esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, a new controller using a new modified Riccati-like equation with three parameters of the PID controller was proposed and its effectiveness was validated from computer simulation and experimental realization. The robustness of the controller was improved using the sliding surface of sliding mode controller and the fuzzy neural networks model. The H_infinity technique was also used for connecting the above controller and the parameters of the Riccati-like equation. The stability of the proposed control was proven based on the Lyapunov stability criterion. The formulated controller was applied to vibration control of the vehicle seat suspension system with MR (magnetorheological) damper subjected to severe road excitations: random bump road and random step wave road. It was demonstrated that the proposed controller can bring control robustness, showing well-controlled unwanted vibrations at the seat and the driver position in terms of the displacement and accelerations. In addition, it was shown from comparative work that the proposed controller can provide the best vibration control performance compared two existing controllers, which are kinds of hybrid controllers designed for robustness guarantee of control system. The superior property of the proposed controller was also confirmed by calculating two factors of transmission: ToD and ToA. The results presented in this work are self-explanatory, justifying that the proposed controller can apply to any physical system subjected to disturbances and/or uncertainties. As a second phase work, a new robust controller will be formulated by considering both the variation of the main system parameters such as sprung mass and unmodeled dynamics of the driver such as human body model.  
Figure 8: Applied current of random-step-wave excitation: (**a**) general view; and (**b**) large view.  
Figure 9: Experiment results of power spectral density: (**a**) acceleration of seat; and (**b**) acceleration of driver.  
**Author Contributions:** [PERSON] derived all equations and did simulations and experiment to achieve performance results and [PERSON] created the idea and made the problem formulation. In addition, both authors wrote the paper together by carefully checking the main technical contribution and sound as well.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2017R1A2B3003026). This financial support is gratefully acknowledged.  
**Conflicts of Interest:** The authors declare that there is no conflict of interest.
# A Privacy-Preserving Routing Protocol Using Mix Networks in Opportunistic Networks
## 4 Evaluation
### Simulation Environment and Scenarios  
We present an experiment using an enhanced version of the Opportunistic Network Environment (ONE) simulator [15] that included our anonymous mixnet routing. We chose different scenarios to analyze the performance of our schema. Node contacts from the scenarios were defined by physical contacts obtained from real mobility traces from the Crawdad database ([[http://crawdad.org/](http://crawdad.org/)]([http://crawdad.org/](http://crawdad.org/))), a community resource for collecting wireless data at Dartmouth College. Each scenario corresponded to a different network with different nodes, and different mobility and contact patterns.  
The first scenario, Info5, was based on real mobility traces [16] obtained during the 2005 edition of the Infocom conference over the course of almost three days. Contacts from these mobility traces represent 22,459 contacts from 41 different nodes. The second scenario, Cambridge, was based on 10,641 real contact traces from 51 students from the system research group of the University of Cambridge carrying small devices for six days [17]. Lastly, the third scenario, Taxis [18], contained 449,226 mobility contacts from 304 taxis during one month in the city of Rome.  
Table 1 summarizes the main characteristics of the three scenarios. These include the connections per minute, which is the average number of connections a network has in one minute during the whole duration of the scenario; total connection time, which is the sum of the duration of all connections in the network, \(\sum_{e_{i}\in G}\lambda(e_{i})\); and the maximal and average degree of all nodes \(v\in V^{*}\), where the degree of the node is determined by \(N^{*}(v)\) (see Section 3.1).  
These scenarios are commonly used in the evaluation of routing algorithms in the OppNet literature. They provide a realistic setup to measure the feasibility of our proposal.  
In our experiment, we set the simulation time of 86,400 s (24 h). That is, we simulate each scenario for one day.
# Trusted GNSS-Based Time Synchronization for Industry 4.0 Applications
###### Abstract  
The protection of satellite-derived timing information is becoming a fundamental requirement in Industry 4.0 applications, as well as in a growing number of critical infrastructures. All the industrial systems where several nodes or devices communicate and/or coordinate their functionalities by means of a communication network need accurate, reliable and trusted time synchronization. For instance, the correct operation of automation and control systems, measurement and automatic test systems, power generation, transmission, and distribution typically require a sub-microsecond time accuracy. This paper analyses the main attack vectors and stresses the need for software integrity control at network nodes of Industry 4.0 applications to complement existing security solutions that focus on Global Navigation Satellite System (GNSS) radio-frequency spectrum and Precision Time Protocol (PTP), also known as IEEE-1588. A real implementation of a Software Integrity Architecture in accordance with Trusted Computing principles concludes the work, together with the presentation of promising results obtained with a flexible and reconfigurable testbed for hands-on activities.  
trusted computing; industry 4.0; cyber-physical system; time synchronization; cybersecurity; global navigation satellite system; embedded system
# Fracture Prediction in Weldox 700E Steel Subjected to High Velocity Impact Using LS-DYNA
## 3 Results and Discussion
### Validation
#### 3.2.1 Validation for Deformation with No Fracture in Taylor Rod  
The developed mathematical model is first validated using the Taylor rod impact problem [35]. The Taylor rod, assumed to be of circular shape, is impacted at the rigid flat target. In view of the requirement, the fracture model is not applied with the material model for the present validation work. The dimensions for the problem domain and the values of material constants are taken from the literature [35]. The problem domain is similar to as shown in Figure 3 with the difference that the circular projectile (termed Taylor rod) is deformable, and the flat target is assumed to be rigid. Due to the symmetry of the problem, the quarter model is applied for investigation. The radius of the circular projectile is 3.935 mm, and the initial length is 25.4 mm. The impact velocity is 338 m/s. Table 4 shows the comparison of the present work and experimental data [35] in terms of length ratio and radius ratio. The length ratio is defined as the ratio of the final deformed length (\(L_{f}\)) to the initial undeformed length (\(L_{i}\)), whereas the radius ratio is defined as the ratio of the final radius of the Taylor rod at the impacting face (\(r_{f}\)) to the initial radius (\(r_{i}\)). It is observed from Table 4 that the result from the present work is in close confirmation to the experimental results from the literature [35]. Further, the final deformed shape of the Taylor rod is also compared with the experimental shape. The deformed shapes of the Taylor rod from experimental work and present work are placed adjacent to each other as shown in Figure 5. The black solid line represents the experimental shape [35], and the red color mesh represents the predicted shape from the present work. It is observed that the deformed shape of the Taylor rod is in close confirmation to the experimental shape [35]. There is some deviation in the results, which may be due to some approximation for material constants and friction conditions at the contact interface.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Length Ratio** & **Radius Ratio** \\ \hline Experimental & 0.66 & 2.220 \\ Present work & 0.65 & 2.219 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Validation of the deformed shape for Taylor rod with experimental work [35].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect Ratio** & **No. of Elements** & **No. of Nodes** \\ \hline Aspect ratio 1 & 535,800 & 568,465 \\ Aspect ratio 2 & 267,900 & 291,165 \\ Aspect ratio 3 & 214,320 & 235,705 \\ Aspect ratio 4 & 133,950 & 152,515 \\ Aspect ratio 5 & 107,160 & 124,785 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of elements and number of nodes in the target plate for mesh convergence study.
# A Micro-Processor-Based Feedback Stabilization Scheme for High-Q, Non-Linear Silicon Resonators
## 1 Introduction
However, in the case of non-linear applications, the signal wavelength cannot be dithered since this will push the device past the'switch-off' point on the long wavelength side. In this work a means is presented by which cavities operating in the non-linear regime can be actively stabilized using a simple thermal control element. In Section 2 the device technology and fabrication processes are presented along with experimental setup designs and details of the stabilization scheme. Section 3 details the results of linear and non-linear optical measurements under direct stabilization control.
###### Abstract  
This paper presents climatological features of the longitudinal structures WN4, WN3, and WN2 and their drivers observed in the lower thermospheric temperatures and in the ionospheric TEC. For this purpose, two long-term data sets are utilized: the satellite SABER/TIMED temperature measurements, and the global TEC maps generated with the NASA JPL for the interval of 2002-2022. As the main drivers of the longitudinal structures are mainly nonmigrating tides, this study first investigates the climatology of those nonmigrating tides, which are the main contributors of the considered longitudinal structures; these are nonmigrating diurnal DE3, DE2, and DW2, and semidiurnal SW4 and SE2 tides. The climatology of WN4, WN3, and WN2 structures in the lower thermosphere reveals that WN4 is the strongest one with a magnitude of \(\sim\)20 K observed at 10\({}^{\circ}\) S in August, followed by WN2 with \(\sim\)13.9 K at 10\({}^{\circ}\) S in February, and the weakest is WN3 with \(\sim\)12.4 K observed over the equator in July. In the ionosphere, WN3 is the strongest structure with a magnitude of 5.9 TECU located at \(-\)30\({}^{\circ}\) molid latitude in October, followed by WN2 with 5.4 TECU at 30 molip in March, and the last is WN4 with 3.7 TECU at \(-\)30 molip in August. Both the climatology of the WSA and the features of its drivers are investigated as well.  
climatology of nonmigrating tides; global structure; seasonal and interannual variability; climatology of longitudinal TEC structures and WSA anomaly 2024202415478[[https://doi.org/10.3390/atmos1504078](https://doi.org/10.3390/atmos1504078)]([https://doi.org/10.3390/atmos1504078](https://doi.org/10.3390/atmos1504078))
## 3 Results and Discussions  
### Relaxation Time Measurements  
In order to verify the repeatability of the experimental data and reduce the impact of accidental errors on the measurement results, the same measurement device was used to perform repeated observations on the same sample. The apparent resistivity in the time domain can be calculated using the following formula [32]:  
\[\rho(t)=G\cdot\Delta U(t)/I, \tag{4}\]  
where \(\Delta U(t)\) and \(I\) are the voltage difference and the current injected into the ground, respectively. In addition, the parameter \(G\) represents the geometric factor, the expression of which can be derived according to the electrode arrangement of the quadrupole method [33].  
\[G=2\pi/(1/AM-1/BM-1/AN+1/BN) \tag{5}\]Subsequently, the apparent resistivity in the time-domain is converted into the frequency by the Fourier transform, and the expression of complex resistivity is then obtained with  
\[\rho(iw)=G\cdot\Delta U(iw)/I(iw). \tag{6}\]  
According to the complex resistivity parameter in Equation (6), the resistivity phase distribution can be obtained, which is shown in Equation (7) [34].  
\[\varphi=\arctan(\rho^{\prime\prime}/\rho^{\prime}) \tag{7}\]  
Here, the parameters \(\rho^{\prime\prime}\) and \(\rho^{\prime}\) represent the real and imaginary parts of the complex resistivity, respectively.  
The relaxation time that needs to be studied in this paper is related to the phase peak position (\(w_{\varphi}{}^{peak}\)) and the relaxation time (\(\tau\)), which can be obtained using the following relationship in Equation 8 [34]. The parameters \(m\) and \(c\) are the chargeability and exponent, respectively.  
\[w_{\varphi}{}^{peak}=1/\tau\cdot(1-m)^{1/2c} \tag{8}\]  
Furthermore, ignoring the trivial influence of chargeability \(m\) and exponent \(c\). The relaxation of time can be calculated by using the frequency of the phase peak \(f_{peak}\)[35].  
\[\tau=1/2\pi\!f_{peak} \tag{9}\]  
Therefore, the distribution of relaxation time within different grain sizes was obtained through experiments and data processing, and the results can be seen in Table 1.  
The relaxation time curves of eight quartz sand samples of different grain sizes are shown in Figure 6. In this figure, it can be seen that, with the increase in grain size, the numerical value of relaxation time also increased.
## 3 Results
### Water Harvesting  
The harvesting (condensation) of relative air humidity is the main factor in this research, and is calculated as follows:  
\[\text{Kg}_{\text{water harvested}}=\frac{(\text{PSH}*3600)(\text{Humidity}*\%\text{ reduction})}{\text{Time to condense}} \tag{13}\]  
The application of the above equation involves all the factors calculated above. The results of the water harvested for the case study are shown in the Tables 4-6.  
The above tables show the behaviour of AWG water harvesting for each refrigeration package proposed and for the study region's conditions. The month with the least amount of water collection was January, with minimum quantities of 3.6 to 0.89 L/day, whereas August was the month with the highest amount of harvesting with a maximum of 18 to 3.9 L/day. The variations in the amount of water captured by the AWG were due to the daily hours of operation in each month of the year (equal to the PSH), the amount of moisture present in the air and the refrigeration packages used.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Month**} & \multicolumn{4}{c}{**Water Harvested with 1 Ton of Refrigeration (Litres)**} \\ \cline{2-7}  & \multicolumn{2}{c}{**25\% Reduction**} & \multicolumn{2}{c}{**50\% Reduction**} & \multicolumn{2}{c}{**75\% Reduction**} \\ \cline{2-7}  & **Max** & **Min** & **Max** & **Min** & **Max** & **Min** \\ \hline January & 5.4 & 3.56 & 7.17 & 4.99 & 10.1 & 7.49 \\ February & 6.47 & 4.27 & 8.59 & 5.98 & 12.2 & 8.98 \\ March & 8.11 & 5.35 & 10.77 & 7.50 & 15.30 & 11.26 \\ April & 9.24 & 6.09 & 12.26 & 8.54 & 17.42 & 12.82 \\ May & 9.43 & 6.22 & 12.51 & 8.71 & 17.77 & 13.08 \\ June & 9.52 & 6.28 & 12.63 & 8.80 & 17.94 & 13.21 \\ July & 9.00 & 5.94 & 11.95 & 8.32 & 16.97 & 12.49 \\ August & 9.65 & 6.37 & 12.81 & 8.92 & 18.19 & 13.39 \\ September & 8.29 & 5.47 & 11.00 & 7.66 & 15.62 & 11.50 \\ October & 8.15 & 5.38 & 10.82 & 7.54 & 15.37 & 11.32 \\ November & 6.27 & 4.14 & 8.33 & 5.80 & 11.83 & 8.71 \\ December & 5.49 & 3.62 & 7.29 & 5.08 & 10.35 & 7.62 \\ \hline \end{tabular}
\end{table}
Table 4: Maximum and minimum amounts of water harvested in the months of the year with a compressor with 1 ton of refrigeration capacity.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{} & \multicolumn{2}{c}{**1/4 Ton of Refrigeration**} & \multicolumn{2}{c}{**1/2 Ton of Refrigeration**} & \multicolumn{2}{c}{**1 Ton of Refrigeration**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Reduction**} & \multicolumn{2}{c}{**Reduction**} & \multicolumn{2}{c}{**Reduction**} \\ \cline{2-10}  & **25\%** & **50\%** & **75\%** & **25\%** & **50\%** & **75\%** & **25\%** & **50\%** & **75\%** \\ \hline Max & 19.32 & 27.58 & 38.72 & 9.66 & 13.79 & 19.36 & 4.83 & 6.89 & 9.68 \\ Min & 23.48 & 35.39 & 52.60 & 11.74 & 17.69 & 26.30 & 5.87 & 8.84 & 13.15 \\ \hline \end{tabular}
\end{table}
Table 3: Condensation times for each refrigeration package in seconds.
## 3 Material and Method
### Features Fusion and Classification  
Finally, a CCA-based feature reduction is applied to the extracted features. A serial-based fusion approach is used to fuse the resultant reduced vectors. A vector of dimensions \(n\times 2000\) is obtained simply by concatenating the features. This is used for classification and fed into the classifiers. Ten different types of classification techniques were implemented for the analysis of classification accuracy. In the past, agricultural applications suffered from the unavailability of data due to its complex structure and collection cost, especially for mango plants because these plants are available only in select regions. The cost of labeling for data acquisition is also very high [37]. Hence, this issue encouraged us to collect data by ourselves from mango growing areas in Pakistan. We adopted 2 strategies in this research: (1) data augmentation, and (2) a segmentation technique to segment the diseased parts and veins of mango leaves. This is a unique technique as it has not been performed in earlier agricultural applications, especially with the mango plant. As a final point, the segmented images were entered into a computer-based system for feature fusion, and then for identification. The computation was about 45 min as the segmentation of one image took almost 7.5 s. All simulations were performed on a personal computer with the following specifications: 64-bit Windows operating system with MATLAB version 2018, 32 GB RAM with an Intel(r) Xeon(r) processor and central processing unit of 2.2 GHz, GPU GeForce GT\(\times\)1080.
## 3 Microseismic Evolution Characteristics of Rockburst Formation Process
### Special Types of Rockbursts  
Because different types of rockbursts show different formation mechanisms and microseismic evolution laws, it is necessary to distinguish the different types of rockbursts for the early warning. However, scholars have not reached a consensus on the classification of rockbursts. Different classification criteria and results are often provided from different perspectives. Moreover, with the deepening of the understanding and research on rockbursts, some new types of rockbursts have been distinguished and defined. For example, [PERSON] et al. [38] classified an intermittent rockburst as one that has the same or higher level and occurring several times within a certain duration in the same area. For tunnel engineering, there are two rockburst classification methods with the highest frequency: one is to divide rockburst into strainbursts and structure-type rockbursts [39] and the other is to divide rockbursts into immediate rockbursts and time-delayed rockbursts [40] (also known as delayed rockbursts [41]). By sorting out the related research results of structure-type and delayed rockbursts, this section summarizes the microseismic evolution characteristics and formation mechanism of these special types of rockbursts.
## 3 Method
### SCR Analysis  
To extract the response energy of the point target from the background clutter, the signal-to-clutter ratio (SCR) of the point target was first calculated and analyzed [18]. SCR is defined as the ratio of the peak power in the target impulse response to the average background clutter power estimated from the region close to the target and is used to measure the contrast of the point target with respect to the background clutter [2], as presented in Equation (3). If the point target SCR is greater than 20 dB, the effect of background noise on the calibration results is less than 0.5 dB [19], thus determining whether the point target SCR is greater than 20 dB, which is used as the reference standard towards employing the point target for radiation calibration.  
\[\text{SCR}=\frac{\sigma_{pq}^{T}}{\sigma_{pq}^{C}}=\frac{\sigma_{pq}^{T} \text{sin}\theta}{\langle\sigma_{pq}^{C}\rangle\delta_{a}\delta_{r}} \tag{3}\]  
Here, \(\sigma_{pq}^{T}\) represents the point target RCS, \(\theta\) represents the local incidence angle of the point target, \(\left\langle\sigma_{pq}^{C}\right\rangle\) represents the average background clutter RCS, and \(\delta_{a}\) and \(\delta_{r}\) represent the SAR image pixel space in the azimuth and range directions, respectively.  
Figure 6: Automatic corner reflector review of azimuth and elevation angle. (**a**) Reviewing the azimuth angle with north marker, (**b**) Reviewing the elevation angle with level meter.
###### Abstract  
This study classified chaotic time series data, including smooth and nonsmooth problems in a dynamic system, using a convolutional neural network (CNN) and verified it through the Lyapunov exponent. For this, the classical nonlinear differential equation by the Lorenz model was used to analyze a smooth dynamic system. The vibro-impact model was used for the nonsmooth dynamic system. Recurrence is a fundamental property of a dynamic system, and a recurrence plot is a representative method to visualize the recurrence characteristics of reconstructed phase space. Therefore, this study calculated the Lyapunov exponent by parametric analysis and visualized the corresponding recurrence matrix to show the dynamic characteristics as an image. In addition, the dynamic characteristics were classified using the proposed CNN model. The proposed CNN model determined chaos with an accuracy of more than 92%.  
convolutional neural network; Lyapunov exponent; recurrence plot; chaos +
Footnote †: journal: Applied Sciences