esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Degrees-of-Freedom (2 DOF) Magnetic Spring-Based Electromagnetic Energy Harvester
###### Abstract  
This paper aims to study a two-degree-of-freedom (2 DOF) magnetic spring-based electromagnetic generator system theoretically and experimentally. The proposed linear generator system contains a nonlinear nonmagnetic shaft (nonmagnetic materials), two floating magnets, and two fixed magnets. All magnets are placed in the system in a repulsive configuration. First, the magnetic properties of the proposed system are studied to provide insight into its role in the system's nonlinear oscillatory behavior. The magnetic restoring forces are analyzed, and both linear and nonlinear coefficients of the oscillator system are analyzed based on these forces. The mathematical model of the proposed system is presented to study the dynamic behaviors of the oscillator system. The magnetic restoring forces and dynamic behaviors for different oscillator heights are also studied. Finally, the proposed system's energy generation capacity is examined using electromechanical coupling.  
Keywords:two degrees of freedom; magnetic spring-based oscillator system; linear and nonlinear stiffness; dynamics analysis; electromagnetic; energy harvester +
Footnote †: journal: Applied Sciences
# Quantum Coding via Quasi-Cyclic Block Matrix
## 2 Preliminaries
### Error of Quantum Error-Correction Code and Bound  
As with different quantum codes, errors are also labeled with strings in field \(F_{2}\). This will occur when the states are transmitted through quantum channels. The traditional approach to error correction for quantum codes is to consider the single qubit flip error, the phase error or the phase-flip error, which can be described with three Pauli operators \(\sigma_{1}\), \(\sigma_{2}\) and \(\sigma_{3}=\omega\sigma_{1}\sigma_{2}\), respectively. Here, \(\omega\) is the primitive root of unity. Every error on \(N\) qubits can be denoted as \(e=\sigma_{1}^{X}\sigma_{2}^{Z}\), for \(X=(x_{1},x_{2},\ldots,x_{n})\) and \(Z=(z_{1},z_{2},\ldots,z_{n})\in F_{2}^{N}\). Reflexive stabilizer codes and CSS codes have a one-to-one correspondence by choosing a basis for the error group. In terms of the Pauli matrices, the single qubit quantum error can be described as \(X(a)\) and \(Z(a)\) for \(a\in F_{2}\), whose action on \(|x\rangle\in C\) is given by  
\[X(a)|x\rangle=|x+a\rangle\quad\text{ and }\quad Z(a)|x\rangle=\omega^{ax}|x\rangle \tag{6}\]  
Therefore, it acts on an \(N\)-qubit basis state \(|Q\rangle=(q_{1},q_{2},\ldots,q_{N})\) in \(F_{2}^{N}\) as follows  
\[e|Q\rangle=(-1)^{Z\cdot Q}|X+Q\rangle\] \[=(-1)^{z_{1}q_{1}+\ldots+z_{N}\cdot q_{N}}|x_{1}+q_{1},\ldots,x_{ N}+q_{N}\rangle. \tag{7}\]  
Furthermore, the quantum Hamming bound of a general quantum code \(\mathcal{C}[[N,k,d]]\) is required to satisfy the following inequality condition [36]  
\[\sum_{l=0}^{t}3^{l}\binom{N}{l}\leq 2^{N-k}, \tag{8}\]  
which may correct up to \(t=[(d-1)/2]\) quantum error bits.
# Predicting Decompensation Risk in Intensive Care Unit Patients Using Machine Learning
## 4 Discussion  
### Model Selection and Explanation  
This study found that the CNN-LSTM model outperformed the LSTM model in predicting a sequence of decompensation risks, although the performance differences were not significant. We also observed that traditional ML models, particularly RF, demonstrated comparable performance, although their task was to predict the risk of an event within the forecast window rather than a sequence. These results are equivalent to those reported in [2,3,11], although the tasks are slightly different, i.e., a different set of variables were used, or their datasets were extracted from sources other than MIMIC-IV.  
To the best of our knowledge, no prior published work has attempted to predict a sequence of decompensation risks rather than single events. This is a critical point, as decompensation is particularly challenging to identify, especially at its early stages. Predicting a sequence facilitates early detection of increased decompensation risk and pace, potentially leading to saving more lives.  
This manuscript also introduces a novel decompensation score, calculated as the area under the curve of the decompensation risk sequence. A decompensation score could provide clinicians with a single value, ranging from 0 (indicating the lowest risk) to 24 (highest risk), enabling decompensation monitoring of patients during their stays in the ICU. This proposed score is innovative in that it summarises the risk of decompensation over a time period (e.g., 24 h) rather than a single event. Our score not only provides insight into the severity of the risk but also its proximity.  
Figure 10: SHAP plots. (**a**) Cluster C0. (**b**) Cluster C5.  
To understand the logic behind the CNN-LSTM model's predictions, we performed PCA and k-means on the model predictions. In this way, predicted decompensation risks were stratified into several clusters in terms of their severity. We performed a SHAP analysis on the overall model to find associations between the input features and the decompensation risk.  
Furthermore, a similar analysis was carried out on two selected clusters, representing two levels of decompensation severity, to investigate changes in feature importance and the associations with the predicted decompensation risk. We found differences in the features associated with decompensation risk, depending on the specific risk cluster. These results are significant as they suggest that clinical and physiological mechanisms leading to decompensation may be time-varying. Nevertheless, we acknowledge that further investigation is needed. The results, however, indicate that the proposed methodological approach is useful not only in predicting risk but also in providing valuable insights into the reasons behind the model's predictions.
# Output Power Smoothing Control for a Wind Farm Based on the Allocation of Wind Turbines
## 2 Wind Energy Conversion System Description
### Configuration of the Wind Farm  
The layout of the wind farm model built in this paper is shown in Figure 2. The wind farm comprised nine PMSG based wind turbines arranged in three rows and three columns. The traditional grid-connection mode of the PMSG based WECS, which was composed of the PMSG and the back-to-back converter, was not adopted in the wind farm model of this paper. Here, the alternating voltage generated by the PMSG was converted into the direct voltage by the generator-side converter and then connected to the DC bus. Apart from the high power transfer capability and cost-effectiveness in a longer transmission system, HVDC is also considered as an environmentally friendly technology owing to its low corona losses and ozone generation rates [22]. Thus, the power is delivered and converged in the cables through the mode of high voltage direct current (HVDC) in this paper. Afterwards, the direct current power was inverted to the alternating current power and transmitted into the grid through the shared grid-connected inverter. The number of grid-connected inverters was decreased, as compared to the traditional grid-connection mode of the PMSG based WECS and then the cost was reduced. The nine wind turbines were divided into three groups according to the topographic location, turbines 1-3 is group one, turbines 4-6 is group two and turbines 7-9 is group three. The BESS was connected to the HVDC DC link to be the support of the CWTs as shown in Figure 2.  
Figure 2: The layout of the wind farm model.
## 3 Determination of the Wind Load
### Stochastic Simulation  
Based on the wind tunnel tests, the wind force of the individual members was assumed to have the same wind load characteristic as that of the whole body ([PERSON] et al. [18]). In addition, the same mean root square of wind force was used. [PERSON] [36] noted that the  
Figure 10: Force coefficient in CFD simulation and wind tunnel tests.  
Figure 9: Drag coefficient values in current and previous studies.  
nondimensional auto-power spectrum of the wind load is independent of the height. Thus, \(S_{F}(f)\) can be expressed as follows:  
\[S^{\prime}{}_{F}(f)=\frac{S_{F}(f,z_{i})}{\sigma_{F}^{2}(z_{i})}=\frac{S_{F}(f,z _{j})}{\sigma_{F}^{2}(z_{j})} \tag{10}\]  
where \(S^{\prime}{}_{F}(f)\) is the nondimensional auto-spectrum, \(\sigma_{F}\) is the RSM of the wind force of each member (assumed to be the same as that for the whole structure), and \(z_{i}\) is the height of the \(i\)-th member (determined according to [37]). The cross-power spectrum \(S_{Fij}(f,z_{i},z_{j})\) can be simplified as  
\[S_{Fij}(f,z_{i},z_{j})=\sigma_{F}(z_{i})\sigma_{F}(z_{j})S^{\prime}{}_{F}(f) coh(p_{i},p_{j}) \tag{11}\]  
where \(coh(p_{i},p_{j})\)[37] is the coherence function of the wind load between members. \(p_{i}\) and \(p_{j}\) is the coordinate of the \(i\)-th and \(j\)-th member. It is related to the position of the member. Therefore, the spectrum of the base moment can be deduced as  
\[S_{M}(f)=\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}S_{Fij}(f,z_{i},z_{j})z_{i }z_{j} \tag{12}\]  
according to Equation (11), it can be expressed as,  
\[S_{M}(f)=\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\sigma_{F}(z_{i})\sigma_{F} (z_{j})S^{\prime}{}_{F}(f)coh(p_{i},p_{j})z_{i}z_{j} \tag{13}\]  
Thus,  
\[S^{\prime}{}_{F}(f)=\frac{S_{M}(f)}{\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N }\sigma_{F}(z_{i})\sigma_{F}(z_{j})coh(p_{i},p_{j})z_{i}z_{j}} \tag{14}\]  
where the spectrum of the base moment \(S_{M}(f)\) can be deduced by the results of the HFFB tests. According to Equation (14), the wind force history of members can be generated according to the moment spectrum and the fluctuating wind force coefficients obtained in the wind tunnel tests. Based on the above deduction, the WAWS was carried out and wind load time history of each member was obtained. Each element in the FEM model, corresponding to each member, was meshed uniformly with several nodes. The wind load of the member was distributed uniformly on nodes attached on it. The wind load time history on the top point of the boom-down crane generated by WAWS is shown in Figure 11. As shown in Figure 12, the sum of the members' wind force time history is calculated as the base shear force, which was compared with the results of the wind tunnel tests of the corresponding case.  
Figure 11: Simulation of the wind force time history.
# Active Control of Low-Frequency Noise through a Single Top-Hung Window in a Full-Sized Room
## 2 Materials and Methods
### Active Control Formulation  
A multichannel, feedforward filtered-\(\pi\) least mean square (FXLMS) algorithm was adopted, as illustrated by the block diagram in Figure 3[23]. A single reference microphone (\(J=1\)) provided advanced information of the impinging noise to \(K=4\) control filters, which drove \(K=4\) control sources to minimize the sum-of-the-squared pressures at the \(M=4\) error microphones. The \(m\)th error microphone signal \(e_{m}(n)\) can be expressed as  
\[e_{m}(n)=d_{m}(n)-\sum_{k=1}^{K}s_{mk}(n)*\left[\mathbf{w}_{k}^{\rm T}(n)\mathbf{x}(n) \right], \tag{1}\]  
where \(d_{m}(n)=p_{m}(n)*x(n)\) is the disturbance to be cancelled at the \(m\)th error microphone and \(s_{mk}(n)\) is the real secondary path transfer function from the \(k\)th control source to the \(m\)th error microphone. \(\mathbf{w}_{k}(n)=\left[\begin{array}{cccc}w_{k,0}(n)&w_{k,1}(n)&\cdots&w_{k,L- 1}(n)\end{array}\right]^{\rm T}\) is the coefficient vector of the control filter with order  
Figure 1: (**a**) Layout of the four secondary sources around a customized wooden frame and four error microphones placed 30 cm away, and (**b**) the primary source and reference microphone on the outside of the fully opened top-hung window.  
Figure 2: Room dimensions, observation microphone layout, primary source, reference microphone, and error microphone placement.  
\(L\), and \(\mathbf{x}(n)=\left[\begin{array}{cccc}x(n)&x(n-1)&\cdots&x(n-L+1)\end{array}\right]^ {\text{T}}\) is the reference signal vector of \(\mathbf{W}(z)\). The \(k\)th equation of the FXLMS algorithm is given by  
\[w_{k}(n+1)=\mathbf{w}_{k}(n)+\mu\sum_{m=1}^{M}\mathbf{x}_{km}^{{}^{\prime}}(n)e_{m}(n), \tag{2}\]  
where \(\mu\) is the step size and \(\mathbf{x}_{km}^{{}^{\prime}}(n)=\hat{s}_{mk}(n)*\mathbf{x}(n)\) is the filtered reference signal from the measured secondary path estimate \(\hat{s}_{mk}(n)\). Hence, the \(k\)th secondary source output is given by  
\[\mathbf{y}_{k}(n)=\mathbf{w}_{k}^{\text{T}}(n)*\mathbf{x}(n). \tag{3}\]  
The parameters used in the execution of the FXLMS algorithm are summarized in Table 1. The filter order and sampling rate were maximized to fully utilize the available resources on the real-time platform described in Section 2.1. The control filter length was also maximized to account for the non-ideal acoustic conditions described in the following section.
# Active Learning Strategy for Surrogate-Based Quantile Estimation of Field Function
## 3 Active Learning for Surrogate-Based Stochastic Process Quantile Estimation
### Auxiliary Optimization Problem and Active Learning Process  
In order to find the most relevant candidate sample \(\bar{\mathbf{u}}\) to be added to the current dataset, an auxiliary optimization problem can be formulated:  
\[\min_{\bar{\mathbf{u}}}\ A(\bar{\mathbf{u}}) \tag{26}\]  
where the objective function to be minimized corresponds to the confidence interval area calculation for quantile estimation as defined previously. The auxiliary optimization problem can be solved using an evolutionary algorithm (Covariance Matrix Adaptation--Evolution Strategy [28; 29]). In cases of high-dimensional problems, gradient-based algorithms may be used (with analytical calculation thanks to the availability of the gradient of Gaussian-process) which scale better with the dimension [30].  
Once the optimal solution \(\mathbf{u}^{*}\) has been found, the exact black-box model \(\mathcal{Q}_{\mathbf{X}}(\mathbf{u}^{*})\) is evaluated. The input and output datasets are updated with the resulting stochastic process trajectory \(\mathbf{X}(t,\mathbf{u}^{*})\) giving \(\mathcal{U}_{M+1}=\{\mathbf{u}_{1},\ldots,\mathbf{u}_{M},\ \ \mathbf{u}_{M+1}=\mathbf{u}^{*}\}\) and \(\mathcal{X}_{M+1}=\{\mathbf{X}(t,\mathbf{u}_{1}),\ldots,\mathbf{X}(t,\mathbf{u }_{M}),\mathbf{X}(t,\mathbf{u}_{M+1}=\mathbf{u}^{*})\}\). Then, based on these new input and output datasets a Karhunen-Loeve decomposition is carried out and for each KL mode, a GP is built. The procedure of adding a new sample into the datasets is performed until a stopping criterion is met. The stopping criterion can be based either on the value of the confidence interval area or on a maximal number of exact black-box model evaluations. The proposed active learning strategy for quantile estimation refinement is summarized in Figure 3. In the following sections, the proposed active learning approach is applied on an analytical problem and a launch vehicle design problem.
## 3 Bearing Estimation Methods
### Vertical Polarization Filtering Method  
The vertical polarization filtering method uses the phase difference between the vertical and horizontal components for various wave types. Using the vertical component signal and/or its time derivative as time-domain filters for the horizontal signals, the horizontal polarization \(S_{H}\) waves and noise are suppressed [23; 28; 29]. The vertical polarization \(P\) waves and shallow-angle \(S_{V}\) waves are enhanced, which provides primary information regarding source bearing. Applying the vertical polarization filter on the rotational analysis, the maximum radial power obtained through vertical filtering is as follows [23]:  
\[\Psi_{z}(\theta)=\text{cos}^{2}\theta\text{$\sum$}(x_{t}z_{t})^{2}+\text{sin}^{ 2}\theta\text{$\sum$}(y_{t}z_{t})^{2}+2\text{cos}\theta\text{sin}\theta\text{$ \sum$}(x_{t}z_{t})(y_{t}z_{t}) \tag{3}\]
# Safe Functional Modified CuO Nanoparticles?
## 2 Safe(r)-By-Design CuO Nanoparticles
### Are the Modified CuO Nanoparticles Discussed in This Section Safe?  
None of the studies previously discussed in this section provided evidence for the elimination of hazard by safe(r)-by-design doped, coated or capped CuO nanoparticles. Still, one might argue that such elimination is not necessary for the absence of risk, as there may be exposure levels that do not give rise to negative impacts (no-negative effect levels) and exposure may remain below these levels. However, there is as yet no proof that this actually applies. Such proof is also complex. No-negative effect levels for modified CuO nanoparticles have not as yet been established. Their establishment is likely to be difficult, as [PERSON] et al. [44] have shown that there are substantial differences in the response to coated nanoparticles between species, and it is also known that the toxicity of Cu ions (central to the safety claim of [PERSON] et al. [39], shows large differences between species, between varieties and even between individuals [64; 65; 66]. Furthermore, as pointed out above, there are currently no data about the robustness of CuO nanoparticle coatings or cappings in the real world. In the real world, no-negative effect levels in practice depend on the presence of other substances. There may be co-exposure to other nanoparticles, e.g., to ZnO nanoparticles, which may give rise to strong interactions [67]. Furthermore, background-exposure data regarding other substances that induce the generation of ROS (central to the safety claim of [PERSON] et al. [40]) and can release Cu ions are needed to establish no-negative effect levels for modified CuO nanoparticles in the real world. Such exposure data are currently patchy at best. Finally, realistic fate and exposure studies are needed to show that actual exposure remains below no-negative effect levels. Such studies are currently lacking. It can be concluded that the safe(r)-by-design studies regarding the modified doped, coated or capped CuO nanoparticles discussed here do not prove that these modified nanoparticles are indeed safe. Providing such proof does not seem feasible in the near future, due to its complexity and the present lack of data.
# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
## 3 Experiment Results and Analysis
### Result and Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Fusion Method**} & \multicolumn{6}{c}{**Visibility Range (km)**} \\ \cline{2-7}  & **0–10** & **11–20** & **21–30** & **31–40** & **41–50** & **Total** \\ \hline Random fusion (\%) & 55.62 & 57.16 & 69.55 & 69.21 & 68.71 & 67.48 \\ Average fusion (\%) & 78.39 & 80.95 & 88.79 & 87.46 & 86.58 & 84.53 \\ Weight fusion (\%) & 86.12 & 93.61 & 92.25 & 92.21 & 91.60 & 90.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Visibility accuracy of different fusion strategies.
## 4 Experimental Results
### Improved UDN
#### 4.1.1 Comparison of Activation Functions  
We experimented with Sigmoid, Relu, Softplus, Tanh, Swish, and FTSwish (T = -0.2) on a computer having Intel Core-i5 CPU. From Figure 8a, b, we can see that Swish outperforms other activation functions (AFs) with the quickest training loss decreasing and the quickest training accuracy increasing. Figure 8c represents the consuming time for running five epochs, and the y-axis represents times (seconds). Swish is also the least time-consuming AF. Therefore, using Swish as the activation function can significantly improve the calculation speed without sacrificing detection accuracy. In following experiments, the activation function we adopt is Swish.  
Figure 8: Comparisons of different activation functions (AFs). (**a**) Training loss; (**b**) Training accuracy; (**c**) Training time (in seconds) consuming for five epochs.
## 3 Results and Discussion
### Comparison of Impact Using MSIS-2017
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station No.** & **Vibration Source** & \(\rm{\bf{I_{v}}}\) **(mm2/s)** & \(\rm{\bf{t_{HV}}}\) **(s)** & \(\rm{\bf{PGV_{Hmax}}}\) **(mm/s)** \\ \hline \multirow{2}{*}{MVMS no. 1} & Blasting works & 0.32 & 2.54 & 1.31 \\ \cline{2-5}  & Underground mine tremors & 3.98 & 5.36 & 3.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculated vibration parameters for assessment using MSIS-2017. Figure 18: Comparison of the variability over time of the \(\rm{PGV_{H}}\) vector of horizontal vibrations recorded by the MVMS station. The assessment conducted using the MSIS-2017 categorizes vibrations induced by both blasting works in the open-pit mine and tremors in the underground mine as Grade 0--weakly perceptible, no damage. It should be added that MVMS measuring stations are installed on building foundations, which is consistent with the guidelines of the standard. Evaluation using the MSIS requires ground vibration measurements, which in this case introduces a certain discrepancy with the assumptions. However, this does not hinder the proposed analysis, as its aim is to compare the intensity and structure of recorded vibrations under the same  
Figure 19: Comparison of the duration of vibrations recorded by the MVMS station. Figure 20: Evaluation of vibration intensity using MSIS-2017—MVMS Station. measurement conditions, rather than to make a significant assessment of vibration effects on the object.