esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A MATLAB/OCTAVE Code for VC Nodes  
This appendix provides the codification of the proposed MBF method for power systems that comprise voltage-controlled nodes. This implementation is reported in Figure A1 and works in MATLAB and OCTAVE environments.  
```
clc;clear Sbase=100;%AM/A Vbase=230;%kV % LineInfo.[ {i;R[pu]X[pu]]Y[pu] Lineas=[120.010080.050400.05125;
130.007440.037200.03875;
240.007440.037200.03875;
340.012720.0636060.06375];
#Nodetypetype1 ItypoV[R][R|VAr|] %T:0-->SLACK,1-->VC,2-->RQ] % [kT Pgk Qgk Pdk QdkV0] Nodos=[1100 0 50 30.9900 1 ;
2200 0 170 105.35 1 ;
3200 0 200 123.94 1 ;
41318 0 80 49.580 1.02 ];
#Permittransformation
*Zbase=(Ushape)*Z/Shape; Lines(:,3:4)=Lineas(:,3:4);Nodes(:,3:6)=Nodes(:,3:6)/Shape;
#Branch-to-nodeandImpedancematrices NN=size(Nodes,1); NL=size(Lineas,1); A=zeros(NN,NL); ZL=zeros(NN,NL); Yb=zeros(NN,NN); fori=1:N; Ni=Lineas(i,1);Nj=Lineas(i,2);A(Ni,i)=1;A(Nj,i)=-1; ZL(i,i)=Lineas(i,3)+1j*Lineas(i,4);Ys=1i*Lineas(i,5); Yb(Ni,Ni)=Yb(Ni,Ni)+Ys; Yb(Nj,Nj)=Yb(Nj,Nj)+Ys; end Ao=A(1,:);Ad=A(2:end.);Yold=Ads(ZL\Ad.')+Yb(2:end.2:end);Zdd=inv(Ydd); Ydo=Ad*(ZL\Ad.');Ydo=Nodes(2:end.7);Vo=Nodes(1,7); Yp=Ax(=ZL\Ad.')+Yb;Gd=Nodes(2:end.5)+1j*Nodes(2:end.6);e=1e-10;% Tolerance max=1000; PV=Nodes(Nodes(ods.:,2)==1,:);Sdpv=zeros(size(Sd,1),1); forpv=1:size(PV,1); yvu=PV(pv,1)-1;Ydo(pvn,1)=PV(pv,7); Qpv=-image(coni[S(pvn,1))+coni(Ydo(pvn,1))*Yb(pvn+1,:)*[Vo;Vdo]); Sdpv(pvn,1)=PV(pv,3)+1j*Qpv;end fort=1:tmax Sdx=Sdpv-Sd;Vd=Zdd*(diag(coni(1./Vdo)*conj(Sdx)-YdosVo); forpv=1:size(PV,1) yvn=PV(pv,1))-1; Vd(pvn,1)=PV(pv,7)*exp(1j*angle(Vd(pvn,1))); Qpv=-image(coni[Sd(pvn,1))+coni(Ydo(pvn,1))*Yb(pvn+1,:)*[Vo;Vd]); Sdpv(pvn,1)=PV(vy,3)+1j*Qpv;end ifmax(abs(yd)-abs(Vdo))<=eV=[Vo;Vd];VL=Ao.'*Vo+Ad.'*Vd;IL=ZL.VL;Sloss=(ZL*(abs(IL)).^2); break;else Vdo=Vd; end [Vmin,No]=min(V);Sslack=sum(Sd)+sum(Sloss); %%Conergencetest Proof=zeros(NN-1,2); fori=1:NN-1; Proof(i,:)=[1i+abs(Zdd(i,i))*abs(Sd(i))/min(abs(V))]; plot(Proof(:,1),Proof(:,2),'--*');end
```  
**Figure A1**. MATLAB implementation of the matricial iterative sweep power flow while considering VC nodes.
## 4 Simulation and Results  
We performed the simulation using MATLAB 2019 and set up the environment as detailed in this section. In the training process, we used the model shown in Figure 5. We applied the MMSE algorithm to find the parameters in the equalizer and GPR target, as in Section 3.2. Thereafter, we created 3000 random samples \(u[k]\in\{0,1\}\) with a length of 10,000 and reshaped the signal \(a[j,k]\in\{-1,1\}\) with a size of \(100\times 100\). In this study, we focus on combating ITI and ISI. Accordingly, we use the small block size of 100 \(\times\) 100 data for 2D interference. The channel output \(y[j,k]\) is supplied to the input of the neural network, and the error signal \(e[j,k]\) is supplied to the output of the neural network as a label. Therefore, the neural network was trained with 3000 random samples. In the testing process, we created 1000 random samples \(u[k]\) with a length of 10,000 and reshaped the signal \(a[j,k]\) with a size of 100 \(\times\) 100. The signal \(a[j,k]\) is fed into the channel to suffer 2D interference and additive noise. The channel output \(y[j,k]\) is converted into an equalized signal, \(z[j,k]\). Further, the signal \(z[j,k]\) goes through serial detection, which utilizes our proposed method for creating the soft output, to detect and recover the modulated signal \(\epsilon[j,k]\). Finally, \(\epsilon[j,k]\) is demodulated into the original signal \(\epsilon[k]\). In this paper, we define the channel signal-to-noise ratio (SNR) as \(10\log_{10}(1/\sigma^{2})\), where \(\sigma^{2}\) is the AWGN power. As shown in Figures 10-12, we changed the SNR from 10 to 15 dB to train the neural network in the training process. Thereafter, in the testing process, we used the neural network models from 10 to 15 dB to simulate the BER performance. In addition, Figures 10-12 have hidden layers [100] (one layer), [60 40] (two layers), and [30 50 20] (three layers), respectively. We simulated an AD of 3 Tb/in\({}^{2}\) (0.465 Tb/cm\({}^{2}\)) (\(T_{\times}=T_{z}=14.5\) nm) [18; 19]. The coefficient of the channel, which does not include the TMR effect (\(\Delta_{off}=0\)) or media noise (\(\Delta_{x}=\Delta_{z}=0\)), was used in the simulation as follows:  
\[\mathbf{H}=\left[\begin{array}{ccc}0.0824&0.3876&0.0824\\ 0.2125&1&0.2125\\ 0.0824&0.3876&0.0824\end{array}\right]. \tag{29}\]Figure 11: The BER performance of serial detection with two hidden layers neural network.  
Figure 10: The BER performance of serial detection with one hidden layer neural network.
## 1 Introduction
### Organization  
The rest of the paper is organized as follows. The system model is described in Section 2. We next present the joint energy-efficiency maximization problem of clustering decision and power allocation in Section 3. Simulation results are given in Section 4. Finally, we provide concluding remarks in Section 5.
## 2 Materials and Methods
### Microwave Plasma-Atomic Emission Spectrometry (MP-AES) Determination of Cu and Pd in the Complexes
#### 2.3.4 Synthesis of PdL2  
0.002 mol (0.4609 g) Pd(NO\({}_{3}\))\({}_{2}\).H\({}_{2}\)O in 10 mL H\({}_{2}\)O;  
0.008 mol (1.1318 g) of 6-propyl-2-thiouracil (L2) in 10 mL DMSO;  
0.004 mol (0.1600 g) of NaOH in 5 mL H\({}_{2}\)O.  
All the metal complexes were synthesized according to a previously described procedure [40], with a modification in the duration of procedures (24 h for palladium(II) complexes) and/or solvents [9; 10; 11; 12; 13].  
Figures 1 and 2 demonstrate the synthesis of transition metal complexes with two free ligands.  
The synthetic scheme that is shown in Figures 1 and 2 is similar to that reported by other authors [41; 42].  
IR (cm\({}^{-1}\)) of CuL1: 3115 (NH), 3080 (NH), 3003 (=CH), 2931 (CH\({}_{3}\)), 2888 (CH\({}_{3}\)), 1753, 1637 (C=O), 1578, 1559, 1426, 1385, 1350, 1284, 1241(C=S), 1207, 1199, 1167, 1033, 962, 933, 906, 872, 837, 809, 755, 656, 621, 597, 553, 512, and 457.  
ATR (cm\({}^{-1}\)) of CuL1: 3111 (NH), 3088 (NH), 3001 (=CH), 2934 (CH\({}_{3}\)), 2882 (CH\({}_{3}\)), 1752, 1699, 1633 (C=O), 1575, 1541, 1424, 1386, 1349, 1283, 1241 (C=S), 1207, 1195, 1164, 1032, 962, 932, 908, 872, 834, 806, 755, 656, 628, and 621.  
Raman (cm\({}^{-1}\)) of L1: 3085 (NH), 2921, 1635 (C=O), 1558, 1419, 1382, 1353, 1245 (C=S), 1199, 1177, 1043, 985, 961, 931, 834, 789, 657, 597, 554, 512, 458, 258, and 214.  
Figure 1: Synthesis of metal complexes of 6-methyl-2-thiouracil (L1) and 6-propyl-2-thiouracil (L2) with Cu(II).  
Figure 2: Synthesis of metal complexes of 6-methyl-2-thiouracil (L1) and 6-propyl-2-thiouracil (L2) with Pd(II).  
Raman (cm\({}^{-1}\)) of CuL1: 3084 (NH), 2916, 1636 (C=O), 1578, 1549, 1418, 1382, 1281, 1244 (C=S), 1207, 1170, 1043, 986, 961, 650, 622, 596, 573, 553, 512, 473, 456, 257, and 218.  
UV-Vis (DMSO) of Pd(II)L1: \(\lambda_{\rm max}\) = 258, 318 nm.  
IR (cm\({}^{-1}\)) of PdLL: 3442 (H\({}_{2}\)O), 3111 (NH), 3071 (NH), 3052 (=CH), 2993, 2930 (CH\({}_{3}\)), 2892, 2855 (CH\({}_{3}\)), 2750, 2697, 1678 (C=O), 1559, 1521, 1466, 1419, 1400, 1366, 1352, 1284, 1244 (C=S), 1193, 1168, 1064, 954, 830, 656, 614, 598, 576, 553, 513, and 459.  
ATR (cm\({}^{-1}\)) of PdLL: 3400 (H\({}_{2}\)O), 3105 (NH), 3080 (NH), 3049 (=CH), 2889, 2747, 2694, 1673 (C=O), 1636, 1559, 1515, 1465, 1417, 1399, 1365, 1351, 1285, 1242 (C=S), 1232
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 3 Feature-Based Modulation Recognition Methods
### Wavelet Transform  
To address the issue of decreased modulation recognition accuracy in optical communication systems under time-varying noise conditions, [PERSON] et al. [21] proposed an intelligent modulation recognition algorithm based on the fusion of wavelet transform and pattern recognition to meet the real-time classification needs of three typical modulation formats: MPSK, MQAM, and MAPSK. The algorithm significantly improves recognition efficiency under low-SNR conditions through multi-domain feature extraction and the collaborative optimization of data-driven classifiers. In the signal preprocessing stage, considering the characteristics of time-varying noise, the received signal is subjected to multi-scale decomposition using the discrete wavelet transform (DWT). The discrete wavelet basis function is constructed as follows:  
\[\psi_{j,k}(t)=a_{0}^{-j/2}\psi\left(a_{0}^{-j}t-kb_{0}\right), \tag{15}\]  
where \(\psi(t)\) is the mother wavelet function, \(a_{0}\) is the scale discretization base, j is the decomposition level controlling the dilation of the wavelet, \(b_{0}\) is the translation step, \(k\) is the translation parameter, and \(a_{0}^{-j/2}\) is the normalization factor ensuring the energy consistency of the wavelet basis functions across different scales. The received signal is analyzed for time-frequency localization, combined with a threshold processing function based on noise statistics  
\[\hat{x}=T_{h}\left(\omega_{j,k},\delta\right), \tag{16}\]  
to achieve the nonlinear suppression of high-frequency noise components. Here, \(\delta\) is the selected threshold based on noise statistical characteristics, and \(\omega_{j,k}\) is the wavelet coefficient corresponding to decomposition level \(j\) and translation parameter \(k\). Compared to traditional Fourier transforms, this method significantly enhances the processing capability for non-stationary signals.  
In terms of feature extraction, information from the time domain and frequency domain is integrated. The maximum amplitude spectral density is extracted as the pivotal feature, while the normalized peak spacing is amalgamated to augment the separability of modulation modes. For the classifier design, data-mining techniques are leveraged to train classification models through supervised learning, facilitating the rapid differentiation of high-order modulated signals. The wavelet transformation method offers significant advantages in handling time-varying noise and non-stationary signals, effectively extracting multi-domain features and enhancing recognition accuracy. However, the choice of wavelet basis functions and the determination of decomposition levels significantly impact the final performance, necessitating optimization based on specific application scenarios and classification tasks.
# Deterministic Coresets for \(k\)-Means of Big Sparse Data +
## 3 Our Contribution  
We suggest the following deterministic algorithms:  
1. An algorithm that computes a \((1+\varepsilon)\) approximation for the \(k\)-means of a set \(P\) that is distributed (partitioned) among \(M\) machines, where each machine needs to send only \(k^{O(1)}\) input points to the main server at the end of its computation.
2. A streaming algorithm that, after one pass over the data and using \(k^{O(1)}\log n\) memory returns an \(O(\log n)\)-approximation to the \(k\)-means of \(P\). The algorithm can run \"embarrassingly in parallel [20] on data that is distributed among \(M\) machines, and support insertion/deletion of points as explained in the previous section.
3. Description of how to use our algorithm to boost both the running time and quality of any existing \(k\)-means heuristic using only the heuristic itself, even in the classic off-line setting.
4. Extensive experimental results on real world data-sets. This includes the first \(k\)-means clustering with provable guarantees for the English Wikipedia, via 16 EC2 instances on Amazon's cloud.
5. Open-code for for fully reproducing our results and for the benefit of the community. To our knowledge, this is the first coreset code that can run on the cloud without additional commercial packages.
# Induction Coil Design Considerations for High-Frequency Domestic Cooktops
###### Abstract  
The use of wide band gap (WBG) semiconductor switches in power converters is increasing day by day due to their superior chemical and physical properties, such as electrical field strength, drift speed, and thermal conductivity. These new-generation power switches offer advantages over traditional induction cooker systems, such as fast and environmentally friendly heating. The size of passive components can be reduced, and the decreasing inductance value of induction coils and capacitors with low ESR (equivalent series resistance) values contributes to total efficiency. Other design parameters, such as passive components with lower values, heatsinks with low volumes, cooling fans with low power, and induction coils with fewer turns, can offset the cost of WBG power devices. High-frequency operation can also be effective in heating non-ferromagnetic materials like aluminum and copper, making them suitable for heating these types of pans without complex induction coil and power converter designs. However, the use of these new generation power switches necessitates a re-examination of induction coil design. High switching frequency leads to a high resonance frequency in the power converter, which requires lower-value passive components compared to conventional cookers. The most important component is the induction coil, which requires fewer turns and magnetic cores. This study examines the induction heating equivalent circuit, discusses the general structure and design parameters of the induction coil, and performs FEM (finite element method) analyses using Ansys Maxwell. The results show that the induction coil inductance value in new-generation cookers decreases by 80% compared to traditional cookers, and the number of windings and magnetic cores decreases by 50%. These analyses, performed for high-power applications, are also performed for low-power applications. While the inductance value of the induction coil is 90 uH at low frequencies, it is reduced to the range of 5 uH to 20 uH at high frequencies. The number of windings is reduced by half or a quarter. The new-generation cooker system experimentally verifies the coil design based on the parameters derived from the analysis.  
induction coil design; induction cooking; magnetism; resonant converters; wide band gap devices +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://doi.org/10.3390/app14177996](https://doi.org/10.3390/app14177996)]([https://doi.org/10.3390/app14177996](https://doi.org/10.3390/app14177996))
# A Gamified AI-Driven System for Depression Monitoring and Management
## 3 Materials and Methods: A Gamified AI-Driven System
### System Architecture  
The system employs a microservices-based, cloud-centric architecture grounded in human-computer interaction and cognitive behavioral design principles [31; 32]. It consists of three core layers:  
* Multi-platform application: A cross-compatible user interface supporting Android, iOS, and web platforms to ensure broad accessibility.
* Cloud-based infrastructure: A secure and scalable back end hosted on AWS, utilizing DynamoDB (a NoSQL database) for efficient data storage and management. This infrastructure supports API services, user authentication, and real-time data processing.
* AI analytics and simulation core: This layer leverages AWS SageMaker to implement machine learning models that analyze user behavioral patterns, estimate risk levels related to depressive symptom severity and relapse, and simulate synthetic scenarios for testing intervention strategies. The risk estimation component assesses the likelihood of adverse mental health episodes, enabling timely personalized interventions.
## 2 Bridge Damage Identification Methods
### Damage Identification Based on Continuous Wavelet Transform
#### 2.2.3 Brief on Variational Modal Decomposition
The unconstrained optimization problem is formulated using the extended Lagrangian expression \(L\) as follows:  
\[L(\{U_{k}\},\{\omega_{k}\},\lambda)=\alpha\sum_{k}\|\partial_{t}\left[\left( \delta(t)+\frac{\mathrm{j}}{\pi t}\right)*u_{k}(t)\right]\mathrm{e}^{-\mathrm{ j}\mathrm{i}\mathrm{i}\mathrm{i}\mathrm{j}}\|_{2}^{2}+\parallel f(t)-\sum_{k}u_{k}(t) \parallel_{2}^{2}+\left(\lambda(t),f(t)-\sum_{k}u_{k}(t)\right) \tag{10}\]  
The alternating direction method of multipliers is used to iteratively update the intrinsic mode parameters and center frequencies until the optimal solution of the above function is obtained. The steps for solving the variational problem using variational modal decomposition are as follows: initialize \(\{u_{k}^{1}\}\), \(\{\omega_{k}^{1}\}\), \(\lambda^{1}\), and \(n\) to zero and iteratively update using the expressions. In this study, \(\alpha\) is set to 3000. \[\hat{u}_{k}^{(n+1)}(\omega)=\frac{\hat{f}(\omega)-\sum\limits_{i\
eq k}\hat{u }_{i}(\omega)+\frac{\tilde{\lambda}(\omega)}{2}}{1+2\alpha(\omega-\omega_{k})} \tag{11}\]  
In the formula, \(\omega_{k}^{(n+1)}\) update iteratively according to Expression (11). \[\omega_{k}^{(n+1)}=\frac{\int_{0}^{\infty}\omega\mid\hat{u}_{k}(\omega)\mid^{ 2}\mathrm{d}\omega}{\int_{0}^{\infty}\mid\hat{u}_{k}(\omega)\mid^{2}\mathrm{d }\omega} \tag{12}\]  
For \(\tilde{\lambda}^{+1}\), update iteratively according to Expression (12). \[\tilde{\lambda}^{+1}=\tilde{\lambda}^{i}+\tau(f-\sum_{k}u_{k}^{n+1}) \tag{13}\]  
Iterate through Expressions (10) to (13) until the convergence condition shown in Expression (14) is met. \[\sum_{k}\frac{\parallel\hat{u}_{k}^{n+1}-\hat{u}_{k}^{n}\parallel_{2}^{2}}{ \parallel\hat{u}_{k}^{n}\parallel_{2}^{2}}<\varepsilon \tag{14}\]  
In the formula, \(\varepsilon\) is the preset threshold, typically set to \(1\times 10^{-7}\). The iteration ends when the entire update procedure meets the condition specified in Expression (14).
## 5 Experimental Results and Analysis
### Parameter Setting of Morfeco-Ms
#### 5.1.4 Determining the Parameter \(l\)  
After determining \(L=5\), we ascertain the optimal value for \(l\). Based on the experimental findings showcased in Table 12, it becomes evident that the algorithm yields superior results when \(l=3\).
# Numerical Study of the Toughness of Complex Metal Matrix Composite Topologies
## 3 Materials and Methods
### Discrete Elements Method  
#### 3.4.1 Principle  
The Discrete Element Method (DEM) is widely used to model divided media such as raw powders or granular materials. Particular classes of the DEM, named Lattice Element Method (LEM) [32] or Bonded Particle Element Method (BPEM) [15], allow the modeling of continuous media via the introduction of interaction laws between the particles, which local parameters enable simulation of the material apparent behavior. The DEM relies on the Discrete Mechanics framework, and it was successfully applied to the fracture of rocks [33] or concretes [34]. With the DEM, and especially the BPEM, the particles represent a given material volume and the cohesion between these particles is ensured by means of mechanical bonds. In this study, the particles are represented by spheres with different radius to avoid any geometrical anisotropy of the domains. The distribution of particles radius is uniform and vary by a factor \(\pm 0.125\) around the mean value, which is decided with the aim of obtaining a coordination number, or domain cardinality, around 6.2. This value allows the obtaining of an isotropic behavior of the discrete domains [15].  
Since the particles are of spherical shape, voids exist in the domain. However, this does not affect the domain mechanical behavior, and the dynamic properties are recovered by adjusting the elements density to match the expected global mass [15].  
The elastic bonds, which confer its cohesion to the discrete domain, are modeled by cylindrical beams based on the Euler-Bernoulli theory. Thus, two parameters are required to define the beams behavior, namely their elastic modulus \(EM_{b}\) and radius \(r_{b}\). These parameters will be calibrated in the next section.  
The beams are built after the packing stage of the discrete domain (Section 3.4.3). Concerning the failure modeling, once a defined amount of stress \(FS_{b}\) is reached inside a given bond, it is considered broken and it no longer takes part in the computation. This leads to a crack onset in the discrete domain, which propagates according to the local stress state. The value of \(FS_{b}\) must also be calibrated for each material.
# Online Algorithm for Deriving Heart Rate Variability Components and Their Time-Frequency Analysis
## 4 Discussion
Thanks to the reliable determination of the frequency ranges of these components and the possibility of isolating their waveforms obtained in these studies, it will be possible in the future to test hypotheses regarding their origin. However, the verification of such hypotheses will require both appropriate HRV decomposition algorithms and simultaneous measurement of other physiological quantities. It is already suggested that they can support diagnosis, monitoring, and/or therapy in cases of aortic regurgitation, coronary artery bypass grafting, vasovagal syncope, postural tachycardia syndrome, or heart failure [56; 57; 58; 59; 60]. Using the FM distribution in the combined two populations (shown separately in Figure 8), it was checked what percentage of the actual instantaneous frequencies are within the accepted literature frequency range of a given component. For VLF (based on CWT) and ULF (based on VMDon), it is more than 99.9%, and for HF and LF (based on AVMD), it is 94.7% and 95.0%, respectively; thus, the obtained results are in general agreement with the ranges reported earlier. Nevertheless, the results of the analysis of real data show an overlap of these ranges (Figure 8, Table 3), which is known as the mode-mixing problem (particularly for EMD [61]) and poses a significant challenge for all signal decomposition algorithms [32; 62; 63]. The feasibility of observing band overlapping in this work is due to the component extraction procedures used, which do not assume rigid frequency limits, unlike using other approaches [39]. Data from the population-based AM and FM distributions for two datasets (results from AVMD for HF and LF, CWT for VLF, and VMDon for ULF methods gathered together) were used to quantify their ranges for all HRV components. Table 3 contains these ranges defined by the 99 th percentile of the empirical cumulative distribution function. There were also attempts to identify a possible additional very-high-frequency component (higher than 0.4 Hz) using a 2.5 s window in VMDon to extract this range first, but the \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **First Database** & \multicolumn{3}{c}{**Second Database**} \\ \hline
**Component** & **AM (ms)** & **FM (mHz)** & **AM (ms)** & **FM (mHz)** \\ \hline HF & 1.1–233 & 112–398 & 1.1–118 & 110–398 \\ LF & 2.1–188 & 31–142 & 3.2–139 & 28–132 \\ VLF & 7.2–195 & 4.7–30 & 8.2–147 & 4.8–31 \\ ULF & 8.2–221 & 0.002–3.4 & 7.2–134 & 0.002–2.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The AM and FM ranges in the population of all subjects, separately for both datasets. results were significantly worse, and even such a short window still captured primarily the usual HF component.