esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Internet of Medical Things
### IoMT Systems Architecture
#### 5.3.3 Gateway Layer  
The gateway layer in the IoMT architecture plays a crucial role in facilitating communication and data transmission between the physical layer, where devices and sensors are located, and the data layer, where data is processed and stored. It acts as a bridge connecting the two layers and ensures the secure and efficient exchange of information.
## 7 Mechanisms of PGPB (Plant Growth Promoting Bacteria) towards Salinity Tolerance
### Direct Mechanisms
#### 7.1.3 Production of Phytohormones  
Phytohormones play a crucial role in plant development and responsiveness to stress [191]. External phytohormones may help to improve crop productivity by improving biotic and abiotic stress tolerance [192]. Phytohormones like salicylic (SA), abscisic acid (ABA), and jasmonic acids (JA) also play key functions in plant response to salinity [193, 194]. Particular enzymes that facilitate seed germination and plant development are also responsible for enhancing the concentration of certain phytohormones such as gibberellins (GAs) and indle-3-acetic acid (IAA) [195]. IAA is the major natural auxin with a well-documented capacity to control certain aspects of plant performance [196].  
IAA is involved in cell division and extension; and root initiation. The regulation of these processes by IAA is negatively affected by saline conditions. Declining concentrations of IAA in the seedling roots of constrained plants result in reduced and slower germination rates [197]. Additionally, IAA can promote plant defense, for instances application of exogenous IAA decreased tomato infection by _Fusarium oxysporum f_. sp. _radicis-lycopersici_[198]. For example, the IAA producing ability under salt stress was assessed was observed to be 250 \(\pm\) 0.1, 220 \(\pm\) 0.1, and 200 \(\pm\) 0.1 ug/mL for _Rheinheimera_ sp., _Rhizobium_ sp., and _Bacillus subtilis_, respectively [199].  
Phytohormones, e.g., Gas and IAA, are considered as growth hormones, which enhance the number of root tips, as well as root and shoot length resulting in improved nutrient absorption and thus improved plant health during stress and non-stress conditions [200]. It is recognized that phytohormones synthetized by bacteria may contribute to determining plant phenotype [201]. In addition, PGPB modulates ABA biosynthesis and ABA regulated signaling pathways, which can lead to increased salt-stressed crop production. Halotolerant _Dietzia natronolimnaea_ STR1 stimulated salinity (150 mM NaCl) tolerance in rice seedlings through regulation of the ABA signaling cascade [24]. _Novoshingobium_ sp. and _Pseudomonas putida_ are reported to decrease salinity stress in citrus seedlings by enhancing leaf IAA concentration and decreasing accumulation of root chloride under salt stress [202]. Phytohormone signaling cascades influence osmotic equilibrium and other processes of salt resistance and control adaptation of plants to salinity [197, 203, 204]. Auxin-producing root-colonizing bacteriacan provide additional auxin to the rhizosphere, and can help root development during stress, while maintaining leaf growth [205].
## 1 Introduction  
Cyberattacks have significantly increased with the rapid advancement of the Internet, massive data electronic transmission, and the growing number of users. These fast changes and challenges require a powerful mechanism to maintain stable and secure networks. One of these mechanisms is an intrusion detection system (IDS), which monitors the processes prevailing in networks and evaluates them for signs of any possible deviations that violate security policies [1]. Intrusion detection has two approaches: signature based and anomaly based. The signature-based approach detects attacks by looking for exact and specific patterns (i.e., byte sequences or known malicious packets in network traffic). By contrast, researchers use the anomaly-based approach to detect unknown attacks and identify any unacceptable deviations from normal network traffic. Unlike the signature-based approach, the anomaly-based approach shows many false alarms when dealing with large, high-dimensional data. It relies on its knowledge of normal behaviors and any deviation from normal patterns, and it has gained popularity as an effective approach against new attacks.  
An IDS uses machine learning (ML) algorithms to deal with huge volumes of high-dimensional data in order to accurately identify intrusions [2; 3], as well as to generate a model of reliable activities and compare a new behavior against this model. Although there are many ML algorithms for intrusion detection purposes, further investigation is needed to evaluate their efficacy and accuracy (i.e., a low false positive rate (FPR) for a huge volume of data) in intrusion detection. Researchers are aware that some ML algorithms are superior to others in intrusion detection accuracy, but these need more training time to build models on big datasets. Researchers have documented that model building time is a vital aspect of predicting intrusion in real life [4]. Some integrated ML algorithms with IDSs are offline and cannot be deployed in real time because of their high model building time. A delay in IDSs can compromise networks for a period of time before raising any alarm, making fast classification essential to guarantee the quick detection of attacks and ensure that IDSs have a fast data stream monitoring capability. As time plays a crucial role in detecting attacks in a high-speed network, a practical real-time IDS is needed to detect intrusions.  
Consequently, researchers need to conduct further studies on the performance of ML classifier algorithms with feature selection/reduction approaches in order to lower the model building time without compromising intrusion detection accuracy. This study was designed to identify more ML classifiers with the highest detection accuracy and lowest possible model building time for the development of an efficient network IDS.
# Engineering Properties and Environmental Impact of Soil Mixing with Steel Slag Applied in Subgrade
## 2 Materials and Experimental Methods
### Raw Materials
#### 2.1.3 Specim Preparation  
In this study, a set of test soil with different proportions of soil and SL were prepared to carry out the relevant experiments. In order to improve the utilization rate of SL and give full play to the strength advantage of SL, soil specimens mixed with SL at 30%, 50% and 70% of soil by mass were selected. Compared with the existing research, the above content of SL in this study was greatly increased [20; 25]. Before the laboratory tests, soil and SL samples were dried at a temperature of 110 \(\pm\) 5 \({}^{\circ}\)C for 8 h and then cooled at room temperature. The SSL specimens were manually prepared according to the target mix proportion and water content during the whole preparation process. According to the experimental method of Chinese specification (JTG 3430-2020), the SSL specimens with the dimension of \(\Phi\) 152 mm \(\times\) 120 mm and \(\Phi\) 39.1 mm \(\times\) 80 mm were used for compaction, liquid plastic limit, CBR, static and dynamic tests, etc. Some test specimens had to be cured for 7 d after specimen preparation. The specimens for the environmental impact assessment test were taken from specimens that failed the mechanical tests.  
Figure 3: The experimental raw materials and XRD analysis results: (**a**) SL; (**b**) S2; (**c**) SL.
# A Novel Algorithm to Optimize the Energy Consumption Using IoT and Based on Ant Colony Algorithm
## 2 Literature Review
### Routing Algorithms in IoT Platform
Clustering-based routing protocols are energy-efficient protocols that improve the lifespan of an IoT-based network. The purpose of clustering is to minimize the total transfer power by adding it to a single path for a longer network life. In this paper, the fuzzy c-means (FCM) and C-means algorithms are used for the optimal number of cluster heads and their position. The application of FCM in routing helps to change Low Energy Adaptive Clustering Hierarchy (LEACH) parameters during execution. The results show that the hybrid algorithm increases the network lifespan compared to the LEACH algorithm [26].
# Demosaicing by Differentiable Deep Restoration
## 2 Related Work
### CFA Design  
The Bayer pattern [1] is the most commonly used CFA which is designed according to the spectral sensitivity of the human visual system. Several other CFAs have been proposed empirically for various considerations. Luminance channel was introduced to reduce the exposure time with high sensitivity in [12]. CFA with randomly sampled colors [13; 14; 15] was proposed to reduce the aliasing artifacts. In addition to the periodic tiling CFA pattern, [PERSON] et al. [16] developed an irregular CFA with aperiodic tiling Penrose layout. Rather than designing CFA pattern empirically, some CFAs were optimized by minimizing the reconstruction error for a given demosaicing algorithm. A perceptual error was proposed in [17] as optimization objective to select color from R, G, B channels for CFA pattern. In [18], the CFA was iteratively optimized for the proposed equivalent conditions.  
In frequency domain [19], the raw image can be seen as multiplexing of one luminance component at baseband and several chrominance components at high frequency bands. A variety of methods have been proposed to reduce aliasing caused by CFA by maximizing distance among these components in frequency domain. The design of CFA was formulated as a constrained optimization problem. [PERSON] et al. [20] optimized the CFA by parameter selection in the frequency domain with explicitly designed constraints. [PERSON] et al. [21] utilized a geometric method to solve the constrained optimization problem of the specified frequency structure. [PERSON] et al. [22] proposed an automatic CFA generation method with no requirement of human interaction.  
Although the above CFA design methods produce good image results, they are either designed without considering the demosaicing algorithm or designed for only a specific kind of demosaicing algorithm. In comparison, we jointly learn the CFA and demosaicing network for best performance.
## 4 Conclusions  
In conclusion, we have studied the topological plasmonic modes in a graphene pair waveguide array based on Jackiw-Rebbi modes. The interlayer and intralayer spacing of the arrays is uniform but the chemical potential of two graphene in each pair are different. We show the topological bound modes take place with changing the chemical potential of graphene such that two arrays with opposite sequences of chemical potential are interfaced. Such topological bound modes are analogous to Jackiw-Rebbi modes in a Dirac equation. Due to the strong confinement of graphene SPPs, the modal wavelength of the topological bound mode can reach as small as 1/70 of incident wavelength. As the emerging condition of topological bound modes is determined by the chemical potential, the  
Figure 5: The eigenvalue spectra for different chemical potentials. (**a**) Real part and (**b**) imaginary part. The red dots represent the topological bound modes.  
Figure 6: Robustness of the Jackiw-Rebbi against perturbation. The chemical potential (**a**) real part and (**b**) imaginary part of effective refractive index.  
topological bound modes can be dynamically controlled. Moreover, the imaginary part of the effective index of topological bound mode can be smaller than other modes by decreasing the chemical potential. We also show the trivial defect modes emerge in the system, which can be reflected from the wave propagation. Further studies can be carried out from the following three aspects. The first is the structure design by using graphene ribbons, which are easier to be fabricated in experiments than the planar graphene sheets. Secondly, whether the suitably-distributed loss can generate Jackiw-Rebbi modes is also an interesting topic. Thirdly, one may extend the study to higher dimensions, such as the two-dimensional graphene-coated nanowire arrays.  
Conceptualization, D.Z. and S.K.; methodology, C.X.; software, P.Z. and D.Z.; validation, S.K.; formal analysis, C.X.; investigation, S.K.; resources, P.Z.; data curation, C.X.; writing--original draft preparation, D.Z.; writing--review and editing, H.G., M.H. and S.K.; visualization, C.X. and P.Z.; supervision, S.K.; project administration, S.K.; funding acquisition, S.K. and D.Z. This research was funded by National Natural Science Foundation of China (NSFC) (11804259), Program for Distinguished Middle-aged and Young Innovative Research Team in Higher Education of Hubei, China (T201806), the Campus Science Foundation Research Project of Wuhan Institute of Technology (K201821), the Colleges and Universities of National Innovation and Entrepreneurship Training Plan (S201910927024), Science and Technology Plan Research Project of Hubei Education Department (B2019162). The authors declare no conflict of interest.
## 4 Discussion
### Accuracy and Reproducibility of Reverse-Bias Voltage  
The distance from the probe needle to the solar cell surface should be controlled for accurate and reproducible measurements. The non-contact distance is crucial to the measurement. With an increase in the distance, the detected voltage drops. The maximum distance can be estimated by electromagnetics calculation. First, the surface of the module's cover glass (3.3 mm thick) with a relative permittivity of 5.4, and lamination resin (EVA, 0.45 mm thick) with a relative permittivity of 2.6, gives a distance of 3.75 mm. The weighted harmonic average can calculate the measured voltage drop per unit length, and it is 4.78 (=((3.75 mm)/((3.3 mm)/5.4 + (0.45 mm)/2.6))). Namely, if 0.2 V of the error by the difference of the distance is accepted (average voltage was 0.58 V as shown in Figure 18), the acceptable distance from the surface of the glass is (0.2 V)/(0.58 V) \(\times\) (3.75 mm)/4.78 = 0.27 mm. In this experiment, we set the total distance as 4 mm (=3.75 mm + 0.27 mm = 4.02 mm).  
Another crucial parameter is the resolution of the voltage, namely sensitivity to the number of electrical carriers that move during the measurements. The instrument we used specifies 0.1 V of resolution. It is a considerably small value for the detection of fire risk. Since the typical hot-spot power dissipation is 0.53 W/cm\({}^{2}\) for the temperature of 220 \({}^{\circ}\)C, and for 250 cm\({}^{2}\) (156 mm \(\times\) 156 mm) solar cells with 9 A output current solar cells (40 mA/cm\({}^{2}\) current density), the corresponding voltage of the solar cell with fire risk will be 0.53 W/cm\({}^{2}\)/32 mA/cm\({}^{2}\) = 13.3 V, which is substantial enough to the specified resolution of 0.1 V. Also note that \(-\)13 V is known as the maximum reverse-bias voltage for Si solar cells that may lead to hot-spot problems [37; 38; 39].
## 2 Computational Method  
The ideal APoO3 (A = Be, Mg, Ca, Sr, Ba, and Ra) perovskite is a cubic structure with point group 221 (_Pm3m_ symmetric) as shown in Figure 1a. The Wyckoff position for the A atom is set at the corner (0, 0, 0), Po atom is set at the center (1/2, 1/2, 1/2), and oxygen atoms are face-centered (1/2, 1/2, 0), (1/2, 0, 1/2), and (0, 1/2, 1/2), respectively. The structural and electronic properties of APoO3 perovskites are calculated by using the density functional theory as implemented in the Vienna ab initio simulation package (VASP)[36; 37] with the generalized gradient approximation (GGA)[38; 39] for the exchange-correlation function. The \(\Gamma\)-centered Monkhorst-Pack scheme with a \(k\)-mesh of 12 \(\times\) 12 \(\times\) 12 is used for the Brillouin zone integration. The self-consistent total energy criterion is set to be 1.0 \(\times\) 10\({}^{-6}\) eV. In all cases, a plane wave basis set with cut-off energy of 500 eV was used. Equilibrium lattice constant, \(a\), for APoO3 perovskites is obtained from the structure optimization technique. This equilibrium lattice constant is also compared with the minimum in the calculated total energy as a function of the lattice constant, and the differences are negligible. For example, the equilibrium lattice constant for the BaPoO3 is 4.482 (A), and the k-mesh and cut-off energy are identical to the corresponding ones from the structure optimization technique.  
Theoretically, one way to classify the topological insulator's characteristic is the band inversion in momentum space. As suggested by [PERSON][40], the \(Z_{2}\) topological number is calculated by analyzing the parities of the occupied bulk band structure at some specified time reversal invariant momenta in the Brillouin zone. In three dimensions, there are eight time reversal invariant points which lead to four independent \(Z_{2}\) invariants (\(\
u_{0}\), \(\
u_{1}\), \(\
u_{2}\), and \(\
u_{3}\)). When \(\
u_{0}=1\), the material is a topological insulator. Once the material is predicted to be a TI, the direct results of the surface metallic bands by using a large supercell with vacuum are also important. We used a supercell that contained six formula units of APoOwith a vacuum of 20 (A), as shown in Figure 1b. In this calculation, the k-mesh is 6\(\times\)6\(\times\)1, and the energy cut-off is 500 eV.
## Materials and Methods
### Inversion Modeling Algorithm  
The regression relationship used in this study was established as an explicit parametric expression between the VIs and simulated FPAR from the PROSAIL model. The fitting functions could be linear or nonlinear (e.g., exponential or power function). Nonparametric algorithms, on the other hand, usually interpreted the strong nonlinearity of functional dependence between the biophysical/biochemical parameters and the spectral reflectance, such as ANN and SVR regression, which were more accurate and rational for the establishment of the inversion model. In this study, both the ANN and SVR algorithms were employed to establish a hybrid model with the selected VIs. For both the general and specific datasets, 80% of the samples were randomly extracted as the training (modeling) set, while the remaining samples were used for the model testing (validating) set. The  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vegetation Index** & **Formulation** & **References** \\ \hline Normalized difference vegetation & \(\frac{R_{\text{aug}}-R_{\text{aug}}}{R_{\text{aug}}+R_{\text{aug}}}\) & [57] \\ index (NDVI) & \(\frac{R_{\text{aug}}-R_{\text{aug}}}{R_{\text{aug}}+R_{\text{aug}}}\) & [58] \\ optimized soil-adjusted vegetation & \(\frac{R_{\text{aug}}-R_{\text{aug}}}{R_{\text{aug}}+R_{\text{aug}}+0.16}\) & [59] \\ index (OSAVI) & \(\frac{R_{\text{aug}}-R_{\text{aug}}}{R_{\text{aug}}+R_{\text{aug}}+0.16}\) & [49] \\ Optimized soil-adjusted vegetation & \(\frac{R_{\text{aug}}-R_{\text{aug}}}{R_{\text{aug}}+R_{\text{aug}}+R_{ \text{aug}}}\) & [60] \\ index red-edge (OSAVI\({}_{\text{red-edge}}\)) & \(\frac{R_{\text{aug}}-R_{\text{aug}}}{R_{\text{aug}}+R_{\text{aug}}}\) & [61] \\ Renormalized difference vegetation & \(\frac{R_{\text{aug}}-R_{\text{aug}}}{\sqrt{R_{\text{aug}}+R_{\text{aug}}}}\) & [62] \\ index red-edge (RDVI\({}_{\text{red-edge}}\)) & \(\frac{R_{\text{aug}}}{\sqrt{R_{\text{aug}}+R_{\text{aug}}}}\) & [63] \\ Ratio vegetation index (RVI) & \(\frac{R_{\text{aug}}}{R_{\text{aug}}}\) & [64] \\ Soil-adjusted vegetation index (SAVI) & \(\frac{1.5\times R_{\text{aug}}}{R_{\text{aug}}+R_{\text{aug}}+0.5}\) & [65] \\ Simple ratio (SR) & \(\frac{R_{\text{aug}}}{R_{\text{aug}}}\) & [65] \\ Simple ratio red-edge (SR\({}_{\text{red-edge}}\)) & \(\frac{R_{\text{aug}}}{R_{\text{aug}}}\) & [66] \\ Visible-band difference vegetation & \(\frac{2.7\times R_{\text{aug}}-R_{\text{aug}}-R_{\text{aug}}}{2\times R_{ \text{aug}}+R_{\text{aug}}+R_{\text{aug}}}\) & [67] \\ index (VDVI) & \(\frac{2.7\times R_{\text{aug}}-R_{\text{aug}}}{0.2\times R_{\text{aug}}+R_{ \text{aug}}}\) & [68] \\ Wide dynamic range vegetation index & \(\frac{0.2\times R_{\text{aug}}-R_{\text{aug}}}{0.2\times R_{\text{aug}}+R_{ \text{aug}}}\) & [68] \\ (WDRVI) & \(\frac{1.5\times R_{\text{aug}}}{R_{\text{aug}}+R_{\text{aug}}}\) & [68] \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._process of establishing and validating hybrid inversion models based on the PROSAIL model and UAV images is presented in Figure 4.
## 4 Effective Bending Stiffness
### Three-Layered Plate with Surface Stresses  
Let us consider the three-layered plate, which is symmetric in the thickness direction, \(N=3\). In this case, the core thickness is \(h_{c}=h_{2}\), whereas the faces have the same thickness \(h_{1}=h_{3}=h_{f}/2\), so we have \(h=h_{c}+h_{f}\). The effective bending stiffness of the three-layered plate without surface stresses, calculated within the first-order shear deformable plate model, is given by [33]  
\[D_{eff}^{0}=\frac{1}{2}\left\langle\frac{Ez^{2}}{1-\
u}\right\rangle+\frac{1}{ 2}\left\langle\frac{Ez^{2}}{1+\
u}\right\rangle,\qquad\left\langle\left(\dots \right)\right\rangle=\int\limits_{-h/2}^{h/2}\left(\dots\right)\mathrm{d}z, \tag{21}\]  
which results in  
\[D_{eff}^{0}=\frac{1}{12}\left[\frac{E_{f}(h^{3}-h_{c}^{3})}{1-\
u_{f}^{2}}+ \frac{E_{c}h_{c}^{3}}{1-\
u_{c}^{2}}\right], \tag{22}\]  
where \(E_{f}\) and \(\
u_{f}\), \(E_{c}\) and \(\
u_{c}\) are [PERSON]'s modulus and [PERSON]'s ratio of faces and core, respectively. Thus, we have  
\[\mathbb{E}_{B}=\frac{1}{2}D_{eff}^{0}\kappa^{2}.\]  
By considering surface stresses, we get  
\[\mathbb{E}_{S}= \mathcal{U}_{0}\Big{|}_{z=-h/2}+\mathcal{U}_{1}\Big{|}_{z=-h_{c}/ 2}+\mathcal{U}_{2}\Big{|}_{z=h_{c}/2}+\mathcal{U}_{3}\Big{|}_{z=h/2}\] \[= \frac{1}{2}\left[(2\mu_{0}^{S}+\lambda_{0}^{S})\frac{h^{2}}{4}+2 \eta_{0}+\chi_{0}\right]\kappa^{2}\] \[+ \frac{1}{2}\left[(2\mu_{1}^{S}+\lambda_{1}^{S})\frac{h_{c}^{2}}{ 4}+2\eta_{1}+\chi_{1}\right]\kappa^{2}\] \[+ \frac{1}{2}\left[(2\mu_{2}^{S}+\lambda_{2}^{S})\frac{h_{c}^{2}}{ 4}+2\eta_{2}+\chi_{2}\right]\kappa^{2}\] \[+ \frac{1}{2}\left[(2\mu_{3}^{S}+\lambda_{3}^{S})\frac{h^{2}}{4}+2 \eta_{3}+\chi_{3}\right]\kappa^{2}. \tag{23}\]  
Figure 2: Dimensionless bending stiffness \(\overline{D}=D_{\text{eff}}/D\) vs. thickness \(h\) (solid red curve). The dashed blue curve corresponds to the Gurtin–Murdoch model; the horizonal dashed black line describes the classic bending stiffness. Finally, the vertical dashed green line marks the characteristic length \(l\) defined as \(l=(\lambda^{S}+2\mu)h^{2}/D\).  
As a result, the effective bending stiffness becomes  
\[D_{eff}= D_{eff}^{0}+D_{eff}^{GM}+D_{eff}^{SO}, \tag{24}\] \[D_{eff}^{GM}= (2\mu_{0}^{S}+\lambda_{0}^{S})\frac{h^{2}}{4}+(2\mu_{1}^{S}+\lambda _{1}^{S})\frac{h_{c}^{2}}{4}+(2\mu_{2}^{S}+\lambda_{2}^{S})\frac{h_{c}^{2}}{4}+ (2\mu_{3}^{S}+\lambda_{3}^{S})\frac{h^{2}}{4}\,,\] \[D_{eff}^{SO}= \sum\limits_{i=0}^{3}\left(2\eta_{i}+\chi_{i}\right).\]  
where \(D_{eff}^{GM}\) is the contribution to the effective bending stiffness in the framework of the Gurtin-Murdoch model and \(D_{eff}^{SO}\) is the additional term related to the surface bending stiffness in the framework of the Steigmann-Ogden model.