normal A}_{\text{data}}\)
9. \(\text{Otherwise discard the data packet // for redundant data}\)
10. \(\text{Discard D}_{\text{packet}}\) // for redundant data
11. \(\text{\bf endif}\)
12. \(\text{\bf End}\) ```
**Algorithm 3**Cooperative Routing
```
1
###### Abstract  
In this paper, after investigating the coupling effect in a permanent magnet synchronous in-wheel motor, a robust control method for active suspension of hub-driven electric vehicles (EVs) to enhance the performance of the in-wheel motor and the vehicle is proposed. Based on the electric vehicle model addressing the coupling effect between the electromagnetic excitation of the permanent magnet synchronous motor (PMSM) and the transient dynamics in EVs, the influence of the coupling effect on the motor and the vehicle performance is analyzed. The results reflect that the coupling effect in in-wheel motors intensifies the magnetic force oscillation, aggravates the eccentricity of the rotor, deteriorates the motor operation performance, and worsens the ride comfort. To suppress the magnetic force oscillation in motor and enhance the vehicle comfort, the active suspension system considering five aspects of suspension performance is introduced. Simultaneously, on the basis of Lyapunov stability theory, a reliable robust \(H_{\infty}\) controller considering model uncertainties, actuator failure and electromagnetic force interference is designed. The simulation results reflect that the robust \(H_{\infty}\) feedback controller can not only achieve better ride comfort, but also restrain the coupling effect in the motor. Meanwhile the other requirements such as the road holding capability, the actuator limitation, and the suspension deflection are also maintained. The proposed robust control method demonstrates a potential application in the practice of EV control.  
robust control; active suspension; in-wheel motor; unbalanced electromagnetic force; actuator failure +
Footnote †: journal: Applied Sciences
# A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
## 2 Model Design
### Decomposition Submodel
#### 2.1.2 STL for Decomposition  
Traditional time-series decomposition methods usually assume that seasonality and trend are static. However, in real data, both seasonality and trend may change dynamically, so more flexible and adaptive methods are needed. The STL method, as a nonparametric technique, can dynamically decompose the time-series signal, so that the trend and seasonal components can be flexibly extracted without relying on a fixed seasonal window [26; 27]. Therefore, the STL method can be used to analyze wave height data for decomposition.  
For a given wave height time series \(X(t)\), it can be expressed as  
\[X(t)=T(t)S(t)R(t) \tag{3}\]  
\(T(t)\) represents the trend component, reflecting the long-term changes of the time series; \(S(t)\) represents the seasonal component, describing periodic fluctuations; \(R(t)\) represents the remainder component, including irregular changes and noise. For the convenience of decomposition, we take the natural logarithm of both sides of the equation and convert the multiplication relationship into an additive form:  
\[\ln X(t)=\ln T(t)+\ln S(t)+\ln R(t) \tag{4}\]  
In this way, the trend, seasonality, and remainder parts of the wave height data can be decomposed by the STL method to improve the stability and interpretability of the decomposition. The algorithmic implementation of STL decomposition is structured as (Algorithm 1):  
```
Input: Time series \(X(t)\), iterations \(K\), Loess parameters Output: Decomposed components \(T(t)\), \(S(t)\), \(R(t)\) for\(k\gets 1\)to\(K\)do \(X_{\text{detrend}}(t)\gets X(t)-T_{k}(t)\); if\(X(t)\) is missingthen  continue  end if \(S_{k+1}(t)\leftarrow\text{LoessSmooth}(X_{\text{detrend}}(t))\); \(T_{k+1}(t)\leftarrow\text{LoessSmooth}(\text{LowPassFilter}(S_{k+1}(t)))\); \(S_{k+1}(t)\gets S_{k+1}(t)-T_{k+1}(t)\); \(T_{k+1}(t)\leftarrow\text{LoessSmooth}(X(t)-S_{k+1}(t))\);  end if \(R(t)\gets X(t)-T_{K}(t)-S_{K}(t)\);
```  
**Algorithm 1**Optimized STL decomposition algorithm.