esa-sceva/satcom-chunk-collection huggingface dataset:
# Contact Analysis and Friction Prediction of Non-Gaussian Random Surfaces
## 4 Results and Discussion
### Effects of Applied Load  
In this section, a set of applied loads from \(0.5\times 10^{5}\) N/m to \(8\times 10^{5}\)N/m are conducted to contact analysis for surveying the effects of applied load. A non-Gaussian surface of \(R_{q}=0.3\) um, \(K_{u}=4\) and \(S_{k}=1\), as shown in Figure 13, is adopted. As presented in Figure 14, contact area increases with increasing applied load. However, contact pressure displays different trends. For a relatively small load, contact pressure increases with the increase of contact area. A further increase in applied load enhances the Hertzian width, resulting in more asperities to sustain the applied load. This causes the reduction of contact pressure and average contact pressure, as indicated in Figure 15. Thus, the plastic ratio presents an obvious increasing trend for a relatively small load, and then gets saturated gradually with the increase of applied load. A friction coefficient shows a similar trend with the plastic ratio. The increasing trend of a friction coefficient in a relatively small load results from the increase in contact area and pressure. Finally, the friction coefficient becomes constant with an increase in applied load.  
Figure 10: The variation of average contact pressure with the roughness amplitude.  
Figure 11: Rough surface with different skewness.  
Figure 12: Effect of kurtosis on friction coefficient, plastic ratio, and contact area ratio.  
Figure 13: Rough surface with \(R_{q}=0.3\) μm, \(K_{u}=4\) and \(S_{k}=1\).
## 2 Experimental Setup
### Experimental Parameters and Procedures  
The filling density of NSSs and the initial pressure in CVCB are the important factors affecting the dual effect of NSSs on the propane-air explosion in confined space. As shown in Figure 3, three filling methods were selected: (1) The NSSs with the filling density of 21.9 kg/m\({}^{3}\) are selected for the experiment as a small amount of NSSs in CVCB (shown in Figure 3a). (2) The NSSs with the filling density of 38.7 kg/m\({}^{3}\) are selected for the experiment as a moderate amount of NSSs in CVCB (shown in Figure 3b). (3) The NSSs with the filling density of 45.1 kg/m\({}^{3}\) are selected for the experiment, that is, the NSSs are fully filled inside the CVCB (shown in Figure 3c). Therefore, the effects of three different filling densities (21.9 kg/m\({}^{3}\), 38.7 kg/m\({}^{3}\) and 45.1 kg/m\({}^{3}\)) on the suppression of propane explosion were investigated in this study. Based on the Chinese standard AQ3001-2005, calculation method of the filling density is shown in Equation (1):  
\[\rho=\frac{m_{0}}{V}\times N \tag{1}\]  
where \(m_{0}\) is the mass of a single NSSs. \(N\) is the number of NSSs filled. \(V\) is volume of confined space.  
After a gas storage tank leaks, combustible gas with different volume content were formed near the leakage area, and explosions are likely to occur [29]. According to the inquiry [30], the internal pressure of the gas storage tank is 0.2-0.4 MPa when it leaks. The bomb is heated evenly to temperature 343 K. The combustion products from condensing into droplets was prevented. Experimental parameters are shown in Table 2.  
Before the experiment, the NSSs are placed in the combustion bomb with tweezers in order of filling from left to right and from bottom to top. Figure 3 shows the arrangement modes of NSS with different filling densities in CVCB. To reduce the accidental error from various arrangements of NSSs caused by duplicated filling, NSSs are reinstall after completing the different initial pressure experiments at same NSSs filling density. Under different initial conditions, each group of the experiment is repeated at least three times to confirm the reproducibility. Test error of each group is within 5% to ensure the accuracy of test data. Initially, the combustion chamber of CVCB is heated by the heating system to  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Initial Condition** & **Value** \\ \hline Initial temperature T\({}_{0}\) /K & 343 \(\pm\) 2 \\ Initial pressure P\({}_{0}\) /MPa & 0.2, 0.3, 0.4 \\ Filling density \(\rho\)/kg/m\({}^{3}\) & 21.9, 38.7, 45.1 \\ The equivalence ratio & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental initial parameters.  
Figure 3: Arrangement modes of NSS with different filling densities in CVCB.  
specified temperature of 343 K. Stoichiometric propane/air mixture is obtained according to [PERSON]'s partial pressure law. The propane/air mixture is premixed for 5 min t to make the mixture homogeneous. To avoid influence of residual gases, the combustion chamber is flushed by air at least two times after a test.
## 3 Methodology
### Model Configuration  
In addition to the novel application of the Dynamic UNet in this field, several architectural configurations were added to the models. These model configurations further improve upon the current state of the art. The configurations introduced to the model were (Figure 4).  
* ResNet encoders;
* A self-attention layer as part of the upsampling path of the model;
* A blurring algorithm to avoid checkerboard artefacts;
* A bottleneck connection from input to output.  
Figure 4: Experimental layout. Purple represents the location of the self-attention layer (here represented in a simplified view of a ResNet18). Red represents the skip connection from the first to last layer. Green is the bottlenecked connection.
## 3 Implementation and Discussion
### System Integration with IoT  
Table 2 illustrates the average time for an MQTT message to reach the IoT platform. The JSON string is used in the speed test. It can be noted that the QoS0 level of the MQTT protocol needed the least time to transfer the data to the cloud; therefore, it was chosen for the application. On the other hand, the QoS2 level is needed the most since increasing the number of iterations will increase the time needed for data transmission.  
Figure 12 shows the visualization of some measured parameters in IBM Watson IoT. Once the data reach the IoT platform, services such as Watson Analytics or IBM Data Science Experience can be used. Storage of data is also possible with the Cloudant NoSQL database. IBM Watson also allows the binding of other user applications with the IoT platform.
# Acoustic and Dynamic Response of Unbaffled Plates of Arbitrary Shape
## 3 Results
### Effect of Fluid on Dynamic Plate Characteristics  
Next, the effect of the surrounding fluid on the dynamic characteristics of the plate is investigated. The cases considered are a rectangular simply supported plate, and trape  
Figure 5: Pressure jump across a rigid plate at a point. zoidal simply supported and cantilever plates. Table 1 lists the first four values of natural frequency for the cases of no fluid, incompressible fluid, and compressible fluid. All plates were of sandwich type, and the acoustic medium surrounding the plates was air. The material properties and thickness of these plates were as follows: E = 4.41 \(\times\) 10\({}^{9}\) N/m\({}^{2}\), \(\upsilon\) = 0.29, h = 15.78 \(\times\) 10\({}^{-3}\) m, and \(\rho_{\rm M}\) = 130.7 kg/m\({}^{3}\). This type of structure is used in the aerospace industry because it is light and stiff. For such structures, the fluid effects are very significant. The dimensions of the rectangular plate are 0.6 m \(\times\) 0.386 m, and the trapezoidal plate has a base of 1 m, a short base of 0.6 m, and a height of 1 m.  
As expected, it can be observed that the natural frequencies of the plates decrease from the vacuum values. With respect to the support condition, it is noticed that this effect is more significant for the simply supported plate than for the cantilever plate, which signifies that the added mass value of the first plate is higher than that of the second plate. The reduction in natural frequency decreases with increasing modal order. [PERSON] obtained similar findings [19]. With respect to the plate shape, a stronger reduction in the natural frequencies is observed for the trapezoidal plate than for the rectangular plate for the same boundary conditions. In Figures 6-8, the real and imaginary parts of the added mass matrix for the three plates versus the nondimensional frequency are presented. With these figures, we want to observe how the real part affects the natural frequencies and how the imaginary part does it to the damping coefficient, for the plates investigated. For all cases, the value of the added mass increases from the incompressible case (\(k\) = 0) until it reaches a maximum, and then decreases toward zero. If the nondimensional frequency of the mode in vacuum is located before the maximum, the natural frequencies are reduced owing to the fluid. However, the frequencies tend to reach the vacuum value if the nondimensional frequency is located after the maximum. With respect to the imaginary part of the added mass matrix, it is observed that for any reduced frequency these terms are always positive, showing that the fluid influence for these cases is always stabilizing. In relation to the real part of the added mass, there is a minimum value and then tend towards the value of zero,
# Natural Compounds in the Battle against Microorganisms--Linalool
## 3 Antimicrobial Activity
### Mechanism of the Antibacterial Action of Linalool
#### 3.1.1 Alteration of Amino Acid Metabolism  
Bacterial cells exposed to stress from the external environment usually defend themselves by activating some pathways of amino acid biosynthesis (Table 3). Such behavior enables them to survive. [PERSON] et al. [65] showed that in the presence of linalool in a bacterial cell, there is a decrease in the cellular activity of alkaline phosphatase (AKP), which in turn leads to the destruction of the integrity of the cell wall.  
Moreover, it has been proven that this monoterpene contributes to an increase in the concentration of histidine and methionine. These amino acids are involved in the structural phenotype of the bacterial biofilm. Thus, linalool directly contributes to the damage of the biofilm structure, i.e., the physical barrier that effectively protects microorganisms against the action of antibiotics, antiseptics and the host's immune system [58].  
Linalool also contributes to disturbing the pH homeostasis of the bacterial cell by increasing the concentration of lysine while reducing the concentration of L-aspartate. Both of these amino acids, apart from L-glutamate, L-glutamine and L-arginine, play a huge role in the homeostatic mechanisms that determine the proper functioning of the body in prokaryotes [65].
## 7 Design Example and Simulation Results
### Circuital Simulations
Figure 10a shows the I-V and P-V curves of the BP585 PV panel adopted for this application example, where the MPP conditions are the following considering \(T=25\)\({}^{\circ}\)C:  
* \(S=1000\) W/m\({}^{2}\): the MPP voltage is 18.4 V, the MPP current is 4.63 A, and the MPP power is 85 W.
* \(S=600\) W/m\({}^{2}\): the MPP voltage is 17.7 V, the MPP current is 2.77 A, and the MPP power is 49 W.
* \(S=200\) W/m\({}^{2}\): the MPP voltage is 16.2 V, the MPP current is 0.92 A, and the MPP power is 15 W.  
A correct operation of the PV system under the previous irradiance values must reach those MPP conditions. Therefore, the system was tested under variable irradiance conditions to confirm the correct operation of the SMC and P&O algorithm. In this way, Figure 10b shows the simulation of the PV system operating at 1000 W/m\({}^{2}\) up to 13 ms, and at that time, the irradiance changes suddenly to 200 W/m\({}^{2}\). Moreover, this simulation also considers a 35 % peak-to-peak perturbation in the output voltage \(v_{o}\), which is caused by the inverter operation at the second stage of a PV microinverter. The simulation confirms the correct operation of both the P&O algorithm and SMC since the PV power \(p_{pv}\) is always at the MPP voltage for both irradiance conditions, except for the small time-interval between 13 ms and 18 ms, which is the time (5 ms) required by the P&O to track the new MPP condition. After the new MPP condition is reached, the PV voltage describes the stable three-point behavior of the P&O, which confirms the stable operation of the PV system. Figure 10b also reports the duty cycle of the flyback converter, which is never saturated, and thus, the PV system is stable. This is expected since the simulations in Figure 9 confirm that the reachability conditions are fulfilled; hence, the equivalent control condition \(0<u_{eq}=d<1\) is also fulfilled. Moreover, the duty cycle waveform confirms the global stability of the PV system even with perturbations on both the irradiance and the output voltage. Finally, the simulation also reports the switching frequency of the flyback converter, which is always below the designed maximum value (95 kHz). In conclusion, the circuital simulations presented in this section confirm the stability of the SMC under realistic conditions, which ensures that the P&O algorithm is able to track the optimal operating conditions of the PV panel (reference variations) even under varying irradiance levels (line variations) and with voltage oscillations at the output port (load various), which are frequently caused by the inverter connection inside the microinverter.
## 6 Conclusions and Future Research  
Focusing on real-world e-commerce logistics applications, this paper is motivated by the need to solve multi-echelon distribution network design problems with intra-echelon connections, where it is possible to select paths containing \"shortcuts\" for minimizing the overall generalized cost of travel. To capture the additional complexity of multi-echelon network configuration with intra-echelon connections, we propose a QSAP-C model with two types of binary variables for deciding the allocation of warehouses to DCs and the allocation of DCs to delivery stations. Furthermore, this paper designs a matheuristicapproximation method to efficiently obtain the GLB-oriented lower bound estimate. The lower bound estimation procedure is an extension and adaptation to the general assignment case of GLB for solving the standard QAP. The proposed solution approach reformulates the original problem as two-stage decisions and solves them sequentially to obtain the estimated cost. To improve the quality of solutions, both exact and heuristic algorithms with the embedded GLB-oriented lower bound estimator are proposed.  
By using the parametric sensitivity analysis and OD distribution information, the proposed model can effectively evaluate different types of distribution network configuration. The solution framework is also tested using real-world distribution networks of JD logistics in Beijing. The experimental results indicate that OD demand patterns can significantly influence the underlying city logistics distribution and transfer transportation, which further possibly reduces the overall transportation cost to a certain degree. The results also suggest that both the proposed branch-and-bound framework combined approximation schemes and ALNS algorithm can provide high-quality solutions in all instances and work effectively in the real-world case.  
In terms of future works, we will consider more realistic factors to further address the limitations associated with simplistic assumptions in this paper: (1) the transportation cost should also reflect economies of scale, e.g., the unit transportation cost is a nonincreasing function of the rate of flow [50]; (2) reactive transshipment should occur after observing demand [3]; and (3) we will further integrate our proposed optimization model and the time-dependent travel time model in rich arc routing problem developed by [63] to provide more reasonable solutions, where the time-dependent travel time and traffic congestion will be included in a more comprehensive manner.  
Conceptualization, S.W. and X.Z.; methodology, X.Z.; software, Z.N.; validation, Z.N., S.W., and X.Z.; formal analysis, Z.N.; investigation, Z.N. and X.Z.; writing--original draft preparation, Z.N., S.W., and X.Z.; writing--review and editing, S.W. and X.Z.; visualization, Z.N.; supervision, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experiments and Results
### Proposed Model Construction  
In this section, we describe the construction of the proposed model and analyze several factors to achieve the best performance. These factors include architectures for the training model, linguistic features to feed as input features, and the number of sampling points per syllable used as output features.
# Borophene Is a Promising 2D Allotropic Material for Biomedical Devices
## 2 Main Properties of Borophene
### Borophene in Nanotechnologies  
Researchers are looking for new applications for nanotechnologies by creating hybrid nanomaterials that are able to ensure the highest physical, chemical, and electronic properties.  
In regards to physical properties, good flexibility in nanometric structures is highly sought after in the biomedical field. In this context [28], \(8_{6}\) borophene is considered highly suitable when presented on the thin wearable strips, as its flexibility without any torsional stress can alter its molecular structure. Furthermore, all borophene structures are perfectly functional in both strip and tubular conformations, and this behavior allows the molecular, physical, and mechanical characteristics to be maintained in both 2D and 3D conformations. In nanotechnologies, borophene is often used in combination with other metals such as Ag, Si, Cu, and Au, enhancing their properties without negatively impacting on any biomedical applications.
## 2 Materials and Methods
### Hybrid A* Algorithm Improvement
#### 2.5.2 Estimation Function Improvement  
The Hybrid A* algorithm uses the Equation (6) to estimate the cost of each node extension.  
\[f(x)=g(x)+h(x), \tag{6}\]  
where the first term \(g(x)\) is the sum of the estimated cost from the start to the current node, the second term \(h(x)\) is the heuristic value from the current node to the goal, and the sum of the two terms is the evaluation function of the whole algorithm. This paper only improves the \(g(x)\), and use the two  
Figure 10: Schematic diagram of the tire-contact cost estimation. The black solid line is the extension line from the current node to the next node. The dashed is the tire path calculated from vehicle properties. The yellow area is the occupied grid of the tire path.  
Figure 9: Schematic diagram of node extension. (**a**) is the traditional A* algorithm node extension; (**b**) is the nonholonomic constraint node extension of Hybrid A* algorithm. The central red dot is the current node and the green or blue node is the calculated next node.  
heuristic functions of the original algorithm to calculate the \(h(x)\). The calculation of \(g(x)\) is realized by Equation (7).  
\[g(x)=\textit{DirectionCost}\times(g(x_{parent})+\textit{length}(x_{parent},x)+ \textit{TireCost}(x_{parent},x))+\lambda\cdot\textit{SwitchCost} \tag{7}\]  
where _DirectionCost_ contains ForwardCost (FC) and ReverseCost (RC), penalizing the motion cost according to a different direction. \(g\big{(}x_{parent}\big{)}\) represents the sum cost from the start point to the parent node of the current node. \(\textit{length}(x_{parent},x)\) is the length of the curved path between the parent node and the current node. \(\textit{SwitchCost}\) is the direction switch cost to penalize the motion state, when the direction of the vehicle is changed \(\lambda=1\), otherwise \(\lambda=0\). We add a new cost term, \(\textit{TireCost}\big{(}x_{parent},x\big{)}\), based on the original \(g\)-value estimation function, to estimate the cost of tire contact with the surface. This term represents the sum cost of the occupied grid values by the tire line from the current node to its parent node:  
\[\textit{TireCost}(x_{parent},x)=\sum_{i=x_{parent}}^{x}\ (\textit{CostMap}_{i})+ \sum_{j=x_{parent}}^{x}\ (\textit{CostMap}_{j}), \tag{8}\]  
where the first term is the accumulative cost of the left tire, and the second is the right.
# Blockchain-Based E-Voting Mechanisms: A Survey and a Proposal +
## 5 Design
### Discussion  
In addition to the accuracy and robustness of e-voting systems, privacy protection is also crucial, given the transparent and immutable nature of blockchain technology. Even though we focus on the e-voting mechanisms in this paper, various cryptographic and non-cryptographic methodologies can be combined with our proposed solution. Zero-knowledge roofs (ZKPs) [55; 56; 57] are a prominent solution, enabling parties to verify transactions without revealing any additional information. To enhance e-voting systems, ZKPs can be adopted in multiple steps. For example, election authorities (EAs) can register services and generate key pairs with ZKPs for proof of correct constructions. Issuing anonymous voting credentials [58] through these EAs while using ZKPs to verify that the voting is valid without revealing the vote's content can be an effective design decision and applying ZKPs throughout the entire voting process will harden the security of the design. Strategies such as mixing services blend multiple transactions to obscure origins and destinations [59], and a blockchain-empowered data sharing system such as blockshare [60] should also be compatible with our design. Blockshare's blockchain functionalities can manage the sharing of voter credentials, voting data, and other sensitive information securely and verifiably.  
Combining a blockchain-based e-voting system with a blockchain data query solution such as [61; 62] can enhance both the transparency and privacy of the voting process, ensuring the integrity of the data while providing efficient ways to access and analyze voting records. During the voting process, voters cast their votes using our blockchain-based e-voting system. Election officials and authorized parties use the blockchain data query solution to retrieve and verify vote counts. Data analytics tools can also conduct the analysis and generate reports with the help of blockchain data query solutions.
## 3 Results and Discussion
### Kinetic Modelling  
Two mathematical models (i.e., first-order and transference function model (TFM)) were applied to the experimental data to obtain the kinetic parameters that describe and simulate the AD process performance. These models were applied to all the assessed tests, except to the P.o.:Ur. test due to the negative methane yield recorded, which made it not possible to properly fit any kinetic model.
## 4 Monitoring System Framework
### Components of the Multi-Source Monitoring System
#### 4.1.5 Monitoring of the Sideways Pressure of Crushed Stone  
In the pillar-free self-formed roadway mining method, one of the sides of the automatically formed roadway is formed by a stack by goat collapse gangue, which will produce lateral extrusion force on its lateral protective structure while in a loose state. The lateral pressure from the gravel is measured by the FBG lateral pressure sensor illustrated in Figure 11. It measures 30 data in a second.  
The FBG lateral pressure sensor is a high-strength heat-treated steel cylinder. Its working principle is that one end of the sensor is fixed, the other end is connected to an elastic pressure-sensitive diaphragm and a highly stable and sensitive FBG is installed on the diaphragm. When the load causes axial deformation of the elastic sensing diaphragm, the fiber grating will deform synchronously, and its reflection wavelength will be changed. The wavelength reading of the elastic pressure-sensitive diaphragm can be measured by FBG demodulator and the load value can be calculated through the use of a calibration coefficient. The FBG lateral pressure sensor has many advantages including high resolution, strong anti-jamming performance, sensitive response to concentrated loads and reliable measured values.