esa-sceva/satcom-chunk-collection huggingface dataset:
# Unobtrusive Vital Sign Monitoring in Automotive Environments--A Review
## 3 ECG Monitoring in a Car
### Hybrid ECG Monitoring  
A somewhat different research approach was followed by [PERSON] and [PERSON] from Ritsumeikan University in Shiga, Japan. In 2008, these authors proposed using a conductive steering wheel and combine it with a capacitive electrode located in the driver seat [32] (see Figure 3c).  
Figure 3: Overview of several designs for contact-based ECG monitoring (red: active electrodes, blue: driven ground electrode, green: electronics, precise electrode position on steering wheel not revealed): (**a**) [PERSON] [20]; (**b**) [PERSON] [21], [PERSON] [29], [PERSON] [30; 31]; (**c**) [PERSON] [32], [PERSON] [33]; (**d**) [PERSON] [25]; (**e**) [PERSON] [27]; (**f**) [PERSON] [28]; (**g**) [PERSON] [24].  
This way, the habit of steering with one hand can be better accomodated. This proposal also combines the concepts of conductive measurements with capacitive sensing, which may justify the name _hybrid sensing_.  
One year later, the idea of _hybrid ECG sensing_ using the steering wheel as one electrode and the seat as the second (capacitive) electrode, was also proposed by [PERSON] and coworkers, see [34], next to redundant sensing and sensor fusion.  
In 2013, the idea of a steering wheel electrode measuring against a capacitve electrode in the seating area was again presented by [PERSON] and [PERSON] [33] from the Central South University, Hunan, China, possibly without knowledge of [PERSON]'s work.
## 1 Introduction
The same issue for high-energy trajectories was successfully resolved with the patched conic approximation that decouples the design of a sensitive trajectory leg in the close vicinity of a flyby body. The aim of this research is to develop a similar methodology and, by applying it, to perform the analysis and classification of SALT trajectories. Leveraging the concept of the _Earth-Moon region of prevalence_, we assemble a SALT trajectory from the three legs: departing and arriving, designed in the Earth-Moon CR3 BP, and the exterior leg, calculated in the Sun-Earth CR3 BP or directly in the Earth-Moon-Sun BR4 BP. The structure of the paper is as follows. First, we outline the CR3 BP and BR4 BP models, together with the patched three-body approximation of the four-body dynamics. After that, the procedure of planar SALT trajectory design is described in detail. All three trajectory legs are separately examined. Along the way, we also analytically prove the quadrant rule, a well-known property of Sun-assisted lunar transfers. The geometric analysis of the designed SALT trajectories is conducted, and the distinct patterns are revealed that can help to select a good initial guess when designing a SALT trajectory in complex dynamical models.
# Analysis of the Influence of Broad Deaths on Honeybee Population
## 2 Model Formulation
### The DDE Model  
Following the basic models from [14; 25], we extend Equation (1) to  
\[\frac{\mathrm{d}B}{\mathrm{d}t}=\frac{rH^{2}}{K^{2}+H^{2}}-\mu_{b}B-\phi B, \tag{2}\]\[\frac{\mathrm{d}H}{\mathrm{d}t}=\phi B(t-\tau)-\mu_{h}H, \tag{3}\]  
where the term \(\phi B(t-\tau)\) represents the rate at which adult bees emerge from the brood population. The bees that emerge at time \(t\) are those that entered the population at time \(t-\tau\).  
The parameters \(\{r,\mu_{b},\mu_{h},\phi,K\}\in S_{\mathrm{adm}}\), where the admissible set comes from biological significance and mathematical reasoning, and \(S_{\mathrm{adm}}\equiv(0,50000)\times(0,2)^{3}\times(0,50000)\)[14; 20].  
The models in [14; 25] represent the interactions between hive bees, foragers, food, and brood. The dependent variables include the stored food in the hive \(f\), the uncapped brood items \(B\), the number of hive bees \(H\), and the number of foragers \(F\). Hive bees emerge \(\tau\) days after pupation. Bees transition from the hive bee class to become foragers, a process modeled by the recruitment function \(R(H,F,f)\), where \(f\) is a measure of the amount of food stored in the hive and available for colony use [14].  
As noted in [25], these models are complex and cannot be easily analyzed mathematically. Therefore, in Equation (3) above, the function \(R(H,F,f)\) is approximated by the constant \(\mu_{h}\).  
This delay differential equation model (DDE) captures the essential dynamics of honeybee populations by incorporating a time delay \(\tau\) in the transition from brood to adult bees. The inclusion of this time delay is critical, as it reflects the biological reality that there is a lag between when brood are initially cared for and when they emerge as adult bees.  
The analysis of such a delayed model provides insights into the stability and behavior of honeybee populations under different conditions. By examining how the delay \(\tau\) affects population dynamics, we can better understand the potential for periodic oscillations or other complex behaviors that might arise in the colony.  
Through this approach, we aim to extend the applicability of previous models by incorporating realistic biological delays, thus providing a more accurate and comprehensive framework for studying honeybee population dynamics. This model not only aids in theoretical research but also offers practical tools for beekeepers to manage their colonies more effectively by understanding the critical periods and factors that influence bee emergence and overall colony health.
# Hybrid GA-SOCP Approach for Placement and
###### Abstract  
This research addresses the problem of the optimal location and sizing distributed generators (DGs) in direct current (DC) distribution networks from the combinatorial optimization. It is proposed a master-slave optimization approach in order to solve the problems of placement and location of DGs, respectively. The master stage applies to the classical Chu & Beasley genetic algorithm (GA), while the slave stage resolves a second-order cone programming reformulation of the optimal power flow problem for DC grids. This master-slave approach generates a hybrid optimization approach, named GA-SOCP. The main advantage of optimal dimensioning of DGs via SOCP is that this method makes part of the exact mathematical optimization that guarantees the possibility of finding the global optimal solution due to the solution space's convex structure, which is a clear improvement regarding classical metaheuristic optimization methodologies. Numerical comparisons with hybrid and exact optimization approaches reported in the literature demonstrate the proposed hybrid GA-SOCP approach's effectiveness and robustness to achieve the global optimal solution. Two test feeders compose of 21 and 69 nodes that can locate three distributed generators are considered. All of the computational validations have been carried out in the MATLAB software and the CVX tool for convex optimization.  
direct current networks; optimal power flow analysis; metaheuristic optimization; master-slave optimization; genetic algorithms; second-order cone programming Article
## 5 Conclusions
associated PAE, reaching a peak PAE close to 40%. The adoption of a silicon substrate process endows the GaN PA with significant cost effectiveness, making it well suited to mass production and compatible with mainstream CMOS fabrication technologies, thereby facilitating multifunctional and heterogeneous integration. As a result, the proposed MMIC PA not only meets the stringent SWaP-C requirements of millimeter-wave applications such as 5G-A wireless communications and SATCOM but is also ideal for highly integrated T/R-module front ends. Conceptualization: L.P.; methodology: L.P.; software: L.P. and Z.Y. ; validation: L.P., Z.Y., Y.Z., C.Z. and Y.F. ; formal analysis: L.P. and Z.Y. ; investigation: L.P., Z.Y., Y.Z., C.Z. and Y.F. ; resources: L.P.; data curation: L.P. and Z.Y. ; writing--original draft preparation: L.P. and Z.Y. ; writing--review and editing: L.P.; visualization: L.P.; supervision: [PERSON] and Y.L. ; project administration: [PERSON] and Y.L. ; funding acquisition: J.Q. and Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Provincial Key College Student Innovation Training Program of Guangzhou University, grant number 202511078067, the National College Student Innovation Training Program of Guangzhou University, grant number 202411078009, and the Guangzhou Science and Technology Plan Project, grant number 2024A03J0326. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s). The authors declare no conflicts of interest.
## 1 Introduction
In LEACH, when the sensor node is between the BS and the CH, the sensor node transmits data to the CH. At this time, data transmission occurs in the opposite direction of the BS. Energy dissipation associated with data transmission from the sensor node to the CH affects network performance. Figure 2 shows the reverse transmission. In this study, to improve the network performance of IoT systems, we attempted to increase the energy efficiency by eliminating the reverse transmission of sensor nodes. We propose a direction-based LEACH (D-LEACH) based on the transmission direction of the sensor node. D-LEACH reduces the unnecessary energy consumption of LEACH by selecting the direction of data transmission. D-LEACH consists of four phases: layer configuration phase, probability calculation phase for CH selection, setup phase, and steady-state phase. D-LEACH divides the WSNs into several layers using the relative distance between the BS and sensor nodes. The number of sensor nodes in each layer is different for each layer. The top layer has the largest number of sensor nodes and the lowest layer has the smallest number of sensor nodes. Each layer calculates the CH selection probability. The layer with many sensor nodes has a high probability of CH selection. The sensor nodes of each layer select the CH using the selection probability. The sensor node becomes a member node of the CH having the strongest signal intensity among the CHs located closer to the BS than itself. The sensor node uses the relative distance to determine the direction of the data transmission. It transmits the data to the location of the BS using direct transmission, single-hop transmission or multi-hop transmission. The remainder of this paper is organized as follows. Section 2 discusses the cluster-based routing protocol, and Section 3 discusses LEACH. Section 4 introduces the proposed energy-efficient routing protocol, and Section 5 analyzes and compares the performance of the proposed routing protocol. Finally, Section 6 concludes the paper.
## 3 Methods
### GA-BP Neural Network
The GA parameters for the GA-BP neural network are set based on the original BP neural network parameters, as shown in Table 4. These parameters were optimized through multiple rounds of cross-validation to identify the best-performing values, ensuring robust model performance. The values used for the hyperparameters were not arbitrary but derived from this validation process. Additionally, the algorithm effectively handles class imbalance by using oversampling techniques, which helps to balance the distribution of the classes and improves the model's ability to generalize across imbalanced data distributions. Algorithm 2 illustrates the optimization of the BP neural network using the GA algorithm. This code represents the GA part of a GA-BP hybrid model, where the BP part remains unchanged and is used as originally designed. In this approach, the GA is employed to optimize the parameters of the BP neural network. The GA evolves a population of potential solutions (chromosomes) in each iteration, with each individual representing a different configuration of the BP network's parameters. The GA helps the population find the best possible neural network configuration through selection, crossover, and mutation. After the GA optimizes the parameters, the BP part, which is responsible for the actual neural network training and learning, remains unchanged and is used to make predictions. Therefore, the GA fine-tunes the BP network's structure while the BP part performs the learning and prediction tasks. ```
Input: - Dataset in CSV format (features 'X' and target labels 'y') Output: - best_code: The best chromosome encoding obtained by the genetic algorithm - best_fitness_ls: List of the best fitness values of each generation - ave_fitness_ls: List of the average fitness values of each generation  Initialize chrom_len \(\leftarrow\) n_feature * n_hidden + n_hidden * n_output  n_output  Set parameters: size \(\leftarrow\) 16, p_cross \(\leftarrow\) 0.4, p_mutate \(\leftarrow\) 0.5, maxgen \(\leftarrow\) 150  Initialize population chrom_sum with the size of chromosomes - Calculate fitness using chrom_fitness.calculate_fitness - Record the best fitness and average fitness for the generation
```  
**Algorithm 2** GA-BP Neural Network Training \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Size & 16 \\ P\_cross & 0.4 \\ P\_mutate & 0.5 \\ Maxgen & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: GA Parameter Settings.
# Phase Calibrated Ring Oscillator PUF Design and Application
## 6 Experimental Results
### Bias  
Ideally, there should be 50% ones and 50% zeroes in a response. However, our RO PUF implementation on Kintex-7 FPGAs had a bias ranging from 41% to 58% according to our tests. As a comparison, we list the bias of PCPUFs in Table 5. Each result was based on the mean of 100,000 128-bit responses. Without any post-processing, all six PUFs produced bias within \(\pm\)2% of ideal, which proves the effectiveness of the phase calibration on the improvement of the RO PUF bias.
# Multidisciplinary Reliability Design Optimization Modeling Based on SysML
## 3 Representation of Optimization Problems Using SysML
### Definition of Extension Types for Optimization Types  
Multidisciplinary optimization information includes two types, an optimization object model and an optimization process model. The optimization object model consists mainly of the basic elements needed to construct an optimization problem, including optimization objectives, constraint functions, design variables, etc. The optimization process model  
Figure 2: Structural information of the multidisciplinary optimization object model.  
is mainly applied to specific solvers, such as MATLAB, ISIGHT, etc. It is worth noting that these commercial solvers are very mature for optimization solving, while the main functions of SysML modeling software mainly focus on modeling the requirements, structure, function, and behavior information of the system. Therefore, we do not need to consider the process model of optimization too much. Here, we mainly introduce the SysML extension of the optimization object model. The optimization object model is mainly used to construct the basic elements of an optimization problem. As mentioned above, the optimization object information mainly includes parameters, objectives, variables, and constraints, where the parameter element in SysML has the same meaning as the element in optimization, so only the optimization objectives, optimization variables, and optimization constraints are extended here for the metamodel.
## 2 FSO Vertical Channel Model
### Atmospheric Turbulence Channel  
The H-V model is most widely used to represent the vertical atmospheric turbulence. The \(C_{n}^{2}(h)\) model to describe the varying strength of optical turbulence as a function of altitude \(h\) is  
\[C_{n}^{2}(h)= 0.00594(\
u/27)\ ^{2}(\ 10^{-5}\ h)^{2}\exp{(-h/1000)}+2.7\times 10^{-1 6}\ \exp{(-h/1500)}+C_{n}^{2}(0)\ \exp{(-h/100)}, \tag{1}\]  
where \(\
u\) is the root-mean-square (rms) wind speed, and \(C_{n}^{2}(0)\) is the refractive index structure at the ground in m\({}^{-\frac{2}{3}}\). In this study, we assumed \(\
u=21\) m/s and \(C_{n}^{2}(0)\) as  
\[C_{n}^{2}(0)=1.7\times 10^{-13}\text{m}^{-\frac{2}{3}}. \tag{2}\]  
These values correspond to the H-V 5/7 model and weak turbulence assumption.  
The normalized variance of the scintillation index is defined as  
\[\sigma_{I}^{2}=\frac{<I^{2}>-<I>^{2}}{<I>^{2}}=\frac{<I^{2}>}{<I>^{2}}-1, \tag{3}\]  
where \(I\) is the irradiance, and \(<>\) denotes an ensemble average. Following the approach of [PERSON] et al. [23], the irradiance variance can be expressed as follows:  
\[\sigma_{I}^{2}(\mathbf{r},L)=\ \sigma_{I}^{2}(0,L)+\sigma_{I,r}^{2}(\mathbf{r},L), \tag{4}\]  
where \(\mathbf{r}\) denotes the positions at the transverse planes of the configuration and the spatial frequency spaces, and \(L\) is the link distance. The first term in Equation (4) is the on-axis normalized variance, and the second term is the fundamental of the finite beam scintillation index, commonly known as the radial component. The total uplink scintillation index is defined as  
\[\sigma_{I}^{2}(\mathbf{r},L)=\ 8.702k^{\frac{7}{6}}(H-h_{0})^{\frac{5}{6}}sec^{ \frac{11}{6}}(\xi)\times\left[\mu_{1}+1.667\frac{\mu_{1}\Lambda^{\frac{5}{6}} \alpha^{2}(H-h_{0})^{2}sec^{2}(\xi)}{w_{0}^{2}\Big{(}\Omega_{0}^{2}+\Omega^{ 2}\Big{)}}\right], \tag{5}\]  
where \(k=2\pi/\lambda\), and \(\lambda,\xi,H,h_{0}\), and \(w_{0}\) are the wavelength, zenith angle, UAV altitude, height of the OGS, and collimated beam width, respectively. We assumed the wavelength to be 1550 nm, the zenith angle and height of the OGS as 0, and the collimated beam width as 10 cm. A collimated beam is characterized by \(\Omega_{0}=1\), \(\Omega=2L/kw_{0}^{2}\), with receiver beam parameters \(\theta=\Omega_{0}/\Big{(}\Omega_{0}^{2}+\Omega^{2}\Big{)}\) and \(\Lambda=2L/kw^{2}\), where \(w=w_{0}\Big{(}\Omega_{0}^{2}+\Omega^{2}\Big{)}^{\frac{1}{2}}\) is the diffractive beam radius at the receiver. The angles in radians that determines the radial distance of the beam from the optical axis is given by \(\alpha=r/L\), where \(r\) is defined as the radial distance from the beam center line.  
The quantity \(\mu_{1}\) is the integral defined as [24]  
\[\mu_{1}=\ Re\!\int_{h_{0}}^{H}\!C_{n}^{2}(h)\Bigg{\{}\bigg{(}1-\frac{h-h_{0}}{ H-h_{0}}\bigg{)}^{\frac{5}{6}}\bigg{[}\Lambda\bigg{(}1-\frac{h-h_{0}}{H-h_{0}} \bigg{)}+i\bigg{(}1+\theta-\frac{\theta h-h_{0}}{H-h_{0}}\bigg{)}^{\frac{5}{6} }\bigg{]}-\Lambda^{\frac{5}{6}}\bigg{(}1-\frac{h-h_{0}}{H-h_{0}}\bigg{)}^{ \frac{5}{6}}\bigg{\}}dh. \tag{6}\]
# Optimal Non-Integer Sliding Mode Control for Frequency Regulation in Stand-Alone Modern Power Grids
## 2 Non-Integer Order Calculus  
**Definition 1**.: _The Riemann-Liouville fractional integration of order \(\alpha\) of a continuous function \(f(t)\) is defined by [27]_  
\[D^{-\alpha}f(t)=\frac{1}{\Gamma(\alpha)}\int_{t_{0}}^{t}f(\tau)(t-\tau)^{ \alpha-1}d\tau \tag{1}\]  
_where \(t_{0}\) is the initial time and \(\Gamma(.)\) is the Gamma function, which is defined by \(\Gamma(z)=\int_{t_{0}}^{\infty}\bar{t}^{z-1}e^{-t}dt\)._  
**Definition 2**.: _Let \(m-1<\alpha\leq m\) and \(m\in N\), then the Caputo fractional derivative of order \(\alpha\) of a continuous function \(\varphi(t):R^{+}\to R\) is given by [27]:_  
\[D^{\alpha}\varphi(t)=\frac{1}{\Gamma(m-\alpha)}\int_{t_{0}}^{t}\frac{\varphi^{ (m)}(\tau)}{\left(t-\tau\right)^{\alpha-m+1}}d\tau \tag{2}\]  
**Property 1** ([28]).: _For any constant, \(\zeta\in R\) we have \(D^{\alpha}\zeta=0\)._  
**Property 2** ([28]).: _For \(\alpha\in(0,1)\) and \(\omega(t)\in C^{m}[0,T]\), we have:_  
\[D^{\alpha}(I^{\alpha}\omega(t))=D^{\alpha}(D^{-\alpha}\omega(t))=\omega(t) \tag{3}\]  
**Lemma 1**.: _Suppose that \(\psi(t)\in C^{m}[0,T]\) and \(\alpha\in(0,1)\). Then,_  
\[D^{\alpha}\big{|}\psi(t)\big{|}=sgn(\psi(t))D^{\alpha}\psi(t) \tag{4}\]  
**Proof.** As \(\alpha\in(0,1)\), because of Equation (3), one gets:  
\[D^{\alpha}\big{|}\psi(t)\big{|}=\frac{1}{\Gamma(1-\alpha)}\int_{t_{0}}^{t} \frac{\big{|}\psi(t)\big{|}^{\prime}}{\left(t-\tau\right)^{\alpha}}d\tau\]  
Besides, \(\big{|}\psi(t)\big{|}^{\prime}=sgn(\psi(t)).\psi(t)^{\prime}\), so in Equation (2), one has:  
\[D^{\alpha}\big{|}\psi(t)\big{|}=\frac{1}{\Gamma(1-\alpha)}\int_{t_{0}}^{t} \frac{sgn(\psi(t))\psi(t)^{\prime}}{\left(t-\tau\right)^{\alpha}}d\tau \tag{5}\]  
\(\Box\)  
**Theorem 1** ([29]).: _Suppose that for \(\alpha\in(0,\ 1)\), the fractional-order system \(D^{\alpha}y(t)\ =g(y,\ t)\) satisfies the Lipschitz condition and it has an equilibrium point like \(y=0\). Assume that there exists a Lyapunov function \(V\ (t,\ y(t))\) and class-\(K\) functions \(\alpha_{1},\ \alpha_{2}\) and \(\alpha_{3}\) satisfying_  
\[\alpha_{1}(\|y\|)\leq V\ (t,y)\leq\alpha_{2}(\|y\|) \tag{6}\]  
\[D^{p}V(t,y)\leq-\alpha_{3}(\|y\|) \tag{7}\]  
_which \(p\in(0,1)\). Then the equilibrium point of the system \(D^{\alpha}y(t)\ =g(y,\ t)\) is asymptotically stable._  
**Theorem 2** ([30]).: _Consider the following FO system_  
\[D^{\alpha}X=F(x,t). \tag{8}\]  
_Let \(\Lambda:(0,\infty)\times[0,X]\to R^{n}\) introduce as_  
\[\Lambda(\omega,t)=\int_{0}^{t}e^{-\omega^{2}(t-\theta)}F(x,\theta)\mathrm{d}(\theta) \tag{9}\]  
_Then, the FO system (8) can be written as_  
\[\left\{\begin{array}{c}\frac{\partial\Lambda(\omega,t)}{\partial t}=-\omega ^{2}\Lambda(\omega,t)+F(x,t)\\ X(t)=\int_{0}^{\infty}u(\omega)\Lambda(\omega,t)d\omega\end{array}\right. \tag{10}\]_where \(u(\omega)=\frac{2\sin{(\alpha\pi)}}{\pi}\omega^{1-2\alpha}\), \(\alpha\in(0,1)\)._
## 2 Material and Methods
The 3D root mean square (RMS) deviation values were obtained in millimeters (mm). Precision was described as the RMS error variances per group standard deviation (SD). Trueness was defined as the average RMS error discrepancies between the reference file and the experimental model scans. The statistical analyses of the accuracy of the implant analog position, according to the 3D printing angulation, were performed using a statistical software program (IBM SPSS Statistics for Windows, v.26.0; IBM Corp., Armonk, NY, USA). Descriptive statistics were performed by calculating each group's mean, median, and standard deviation. The Shapiro-Wilk test showed that the data were normally distributed. Therefore, the Trueness and precision of the analog position in the 3D-printed models according to the printing orientation (0, 45, and 90 degrees) were analyzed using the parametric ANOVA test, establishing a significance level of \(p\) < 0.05, and subsequently the Bonferroni post hoc test. trueness value was 0.09 mm in the 0- and 45-degrees groups, followed by 0.11 mm in the 90-degrees group. Similarly, the maximum values found were 0.20 mm in the 90-degrees group, followed by 0.18 mm and 0.12 mm in the 0- and 45-degrees groups, respectively. Concerning precision, the best values were found in the 45-degrees group (0.06 \(\pm\) 0.01), followed by the 0-degrees group (0.10 \(\pm\) 0.03) and the 90-degrees group (0.10 \(\pm\) 0.04). When evaluating the precision, the minimum value was 0.04 mm in the 45-degrees group, 0.06 mm in the 0-degrees group, and 0.07 mm in the 90-degrees group. Similarly, the maximum values found were 0.18 mm in the 90-degrees group, followed by 0.15 mm and 0.07 mm in the 0- and 45-degrees groups, respectively. The ANOVA test showed statistically significant differences in trueness (RMS) among the groups (\(p<0.006\)). The Bonferroni test showed that they were found between G2 and G3 (45 and 90-degrees of print orientation) (\(p<0.005\)). No significant differences were found among the rest of the groups (Table 2). Precision was described as the RMS error variances of the standard deviations. The ANOVA test showed statistically significant differences between the groups (\(p<0.002\)). In the post hoc Bonferroni test, the differences appeared between G1 and G2 (0 and 45-degrees of print orientation) (\(p<0.011\)) and between G2 and G3 (45 and 90-degrees of print orientation) (\(p<0.003\)) (Table 3).