esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Typical Characteristics of Signals on Interconnections
Details for the substrate are as follows: dielectric constant \(\varepsilon_{\mathrm{r}}=3\), loss tangent \(\tan\delta=0.001\), substrate thickness \(\mathrm{h}=1.524\) mm, conductor thickness \(\mathrm{t}=17.5\)\(\upmu\)m. [9] The waveforms at the input and output of a microstrip line for RO3003 are represented in Figure 4.  
For matching the circuit, the Smith diagram is used below starting with mismatch in Figure 5a, using TxLine synthesis tool from Microwave Office (MWO) [6], the synthesis tool for different technologies included in MWO [6]. In Figure 5b, the progressive wave is obtained as the magnitude of the return loss is in the center of the Smith chart at 10 GHz, or, based on Nyquist rate frequency, the data rate is 20 Gbit/s. Figure 4: Voltage as variation in time: (**a**) waveform with rise and fall times of the pulses and (**b**) waveform at the output of the interconnect. Figure 5: Matching the ports–Smith chart verification upon synthesis of microstrip line: (**a**) unmatched and (**b**) matched ports after synthesis for microstrip width, substrate and conductor definitions, simulation at 10 GHz, single point. To determine the trace transfer function, below we represent the frequency variation of the insertion loss (IL). The frequency characteristics based on IL are similar to a low pass filter as can be deduced from Figure 6b. For the 10 GHz frequency selected to synthesize the circuit, the reflection loss presents general minimum of \(-80\) dB (for a step in simulation process of 0.01 GHz) for the impedance port in Figure 6a.
## 1 Introduction
### Modeling of Cell Separation/Partitioning
#### 1.2.2 Conventional CFD Methods
From the microfluidicist's outlook, although it is mathematically expensive and laborious, in terms of implementing complex boundary conditions, with gradient-based boundary conditions that are commonly encountered in applied electrical fields, transport systems with surface phase reactions and thermal analysis involving convective heat transfer, this method is much easier to work with compared to the aforementioned ones [73]. Isoperimetric quadratic elements can also be employed to precisely conform to curved fluid-fluid interfaces such as those encountered in capillary flows [69]. Among others, the main disadvantage of FEM is the mathematical difficulties associated with handling highly irregularly shaped elements (e.g., large aspect ratios or highly skewed) or large ranges in element size within a single mesh. In lab-on-a-chip devices, the relevant length scales can range over seven orders of magnitude, from the double-layer thickness (nm) to channel length (cm) and thus, it is often difficult to avoid using such elements while maintaining a computationally tractable problem. Despite these challenges, dramatic developments in computational capabilities during recent years make this method pivotal for several well-known commercial software packages such as NASTRAN, ANSYS, ABAQUS, COMSOL, and FIDAP. A few examples of how these methods performed in the real applications are presented in the following. CFD methods have mostly been applied to investigate the phenomena related to human arteries, such as blockage because of atherosclerosis, the application and performance of ventricular assist devices (VADs) and etc. [PERSON] et al. [74] implemented CFX to study the performance of a CF4 type VAD pump and effects of the pump on the flow path. [PERSON] et al. [75] used the discrete element method to study transport, activation, and adhesion of RBCs through thrombus formation around endothelium. [PERSON] and [PERSON] [76] used a three-phase CFD method to investigate RBCs reflections in disturbed flow regions of human arteries. Another interesting study was conducted by [77], where the authors studied the dynamics of RBCs in capillaries of finger nail-fold for various cases. A more recent study was done by [78], where the researchers used the CFD discrete element method (CFD-DEM) to conceptually design a microfluidic device capable of magnetic sorting of malaria-infected red blood cells. A few examples of results obtained from simulations using CFD methods in similar applications are presented in Figure 5. More details about applications of these methods can be found in [69; 79; 80; 81].
## 1 Introduction  
Many countries' major military facilities and command posts were established underground or within tunnels to enhance defense. With the development and advancement of weapon precision, various missiles and weapons can now hit the portal of a tunnel directly. In addition, the impact of an explosion is sufficient to constitute a great threat to internal personnel and equipment, which mayeven lead to equipment damage and workplace injuries. Therefore, it isnecessary to evaluate the techniques applied for explosions at portals.  
The study of the dynamic response of structures subjected to air blast loading received a lot of attention in the last few decades [1; 2; 3; 4; 5; 6; 7]. In 1993, [PERSON] [8] conducted a 0-90\({}^{\circ}\) explosion test at different distances within a radius five times the size of the tunnel diameter from portals using different amounts of charge. The results showed that the blast pressure in the tunnels was attenuated with an increase in distance. In 2004, [PERSON] et al. [9] usedspherical Comp. B charges on a circular tunnel with a diameter of 0.298 m and a length of 54.3 m. The experiments were conducted with explosives placed at the portal. The results also showed that the blast pressure within the tunnel decreased with an increase in distance, while the impulse response nearly maintained a constant value for different locations in the tunnel. In 2005, [PERSON] et al. [10] investigated the relation between subscale and full-sized explosion tests; the results indicated that the subscale explosion tests could be an effective substitute for full-sized tests.  
The main purpose of this paper is to investigate the explosive pressure response in steel tunnels when subjected to external explosion. Both field tests and the hydrodynamic finite element code LS-DYNA were employed. A comparison with the experimental results indicated that the present numerical method is accurate and efficient for finite element analysis of explosive loading. A simple way to estimate the overpressure in steel tunnels was proposed. The proposed methodology is both useful and efficient and can be further developed for designing protection for military structures and other facilities against explosion.
## 6 Conclusions  
Two novel schemes for data rate enhancement in UWB communication technology have been presented through the course of this paper. The proposed schemes use orthogonal pulses to transmit data in both single user and multiple user scenarios. Instead of transmitting individual orthogonal pulses, this scheme transmits composite pulse of 4 orthogonal pulses and by doing such it manages to increase the data rate in single-user scenario by 4 folds and at the same time accommodates 12 channels for simultaneous data transmission. The correlation properties of the MHP are analyzed through MATLAB simulations as it also validates that the composite pulse can be decoded in the receiver end with a threshold value calculated from the correlation analysis of MHPs with a 10-ns pulse width. The carefully chosen threshold level of 21% satisfies both the low and high ends of the SNR values. One of the major gains in employing the proposed scheme is its capability to work with the existing pulse-based UWB system due to the similarities in features such as the pulse width, pulse repetition interval (PRI), and energy with that of the single orthogonal pulse-based system. Since the difference exists exclusively in the shape of the pulse, it will not affect the transmission and the synchronization; as a result the proposed scheme can be readily incorporated with the existing UWB system with minimal change. The proposed scheme's competence in enhancing number of channels for simultaneous data transmission along with the rate of data being transmitted in sub-GHz UWB communication makes it a leading alternative for future wireless sensing applications.  
Partial support of this work was provided by the NSF-CPS (Cyber-Physical Systems) program with award number CNS-1645863. Opinions expressed are those of the authors and not necessarily those of the National Science Foundation.  
[PERSON], [PERSON]-[PERSON] and [PERSON] contributed to the algorithm design and simulation; [PERSON] contributed to the simulation and writing of the paper.  
The authors declare no conflict of interest.  
Figure 5: Bit error rate (BER) plot for pulse shape modulation for random inputs (\(10^{6}\)).
# Interval Analysis of Vibro-Acoustic Systems by the Enclosing Interval Finite-Element Method
## 2 Formulation of Vibro-Acoustic Coupling System with Uncertain-But-Bounded Parameters
### Interval Vibro-Acoustic Coupling Equation
Assuming that the interval uncertain properties are mainly assembled in structure, and then the structure is subject to uncertain variability, Equation (9) is transferred into the new form to analyze the uncertain vibro-acoustic coupling system. \[\mathbf{Z}^{l}\cdot\mathbf{P}^{I}=\mathbf{F}^{I} \tag{10}\] \[\mathbf{Z}^{l}=\left[ \begin{array}{cc}\mathbf{K}_{s}^{l}- \omega^{2}\mathbf{M}_{s}^{l}&-\mathbf{R}_{cp}\\ -\rho_{a}\omega^{2}\mathbf{R}_{cp}^{T}&\mathbf{K}_{a}-\omega^{2}\mathbf{M}_{a} \end{array} \right]\] \[\mathbf{P}^{I}=\left\{ \begin{array}{c}\mathbf{w}_{s}^{l}\\ \mathbf{p}_{a}^{l}\end{array} \right\};\quad\mathbf{F}^{I}=\left\{ \begin{array} []{c}\mathbf{F}_{s}^{l}\\ \mathbf{F}_{a}\end{array} \right\}\]  
where both the global matrices for structure \(\mathbf{K}_{s}^{I},\mathbf{M}_{s}^{l},\mathbf{F}_{s}^{l}\) and response \(\mathbf{w}_{s}^{I},\mathbf{p}_{a}^{I}\) become the interval quantities with respect to the interval parameters \(\mathbf{u}^{I}\), and then the uncertainties can be expressed as the \(n\)-dimensional closed interval vector with the equivalent form of midpoint value and maximum width,  
\[\mathbf{u}^{I}=\left[\underline{\mathbf{u}},\overline{\mathbf{u}}\right] =\left(u_{i}^{I}\right)_{n}=\left\{u_{i}^{c}+\left[-\Delta u_{i}, \Delta u_{i}\right]\right\}_{n} \tag{11}\] \[=\mathbf{u}^{c}+\left[-\Delta\mathbf{u},\Delta\mathbf{u}\right]\] \[=\mathbf{u}^{c}+\Delta\mathbf{u}^{I}\quad i=1,2\ldots n\]
# Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
## 2 STJL Model Applied to Online Multi-Object Tracking
### Tracker Initialization and Terminate
The higher the similarity, the higher the confidence. The second term evaluates the trajectory length, where \(\beta\) is the control coefficient; the longer the trajectory, the higher the confidence. Both terms fall within the range of 0 to 1. Only robust detection boxes are retained as trial trackers for potential new objects, while weaker ones are discarded. If their trajectory confidence surpasses a threshold, they become official trackers; otherwise, they are eliminated. In the proposed method, the confidence of a tentative trajectory \(T_{\textit{tentative}}^{i}\) is evaluated based on a threshold to determine whether it can transition into a confirmed trajectory. Specifically, as described in Equation (4), if the confidence of the tentative trajectory \(T_{\textit{tentative}}^{i}\) exceeds the threshold \(th2\), the trajectory is classified as a new confirmed trajectory \(T_{\textit{new}}\). Otherwise, the trajectory remains in the tentative state. This decision process is critical for ensuring that only reliable trajectories are promoted to the confirmed state, while less reliable ones remain tentative. \[T_{\textit{tentative}}^{i}=\left\{ \begin{array}{ll}T_{\textit{new}},&\quad if \ T_{\textit{tentative}}^{i}\ \textit{confidence}>th2\\ T_{\textit{tentative}}^{i},&\quad otherwise\end{array} \right., \tag{4}\]  
After the matching process, there may be unmatched detection boxes as well as trackers that are not linked to any detection boxes. Some of these trackers may no longer be linked because the object has left the tracking scene, requiring a decision on whether to terminate the tracker. The tracker's age is used as the criterion for this decision. Tracker age refers to the number of frames since the tracker was last linked to a detection box, as defined in Equation (5). The tracker age \(T_{\textit{age}}\) increments by \(1\) each time the Kalman filter makes a prediction. If at the current time \(n\), the tracker \(T_{i}\) is linked to a new detection box, the tracker age is reset to 0. When the object has left the scene, the tracker is unable to link to a detection box, causing its age to continue increasing. The algorithm terminates the tracker when its age exceeds a certain number of frames. In practice, this termination threshold is set to 30 frames and can be adjusted based on the video's frames per second. \[T_{\textit{age}}=\left\{ \begin{array}{ll}0,&\quad if\ T_{i}(n)\
eq\varnothing \\ T_{\textit{age}}+1,&\quad Otherwise\end{array} \right., \tag{5}\]
# Probing Ferroic States in Oxide Thin Films Using Optical Second Harmonic Generation
## 3 Probing Ferroic States in Thin Films Using SHG
### Optical Second Harmonic in Domain Engineered Ferroic Thin Films
At the strain-driven morphotropic phase boundary, exotic conducting states and enhanced piezoelectric coefficients have been observed [104; 105]. The SHG contributions associated with the two phases have different relative intensities as a function of the angle of incidence [101; 102; 103; 106] as shown in Figure 10c. The pseudo-tetragonal phase (in red) has a symmetric behavior similar to a genuine tetragonal structure (cf. Figure 8b), while the SHG response of the R-phase (in green) reflects its polarization anisotropy. These distinct behaviors allow associating the nonlinear optical output at different incidence angles to the two ferroelectric phases, as shown in Figure 10d,e. In particular, a measurement at \(\theta\) = 45\({}^{\circ}\) will be sensitive to the T-phase, while at normal incidence the SHG output is associated to the R-phase. For a fixed substrate, i.e., strain state, the strain relaxation with increasing thickness will modify the relative ratio of BFO T- and R-phases. This structural evolution results in a symmetry change for BFO films at various thicknesses that can be studied with SHG [107]. In ultrathin films grown in a compressive strain state using Nb-doped STO as substrate, a four-fold rotational symmetry associated with the absence of in-plane anisotropy is attributed to the T-phase [107] as shown in Figure 11a. Figure 10: SHG to distinguish different monoclinic phases. (**a,b**) SHG anisotropy measurements collected at normal incidence for T- and R-phase, respectively. Reproduced with permission from [88] Copyright Wiley-VCH Verlag GmbH and Co. KGaA, 2015. (**c**) SHG intensity for different BFO films at different angles of incidence. (**d,e**) Polarizer measurements allow distinguishing the two phases. (**c-e**) Reprinted with the permission from [101], Copyright AIP Publishing, 2010. Around 100 nm, there is a transition regime where both phases coexist, while at 180 nm the polar axis of the R-phase is fully developed as observed in Figure 11b,c.
# Effects of Phase Shift Errors in Recurrence Plot for Rotating Machinery Fault Diagnosis
## 4 Conclusions  
In this paper, the phase plane and RP are proposed to evaluate the phase shift error effect on the solution of complex asynchronous periodic functions and to quantify the smoothing of a Gaussian peak with white noise. The results show that the proposed method is useful for the analysis of the phase shift errors in both cases.  
When the spectral analysis derives from the integration of the acceleration of a real-time spectrum, instead of a simulated one, many phase shift errors are due to data acquisition and signal conditioning, and most of them are generated by the integration methods. The results clearly show that the behavior of a simple harmonic function and a complex harmonic function is different due to phase shift errors.  
A Gaussian peak with white noise was smoothed and analyzed for several sample points for the same proportional width and three passes each one. The results showed that after the signal smoothing process, the noise is still present and produces phase shift errors, which could be observed and quantified by RP and RQA. Another analysis demonstrated that the data acquisition resolution is a critical factor in order to have a meaningful RP quantification. At least 100 points per peak should be considered in data analysis and smoothing to quantify nonlinearities.  
The present work evaluated the adverse effects of phase shifts due to the acceleration integration and the smoothing process errors, which had a clear impact on the phase plane and the recurrence plot. Thus, phase shift can have a significant influence on machinery nonlinear fault diagnosis. The simulations reported here only represent some of the numerous and varied conditions taking place in a real measurement and further work could include other adverse conditions in the calculations in order to build a more complete analysis.  
Some future studies are required to quantify the effects on rotating machinery fault diagnosis due to common errors generated during the double integration process with real-time measurements, including signals with various vibration sources. Results showed that RP is affected by small changes in vibrations. Therefore, RP has a high sensitivity that could be applied to identify nonlinear mechanical systems such as friction problems in machinery applications.  
Conceptualization, validation, investigation, and writing original draft preparation I.T.-C.; methodology and supervision J.C.J.-C.; technical and writing editing reviewers C.S.L.-C. and [PERSON]-V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results  
### Arthropod Community  
Planthoppers and leafhoppers were more abundant during wet season sampling compared to the dry season (BPH: \(\chi^{2}\) = 21.656, \(p\) < 0.001; WBPH: \(\chi^{2}\) = 46.985, \(p\) < 0.001; GLH: \(\chi^{2}\) = 29.568, \(p\) < 0.001: Figure 1A,B). WBPH was the most abundant herbivore. There was no apparent variety effect on BPH (\(\chi^{2}\) = 0.364, \(p\) > 0.05) or WBPH (\(\chi^{2}\) = 1.392, \(p\) > 0.05) abundance in the field plots; however, more GLH (\(\chi^{2}\) = 20.838, \(p\) < 0.001) were captured in sweep nets at the IR64 plots. More GLH (\(\chi^{2}\) = 7.102, \(p\) < 0.01) were captured in low- than in high-nitrogen plots (Figure 1A,B); but captures of BPH were not affected by nitrogen level (BPH: \(\chi^{2}\) = 0.058, \(p\) > 0.05) (Figure 1A,B). There was a significant season-nitrogen interaction for WBPH (\(\chi^{2}\) = 8.196, \(p\) < 0.01) because of higher captures in low-nitrogen plots than in high-nitrogen plots during the wet season, but not the dry season (Figure 1A,B).  
Egg parasitoids were also more abundant during wet season sampling (_Digosita_ spp.: \(\chi^{2}\) = 72.667, \(p\) < 0.001; _Anagrus_ spp.: \(\chi^{2}\) = 9.075, \(p\) < 0.01; _Gonatocerus_ spp.: \(\chi^{2}\) = 84.525, \(p\) < 0.001: Figure 1C,D). _Digosita_ spp. (mainly \(O\). sp. nr _aesopi_) and _Gonatocerus_ spp. (mainly \(G\). sp. nr. _orientals_) were the most abundant egg parasitoids. At least two _Anagrus_ spp. (including _A. flavelous_ and _A. optabilis_) occurred at relatively low numbers in the plots (Figure 1C,D). More parasitoids were captured in low-nitrogen plots; however, there was no statistically significant effect of nitrogen (3.088 \(\geq\)\(\chi^{2}\) \(\geq\) 0.142, \(p\) \(\geq\) 0.05) or variety (0.740 \(\geq\)\(\chi^{2}\) \(\geq\) 0.344, \(p\) \(\geq\) 0.05) on captures of any of the three parasitoid groups (Figure 1C,D).  
Figure 1: Numbers of planthoppers (**A**,**B), leafhoppers (**A**,**B) and free-living egg parasitoids (**C**,**D) collected at plots of resistant (IR62) and susceptible (IR64) rice under low (gray bars: 0 added) and high (yellow bars: 150 kg N ha\({}^{-1}\)) levels of nitrogenous fertilizer. Sampling was conducted using sweep nets during the 2013 dry (**A**,**C) and wet (**B**,**D) seasons. Standard errors are indicated (N = 6); note differences in scales; results of GLM are indicated as \({}^{**}\) = \(p\)\(\leq\) 0.05 and \({}^{***}\) = \(p\)\(\leq\) 0.001; BPH = brown planthopper, WBPH = whitebacked planthopper, and GLH = green leafhopper; S = season.
## 3 Results
### Single Factor Analysis
#### 3.1.2 Human Disturbance Factors
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sensitivity Level**} & \multicolumn{2}{c}{**Land Use Types**} & \multicolumn{2}{c}{**Distance to Construction Land**} & \multicolumn{2}{c}{**Distance to Road**} \\ \cline{2-5}  & **Area (km2)** & **Proportion (\%)** & **Area (km2)** & **Proportion (\%)** & **Area (km2)** & **Proportion (\%)** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Insensitive \\ Slightly sensitive \\ Moderately sensitive \\ Highly sensitive \\ Extremely sensitive \\ \end{tabular} } & 543.72 & 2.40 & 12,950.13 & 57.07 & 12,931.68 & 56.99 \\  & 2581.43 & 11.38 & 4158.72 & 18.33 & 3957.44 & 17.44 \\  & 19,148.58 & 84.39 & 2524.39 & 11.13 & 2464.58 & 10.86 \\  & 313.51 & 1.38 & 1650.49 & 7.27 & 1776.04 & 7.83 \\  & 103.46 & 0.46 & 1406.97 & 6.20 & 1560.96 & 6.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Area and proportion of ecological sensitivity classification of human disturbance.