esa-sceva/satcom-chunk-collection huggingface dataset:
# Gorilla Glass Cutting Using Femtosecond Laser Pulse Filaments
## 1 Introduction
In this case, a thick soda-line glass (Kerr medium) was placed immediately after the objective lens to achieve long filaments where the thickness of the glass was varied for optimization [38]. However, placement and stable holding of a thick glass after the objective lens is challenging. On the contrary, glass with appropriate thickness is required during glass cutting to achieve long filaments. As a result, a simple but effective femtosecond laser pulse filament-based glass cutting technology is desirable. On the other hand, characteristics of each glass are different. Due to the hardness and brittle nature of Gorilla glass, it is difficult to cut. Femtosecond laser pulse filament-assisted technology can be considered a potential solution for Gorilla glass cutting. This paper reports a novel technique for smooth Gorilla glass cutting using femtosecond laser pulse filamentation technique without placing any additional glass piece after the objective lens. In order to characterize femtosecond laser pulse filaments, we varied the laser energy (E), focus position from the top surface of the glass sample (f), and magnification of the objective lens under a constant scanning speed of the laser beam of 10 mm/s. We examined the impact of the laser parameters on the physical characteristics of the periodic high aspect ratio micro-voids, fabricated by femtosecond laser pulse filaments. The longest filaments/micro-voids of 146 \(\upmu\)m length were evident for a 100\(\times\) objective lens with E = 37 \(\upmu\)J and f = 470 \(\upmu\)m. Further increases in the filament's length are required to cut a 0.5 mm thick Gorilla glass by irradiating the femtosecond laser beam only once. The energy of the laser beam was increased and the scanning speed was decreased to successfully cut the Gorilla glass with excellent surface quality. After femtosecond laser machining, the periodic micro-voids incorporated Gorilla glass samples went through mechanical cleaving process. Uniform mechanical pressure of 400 MPa was applied on both sides of the glass cutting plane to cut the Gorilla glass samples into two pieces. Furthermore, we investigated the glass surface after glass cutting and found excellent smoothness. We believe that the proposed technology will pave the way for smooth and stable display glass cutting.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 3 Methodology
### Haar Wavelet Transformation  
Wavelet transformation (WT) is used to decode a real periodic or non-periodic signal into a linear combination of elementary functions. The principal of the wavelet transformation is based on the decay of the signal on a set of sinusoidal functions. Several wavelet transformation functions exist from which the 'Haar' wavelet transformation was chosen. The idea behind this wavelet analysis is to decompose the signal of \(N=2^{k}\) points on a basis of discontinuous and orthogonal functions resulting from the translation and dilations of functions called the father scaling function \(\varphi(t)\) and the mother wavelet function \(\psi(t)\).  
The real SAR time series obtained from the available S1 images consists of 82 values corresponding to 82 different dates. Since 82 is not a perfect square of 2 (\(N=2^{k}\)), the number of values to be considered in the wavelet decomposition should be limited to the 1 st perfect square less than 82. Thus only 64 values were considered out of the 82 values in the time series. For 64 \(\sigma^{\circ}\) values, the temporal series could be decomposed on six levels where \(2^{6}=64\). However, the 64 values of the time series were able to cover a period of one year (from 15 September 2017 until 28 September 2018) which is considered sufficient to detect irrigation activities since most of the irrigation in the region mainly exist between May and September of each year.  
The 'Haar' wavelet transformation was computed at each plot for \(\sigma^{0}_{\text{P,VV}}\), \(\sigma^{0}_{\text{P,VH}}\), \(\Delta\sigma^{0}_{\text{PG,VV}}\), \(\Delta\sigma^{0}_{\text{PG,VH}}\). Thus, each SAR temporal series of 64 values is transformed into 64 independent wavelet coefficients corresponding to six levels of decomposition. Each plot attains finally 256 different wavelet coefficients (64 coefficients \(\times\) four time series).
# Adaptive Multi-Path Routing Protocol in Autonomous Vehicular Networks
## 5 Mathematical Analysis
### Analytical Results  
In this section we present the results of the mathematical analysis conducted in Section 5.1. Here, we compute for the case when the distance between the two nodes is the maximum transmission range, \(R_{1}\). Note that this work can easily be extended to the case when the distance is \(R_{2}\). As presented in Figure 8, in a very-low-density environment where the density of the nodes (\(\delta\)) is around \(10^{-8}\) nodes/m\({}^{2}\), the probability of finding a high-throughput path (\(p\)) is close to zero (0.00046). Note that this density translates to around \(1.96\times 10^{-3}\) nodes per the area covered by the maximum transmission range (\(R_{1}\)).  
For the probability to reach around 0.5 (50%), \(\delta\) has to be at least \(1.5\times 10^{-5}\) nodes/m\({}^{2}\), or 2.94 nodes per \(R_{1}\). When \(\delta\) reaches \(1.64\times 10^{-4}\) nodes/m\({}^{2}\), or 32.2 nodes per \(R_{1}\), then \(p\) reaches 0.9995, and we can safely assume that a high-throughput path always exists.
# Effects and Mechanisms of Microbial Remediation of Heavy Metals in Soil: A Critical Review
## 5 Microbial Remediation of Heavy Metal Pollution in Soil and Water  
### Microbial Living Environments  
#### 5.1.1 pH  
The pH plays a crucial role in microbial biosorption, and optimum pH is often different for different microorganisms. An unsuitable pH presents many adverse effects on microbial growth for several reasons [70]. First, the pH affects the activity of enzymes in microorganisms, thereby affecting the rate of microbial metabolism of heavy metals [71]. Second, the pH affects the surface charge of the microorganism, thereby affecting its adsorption of heavy metal ions [72]. Moreover, pH affects the hydration and mobility of many metal ions in the soil [16]. Studies by both [PERSON] et al. [73] and [PERSON] [54] showed that the removal rate of heavy metals by microorganisms increases with an increase in the pH over a limited range, but the removal rate begins to decrease after the pH rises to a certain limit. Precisely speaking, for Pb\({}^{2+}\) and Zn\({}^{2+}\) with pH values from 2.0-5.5, the removal rate continued rising. The adsorption capacity (70 mg/g, 20 mg/g) at a pH of 5.5 is seven times higher versus two times higher at a pH of 2.0 (10 mg/g), respectively. However, at pH values higher than 5.5, the removal rate decreases to the same level as a pH of 2.0. [PERSON] et al. [24] showed that the optimum pH range for most bacteria is 5.5-6.5, but there are exceptions. For example, [PERSON] et al. [73] showed that the optimum pH for _Bacillus jeotgali_ is 7. This may be the case because some metal ions form hydroxide precipitates and are less susceptible to microbial adsorption when the pH is increased  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Microbe**} & \multicolumn{4}{c}{**Qmax**} \\ \cline{2-5}  & **Cd** & **Cr** & **Zn** & **Pb** \\ \hline _Bacillus subtilis_ & 101 & & 137 & \\ _Pseudomonas aeruginosa_ & 57.37 & & 13.7 & 79.5 \\ _Streptomyces noursei_ & 3.4 & & 1.6 & 99 \\ _Bacillus licheniformis_ & 142.7 & 62 & & \\ _Bacillus heterozygous_ & 159.2 & 72.6 & & \\ _R Rhizus arrhizus_ & 26.8 & 4.5 & 55.6 & \\ \hline \hline \end{tabular} The contents of the table are derived from [6; 16; 41; 56].  
\end{table}
Table 5: Qmax of different microbes.  
above a certain value [74]. In addition, the optimum pH for aerobic microorganisms may differ from that for anaerobic microorganisms.
# The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals
## 2 Methods
### Detection Based on Shannon Entropy Calculation  
AF may be present in the ECG if there is no cyclic ventricular activity and with disorganized low amplitude atrial activity (f-waves) in the baseline. AF typically causes a highly irregular ventricular rhythm. Therefore, AF detection methods generally perform ventricular activity detection, through identification of the R peak, to measure the associated atrial activity [10; 34]. However, most automated AF detection algorithms use irregular ventricular activity as the sole criterion for detection because f-waves are much harder to detect than QRS complexes due to their relatively lower amplitudes. Therefore, R-peak detection is a very important task. In this paper, R-peak detection was performed using [PERSON] et al.'s method [35].  
During AF, R-R intervals are more irregular and consequently have a larger standard deviation when compared to normal sinus rhythm. Therefore, an R-R interval entropy measure can be employed to distinguish AF from other ECG rhythms. Let X be a finite discrete random variable, with possible values \(x_{1},x_{2},\ldots,x_{m}\), representing the R-R interval time series. The entropy of the sequence is calculated according to the Shannon entropy definition: \(H(X)=-\sum_{i=1}^{m}p(x_{i})logp(x_{i})\)[36], where \(p(x_{i})\) is the probability of occurrence of each \((x_{i})\) value (where \(i=[1:m]\)). A sliding window of fixed length equal to 45 was applied to the R-R interval time series, in order to estimate the entropy fluctuations of the series over time. Since entropy is a measure of the disorder of a system, it follows that a highly irregular heartbeat will result in a large entropy measure. The entropy measure may then be thresholded whereby values above the threshold are deemed to indicate the presence of AF, while those below the threshold indicate the absence of AF. Different threshold values result in different trade-offs between sensitivity and sensitivity of detection; this is explored further in Section 3. Figures 1 and 2 presents examples of the different signals utilized in the AF detection algorithm.  
Figure 1: Illustration of AF detection algorithm, showing annotations, and R-R intervals series. An annotation value of 1 signifies the presence of AF while an annotation value of 0 signifies normal rhythm.
# DkFD: Optimizing Common Pediatric Dermatoses Detection with Novel Loss Function and Post-Processing
## 2 Related Work  
### Object Detection Algorithms  
The two major types of object detection algorithms are one-stage object detection algorithms based on regression and two-stage object detection algorithms based on candidate regions [22; 23].  
The one-stage algorithm directly regresses at various locations in the image, determining the object class and locating the coordinates afterward. It is often simple and quick, but with relatively poor detection accuracy for small and dense objects. YOLO series approaches [24] and CornerNet [25] belong to this type of work.  
The recent two-stage object detection algorithm often employs a Region Proposal Network (RPN) with better precision and accurate localization and is based on candidate regions. In 2014, [PERSON] et al. proposed the R-CNN, which significantly improves detection by using CNN to extract features and selective search to extract candidate boxes. SPP-Net, which was proposed by [PERSON] et al., implements the multi-scale input of CNN and only needs to extract the convolutional features once for the original image. In order to significantly increase the detection accuracy and training efficiency, [PERSON] et al. proposed Fast R-CNN [26], which uses Softmax rather than SVM for classification and incorporates the regression task of regions into the training. However, it continues to use a time-consuming selective search to create candidate boxes. Faster R-CNN [27] was proposed with the RPN, which uses a convolutional neural network to generate the candidate boxes, and the RPN and the subsequent detection network share the convolutional features. It is the first real end-to-end object detection network. Based on the straightforward network connection changes without increasing the model's computation, the Feature Pyramid Network was proposed to improve the effectiveness of small object detection. Faster R-CNN is a classical network for two-stage object detection, and there are numerous derivative networks, such as the HyperNet based on feature fusion, R-FCN based on full convolutional network, Mask R-CNN based on instance segmentation, Cascada R-CNN [28] based on cascade network, and Dynamic R-CNN [29], which can automatically adjust the label assignment criteria and the shape of the regression loss function.  
Taking high accuracy as the main metric of common pediatric dermatoses, we choose Faster R-CNN, a classical algorithm of two-stage object detection, as the benchmark [30]. We improve the Faster R-CNN network for the problem of training overfitting, unstable image quality, unbalanced difficult and easy samples, and intermingling of detection results in the detection of common pediatric dermatoses.
## 2 Materials and Methods
### Volatile Compounds  
Volatile compounds were analyzed by headspace solid phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS) as described in [PERSON] et al. [20] and [PERSON] et al. [38]. The SPME fiber coating used was divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) (50/30 \(\upmu\)m film thickness, Supelco). Vegetable oil (2 g) was weighed in a 25 mL headspace vial. The sample was equilibrated at 50 \({}^{\circ}\)C for 15 min and the adsorption time, with the fiber exposed to the headspace of the sample, was 60 min at the same temperature. The desorption time for the fiber in the injection port of the gas chromatograph was 30 min. The GC-MS instrumentation used was GC 6890 N coupled to a mass selective 5973 detector (Agilent Technologies, Santa Clara, CA, USA). Volatiles were separated using a capillary column HP-5 MS, 5% phenylmethylsiloxane (30 m \(\times\) 0.25 mm I.D. \(\times\) 0.25 \(\upmu\)m, from Agilent Technologies, Santa Clara, CA, USA). Chromatographic conditions were as follows: the oven temperature was held for 5 min at 42 \({}^{\circ}\)C, then increased to 120 \({}^{\circ}\)C at 3 \({}^{\circ}\)C min\({}^{-1}\) and to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\) (5 min hold); injector temperature, 250 \({}^{\circ}\)C; detector temperature 280 \({}^{\circ}\)C; ion source temperature, 230 \({}^{\circ}\)C; quadrupole mass analyzer temperature, 150 \({}^{\circ}\)C. Helium was used as carrier gas at 1 mL min\({}^{-1}\). The mass spectrometer was operated by electronic impact at 70 eV and ions were scanned over the \(m/z\) range of 33-350 at a rate of 4.43 scan/s.  
The identification of each peak was made taking into account the KI (Kovats Index) reported in the literature, and comparing their mass spectra with the one of a commercial library ([PERSON](r), Mass Spectral Database). A semi-quantitative approach was used for quantification, measuring TIC (Total Ion Chromatogram) area and, in the case of overlapping peaks, the quantification of the corresponding compound was conducted by integrating the area of the specific ion and considering the relative ratio in which this ion is present in each compound. Results are expressed in area \(\times\) 10\({}^{3}\)/g oil. Samples were analyzed in triplicate.
## 3 Results
### Comparing the Rioneter Data with D-RAP
In summary, the D-RAP model underestimates the values obtained from the riometers during the solar flares (X-Ray flux changes) while it overestimates the caused impact during the particle events based on the investigated events. Looking through the absorption values recorded by the riometers during the selected events (Table 6), we can conclude that, generally, the X-class solar flares had a larger impact (\(\sim\)1-2.6 dB) than the M-class flares (\(\sim\)0.1-0.7 dB). (Detailed information for the flare and energetic particle events is seen in Supplementary Table S1). However, the largest detected values (\(\sim\)1.2-4.1 dB) were observed during an M3.0 class flare (peak time: 15:47 UT) on 08 September (this conclusion is also true for the ionosonde data). Nevertheless, the duration of the increased X-Ray flux took almost two hours (Figure 4a) during this event, which could explain why the integrated effect on the ionospheric absorption was the highest in this case. Thus, the duration of the flare event seems to be an important parameter. In the case of the riometers, we present the comparison of the modeled and measured values separately for the different stations (Figure 10) because the stations operated at different frequencies (ROV and J\(\gamma\)V riometers operated at 32.4 MHz, while the others operated at 30.0 MHz). Furthermore, the maximum absorption levels should be compared  
Figure 9: X-Ray (panel (**a**)) and proton flux (panel (**b**)) on 10 September, and the changes measured by the Finnish Riometer Network (black curve) and determined by the D-RAP model at the same frequencies (30 and 32 MHz) and at the same locations (red curves) (panels (**c**–**h**)). The X-Ray flux values are from 0.1 to 0.8 nm wavelength band measured by the GOES spacecrafts. The magenta dashed line indicates the peak time of the X-class flare. to the level of the Quiet Day Curve (QDC) before onset, and the QDC level also depends on the station. Looking through the plots, a slightly increasing trend can be recognized at almost every station, the larger the X-Ray flux of the flare, the larger the absorption changes measured by the riometers (the impact of the above detailed M3.0 flare is not presented in Figure 10). Another important thing to note is that the difference between the modeled and observed values seems to be larger at the higher latitude stations (ABI (a), IVA (b), SOD (c)) than at the lower latitude station, JYV (f). In the case of ABI (a) and IVA (b), the model values are much larger than the observed ones in some cases, which agrees with the conclusion of the validation report [27] for the high-latitude stations. The comparison of the model and the measurements during the two investigated energetic particle events were detailed above.
## 2 Experimental Investigation
### Test Results  
The steel material used in the present work was SS275 structural steel in Korean Industrial Standard (KS) [17] (corresponds to ASTM A36). The measured material properties from tension coupon tests are shown in Table 1, which lists the measured thickness \(t\), elastic modulus \(E\), yield stress \(F_{y}\), tensile strength \(F_{u}\), and elongation at fracture \(\varepsilon_{f}\). Load-displacement curves obtained from the tests are plotted in Figure 3. Note that the fracture displacement is defined as the point where a precipitous drop in the load capacity to zero is identified. For the uniaxial tension coupon specimen, the ultimate strength was reached with in-plane (diffuse) necking when the strain is about 21%. The final tensile fracture occurred at approximately 25% drop of the peak load with apparent localized necking as shown in Figure 3a. Comparing to tension coupon specimens, the fracture of in-plane shear specimens propagates more quickly after the ultimate strength (the ultimate strength point is close to the final fracture point).  
Figure 2: Testing set-up for fracture investigation.  
Figure 3: Experimental load–displacement curves and fracture modes: (**a**) Uniaxial tension specimen; (**b**) In-plane shear specimen; (**c**) Tension and shear specimen.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimen** & \begin{tabular}{c} **Measured** \\ **Thickness** \\ **(\(t\), mm)** \\ \end{tabular} & \begin{tabular}{c} **Elastic** \\ **Modulus** \\ **(\(E\), GPa)** \\ \end{tabular} & \begin{tabular}{c} **Yield Stress** \\ **(\(F_{y}\) MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Tensile** \\ **Strength** \\ **(\(F_{u}\), MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Elongation at** \\ **F\({}_{y}\)** \\ **(\%)** \\ \end{tabular} \\ \hline Tension-1 & 7.82 & 214.7 & 276.2 & 414.4 & 1.50 & 38.7 \\ Tension-2 & 7.83 & 214.8 & 271.5 & 410.4 & 1.51 & 40.8 \\ Tension-3 & 7.85 & 206.6 & 271.8 & 410.6 & 1.51 & 39.6 \\ Average & 7.83 & 212.0 & 273.2 & 411.8 & 1.51 & 39.7 \\ Standard deviation (\%) & 0.16\% & 1.81\% & 0.79\% & 0.45\% & 0.34\% & 2.17\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of SS275 steel from tension coupon specimens.
# Design of a SIMO Deep Learning-Based Chaos Shift Keying (Dlcsk) Communication System
## 3 SIMO DLCSK System Model
### Decision Combining Rule  
Reliable communication over multi-path channels highly depends on the condition of the paths, and the probability of deep fade. Spatial diversity is used in conventional coherent communication systems for combating the destructive effects of small-scale fading, and thereby for improving reliability. However, these receivers are very sensitive to the accuracy of the channel estimation process. Since the proposed classifier is trained under different channel conditions, the receiver does not require complex channel estimation techniques or soft data combining methods, such as Equal Gain Combining (EGC) and Maximal Ratio Combining (MRC) [80], for data detection. We can combine hard outputs of several classifiers to achieve a diversity gain through a simple decision rule.  
Based on the received vector \(r_{t,m}\), each of the LSTMs can produce a local decision and report this decision to a Fusion Center (FC), which makes the final decision. There are several methods of fusing the decisions of the classifiers, such as majority voting and ensemble averaging [81]. In this paper, having the binary-valued decisions, the FC applies the majority voting fusion rule to generate the ultimate decision. The class with the highest overall output is selected as the ultimate decision. Mathematically, the decision class \(\text{O}(\mathbf{r})\) can be calculated as [82]  
\[\text{O}(\mathbf{r})=\text{arg}\ \underset{\text{j}}{\text{max}}\ \sum_{m=1}^{M} \mathcal{O}_{jm}\left(\text{C}_{\text{m}}(\mathbf{r})=j\right), \tag{15}\]  
where \(M\) is the number of classifiers, \(j\in\{0,1\}\) denotes the \(j\)th class, \(\text{C}_{\text{m}}(\mathbf{r})\) represents the output of the \(m\)th classifier for the received vector \(\mathbf{r}\), and \(\mathcal{O}_{jm\text{m}}\) is a binary characteristic function that can be defined as  
\[\mathcal{O}_{jm}=\begin{cases}1&\quad\text{if}\ \text{C}_{\text{m}}(\mathbf{r})=j \\ 0&\quad\text{if}\ \text{C}_{\text{m}}(\mathbf{r})\
eq j.\end{cases} \tag{16}\]