esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact Features Extracting Method for a Reciprocating Compressor Based on the ABC-SGMD Model
## 5 Conclusions  
In this study, a fault diagnosis model based on ABC-SGMD and SaDE-ELM was proposed and validated through simulation and experimental methods. The research findings are as follows:  
1. Addressing the issue of poor robustness in traditional fault recognition methods due to the complex mechanism of reciprocating air compressor faults, this paper proposes a novel algorithm model based on ABC-SGMD to automatically extract fault features from the original signal, effectively mitigating the non-stationary characteristics of the original sequence. This method successfully overcomes the mode mixing problem in EMD and the redundant component issue in SGMD.
2. By utilizing the characteristics of features such as refined composite multiscale dispersion entropy to construct a multi-feature fusion model, the effective extraction of impact features is achieved, thereby improving the accuracy and stability of recognition.
3. The fault diagnosis model combining ASGMD with SaDE-ELM demonstrates superiority in handling nonlinear and non-stationary signals from reciprocating air compressors. Validation using simulated fault data yields promising results, demonstrating significant effectiveness and feasibility.
4. The ABC algorithm's limited local search ability can result in slower convergence speeds. To address this, future improvements could focus on enhancing its local search capability and increasing the convergence speed. Additionally, techniques such as parallel computing or distributed computing could be employed to reduce the algorithm's computational complexity.  
Conceptualization, F.B. and J.L.; methodology, J.L. and Z.Z.; validation, J.L. and S.Z.; formal analysis, F.B. and Q.L.; investigation, F.B. and J.L.; resources, J.L.; data curation, X.M.; writing--original draft preparation, F.B., J.L. and Z.Z.; writing--review and editing, F.B. and J.L.; visualization, J.L. and X.M.; supervision, S.Z.; project administration, F.B.; funding acquisition, F.B. and Q.L. All authors have read and agreed to the published version of the manuscript.  
The study was funded by the Project of National Natural Science Foundation of China (Grant Number 52075050), Key University Science Research Project of Jiangsu Province (Grant Number 19 KJA430004), and Jiangsu Key Laboratory of Green Process Equipment and National Natural Science Youth Fund (Grant Number 52206041).  
The data are contained within the article.  
The authors declare no conflicts of interest.
# Phase-Matching Quantum Key Distribution with Discrete Phase Randomization
## 2 The Protocol of PM-QKD
### Phase Error Estimation  
The security analysis of asymptotic case is considered, so there are no statistical fluctuations. The analysis method of the phase error rate that we use comes from [39], which is an important new viewpoint of QKD security, establishing the relationship between the symmetric encoding and privacy with the standard phase-error-correction approach [40], and we summarize briefly as follows.  
If the joint state \(\rho_{\rm AB}\) is a pure of even or odd state, the symmetric encoding PM-QKD protocol is perfectly private, the phase error rate \(E_{ph}=0\), if the joint state \(\rho_{\rm AB}\) is a mixture of even and odd state, \(\rho_{\rm AB}=P_{odd}\rho_{odd}+P_{even}\rho_{even}\), the phase error rate \(E_{ph}\
eq 0\), the effective detection ratios of odd and even components of signal state are estimated by [39]  
\[q_{odd|\mu}=P_{odd|\mu}\frac{Y_{odd|\mu}}{Q_{\mu}} \tag{2}\] \[q_{even|\mu}=P_{even|\mu}\frac{Y_{even|\mu}}{Q_{\mu}}\]  
where \(Q_{\mu}=P_{odd|\mu}Y_{odd|\mu}+P_{even|\mu}Y_{even|\mu}\) is the total gain of mixture signal state \(\rho_{\rm AB}\). \(Y_{odd|\mu}\) and \(Y_{even|\mu}\) are the yield of odd signal state \(\rho_{odd}\) and even signal state \(\rho_{even}\), respectively. \(P_{odd|\mu}\) and \(P_{odd|\mu}\) are the signal state probability of odd and even photon numbers.  
The overall phase error rate comes from the even components, which is estimated by [39]  
\[E_{ph}=P_{even|\mu}\frac{Y_{even|\mu}}{Q_{\mu}} \tag{3}\]  
where \(P_{even|\mu}\) is given by the above section, \(Q_{\mu}\) is given by the experiment results, the important task is to estimate the parameter \(Y_{even|\mu}\).  
For simplicity, we use phase match pairs and discard phase mismatch pairs, so the upper bound of phase error rate comes from the signal state bounded by  
\[E_{ph}\leq 1-q_{1|\mu} \tag{4}\]  
where \(q_{1|\mu}=P_{1|\mu}\frac{Y_{1|\mu}}{Q_{\mu}}\).  
According to the above discussion, we get the final secure key rate by  
\[R_{f}=\frac{2}{D}Q_{\mu}[1-H_{2}(E_{ph})-fH_{2}(E_{\mu})] \tag{5}\]  
where \(Q_{\mu}\) is the total gain of the signal state, \(E_{ph}\) is the phase error rate of the signal state, \(E_{h}\) is the bit error rate of the signal state, \(f\) is the error correction efficiency, \(H_{2}(x)=-x\text{log}_{2}(x)-(1-x)\text{log}_{2}(1-x)\) is the binary entropy function.
# Model Test and Numerical Simulation of Single Pile Response under Combined Loading in Slope
## 4 3D FEM Formulation
### Model Establishment  
The smaller the mesh size was, the higher the accuracy, but the amount of calculation was large [27]. According to the model size of the soil, the C3D8R element was selected to divide the soil model; the grid subdivision interval was 0.1 m, and a total of 16,034 elements were obtained. The Mohr-Coulomb model is widely used in geotechnical engineering, which can commendably simulate the mechanical deformation of the soil under shear failure [28]. The Mohr-Coulomb model was also chosen for this study. Table 1 provides the soil's parameters and characteristics. The linear elastic constitutive model based on the generalized version of Hooke's law was adopted to simulate the pile in the sloping ground, which mainly involved two parameters, the Poisson's ratio (v) and elastic modulus (E). The researchers discovered that the two factors varied with the temperature and other field variables [27; 28]. Table 2 presents the material parameters.  
The C3D8R element was also selected to divide the pile model; the smaller the mesh size was, the higher the accuracy, but the amount of calculation was large. According to the model size of the pile, the grid subdivision interval was 0.02 m, and a total of 1920 elements were obtained. In order to simulate the interaction between pile and soil, the normal behavior was used; rigid contact was used in the properties of the pile side and pile end surface. Simultaneously, considering the friction between the pile side and the soil under the load, the friction formulation used a penalty, and the friction coefficient was set as 0.35 in the pile side's tangential action. The boundary condition was set so that the displacement constraints in X and Y directions were applied around the model, all degrees of freedom were constrained at the bottom of the model, and the influence of the initial stress field of soil was considered.
## 6 Discussion and Comparison
### Limitations of the Proposed Method  
There are certain limitations, such as a small sample size, lack of stratification for severity, and subtypes of schizophrenia. The sample size of the available benchmark dataset is small; it consists of 84 subjects only. Moreover, it is also not clear what the level of severity of the subjects with schizophrenia is. Also, the information about the subtypes of schizophrenia in the dataset is not known, and the performance of the method is clear in this context. In addition, there is the potential impact of variables such as medication use, gender, and illness phase on EEG patterns in schizophrenia. Future iterations of our model will aim to incorporate these factors to refine its diagnostic accuracy further. Preliminary data suggest potential variations in EEG signals correlated with these factors, indicating a promising direction for enhancing model robustness.
# Chemicals from Brominated Flame Retardants: Analytical Methods, Occurrence, Transport and Risks
## 3 Distribution of BFRs in Different Matrices
### Biota  
[PERSON] et al. [57] investigated the distribution, bioaccumulation, and bio magnification of TBBPA in various biota species from two mangrove wetlands in the Pearl River Estuary (PRE) and the Jiulong River Estuary (JRE) in South China. The TBBPA levels ranged from 0.56 to 22.1 ng/g lipid weight in the biota species from the PRE and from not detectable (nd) to 56.3 ng/g lipid weight in the biota species from the JRE. The bioaccumulation of TBBPA in these species were evaluated using the Bioaccumulation Factor (BAF), which is defined as the ratio of the concentration of a substance in an organism to the concentration in the surrounding environment (sediment or water).  
[PERSON] et al. [60] measured TBBPA-BDBPE concentrations in herring gull egg pools from the Laurentian Great Lakes of North America, with levels ranging from below the limit of detection (LOD) to 42.8 ng/g wet weight. These findings indicate significant bioaccumulation in herring gulls, which are top predators in their ecosystem, highlighting the potential for bio magnification through the food chain.  
[PERSON] et al. [59] reported TBBPA levels in zooplankton samples from the Yellow Sea and Bohai Sea, ranging from not detectable (ND) to 9.83 \(\upmu\)g/kg (wet weight). The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Chemicals** & **Concentration** & **Occurrence** & **Country/Region** & **Reference** \\ \hline \multicolumn{5}{c}{Human sample} \\ \hline \multirow{5}{*}{Tetrabromobisphenol (TBBPA)} & \textless{LOD-42 ng/g lw} & Breast milk & Beijing, China & [56] \\ \cline{2-5}  & \textless{LOD-15.1 ng/g lw} & Breast milk & French & [77] \\ \cline{2-5}  & 4.73 ng/g lw & Breast milk & China & [78] \\ \cline{2-5}  & ND-1.08 ng/g & Hair & China & [79] \\ \cline{2-5}  & 0.0793–1.15 \(\upmu\)g/L & Urine & China & [80] \\ \hline Novel halogenated flame & Maximum 6930 pg g\({}^{-1}\) & \multirow{2}{*}{Breast milk} & \multirow{2}{*}{Canada} & [81] \\ retardants (NHFRs) & lipid & & & \\ \hline \multicolumn{5}{c}{methoxy-polybrominated} & Maximum 1600 pg g\({}^{-1}\) & \multirow{2}{*}{Breast milk} & \multirow{2}{*}{Canada} & [81] \\ diphenyl ethers (MeO-PBDEs) & lipid & & & \\ \hline \multicolumn{5}{c}{ND: no detected; LOD-limit of detection.} \\ \end{tabular}
\end{table}
Table 2: _Cont._bioaccumulation in zooplankton, which are primary consumers, suggests an entry point for TBBPA into the marine food web.  
The presence of TBBPA-BDBPE in biota, especially in mangrove wetlands and zooplankton, indicates significant bioaccumulation potential. This poses ecological risks through trophic transfer and bio magnification, ultimately affecting wildlife and human health due to the consumption of contaminated species. Detailed BAF calculations specific to each species and environmental media are essential for a comprehensive understanding of bioaccumulation dynamics.
## 2 Materials and Methods
### Implementation of SMOTE
#### 2.3.2 \(k\)-Nearest Neighbors  
Once the minority samples are identified, SMOTE employs the \(k\)-Nearest Neighbor (KNN) algorithm to find similar samples in the feature space. This step is critical for maintaining the underlying distribution of the minority class while generating synthetic samples. The value of \(k\), typically set to 5, is a hyperparameter that can significantly influence the quality of the synthetic samples. The distance between two samples is calculated using the Euclidean distance metric. The \(k\)-nearest neighbors of a sample \(x_{i}\) are then defined as the set, as follows:  
\[\text{KNN}(x_{i})=\left\{x_{j}\in S_{\text{min}}\mid d(x_{i},x_{j})\text{ is among the $k$ smallest distances}\right\}, \tag{3}\]  
where \(x_{j}\) denotes a particular minority-class instance in \(S_{\text{min}}\) such that its distance \(d(x_{i},x_{j})\) to the query point \(x_{i}\) is ranked among the shortest \(k\) distances. Only those \(x_{j}\) values yielding the smallest \(k\) distances \(d(x_{i},x_{j})\) are included in \(\text{KNN}(x_{i})\). In other words, while \(x_{j}\) may initially range over all samples in \(S_{\text{min}}\), the algorithm restricts the final set of neighbors to exactly \(k\) points once it ranks their distances from \(x_{i}\). This step is a standard feature of the KNN algorithm [17].  
This KNN approach allows SMOTE to create synthetic samples that are statistically similar to existing minority samples, thereby preserving the inherent characteristics of the minority class [17].
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 6 Analysis
### Unlinkability  
In our proposed scheme, the original identities of the participants are not transmitted over the unsecure network; instead, every participant's identity is converted into an alias. Moreover, the authentication, secure communication and key update phases are independent of each other. In addition, after every authentication procedure performed by \(OBU_{i}\), the value \(C_{i}\) updates itself. Therefore, for two or more authentication messages that are sent by the same user, the adversary \(A\) cannot determine whether they have the same origin. Thus, \(A\) cannot trace the location of a user by intercepting messages.  
Resistance to Impersonation Attack  
In the authentication procedure of our improved scheme, if an adversary wishes to impersonate \(OBU_{i}\), it must obtain both the \(A_{i}\) and \(ID_{i}\) of \(OBU_{i}\). Otherwise, it cannot compute a valid authentication request, since the original identity of \(OBU_{i}\) is converted into an alias via an XOR operation with a random number \(r_{i}\) chosen by itself and this random number \(r_{i}\) is hidden by its \(A_{i}\). Moreover, the adversary can successfully impersonates \(OBU_{i}\) only by correctly guessing the random number, which is difficult because the random number is reselected with each authentication. Furthermore, in the secure communication procedure, the original identity of \(OBU_{i}\) is also converted into an alias with a random number \(r_{i}(AID_{i}=ID_{i}\bigoplus h(r_{i}P_{pub_{j}}))\). The adversary cannot successfully impersonate the OBU since the random number cannot be guessed.  
Resistance to Internal Attack  
In our proposed scheme, an internal attack refers to the case in which the owner of a vehicle, who possesses the common secure key \(PSK\), attempts to reveal the session key for a communication channel. Under our improved scheme, in the secure communication procedure, even if the adversary can intercept all exchanged messages, \((AID_{i}\), \(u_{i})\) of \(OBU_{i}\) and \((AiD_{j}\), \(u_{j})\) of \(OBU_{j}\) and compute \(T\) and \(R\) using the secure key \(PSK\), it cannot determine the user's original identity or compute the session key \(k\) under the assumption of the hardness of \(ECCDH\) problem in \(G\).
# Carbon Dots with Up-Conversion Luminescence as pH Nanosensor
## 2 Materials, Their Characterization, and Research Methods
### Preparation and Characterization of Aqueous Solutions of Carbon Dots  
Deionized water (Millipore Simplicity UV water purification system) was used to prepare aqueous solutions of CDs. Aqueous solutions of CD-poly, CD-COOH, and CD-OH  
Figure 1: STEM image of the synthesized nanoparticles (**a**) and their elemental analysis (**b**).  
were prepared with a nanoparticle concentration of 0.1 mg/mL (obtained from gravimetric analysis).  
To change the pH in the range from 2 to 12, either an aqueous solution of HCl (Sigma Aldrich, concentration 1 M, pH = 0) or an aqueous solution of NaOH (Dia-M, concentration 1.8 M, pH = 14) was added to the solution. Measurements for the pH value of aqueous solutions of CDs were carried out using the ionometric converter Akvilon I-500 equipped with an InLab Nano pH electrode (Mettler Toledo). All measurements were carried out at a fixed temperature of 22 \({}^{\circ}\)C.  
Zeta potentials and sizes of nanoparticles in aqueous solutions were measured using dynamic light scattering on a Malvern Zetasizer NanoZS instrument. According to the data obtained, the sizes of the studied CDs were 182 \(\pm\) 21, 105 \(\pm\) 12, and 176 \(\pm\) 23 nm for CD-poly, CD-COOH, and CD-OH, respectively. The corresponding zeta potentials were \(-\)39.7 \(\pm\) 4.3, \(-\)29.1 \(\pm\) 4.5, and \(-\)17.2 \(\pm\) 1.3 mV. The obtained values of the zeta potentials indicate the colloidal stability of aqueous solutions of all CDs.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 4 Results and Discussion
### Results after Varying the Packet Size
#### 4.1.3 Simulation Results of Nakagami Propagation Model regarding Packet Size  
In the Nakagami propagation model, the mRSU surpasses the Nakagami model, notably in terms of the throughput and packet delivery ratio, because the multi-path process allows for numerous inputs at the receiver and higher throughput. Moreover, the capacity to receive numerous route signals minimizes the likelihood of packet drops.
# A Single Motor-Driven Focusing Mechanism with Flexure Hinges for Small Satellite Optical Systems
## 2 Materials and Methods
### Design of Flexure Hinge (FlexHe)
#### 2.4.1 FEM Modeling of the FlexHe
As a result, it showed that the fatigue life could be unlimited, even though the maximum load was increased by 150%. Figure 4: Static simulation results of the flexure hinge; **(a)** Simulation setup, **(b)** Distribution of the Von-mises stress along with the flexure hinge and stress concentration on each slit; the plotted graphs of **(c)** Reaction force at the fixed support versus deformation, **(d)** Von-mises stress versus deformation, and **(e)** Strain energy versus deformation.
# Advancing the State of the Fog Computing to Enable 5G Network Technologies
## 7 Convergence of 5G and Fog Computing
### The Needs for Fog and 5G Network Technologies
#### 7.1.5 Security and Privacy Protection  
Devices that have proximity with end users and employ fog nodes are sometimes carried to an unsecured location. These devices may be vulnerable to unwanted attacks such as the man-in-the-middle attack [63]. It means that fog nodes devices could be switched to fake fog nodes virtually. This challenge could be resolved through encryption and decryption approaches. Another pertinent challenge is data confidentiality and integrity. The dispersed devices mostly cause this at the edge, creating a huge volume of data that must be transmitted and stored in fog nodes for computational task and storage. Nevertheless, fog nodes interact with gadgets and data pools in cloud computing. These complex processes make data vulnerable to hijacking. There is a proffered solution to solve this problem, known as light-weight encryption algorithms and masking techniques [64]. In fog computing, there are problematic areas where collaboration takes place, which may lead to safety and privacy issues. These areas include resource access control, quality of service and security, secured collaboration, and distributed decision enforcement as well as information sharing policy [64]. A study to provide the solution for the challenges mentioned above puts forth a policy based on resource management and access control system to ensure that users have secured interoperability and partnership with diversified resources [45].
# Equivalent Linearization Methods for a Control System with Clutching Inerter Damper
## 7 Conclusions  
The equivalent linearization methods for a system with a clutching inerter damper are developed in this study. First, the equations of motion for the system with IMD and CID are built using mechanical analysis. The simulation results show the superiority of the CID system. Subsequently, three ELMs are proposed based on different principles, which can be applied using the derived formulas or procedures. Finally, a numerical study is conducted to verify the feasibility of different ELMs.  
All the ELMs proposed in this paper are proved to have enough accuracy for small inertance-mass ratios, with the error being within an acceptable range. As the inertance-mass ratio increases, the error also increases for all methods. Responses to free vibration and harmonic excitations show the accuracy of ELM1 and ELM2 in describing the inherent properties of the structure. However, it seems that the CID system will show a high demand for damping under seismic excitations and ELM3 will be more accurate. From another point of view, ELM1 and ELM2 will always yield larger results than the CID system, compared with ELM3, which is a more secure or conservative choice for response estimation and structure design. Since ELM2 has a clear physical meaning and direct formulas, it should be a recommended method for small inertance-mass ratios.  
On the one hand, the CID system retains the mass amplification effect of the IMD system, which is effective in mitigating the intensity of the input and extending the structural vibration period. On the other hand, the equivalent damping effect of the CID system reduces the sensitivity of the system to the frequency of input and further mitigates the response. The ELS accurately reflects the characteristics of the CID system and can evaluate the response of the nonlinear system with acceptable error. The feasibility of the ELS can be used for the performance design of the CID system.  
All authors made scientific contributions: [PERSON] conceived the idea of this research, [PERSON] made the calculations and wrote the manuscript, and [PERSON] revised the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Award No. 51678116.  
The authors declare no conflict of interest.
## 4 Future Directions
### Machine Learning for System-Level Design Optimization of Electrical Drive Systems  
Machine learning algorithms have been used to optimize the dimensions of several electrical machines, like PM motors. They can be used to design the whole electrical drive systems, including both electrical machines and their power electronics, and control systems. Recently, DL has been successfully employed to design the controller to drive the electrical machines [154; 155]. As there are many types of control algorithms, such as field-oriented control, direct torque control, and model predictive control, more research work shall be conducted.  
Currently, selected machine learning approaches do not show significant advances for solving particular optimization problems involving electromagnetic devices when compared with conventional parametric and semi-parametric modeling techniques. The main reason is that their effectiveness depends on the complexity of the considered optimization  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Par.** & **Unit** & **DEA** & **RBF** & **Kriging** & **ANN-BP** \\ \hline \(R_{2}\) & m & 3.18 & 3.16 & 3.11 & 3.10 \\ \(h_{2}/2\) & m & 0.428 & 0.365 & 0.267 & 0.232 \\ \(d_{2}\) & m & 0.211 & 0.244 & 0.340 & 0.394 \\ \(B_{\text{stray}}\) & mT & 1.032 & 0.957 & 0.943 & 0.938 \\ \(E\) & MJ & 180.00 & 179.95 & 179.94 & 179.94 \\ \(F\) & - & 0.344 & 0.319 & 0.315 & 0.313 \\ FEM & - & 2310 & 202 & 157 & 159 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimization results of SMES with different models.  
Figure 9: Convergence processes of sequential optimization method (SOM) with three surrogate models (RBF, Kriging, and ANN) for the optimization of SMES.  
problems, which, for instance, is a function of the number of parameters to be optimized. In case the number of design parameters and objectives and, consequently, the overall complexity of the analysis increases, machine learning algorithms typically feature promising opportunities and feature crucial benefits, e.g., for the system-level multidisciplinary design optimization of electrical drive systems for (hybrid) EVs.