esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Kinetic Modelling
#### 3.4.1 First-Order Kinetic Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Substrate** & \(\mathbf{G_{max}}\) **(NL CH\({}_{4}\) kg\({}^{-1}\) VS)** & \(\mathbf{k}\) **(days\({}^{-1}\))** & **R\({}^{2}\)** & **S.E.E.** & **Error (\%)** \\ \hline _P.o._ & 82 \(\pm\) 2 & 0.20 \(\pm\) 0.01 & 0.9936 & 3.277 & 4.0 \\ _P.o.:Pep._ & 388 \(\pm\) 2 & 0.46 \(\pm\) 0.01 & 0.9987 & 6.714 & 0.2 \\ _P.o.:Cas._ & 414 \(\pm\) 6 & 0.28 \(\pm\) 0.01 & 0.9934 & 6.025 & 0.8 \\ _P.o.:Syn.Cas._ & 377 \(\pm\) 2 & 0.36 \(\pm\) 0.01 & 0.9987 & 6.548 & 0.1 \\ _P.o.:R.s._ & 184 \(\pm\) 4 & 0.29 \(\pm\) 0.02 & 0.9879 & 9.495 & 5.1 \\ \hline \hline _P.o.: Posidonia ocanica;_ **Cas.**: Casein; **Pep.**: Peptone; **Syn.Cas.**: Synthetic casein made of amino acids; _R.s.: Rayhiadocelis subcapitata._ & \(\mathbf{G_{max}}\); Ultimate methane production; \(\mathbf{k}\): Specific rate constant or apparent kinetic constant; **R\({}^{2}\)**. Determination coefficient; **S.E.E.**: Standard error of estimate; **Error**: Difference between the experimental and calculated ultimate methane production. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Values of the kinetic constant obtained from the first-order model for the different mixtures tested. Values are given as means \(\pm\) standard deviations. biomass of _Typha_ sp. exhibited \(k\) values proximate to those of microcrystalline cellulose (used as a positive control), achieving a value of 0.170 d\({}^{-1}\). In a study by [PERSON] et al. [48], the floating macrophyte _Egeria densa_ demonstrated a \(k\) value of 0.27 d\({}^{-1}\), an intermediate figure in comparison to plants examined in the [PERSON] et al. [47] investigation but resembling the value observed for _Hyacinth_ sp. The notable feature of _Egeria densa_ is its relatively low lignin content (6.3%), emphasizing its impact on the hydrolysis rate. [PERSON] et al. [49] investigated the impact of chemical pretreatment on biogas production derived from _Hyacinth_ sp. The pretreatment procedure involved varying H\({}_{2}\)SO\({}_{4}\) concentrations from 0 to 5% \(v/v\) and adjusting the residence time between 0 and 90 min. The findings revealed alterations not only in cellulose content but also in glucose levels, COD, and the COD/N ratio. Optimal pretreatment conditions were identified as a 5% \(v/v\) H\({}_{2}\)SO\({}_{4}\)  
Figure 3: Variation of the experimental values of methane production with time and theoretical curves obtained with the first-order model (mixture _P. oceanica_—peptone) (**a**) and transference function model (mixture _P. oceanica_—casein) (**b**). concentration with an exposure time of 60 min, as the slurry pretreated under these conditions exhibited a COD/N ratio of 388 [49]. For this pretreated _Hyacinth_ sp., a \(k\) value of 0.021 d\({}^{-1}\) was achieved, which was much lower than those reported by [PERSON] et al. [47] (0.35-0.53 d\({}^{-1}\)) and those attained in the present research, although the biogas production for pretreated _Hyacinth_ sp. biomass increased by 131% compared to the untreated biomass.
# Face Spoofing Detection with Stacking Ensembles in Work Time Registration System
## 3 The Proposed Method
### Smartphone Detection Using CNN  
Complementing the bezel detector, a smartphone detection algorithm was developed using a convolutional neural network architecture. This module works closely with other parts of the system and helps to detect if a face is displayed on an electronic device, such as a smartphone or a tablet, through features such as shape, bezel, or on-screen elements.  
First, a model based on YOLOv4 was developed, but it did not work well in this case, as it was trained to detect whole objects, while in this use case, most of the time, only parts of the device are visible in the camera view. This necessitated the creation of a separate model for this task. The architecture of this model is presented in Figure 4.  
The CNN was trained to detect whether there is a smartphone in the image and whether it appears as part of the image (for example, the presented person is holding it), or is used to actively display a spoofed face. Therefore, while this algorithm can provide useful information about the authenticity of the photo, it is best used as part of the whole system.
## 2 Road Excitation Model  
### Parameter Optimization for Power Spectral Density  
Distance between road surface and base plate is typically defined as a function of road irregularities. Road profile is typically assumed to be homogeneous and isotropic Gaussian random process, and its statistical characteristics can be described by PSD. According to the International Standards Organization (ISO) 8601 [10], PSD of road roughness can be defined as Equation (1).  
\[G_{q}(n)=G_{q}(n_{0})(\frac{n}{n_{0}})^{-w} \tag{1}\]  
where \(n\) is spatial frequency in \(m^{-1}\), \(n_{0}\) is reference spatial frequency with a value of 0.1 \(m^{-1}\), \(G_{q}(n_{0})\) is PSD value for reference spatial frequency in \(m^{3}\), \(w\) is termed waviness, and reflects approximate frequency structure of the road profile, commonly taken as \(w=2\). Also, \(G_{q}(n_{0})\) represents different road levels ranging from \(A\) (very good) to \(H\) (very poor), depending on its values.  
Equation (1) leads to estimation errors (overrated phenomenon) especially at low frequencies. To deal with this problem, PSD of road roughness based on rational white noise signal is proposed [11], and Equation (1) can be modified as follows.  
\[G_{q}(n)=\frac{\alpha\rho^{2}}{\pi(\alpha^{2}+n^{2})} \tag{2}\]  
where \(\alpha\) is constant related to road feature; \(\rho^{2}\) represents variance of road roughness and \(n\) is spatial frequency in \(m^{-1}\). More details are stated in [12].  
Time domain representation of the road can be given as [11].  
\[\dot{q}(t)=-\alpha vq(t)+w(t) \tag{3}\]  
where, \(v\) represents velocity of the vehicle in m/s and \(w(t)\) is the white noise sequence. Covariance of white noise can be obtained as follows.  
\[\text{cov}[w(t)]=E[w(t)w(t+\tau)]=2\rho^{2}\alpha v\delta(\tau) \tag{4}\]  
where \(\tau\) represents time-shift in \(s\) and \(\delta(\tau)\) is impulse function.  
Then, the method of Least squares nonlinear parameter estimation is used to optimize the road parameters, and the following equation was utilized.  
\[J=\sum_{i=1}^{N}\left[\Phi(n_{i},\alpha,\rho)-G_{q}(n_{i})\right]^{2}\ \rightarrow\text{min} \tag{5}\]  
where \(N\) is the number of spatial frequency and \(n_{i}\) is the sampling frequency points.  
Using the ISO-8601 [10], we know that PSD of road roughness is four times for adjacent road levels. We obtain information to other road information based on one of them road parameter.  
Based on the above analysis, optimal parameters for varying road level are listed in Table 1.  
Considering values presented in [12], PSD of road roughness between rational white noise and ISO standard are compared using Equations (1)-(3). PDS road roughness results are shown in Figure 1.  
It can be seen from Figure 1 that PSD of road roughness generated between rational white noise and ISO standard are consistent.
## 3 Results and Discussion
### Effect of Surface Material and Nutrient Composition on Biofilm Formation and eGFP Expression  
Following the characterization of surface materials, _E. coli_ biofilm formation on SST, PVC, and SIL, and in LB, TB, and M9 ZB media, was evaluated for 9 days through the analysis of the number of total and culturable cells, eGFP expression, and plasmid copy number (Figure 4).  
Figure 4: Evaluation of _E. coli_ biofilms grown in LB (*), TB (*), and M9 ZB (*) on stainless steel (SST; left column), polyvinyl chloride (PVC; middle column), and silicone rubber (SIL; right column), concerning the number of biofilm total cells (**a–c**), culturable cells (**d–f**), eGFP production (**g–i**), and plasmid copy number (**j–l**). * not quantifiable. The means \(\pm\) standard variations (SD) from three independent experiments are presented. Different lowercase letters indicate statistically significant differences between culture media on each day (\(p<0.05\)).
## 5 Conclusions  
This paper mainly studies the performance of the average outage probability of an RF-UWOC hybrid two-hop relay transmission system that adopts the \"Decode-and-Forward\" mechanism in a complex fading transmission environment, when the receiving end selects two detection technologies, namely intensity modulation with direct detection (IM/DD) and heterodyne detection (HD), respectively. Firstly, by using the advanced transcendental Meijer-G function, the theoretical closed-form expression of the end-to-end outage probability of the hybrid two-hop system is derived. Subsequently, based on the power series expansion characteristics of the Meijer-G function near zero, the asymptotic expression of the outage probability under the condition of high average SNR is obtained. Theoretical analysis and numerical simulation results show that the transmission distance of the RF link, the parameters of the Generalized K distribution that can characterize the small-scale fading and large-scale shadowing effect of the RF link, as well as the parameters in the UWOC link that represent ocean turbulence, pointing error, underwater path loss, etc., will all have a significant impact on the system performance of the hybrid two-hop RF-UWOC. Specifically, as revealed by the laws of the asymptotic expression of the outage probability, the system outage probability will be jointly determined by the comprehensive influence of the parameters characterizing ocean turbulence and pointing error, the detection scheme, and the values of the parameters of the Generalized K distribution of the RF link. Under the same parameter configuration conditions, the outage probability of the system still decreases with the increase in the average SNR. Under the same average signal-to-noise ratio conditions, the heterodyne detection, which represents coherent detection, is far superior in performance to the IM/DD scheme of non-coherent detection. This indicates that increasing the received SNR and adopting coherent detection are both beneficial for improving the system performance. Finally, the Monte Carlo numerical simulation results provided in this paper are in good agreement with the theoretical calculation results, indicating that the derived theoretical closed-form expression and asymptotic expression of the outage probability of the hybrid two-hop RF-UWOC system are very accurate. They can provide rapid calculation support for the comprehensive performance analysis of the hybrid RF-UWOC system from a theoretical perspective in the future.  
Conceptualization, M.J.; methodology, Y.L. ([PERSON]) and Y.L. ([PERSON]); validation, M.G. and Y.L. ([PERSON]); formal analysis, Y.L. ([PERSON]); writing--original draft preparation, M.G.; writing--review and editing, Y.L. ([PERSON]); supervision, Y.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. U23B20144).  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 3 Methodology
### Long Short-Term Memory Neural Network Model for Inferring Users' Nationality
#### 3.3.2 Model Training  
After pre-processing, trajectories are defined as discrete location sequences representing the past time-space transitions of users over the territory. We therefore replace discrete locations with the corresponding embedding vectors, obtaining a new representation of trajectories as sequences of dense vectors (see Figure 2).  
Before being fed to the LSTM, sequences are subjected to a segmentation phase, where they are partitioned into multiple segments of fixed length. This is done by a fixed-width sliding window scanning each trajectory. The window moves forward by one location until it reaches the end of the sequence: at each step, the locations in the window are gathered as training features. The segment length depends on both specific purposes and dataset restrictions. Its choice is particularly influenced by the time resolution of the trajectories, whereby a higher time resolution leads to a larger window in terms of locations (e.g., 4 h window contains 16 locations if _time_resolution_\(=15\) min while just four if _time_resolution_\(=1\) h).  
The LSTM model is finally trained with a collection of these fixed-length trajectory segments, encoded as embedding sequences, where each is labeled with the nationality of the user generating it. For example, if the window length is equal to four locations, the location sequence (\(LOC_{t-3}\), \(LOC_{t-2}\), \(LOC_{t-1}\), \(LOC_{t}\)) is identified as input features and the corresponding nationality \(NAT\) as a target variable. Therefore, to link trajectories to nationalities, the output of the LSTM is fed into a softmax function, as reported in Equation (8), where \(h_{last}\) is the output of the LSTM at the last step and _n_NAT_ is the total number of nationalities:  
\[P(NAT=j\,|\,h_{last})=\frac{\exp\left(W_{j}h_{last}^{\prime}+b_{j}\right)}{ \sum_{k=1}^{nNAT}\exp\left(W_{k}h_{last}^{\prime}+b_{k}\right)}. \tag{8}\]  
Given a trajectory sequence \(T\) labeled with a nationality \(NAT\), we train the model to maximize the log-likelihood, with respect to any weight in the network, as reported in Equation (9). The model is trained through backpropagation by mini-batch stochastic training.  
\[NAT(T)\rightarrow\sum_{T\in NAT}\log(p(NAT|T)). \tag{9}\]  
Figure 2: New trajectory representation as a sequence of location embeddings.  
The prediction is therefore based on both the current sequence of locations and historical trajectories of other users and nationalities. The flowchart of the whole process from raw traces to the final classification is illustrated in Figure 3.
## 2 Materials and Methods
### Data Processing  
Aerial images captured by drones were used to produce an accurate vegetation map. Images were processed and edited using photogrammetry software such as PhotoScan Professional (Agisoft LLC, St. Petersburg, Russia). Because each drone image-capturing point has GPS coordinates and captures overlapping images, a single image can be generated by editing. Additionally, an orthomosaic image can be created to produce a digital elevation model (DEM) and contour lines, which can be used to determine the dividing lines of the actual terrain. These processed images were then used to create the vegetation maps using QGIS software (QGIS Development Team, version 3.22.9-Bialowieza, Poland).
# Review of Magnetoelectric Effects on Coaxial Fibers of Ferrites and Ferroelectrics
## 6 Discussion
A ferroelectric phase of particular interest for use in ME nanocomposites is polyvinylidene fluoride (PVDF), which is a fluoropolymer. It is corrosion resistant and is ideal for synthesizing flexible ME composite structures. There have been recent reports on the synthesis and characterization of fiber mats and thin-film composites of PVDF and spinel or hexagonal ferrites [70; 98; 99]. Fiber mats of PVDF with nanoparticles of CFO were investigated in terms of the strength of ME coupling strength by H-induced polarization and the maximum polarization was found to decrease by as much as 10% [97]. In another study, ME coupling was determined by H-induced variation in P\({}_{\rm r}\) in films of PVDF-based ferroelectric containing SrM fibers [99]. Fractional variation in P\({}_{\rm r}\) ranged from 4% to 9%, depending on the amount of SrM fibers in the films.
# Interactive Airfoil Optimization Using Parsec Parametrization and Adjoint Method
## 2 Theory of Adjoint Optimization
### Adjoint Method: Continuous and Discrete Approach  
There are two main approaches to adjoint optimization: continuous and discrete [3; 4]. They differ in the sequence of mathematical operations used during their derivation. Each of those two approaches can then be treated from either a Lagrange or a duality viewpoint as both yield the same results [7]. In this paper, the Lagrange viewpoint is used as it connects more naturally to the constrained optimization.  
The continuous approach favored by [PERSON] seems more natural from the perspective of physics and continuum mechanics. In this approach, the cost function of a continuous problem is defined first. Then, the adjoint partial differential equations (PDEs) are derived from the continuous flow equations, together with their boundary conditions (BCs) and possible initial conditions (ICs). All the equations are then discretized separately and numerically solved. After solving those equations, it is possible to compute the gradient of the cost function with respect to the design variables. In this approach, great care has to be taken when deriving the adjoint equations, especially their BCs [3]. This makes this approach very complex and challenging in terms of math, although it can be computationally more efficient than the discrete version and can have lower memory requirements [4]. The continuous version is also critical for understanding the behavior of the adjoint equations and their physical significance and for assessing the possibility of ill-posed problems [3; 4]. The continuous adjoint method workflow is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimization Method** & **RSM** & **Genetic Algorithm** &
\begin{tabular}{c} **Finite** \\ **Difference Method** \\ \end{tabular} & **Adjoint Method** & **Simplex Method** \\ \hline Computational complexity & \(m^{2}\) & \(k\) & \(m+1\) & \(\approx 2\) & \(\approx 1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computation cost of 1 optimization iteration—compared to single flow solution [2].  
Figure 1: Flowchart of the continuous adjoint method.  
The discrete adjoint method differs in terms of the order of operations. The beginning is the same as in the continuous approach, i.e., the definition of the cost function in the continuous problem. After that, the continuous problem is discretized as a whole. This means the discretization of the fluid flow PDEs with the corresponding BCs and ICs implemented and the formulation of the discrete analog to the continuous cost function as a function of many variables. Only then is the adjoint set of algebraic equations derived from the discretized flow PDEs. The advantages and disadvantages of this approach can be found in [4]. This approach avoids much of the complexity of the continuous approach, but great care must be taken when discretizing the problem. The discrete adjoint method workflow is shown in Figure 2.
## 2 Dynamic Analysis of the Financial System
### Bifurcation Analysis of Financial System (1)  
There are three parameters \(a,b\), and \(c\) in the financial system (1), and they exhibit chaotic behavior when \(a=0.9\), \(b=0.1\), and \(c=1\). If one wants to control the system through its parameters, which parameter will be more effective? Therefore, a further discussion of this problem is given by the bifurcation with respect to the parameters, and the bifurcation of system (1) is shown in Figure 2a according to the bifurcation algorithm of a dynamical system in Algorithm 1. Meanwhile, the Lyapunov exponent spectrum is also shown in Figure 2b.  
Figure 1: Attractor of financial system (1). (**a**) Phase diagram of \(x\) vs. \(y\). (**b**) Phase diagram of \(y\) vs. \(z\).  
```
Input: Parameters \(a,b\), initial values \(x(0),y(0),z(0)\), \(c\in[c_{l},c_{r}]\), step of parameter \(c\): \(h_{c}=0.01\); \(t\in[0,T]\), step of time \(t\): \(h=0.001\), allowable error \(\delta_{0}\) Output: Poincare cross-section points \(P\) when \(x=y\)
1 Calculate \(N_{c}=\text{round}\left((c_{r}-c_{l})/h_{c}\right)\), \(N=T/h,i=1,j=1\);
2 Generate variables \(x,y\) and \(z\) based on system (1):
3 for\(i\leqslant N\)do
4\(x(i)=x(i-1)+\dot{x}(i-1)h\);
5\(y(i)=y(i-1)+\dot{y}(i-1)h\);
6\(z(i)=z(i-1)+\dot{z}(i-1)h\);
7\(i=i+1\);
8
9 end for
10 for\(j\leqslant N_{c}\)do
11 if\(|x(j)-y(j)|\leqslant\delta_{0}\)then
12\(P(j,:)=z(j)\);
13
14 end if
15\(j=j+1\);
16
17 end for
```  
**Algorithm 1**Algorithm of Bifurcation with Respect to Parameter \(c\)  
Based on the Lyapunov exponent spectrum and the bifurcation diagram with respect to parameter \(c\), the system (1) exhibits chaotic behavior when \(c\in(0.6,1.1)\), but it exhibits periodic behavior when \(c\in(0.1,0.6)\cup(1.1,2)\). So, the dynamic behavior of the system (1) can be controlled by the demand elasticity of the commodity \(c\). For example, the attractor of the financial system is shown in Figure 3 when \(c=2\). Obviously, it can be in a periodic state when the parameter \(c\) changed from \(1\) to \(2\).  
**Remark 1**.: _Similarly, the bifurcation algorithm can also be used to calculate the bifurcation diagrams with respect to parameters \(a\) and \(b\). According to the results of bifurcation, the system (1) keeps at a state of chaos when the parameters \(a\) and \(b\) belong to the interval \([0,3]\). Therefore, if one wants to control the behavior of the financial system (1) to be periodic, then the parameter \(c\) may be better than parameters \(a\) and \(b\)._  
Figure 2: Bifurcation diagram and Lyapunov exponent spectrum of financial system (1) with \(c\). (a) Bifurcation diagram. (b) Lyapunov exponent spectrum.