esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectral Broadening in a Continuously Pumped Singly Resonant Second-Harmonic Cavity
## 4 Conclusions  
In conclusion, we show that new nonlinear regimes occur with high power buildup in a cw-pumped SHG cavity. By controlling the detuning over several cavity linewidths, we investigate the system dynamic and observe new comb regimes that extend over a broad spectral range. Most significantly, the obtained results illustrate the onset of sideband oscillations far away from the main quadratic frequency comb around the pump wavelength. Exploiting the wide spectral coverage and the coherent multifrequency emission in quadratic nonlinear cavities it is possible to enable a dramatic extension of applications for high-resolution spectroscopy, optical telecommunication, and quantum photonic sources. The studied quadratic nonlinear system is suitable to be miniaturized by replacing the optical bench-top mounting system with a quadratic nonlinear waveguide resonator. For instance, a new on-chip synthesizer could be based on the previously investigated design [17], which utilizes the AlGaAs platform. AlGaAs, with its efficient \(\chi^{(2)}\) nonlinearity, can operate with relatively low pump power, and its compatibility with the CMOS industry makes it a promising candidate to develop a waveguide resonator for multifrequency integrated sources on small photonic chips. Furthermore, our measurements may open the way for future theoretical and experimental investigations on the quantum correlations between the generated beams.  
Management, M.P.; setup implementation, measurements and data analysis, S.M. and M.P.; theoretical study, T.H.; results discussions, all; writing--original draft preparation, S.M. and M.P.; writing--review and editing, all; supervision, M.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by European Union's Horizon 2020 research and innovation program (Qombs Project, FET Flagship on Quantum Technologies Grant. No. 820419). T.H. acknowledges funding from the Swedish Research Council (Vetenskapsradet, Grant. No. 2017-05309).  
The authors gratefully acknowledge suggestions and stimulating discussions from [PERSON] and [PERSON]. They thank G. Notariale for technical support.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
PDH  
Pound Drever Hall
PSD
Power spectral density  
RF  
Radio frequency  
SH  
Second harmonic generation
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 5 Simulations and Evaluation
### Methods for Comparison
#### 5.2.2 DPD-MUSIC  
In order to effectively resist reverberation, a direct-path dominance (DPD) test is often applied to select those time-frequency (TF) bins that contain more direct components. The test can be expressed as  
\[\mathfrak{T}_{DPD}=\{(\tau,k_{h}):\frac{\sigma_{1}(\tau,k_{h})}{\sigma_{2}( \tau,k_{h})}\geqslant\lambda_{DPD}\}, \tag{22}\]  
where \(\lambda_{DPD}\) is the threshold for the DPD test to choose the TF-bins, \(\sigma_{1}(\tau,k_{h})\) and \(\sigma_{2}(\tau,k_{h})\) are the largest and second-largest singular values of the covariance matrix \(\mathbf{\tilde{R}}_{nm}(\tau,k_{h})\) obtained using a rectangular window over the time and frequency indices.  
\[\mathbf{\tilde{R}}_{nm}(\tau,k_{h})=\frac{1}{J_{\tau}I_{\
u}}\sum_{x_{t}=0}^{I _{\tau}-1}\sum_{y_{t}=0}^{I_{\tau}-1}\mathbf{x}_{nm}(\tau+\iota_{\tau},k_{h}+ \iota_{\
u})\mathbf{x}_{nm}^{H}(\tau+\iota_{\tau},k_{h}+\iota_{\
u}) \tag{23}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Training Stage** & **Testing Stage** \\ \hline DOA & \(\theta\): step-10\({}^{\circ}\), \(\phi\): step-10\({}^{\circ}\) & Randomly \\ SNR & Randomly & step-5 dB \\ \(RT_{60}\) & Randomly & step-0.2s \\ Speech & Librispeech Dev & Librispeech Test \\ Noise & White Gaussian Noise & White Gaussian Noise \\ Room & Room 1–4 & Room 1–6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for the simulated datasets.  
The window contains \(J_{\tau}\) time frames and \(J_{v}\) frequency bins. Based on the selected TF bins, SH-MUSIC method is still used for the sound localization task [28; 43].
# Sustainable Removal of Cr(VI) by Line Peel and Pineapple Core Wastes
## 3 Results and Discussion
### Adsorption in the Optimized Conditions
The \(n\) constant of the Freundlich isotherm also provided information about the intensity of the adsorption. Widely, it is accepted that values of \(n\) between 1-10 and lower than 1 correspond to good and poor adsorption features respectively. In this study, the \(n\) value was found to be above 1, indicating that Cr has a great affinity onto PP which is in concordance with the high uptake obtained [34]. After compiling all the input parameters required to run the FAST v2.1 model (Table 5), the simulation of the breakthrough curves of a column of the working volume of 100 L (internal diameter 30 cm) was performed for treating a synthetic solution with a Cr(VI) concentration of 40 mg/L. The breakthrough curves were simulated at various flow rates and the results are presented in Figure 6a. Comparing these curves, it was demonstrated that the time frame in which the contaminant is totally adsorbed is dependent on the operation time under which no contaminant is detected in the outflow. Therefore, the breakthrough curves obtained with the simulation proved the feasibility of the scale up. Figure 6: Breakthrough curves obtained from the FAST v2.1 simulation: (**a**) at different flow rates; (**b**) exhaustion bed time versus flow rate. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Unit** & **Value** \\ \hline Biosorbent & kg & 62500 \\ Bed density & g/cm\({}^{3}\) & 0.45 1 \\ Particle density & g/cm\({}^{3}\) & 1.25 1 \\ Particle diameter & mm & 0.2 \\ Flow rate & L/min & 0.1–1 \\ Influent concentration & mg/L & 40 \\ Freundlich constant (\(K_{F}\)) & (mg/g)(L/mg)\({}^{n}\) & 1.27711 \\ Freundlich exponent (\(n\)) & & 2.8948 \\ Surface diffusion coefficient (\(D_{s}\)) & m\({}^{2}\)/s & 8.20\(\cdot\)10\({}^{-13}\) \\ Film diffusion coefficient (\(k_{L}\)) & m\({}^{2}\)/s & 0.82-8.41\(\cdot\)10\({}^{-8}\) \\ \hline \hline \end{tabular} \({}^{1}\) determined experimentally.  
\end{table} 
Table 5: Parameters required for the simulation using FAST V2.1 model. Based on these curves, valuable information can be predicted like the exhaustion bed time. In Figure 6b, the exhaustion bed time, in which concentration attained in the outflow was 90% of its initial value, is represented versus the flow rate. As depicted the power function fitted well the obtained data. Thus, this new function can be used for predicting the behavior of the simulated column adsorption.
## 3 Stability Analysis and Discussion
### 2-DOF Model of VHCVSS
#### 3.2.1 Modeling  
Mathematically, the 2-DOF model associated with VHCVSS milling process, shown in subgraphs (b) and (c) of Figure 2 can be written as  
\[\left[ \begin{array}{cc}m_{x}&0\\ 0&m_{y}\end{array} \right]\left[ \begin{array}{c}\tilde{x}(t)\\ \tilde{y}(t)\end{array} \right]+\left[ \begin{array}{c}c_{x}&0\\ 0&c_{y}\end{array} \right]\left[ \begin{array}{c}\dot{x}(t)\\ \dot{y}(t)\end{array} \right]+\left[ \begin{array}{c}k_{x}&0\\ 0&k_{y}\end{array} \right]\left[ \begin{array}{c}x(t)\\ y(t)\end{array} \right]\right]=\sum\limits_{j=1}^{N}\int_{0}^{a_{p}}\left[ \begin{array}{cc}-h_{xxj}(t,z)&-h_{xyj}(t,z)\\ -h_{yxj}(t,z)&-h_{yyj}(t,z)\end{array} \right]\left[ \begin{array}{c}x(t)-x(t -\overline{\gamma}_{j}(t,z))\\ y(t)-y(t-\overline{\gamma}_{j}(t,z))\end{array} \right]dz \tag{28}\]  
where the cutting force coefficients \(h_{j,xx}(t,z)\), \(h_{j,xy}(t,z)\), \(h_{j,yx}(t,z)\), and \(h_{j,yy}(t,z)\) can be presented by  
\[ \begin{array}{rcl}h_{xxj}(t,z)&=&qf_{z}^{d-1}g_{j}(t,z)\sin^{q}\phi_{j}(t,z) (K_{t}\cos\phi_{j}(t,z)+K_{r}\sin\phi_{j}(t,z))\sin\phi_{j}(t,z),\\ h_{xyj}(t,z)&=&qf_{z}^{d-1}g_{j}(t,z)\sin^{q}\phi_{j}(t,z)(K_{t}\cos\phi_{j}(t, z)+K_{r}\sin(\phi_{j}(t,z))\cos\phi_{j}(t,z),\\ h_{yxj}(t,z)&=&qf_{z}^{d-1}g_{j}(t,z)\sin^{q}\phi_{j}(t,z)(-K_{t}\sin\phi_{j}( t,z)+K_{r}\cos\phi_{j}(t,z))\sin\phi_{j}(t,z),\\ h_{yyj}(t,z)&=&qf_{z}^{d-1}g_{j}(t,z)\sin^{q}\phi_{j}(t,z)(-K_{t}\sin\phi_{j}( t,z)+K_{r}\cos\phi_{j}(t,z))\cos\phi_{j}(t,z).\end{array}  \tag{29}\]  
Figure 7: Comparison of the SLDs by theoretical prediction and time-domain simulation for different processes. (**a**) Process I; (**b**) Process II; (**c**) Process III; (**d**) Process IV. \[
## 2 Interactive Geospatial Database and Data
The standard penetration test (SPT)-\(N\) (blow counts) value from each survey site was corrected by \(N_{60}=(ER/60)\times N\) considering an \(N\)-value 60% of the energy efficiency (ER) [29]. Moreover, \(N_{60}\) was linearly extrapolated based on 50 \(N\)-values, and the maximum \(N_{60}\) was regulated under 300. When an \(N\)-value is 150 or above, reliability of the associated geotechnical characteristic value cannot be guaranteed. In this study, the extrapolated \(N_{60}\) (\(N_{60}^{exp}\)) was extracted as an average for each geo-layer. The seismic response parameter was determined using an empirical correlation [30], with \(N_{60}^{exp}\) based on the shear wave velocity (\(V_{S}\)) within each geo-layer (FL, AS, WS, WR, and SR). If \(N\) value is not recorded above engineering bedrock (SR or normal rock), the representative vs. values were determined to be 190 m/s for FL, 280 m/s for AS, 350 m/s for WS, 650 m/s for WR, and 1300 m/s for bedrock [22]. Datasets in the geospatial DB were overlaid on a regular 100-m-mesh layer and the DEM that had been resampled to a \(100\times 100\) m mesh size. An intergraded mesh spatially joined by the geospatial DB provided geospatial information about the terrain, geology, and subsurface, and assigned a value to every vertex in the spatial structure. Thus, the DB could be analyzed and visualized by assigning a preprocessing phase (Figure 1) to the geotechnical characteristic values and interpolating data between vertices according to the mesh topology. Interactive DBs are playing an important role in the design, calibration, comparison, and optimization of proposed geospatial and geotechnical models [30]. In particular, units of sub-grouped meshes in two-dimensional domains are becoming the representative zonal statistics for detailed regional assessments.
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 4 Outcomes and Discussion
### Error Analysis
#### 4.8.1 Confusion Matrix for LSTM with FastText Embeddings  
Using a confusion matrix, we examined the classification errors. As can be seen in Figure 2, the LSTM model classified most of the cases correctly in both classes, obtaining a Precision (0.96) and Recall (0.96) balance. Just looking at the confusion matrix shows that we have 857 false positives (Not Offensive \(\rightarrow\) Offensive) and 891 false negatives (Offensive \(\rightarrow\) Not Offensive) over a total of 46,025 samples. These misclassifications are examples of the nuances where the model fails, either because they are borderline examples of hate speech vs. a benign statement or the problem itself originates from the context, either sarcasm, an implicit slur, or a code-mixed case.  
A closer look at the false negatives indicates that the model sometimes misses more nuanced hate speech when offending material is couched in metaphors or euphemisms. On the other hand, false positives arise when an emotionally charged but inoffensive comment is over-censored. In short, our results show that LSTM + FastText can achieve very high performance in structured scoring systems, but deployments in the wild require ongoing adjustment, more granular annotation, and potentially adding context-based inference or external knowledge bases to account for idiosyncrasies in Roman Urdu terms. Additionally,  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Recall** & **Precision** & **Accuracy** & **F-Score** \\ \hline RNN & 94.1 & 93.4 & 85.2 & 75.9 \\ CNN & 92.5 & 84.3 & 76.5 & 95.1 \\ GRU & 73.6 & 85.0 & 93.5 & 76.6 \\ LSTM & 78.7 & 73.1 & 81.8 & 96.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of DL Models with FastText.  
future work might employ ensemble techniques or explainable AI methods to further minimize these important error types.
###### Abstract  
Research on wall rock alteration is of great importance to the understanding and exploration of ore deposits. The microscopic changes of the same mineral in different alteration zones can provide information about the migration and enrichment of ore-forming elements. In this paper, a typical profile of a high-grade iron ore body in Gongchangling iron deposit was investigated and sampled. The samples were checked by polarized microscopy, and alterations zonation were delineated according to the hydrothermal mineral assemblages and paragenesis. Moreover, hyperspectral images of wall rocks from each alteration zone were obtained by Norsk Elektro Optikk (NEO) HySpex-320 m imaging system. A customer Interactive Data Language (IDL) software package was used to process the images, and spectral features were extracted from the selected samples. The results indicate that spectral characteristics manifest obviously regular variations; i.e., from proximal to distal for the high-grade iron ore body, the wavelengths at ca. 1200 nm of chlorite and garnet, which accounts for most of the hydrothermal alteration minerals, become longer, and the absorption depths gradually smaller. The spectral features at 1200 nm of chlorite and garnet are always caused by the crystal field effect of Fe\({}^{2+}\); therefore, the wavelength variations indicate the increase of Fe\({}^{2+}\) and a reduced environment, which can provide more detailed information about the metallogeny and water-rock interaction. Since the hyperspectral features of the altered rocks can disclose unique mineralogical and structural information, the conventional classification of alteration zonation should be combined with the spectral feature, i.e., spectral alteration zonation, which is of great help to the understanding of the forming conditions of wall rock alteration and also the high-grade iron ore bodies.  
hyperspectral; close range HySpex-320 m; spectral alteration zonation; wall rock alteration; Gongchangling Iron Deposit +
Footnote †: journal: Applied Sciences
## 1 Introduction
### Overview of Measuring Methods
However, the reflection coefficient was determined based on a number of Doppler frequency shifts measurements at different laser beam angles obtained by scanning the field of the standing wave tube made from glass (during operation with the test specimen), backed by a rigid wall with retroreflective tape, which is very far from the technique numerically investigated in this article. Despite of the wide range of the measurement approaches briefly mentioned above, in this article, we focus on the determination of the acoustic properties (sound transmission loss \(nTL\) transmission coefficient \(\tau\)(-) and acoustic absorption \(\alpha\)(-)) of membrane-type specimens excited by well-defined plane wave excitation by means of surface displacement (in the impedance tube environment). Numerical models were used to simulate the membrane-type specimen being mounted in an impedance tube. Both properties were determined by means of incident pressure and specimen surface mobility information under normal incidence plane wave excitation. The data processing procedure yields both \(\alpha\) and \(\tau\). However, because these two quantities are related to each other (as will be discussed in Section 2, near Equations (6)-(8)), emphasis is put on the determination of the transmission coefficient \(\tau\). Subsequently, the proposed method was experimentally tested. By combining the known incident sound power and the frequency response of the average surface mobility, both acoustic characteristics can be determined for normal incidence plane waves. The paper is outlined as follows: Section 2 Materials and Methods including the theoretical explanation of the approach used for the determination of \(nTL\), \(\alpha\) and \(\tau\) and the numerical study focused on theoretical verification for two different specimens and six different boundary scenarios. In the Section 3, the laboratory experiment is introduced. Subsequently, numerical simulations and experimental results were analysed (Section 4). This work should be considered as an intermediate step towards a method to measure the acoustic characteristics of membrane structures (e.g., tent structures) in situ.
# Pipe Crack Recognition Based on Eddy Current NDT and 2D Impedance Characteristics
## 3 Feature Extraction of the ECT Signal Based on the Impedance Diagram
### Extraction of the Characteristics of the Impedance Diagram by Image Processing Algorithm  
The Hough transform [22] is a fast algorithm for finding straight lines, circles, and other simple shapes in a binary image. The standard parametric equation of a straight line (termed l) in the image space is as follows:  
\[\rho=x\cos\theta+y\sin\theta \tag{8}\]  
where \(\rho\) is the distance between l and the origin and \(\theta\) is the angle between l and the x-axis. According to Equation (8), the different points of line l are represented as a sinusoid that intersects at one point in the parameter space. If the intersection is determined, that is, the local maximum, the line transformation is realized. The pixel gray value at \((x_{i},y_{i})\) in the binary image whose size is N \(\times\) N is represented as I \((x_{i},y_{i})\). In the parameter space, M discrete values are for \(\theta\) evenly selected between \((0,\pi)\). The number of the samples of \(\rho\) is Q. Then, the standard Hough transform may be expressed as:  
\[\begin{split} H(d_{q},\theta_{m})=\sum\limits_{i,j=0}^{N-1}I(x_{ i},y_{i})\big{|}_{d_{q}-\frac{1}{2}\leq x_{i}\cos\theta_{m}+y_{i}\sin\theta_{m} \leq d_{q}+\frac{1}{2}}\\ \text{m}=0,1,\ldots,\text{M}-1;\text{ q}=0,1,\ldots,\text{Q}-1\end{split} \tag{9}\]  
The contour extraction algorithm [23] is also known as an approach for emptying a shape of its internal points. Specifically, if a black point exists in the input binary image and the neighboring points are also black, the point is considered as an internal point and is hollowed out. When all the internal points are hollowed out, the contour line is achieved. Figure 8 shows the extraction process of the geometric features of the impedance diagram. Since noise or individual erroneous data points may exist in the original image, the morphological filtering is performed by combining the opening closing operations. In general, the opening operation removes the isolated dots and burrs, while the closing operation fills the holes, preserving the image position and shape. Table 2 provides the characteristic parameters of Figure 5c achieved by the feature extraction algorithm.  
Figure 7: Criterion values for ten geometric characteristics, which are respectively the expectation of the slope of fitted straight lines, the expectation of closed curve areas, the variance of closed curve areas, the variance of the slope of the fitted straight lines, the number of the closed curves that exceed the threshold area, the expectation of closed curve lengths, the variance of closed curve lengths, the number of fitted straight lines, the maximum difference of the fitted line slopes and the number of closed curves of the impedance diagram.
## 1 Introduction  
The interaction between electromagnetic waves, for instance light, and matter was always a topic that aroused interest to the scientific community [1; 2; 3; 4]. Mainly because some phenomena were discovered recently, as the extraordinary optical transmission (EOT) and based on them it has been possible to develop new devices [1; 2; 5; 6].  
The resonant behavior characteristic from EOT is affected by the complex dielectric function, which also depends on the incident wavelength. The complex dielectric function is often presented as a fitting of the Drude-Lorentz model, for each material [1; 2; 7; 8].  
The variation of the complex dielectric function with physical or chemical events can be useful to develop a sensor. This kind of sensors can be designed for many application in different fields such as medicine, chemistry, defense, communications, energy or environment [1; 2; 6].  
A nanoantenna, which is a subwavelength hole structure that supports EOT, can be tuned and its material can be chosen in order to fulfill spectral specifications (for example, gain, bandwidth or peak position). Thus, it is possible to design the sensor optical response [1; 2; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20].  
It is expected that this new kind of sensor, in the near future, will be classified as standalone, miniaturized, portable and they will have high sensibilities and huge detection limits [1; 2; 17; 18; 19; 20]. However, the improvement of computational methods and of new techniques of manufacturing are leading the process of creating new nanotechnological devices. Thus, sensors based on nanoantennas are not exceptions and they are quite important factors to decrease the fabrication costs and increase the reproducibility and the probability to commercialize these devices [6; 17; 18; 19; 20]. It is needed to find models to be able to clarify how the spectrum changes regarding the parameters tuning [1; 2; 21; 22; 23; 24; 25; 26; 27]. This article not only aimed to analyze the parameter influence, but it also presents simulations from two different sensor applications.
## 2 General Situation and Research Methods of the Research Area
### Leaf Reflectance Spectrum Collection and Chlorophyll Determination  
We selected 60 functional leaves in the middle of _Quercus aquifloiides_ plants at each altitude gradient. Spectral data were collected using an ASD FieldSpec3 portable near infrared spectrometer (Analytical Spectral Device, Almero, Netherlands). The band range of ASD spectrometer is 300-2500 nm. The sampling interval is 1.4 nm (350-1000 nm band) and 2 nm (1000-2500 nm band) respectively in different band ranges. The spectral reflection curve was set to 10 repetitions, and the final output spectral data was the average value of the 10 data. The operation procedures of the instrument were as follows: optimizing spectrometer \(\rightarrow\) dark scanning \(\rightarrow\) white board scanning \(\rightarrow\) adjusting transmission mode \(\rightarrow\) aiming optical fiber at the sample \(\rightarrow\) saving the value after reading is stable (Figure 1) [16].  
The relative Chlorophyll content index (CCI) was measured by CCM-200 plus chlorophyll meter (OPTI-Science, Tyngsboro, MA, USA), with the resolution of \(\pm\)0.1 CCI and the measurement range of 0.71 cm\({}^{2}\). The principle of this instrument is to measure the relative content of chlorophyll in the measured object by the optical absorptivity when the light source is excited in red light and infrared light [19].