esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Procedure
### Estimation of Stress Intensity Factors
\begin{array}{rcl}u_{rk}&=&\left\{\begin{array}{c}\sum\limits_{n=1}^{N}A_ {\text{In}}f_{\text{In}}(r_{k},\theta_{k})-\sum\limits_{n=1}^{N}A_{\text{In} }f_{\text{In}}(r_{k},\theta_{k})\end{array}\right\}\cos\theta_{k}\\ &&+\left\{\begin{array}{c}\sum\limits_{n=1}^{N}A_{\text{In}}g_{\text{In}}(r_ {k},\theta_{k})-\sum\limits_{n=1}^{N}A_{\text{In}}g_{\text{In}}(r_{k},\theta_{ k})\end{array}\right\}\sin\theta_{k}+T_{x}\cos\theta_{k}+T_{y}\sin\theta_{k}\\ \\ u_{\theta k}&=&-\left\{\begin{array}{c}\sum\limits_{n=1}^{N}A_{\text{In}}f_ {\text{In}}(r_{k},\theta_{k})-\sum\limits_{n=1}^{N}A_{\text{In}}f_{\text{In} }(r_{k},\theta_{k})\end{array}\right\}\sin\theta_{k}\\ &&+\left\{\begin{array}{c}\sum\limits_{n=1}^{N}A_{\text{In}}g_{\text{In}}(r_ {k},\theta_{k})-\sum\limits_{n=1}^{N}A_{\text{In}}g_{\text{In}}(r_{k},\theta_{ k})\end{array}\right\}\cos\theta_{k}-T_{x}\sin\theta_{k}+T_{y}\cos\theta_{k}+Rr_{k} \end{array}  \tag{8}\]  
The \(T_{x}\) and \(T_{y}\) express the rigid-body translation in the \(x\) and \(y\) directions, respectively, and \(R\) is the rigid-body rotation. The subscript \(k\) (\(k=1,2,\ldots,\) M) denotes the index of the function evaluated at a point \((r_{k},\theta_{k})\) in the displacement field at which the displacement values are \(u_{rk}\) and \(u_{\theta k}\). The M is the total number of displacement data points used in solving displacement equations. The unknown coordinates around a crack tip may thus be given in polar coordinates as Equation (9):  
\[r_{k}=\sqrt{\left(x_{k}-x_{0}\right)^{2}+\left(y_{k}-y_{0}\right)^{2}},\quad \theta_{k}=\tan^{-1}\left(\frac{y_{k}-y_{0}}{x_{k}-x_{0}}\right) \tag{9}\]  
The \(x_{0}\) and \(y_{0}\) are the location of the crack tip relative to an arbitrary Cartesian coordinate system. At any point in the displacement field, the coordinates \(r_{k}\) and \(\theta_{k}\), and displacements \(u_{rk}\) or \(u_{\theta k}\) can be substituted into Equations (7) and (8). Therefore, stress intensity factors, higher-order terms and crack tip locations can be derived automatically from the displacement field determined by the DIC method.
## 2 Materials and Methods  
### Materials  
Deep well, 10 mL, 24-well plates were purchased from VWR (Randor, PA, USA). PDMS sheets (CultureWell Silicone Sheet, 0.25 mm thick, 13 cm \(\times\) 18 cm) were purchased from Grace Bio-Labs (Bend, OR, USA). Laser-etching of PDMS sheets was performed at the Cornell Nanoscale Facility (Ithaca, NY, USA). Spacers and clamps for the SPMESH sheet extraction devices were machined at the Cornell University LASSP machine shop (Ithaca, NY, USA). Twenty mL amber SPME vials and caps were purchased from MilliporeSigma (Burlington, MA, USA). Sodium chloride (NaCl), methanol and ethanol were purchased from VWR (Radnor, PA). IBMP and linalool were purchased from MilliporeSigma (Burlington, MA), and their internal standards \(d_{3}\)-isobutyl-2-methoxypyrazine (\(d_{3}\)-IBMP) and \(d_{3}\)-linalool were purchased from C/D/N Isotopes (Point-Claire, Quebec). _Vitis vinifera_ grapes (Pinot norir \(n=1\) (for calibration), Cabernet Sauvignon \(n=69\), Merlot \(n=43\), Chardonnay \(n=1\) (for calibration), Muscat Blanc \(n=5\), Muscat of Alexandria \(n=12\), Malvasia Bianca \(n=1\) and Symphony \(n=1\)) were collected by E & J Gallo Winery (Modesto, CA, USA) from vineyards in Modesto, Lodi, Lake County, Fresno, Madera, Alexander Valley, Paso Robles, Napa Valley, Dry Creek Valley and Central Coast AVAs throughout July to September of the 2019 harvest, at commercial maturity (18 to 25 \({}^{\circ}\)Brix).
## 2 Correntropy Criterion  
The correntropy evaluates the resemblance of random variables \(X\), \(Y\in R\) and it is calculated based on the joint distribution function \(F_{XY}(x_{1},y_{1})\), the correntropy function is specified as follows:  
\[V(X,Y)=E(\chi(X,Y))=\int\int\chi(x_{1},y_{1})dF_{XY}(x_{1},y_{1}) \tag{1}\]  
where \(E(\cdot)\) denotes the expectation operator, \(\kappa(X,Y)\) represents a kernel function with non-negative symmetric properties. \(F_{XY}(x_{1},y_{1})\) signifies the probability density function of \(X\), \(Y\). The Gaussian kernel is utilized as the kernel function and its definition is presented as:  
\[\chi(x,y)=G_{\overline{\sigma}}(e_{\zeta})=\frac{1}{\sqrt{2\pi\overline{ \sigma}}}\exp(-\frac{e_{\zeta}{}^{2}}{2\overline{\sigma}^{2}}) \tag{2}\]  
where \(e_{\zeta}=x_{1}-y_{1}\) is the difference between \(x\) and \(y\), and the \(\overline{\sigma}>0\) denotes the kernel bandwidth of the correntropy function.  
In several practical circumstances, data samples are frequently limited and the probability density function is not known. Consequently, the computation of correntropy estimation is performed using a selection of samples:  
\[\overline{V}(X,Y)=\frac{1}{\overline{M}}\sum_{i=1}^{\overline{M}}G_{\overline {\sigma}}(e_{\zeta}(i)) \tag{3}\]  
where \(e_{\zeta}(i)=x_{1}(i)-y_{1}(i)\), \(i=1,2,\ldots\overline{M}\) are the data samples of the \(F_{XY}(x_{1},y_{1})\).  
Expanding the Gaussian kernel function, one can obtain:  
\[V(X,Y)=\sum_{N=0}^{\infty}\frac{(-1)^{N}}{2^{N}\overline{\sigma}^{2N}N!}E[(X-Y )^{2N}] \tag{4}\]  
It is clear that the correntropy function can be represented as the sum of moments of the error variable \(X-Y\) with even orders. The kernel bandwidth \(\overline{\sigma}\) is used to allocate weights to higher-order moments. Notably, the second-order moment plays a significant role in determining the correntropy when the kernel bandwidth selected is significantly large [27].  
Considering the error sequence \(e_{\zeta}(i)\), when \(e_{\zeta}(i)=0\), the Gaussian kernel function will be the maximum value; the objective function of the MCC is formulated as:  
\[L_{\text{MCC}}=\text{min}(\sum_{i=1}^{\overline{M}}G_{\overline{\sigma}}(0)-G_ {\overline{\sigma}}(e_{\zeta}(i))) \tag{5}\]
## 3 Results
### Self-Powered Electronic Board Aproach  
Experimental results have also demonstrated that the modulation of the EL signal using the developed electronic board for self-powered modulation achieves similar results to the conventional approach using a power supply, as shown in Figure 8. Not only is the visual quality comparable to the conventional approach, but the SNR also indicates similar quantitative quality in both methods. The same trend, which identifies square modulation as providing the best SNR results, was also observed in this experiment.  
Detailed pictures of some of cells of the PV module above have also been acquired using the self-powered approach. The increase in resolution in terms of pixels per cm\({}^{2}\) allows a better identification of possible faults that appear in solar cells. Figure 9 includes, for all the modulation, three groups of four cells that present some anomalies. The first row shows solar cells with some cracks that have led to the creation of isolated areas where current can be extracted/injected and therefore EL signal is low resulting in dark areas. The second row includes a solar cell (bottom-left solar cell) where one of the corners is inactive. Finally, the third row shows two cells (bottom solar cells) with clear inhomogeneities. That patron of inhomogeneities is observed in the cells close to the PV module frame, which may indicate derivation issues.  
Regarding the computational cost of the processing approach using the FFT, a total processing time of 6.5 s was reported for analyzing 1000 images of 640 \(\times\) 512 pixels on an Intel Core i7-11800H processor. Most of the computation time is spent applying the FFT to all pixels, allowing the estimation of the computational cost function as shown in Equation (2).  
\[\mathrm{T(N,p)=c\cdot p\cdot Nlog_{2}N} \tag{2}\]  
where T(N, p) represents the processing time as a function of the number of images to be processed (N) and the number of pixels per image (p), and c is a constant that depends on the FFT implementation and hardware characteristics. For the current experiments using the aforementioned processor and a python script with NumPy library for FFT calculation,  
Figure 8: EL images using self-powered approach based on the electronic board. Exposure time of 2 ms, 53 fps and 1000 images per sequence.  
constant c corresponds to 2\(\cdot\)10\({}^{-9}\) s-pixel\({}^{-1}\). Therefore, processing half the images would result in a total processing time of 2.9 s.
# Research on Seismic Signal Analysis Based on Machine Learning
## 2 Application of Machine Learning Method in Seismology  
### Background  
The advantage of machine learning techniques is that the main features of different types of data can be extracted directly from the data to be classified. By using this technique, we can come up with solutions without prior statistical distribution of features of different types of data, thus identifying some human experience to distinguish missing features in earthquake classes [18; 25].  
Feature extraction is a commonly used dimensionality reduction technique. Some of the most representative features are selected from the original feature space to express the dataset according to a certain evaluation criterion [26]. In the practical application of classification and recognition of natural earthquakes and other signals such as artificial blasts, machine learning organically unifies the functions of feature extraction, feature selection, and feature classification and realizes the overall optimization of performance and efficiency. [PERSON] et al. [25] first distinguished the seismic waveform and noise signal through a multilayer convolutional neural network (CNN). [PERSON] et al. [27] found that the CNN network had a good generalization ability for different types of earthquake and noise samples. [PERSON] et al. [28] used convolution and recursive neural networks to classify local blasts and natural earthquakes and finally obtained 99% accuracy by taking the spectrum of seismic sensors as input. [PERSON] et al. [3] converted the seismic waveform into a frequency spectrum as input. They used the ResNet network to classify seismic and blast events, and it worked very well. [PERSON] et al. [29] combined convolutional neural networks and traditional physical features to distinguish blast events and also visualized neural networks. [PERSON] et al. [30] used the input information, including the seismic waveform of multiple stations and the seismic time-frequency data of a single station, to classify the observation data of natural earthquakes and quarry blast recorded in Utah in 2012.
## 2 Literature Review
### Traditional ASR Approaches and Their Limitations  
Traditional ASR approaches for mixed-language recognition often require the manual development of language-specific components, such as bilingual pronunciation dictionaries and telephone sets. [PERSON] et al. [34; 35] used phoneme-mapped articulatory dictionaries and modified language models to enhance recognition accuracy. However, these methods are labor-intensive, and the structural differences between languages add another layer of complexity, making it difficult for models to handle the phonetic and syntactic variations between Mandarin and English. [PERSON] et al. [36] highlighted the difficulty of modeling cross-linguistically uncommon word forms in mixed-language speech. To tackle these challenges, techniques like text augmentation had been employed, including statistical machine translation [32] and word embedding methods [37], which achieved the best error rate of 44.96%. Additionally, incorporating language-specific features such as parts of speech (POS) into language models led to a 32.81% relative improvement in perplexity on the SEAME development set [38]. These challenges create substantial hurdles in achieving high recognition accuracy in mixed-language ASR due to the wide disparities in linguistic units between languages like Mandarin and English.
# A Fully Integrated Low-Power Multi-Mode RF Receiver for BDS-3/GPS
## 2 GPS and BDS-3 Signal Properties
### Overall Requirements of the BeiDou Navigation Satellite System-3  
The overall requirements for the proposed receiver have been put forward by the China Satellite Navigation Office in their general specifications [10]. Analyzing the overall requirements, the main difficulties in this design are as follows:  
1. The RF signal attenuates significantly at a high frequency of 1575.42 \(\pm\) 2.046 MHz due to the parasitic effect caused by routing and packaging. The transconductance can be increased by enlarging W/L or the current to boost the gain. However, large-sized transistors make the parasitic capacitance influence more serious. A large current results in heightened power consumption. Therefore, it is difficult to realize an RF receiver with high working frequency and low power.
2. In the receiver design, RF circuits with excessive gain lead to greater power consumption and more complex RF layouts. If the gain of the RF circuits is reduced, the gain of the intermediate frequency (IF) circuits must be increased to meet the gain requirements of the system. However, the IF circuits with excessive gain will bring instability to the system. Since the gain of the RF circuits decreases, the noise contribution from the IF circuits increases. Therefore, RF and IF circuits must reasonably allocate the cascade gain to ensure system stability.
3. External LNA [4] is generally used to provide better noise performance in practical applications. However, this external LNA is not expected in our design in the case of weak interference, which puts forward higher requirements for noise performance. The noise figure (NF) will be worsened due to crosstalk and noise coupling on the substrate of the system-on-chip. Moreover, the low-power requirement also limits the further improvement of NF. Therefore, it is difficult to realize the receiver-on-chip with a low NF.
4. An image rejection ratio (IMRR) of 25 dB is required. Generally, increasing the IF or the CBPF order can improve the IMRR, but the current and area increase. So it is not conducive to a low-power design.  
Therefore, designing a high-performance BDS-3/GPS receiver that prioritizes low power consumption and occupies a small area is challenging.
## 3 Case Study
### Original Dataset  
Each CDR of this original dataset contains the following fields:  
* _From:_ Identifier of the calling phone,
* _To:_ Identifier of the receiving phone,
* _Day:_ Day of the call,
* _Time:_ Time of the call,
* _Duration:_ Duration of the call, in seconds, and
* _Cell Tower:_ Location of the call-originating mobile tower.  
Since all the mobile phone records were registered with false names in this dataset, numerical identifiers for callers and receivers are provided. The 9834 different calls records have no missing values. Figure 1a shows the first three records of this dataset. This first example describes a call made the day 1 by the caller #349 to the receiver #23 at a time between 00:00 and 00:59 with a duration of 1634 milliseconds.  
Figure 1: Call detail datasets used in this proposal. (**a**) original dataset from VAST 2008; (**b**) processed datasets (duration of the calls made per hour); and (**c**) processed datasets (duration of the calls made per six hours).
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 4 Significance of Communications in IoMT
### Security and Privacy Mechanisms  
Security and privacy mechanisms are essential for protecting data integrity, confidentiality, and privacy in both physical and virtual environments. Encryption, authentication, access control, and secure communication protocols (e.g., TLS/SSL) are implemented to mitigate risks associated with unauthorized access, data breaches, and cyberattacks in AMI systems, including those operating within the metaverse [61]. Cryptography plays a crucial role in ensuring data security by enabling secure communication despite potential threats. Encryption algorithms transform input (plain-text) into encrypted output (cipher-text) using keys. Symmetric key encryption methods include block cipher, which operates on single data blocks by breaking down the message into blocks for individual processing with a cryptographic key, and stream cipher, which encrypts and decrypts shared data using symmetric key mechanisms. Asymmetric key encryption, also known as public-key encryption, uses two different keys, one for encryption and another for decryption, in contrast to symmetric key encryption, where the same key is used for both encryption and decryption.
## 2 Experimental Section
### Experimental Design  
The influencing factors, including the solvent used for the extraction of the essential oil, liquid/solid ratio, microwave power, extraction time and particle size of sample, were systematically evaluated using an experimental design process. Based on the single factor experimental results, a 2\({}^{3}\) factorial portion central composite design (CCD) combined with response surface methodology (RSM) was used for optimizing the microwave power (X\({}_{1}\): 400-600 W), extraction time (X\({}_{2}\): 10-20 min) and liquid/solid ratio (X\({}_{3}\): 15-25 mL/g). Twenty different experiments with six center points (six replicates for each center point) were used in the CCD test to fit the full quadratic equation model. The general equation was as follows:  
\[Y=\beta_{0}+\sum_{i=1}^{k}\beta_{i}X_{i}+\sum_{i=1}^{k}\beta_{ii}X_{i}^{2}+ \sum_{\begin{array}{c}i=1\\ i<j\end{array}}^{k-1}\sum_{j=2}^{k}\beta_{ij}X_{i}X_{j}\ (k=3) \tag{3}\]  
where \(Y\) represents the response variable; \(\beta_{0}\), \(\beta_{i}\), \(\beta_{ii}\), and \(\beta_{ij}\) are the regression coefficients of the intercept, linearity, square, and interaction terms, respectively; \(X_{i}\) and \(X_{j}\) are the independent coded variables affecting the response variable \(Y\); and \(k\) is the number of variables. Each factor for the test variables was set at one of five different levels (_i.e._, \(-1.68\), \(-1\), \(0\), \(+1\), \(+1.68\)) to evaluate the equation. The dependent variables were set as the yields of the polysaccharides and essential oil from the powdered leaves of _T. chitensis_ var. _mairei_. The actual and coded levels of the independent variables used in the experimental design are shown in Table 1. All of the experiments were conducted in triplicate and the yields reported in the table represent the average values of the three experiments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Runs**} & \multicolumn{3}{c}{**Factors**} & \multicolumn{3}{c}{**Yields (\%)**} \\ \cline{2-6}  & \(\mathbf{X_{1}\) (W) \({}^{a}\)} & \(\mathbf{X_{2}\) (min) \({}^{b}\)} & \(\mathbf{X_{3}\) (mL/g) \({}^{c}\)} & **Polysaccharides** & **Essential Oil** \\ \hline
1 & 1 (600) & 1 (20) & \(-1\) (15) & 6.07 & 0.23 \\
2 & 0 (500) & 0 (15) & 0 (20) & 6.22 & 0.27 \\
3 & 0 (500) & 0 (15) & 0 (20) & 6.32 & 0.27 \\
4 & 1 (600) & \(-1\) (10) & 1 (25) & 6.01 & 0.18 \\
5 & \(-1\) (400) & \(-1\) (10) & \(-1\) (15) & 5.22 & 0.16 \\
6 & 1 (600) & 1 (20) & 1 (25) & 6.34 & 0.25 \\
7 & \(-1\) (400) & 1 (20) & 1 (25) & 5.77 & 0.24 \\
8 & 1 (600) & \(-1\) (10) & \(-1\) (15) & 5.64 & 0.16 \\
9 & \(-1\) (400) & \(-1\) (10) & 1 (25) & 5.52 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental conditions used for the central composite design analysis and the corresponding measured responses.
## 4 Conclusions  
The experiment was meant to investigate the transformative potential of microwave supported heat-moisture treatment at 30% of initial moisture content of gluten-free flours, specifically buckwheat and teff. The aim was to understand whether this low-cost method could effectively modulate the behavior of these native flours in the context of 3D extrusion printing. The research primarily focused on three critical aspects of the extrusion printing process: the sol-state viscosity during extrusion, the speed of sol-gel transition (gelation time) during formation, and the elastic modulus (G\({}^{\prime}\)) for structural support during the self-supporting stage. These factors collectively determine the printability and integrity of 3D-printed food structures.  
The results revealed that microwave heating significantly influences the rheological properties of the flour-based ink. First, it reduces the viscosity of the ink, rendering it more amenable to extrusion. This effect is crucial as it enhances the extrudability of the ink, allowing for precise and consistent deposition of material during printing. Achieving the desired sol-state viscosity is essential for the success of 3D food printing. The increase in elastic modulus (G\({}^{\prime}\)) observed in the microwave-treated flours is also the crucial result for further investigation using MW supported heat moisture treatment. This elevated G\({}^{\prime}\) imparts self-supportability to the printed structures, minimizing the need for external scaffolding or support materials. This not only simplifies the printing process but also opens up possibilities for intricate and delicate designs, expanding the potential of 3D food printing.  
The study demonstrates that microwave heating of native gluten-free flours with 30% initial moisture content represents a cost-effective and highly promising method for modulating the rheological properties of edible inks in 3D food printing. The ability to optimize sol-state viscosity and elastic modulus has the potential to make 3D printing more accessible, efficient, and versatile. These findings can start the development of customized and nutritionally tailored edible inks using natural diversity of plant-based starch-rich flours.  
Conceptualization, T.L. and J.H.; methodology, T.L., K.B., A.O. and J.H.; validation, J.H.; formal analysis, T.L. and K.B.; investigation, T.L., K.B. and A.O.; resources, J.H.; data curation, J.H.; writing--original draft preparation, T.L. and J.H.; writing--review and editing, J.H.; project administration, T.L.; funding acquisition J.H. All authors have read and agreed to the published version of the manuscript.  
The project is financed by the National Science Centre, Poland under the program POLS project number 2020/37/K/ST5/03602.  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 5 LOS Velocity Calibration and Uncertainties
### Measurement Uncertainties Assessment Procedure
#### 5.6.3 Combined Uncertainty on \(V_{ref}\)  
In this study, classic anemometry is used to provide \(V_{ref}\) (Equation (2)). \(u_{c,V_{ref}}\) is derived by combining uncertainties from the reference instruments with the calibration process uncertainties. For the sake of simplicity, and as \(\varphi\), \(V_{hor}\), \(\theta\) and \(\theta_{los}\) are measurements taken by independent systems, their uncertainties can reasonably be assumed uncorrelated. The law of propagation of uncertainties gives:  
\[u_{c,V_{ref}}=\sqrt{\left(\frac{\partial V_{ref}}{\partial V_{hor}}\cdot u_{c,V_{hor}}\right)^{2}+\left(\frac{\partial V_{ref}}{\partial\varphi}\cdot u_{ \varphi}\right)^{2}+\left(\frac{\partial V_{ref}}{\partial\theta_{r}}\cdot u_{ c,\theta_{r}}\right)^{2}} \tag{10}\]  
where \(\theta_{r}=\theta-\theta_{los}\) is the relative wind direction. Based on Equation (2), the partial derivatives are computed for each valid 10-min period:  
\[\left\{\begin{array}{l}\frac{\partial V_{ref}}{\partial V_{hor}}=\cos\varphi \cos\theta_{r}\\ \frac{\partial V_{ref}}{\partial\varphi}=-V_{hor}\sin\varphi\cos\theta_{r}\\ \frac{\partial V_{ref}}{\partial\theta_{r}}=-V_{hor}\sin\theta_{r}\cos\varphi \end{array}\right. \tag{11}\]  
At this stage of the uncertainty assessment procedure, only the values of \(u_{c,V_{hor}}\), \(u_{\varphi}\) and \(u_{c,\theta_{r}}\) are missing. The uncertainty budget (Section 5.6.4) provides their estimation. Note that angle uncertainties must be expressed in radians when computing the combined uncertainty.