esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A MA-SDE method has been successfully developed for the simultaneous separation of the polysaccharides and essential oil from the powdered leaves of _T. chinensis_ var. _mairei_. First, extraction solvent of essential oil, particle size of sample, extraction time, microwave power, and liquid/solid ratio were optimized using single factor experiment. Then, based on the single-factor experimental results, a CCD combined with RSM was used for further optimizing the microwave power, extraction time, and liquid/solid ratio. Based on the results of single factor experiments and CCD tests, the optimal conditions for the extraction of the essential oil were determined to be dichloromethane as the extraction solvent, a sample particle size of 60-80 mesh, liquid/solid ratio of 22.5 mL/g, extraction time of 17.5 min and microwave power of 547 W. MAE is well known as a technique for green extraction [39; 40] and adheres to the technical advantages of MAE. Compared to conventional HRE and HDE methods, the MA-SDE method provided higher yields, less energy consumption, and shorter extraction duration, which suggested that this method is highly effective for the extraction of the
# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 4 Typical Hardware-Based LQEs: A Survey
### RSSI Based LQEs and Their Mapping Models  
According to Figure 3, the LR model and polynomial regression model (for short, PR model) are usually used as mapping models between \(RSSI\) and \(\mathit{PRR}\) for \(RSSI\) based LQEs. In [20], the mapping model between averaged \(RSSI\) (defined as \(\mu_{\mathit{rssi}}\)) and \(\mathit{PRR}\) was obtained based on logistic regression. Using the measured data in the runway, a mapping model was obtained, as shown in Equation (10).  
\[\mathit{PRR}=\begin{cases}1-\frac{1}{1+223.5424\times e^{2.1771\times\mu_{ \mathit{rssi}}+198.4593}},&-96<\mu_{\mathit{rssi}}\leq-86\\ 0,&\mu_{\mathit{rssi}}\leq-96\end{cases} \tag{10}\]  
In [19], the mapping model between normalized \(RSSI\) and \(\mathit{PRR}\) was obtained based on polynomial regression. As normalized \(RSSI\) is used, the PR Model is self-adaptive essentially. Therefore, the model given in [19] was used directly:  
\[\mathit{PRR}=-3943.5R_{\mathit{avg}}^{6}+6506.6R_{\mathit{avg}}^{5}-4279R_{ \mathit{avg}}^{4}+1430.9R_{\mathit{avg}}^{3}-256.47R_{\mathit{avg}}^{2}+23.77R_ {\mathit{avg}}^{1}+0.022 \tag{11}\]  
where \(R_{\mathit{avg}}\) is the mean value of normalized \(RSSI\) obtained by the median filter, and its value ranges from 0 to 0.5.
# Tsunami Modeling and Satellite-Based Emergency Mapping: Workflow Integration Opportunities
## 4 Discussion and Conclusions
CEMS-RM is continuously seeking to improve timeliness of the service and uses as much as possible information which is available in the first hours after events. In this view and to further exploit this information, an additional product was recently introduced in CEMS-RM (since April 2019). The First Estimate Product (FEP) is an early information product which aims at providing an extremely fast (yet rough) assessment of most affected locations within the area of interest. FEP can be used to (1) highlight possibly affected areas, (2) review the initial product specifications (product type, areas of interest) or (3) decide on cancellation of initially requested delineation or grading products. In the current setup, FEP information is derived from the earliest suitable available post-event image. Rapid tsunami loss estimation can represent an effective way to generate a FEP [12]: coastal area inundation parameters (extent and maximum water height) derived from models and integrated with available exposure data, provide information that can be exploited to quickly generate first damage estimates, without waiting for the availability of any post-event image. In big disasters like tsunamis, FEP helps researchers to understand the situation quickly and supports the identification of focus areas for further image tasking and analysis. Lastly, it has to be remarked that in the last few years new satellite constellations composed of hundreds of small platforms provide daily high-resolution images of the entire Earth, i.e., the 3 m Planet data. A similar capacity shifts the focus from identifying the priority target areas for satellite sensing to how to efficiently extract useful information from a huge amount of data. This is the main reason why the trending research topics in the SEM domain deal with how to quickly generalize pre-trained deep learning networks to identify areas affected by disasters. While waiting for algorithms with sufficiently high accuracy and complete level of automation, there is still the need to identify the high priority areas to be interpreted by image analysts. Conceptualization, A.A. ([PERSON]), A.A. ([PERSON]), P.B., F.G.T. and A.W. ; Investigation, A.A. ([PERSON]) and A.A. ([PERSON]); Visualization, A.A. ([PERSON]), A.A. ([PERSON]) and A.W. ; Writing--original draft, A.A. ([PERSON]), A.A. ([PERSON]), F.G.T. and A.W. ; Writing--review and editing, A.A. ([PERSON]), F.G.T. and A.W. This research received no external funding. The authors declare no conflict of interest.
# LCNS Positioning of a Lunar Surface Rover Using a DEM-Based Altitude Constraint
## 4 Covariance Analysis
### Measurement Covariance Modelling  
The measurement covariance matrix \(\Sigma_{k}\) (used in the evaluation of the Kalman gain matrix, as shown in Equation (6)) is defined in Equation (12). Note that the size of matrices \(\Sigma_{pp}\) and \(\Sigma_{pp}\) is \(mxm\), where \(m\) is the number of visible LCNS satellites. \(\Sigma_{pp}\) and \(\Sigma_{pp}\) are further defined in Section 4.5.1. Furthermore, the definition of \(\sigma_{DEM}\) and \(n\) is provided in Section 4.5.2.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **State Vector Element** & **Value** \\ \hline \multirow{3}{*}{LCNS ODTS Uncertainty (1-sigma)} & Position (\(\sigma_{\text{s}\text{,}p}\)) & 15 m \\  & Velocity (\(\sigma_{\text{s}\text{,}p}\)) & 0.15 m/s \\  & Clock (\(\sigma_{\text{s}\text{,}\hat{\mu}}\)) & 10 m \\  & Clock drift (\(\sigma_{\text{s}\text{,}\hat{\mu}}\)) & 0.1 m/s \\ \hline \multirow{3}{*}{EKF Process Noise (1-sigma)} & Position (\(\sigma_{p}\)) & 0.01 m/\(\sqrt{\text{s}}\) \\  & Velocity (\(\sigma_{\text{0}}\)) & 0.15 m/s/\(\sqrt{\text{s}}\) \\  & Clock (\(\sigma_{\text{\hat{\mu}}}\)) & 1 m/\(\sqrt{\text{s}}\) \\  & Clock drift (\(\sigma_{\text{\hat{\mu}}}\)) & 10 m/s/\(\sqrt{\text{s}}\) \\ \hline \multirow{3}{*}{Initial EKF Uncertainty (1-sigma)} & Position & 100 m \\  & Velocity & 10 m/s \\ \cline{1-1}  & Clock & 100 m \\ \cline{1-1}  & Clock drift & 1 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Configuration settings used for the EKF covariance analysis; all values are 1-sigma.  
\[\Sigma_{k}=\begin{bmatrix}\Sigma_{pp}&0&0\\ 0&\Sigma_{\hat{p}\hat{p}}&0\\ 0&0&n\sigma_{DEM}\end{bmatrix} \tag{12}\]
## 6 Discussion  
In this paper, a novel LPSA algorithm is proposed for pulse compression based on a chirp transform spectrometer. Limited by the dispersion characteristic match between the expander chirp signal and the SAW compressor, and the large attenuation and limited operational bandwidth of the SAW devices in the classical two-channel pull-push structure, a digital pulse compression method may be a reasonable alternative to SAW filters. Considering the quadratic phase characteristic of the chirp signal, a fast digital pulse compression method by linear sampling and accumulation was developed. Compared to the classical digital pulse compression that is widely applied in radar systems, the proposed  
Figure 11: Frequency resolutions obtained by the LPSA algorithm and the classical time-domain pulse compression method.  
Figure 10: Experiment results with four input frequencies compressed by the SAW filter.  
novel method significantly decreases the computational requirements, with a two-stage frequency resolution subdivision for sparse spectrum measurements at the expense of small dynamic range.  
Considering the parameters proposed in the simulations, the input IF chirp signal had a bandwidth from 1.1 GHz to 2.1 GHz for pulse compression. In classical digital pulse compression methods applied in radar systems, the realization of pulse compression in the time-domain requires significant computation. The frequency-domain pulse compression method with less computation cannot be used in the CTS system, as the spectrum measurement is based on time intervals of the IF chirp signals. In the novel LPSA algorithm with two-stage frequency subdivision, if 100 sampling points are used for spectrum sensing (with a corresponding frequency resolution of 3.1 MHz), then the total number additions and multiplications will be 64,600 and 323, respectively. In the second-stage frequency resolution subdivision, only the channels with signals above the threshold will be calculated. So, the calculation is proportional to the sparsity of the measured spectrum. When dealing with sparse spectra with small dynamic ranges, the LPSA method can further save on computation.  
From the derived frequency resolution in the LPSA algorithm, we can see that the frequency resolution is mainly determined by the compression time, that is, the bandwidth of the IF chirp signals. The pulse amplitude accuracy of the novel LPSA algorithm is mainly determined by the number and accuracy of the orthogonal sampling points. Usually, 800 sampling points with 8 G sampling rate can achieve an amplitude accuracy above 98%. Also, the approximate factor p will influence the amplitude accuracy slightly; this can be optimized by improving the sampling rate to keep p close to zero. It can be also observed that the different conversion losses for different input frequencies for the mixer have a significant influence of the amplitude accuracy. This can be calibrated with the conversion loss characteristic of the mixer.
## 3 Evaluation of the Energy Conversion Subsystem and Energy Storage Subsystem
### Conversion Process
#### 3.1.1 Electrolysis  
The SailREC System, as envisioned in this study, has seawater as a primary source of water. Since direct seawater electrolysis is not established yet, conventional polymer electrolyte membrane (PEM) or alkaline electrolysis of seawater might be performed after purification steps [19]. Solid oxide electrolysers are still in the research and development phase in terms of technological maturity, apart from having the challenges of thermal cycling, chrome migration, and corrosion while offering higher electrical efficiency. Likewise, direct seawater electrolysis faces the challenge of designing selective electrodes that suppress chloride chemistry [20]. The use of PEM electrolysers is foreseen, as they have a faster response to supply changes, higher power density, and less maintenance compared to alkaline electrolysers, though PEM electrolysers have a higher cost in the application. The electrolysers would be fed by purified water. The by-product of the purification of seawater is brine. This would have to be disposed of, and unlike in coastal waters, discharge in the high seas is not expected to have a negative impact on the environment, but further inspection of this and the legal regulations would be required.
# NaYF4 Microstructure, beyond Their Well-Shaped Morphology
## 3 Results and Discussion  
### Confocal Microscopy and Polarized Luminescence  
With our confocal set-up, the polarized spectra of our NPs deposited by drop-casting of diluted NaYF\({}_{4}\) NPs in water are measured. The low concentration coupled to a sonication of the solution just prior deposition leads to dispersed NPs on the glass coverslip. Without reference, the orientation of the NP cannot be known nor the expected orientation of the polarization. We measured the spectra for a wide range of polarization angle. In these conditions, the axes of the two orthogonal electric dipoles will be given by the least brilliant and the brightest spectra. In Figure 2a we present the photoluminescence (PL) spectrum for two orthogonal polarizations (red and blue) and the difference between them in green. Moreover, by plotting the PL intensity versus the polarization angle (see Figure 2b), the typical dipole-like behavior expected from lanthanide ions appears to be in good approximation with the [PERSON] law (see Figure 2c).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Phase** & **Space** & **Cell** & **Atomic** & **Occupancy** & **Re Site** & **COD** & **Ref.** \\  & **Group** & **Parameters (Å)** & **Positions** & & & **Symmetry** & **ID** & \\ \hline Hexa- & P6\({}_{3}\)/m & \(a=b=\) & Y & 2/3 1/3 1/4 & 0.75 & \(C_{3h}^{2}\) & & \\  & P6\({}_{3}\)/m & 5.99276 & Na & 2/3 1/3 1/4 & 0.25 & & 1517675 & [26] \\  & 176 & \(c=\) & Na & 0 0 z & 0.25 & & & \\  & 3.52281 & F & x y 1/4 & 0.25 & & & \\ \hline Hexa- & P6 & 5.99276 & Y & 0 0 0 & 0.75 & \(C_{3h}^{2}\) & & \\  & 174 & & Na & 2/3 1/3 1/2 & 0.5 & & 1517672 & [26] \\  & & \(c=\) & Na & 1/3 2/3 z & 0.5 & & & \\  & & 3.52281 & F & x y 1/2 & 1 & & \\ \hline  & & \(a=b=\) & Y & 1/3 2/3 1/2 & 0.25 & \(C_{3h}^{1}\) & & \\  & 5.9148 & Y & 0 0 0 & 1 & \(C_{3}^{1}\) & 1517674 & [26] \\ Hexa- & P62m & & Na & 1/3 2/3 1/2 & 0.75 & & & \\  & 189 & \(c=\) & F & x 0 0 & 1 & & \\  & 3.52281 & F & 0 1/2 & 1 & & & \\ \hline cubic & Fm3m & \(a=b=c=\) & Y & 0 0 0 & 0.5 & \(O_{h}\) & & \\  & 225 & 5.47000 & Na & 0 0 0 & 0.5 & & 1517676 & [27] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Crystallographic data on synthesized phases of NaYF\({}_{4}\) for phase identification and rare-earth (Re) ion site symmetry.  
However, if we look in detail this dependence for several wavelengths, the extreme values are not obtained for the same angles as expected. Indeed, the polarization is due to the site symmetry of the lanthanide ion and the electrons can optically relax by emitting \(\alpha\), \(\sigma\) or \(\pi\) photons [28] with orthogonal polarization. Then a PL dependence can only present two angles for the PL optima values for all measured transitions, which is not the case: the 665.0 nm transition has a maximum shifted of 20\({}^{\circ}\) as compared to another transitions at 662.9 nm. This observation leads to conclude that the local symmetry orientation is not the same for all the emitters. To understand this observation, we investigated the microstructure of the particles in more details.
# A Chest Strap-Based System for Electrocardiogram Monitoring
## 2 Methodology  
### The System Architecture of the ECG Monitoring System  
In recent years, growing public awareness of exercise has increased attention to health monitoring during physical activity. To enable comfortable physiological signal monitoring during exercise, this study proposes a portable chest strap ECG monitoring system (CEMS). The CEMS can monitor a high-quality ECG signal without interfering with daily activities. As illustrated in Figure 1, ECG signals are collected via the silver-coated textile electrode (STE) embedded in the chest strap and transmitted to an embedded system. The signals are first processed through a dual-stage filtering circuit, comprising a notch filter to remove industrial frequency interference and a bandpass filter to eliminate baseline drift and high-frequency noise. After filtering, the signals are digitized by the analog-to-digital converter (ADC) of the microcontroller unit (MCU) and transmitted via a Bluetooth module to a mobile device for real-time ECG waveform visualization. Meanwhile, the ECG data are also stored in the system's flash memory for subsequent input into an exercise intensity assessment model, allowing for accurate evaluation of the user's exercise intensity.  
Unconstrained physiological signal monitoring is essential during physical activity. The structure of the proposed CEMS is illustrated in Figure 2a. The STE is sewn onto an elastic band, and its excellent flexibility allows it to stretch freely with the band. The soft and comfortable texture of the textile enables the CEMS to be worn securely around the chest, ensuring good contact with the skin. Figure 2b displays the printed circuit board (PCB) of the CEMS, which is enclosed in a plastic housing and attached to the elastic band using Velcro. The system's compact size meets the needs of individual users and allows high-quality ECG monitoring without adding weight or restricting movement. This design provides a novel and practical solution for wearable ECG monitoring during daily exercise.  
Figure 1: The overall operational architecture of the CEMS.  
Figure 2: Structure of the CEMS. (**a**) Wearable configuration of the CEMS. (**b**) PCB of the CEMS signal conditioning circuit.
# A Survey of the Recent Trends in Deep Learning Based Malware Detection
## 2 Trends in Malware Detection
### Shortcomings of Deep Learning Based Methods for Malware Detection  
The approach of deep learning has taken over the field of malware analysis because of its capability of automatic feature engineering but since still it is in the phase of evolution, therefore, certain issues still need to be catered to. One of the issues faced by deep learning-based methods is small data. The solution published by [43] indicates that the system was tested against small data and malware was executed for a very small time which can be easily catered by malware writers through evading techniques. Similarly, the research work of [46] used small data for training to avoid computational constraints but it affected the generalization. Again, the same problem was seen in the work of [47] due to which results of the given solution cannot be relied upon when implementing the presented framework in a real-time environment. Solutions given by [53, 57, 25] also suffer from the same problem.  
Another problem that can be seen is the size of the input. Since CNN works over images and it is observed that most of the produced solutions work over the fixed size of images only. Solutions presented by [48, 54] could perform better by handling variable size input data. Ref. [52] have not mentioned the execution time of samples for extracting API calls. In case samples would not have run for enough time, then claimed results would be non-reliable. Some of the proclaimed solutions have not catered to obfuscated samples due to which if they are implemented in real-time, their results will be affected on encountering packed or obfuscated samples.  
Solutions communicated by [44, 49, 51] have not catered to the circumstances where evasion techniques could have been applied. In the research work of [45], sparsity constraint was not considered. Most of the solutions adapting dynamic analysis did not pay heed to multipath execution problems. Comparison between approaches carried out by [35] is not reliable because, in one of the approaches, features were not normalized whereas the value of features had a big range. Research work of [57] made use of only malware samples for malware classification although the real time system receives benign as well as malicious files so the system should have been trained on both types of files. Secondly, even malware families that were considered for training were too few.  
The solution proposed by [82] is a stacked approach consisting of two stages. In the first stage, multiple base line machine learning based classifiers were used using the static features only. In the 2 nd stage, the final classifier was used which worked over the dataset created by the predictions of the base classifiers used in the 1 st stage. Similarly proposed methodology in [83] is also following the ensemble method. The first stage of ensemble classification in [83] is using multiple machine learning algorithms which are trained using static features only.
## 5 Experimental Results and Contributions
### Monitoring and Information Display System
#### 5.1.1 Virtual Reality System
During the exercise, the user sees two avatars, as shown in Figure 5. The avatar on the left represents the user, while the avatar on the right represents the expert user who entered the exercise into the system. During the exercise, the patient is able to observe both their own movements and the movements that must be followed by the expert user. In this way, the patient knows the movements to be made and is able to check whether these movements are being made correctly. Figure 5 shows a counter with the number of repetitions made as well as the repetitions that still need to be made. After finishing the exercise, the user is able to see the mistakes made, according to the repetitions made and for each part of the body, as can be seen in Figure 6.  
The data generated during the execution of the exercise provides information, such as the level of performance of an exercise, and allows the patient to be aware of the progress in the rehabilitation process. All this is controlled by the doctor who is able to consult this information remotely so that they are able to know the patient's progress, adapting the  
Figure 5: Virtual reality scenario. Figure 6: Errors when the user has done the exercise. patient's rehabilitation process according to this progress and ensuring that it is carried out correctly.
## 3 Mathematical Model
### Heat Source Model  
To more closely match the formation of laser-arc hybrid welding \"nail\" shape, a combination of heat sources was used. The welding pool is deep and the diameter is small in laser welding, while the welding pool is shallow and the diameter is large in arc welding. Given this, the combined heat source was selected as a Gaussian columnar heat source model with linear decay along the depth direction and a double ellipsoidal heat source model.  
The expression for the Gaussian columnar heat source model with linear decay along the depth direction is as follows:  
\[Q_{0}=\frac{3P\eta_{1}}{\pi R^{2}}\cdot D(z)\cdot\exp(-\frac{3}{R^{2}}r^{2}) \cdot u(z) \tag{3}\]  
where \(Q_{0}\) is the heat source, \(P\) is the laser power, \(\eta_{1}\) is laser absorption efficiency of the workpiece, \(D(z)\) is the decay function of the peak heat flow, which indicates the peak heat flow decay rate in the depth direction, \(R\) is the effective action radius of the laser heat source, \(r\) is the distance from a point within the effective radius of action of the heat source to the heat source, and \(u(z)\) is the unit step function.  
Figure 2: Model grid for simulation.  
Based on the data obtained after cutting and processing the welded parts, the expression of the laser heat source model is simplified:  
\[Q_{0}=\frac{9P\eta_{1}}{\pi R^{2}H}\cdot\exp(-\frac{6r^{2}}{R^{2}\log(\frac{H}{ h})}) \tag{4}\]  
where \(H\) is the depth of action of the laser heat source, and \(h\) is the depth of a point.  
The power density expression for the first \(1/4\) ellipsoid of the double ellipsoid heat source model is:  
\[Q_{1}=\frac{6\sqrt{3}\eta_{2}f_{1}UII}{\pi^{\frac{3}{2}}a_{1}bc}e^{-3(x-vt)^{2 }\frac{1}{a_{1}^{2}}}e^{-3y^{2}\frac{1}{p^{2}}}e^{-3z^{2}\frac{1}{c^{2}}} \tag{5}\]  
The power density expression for the last \(1/4\) ellipsoid of the double ellipsoid heat source model is:  
\[Q_{2}=\frac{6\sqrt{3}\eta_{2}f_{2}UII}{\pi^{\frac{3}{2}}a_{2}bc}e^{-3(x-vt)^{2 }\frac{1}{a_{2}^{2}}}e^{-3y^{2}\frac{1}{p^{2}}}e^{-3z^{2}\frac{1}{c^{2}}} \tag{6}\]  
\[f_{1}+f_{2}=2 \tag{7}\]  
where \(Q_{1}\) and \(Q_{2}\) are the heat source, \(\eta_{2}\) is the arc energy absorption efficiency of the workpiece, \(a_{1}\) and \(a_{2}\) are the radii of the first and second half of the melt pool, \(b\) and \(c\) are the effective action width and effective action depth of arc heat source, respectively, and \(f_{1}\) and \(f_{2}\) are the energy fractions of the anterior and posterior ellipsoids.