esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic and Active THz Graphene Metamaterial Devices
## 2 Fundamentals of Graphene  
### Linear Optical Property  
The optical properties of graphene originate from the two-dimensional electronic band where the conduction and valence bands contact at the Dirac point, forming the honeycomb lattice. The optical response of graphene electrons at visible and infrared frequencies is independent of incident light frequency and can be described by universal conductivity \(\mathrm{G}=e^{2}/4\hbar\). Compared to other semiconductor or metal materials with the same atom layer thickness, monolayer graphene has an extreme optical absorption (with \(\pi\alpha\) = 2.3%) [42]. In contrast, the reflection (\(<\)0.1%) is almost negligible in the visible light range. The opacity is determined only by the precision structure constant \(\alpha=\mathrm{E}^{2}/\hbar\mathrm{c}\)[43]. Without inter-layer electron coupling, the optical absorption of uniform multilayer graphene is dependent on the layers, and the opacity increases linearly by 2.3% with the stacking of layers.  
Linear response theory is applied to the light-matter interaction of graphene at far-infrared and terahertz frequencies. Under a random phase approximation condition, the dynamic optical conductivity of graphene is expressed as [44; 45]:  
\[\begin{array}{rl}\sigma_{g}\big{(}\omega,\mu_{c},\Gamma(or\ \tau^{-1}),T\big{)}=& \frac{-i\epsilon^{2}(\omega+i2\Gamma)}{\pi\hbar^{2}}\bigg{[}\frac{1}{(\omega+i 2\Gamma)}\int_{0}^{\infty}\epsilon\Big{(}\frac{\partial f_{d}(\epsilon)}{ \partial\epsilon}-\frac{\partial f_{d}(-\epsilon)}{\partial\epsilon}\Big{)}d \varepsilon-\\ &-\int_{0}^{\infty}\frac{f_{d}(-\epsilon)-f_{d}(\epsilon)}{(\omega+i2\Gamma) ^{-}-4(\epsilon/\hbar)^{2}}d\epsilon\bigg{]}\end{array} \tag{1}\]  
where \(f_{d}(\epsilon)=1/[\exp(\epsilon-\mu_{c}/k_{B}T)+1]\) is the Fermi distribution; \(\Gamma\) is the carrier scattering rate determined by defect scattering, ionized impurity scattering, and acoustic and optical phonon scattering; and \(\mu_{c}\) is chemical energy dependent on carrier density.  
Graphene conductivity comprises intraband and interband conductivity, which are induced by electron-phonon scattering relaxation and interband electron transition, respectively. When \(|\mu_{c}|\gg K_{B}T\), intraband conductivity is expressed by the Drude-Boltzmann model similar to metal, and it is controlled by chemical energy \(\mu_{c}\). For interband conductivity, there is a critical frequency \(\omega_{inter}\) related to chemical energy \(h\omega_{inter}=\mu_{c}\). The frequency is lower than \(\omega_{inter}\), and the interband conductivity is almost negligible in the total conductivity. Reference [46] presents the detailed evolution of graphene optical conductivity from sub-THz to near-IR with frequency, corresponding to different modulation mechanisms. In the sub-THz region, graphene is usually treated as a frequency-independent film, and the terahertz wave is controlled by the Fermi level. Surface plasmon polarization can be supported on graphene with more manipulation potential in the terahertz and mid-infrared frequency bands.
# Long-Term Structural State Trend Forecasting Based on an FFT-Informer Model
## 2 Long-Term Structural State Trend Forecasting Based on FFT-Informer Model
### Feature Extraction by FFT  
Extracting appropriate features is essential to ensure the performance of machine learning algorithms. Fast Fourier transform (FFT) [35] is a feature extraction method often used to pre-process time series. As a time-frequency domain signal feature extraction method, the resulting approximation reflects the amplitude, vibration frequency, trend, and other characteristics of the original data to a certain extent and is suitable for further processing of the data. Because of the fluctuation characteristics of structural state data, FFT is applied to extract maximum amplitude and its phase key amplitude and phase of the series as the data characteristics.  
The Fourier transform is applied widely in the signal processing field and can be regarded as a linear transformation operator. Fast Fourier transform (FFT) is a general term for applying computers to calculate Discrete Fourier transform (DFT), which is more efficient and faster. Let \(Z\) be a time series of length \(N\). Using sliding windows, \(Z\) is divided into \(m\) data segments with length \(n\). Its Discrete Fourier transform (DFT) is:  
\[X(n)=\sum_{k=0}^{N-1}c_{k}e^{-\frac{2\pi}{N}nk} \tag{1}\]  
Amplitude and phase are extracted by FFT as features. Since the trigonometric function reflects the amplitude and phase well, the extracted features can be fitted with the original data through the trigonometric function as Equation (3). Vector \(A_{m}\) represents the feature information (maximum amplitude \(a_{m1}\), its phase \(a_{m2}\), and moving \(a_{m3}\)) obtained by trigonometric function fitting.  
\[A_{m}=[a_{m1},\cdots,a_{m3}] \tag{2}\]  
\[\rho(x_{i})=a_{m1}\sin\left(a_{m2}\frac{X\pi}{n/2}\right)+a_{m3} \tag{3}\]  
The original structural state data \(Z\) is transformed into the feature vector \(A\) as shown in Figure 2. Equation (3) is used to fit the window data, in which \(a_{m1}\) and \(a_{m2}\) are fixed by maximum amplitude and its phase. The parameter vectors \(A_{m}\) as Equation (2), as the feature vectors, are the input of the Informer model.  
In addition, the least-squares approximation solution is the optimal approximation of a time series minimalizing the sum of the squared distances between the data points (samples) and an approximating polynomial. So, the least-squares approximation is applied to fit the original data and Equation (3). The solution of the least-squares approximation  
Figure 2: Feature extraction based on FFT.  
fits the coefficients of the approximating polynomial, minimizing the approximation error \(\sigma\) between the data, samples, and the polynomial.  
\[\sigma=\frac{1}{n}\sum_{i=1}^{n-1}(p(x_{i})-y_{i})^{2} \tag{4}\]  
where \(p(x)\) is the approximating of the fitted data. The fitted coefficients of Equation (4) are then used as features of the data in this window.
## 6 Conclusions  
HOS-Re adopts a ML approach and proposes higher order statistical features that extracts useful information from the non-Gaussian signal distribution. Results from presently proposed HOS-Re approach, achieve attractive classification accuracy with MIFS on SignFi dataset [29]. HOS-Re extracts third-order cumulant feature that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly mutually informative feature subset as input to multilevel SVM classifier with Gaussian RBF kernel to measure the performance. MIFS and CIFE report better performance compared to other feature selection algorithms. However, MIFS reports the most striking performance in comparison to CIFE in extracting the mutually informative cumulant feature subset. The importance of reducing the uncertainty and preserving useful information considering the non-linear relationship for achieving better recognition accuracy is evident while looking at the other feature selection criteria in the present study.  
MIFS reports the best overall trade-off accuracy because it selects highly mutually informative cumulant feature subset and it significantly reduces the uncertainty among the features and the gesture class. For \(10\times 5\) cross-validations, HOS-Re with MIFS achieved average recognition accuracy of 97.84%, 98.26% and 96.34% for the _lab_, home and _lab_ + _home_ environment respectively. The average recognition accuracy for 150 sign gestures with 7,500 instances, collected from five different users was 96.23% in the lab environment. This work presents clear evidence that with proper feature extraction and selection technique, ML algorithms can achieve notable recognition accuracy, even without applying any form signal processing technique.  
Conceptualization, H.F.; methodology, H.F.; software, H.F.; validation, H.F.; formal analysis, H.F.; investigation, H.F.; writing--original draft preparation, H.F.; writing--review and editing, H.F., H.A., S.K.P., A.C., H.H., K.N.; visualization, H.F.; supervision, H.A.  
This work was supported by Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programme. The authors of this article would like to acknowledge the authors of SignFi dataset for providing Wi-Fi CSI traces, labels, and videos of the 276 sign words in GitHub.  
The authors declare no conflict of interest.
# Regional Seismicity of the Northeastern Tibetan Plateau Revealed by Crustal Magnetic Anomalies
## 6 Relationships Between the Magnetic Anomalies and Earthquakes
In terms of the intensity variations of the magnetic anomalies, the extreme positive and negative values of the seven geomagnetic elements (\(\Delta F\), \(\Delta H\), \(\Delta X\), \(\Delta Y\), \(\Delta Z\), \(\Delta D\), and \(\Delta I\)) in the study area are -311 to 425 \(n\)T, -319 to 349 \(n\)T, -343 to 313 \(n\)T, -277 to 214 \(n\)T, -307 to 401 \(n\)T, -33\({}^{\circ}\) to 25\({}^{\circ}\), and -5\({}^{\circ}\) to 27\({}^{\circ}\), respectively. In contrast, the magnetic anomalies at the epicenters are much smaller than the extreme values of the positive and negative anomalies. The proportions of earthquakes in areas with negative magnetic anomalies in terms of \(\Delta X\) and \(\Delta H\) are not very high. However, combined with the analysis of the intensity of the magnetic anomalies at the epicenters, we conclude that the earthquakes do not occur in areas with extreme positive anomalies but in strong-weak transitional areas near negative magnetic anomalies or the zero contour line. In particular, for the \(\Delta Z\) component, most earthquakes occur near the zero contour for \(\Delta Z\) (Figure 6b). The deviation of earthquakes from the zero contour line could be due to uncertainties in the earthquake locations or a failure to fully and accurately separate the crustal magnetic field from the geomagnetic field [16]. The \(\Delta Z\) gradient may be used to assess the likelihood of seismicity. However, the EMM2017 model only represents the static crustal magnetic field, and such forecasting is only suitable for the medium and long terms.
# Extended L-Band InAs/InP Quantum-Dash Laser in Millimeter-Wave Applications
## 3 Results and Discussion
### Hybrid SMF, FSO, and WL Transmission Performance Analysis  
Lastly, to further substantiate the SIL QD-DWL as a candidate source for future optical networks accommodating MMW technology with hybrid channels and operating in an extended L-band window, a 2-Gbits/s modulated 30-GHz MMW beat tone was realized by altering the FSR of the QD-DWL to 35 GHz. In this case, a hybrid-FSO channel was considered, consisting of 20-km SMF, fixed 5-m FSO, and 2-m WL sub-links, as shown in Figure 3. The measured BER as a function of the received power in the BtB (i.e., 0-m WL length) and 2-m WL length configurations are shown in Figure 6 and summarized in Table 1. In this case, \(P_{OR}\) for the 0-m case was noted to be -7.3 dBm; however, when the WL link was increased to 2 m, \(P_{OR}\) was increased by 4.3 dB to reach a value of 3 dBm and was ascribed, in part, to free-space path losses and possible channel fading. It is noteworthy to mention here that despite experiencing a longer transmission channel (SMF and FSO sub-links), the \(P_{OR}\) exhibited a smaller value than that of the 28-GHz MMW carrier over the hybrid-WL channel when compared in BtB configurations. This could be attributed to the frequency-dependent chromatic-dispersion-induced power fading of the MMW beat tones while propagating through the SMF. To further support this statement, Equation (3) that provides MMW power distribution along a fiber length was used [19]:  
\[\Delta L=\frac{4\pi^{2}c}{2\lambda_{IL_{0}}^{2}D\omega_{MMW}^{2}} \tag{3}\]  
where any even (odd) multiple of \(\Delta L\) would result in an MMW power peak (null). For the SMF fiber length of \(L\) = 20 km, \(D\) = 20 ps/(nm\(\cdot\)km) and \(\lambda_{IL_{0}}\)= 1610 nm, \(\Delta L\) were calculated to be -3.69 and -3.21 km for the 28- and 30-GHz MMW beat tones, respectively. This implies the 30-GHz MMW carrier operation close to the even multiple of \(\Delta L\) compared to the 28-GHz MMW beat tone, thus exhibiting a less power-fading effect, which is in good agreement with the transmission results summarized in Table 1. In this case, a smaller \(P_{OR}\) of -7.3 dBm to reach the FEC limit was observed by the higher-frequency MMW carrier compared to by the lower-frequency counterpart, which exhibited \(P_{OR}\) of -5.1 dBm, converted to -2.2 dB power improvement. Hence, a proper selection of fiber length is paramount to minimize the chromatic-dispersion-induced power-fading effect in RoF network architectures. Lastly, at \(\sim\)0.5 dBm, the SNRs were observed to be \(\sim\)15.5 and \(\sim\)10.5 dB for the 0- and 2-m cases, showing a reduction of \(\sim\)5.1 dB. This is consistent with the 28-GHz transmission results on increasing the WL sub-link distance and is ascribed to the free-path loss and possible channel-fading effects.
# Investigation of Potential of GNSS-R Polarization: Theoretical Simulations
## 3 Theoretical Simulations  
Using the developed models presented in Section 2, we describe the corresponding scattering properties and simulated spaceborne DDM output at various polarizations.
# Current-Feedback Instrumentation Amplifier Using Dual-Chopper Fill-In Technique
###### Abstract  
In this study, we describe a dual-chopper glitch-reduction current-feedback instrumentation amplifier (CFIA) with a ripple reduction loop. The amplifier employs the chopping technique to reduce low-frequency noise, such as \(1/t\) noise. A glitch caused by chopping occurs at each chopper clock edge and results in intermodulation distortion (IMD). Owing to the input offset, the chopping technique also produces ripples. In this study, the glitch-induced IMD was reduced using a fill-in technique whereby only neat signals were alternately used as outputs by avoiding the glitch section with dual-chopping channel CFIA. To avoid using a high-order, low-frequency filter, a ripple reduction loop was implemented to reduce the ripple generated by chopping. The CFIA is based on a low-noise chopper fully differential difference amplifier with a cascade stage and a Monticelli-class AB output stage, which can drive a larger load and increase power efficiency. The proposed dual-chopper CFIA was fabricated using a 0.18 \(\upmu\)m CMOS standard process, and its current consumption with a 1.8-V power supply is 29.5 \(\upmu\)A. The proposed CFIA has a gain of 51 V/V, input referred noise of 53.3 nV/ \(\surd\)Hz at 1 Hz, and a noise efficiency factor of 4.48.  
chopping: fill-in technique; chopper glitch reduction; ripple reduction loop; class AB output
# Fracture Toughness Determination on an SCB Specim by Meshless Methods
## 3 Mathematical Formulation
This term will be used to define the gap, \(g\), for fracture toughness calculations through numerical modeling. Computationally, to determine the \(K_{IC}\) within the obtained data, a numerical iterative routine was developed to analyze the stress field derived from numerical meshless and FEM analyses. It means that the stress field together with the point coordinates referenced to the crack tip must be extracted on a predefined window after the crack tip. Then, this data will be considered as an input for the developed numerical algorithm. This algorithm links the SIF computation routine and the stress calculation following principal stresses in the vicinity of the crack tip considering mode I condition. Assuming the plane problem of a homogeneous isotropic solid, [PERSON] series expansion on the plane stress would be appropriate as stated in the following formulations. Notice that the significance of \(K_{\text{I}}\) is demonstrated in Equation (10), [35]. Considering the maximum applied load, this mode I SIF will be the fracture toughness. \[\sigma_{yy}=\sum_{n=1}^{\infty}\Big{(}A_{\text{In}}\frac{n}{2}\Big{)}r^{\frac {n}{2}-1}\Big{\{}\Big{[}2-(-1)^{n}-\frac{n}{2}\Big{]}cos\Big{[}\Big{(}\frac{n} {2}-1\Big{)}\theta\Big{]}+\Big{(}\frac{n}{2}-1\Big{)}cos\Big{[}\Big{(}\frac{n }{2}-3\Big{)}\theta\Big{]}\Big{\}}, \tag{9}\]  
\[K_{IC}=A_{\text{II}}\sqrt{2\pi}. \tag{10}\]  
In which \((r,\theta)\) represents the polar coordinates of the points referenced to the crack tip located in the defined window after the crack tip.
## 1 Introduction
Currently, most groundwater modeling applications use VisualMODFLOW, which delivers good simulation results [22; 23]. However, the GMS (10.6.6) software used inthis study integrates various groundwater numerical simulation tools, allowing for both groundwater modeling and the creation of visual geological models. By modifying geological model parameters, the hydrogeological parameters in the groundwater simulation can be adjusted. The software achieves an integrated design for modeling, inputting, and modifying various hydrogeological and geometric parameters, running the model, inverting and calibrating parameters, and displaying output results, making the entire process systematic and standardized. This study utilized the GMS software, incorporating the latest hydrogeological data accumulated over recent years from the III3 Mining Area of the Zhuxianzhuang Coal Mine in northern Anhui, including hydrogeological profiles and borehole data. Factors such as aquifer development, permeability, groundwater hydraulic properties, and dynamic characteristics were considered to establish a numerical groundwater flow model. This model simulated karst water dynamics under deep mining conditions, and its accuracy was validated. This model also used to assess the drainage potential of karst water in the deep sections of the mining area and evaluate the connectivity and water-conducting properties of the F22 fault within the mining area. The study provides a visualized model of the karst water flow field under deep mining conditions, aiming to demonstrate the drainage potential of karst water in the study area. The hydrogeological model developed in this study provides a theoretical foundation and a typical case study for water hazard prevention and control in deep mining under similar geological conditions worldwide.
## 2 Related Research
### Data Imbalance Problem  
Relevant studies have been reported on classification problems with imbalanced data. Synthetic minority oversampling technology (SMOTE) [5] is a classical method to address the class imbalance problem. Oversampling is carried out to expand data for classes with a small amount of data. [PERSON] et al. [6] proposed a new variant of SMOTE, region-adaptive SMOTE (RA-SMOTE), and used machine learning algorithms to evaluate its effectiveness. [PERSON] et al. [7] used different weights to analyze the data and defined security levels to identify specific classes of artificially generated data. [PERSON] et al. [8] used the ToN-IoT dataset for network traffic data and proposed the chi2 technology for feature screening and SMOTE to improve the class imbalance problem. [PERSON] et al. [9] used the CICIDS2017 dataset to study the problem of data imbalance. This research used several data sampling techniques, including oversampling, undersampling, and mixed sampling techniques, to improve the data imbalance problem. The study concluded that the best results were obtained using the OSS-BSMOTE hybrid sampling technique.  
The abovementioned related studies mainly used data augmentation to solve the data imbalance problem. Many new approaches for data generation technologies have been proposed recently, such as autoencoders and generative adversarial networks (GANs). Developing an effective data augmentation technique to improve the detection of rare attack data for network intrusions is a worthy research problem.
## 1 Introduction
The goal of the described research is to develop an approach for establishing acceptable capacity limits or design requirements for units that serve a flattened load profile with a significant harmonic content. As with the research performed previously by the authors, a transformer aging model based upon IEEE Std. C57.91 and IEEE Std. C57.110 was utilized to identify load profile effects on internal temperatures and thereby capacity limits [6]. The derating and design parameter identification procedures were applied to the same 50 MVA, 118/14.3 kV, Z = 12%, SPX Waukesha transformer utilized to establish excessive transformer aging due to a flattened profile [6]. The model was built utilizing data from the SPX manufactured test report. The analysis assumes the unit's solid dielectrics  
Figure 1: Paper insulation aging as a function of per unit load for a 50 kVA liquid immersed transformer. were comprised of thermally upgraded kraft (TUK) paper and the liquid dielectric consisted of mineral oil, both of which are considered industry standards.
## 3 Results
### Rabbit Brain Imaging
#### 3.2.3 Block-Wise Local SVD Filtering for H-Doppler  
Figure 11 shows the comparison results of block-wise local SVD (BWL-SVD) filtering for conventional UCDI and H-Doppler processed with a fixed block and step size. The effects of different block parameters on the results were compared in detail [15]. The block parameters used in this experiment are block size = height: 128, width: 24 and overlap = height: 120, width: 20. According to the threshold selection strategy mentioned in Section 2.2, as the singular vector Doppler frequency curves of different blocks change, the thresholds of BWL-SVD result in a range rather than a fixed value with the same predefined Doppler frequency. For our experiment, the conventional UCDI method with BWL-SVD filtering has low-order thresholds ranging from 22 to 31 and high-order thresholds ranging from 232 to 264. H-Doppler method shares the same low-order thresholding as UCDI but does not require high-order thresholding. The images reveal that the Doppler results obtained by the H-Doppler retain more of the blood flow signals in the deep tissue. The zoomed images show more details. The CNRs of the conventional UCDI with BWL-SVD filtering were 20.1 dB (white) and 19.7 dB (yellow). The CNRs of the H-Doppler approach we proposed were upgraded to 23.2 dB (white) and 24.7 dB (yellow). To further illustrate the superiority of the H-Doppler method based on BWL-SVD, we selected two vessels at the same depth in Figure 11 (pointed by the arrows) for quantitative comparison. The target pixel numbers and mean intensities of the blood signals are calculated. The results show that the target pixel numbers and mean intensities of the H-Doppler approach in the yellow region are 27,663 pixels and 24.7 dB, surpassing the 2225 pixels and 15.1 dB of the traditional UCDI method. In the white region, the H-Doppler results are 16,077 pixels and 22.3 dB, surpassing 1841 pixels and 7.8 dB of the UCDI method.
###### Abstract  
The pulsed laser has gradually become the standard method of studying the single-event effects of micro-nano devices, and it is a powerful supplement to heavy ion experiments on single-event effects. In particular, with the continuous improvement of the technological range and operating frequency of electronic devices, the femtosecond pulse laser has the potential for broad applications and practical value in the single-event effects testing and radiation hardness evaluations of devices. The single-event effects of single-photon and two-photon absorption were studied with a laser of 1064 nm and 1260 nm, and the influences of SPA (single-photon absorption) and TPA (two-photon absorption) on the single-event transient voltage under different pulse laser energies were analyzed. The test results of the TPA SEE (single-event effect) were basically consistent with those of the SPA SEE, and the trend of the change in the transient voltage pulse was consistent. These results lay a foundation for the application of the femtosecond pulse laser in the performance evaluation and screening of space electronic devices and the radiation hardness evaluation of satellite electronic systems.  
single-event effect; pulse laser; single-photon absorption; two-photon absorption; single-event transient Article