esa-sceva/satcom-chunk-collection huggingface dataset:
# Isothiocyanates: An Overview of Their Antimicrobial Activity against Human Infections
## 4 Isothiocyanates Antimicrobial Properties against Human Pathogens
### Escherichia coli
The authors also indicated that AITC and PEITC were capable of affecting the bacterial cell membrane integrity in a dose-dependent manner. In addition AITC and PEITC increased the hydrophilic character of the membrane changing the physico-chemical characteristics of the bacterial membrane [49]. Recently, [PERSON] et al. showed that BITC was more effective than AITC in the inhibition of EHEC _E. coli_ growth [45]. The study was performed comparing the antibacterial activity of synthetic SFN, AITC, BITC, phenyl ITC (PITC) and isopropyl ITC (IPRITC) against different clinical and laboratory EHEC _E. coli_ strains, including WT (_E. coli_ MG1655) and Shiga toxin-producing _E. coli_ (O157:H7 _E. coli_). Results indicated that BITC displayed the highest inhibitory growth ability and the highest bactericidal activity against wild type and Stx-producing _E.coli_ strains compared to the other assessed ITCs. Specifically, BITC showed MIC = 0.07 mg/mL and MBC = 0.15 mg/mL against WT EHEC _E. coli_ strain compared to AITC showing MIC = 0.4 mg/mL and MBC = 0.79 mg/mL against the same strain. Furthermore, BITC showed similar MIC values against WT _E. coli_ as well as for clinical isolates carrying Shiga toxin-converting prophages. All the other ITCs showed similar trends although with less efficacy compared to BITC. The only minor exception was SFN, which was more effective against the 86-24 strain carrying Shiga toxin converting prophages than the other strains, although its activity was in any case inferior to BITC [45]. It is known that the conventional antibiotic treatment against EHEC _E. coli_ can induce the production of Shiga toxins from bacteria. For this reason, the authors tested whether AITC, BITC, PITC, SFN and IPRITC could produce similar effects. Results indicated that the ITCs included in the study (AITC, BITC, PITC, SFN and IPRITC) alone did not induce prophage development exerting a protective effect on the Shiga toxin production [45]. This is one of the most important findings because ITCs could be used not only to replace antibiotics but also as a combined treatment with them. Finally the ITCs' mechanism of action against _E. coli_ Shiga toxin production strains was also investigated [45]. Specially the authors evaluated the effect of ITCs (AITC, BITC, PITC, SFN and IPRITC) on the \"alarmone\" penta/tetraphosphate (p)ppGpp that was able to influence the activity of RNA polymerase and the development of bacteriophage and Shiga toxin production. Results indicated that all tested ITCs included in the study induced an increase in (p)ppGpp \"alarmone\" level and indirectly downregulate RNA synthesis affecting prophage development and consequently the Shiga toxin production [45].
# A Review of Femtosecond Laser-Induced Emission Techniques for Combustion and Flow Field Diagnostics
## 4 Femtosecond Laser-Induced Filament Emission
### Tagging Velocimetry Based on Femtosecond Laser-Induced Emission
As shown  
by measuring the FLEET spectra, based on two-line thermometry, the flow field temperature can be obtained [150; 157]. By analyzing the FLEET images, the boundary layer [146; 161], shear layer [147; 162], vorticity [163], and mixture fraction [156] can be obtained. By analyzing the FLEET signal intensity and lifetime, the pressure information of the flow field can be inverted [164]. The FLEET technique has the following advantages. Firstly, FLEET can achieve velocity measurement with only one fs laser, whose experimental system is simple. Secondly, FLEET uses fs laser as a light source, which can induce efficient multi-photon photolysis, therefore, it can be used to realize multi-dimensional measurement [140; 153; 165]. Thirdly, FLEET uses molecular nitrogen as a tracer, so the interference on the reactions of the flow field can be minimized. Fourthly, FLEET enables the measurement of other parameters in the flow field. However, there are still some technical defects in the current FLEET technique, such as poor signal-to-noise ratio (SNR). Many researchers have tried to enhance the signal intensity by using different wavelengths or adding inert gases [139]. At present, the most effective way to enhance signal intensity is using laser-induced chemiluminescence of the radicals that have strong emission intensity. Another fs-laser-based tagging velocimetry is femtosecond laser-induced cyano chemiluminescence (FLICC) [166], which is proposed by our group. When an fs laser focused at methane-seeded nitrogen gas flows, the high-intensity emission originating from CN (\(B^{2}\Sigma^{+}\to X^{2}\Sigma^{+}\)) fluorescence was observed. The emission is strong and can last for hundreds of microseconds with a proper methane concentration. Therefore, velocity measurement can be achieved through CN fluorescence imaging. As shown in Figure 9, FLICC is an ideal tool for near-wall velocity measurements, which can be used to study, e.g., the boundary layer structure in supersonic flows. Although the FLICC technique requires hydrocarbons to be seeded in the measured flow field, it has a natural advantage when it comes to combustion flow fields.
# Free Vibration Analysis of Arches with Interval-Uncertain Parameters
## 4 Numerical Simulation and Discussion
### Comparisons of CPS Model with MCS in Calculating Natural Frequencies  
In order to assess the accuracy and efficiency of the CPS model in estimating an uncertain natural frequency, the MCS will be used for comparison. A sufficient number of samples is the guarantor ensuring the accuracy of the simulation in the MCS, and 500 samples are utilized in this MCS. The expansion order \(n\) of the CPS model takes the value 3 and the number of the integral point \(m\) takes the value 5. Variables \(\omega_{1}^{ll}\) and \(\omega_{1}^{ll}\)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Value** \\ \hline Cross-sectional width b & 0.06 m \\ Cross-sectional height h & 0.08 m \\ Density of mass \(\rho\) & 7860 kg/m\({}^{3}\) \\ Young’s modulus E & \(2.1\times 10^{11}\) pa \\ Poisson’s coefficient \(\
u\) & 0.3 \\ Shear factor \(\kappa_{0}\) & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical and physical parameters of arch.  
Figure 3: Waterfall diagrams of the first fourth-order natural frequencies of the arch versus the central angle with different radii: **(a)** first order; **(b)** second order; **(c)** third order; and **(d)** fourth order.  
represent the upper bound and lower bound of the \(i\)th natural frequency, respectively. Variables \(e_{i}^{II}\) and \(e_{i}^{L}\) represent the upper bound and lower bound of the \(i\)th frequency changing rate, respectively, which can be regarded as an indication of the fluctuation in natural frequency and can be formulated as  
\[\begin{cases}e_{i}^{II}=\frac{\omega_{i}^{II}-\omega_{i}}{\omega_{i}}\times 1 00\%\\ e_{i}^{L}=\frac{\omega_{i}^{L}-\omega_{i}}{\omega_{i}}\times 100\%\end{cases} \tag{33}\]  
Accounting for inevitable manufacturing tolerances, the cross-sectional height is considered as an uncertainty parameter, with a deviation degree of 5%. The radius takes values of 1 m and 3 m, while the central angle still varies in the interval [\(\pi/6\), \(\pi\)] for legibility. Figures 4 and 5 show the comparisons of changing rates of the first fourth-order uncertain natural frequencies. It is shown that the results obtained from both models exhibit excellent agreement in terms of the changing rate for each order's frequencies, thereby formalizing the accuracy of the proposed interval model and its applicability in determining frequency
## 5 Transient Performance of the System in DC Faults
### The Preferable Case for HVDC Grid Application  
In this section, we evaluated the feasibility of applying two fault handling solutions to HVDC grids based on the simulation results. The summary of our analysis is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Simulation Condition** & **Case-1** & **Case-2** \\ \hline Structure & & HB-MMC + HCB & FB-MMC + RCB \\ DC-fault-handling capability & - & O & O \\ Current-limiting reactor & & Required & Not necessarily required \\ \hline Total interruption time (ms) & & 3–45 (ms) & \(-\)11 (ms) \\ Maximum DC fault current in fault period (KA) & Variable fault location & 2–4 (kA) & 3–25 (kA) \\ Energy dissipation across circuit (Welaker (MI)) & & 0.32–0.51 (M) & 0.01–0.05 (M) \\ \hline Maximum AC current in fault period (kA) & 5 km from the sending & 2 (kA) & 0.15 (kA) \\ Maximum AC current in reclosing period (kA) & end converter side & 0.62 (kA) & 1.02 (kA) \\ \hline Maximum DC current in reclosing period (kA) & 5 km from the sending & 0.8 (kA) & 2 (kA) \\ Maximum DC system overvoltage (kV) & end converter side & 175 (kV) & 205 (kV) \\ Time to recovery (ms) & (pole-to-pole) & \(-\)0.1 (s) & 10 (s) \\ \hline Maximum DC overvoltage in reclosing period (kV) & 5 km from the sending & & \\  & end converter side & 91 (kV) & 152 (kV) \\  & (pole-to-ground) & & \\ \hline Stress on insulation & - & Relatively low & Relatively high \\ \hline Feasibility in HVDC grid application & - & \(\  
Although in Case-1 the HCB alone copes with a large DC fault current, the power system experienced lower overvoltage stress. However, considering the high energy dissipation in the HCB, a sophisticated insulation design is required.  
Table 2 shows that in Case-1, the fault current, overvoltage magnitude, and interruption time are low. Conversely, in Case-2, the abovementioned parameters are relatively high. Therefore, additional consideration for the insulation design is required to ensure the reliable long-term operation of the DC grid. Furthermore, the oscillating voltage in the transient state is expected to stress the overall system components. As a result, we conclude that Case-1 would be a better solution for HVDC grid application.
## 1 Introduction  
As part of human exploration of the Moon, a wide variety of commercial and international missions have been undertaken to send robotic landers and rovers to the surface of the Moon [1]. To support the growing number of ongoing and scheduled robotic activities and improve their autonomous operation capabilities, future lunar missions will require reliable infrastructure to provide navigation and communication services to the exploreres on the Moon. For this purpose, robotic rovers will be used for resource mapping and scientific observation missions on the lunar surface. Earlier studies have reported that a positioning accuracy of less than 100 m is required to support these goals [2].  
There are several ongoing feasibility studies on dedicated lunar orbiting satellite constellations like the Global Navigation Satellite System (GNSS) around the Earth [3; 4; 5]. While they can provide robust and precise navigation services to the entire Moon's surface and its proximity, the transportation cost to inject many satellites into multiple lunar orbits is not affordable at the early stage of lunar exploration programs. To reduce the initial deployment cost of the lunar navigation satellite constellations, the authors proposed a new dual-satellite navigation method called multi-epoch double-differenced pseudorange observation (MDPO), which requires range observations from only two navigation satellites in a single orbit plane [6]. Furthermore, it is still quite challenging for lunar orbiting satellites to achieve reasonable orbit and clock determination as well as prediction accuracies, especially for a small satellite with its limited size, weight, and power. The MDPO algorithm efficiently eliminates these error sources by taking a double-differenced measurement between a lander and a rover.  
Although there are several operational limitations in availability and real-time property, MDPO can handle the typical pseudorange observables provided by the satellites in the future full constellation. Since there is no special treatment required for the first two navigation satellites, it is quite suitable for a temporal precursor mission by the completion of the full constellation. More details about the MDPO algorithm and comprehensive numerical analysis results can be found in the early work [7].  
In this study, to evaluate the performance of the MDPO algorithm with real pseudorange observations, we applied it to GNSS pseudorange measurements obtained from a pair of GNSS ground stations emulating a lander and a rover on the lunar surface. Since the resulting positioning accuracy varied largely by satellite geometry, the consistency of the navigation performance was evaluated by the estimated measurement errors of the double-differenced pseudorange observations.
## 1 Introduction
### Key Assumptions and Operational Conditions  
In this work, the load is purely resistive, eliminating the need for grid connection control (PLL), which is not the focus of this study. However, since the filter inductor's reactance is significantly higher than the load resistance at the switching frequency (tens of kHz), the analysis and simulation will also remain highly accurate and applicable to the grid-connected CSI. For instance, with \(L=500\) uH and a switching frequency of 50 kHz,  
Figure 2: Basic CSI current vectors. Vectors (e.g., **ab**) represent switching states with magnitude \(\frac{2}{\sqrt{3}}I_{DC}\). Asterisks denote reference values.  
the reactance (\(X_{L}=2\pi f_{sw}L\)) is 157 \(\Omega\), which is significantly larger than the 10 \(\Omega\) load resistance used in the simulation.  
This study will assume the converter's continuous mode of operation. This means the DC link inductor current will not drop to zero and will remain relatively constant for the example shown in the application section.  
Please note that the reference space vector used in the synthesis is constant during the switching period, which means the amplitude of the sinusoidal load currents remains unchanged. This condition can be achieved in practice by maintaining a high switching frequency.
## 2 Basic Concepts of DQ0-Based Models
### Basic Definitions
#### 2.1.3 Energy in Terms of DQ0 Quantities  
Assume a symmetrically configured three-phase inductor, with currents \(i_{a}\), \(i_{b}\), \(i_{c}\), as shown in Figure 4.  
The total energy stored in the inductor is  
\[E=\frac{1}{2}L\left(i_{a}^{2}+i_{b}^{2}+i_{c}^{2}\right), \tag{20}\]  
Rewrite this expression as  
\[\begin{split} E&=\frac{1}{2}L\begin{bmatrix}i_{a}&i_{b}&i_{c} \end{bmatrix}\begin{bmatrix}i_{a}\\ i_{b}\\ i_{c}\end{bmatrix}^{\mathsf{T}}=\frac{1}{2}L\begin{pmatrix}T_{\theta}^{-1}& \begin{bmatrix}i_{d}\\ i_{q}\\ i_{0}\end{bmatrix}\end{pmatrix}^{\mathsf{T}}T_{\theta}^{-1}\begin{bmatrix}i_{d }\\ i_{q}\\ i_{0}\end{bmatrix}\\ &=\frac{1}{2}L\begin{bmatrix}i_{d}&i_{q}&i_{0}\end{bmatrix} \left(T_{\theta}^{-1}\right)^{\mathsf{T}}T_{\theta}^{-1}\begin{bmatrix}i_{d }\\ i_{q}\\ i_{0}\end{bmatrix}.\end{split} \tag{21}\]  
Again we use the identity (18) to obtain  
\[E=\frac{3}{4}L\begin{bmatrix}i_{d}&i_{q}&i_{0}\end{bmatrix}\begin{bmatrix}1&0 &0\\ 0&1&0\\ 0&0&2\end{bmatrix}\begin{bmatrix}i_{d}\\ i_{q}\\ i_{0}\end{bmatrix}, \tag{22}\]  
or equivalently,  
\[E=\frac{3}{4}L\begin{pmatrix}i_{d}^{2}+i_{q}^{2}+2i_{0}^{2}\end{pmatrix}. \tag{23}\]  
Similarly, the energy stored in a symmetrically configured three-phase capacitor \(C\) is  
\[E=\frac{3}{4}C\begin{pmatrix}v_{d}^{2}+v_{q}^{2}+2v_{0}^{2}\end{pmatrix}. \tag{24}\]  
Figure 4: Symmetrically configured three-phase inductor.  
In addition, we will now show that the derivative of energy is power, as expected. Referring to the circuit in Figure 4, the product rule yields  
\[\frac{\mathrm{d}}{\mathrm{d}t}E=\frac{3}{2}L\left(i_{d}\frac{\mathrm{d}i_{d}}{ \mathrm{d}t}+i_{q}\frac{\mathrm{d}i_{q}}{\mathrm{d}t}+2i_{0}\frac{\mathrm{d}i_{ 0}}{\mathrm{d}t}\right). \tag{25}\]  
Now, using the dynamic model of the inductor in (14),  
\[\frac{\mathrm{d}}{\mathrm{d}t}E=\frac{3}{2}L\Bigg{[}i_{d}\left(i_{q}\frac{ \mathrm{d}\theta}{\mathrm{d}t}+\frac{1}{L}(v_{d,1}-v_{d,2})\right)+i_{q}\left(- i_{d}\frac{\mathrm{d}\theta}{\mathrm{d}t}+\frac{1}{L}(v_{q,1}-v_{q,2})\right)+2i_{0 }\left(\frac{1}{L}(v_{0,1}-v_{0,2})\right)\Bigg{]}, \tag{26}\]  
or more simply,  
\[\frac{\mathrm{d}}{\mathrm{d}t}E=\frac{3}{2}\left(v_{d,1}i_{d}+v_{q,1}i_{q}+2v_ {0,1}i_{0}\right)-\frac{3}{2}\left(v_{d,2}i_{d}+v_{q,2}i_{q}+2v_{0,2}i_{0} \right), \tag{27}\]  
which is identical to  
\[\frac{\mathrm{d}}{\mathrm{d}t}E=P_{1}-P_{2}. \tag{28}\]  
The change in stored energy is equal to the sum of powers flowing into the inductor.
# Network Attack Classification in IoT Using Support Vector Machines +
## 4 SVM Models Training and Evaluation
### Complexity  
The SVM models have proven to correctly classify local node activity to either malicious or benign. The SVM relies on finding the appropriate support vectors for the input dataset. The number of support vectors required to create the hyperplane, dictate the computation overhead and complexity of each model. The least SVM computation overhead that can be achieved for \(R\) lineal functions is the number of operations that are proportional to \(R^{3}\), given that the support vectors are known beforehand and it is only required to compute their coefficients [6]. The SVM cost increases when the support vectors need to be identified and the choice of the kernel function.  
The SVM's computational requirements have made it possible to be applied in non-constrained nodes, in which computational power and memory are not limited resources [4, 5, 21]. In a WSN with constrained nodes, the SVM can be placed at the central node/gateway to monitor the entire network. This comes with the cost of a communication overhead, as it increases the network traffic with monitoring network packets.
# Effects of Bridge Piers on Flood Hazards: A Case Study on the Jialing River in China
## 2 Materials and Methods  
### Study Area  
The project area is in the upper reach (Fengxian section) of the Jialing River, which is one of the main tributaries on the left bank of the Yangtze River (Figure 1). The main stream of the Jialing River (Fengxian section) is about 102 km long, the watershed area is 2531 km\({}^{2}\), and the average bed slope of the river is 1.4%. This is a warm temperate mountain climate area with an annual average temperature of 11.4 \({}^{\circ}\)C, an average annual rainfall of 623.6 mm, a maximum rainfall of 939.3 mm (in 1958), and a minimum rainfall of 422.3 mm (in 1969). The precipitation from April to October (wet season) is on average 577.3 mm, which is 92% of the entire year's precipitation. From November to March (dry season), the precipitation is on average 46.3 mm (8% of the entire year's precipitation). The elevation on both sides of the Jialing River is in the range of 1400-1800 m, the relative height is 500-800 m with a steep slope of about 30-40 degree. Due to the narrow and steep river channel, the floods are of short durations and high peaks and most have a single peak. According to the statistics of the measured data, the floods in the upper reach of the Jialing River are mainly formed by high-intensity rain ([PERSON] et al., 2019). For example, the 1933 rainstorm, with a rainfall intensity of 73.6 mm/day, produced 500 m\({}^{3}\)/s peak flow at the upper reach and 970 m\({}^{3}\)/s peak flow in the lower reach, which is approximately a 10-year flood. Heavy rains generally occur from July to September.  
The hydrological stations in the upper reaches of the Jialing River are the Fengzhou Station (106\({}^{\circ}\)38\({}^{\prime}\) E, 33\({}^{\circ}\)58\({}^{\prime}\) N), the Ciba Station (106\({}^{\circ}\)25\({}^{\prime}\) E, 33\({}^{\circ}\)51\({}^{\prime}\) N), and the Lueyang Station (106\({}^{\circ}\)08\({}^{\prime}\) E, 33\({}^{\circ}\)20\({}^{\prime}\) N), with catchment areas 688 km\({}^{2}\), 2722 km\({}^{2}\) and 19,206 km\({}^{2}\), respectively. The maximum peak flow rate measured at Fengzhou Station was 972 m\({}^{3}\)/s (in 1990). The maximum peak flow rate measured at Ciba Station was 4670 m\({}^{3}\)/s (in 1981). The Lueyang Station's maximum flood peak flow in 1981 reached 8630 m\({}^{3}\)/s.  
This section of the Jialing River is meandering. The highway crosses the main stream of the Jialing River four times. The four bridges designed to allow this are the following: the Xizhuang Bridge, the Fengxian Bridge, the E Ramp Bridge, and the Jialing River Bridge. The length of the river from the Xizhuang Bridge to the Jialing River Bridge is 2230 m. The span of the bridge piers is not equal, and there is a span of 40 m in the main channel and 30 m on the bank. The lengths of the bridges are greater than 100 m, and the bridges are designed for a 100-year flood.
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 3 Data and Methods
### Auxiliary Data  
#### 3.2.1 DEM Data  
For the identification of potential aquaculture areas (low topography, flat regions), we derived terrain information using the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) in Version 3 published by NASA JPL [47; 48], which provides void-filled data with a high spatial resolution of 30 m. We used the elevation (height above sea level in meters) and slope (in decimal degree) information, which were considered as the most important parameters influencing the presence or absence of aquaculture ponds in the low-lying coastal regions, and used for topographical masking.
## 3 Influence of Boundary Distance in the Integral Response Deformation Method
### Theoretical Basis and Engineering Specification Consensus  
According to the classical theory of body wave (P- and S-wave) propagation in elastic media [25], the displacement amplitude of a wave field radiating from a point source obeys a geometric spreading law:  
\[u(r)\text{ex}1/r \tag{1}\]  
Figure 8: Shear stress computed via the IRDM under different \(R_{B}\) values, where \(R_{B}\) is a dimensionless bottom boundary distance ratio \(R_{B}=H_{B}/H\).  
where \(r\) is the radial distance from the source. Consequently, the kinetic energy density decays proportionally to \(\frac{1}{r^{2}}\).  
If the lateral boundary distance is taken as \(r=3\ L\), the total round-trip distance for a reflected wave becomes \(2r=6\ L\), and the reflected energy density is \(\left(\frac{1}{2r}\right)^{2}=\frac{1}{36}\approx 2.78\%\). This implies that less than 3% of the original wave energy returns to the structure, meeting the noninterference threshold proposed by [PERSON]'s radiation damping theory [PERSON] (1996), which stipulates that reflected energy below 5% has negligible influence. Solving the energy decay inequality \(\left(\frac{1}{2r}\right)^{2}\leq 0.05\) yields \(r\geq 2.24\ L\), and adopting \(r=3\ L\) as a conservative integer multiple ensures safe truncation.  
The proposed criterion aligns with the Standard for Seismic Design of Underground Structures ([PERSON] and [PERSON], 2016): \"The boundary distance in time history analysis should not be less than three times the characteristic dimension of the structure to ensure full attenuation of far-field wave effects\".  
Japan's Tunnel Seismic Design Guide ([PERSON] and [PERSON], 2017) and ASCE standards in the U.S. ([PERSON] and [PERSON], 2017) also adopt similar guidelines.  
This consistency demonstrates the broad professional consensus supporting the 3 L guideline as a practical, validated approach in seismic modeling.