esa-sceva/satcom-chunk-collection huggingface dataset:
# A Backwards-Tracking Lagrangian-Eulerian Method for Viscoelastic Two-Fluid Flows
## 3 Numerical Method
[27], the new method is expected to be more robust due to the structured nature of the Lagrangian nodes as well as the staggered arrangement between the storage locations of the velocity and the viscoelastic stress. Although the computational efficiency is not assessed in detail in this work, the computational cost is, in fact, reduced. As a reference, the computational performance of the original Lagrangian-Eulerian method was studied for the flow of a four-mode PTT fluid over a confined cylinder [27]. Four Lagrangian nodes per fluid cell were initialized for the node set. Out of the total stress calculation time, approximately 50% was spent on solving the ODE systems, 30% on the unstructured interpolation, and 10% on the redistribution of the Lagrangian node set. In the new method, the unstructured interpolation, as well as the redistribution, is completely removed. Furthermore, the number of Lagrangian nodes is reduced to be on the same order as the number of Eulerian cells. Thus, a large reduction in computational time should be expected. The proposed method is implemented in the software IBOFlow(r)[37], an in-house CFD code that was developed at the Fraunhofer-Chalmers Research Institute for Industrial Mathematics in Gothenburg, Sweden. In addition to viscoelastic flow, the solver has previously been employed to simulate conjugated heat transfer [38; 39; 40], and fluid-structure interaction [41], as well as free surface flow of shear-thinning fluids with applications for seam sealing [42; 43], adhesive application [44], and 3D-bioprinting [45].
## 3 Results and Discussion  
### Characterization  
The structures of the prepared polyurethanes were analyzed with \({}^{1}\)H-NMR, ESI-MS, and elemental analysis. The proton peaks of secondary carbon (i) and tertiary carbon (h) formed by a ring opening reaction of the epoxy group were observed in the 4.05-4.32 (ppm) range, as shown in Figure 2.  
\({}^{1}\)H NMR (300 MHz, Acetone): \(\delta\) (ppm) = 1.36 (8H, _m_), 1.53 (8H, _m_), 2.52, 2.69 (4H, _d_), 3.05 (2H, _m_), 3.26-3.90 (12H, _m_), 4.32 (2H, _m_), 4.05 (4H, _d_). EA calc'd for C\({}_{42}\)H\({}_{57}\)O\({}_{12}\)P: C, 64.27; H, 7.32; O, 24.46; P, 3.95. found: C, 60.95; H, 5.99; O, 22.86. LC-ESI-MS: found 785.3 (M + H)\({}^{+}\), calc'd 784.3.  
Figure 2: \({}^{1}\)H-NMR spectrum of EP-DOPO.  
Polyurethane was prepared by reacting polyol with IPDI followed by capping the terminal-NCO (isocyanate) group with a phenol OH group, as shown in Figure 3.  
The molecular weights of the prepared polyurethanes were analyzed with GPC (Figure 4) and the molecular weights and dispersity data are shown in Table 1.  
The prepared polyurethanes were analyzed with GPC to check whether two polyurethanes have a similar range of molecular weight. It turned out that both polyurethanes have a similar range of molecular weight: 9338 g/mol for EPPU and 7556 g/mol for Ref PU. However, EPPU-2 has a small molecular weight because it is synthesized by reacting small molecular weight of EP-DOPO with diisocyanate (Figure S1).
# Visualizing Small Objects Using Amplitude-Modulated Laser Light at Microwave Frequencies
## 4 Experimental Results and Comparison between Two Experiments
### Comparison between the LADAR and the SAIL Results  
We applied two types of imaging systems to the targets and acquired good images successfully. Here, we discuss the advantages and disadvantages of these two diagnostics and suitable experimental situations for each diagnostic in terms of the measurable range and analysis difficulty.  
Firstly, since our SAIL system employed the ISAR technique--i.e., the targets were moved instead of the LADAR--this system can only observe moving targets. In addition to this disadvantage, this system requires that the positions of the moving targets at each measurement be pre-known when used with the present analysis method. A reconstruction method that does not need to know the target position in advance needs to be developed in future research. However, the LADAR can be applied to both moving and stationary targets. As this system needs to scan the beam spatially, moving targets must stay in the same position during the scan. In the present system, the scan speed was very slow since an electrically controlled stepping motor was utilized.  
Secondly, a suitable target range between these diagnostics is considered to be around 1000 m in terms of the cross-range resolution. For the fine cross-range resolution, the LADAR system requires that the projection beam have a spot size in the target range that is as small as possible. Nevertheless, a transmitter with a large aperture is utilized, and the spot size with range of over 1000 m becomes over few centimeters in size. However, the SAIL system does not require a collimated projection beam, but it does require a wide azimuth look angle to improve the cross-range resolution; that is, SAIL requires a diverged projection beam. In general engineering and developing a diverged projection beam is  
Figure 8: Synthetic images of two corner cube targets (**a**,**c**) and corresponding photographs of the targets (**b**,**d**). The two reflectors are separated by 110 mm in the cross-range direction; (**a**,**b**) diagonal separation of 53 mm in cross-range and in-range separation of 40 mm (**b**,**d**).  
easier than collimation. However, since a diverged projection beam indicates a low-gain transmitter, the reflection power from the target will often fall below noise level in this case. To achieve a well-resolved and high-contrast image, even when utilizing a low-gain transmitter, the IF data taken by each repetition measurement must be well correlated before the Fourier transformation process is applied. In order to achieve that, the precise estimation of the target positions is a key issue that will be researched in the near future.
# The Necessity of LED to Ambient Light Ratio Optimization for Vehicular Optical Camera Communication
## 3 LED to Ambient Light Ratio Optimization
### A Top-Down Explanation on How L2A Ratio and LED Density Determine Bit Detection Accuracy  
#### 3.3.1 Effect of Bvar and Wvar on LED Bit Detection Accuracy  
In all problem of data classification, the data would be classified better when the data within each class is close to each other while the data from different class is far to each other. Between-class variance (Bvar) and within-class variance (Wvar) are the statistical quantification for these characteristics of data. As illustrated in Figure 4, Bvar indicates the separation in the features of two classes while Wvar indicates the scattering of features within each class. LED bit detection is indeed a problem of classifying On and Off LED from obtained LED features. Consequently, the bit detection accuracy is high when Bvar is large and Wvar is small.  
Figure 4: Between-class variance and within-class variance.  
Figure 3: Decisive factors of LED bit detection accuracy.  
Let \(\mathbf{x}=(x_{AGR},x_{GRI})\) denote the features of an LED, \(\mathbf{x}_{On}\) and \(\mathbf{x}_{Off}\) denote the On and Off states of the LED features. The between-class variance \(\sigma_{b}\) is calculated as:  
\[\sigma_{b}=(\boldsymbol{\mu}_{On}-\boldsymbol{\mu}_{Off})^{2}, \tag{5}\]  
where \(\boldsymbol{\mu}_{On}=E[\mathbf{x}_{On}]\) and \(\boldsymbol{\mu}_{Off}=E[\mathbf{x}_{Off}]\) are the means of the On and Off LED features.  
The within-class variance \(\sigma_{w}\) is calculated as:  
\[\sigma_{w}=E[(\mathbf{x}_{On}-\boldsymbol{\mu}_{On})^{2}]+E[(\mathbf{x}_{On} -\boldsymbol{\mu}_{On})^{2}]. \tag{6}\]  
To achieve the best distinguishability between On and Off LEDs, the variance ratio of between-class to within-class variances needs to be maximized:  
\[R_{B2W}=\frac{\sigma_{b}}{\sigma_{w}}, \tag{7}\]
## 1 Introduction
The interpolation method is often adopted to approximate a given power density, which inevitably introduces errors and may even distort the evolutions of some essential properties, especially in violent discharges [31]. Therefore, a more efficient method should be developed to quickly and accurately describe the CO\({}_{2}\) conversion in RF discharges under Martian pressure at a given power density. In recent years, with the development of machine learning, data-driven models, such as artificial neural networks (ANNs) and deep neural networks (DNNs), have been introduced into the studies of low- temperature plasmas [32; 33]. Compared to ANNs, DNNs can extract abstract high-level features and produce accurate predictions in complex chemical, physical, and biological systems [34; 35; 36]. To better understand the underlying physical and chemical properties of plasma, considerable effort has been devoted to applying DNNs to various plasma processes. [PERSON] et al. [37] investigated the possibility of DNNs for integrating diagnostic data in the active control of fusion-relevant plasmas in complex scenarios. Based on a DNN, [PERSON] et al. [38] developed a microwave interferometry method for time-varying plasmas, and the DNN was used to effectively eliminate the noise and accurately diagnose the time-varying electron density and collision frequency. The dielectric barrier discharges driven by sinusoidal or pulsed voltages can also be calculated by the optimized DNN models [39; 40]. A neural network was constructed to investigate the effects of chamber size, gas flow rate, and input power on CO\({}_{2}\) conversion in a DBD reactor [41], and the results showed that neural networks have great potential for evaluating and predicting the performance of plasma-assisted CO\({}_{2}\) conversion. More critically, the studies in Refs. [42; 43] showed that DNNs could offer mesh-less implementations for modeling multi-physics and multi-scale systems. It is, therefore, feasible to model and predict the discharge characteristics and plasma chemistry of RF CO\({}_{2}\) discharge under Martian pressure using DNNs. In this paper, a DNN is constructed based on the characteristics of RF CO\({}_{2}\) discharges and trained using simulation data obtained from the fluid model, and then, this DNN is employed to describe the discharge characteristics and plasma chemistry of RF CO\({}_{2}\) discharges with various driving frequencies and input power densities. The second section is concerned with the methodology used for this study, including the descriptions of the fluid model and the DNN algorithm. The third section begins by illustrating the validity and efficiency of DNN prediction by comparing the prediction results with the fluid simulation results, and then, the well-trained DNN is applied to investigate the effects of driving frequency and input power density on the CO\({}_{2}\) conversion in RF discharges under Martian pressure, which further indicates that the DNN is capable of obtaining enough prediction results based on the limited training data. Finally, a summary is given.
## 3 Results
### Performance Comparison of the Unseen Test Set
For example, the soft ferrite phase provides this steel grade its high ductility. The martensite phase--which has extremely high strength, such as regarding the quenched grades that are used for springs and cutting tools--is responsible for the high tensile strength. Combining the advantages of the two phases, called dual phase steels [34], the soft ferrite phase surrounds the hard islands of the martensite phase, which creates the very high initial work hardening rate exhibited by these steels. Banitic steels have a more complex microstructure, which contains upper and lower bainite in combination with martensite and carbides. The lower the bainitic transformation temperature, the finer the bainitic structure is, and it results in higher hardness, toughness, and wear resistance [35; 36]. Furthermore, minimizing the martensite and cementite fractions in the microstructure contributes to improved damage resistance [35]. Grain size [37] and precipitates strength [38] effect should be considered. [PERSON] et al. [37] reported the mechanical properties and grain size in steels after warm-rolling and annealing process. The original grain size of ferrite was \(\sim\)0.53 \(\upmu\)m, the yield strength and tensile strength were 951 MPa and 968 MPa, respectively, and the total elongation rate was 11.5% after warm-rolling at 600 \({}^{\circ}\)C. Additionally, after the next 4 h of annealing, the grain size of ferrite and particle size of cementite increased to \(\sim\)1.35 \(\upmu\)m and \(\sim\)360 nm, and the yield strength and tensile strength decreased to 600 MPa and 645 MPa, respectively, with a total elongation increase of 20.9%. This implied a finer grain with better mechanical properties. We investigated the microstructure analysis using an optical microscope at magnification up to 500\(\times\), and it is difficult to analyze the above effect at this magnification.
## 2 Literature Review
### Facebook Prophet  
Facebook Prophet (FBP) is a decomposable time series model, based on additive modelling [30]. Recently, it has gained significant attention due to its capability to accurately forecast time series data. For instance, [PERSON] et al. compared FBP to autoregressive integrated moving average (SARIMA) and concluded that FBP outperformed SARIMA for the prediction of electricity and natural gas demand [31]. Additionally, [PERSON] et al. predicted PV short circuit current for the next day, deeming it to be a reliable forecasting method [32].  
FBP delivers its peak performance when dealing with a time series with strong seasonal effects [33]. This applies to solar irradiance and is one of the main reasons to believe that this algorithm is suitable for solar irradiance forecasting. However, in the literature, FBP has not yet been utilised for solar irradiance prediction.  
FBP models the time series data as follows:  
\[y(t)=g(t)+s(t)+h(t)+\epsilon_{t} \tag{1}\]  
where the trend is \(g(t)\), the seasonality is \(s(t)\), and the holidays are \(h(t)\). It is worth mentioning that holidays and weekly trends were not accounted for, as these have no influence on solar irradiance, \(\epsilon_{t}\) indicates the changes not represented by the model and is assumed to be normally distributed. It has intuitively adaptable parameters, designed to be used by analysts that have domain knowledge rather than statistical expertise. Therefore, it is important to know the characteristics of the subject that is being predicted, in this case, the behaviour of solar radiation.
# Software Reliability Growth Model with Dependent Failures and Uncertain Operating Environments
## 3 NHPP Software Reliability Growth Model (SRGM)  
### Model Formulation  
(1) In general, \(m(t)\) is obtained from a differential equation as follows [2,31]:  
\[\frac{m(t)}{dt}=b(t)[a(t)-m(t)] \tag{7}\]  
where \(a(t)\) is defined as the expected total number of initial failures plus the number of newly introduced errors that remain until testing, and \(b(t)\) is defined as the rate of detected failures per fault.  
(2) The mean value function of the model when considering uncertainty of the operating environment can be obtained from the following differential equation [32]:  
\[\frac{m(t)}{dt}=\eta b(t)[N-m(t)] \tag{8}\]where \(b(t)\) is the fault detection rate function. Let \(\eta\) represent the uncertainty of the operating environment. Here, \(\eta\) is a random variable that follows gamma distribution \(g(x)\) with parameters \(\alpha\) and \(\beta\), which is given by  
\[g(x)=\frac{\beta^{\alpha}x^{\alpha-1}e^{-\beta x}}{\Gamma(\alpha)}. \tag{9}\]  
(3) The mean value function of the model when considering dependent failures can be obtained from the following differential equation [16; 17]:  
\[\frac{m(t)}{dt}=b(t)[a(t)-m(t)]m(t) \tag{10}\]  
where \(a(t)\) is defined as the expected total number of failures that remain prior to testing, and \(b(t)\) is the defined detection rate of failures per fault.
# Segmentation of Brain Tumors from MRI Images Using Convolutional Autoencoder
## 2 Materials and Methods
### Network Architecture
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer No** & **Layer Name** & **Block** & **Layer Properties** \\ \hline
1 & Image Input & / & 256 \(\times\) 256 \(\times\) 1, images \\
2 & Convolutional & Block A & 16 3 \(\times\) 3 \(\times\) 1 convolutions with stride [1 1] and padding ’same’ \\
3 & Normalization & Block A & Batch Normalization \\
4 & Convolutional & Block A & 16 3 \(\times\) 3 \(\times\) 16 convolutions with stride [1 1] and padding ’same’ \\
5 & Normalization & Block A & Batch Normalization \\
6 & MaxPool & Block A & 2 \(\times\) 2 MaxPool with stride [2 ] and padding ’same’ \\
7 & Convolutional & Block A & 32 \(\times\) 3 \(\times\) 16 convolutions with stride [1 1] and padding ’same’ \\
8 & Normalization & Block A & Batch Normalization \\
9 & Convolutional & Block A & 32 \(\times\) 3 \(\times\) 32 convolutions with stride [1 1] and padding ’same’ \\
10 & Normalization & Block A & Batch Normalization \\
11 & MaxPool & Block A & 2 \(\times\) 2 MaxPool with stride [2 ] and padding ’same’ \\
12 & Convolutional & Block A & 64 3 \(\times\) 3 \(\times\) 32 convolutions with stride [1 1] and padding ’same’ \\
13 & Normalization & Block A & Batch Normalization \\
14 & Convolutional & Block A & 64 3 \(\times\) 3 \(\times\) 64 convolutions with stride [1 1] and padding ’same’ \\
15 & Normalization & Block A & Batch Normalization \\
16 & MaxPool & Block A & 2 \(\times\) 2 MaxPool with stride [2 ] and padding ’same’ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: New CNA architecture for brain tumor segmentation. All network layers are listed along with their parameters. Figure 2: Schematic representation of the CNA segmentation architecture containing the input layer, three Blocks A, Block B, three Blocks C, Classification Block, and Output Layer. Block A represents the encoder, Block C the decoder. Conv layers retain the input dimensions at the output, and Pool and Trans-Conv reduce, i.e., increase the output size. CONV—convolutional layer; RELU—rectified linear unit activation function; BN—Batch Normalization, POOL—association layer; TRANS-CONV—transposed convolutional layer; SOFTMAX—softmax activation layer.
# Burnt-Area Quick Mapping Method with Synthetic Aperture Radar Data
## 3 Results  
### Radar Burn Ratio and the Difference for the Burnt Area Detection  
Figure 3 is a co-polarized (VV) and cross-polarized (VH) SAR image from Sentinel-1a, along with the difference values and the Hotspot distribution data. The range of the ratio values for the RBR variable, ranges from 1 to 1.5 dB, while the RBD variable has a pixel value range of \(-2.5\) to 1 dB. The hotspot distribution pattern on the RBR variable has the same pattern, which is in bright pixels or high ratio values in both the RBR VV and RBR VH variables. Meanwhile, in the RBD variable, the hotspot distribution pattern can be identified, but in reverse where the hotspot points are in dark pixels or low difference values. From the visualization above, it can be seen that the distribution of the hotspots has the same distribution pattern for the four variables where changes in land cover, due  
to land fires, cause changes in the backscatter value during the time series observations (before-after).  
The results of the experiment, is shown in Figure 4, in which, the radar burn difference shows significant burnt areas in South East and South West Kalimantan Island, as a dark area. While the radar burn ratio shows a difference between co-polarized (VV) and cross-polarized (VH), where there are more significant bright areas of the RBR VV that correlate with burnt area of the RBD. The RBR bright spots and the dark spots of the RBD correlates to the red fire location from the hotspot data.  
Figure 4: Sample of the cross-polarized and co-polarized image and its difference from the Sentinel-1a SAR images with the actual Hotspot data.  
Figure 3: Flow chart of the burnt area identification process using cloud computing.  
The resulting images for RBR and RBD for orth polarization can be seen on Figure 5 with corresponding RBR bright spots and RBD dark spots. The average RBD value for both the VH and VV polarization in the burnt area is very low, ranging from \(-1.5\) to \(-2\) dB, in contrast to the unburnt area, which has an average value of 0.1 to 0.3 dB. These values indicated that the burnt area can be detected while the RBR value of both the burnt and unburnt areas did not show significantly different values with 1.1 dB for the cross-polarized RBR and 1.3 dB for the co-polarized RBR, the unburnt area is at 1.0 dB as the baseline value for both components. From the values represented in Figure 6, we selected three components that have the largest margin between the burnt and unburnt areas to create a composite image, namely: RBD VH and VV and RBR VV. The composite image is then quantified with CART, random forest, and SVM, and compared with the Hotspot data.  
Figure 5: Cross-polarized and co-polarized radar burn ratio and radar burn difference from the Sentinel-1a SAR images.  
Figure 6: The average backscatter of the burnt and unburnt areas in each variable (RBD_VH, RBD_VV, RBR_VH, and RBR_VV).