esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Two-Tier Camera-Based WMSNs Topology
### Proposed Upper-Tier Structure: Wireless Network of Stronger Sensor Nodes without Camera  
Sensor nodes in the proposed upper-tier are not equipped with cameras but they have stronger processors, communication units, and more storage capacity. Stargate [22], Imote2 [18], and Yale XYZ [24] are practical instances of stronger sensor nodes. Their task is lower-tier management, information gathering and processing such as simplified image stitching, and transmitting the final result of this process to Sink. Moreover, all nodes in this tier are equipped with Global PositioningSystem (GPS). Accordingly, Sink knows their geographical position and easily sends its queries to the related cluster head.  
Each of the stronger sensor nodes has the responsibility of multiple camera nodes in the lower-tier. Therefore, upper-tier contains fewer sensor nodes and less density compared to the lower-tier. At the beginning, stronger sensors nodes are placed by network manager so as their density would be uniform in the total covering field. However, the radio-covered areas by upper-tier sensor nodes have overlaps to increase the tolerance of the whole network versus a failure of upper-tier nodes, to prolong network lifetime an appropriate subset of nodes in this tier must be chosen to be in active mode. Clustering algorithms are suitable for selecting this subset. After determining the number of clusters, which depends on the application, and performing clustering action, it is adequate to set only one node (cluster head) in each cluster to active mode and leave the others in sleep mode. Moreover, to achieve good coverage; firstly, all nodes in the upper-tier should be classified into clusters with almost equal number of members; secondly, the selection of cluster heads (active nodes) should be in a way that their locations in upper-tier become uniform. The proposed algorithms in [25-27] are instances that can be employed here.  
After clustering and selecting cluster heads, a routing protocol is necessary for data transmission between each cluster head and Sink. As we know, hierarchical protocols are suitable for this tier because of their speed and low energy consumption. The protocols in [28,29] are instances which are suitable for this purpose. Figure 1 depicts the structure of the upper-tier.
## Article
###### Abstract  
With the prevalence of renewable energy sources such as wind power in the power system, analyzing the fault characteristics of systems composed of DFIGs is becoming increasingly important. Therefore, this article analyzes, at first theoretically, the fault characteristics of a doubly fed induction generator (DFIG) during fault periods. It was found that the fault current of the DFIG exhibited the frequency offset phenomenon, which is affected by the depth of voltage dips and can negatively impact traditional distance protection. Furthermore, a method using a dynamic voltage restorer (DVR) based on superconducting magnetic energy storage (SMES) was adopted to compensate for the fault voltage of DFIG, which can mitigate the voltage dips of the DFIG. This method can not only achieve the fault ride through for DFIG but also significantly improve the frequency offset of the fault current during fault periods. Finally, a model composed of a 2.5 MW DFIG-based wind turbine and a 2.5 MW DVR-based SMES was built using a real-time digital simulator (RTDS) platform, and the simulation results showed that the fault stator voltage of DFIG can be compensated at a rated value of 0.69 kV, and the frequency of fault current can be maintained at 50 Hz These results validate the excellent performance of the method in achieving the fault ride through of DFIG and improving the frequency offset of the fault current by comparing multiple type faults while employing different protection methods.  
double-fed induction generator; dynamic voltage restorer; superconducting magnetic energy storage; fault characteristics; fault ride through; frequency offset
## 3 Results and Discussion
Please note that for fair comparison, the individual layer thicknesses are properly chosen such that the InSb-SiO\({}_{2}\) multilayers have exactly the same total thicknesses for these different values of \(t_{\text{m}}/t_{\text{d}}\). To understand this interesting phenomenon, we turn to the effective relative permittivities expressed by Equations (2) and (3). As we have shown in Figure 1c, the InSb-SiO\({}_{2}\) multilayers with \(t_{\text{m}}/t_{\text{d}}=1\):1 have hyperbolic dispersion relationship since \(\varepsilon_{\parallel}\varepsilon_{\perp}<0\) at \(f=1.6\) THz. For both \(t_{\text{m}}/t_{\text{d}}=1\):2 and \(t_{\text{m}}/t_{\text{d}}=2\):1; however, Figure 5c,d shows that the InSb-SiO\({}_{2}\) multilayers exhibit elliptical dispersion relationship because of the anisotropic uniaxial effective relative permittivities at \(f=1.6\) THz, i.e., \(\varepsilon_{\parallel}\varepsilon_{\perp}>0\) and meanwhile  
Figure 4: Electric field components \(E_{x}\), \(E_{y}\), \(E_{z}\) at \(y=0\) and \(z=1.2\,\upmu\)m, highlighting the totally negligible \(E_{y}\) component and the field enhancement in the gap. Figure 3: Local field enhancement contributed from electric field components \(E_{x}\) and \(E_{z}\) in side view at \(y=0\). The gold antenna (**a**,**c**) directly sits on the PI substrate, or (**b**,**d**) sits on the PI substrate buffered by the HMM with hole array. \(\epsilon_{\parallel}\
eq\epsilon_{\perp}\). Therefore, these results reveal that the hyperbolic regime of the HMM composed of the InSb-SiO\({}_{2}\) multilayers is vital for achieving large field enhancement. Please note that in this work we choose the InSb-SiO\({}_{2}\) multilayers to form the hyperbolic metamaterial because they are typical hyperbolic metamaterial structures that are easy to fabricate through thin-layer deposition. In principle, we expect similar results can also be obtained by using other types of hyperbolic metamaterials.
## 4 Experiment Verification
### Impact Force at Different Inlet Pressures  
The rotation speed of the blade was set to 1470 rpm, the abrasive particle diameter was set to 0.4 mm, and the system pressure was set to 4, 6, 8, 10, and 12 MPa. The experiment was repeated five times for each pressure parameter, and the impact force of each experiment was measured with a pressure sensor. According to Equation (7), combined with the simulation results of different inlet pressures in Figure 6, after calculation and experimental measurement, the average value of five impact forces is shown in Table 5. It can be seen that the variation trend of the impact force measured by the test is the same as the simulation. With the increase in pressure, the impact force at 12 MPa is approximately 1.28 times that at 4 MPa. However, the impact force obtained from the test is relatively less, approximately 80% of the simulated impact force. This finding is due to the impact of jet diffusion and air resistance, and the impact force of the jet on the surface of the object is smaller than  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Blade Rotation** & **Average** & \multirow{2}{*}{**Number of**} & **Weight of Rust** & **Weight of Rust** \\  & **Rotation** & **Erosion Rate** & & **Removed by** & **Removed by** \\  & **Speed (rpm)** & **(kg/m\({}^{2}\))** & **(kg/m\({}^{2}\))** & **(Simulation (g/s))** & **Experiment (g/s)** \\ \hline
1 & 600 & \(6.25\times 10^{-9}\) & 9370 & 0.97 & 0.88 \\
2 & 800 & \(1.35\times 10^{-8}\) & 11,330 & 2.75 & 2.35 \\
3 & 1000 & \(1.78\times 10^{-8}\) & 15,340 & 4.91 & 4.65 \\
4 & 1200 & \(2.09\times 10^{-8}\) & 19,430 & 7.31 & 6.96 \\
5 & 1470 & \(2.25\times 10^{-8}\) & 23,460 & 9.50 & 8.28 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Weight of rust removed at different blade rotation speed.  
Figure 11: Comparison of the specimen morphology before and after rust removal.  
the theoretical impact. The comparison result is the same as the comparison result at different blade rotation speeds. Compared with the conclusions of relevant literature [26], the simulation and experimental methods in this paper can be considered feasible.
# Security Audit of a Blockchain-Based Industrial Application Platform
## 5 Discussion: Application of BISS:4.0 to Real-World Scenarios
### Scenario 2: Multi-Worm Attack  
* _Intention:_ in this attack scenario, an attacker uses a multi-worm attack to destroy production equipment.
* _Attack path and impact:_ the infection occurs via a portable data storage device and spreads through exploitations in the operating system. The virus searches the infected host for software that can access programmable logic controllers (PLCs). PLCs are used for automation and monitoring of electromechanical devices. The malware updates itself, especially the specific attack code. The goal of the attack code is to damage the electromechanical equipment controlled by the host. At the same time, the virus sends false feedback to the main controller. Therefore, the monitoring systems cannot detect the misuse until the self-destruction of the equipment has started [45].
* _Countermeasure:_ BISS:4.0 with its blockchain technology can prevent attacks by securing two attack surfaces. The first is the misuse of devices. With blockchain technology, the programs to execute processes of the devices are stored in the blockchain and are thus protected from tampering. The second prevented attack surface is the false feedback. Sensor data are written directly from the sensors or sensor systems to the blockchain. Another countermeasure such as encapsulating the systems connected to the devices, such as using read-only systems to execute device code and read-only sensor systems or banning all portable media and using security software to intercept malware before it can be transmitted over the network, could also be a feasible approach but often cannot be consistently enforced.
###### Abstract  
A fast and cost-effective melamine detection approach has been developed based on surface enhanced Raman spectroscopy (SERS) using a novel hydrogen bonding-assisted supramolecular matrix. The detection utilizes Fe\({}_{3}\)O\({}_{4}\)/Au magnetic nanoparticles coated with 5-aminoorotic acid (AOA) as a SERS active substrate (Fe\({}_{3}\)O\({}_{4}\)/Au-AOA), and Rhodamine B (RhB) conjugated AOA as a Raman reporter (AOA-RhB). Upon mixing the reagents with melamine, a supramolecular complex [Fe\({}_{3}\)O\({}_{4}\)/Au-AOA\(\bullet\)**melamine**\(\bullet\)AOA-RhB] was formed due to the strong multiple hydrogen bonding interactions between AOA and melamine. The complex was separated and concentrated to a pellet by an external magnet and used as a supramolecular matrix for the melamine detection. Laser excitation of the complex pellet produced a strong SERS signal diagnostic for RhB. The logarithmic intensity of the characteristic RhB peaks was found to be proportional to the concentration of melamine with a limit of detection of 2.5 \(\upmu\)g/mL and a detection linearity range of 2.5-15.0 \(\upmu\)g/mL in milk. As Fe\({}_{3}\)O\({}_{4}\) nanoparticles and AOA are thousands of times less expensive than the monoclonal antibody used in a traditional sandwich immunoassay, the current assay drastically cut down the cost of melamine detection. The current approach affords promise as a biosensor platform that cuts down sample pre-treatment steps and measurement expense.  
surface enhanced Raman spectroscopy (SERS); Fe\({}_{3}\)O\({}_{4}\) magnetic nanoparticles; melamine; detection; supramolecular hydrogen bonding; matrix +
Footnote †: journal: Applied Physics Letters  
Article
## 3 Data Processing and Kinematic Analysis  
The extraction and processing of sensor data are described in this section, as shown in Figure 2. The VLP-16 LiDAR [18] (VLP-16, Velodyne Lidar, San Jose, CA, US, 2020) was selected as the sensor used for this research as it provides a horizontal detection range of 360 degrees and a vertical range of 30 degrees, respectively. It also generates approximately  
Figure 1: Overview of the developed predicted safety algorithm.  
300,000 points per second, which allows for fast and high-resolution detection. In addition, the point cloud of each Lidar includes the information of the \(x,y,\) and \(z\) coordinates that can be used to identify the position of the detected objects. In the study, MATLAB (R2020a, MathWorks, Natick, MA, US, 2020) software was used to store, process, and visualize the point cloud data. The data processing for object tracking and track management was taken through the steps discussed below.
## 3 Materials and Methods
### Measurements
#### 3.6.5 Mathematical Models  
The kinetics and mechanism of 5-FU release were determined by fitting experimental data to theoretical mathematical models, zero-order and first-order, Higuchi and Korsmeyer-Peppas, using the following equations:  
Zero-order:  
\[F=kt \tag{6}\]  
First-order:  
\[\log F=\log F_{0}-\frac{kt}{2.303} \tag{7}\]  
Higuchi model:  
\[F=k\sqrt{t} \tag{8}\]  
Korsmeyer-Peppas model:  
\[\frac{M_{t}}{M_{\infty}}=kt^{n}\ (\frac{M_{t}}{M_{\infty}}<0.6) \tag{9}\]  
where \(F\) is the amount of drug released, \(F_{0}\) is the initial concentration of 5-FU, \(t\) is the release time increment, \(k\) is the model constant, \(M_{t}/M_{\infty}\) is the fraction of 5-FU released during time \(t\) and \(n\) is the exponent in the Korsmeyer-Peppas model [45; 46].
## 2 Methodology
### Wavelet Transformation (WT)  
In the literature, wavelet transformations are typically categorized into two variants: continuous wavelet transformation (CWT) and discrete wavelet transformation (DWT). Discrete wavelet transformation (DWT) is frequently favored over continuous wavelet transformation (CWT) because of its reduced computational demands.  
Discrete wavelet transform (DWT) serves as a signal processing technique and an alternative to the Fourier transform by decomposing time series data into independent sub-signals across various frequencies, thereby facilitating the identification of significant features [77; 78; 79]. By incorporating both temporal and spectral dimensions, it provides a thorough time-frequency analysis of the signal. The mother/main wavelet function is represented as \(\psi\)\(y(t)\), which is the fundamental component of DWT, serves as a base function. This wavelet captures distinct frequencies by modifying different scales (\(s_{0}\)) and translating in time (\(\tau_{0}\)), which allows for precise localization in the time-frequency domain. The formulation of the mother wavelet is expressed in Equation (13):  
\[\psi_{m,n}(t)=\frac{1}{s_{0}^{m}}\psi\bigg{\{}\frac{t-n\tau_{0}s_{0}^{m}}{s_{0 }^{m}}\bigg{\}} \tag{13}\]  
where  
\(m\) and \(n\) are integers representing the scale and shift parameters, respectively;  
\(\psi_{m,n}(t)\) is the wavelet function.  
Typically, the scale parameter \(s_{0}\) is set to 2, and the shift parameter (\(\tau_{0}\)) is set to 1. According to [PERSON]'s theory, DWT decomposes a signal into a series of approximation and detail components, which are linearly independent. In this framework:  
* \(s_{0}\) represents the precision step for the signal's expansion.
* \(\tau_{0}\) denotes the localization parameter for handling discrete time series data (\(x_{i}\)), where the data are sampled at discrete intervals (\(i\)).  
The inverse DWT, which reconstructs the original signal from its approximation and detail components, is defined in Equation (14), following the framework established by [PERSON] [80]:  
\[x(t)=T+{\sum}_{m=1}^{M}{\sum}_{t=0}^{2^{M-n-1}}{W}_{m,n}2^{-\frac{m}{2}}\psi( 2^{-m}t-n) \tag{14}\]  
where \(W_{m,n}2^{-\frac{m}{2}}{\sum}_{t=0}^{N-1}\psi(2^{-m}t-n)x(t)\) is the wavelet coefficient for the discrete wavelet at scale \(s=2^{m}\) and \(\tau=2^{m}\). In this study, a 5-level decomposition was chosen for the wavelet transformation because it improved the model results. The calculation for the level is given by Equation (15):  
\[L=int(N) \tag{15}\]  
where \(L\) is the level of the decomposition and \(N\) is the number of runs.
# A Feedback Control Method to Maintain the Amplitude of the RF Signal Applied to Ion Traps
## 4 Experiments and Results  
Without the use of any control, the amplitude of the RF signal drifts. In Figure 6, the amplitudes of the RF signals with and without the use of the PI controller for 5 h are presented. When the PI controller is not used, the amplitude of the RF signal drifts; otherwise, it does not drift. This indicates that the FPGA-based controller prevents the secular frequency of trapped ions from drifting. This FPGA-based feedback controller has an error range of approximately 5 mV attributed to the limitations of the resolution of the ADC. When 10 Hz noise is added to the RF voltage amplitude, the results without and with the FPGA-based feedback controller are shown in Figures S3 and S4, respectively.  
Figure 7 shows fluctuation in the amplitude of the RF voltage with the baseline method and FPGA-based controllers. It represents that the performance of the new method is better than that of the method developed in the previous work. The standard deviations of the amplitude of the RF signal obtained by the ADC are 61.58 \(\upmu\)V with the PI controller and 63.28 \(\upmu\)V with the lag compensator, which are 0.099% and 0.102% of the amplitude, respectively. These results show that there is no drift. If a small drift persists, the lag compensator is more advantageous than the PI controller. Table 1 shows the fluctuation in the amplitude of the RF signal using the three different feedback controllers. This new method can reduce the fluctuation of the 1 MHz secular frequency of the trapped ion to less than 1 kHz. The voltages in Figure 7 and Table 1 are the outputs of the rectifier measured by ADC. When the helical resonator is not mounted on a vacuum chamber, the fluctuation in the amplitude of the RF signal is 1.806% with the use of a commercial universal PI controller, which is much greater than the 0.05% when the helical resonator is mounted on the vacuum chamber [13]. The fluctuation in the amplitude of the RF signal can be reduced more when using the FPGA-based feedback controller with the helical resonator mounted on a vacuum chamber than when employing the baseline method under the same conditions. By using the FPGA-based feedback control, we can also change the control method to suit the experimental conditions. Moreover, researchers can change the gains, set-point voltage, and input and output of a controller using a computer in this system. Remotely changing the conditions of the RF signal applied to the ion trap reduces the possibility of the changes that can be generated by researchers.  
Figure 6: Results of the amplitude of the RF signal for 18,000 s. (**a**) The amplitude of the RF signal without the control. The amplitude drifts from 79.50 to 81.34 mV. (**b**) The amplitude of the RF signal when the FPGA-based PI controller is used. The voltage does not drift. When the FPGA-based feedback controller is used, no overshoot occurs, and the settling time is less than 1 s.
## 3 The Variation Law of Groundwater during Tunnel Construction
### Influence of Tunnel Drainage Mode on Groundwater Level
The other measuring points were all outside the grouting circle. The thicker the grouting circle was, the larger the lowest water level was. After the secondary lining was finished, the recovery degree of water level was larger than that of without grouting. The influence of grouting thickness increasing on water level was relatively small. The range of water level decline within 5 m below the groundwater level after grouting in the tunnel was 0.07-0.66 m. The smaller the burial depth, the smaller the decrease in water level. After the secondary lining construction, the water level gradually recovered, with a degree exceeding 90%. The range of groundwater decline after recovery was 0.06-0.07 m, which can maintain normal plant growth. Based on the consideration of cost control and ecological environment safeguards, it is suggested that the reasonable grouting thickness is about 2 m.  
The Influence of Permeability Coefficient of Water-Blocking Grouting Circle on Tunnel Groundwater Level  
Different grouting parameters have significant differences in the permeability coefficient of the water-blocking grouting circle. The permeability coefficient of different water-blocking grouting circles with the same thickness will significantly impact groundwater discharge and water level. To analyze the effect of different permeability coefficients of water-blocking grouting circles on groundwater, three situations were studied: a grouting circle thickness of 2 m with permeability coefficients of 4.9 \(\times\) 10\({}^{-1}\) m/d, 4.9 \(\times\) 10\({}^{-2}\) m/d, and 4.9 \(\times\) 10\({}^{-3}\) m/d. The results are shown in Figure 11. The smaller the permeability coefficient, the more significant the water-blocking effect of the grouting circle, the smaller the change in groundwater level, and the higher the degree of water level recovery at each measuring point. Within a range of 5 m below the groundwater level, the range of water level decline during tunnel construction was from 0.02 m to 0.66 m, and the degree of water level recovery exceeded 90%. After recovery, the maximum range of groundwater level decline was less than 0.07 m, so that it was able to ensure the normal growth of plants. The permeability coefficient of the grouting circle was 4.9 \(\times\) 10\({}^{-3}\) m/d, which can effectively control the groundwater level decline and prevent the growth of plants from being affected. Figure 10: Water level at different grouting ranges at various measuring points during the construction process. (**a**) The lowest water level. (**b**) The final recovery water level.
## 1 Introduction
The amplitude modulation is a consequence of the near resonating condition between structural vibration and water sloshing. Damping in liquid-tank systems is generated by wave breaking and the impact of liquid on the container walls, as a result of a complex free surface fluid-structure interaction problem [25; 26; 27]. For the interested reader, damping and beating phenomena have been widely treated in the literature on mechanical vibrations (e.g., see [28]). Given the relevance of fluid-structural interaction phenomena in different branches of science and engineering, in recent years several studies have been developed in order to deeply understand the dynamics of sloshing phenomena and model their behaviour in mechanical coupled systems [29; 30; 31] with the majority of the applications in the field of civil engineering applications [32]. In this framework, the use of tanks with different bottom shapes has, in fact, revealed interesting properties for technological applications, such as the Tuned Sloshing Dampers (TSDs) not only for the case of sloped bottom shape which represents one of the most studied [33; 34; 35; 36; 37; 38] but also for different various geometries [39; 40; 41; 42; 43]. Besides the civil engineering applications, significant advantages can be obtained also in different fields by simply modifying the bottom shape of the container. In this way, it is possible to operate on the main properties of the sloshing phenomenon, i.e., natural frequencies, damping ratios, effective mass and stiffness, which regulate the effectiveness of the coupled dynamic systems. Even if it is known that the fluid dynamic response is affected by the bottom geometry of the tank, there are some aspects which are still open and require more investigations. In this work an experimental campaign aimed at investigating the natural sloshing frequencies and the damping properties of a liquid inside a tank characterised by different bottom shapes is carried out and some results compared through analytical solutions available in the literature.