esa-sceva/satcom-chunk-collection huggingface dataset:
# Voltage Tunable Spoof Surface Plasmon Polariton Waveguide Loaded with Ferroelectric Resonators
###### Abstract  
A real-time tunable planar plasmonic waveguide based on a voltage-adjustable ferroelectric resonator is designed and investigated. The laminated ferroelectric compound resonator is composed of a ferroelectric Ba\({}_{0.85}\)Ca\({}_{0.15}\)Zr\({}_{0.9}\)Ti\({}_{0.1}\)O\({}_{3}\) (BCZT) layer, a PCB layer, as well as a localized spoof plasmonic metal layer, where the BCZT layer is beneficial for enhancing the voltage tunability in the spoof surface plasmon polariton (SSPP) waveguide. The simulated results show that the tuning range of the notch in the transmission curve, generated by the coupling between the ferroelectric compound resonator and the plasmonic waveguide, can achieve a variation of up to 8.8% thanks to the large tunability value in the BCZT ferroelectric layer. In addition, the notches consist of Fano resonant frequencies, the generation mechanism of which is elaborately discussed in terms of the temporal coupled mode theory.  
ferroelectric compound resonator; real-time; voltage tunability; dielectric properties; SSPP waveguide 2025
# Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service
## 3 Optimization Model
### Graph Instance Definition  
The street map can be modeled as an undirected weighted graph to represent users' ability to walk on the streets in both directions where they need to pick up or leave the shared vehicle (see Figure 1). The street-level graph is modeled as follows:  
\[G=(V,E,P,W).\]  
The set of nodes \(V\) is composed of two subsets: \(V=S\cup B\), where \(S\) is the subset of street nodes (i.e., nodes on roads and streets), while subset \(B\) contains buildings. Both \(S\) and \(B\) are of type node, hence they are naturally members of vertices in \(G\).  
Buildings contain users. A weight \(p_{i}\in P\) associated with each node \(b_{i}\in B\) corresponds to the estimation of the number of people living in this building. The weight for each node in \(S\) is set to zero, making the assumption that target users are only located in buildings. This is similarly to the study taken by [PERSON] and [PERSON] [53]).  
The set of edges \(E\) consists of two subsets: \(E=R\cup L\), where \(R\) is a set of streets/roads, and \(L\) is a set of links connecting residential buildings to nearby streets.  
Each edge \((u,v)\in R\) is valuated by a weight, \(w_{i}\in W\), representing the walking distance from \(u\) to \(v\) where \(u,v\in S\).  
Each edge \((u,b)\in L\) where \(u\in S\) and \(b\in B\) is valuated by 0. In other words we consider the distance between the building and its nearest adjacent street is considered negligible. This process is called \"snapping\" and is common in every routing service where the starting point is first projected on a road before starting to build a route [54].
## 2 Related Work
### Overview of Existing Defense Strategies against Stealthy False Data Injection Attacks (FDIAs)  
Existing defense systems to deal with stealthy false data injection attacks (FDIAs) can be grouped into two broad categories: anomaly-based intrusion detection systems (AIDS) and provenance-based trust management systems (TMS) [9; 13; 22; 31; 37; 38].  
The goal of AIDS is to detect malicious nodes and exclude their readings from the aggregation of sensor measurements while estimating the most trustworthy aggregate measurement at a given time step. When AIDS is used to deal with FDIA, the simple average is usually used to aggregate the measurements of the normal sensors. Existing AIDS can be broadly split into two categories: confidence interval-based AIDS [9; 22; 37] and clustering-based AIDS [38]. Both types of AIDS are based on the hard clustering concept that classifies a sensor node as either malicious (1) or normal (0). Confidence interval-based AIDS divides the search space into only two clusters (one for each class: malicious and normal). On the contrary, clustering-based AIDS allow multiple clusters for each class; this type of AIDS performs better on search space with local patterns.  
AIDS uses unsupervised hard clustering algorithms to perform clustering of the search space to classify the sensor nodes as malicious and normal. Different search spaces have been explored for detecting stealthy malicious nodes, including measurement space, forecasting residual space, Spatio-temporal correlation space, the skewness space of residuals in the forecasting demand model, and reputation space [9; 22; 37; 38]. The major limitation of AIDS is that they use a set of arbitrary rules to cluster the search space and to label the clusters as malicious and normal. Past studies suggest that AIDS suffer from low malicious node detection accuracy and a high false positive rate (FPR) [9].  
The goal of TMS, in contrast to AIDS, is not to classify the sensor nodes as malicious and normal [13; 31]. Instead, they carry out unsupervised fuzzy clustering in the sensor measurement space to assign a reputation score to each sensor to control its influence on the sensor measurement aggregation process. During the aggregation process, TMS assigns a trustworthiness score to each measurement at time step \(t\) based on the data similarity and provenance similarity among measurements, and the trustworthiness of its data provenance, which is usually set to the reputation score of the least reputed node in the provenance. TMS then determines the most trustworthy measurement at time step \(t\) using the weighted average method. Most of the existing TMS use linear models to update the reputation scores of nodes based on the trustworthiness scores of their measurements received at time step \(t\). However, the linear models may not be able to accurately estimate the reputation scores of nodes in the face of stealthy FDIA.
# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
## 6 Discrete Time Control Method
The curve parameters which localize \(\mathbf{P}_{\mathrm{back}}(t)\) can be achieved by Equation (26). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**v** & **u** & **I(u,v)** & **L(u,v)** \\ \hline
**0** & **0** & **0** & **0** \\
0 & 0.01 & 0.01 & 0.01 \\
0 & 0.02 & 2.8427 & 2.8427 \\
0 & 0.03 & 5.6792 & 5.6792 \\
0 & 0.04 & 8.5367 & 8.5367 \\
0 & 0.05 & 11.3858 & 11.3858 \. &. &. &. \\
3 & 0 & 0 & 687.7763 \\
3 & 0.01 & 0.001 & 687.7773 \\
3 & 0.02 & 1.8897 & 689.6662 \\
3 & 0.03 & 3.7465 & 691.5228 \. &. &. &. \\
3 & 0.98 & 115.2653 & 803.0417 \\
3 & 0.99 & 116.2936 & 804.07 \\
3 & 1 & 117.343 & 805.1194 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data for converting between arc length and curve parameter. \[ \begin{split} u_{back}(t)&=u(L_{R}(t)+\Delta L_{back})\\ v_{back}(t)&=v(L_{R}(t)+\Delta L_{back})\end{split}  \tag{26}\]  
If \(i_{\text{back}}(t)\) and \(u_{\text{back}}(t)\) do not exceed maximum parameters of PCBC, \(\mathbf{P}_{back}(t)\) can be obtained by equation below:  
\[\mathbf{P}_{back}(t)=\mathbf{B}(u_{back}(t),v_{back}(t)) \tag{27}\]  
Otherwise:  
\[\mathbf{P}_{back}(t)=\mathbf{P}(n-1) \tag{28}\]  
where \(\mathbf{P}(n-1)\)is the given target position of the robot. Figure 9 illustrates the case in that the robot slip out of trajectory and attempting to get back the initial trajectory by employing the re-path algorithm. Figure 8: Re-path algorithm flow chart. Figure 7: Cumulative errors of actual robot position respect to reference trajectory.
## 3 Results and Discussion
### Headspace Volatile Analysis Using SPME-GC-MS of Mallab Samples (Roasted/Unroasted)
#### 3.4.5 Furans  
Very low amounts of furan and its derivatives were detected, found at the highest levels in WG (5.41%), followed by 2.4 and 3.8% in WE and RE samples, respectively. As expected, furan increased up to 6.37% upon roasting of RE samples, likely resulting from sugar oxidation in the seeds [45]. Furthermore, 2-Butylfuran (peak 95) and 2-pentylfuran (peak 96) were the most abundant furans in WG at 1.5 and 2.6%, respectively. Furan and its derivatives, i.e., 2-alkylfurans, 2-acetylfuran, furufural and furufryl alcohol, are produced via Maillard reaction in many heat-processed foods and beverages to contribute to their sensory properties. Furans can also serve as indicators of time-temperature effects during the production and storage of food items. In addition, 2-Akylfurans such as 2-butyl (peak 95) and 2-pentylfurans (peak 96) are mainly formed from lipid degradation [46], and some impart a mild, sweet and fruity taste, including 2-butylfuran, a constituent of many plant species and some cooked foods [47].
# Chloroplasts at the Crossroad of Photosynthesis, Pathogen Infection and Plant Defense
## 2 The Chloroplast is a Major Synthesis Site for Many Plant Hormones
### The Chloroplast is a Major Site of ABA Biosynthesis
ABA treatment induced \(\cdot\)NO production in guard cells, mediated by nitrate reductase and a \(\cdot\)NO synthase-like enzyme [76; 77; 78]. Furthermore, ABA treatment caused sustainable increases of calcium ions (Ca\({}^{2+}\)) in _Commelina communis_ and _Arabidopsis_ guard cells [80]. The sequential H\({}_{2}\)O\({}_{2}\), \(\cdot\)NO, and Ca\({}^{2+}\) spikes are required for ABA-induced reduction of guard cell turgor and subsequent stomatal closure as well as ABA-induced gene expression in the guard cell nucleus [80,81]. ABA suppresses the post-invasion PTI basal response [82]. ABA-hypersensitive _Arabidopsis_ mutants displayed increased susceptibility to _P. syringae_ while _Arabidopsis_ mutants with defective ABA synthesis or perception showed increased resistance [83,84]. In addition, exogenous ABA application results in increased susceptibility of many plant species to bacterial and fungal pathogens [85,86, 87, 88, 89, 90]. For example, ABA pretreatment caused reduced callose deposition and enhanced multiplication of _P. syringae_ pv. _tomato_ (_Pst_) wild-type strain DC3000 in _Arabidopsis_[83]. ABA influences the production and signaling of other hormones (Figure 1). The JA signaling pathway contains two distinct and antagonistic branches: the ERF (ethylene response factor)-branch is responsible for activating pathogen-responsive genes and repressing wounding-responsive genes and is co-regulated by ET; the MYC (myelocytomatosis) branch is responsible for activating wounding-responsive genes and repressing pathogen-responsive genes, which is co-regulated by ABA [91,92]. _Arabidopsis_ plants constitutively expressing MYC2, a key transcriptional activator of JA responses, were hypersensitive to both JA and ABA [93]. Several studies showed that ABA has synergistic impacts on the MYC branch and antagonistic impacts on the ERF branch [93, 94, 95]. ABA has negative effects on SA signaling. ABA was found to locally down-regulate SA biosynthesis by transcriptional regulation of isochorismate synthase 1 (ICSI, i.e., SA-induction-deficient 2 [SID2]), an SA biosynthetic enzyme, which resulted in suppression of SA-induced defenses [96]. ABA treatment also suppressed SAR development via inhibiting SA signaling and this process appeared to be independent of JA/ET signaling [97]. Both endogenous ABA levels and pathogen effector-induced increases of ABA are involved in the antagonism between ABA and SA [96]. The effector-mediated manipulation of ABA biosynthesis and signaling is a key virulence mechanism for pathogens [83]. JA and SA signaling has reciprocal antagonistic interactions [98, 99, 100]. Therefore, ABA may indirectly antagonize SA signaling via its activation effect on JA signaling [101].
## 3 Results and Discussion
### RF Treatment of Nutrient Broths without Bacteria
#### 3.2.2 Frequency  
One of the characteristics of free-running oscillation is the reduction of the initial frequency (i.e., without sample load) caused by the absorption of RF energy by the load. Apart from the frequency, the absorption of the RF energy by the sample is mainly affected by the temperature-dependent dielectric properties of that sample, i.e., the dielectric constant \(\varepsilon\)' and the dielectric loss factor \(\varepsilon\)'' [20]. Therefore, it was important to validate that the RF frequency remained within the predefined frequency range limits of 27.12 \(\pm\) 0.50 MHz during the entire treatment of the nutrient broths for which the setup was calibrated.  
Figure 4 shows the RF frequency evolution in TSB and BHI. For both media, it was observed that the initial RF frequency varied only slightly among different replicates, ranging from 26.92 to 26.95 MHz and from 26.87 to 26.96 MHz, for TSB and BHI, respectively. As such, initial frequencies ranged in a bandwidth of just 0.09 MHz. As the treatments progressed, the frequency decreased with increasing sample temperature, approximately reaching a constant value after 15 min. As can be seen in Figure 3, this treatment time corresponded to temperatures of 71-76 \({}^{\circ}\)C, indicating that the influence of temperature on the dielectric properties started to become less important at these temperatures. The constant frequency was on average higher in TSB (26.86 \(\pm\) 0.05 MHz) than in BHI (26.82 \(\pm\) 0.05 MHz), but differences were not significant. Overall, the RF frequency remained within the predefined range of 27.12 \(\pm\) 0.50 MHz during the entire treatment for both broths. Nevertheless, more precise fine-tuning is necessary in future studies to achieve the more narrow frequency range of 27.12 \(\pm\) 0.16 MHz designated for ISM applications. This fine-tuning is expected to be feasible, since the entire set of treatments ranged in a bandwidth of 0.19 MHz, which is smaller than the ISM band of 0.32 MHz. Therefore, the initial calibration needs to improve with the specific media or food product and sample size in mind. Parameters to adapt in order to fine-tune the frequency include the voltage, the current, and the position of the secondary coil.
# Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs
## 1 Introduction
However, a great amount of data transmission is produced when the clusters are near the base station. This leads to the premature death of the cluster head nodes. Therefore, the algorithm cannot fundamentally solve the \"hot spots\". It is obvious that different swarm intelligence algorithms have their unique advantages [12]. Compared with traditional intelligent algorithms, the later developed algorithms perform better. In general, selecting and improving a new swarm intelligence algorithm has practical research significance. Our goal is to effectively solve the problem of clustering routing quickly. To improve the utilization of network energy, this paper proposes dynamic layered dual-cluster heads routing algorithm based on Krill Herd (KH) optimization in UWSNs. When the distance between sink nodes is minimal, a huge amount of energy is consumed. In this case, the underwater network sets the non-uniform clusters according to the size of the clusters and the distance between nodes and the sink node. It can reduce the premature death of the upper nodes. The dynamic hierarchical mechanism is introduced. It can reduce the cluster head nodes that are repeatedly selected by the same node. Meanwhile, the network energy consumption is balanced. Finally, the core of the KH optimization is used to choose the master cluster head nodes and vice cluster head nodes. It solves the problem that the cluster head nodes are under heavy load, and effectively prolongs the survival time of cluster nodes.
# Research on a Low Melting Point Al-Si-Cu (Ni) Filler Metal for 6063 Aluminum Alloy Brazing
## 3 Results and Discussion
### Bazeability of 6063 Aluminum Alloy with Al-Si-Cu (Ni) Filler Metal
Therefore, the microhardness of the brazing seam appears to decrease from the brazed seam center area to the base metal. Figure 13 shows the shear fracture morphology of 6063 aluminum alloy joints brazed with Al-Si-20 Cu and Al-6.5 Si-20 Cu-(1.0, 1.5, 2.0) Ni filler metals. The fracture morphology of the brazed joints indicates the characteristics of brittle fracture. Table 7 lists the results of the EDS analysis of the typical phases of the fracture morphology of Al-Si-Cu (Ni) brazing joints. The results show that A, B, and C were the primary Si phase, the \(\theta\)(Al\({}_{2}\)Cu) phase, and the \(\theta\)(Al\({}_{2}\)Cu) + \(\delta\)(Al\({}_{3}\)Ni\({}_{2}\)) phase, respectively. In the process of brazing, elements in the matrix and liquid filler metal inter-diffused, so that the filler metal and the base metal had a better metallurgical bond. A small amount of Mg was detected in the joint fracture, so it can be judged that the fracture location of the brazed joint was roughly in the interface area of the brazed seam. Based on the analysis of the microstructure, shear strength, and shear fracture morphology of the joint, when Al-6.5 Si-20 Cu brazed the 6063 aluminum alloy, there was a large \(\theta\)(Al\({}_{2}\)Cu) brittle phase in the brazing seam, but the large \(\theta\)(Al\({}_{2}\)Cu) phase had poor compatibility with the substrate deformation, the strain was concentrated at the phase  
Figure 11: Element mapping of the 6063 aluminum alloy joint brazed with Al-6.5 Si-20 Cu-1.5 Ni filler metal at 570 \({}^{\circ}\)C for 10 min. Figure 12: (**a**) Shear strength of 6063 aluminum alloy joints after brazing with Al-Si-Cu (Ni) filler metals at 570 \({}^{\circ}\)C for 10 min. (**b**) Microhardness of the brazed joints. interface, the crack initiation near the \(\theta\)(Al\({}_{2}\)Cu) phase was accelerated, and the fracture of the welded joint was accelerated, so the shear fracture of the brazed joint was a smooth and typical brittle fracture. When 6063 aluminum alloy was brazed with the Al-6.5 Si-20 Cu-Ni filler metal, the existence of Ni not only made the brittle phase of the Al\({}_{2}\)Cu finer and more dispersed but also improved the deformation coordination of the \(\theta\)(Al\({}_{2}\)Cu) phase and matrix and the shear strength of the brazed joint, reduced the strain concentration degree, and presented a mixed fracture mode.
# Physics-Informed Graph Neural Networks for Attack Path Prediction
## 2 Materials and Methods
### Path Prediction Training
#### 2.5.2 Physics-Inspired Loss  
The physics-informed loss ensures that the predicted \(\hat{Y}\) adjacency matrix adheres to domain knowledge. In our case, it implies that the graph described in the predicted matrix **(1)** contains no branches, **(2)** consists of a single connected path component, with all other nodes remaining isolated, **(3)** contains no cycles. If the network's prediction violates these constraints, \(\mathcal{L}_{\text{pinn}}\) and thus \(\mathcal{L}(\cdot)\) increase. Formally, given \(\alpha,\beta,\zeta\in[0,1]\) the physics-inspired loss is written as follows:  
\[\mathcal{L}_{\text{pinn}}=\alpha\cdot\mathcal{L}_{\text{degree}}+\beta\cdot \mathcal{L}_{\text{connectivity}}+\zeta\cdot\mathcal{L}_{\text{cycle}} \tag{7}\]
# A Probable Approach to Reliability Assessment of Reinforced Plates
## 5 Mean Value and Dispersion of Failure Loading  
Let us substitute expressions for the distribution function \(F_{1}(P,\eta)\) in Formulas (24) and (25) and introduce dimensionless loading \(p=P\sqrt{\hbar}/K_{0}\). We will obtain statistical characteristics of strength for the following individual cases:  
under biaxial symmetric tension  
\[\langle p\rangle=\int\limits_{0}^{\infty}\left(1-e^{-\left(\frac{p(\omega-1)( \lambda+\omega)}{4\sqrt{2}\omega}\right)^{-2}\right)}^{N}dp; \tag{30}\]  
\[D(p)=2\int\limits_{0}^{\infty}\left(1-e^{-\left(\frac{p(\omega-1)(\lambda+ \omega)}{4\sqrt{2}\omega}\right)^{-2}\right)}^{N}pdp-\langle p\rangle^{2}; \tag{31}\]  
under uniaxial tension  
\[\begin{array}{l}\langle p\rangle=\int\limits_{0}^{\infty}\left(1-\frac{2}{ \pi}\left\{\begin{array}{c}0.5\arccos(0.5(1-\varkappa))\\ \int\limits_{0}^{0.5\arccos(0.5(1-\varkappa))}\end{array}e^{-\frac{(\sigma_{1} (0,\varkappa)+\sigma_{2}(0,\varkappa)\cos 2\alpha)^{-2}}{p^{2}}}d\alpha+ \\ +\int\limits_{0.5\arccos(0.5(1-\varkappa))}^{\pi/2}e^{-\frac{(\sigma_{2}( \varkappa)+\sigma_{2}(\varkappa)+\sigma_{2}(\varkappa)\cos 2\alpha)^{-2}}{p^{2}}}d \alpha\right\}\right)^{N}dp\\ \\ D(p)=2\int\limits_{0}^{\infty}\left(1-\frac{2}{\pi}\left\{\begin{array}{c}0.5\arccos(0.5(1-\varkappa))\\ \int\limits_{0}^{0.5\arccos(0.5(1-\varkappa))}\end{array}e^{-\frac{(\sigma_{1} (0,\varkappa)+\sigma_{2}(0,\varkappa)\cos 2\alpha)^{-2}}{p^{2}}}d\alpha+ \\ +\int\limits_{0.5\arccos(0.5(1-\varkappa))}^{\pi/2}e^{-\frac{(\sigma_{1}( \varkappa)+\sigma_{2}(\varkappa)+\sigma_{2}(\varkappa)\cos 2\alpha)^{-2}}{p^{2}}}d \alpha\right\}\right)^{N}pdp-\langle p\rangle^{2}\end{array} \tag{33}\]  
under biaxial symmetric compression  
\[\langle p\rangle=\int\limits_{0}^{\infty}\left(1-e^{-\left(\frac{p(\omega-1)} {6\alpha}\sqrt{\frac{(3\varkappa-1)^{3}}{\ln(2\omega+1)}}\right)^{-2}}\right) ^{N}dp \tag{34}\]  
\[D(p)=2\int\limits_{0}^{\infty}\left(1-e^{-\left(\frac{p(\omega-1)}{6\alpha} \sqrt{\frac{(3\varkappa-1)^{3}}{\ln(2\omega+1)}}\right)^{-2}}\right)^{N}pdp- \langle p\rangle^{2}. \tag{35}\]  
Figure 4: Probability of failure under biaxial symmetrical compression.  
In Figure 5, the effect of the applied loading ratio, the number of inclusions, and the Poisson's ratio of the matrix on the mean values of failure loading calculated according to Formulas (30), (32), and (34) are shown.  
Figure 6 shows the dependence of the failure loading dispersion calculated according to Formulas (31), (33), and (35) on the applied loading ratio, the number of inclusions, and the matrix Poisson's ratio.  
In article [26], a probabilistic description of experimental statistical regularities and strength characteristics of stochastically defective isotropic materials was carried out. It is shown that the method considered in this article allows the selection of parameters to match the theory well with the experimental data. The considered method of researching composite material failure is consistent with the results of the work [26].