esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Node Privacy Feature Decoupling Graph Autoencoder Based on Attention Mechanism
## 6 Simulation Results
### Overall Utility and Privacy Performance  
This study compares DU-GAE with VGAE, PVGAE, GAE-MI, and APGE, with experimental results presented in Table 2. The findings reveal that without any privacy protection techniques, VGAE suffers from severe privacy leakage issues, where attackers can easily predict node privacy attributes with extremely high accuracy based on the published graph embeddings.  
The experimental results demonstrate that our proposed model effectively resists attribute inference attacks, outperforming other privacy-preserving methods in protecting privacy attributes while simultaneously achieving the highest accuracy in utility attribute inference and minimal data utility loss, thereby achieving an optimal privacy-utility trade-off. Specifically, in the Rochester dataset, our method achieves a utility attribute inference accuracy of 0.865 while reducing privacy attribute inference accuracy to the lowest level of 0.575. Notably, our model also achieves the lowest Macro-F1 score for privacy attribute inference compared to other privacy protection methods, indicating successful suppression of privacy information representation in the graph embeddings. Additionally, the variances of each indicator in our method are relatively small, which reflects that the method we proposed has stable performance and good generalization performance in different datasets. For example, in the Yale dataset, both the classification accuracy of utility labels and privacy labels and the variance of Macro-F1 achieved the lowest 0.001, demonstrating the stability of the model.  
Unlike PVGAE which decouples privacy latent spaces for all nodes, our approach selectively identifies key nodes through an attention mechanism to construct privacy information distributions. This strategy effectively reduces the utility loss caused by privacy decoupling, demonstrating that uniformly decoupling privacy information across all nodes excessively suppresses non-private information representation. Furthermore,these results confirm that different nodes contribute variably to privacy protection, with some nodes being more critical for data utility--excessive decoupling of such nodes would significantly impair the representational performance of graph embeddings.
# Radio Frequency Electromagnetic Fields Exposure Assessment in Indoor Environments: A Review
## 4 Discussion
[19], the contribution of different sources to the measured in 2006, 2009 and 2012 varied with time, due on one side to the rapid evolution on one side of the telecommunication systems, and on the other side to the exponential growth in everyday life of use of technologies emitting RF-EMF. Therefore, results about the contribution of the different sources obtained in the examined studies have to be considered a time-dependent picture of the continuous evolving exposure to RF-EMF. This is even more true if considering the forthcoming of 5G, with the expected huge increase of devices connecting by RF one each other. Even if all these technologies will be always more efficient in terms of power control and minimization of the emitted EMF, the assessment of the levels of exposure will still be crucial in order to ensure the safety of the new devices. In order to be able to take into account variability and uncertainty of the new exposure scenarios, stochastic approaches will be essential. These methods, being feasible to manage the uncertain aspects of the exposure scenarios, allow obtaining a stochastic and probabilistic description of levels of exposure in various environments, thus overcoming limitations of deterministic methods based only on measurements and computational methods, and giving results that could be truly representative for general population exposure.
# Design of Static Random-Access Memory Cell for Fault Tolerant Digital System
## 2 Background  
### Soft Error and Single Event Transient (SET)  
Due to the defects during the fabrication process or circuit design error, there may occur permanent physical flaws in the hardware, leading to a fault or error in the electronic device's operation. This is a so-called \"hard error\" of the electronic device. Even in hardware free from this hard error, it has been observed that the data bits are spontaneously or dynamically changed to incorrect values, through various noise sources affecting the electronic device after fabrication. This failure is not repeatable, non-destructive, and non-permanent, which is referred to as a \"soft error\" to differentiate it from the hard error.  
There are two types of soft error: intermittent faults and transient faults [1]. Intermittent faults occur from time to time in irregular intervals, usually at the same locations. The causes of intermittent faults are power supply noise, temperature changes, aging of circuit components, and signal coupling of parasitic capacitances. The intermittent fault has become less critical in the advanced technology because it can be minimized through recent well-developed design and manufacturing techniques. However, the transient errors, which are caused mainly by cosmic rays and alpha particles, occur randomly and are much more unpredictable and unavoidable. Alpha particles or high-energy neutrons in the atmosphere can accidentally impact the transistors in the electronic device, which leads to the generation of charges. Then, the voltage of the nodes in the circuit can be changed to the incorrect state, so a logic error can occur.  
In integrated circuits, the diffusion nodes (drain or source) of the \"off\" transistors are sensitive to the voltage change due to the particle impact. The voltage change occurs in the following procedure. If an energetic particle hits a source or drain node and passes through the transistor, electron-hole pairs are generated near the p-n junction of the source or drain diffusion. Along the path of particle injection, the depletion region of the p-n junction is extended, which leads to the generation of a high electric field. Thus, the generated carriers (i.e., electron-hole pairs) are collected by the electric field, which creates a temporal junction current. This current results in a sudden voltage change at the source or drain node. For example, in off nMOSFET, the source or drain is n+-doped and the electrons are collected towards the n+ side, and the node voltage is substantially lowered. This phenomenon is called single event transient (SET). For a pMOSFET whose drain or source is p+-doped, the node voltage can be unintentionally raised due to SET.
# AMP-GSM: Prediction of Antimicrobial Peptides via a Grouping-Scoring-Modeling Approach
## Materials and Methods
### Feature Selection Methods  
We have a total of 1508 features in our dataset. These features have an impact on how well the classification algorithms work. Therefore, a feature selection method is required to lower the model's dimension and make it simpler to classify and comprehend. Feature selection can also be performed based on grouping with the proposed AMP-GSM method. We wanted to compare the results we obtained with the results obtained using certain traditional feature selection methods. Therefore, we applied conditional mutual information maximization (CMIM) [53], minimum redundancy maximum relevance (mRmR) [54], information gain (IG) [55], and extreme gradient boosting (XGB) [56] methods, which are also frequently used in the literature.  
Conditional mutual information maximization (CMIM) strikes a balance between the candidate feature's ability to forecast the future and its independence from other characteristics that have already been chosen by using conditional mutual information to calculate distance [57].  
The mRMR algorithm is a filtering method that attempts to select the most relevant features with the class labels, while simultaneously aiming to minimize the redundancy between the selected features [58]. This algorithm starts with an empty set, uses mutual information to balance the features, and then combines sequential search with forward selection to identify the best subset of attributes.  
Information gain (IG) is utilized for feature selection by assessing each variable's gain in relation to the target variable. For each of the independent features, we determine the information gain. The traits are then be ranked according to their individual information gains in descending order. We choose a cutoff point and incorporate all features above the cutoff point into the machine learning algorithms.  
Extreme gradient boosting (XGB) is another commonly used feature selection method. Importance in XGB assigns a score based on the usefulness or value of each feature in building the boosted decision trees within the model. An attribute's relative relevance rises as an increasing number of decision trees use it to make important decisions.
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 3 Experimental Design
### Experimental Setup and Procedures  
In this study, a microfiber composite (MFC) patch, (type: M2814-P2, dimension: 37 mm \(\times\) 17 mm \(\times\) 0.180 mm, internal capacitance: 30.79 nF) was used as PD in all experimental tests. The MFC patch was attached to the surface of one end of a rectangular aluminium beam (dimension: 205 mm \(\times\) 20 mm \(\times\) 1 mm), which was then excited by various sources of excitation. The other end of the cantilever beam carried two permanent magnets, one on the top surface and the other on the bottom surface, as shown in Figure 10. The magnets were configured such that they were attracting each other. Since they were very strong magnets, such configuration resembled firmly attaching a proof mass at the tip of the cantilever. Permanent magnets were used for the convenience of changing tip mass, which changes the resonance frequency of the cantilever beam, as well as the displacement at the free end [39].  
The cantilever beam was tested under three testing scenarios, namely the shaker test, cycling test and walking test. For all scenarios, the voltage produced by the PD was rectified by both the FBR circuit and the proposed H-Bridge circuit. The components used in the H-Bridge and the FBR circuits are listed below.  
Figure 10: Experimental setup of shaker test.  
Figure 9: Peak voltage and current waveforms generated by the PD.  
FBR circuit with standard forward voltage diodes (V\({}_{\text{f}}\) = 0.60 V, IN4007).
* PMOS, AP2306 AGN  
The forward voltage of diodes and the threshold voltage of the MOSFET were checked and verified with a voltmeter. The ensuing output voltage and current were measured by the ammeter, the voltmeter, and the oscilloscope. The input and output power were calculated according to the equations presented in Section 3.1.
# FE-Aided Synchronization Analysis of Line-Start Synchronous Reluctance Motors
## 6 Conclusions  
The analytical model of an LS-SynRM used to approximate the synchronization process showed that the pull-in energy of an LS-SynRM becomes half that of an LSPM when both motors are characterized with identical steady state torque. The analytical model was validated with the dynamic analysis results. The FE-aided analytical approach was proposed to estimate steady state torque of an LS-SynRM with multiple flux barriers and a rotor cage. The steady state average torque and alternating torque were estimated without equivalent rotor parameters. This allows the analysis of the critical inertia to be predicted easily in terms of the numerical computation cost. The comparison of results from the proposed approach and the FEA for the estimation of the critical inertia differed by up to 15%. In consideration of the wide space of the design variables due to the complicated rotor geometry of LS-SynRMs, the approach described in this paper can help a designer to estimate the critical inertia for LS-SynRMs in the design stage.  
Conceptualization, U.-J.S.; methodology, U.-J.S.; software, U.-J.S.; validation, D.-J.K.; investigation, P.-W.H.; resources, P.-W.H.; writing--original draft preparation, U.-J.S.; writing--review and editing, U.-J.S.; visualization, D.-J.K.; supervision, Y.-D.C.; funding acquisition, Y.-D.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a Korea Institute of Energy Technology Evaluation and Planning grant funded by the Korea government, Grant No. 20192010106780 (A Construction and Operation of Open Platform for Next-Generation Industrial Super Premium Efficiency Motors).  
The authors declare no conflict of interest.  
Figure 11: Steady state torque characteristics of the second prototype.  
Figure 12: Estimated critical inertia of the second prototype for load torque quadratic with rotor velocity.
## 2 Materials and Methods
### Data Analysis  
The raw gene sequencing reads were demultiplexed, quality-filtered using Trimmomatic [31], and merged using FLASH software (version 1.2.11, Johns Hopkins University, Baltimore, MD, USA) [32]. The operational taxonomic units (OTUs) with a 97% similarity cutoff were clustered using UPARSE (version 7.1, Taxon Biosciences, Inc., Tiburon, CA, USA) [33]. Moreover, the UCHIME algorithm was used to determine and eliminate the chimeric sequences. The OTU-based assessments of the alpha diversity indexes, including community richness (Chao1, ACE) and community diversity (Shannon, Simpson) and coverage, were conducted by [PERSON] [34]. The distribution between the samples and microbial communities was predicted and visualized using a co-occurrence network. A redundancy analysis (RDA) was carried out to reveal the correlations between the microbial genera and physicochemical indexes by using Canoco 5 software (Chinese Academy of Sciences, Beijing, China) [35]. The Spearman correlations between the volatile compounds and microbiological genera were shown on the correlation heat maps drawn with OriginPro 2022 (OriginLab Corporation, MA, USA) and heatmap package software (version 1.0.12, University of Alberta, Edmonton, AB, Canada.). All statistical analyses were performed with SPSS6.0 (SPSS Inc., Chicago, IL, USA).
# Tensor-Based Approaches for Nonlinear and Multilinear Systems Modeling and Identification
## 4 Tensor Operations and Decompositions
### PARAFAC and Tucker Decompositions
\begin{table}
\begin{tabular}{c c} \hline
**Third-Order Tensor** & **Nth-Order Tensor** \\ \hline \(\mathcal{X}\in\mathbb{K}^{I_{N}\times I_{N}}\) & \(\mathcal{X}\in\mathbb{K}^{I_{N}}\) \\ \hline \(\mathbf{A}\in\mathbb{K}^{I\times R}\), \(\mathbf{B}\in\mathbb{K}^{I\times R}\), \(\mathbf{C}\in\mathbb{K}^{K\times R}\), & \(\mathbf{A}^{(n)}\in\mathbb{K}^{I\times R}\) \\ \hline \(x_{i,jk}=\sum\limits_{r=1}^{R}a_{i_{n}}b_{r}c_{2r}\) & Scalar writing & \(x_{\mathbf{I_{N}}}=\sum\limits_{r=1}^{R}\sum\limits_{n=1}^{N}a_{i_{n}}^{(n)}\) \\ \hline \(\mathcal{X}=\mathcal{I}_{R}\times_{1}\mathbf{A}\times_{2}\mathbf{B}\times_{3} \mathbf{C}\) & with mode-\(n\) products & \(\mathcal{X}=\mathcal{I}_{R}\overset{N}{\underset{n=1}{\circ}}\mathbf{A}^{(n)}\) \\ \hline \(\mathcal{X}=\sum\limits_{r=1}^{R}\mathbf{A}_{r}\circ\mathbf{B}_{r}\circ\mathbf{C }_{r}\) & with outer products & \(\mathcal{X}=\sum\limits_{r=1}^{R}\sum\limits_{n=1}^{N}\mathbf{A}^{(n)}_{r}\) \\ \hline \(\mathbf{X}_{|I_{N}\times I}=(\mathbf{A}\circ\mathbf{B})\mathbf{C}^{T}\) & & \\ \(\mathbf{X}_{|K_{N}\times I}=(\mathbf{B}\circ\mathbf{C})\mathbf{A}^{T}\) & Matrix unfoldings & \(\mathcal{X}_{\mathcal{S}_{1}\mathcal{S}_{2}}=\binom{\circ}{\underset{n\in \mathcal{S}_{1}}{\circ}}\mathbf{A}^{(n)}\bigg{(}\overset{\circ}{\underset{n\in \mathcal{S}_{2}}{\circ}}\mathbf{A}^{(n)}\bigg{)}^{T}\) \\ \(\mathbf{X}_{|K_{N}\times I}=(\mathbf{C}\circ\mathbf{A})\mathbf{B}^{T}\) & & \\ \hline \(x_{|IK}=(\mathbf{A}\circ\mathbf{B}\circ\mathbf{C})1_{R}\) & Vectorized form & \(\mathbf{x}_{\mathbf{I_{1}}\cdot\mathbf{I_{N}}}=(\mathbf{A}^{(1)}\circ\mathbf{A} ^{(2)}\circ\cdots\circ\mathbf{A}^{(N)})1_{R}\) \\ \hline \end{tabular}
\end{table} 
Table 10: PARAFAC decomposition of a tensor of order three and order \(N\). Note that a Tucker decomposition is generally not essentially unique, unless additional constraints are imposed, such as a perfect knowledge of the core tensor, certain sparseness or structural constraints on the core tensor or the matrix factors [80; 81]. Consult [82] for a review of uniqueness results for Tucker models.
# A Precise Prediction of Tunnel Deformation Caused by Circular Foundation Pit Excavation
## 3 Finite Element Model
### Constitutive Model and Model Parameters  
In the references paper [12], the ability of three different soil constitutive models to predict a tunnel's response to basement excavation has been evaluated. It is found that the hypoplastic model yielded the best predictions of tunnel heave among the adopted models. The nonlinearity of soil can be captured by a hypoplastic (HP) constitutive model. Various HP models have been developed in the 1990s ([PERSON] 1991; [PERSON] 1996; [PERSON] 1996; [PERSON] et al. 1996) [32; 33; 34; 35] as well as recently ([PERSON] 2012 2014, 2013) [36; 37; 38]. The model proposed by [PERSON] (1996) [34] was adopted to describe the behavior of Toyoura sand. This model was incorporated into the software package ABAQUS using open-source implementation which can be freely downloaded from the web ([PERSON] et al. 2008) [39]. The model specifies eight material parameters (\(q_{\mathrm{C}}\), \(h_{\mathrm{s}}\), \(n\), \(e_{\mathrm{d}0}\), \(e_{\mathrm{c}0}\), \(e_{\mathrm{i}0}\), \(\alpha\) and \(\beta\)). [PERSON] and [PERSON] (1997) [40] improved the model for predictions of small-strain stiffness and the recent stress history, leading to five additional parameters (\(m_{\mathrm{T}}\), \(m_{\mathrm{R}}\), \(R\), \(\beta_{\mathrm{r}}\) and \(\chi\)). See Table 1 and the literature mentioned above for their physical meaning.  
Figure 2: Finite element model (in model scale, unit: mm).  
Six parameters of Toyoura sand (\(\varphi_{\rm c}\), \(h_{\rm s}\), \(n\), \(e_{\rm d0}\), \(e_{\rm c0}\) and \(e_{\rm i0}\)) were obtained from [PERSON] and [PERSON] (1999) [41], while the triaxial test results reported by [PERSON] and [PERSON] (1999) [42] were used to calibrate the parameters of \(\alpha\) and \(\beta\). Five parameters (\(m_{\rm T}\), \(m_{\rm R}\), \(R\), \(\beta_{\rm r}\) and \(\chi\)) of the intergranular strain can be calibrated from the stiffness degradation curve of Toyoura sand ([PERSON] et al. 1978) [43], as shown in Figure 3. The void ratio of soil was considered as a state variable in the HP model. The parameter selection list is presented in Table 1. The parameter selection procedure and values have been reported by [PERSON] et al. (2015) [12]. The tunnel and diaphragm wall can be considered as an elastic material. Their elastic modulus and Poisson's ratio were 70 GPa and 0.2, respectively.
## 6 The Stability Amplification Factor  
The stability amplification factor, introduced by [PERSON], is used to enlarge an unconditional stability interval for an SDIM [28]. In order to verify the unconditional stability range for CVM, a free parameter \(\sigma\) is added. As a result, after including the stability amplification factor in Equation (3), the coefficients \(B_{1}\) to \(B_{3}\) and \(p_{i+1}\) become:  
\[\begin{array}{lcl}B_{1}&=&\frac{1}{D}\beta_{3}\quad\quad,\quad B_{2}\ =\ \frac{1}{D}[(1+\alpha)\beta_{1}+(\beta_{1}\gamma_{2}-\beta_{3})2\xi\Omega_{0} ],\\ B_{3}&=&\frac{1}{D}[(1+\alpha)\beta_{2}+\alpha\beta_{3}+(\beta_{2}\gamma_{2}- \beta_{3}\gamma_{1})2\xi\Omega_{0}]\quad\quad,\quad\ p_{i+1}\ =\ \frac{1}{D}\beta_{3}(F_{i+1}-F_{i}).\end{array} \tag{19}\]  
in which:  
\[\overline{D}\ =\ (1+\alpha)+\gamma_{2}2\xi\Omega_{0}+\sigma\beta_{3}\Omega_{0} ^{2}, \tag{20}\]  
In order to verify the stability condition of CVM, with the inclusion of a free parameter \(\sigma\) the upper stability limit with \(\delta_{i}\) is displayed in Figure 2a,b for the different values of \(\rho\ =\ 0.5\), and \(\rho\ =\ 1.0\) for the different values of the viscous damping ratios, 0, 0.1 and 0.2, respectively. The case of \(\sigma\ =\ 1\) implies that there is no inclusion of a free parameter of CVM. When \(\sigma\ =\ 2\), CVM is unconditionally stable within the range of \(0<\delta_{i}<2\) and for \(\sigma\ =\ 3\), CVM is unconditionally stable within the range of \(0<\delta_{i}<3\). It can be understood that the large value of the stability amplification factor enlarges the unconditional stability range. However, later on in the paper, it will be shown that the larger value of \(\sigma\) will decrease the accuracy of an integration method [22]. Although the value of \(\delta_{i}\) is not known before calculation, [PERSON] recommended that \(\delta_{i}<2\)[20; 21; 22; 23]. In practice, it is rare to experience that real structure whose instantaneous stiffness is greater than two times of the initial stiffness, i.e., \(\delta_{i}>2\). In fact, the case of \(\sigma\ =\ 2\) is thoroughly investigated in the following study.  
Figure 2: \(Cont.\)
## 5 Results and Discussion
### Validation Result  
#### 5.3.1 Result of Repeated Measurements  
All the supporting block planes are extracted, and the flatness and the standard deviation of measuring points of the supporting block planes are calculated. Figures 18 and 19 show the experimental results.  
Figure 17: Statistical results of standard deviation of the measuring points and the flatness.  
Figure 18: Standard deviation of measuring points for repeated measurements.  
The experimental results show that the variation range of standard deviation of each supporting block plane is within 0.010 mm. The difference between the maximum flatness and the minimum flatness of each supporting block plane is within 0.030 mm. It shows that the detection method has high repetitive measurement accuracy.
## 4 The Scheduling Algorithm Based on GA  
In recent years, the genetic algorithm (GA) has emerged as a widely adopted optimization technique, demonstrating remarkable success in scheduling problems due to its robust global search capability and adaptability. This is particularly evident in complex multi-objective, multi-constraint optimization scenarios such as carrier-based aircraft scheduling, where GA has shown superior performance [23; 26].  
By simulating the processes of natural selection and genetic variation, GA iteratively evolves an initial population toward high-quality solutions. This evolutionary mechanism makes GA exceptionally suitable for solving high-dimensional, nonlinear scheduling problems with intricate constraints, where traditional optimization methods often struggle.  
In order to better construct the optimization model of carrier-based aircraft dispatch operation scheduling, before establishing the model, the assumptions are as follows:  
S1: The operation is non-preemptive, that is, uninterruptable;  
S2: Ignoring the influence of aircraft folding or unfolding wings on the taxiing path, the taxiing process is carried out according to the path library, that is, the taxiing time is mainly determined by the aircraft's parking position and the selected takeoff position;  
S3: The task is static scheduling, without other external interference;  
S4: The arrangement information, station point information and takeoff position information of carrier-based aircraft on deck are known;