esa-sceva/satcom-chunk-collection huggingface dataset:
# A SAM-Based Detection Method for the Distance Between Air-Craft Fire Detection Lines
## 2 Materials and Methods
### Loss Function
#### 2.6.1 Dice Loss Function  
The Dice loss function is a loss function based on the Dice coefficient (Sorensen-Dice coefficient), which is widely used in computer vision tasks such as image segmentation and object detection, especially performing well in dealing with datasets with category imbalance characteristics. The purpose of introducing the Dice loss function is to measure the degree of overlap between the prediction mask and the true mask, enabling the model to learn more accurate information.  
The Dice coefficient is a measure of the similarity of two sets, which is defined as the size of the intersection of two sets divided by the size of the union. In the second classification problem, it can be expressed as follows:  
\[Dice\ Coefficient=\frac{2\times TP}{2\times TP+FP+FN} \tag{5}\]  
where TP is the true positive, FP is the false positive, and FN is the false negative.  
The Dice loss function minimizes the loss by maximizing the value of the Dice coefficient. Therefore, the Dice loss function is defined as one minus the Dice coefficient:  
\[Dice\ Loss=1-Dice\ Coefficient=1-\frac{2\times TP}{2\times TP+FP+FN} \tag{6}\]  
To avoid division by zero and improve numerical stability, a smoothing term \(\epsilon=10^{-6}\) is added to both the numerator and the denominator:  
\[Dice\ Loss=1-\frac{2\times TP+\epsilon}{2\times TP+FP+FN+\epsilon} \tag{7}\]
# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 4 Discussion
### Research Objectives
#### 4.3.2 Time Series Analysis  
The time series analysis focused on studying the linear trend of the VIs recorded from the vineyards during a 60-day temporal window repeated for the six vintages. Hence, the causes that unsettled the linear regression line's slope between the VIs values and the DOY were investigated. First, the average slope was not linked to the direction of rows unlike what was stated for the average VIs. Because the VIs are strictly linked to the plant vigour, the NS direction and EW direction retrieved a comparable daily biomass increasing rate in which NS and SW-oriented vineyards reached higher maximum values than the EW and SE ones. Also, the literature review agreed that rows' orientation can affect the maximum biomass production and yield, but no effects were found concerning vegetative growth [48]. However, the direction was significant for the linear regression line's slope CV computed in both VIs. The NS-oriented vineyards resulted in more instability than the SW ones. The statement is hence assumed because no comparisons were found in the literature review. Perhaps the CV of the NS-oriented slope is linked to the ways in which those vineyards reach the maximum VIs value.  
As mentioned in the section before, the training system plays a key role regarding the intercepted sunlight. The HS, which is able to catch almost all the sunlight addressed to the vineyard, showed the highest average slope. Thus, a large intercepted light boosted the daily biomass increase compared to the other training systems. Moreover, the HS showed the highest CV of the regression line's slope, while the VS was the lowest. So, concerning the training system, not the quality but the amount of intercepted light played a crucial role in the VIS development [54].  
Regarding the planting altitude and its related climate condition, this study stated a slower VIs development in vineyards planted at altitudes higher than 200 m (hill) than in vineyards planted above 200 m (plain). Consequently, average temperature may be the main driver that reduces the daily increase in biomass in hilly vineyards. Also, the flat areas contribute to a faster grapevine development than the sloped ones. However, because soil management was not noted in this paper, it was impossible to verify the role of grass cover, which may interfere with VIs' development and the vineyard's location. No other items seem to affect the VIs development during the monitored temporal window.
## 2 Materials and Methods
### Evaluation of Cytotoxic Activity  
2.6.1. Cell Treatment and Exposure to the Extract  
Two cell lines were used to evaluate the cytotoxicity of the _S. aspera_ extract. The TE671 rhabdomyosarcoma cancer cell line was obtained from the European Collection of cell cultures (ECACC). The A172 glioblastoma cell line was obtained from a male patient of 53 years (ECACC, London, UK, Cat. Nr 88062428). Cells were grown in a cell culture flask (75 cm\({}^{2}\) surface area) in Dulbecco's modified Eagle's Medium (DMEM) (ThermoFisher Scientific Inc. (Gibco), Waltham, MA, USA Cat. Nr. 10566016) with a high concentration of glucose (4500 mg/mL), enriched with 15\(\%\) fetal bovine serum (FBS) (ThermoFisher Scientific Inc. (Gibco), Waltham, MA, USA Cat. Nr. 26140-079), L-glutamine (2 mM) (ThermoFisher Scientific Inc. (Gibco), Waltham, MA, USA Cat. Nr. A12860-01). In order to avoid pathogen contamination a dual antibiotic solution of penicillin G (100 IU) and streptomycin (100 ug/mL), was added (ThermoFisher Scientific Inc. (Gibco), Waltham, MA, USA Cat. Nr. 15140-122), in addition to an amphoteric B solution (ThermoFisher Scientific Inc. (Gibco), Waltham, MA, USA Cat. Nr. 14140-122). The cell population was measured with the use of a Coulter counter (CellTaq \(\alpha\), Nihon Kodhen Inc., Tokyo, Japan), in order to determine the absolute initial number of cells inoculated in each respective experimental setup. Cells were inoculated in 96 well plates (1.5 \(\times\) 10\({}^{3}\) cells/mL) and were allowed to grow for 24 h until \(\sim\)80\(\%\) confluence. After 24 h cells were exposed to successively diluted concentrations of the extract (t = 0 h), ranging from 0.02 to 3.125 mg/mL for 24, 48 and 72 h. Extracts were diluted in DMSO. Control experiments included both cells with no further treatment (no extract), as well as cells with vehicle only (DMSO at a final concentration of 4% (data not shown)). Vehicle had no effect on cell proliferation and viability.  
Experiments were performed in 96-well plates (CellStar(r) Sigma-Aldrich Chemie GmbH, Taufkirchen, Germany DE Cat. Nr. M3687-60 EA) as described previously by [14].
## 5 Conclusions  
The differential boost inverter is an interesting inverter topology providing a transformerless voltage step-up capability suitable for DC-AC modules in PV systems. This paper dealt with the nonlinear behavior and slope compensation of a grid-tied boost inverter under a differential peak current mode control for its operation as an AC module in a PV system. The module performs maximum power point tracking, DC PV voltage regulation and grid synchronization. A three-loop differential peak current mode-controlled PV-fed grid-tied boost inverter has been studied and its slope compensation has been addressed. The paper began with presenting detailed simulations that has shown that the system can exhibit fast-scale instability. This fact has been verified using numerical simulations from the switched model of the inverter for different irradiance levels. Accurate closed-form design-oriented expressions to predict fast-scale instability have been derived in terms of the system parameters and adaptive slope compensation schemes have been proposed to suppress the observed instability. The effectiveness of the proposed schemes has been confirmed by detailed numerical simulation performed on the circuit-level switched model. The results demonstrate the viability of differential peak current mode control with the proposed adaptive slope compensation for differential boost inverters that can be used as AC modules in PV systems.  
Figure 14: The time varying duty cycle calculated from (12) (\(D(t)\)) obtained from numerical simulation (\(d(nT)\), \(n\in\mathbb{N}\)) for guaranteeing a _deadbeat_ response within the complete duty cycle range.  
**Author Contributions:** These authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors would like to acknowledge the support by the Spanish Ministerio de Economia y Competitividad under grants DPI2017-84572-C2-1-R. A.E.A. and M.A.-N. acknowledge financial support from the Researchers Supporting Project number (RSP-2020/150), King Saud University, Riyadh, Saudi Arabia.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not aplicable.  
**Data Availability Statement:** Not aplicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Scanning the Coverage Area with Directional BS Antennas (LOS Channels)
### Deterministic Scanning Algorithm
Similarly, Figure 8c shows six slots targeting the 161 m contour using an 8 \(\times\) 8 element array with an azimuth scan step of \(\Delta\theta_{l}=10^{\circ}\), and Figure 8d shows 15 slots targeting the 502 m contour using a 12 \(\times\) 12 element array with an azimuth scan step of \(\Delta\theta_{l}=4^{\circ}\). As the dimension of the antenna increases, its beamwidth decreases and more scan points are required to make sure the BS antenna covers the region of interest contiguously. Figure 8e shows the combined SNR levels corresponding to all scan positions as listed in Table 3 for the deterministic case. We observe that the SNR levels within the entire area of interest are maintained at the required values; i.e., higher than \(-13\) dB, which allows us to keep a low value of \(P_{M}\) at the output of the GLRT detectors. It should be noted that the antenna configuration proposed above is not necessarily a unique solution for a given microcell. Different antenna configurations may be exploited in the scanning procedure to obtain a similar SNR performance to that shown in Figure 8.  
Figure 7: Received power levels along the \(x\)-axis for different array configurations under LOS conditions.
## 4 Expressway PTV Detection Algorithm for Over-the-Horizon  
### Overview of the Overall Framework of the Algorithm  
This article uses the transaction data collected by ETC gantries for PTV detection beyond the line of sight in the rear. The algorithm is mainly divided into four modules: data preprocessing module, vehicle speed prediction module, vehicle positioning module, and PTV evaluation module. Figure 1 shows the overall framework structure of over-the-horizon detection.  
1. Data preprocessing module: First, clean the abnormal data, and then check the integrity of the data. Construct the driving trajectory of each vehicle, and then use equivalent conversion to calculate the traffic flow of each section at a certain time.
2. Vehicle speed prediction module: Due to the inability to calculate the current driving speed of the vehicle, we use a machine model to predict the driving speed of the vehicle in the current section. The algorithm first applies WT to the speed data of the vehicle's historical section for noise reduction. Then, the LightGBM model is used to train and learn the speed characteristics of historical vehicle sections, and the final output is the predicted speed of the vehicle in the current section.
3. Vehicle positioning module: Set the gantry spacing through the ETC simulation system and use this spacing as the distance for over-line-of-sight detection. Through a comprehensive analysis of the characteristics of roads and vehicles, the rationality of the distribution of gantry structures is demonstrated, the detection range of over-the-line-of-sight is divided, and the position of vehicles in transit is refreshed through the transaction data time and location collected by ETC gantry structures.
4. PTV evaluation module: By extracting vehicle and road features from ETC data, the evaluation factors of the PTV are constructed. Finally, fuzzy set theory is used to detect the threat of a single vehicle driving, improving the accuracy of model judgment.
# Deep Learning-Based Acoustic Echo Cancellation for Surround Sound Systems
## 4 Proposed GCRN-Based Surround AEC
### Feature Extraction and Signal Reconstruction  
In the GCRN model, the compressed complex spectra of the microphone signal and the four-channel B-format signals were used as the input features. All signals were sampled at 16 kHz. A 20 ms Hanning window with 50% overlap between adjacent frames was used to produce a set of time frames in this work, and a 320-point STFT (short-time Fourier transform) was adopted to generate input features, leading to a 161-dimensional spectral feature in each frame. The compressed complex spectra of the microphone signal \(y(n)\) and the four-channel B-format signals \(x_{m}^{B}(n)\) were concatenated as input feature maps, which have a shape of \([10,T,161]\), where \(T\) represents the total frames. They were taken as the input features to train the GCRN model. As for the output, the real and imaginary parts of the compressed complex spectrum of near-end speech \(s(n)\) were used. The loss function can be formulated as follows:  
\[\mathcal{J}\big{(}S^{c},\hat{S}^{c}\big{)}=0.5\cdot\big{(}|S_{R}^{c}|-|\hat{S} _{R}^{c}|\big{)}^{2}+0.5\cdot\big{(}|S_{I}^{c}|-|\hat{S}_{I}^{c}|\big{)}^{2}. \tag{4}\]  
In the enhancement stage, the estimated real and imaginary parts were used to recover the spectrum near-end signal, which was then used to reconstruct the estimated near-end signal in the time domain by the inverse STFT and the overlap-add method. The estimated magnitude and phase information is given by  
\[|\hat{S}^{c}|=\bigg{(}\sqrt{|\hat{S}_{R}^{c}|^{2}+|\hat{S}_{I}^{c}|^{2}}\bigg{)} ^{\frac{1}{\beta}},\hat{\theta}=\arctan(\frac{\hat{S}_{I}^{c}}{\hat{S}_{R}^{c} }), \tag{5}\]  
finally, the spectrum can be written as:  
\[\hat{S}^{c}=|\hat{S}^{c}|e^{\hat{\theta}}, \tag{6}\]  
Figure 3: The architecture illustration of the GCRN model for surround AEC.  
where \(|\hat{S}^{c}|\) and \(\hat{\theta}\) are the estimated magnitude and phase information of the near-end speech \(s(n)\), respectively.
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
## 2 Materials and Methods
### Relevance of the Elevation Angle for the Satellite Channel
Even though we illustrate the received signal through the well-known Loo model in Figure 6, there are more LEO channel models depending on a line-of-sight component, and thus, directly depending on the elevation angle. The, elevation angle characterization proposed in this article can be a valuable tool to determine CDF curves of the received signal, based on the first and second order order statistics of the elevation angle. For example, to determine the expected CDF of the Loo model based on the elevation angle expected value, and determine how far apart other curves will be based on the measures of dispersion of the elevation angle.
# Elliptical Structures of Gravity Waves Produced by Typhoon Soudelor in 2015 near Taiwan
###### Abstract  
Tropical cyclones (TCs) are complex sources of atmospheric gravity waves (GWs). In this study, the Weather Research and Forecasting Model was used to model TC Soudelor (2015) and the induced elliptical structures of GWs in the upper troposphere (UT) and lower stratosphere (LS) prior to its landfall over Taiwan. Conventional, spectral and wavelet analyses exhibit dominant GWs with horizontal and vertical wavelengths, and periods of 16-700 km, 1.5-5 km, and 1-20 h, respectively. The wave number one (WN1) wind asymmetry generated mesoscale inertia GWs with dominant horizontal wavelengths of 100-300 km, vertical wavelengths of 1.5-2.5 km (3.5 km) and westward (eastward) propagation at the rear of the TC in the UT (LS). It was also revealed to be an active source of GWs. The two warm anomalies of the TC core induced two quasi-diurnal GWs and an intermediate GW mode with a 10-h period. The time evolution of dominant periods could be indicative of changes in TC dynamics. The FormoSat-3/COSMIC (Formosa Satellite Mission-3/Constellation Observing System for Meteorology, Ionosphere, and Climate) dataset confirmed the presence of GWs with dominant vertical wavelengths of about 3.5 km in the UT and LS.  
gravity wave; tropical cyclone; Weather Research and Forecasting (WRF) model; FormoSat-3/COSMIC +
Footnote †: journal: _arxiv
# Accurate Performance Analysis of Coded Large-Scale Multiuser MIMO Systems with MMSE Receivers
## 4 Performance Analysis of Coded Multiuser MIMO System with an MMSE Detector  
In this section, we derive an upper bound on the BER of the encoded large-scale multiuser MMSE-MIMO systems that employ an \(M\)-ary quadrature amplitude modulation (QAM) constellation with gray mapping.  
By using an MMSE Receiver in massive MIMO systems, at the base station, the decision variable \(z_{k}\) for the transmission sequence \(x_{k}\) can be expressed as  
\[z_{k}=\xi_{k}x_{k}+\eta_{k},\hskip 28.452756 pt1\leq k\leq K, \tag{21}\]  
where \(\xi_{k}=\sqrt{\frac{E}{K}}\mathbf{w}_{k}{}^{H}\mathbf{g}_{k}\), and the noise-plus-interference term \(\eta_{k}\) is a random variable with zero mean and variance \(\sqrt{\frac{E_{k}}{K}}\sum_{i=1,i\
eq k}^{K}\mid\mathbf{w}_{k}{}^{H}\mathbf{g} _{i}\mid^{2}+\parallel\mathbf{w}_{k}\parallel^{2}\). \(\mathbf{w}_{k}\) and \(\mathbf{g}_{k}\) are the \(k\)-th columns of the matrices \(\mathbf{W}\) and \(\mathbf{G}\), respectively.  
Then, for the transmitted data of the \(k\)-th user, the received SINR at the BS can be expressed as  
\[\gamma_{k}=\frac{\mathbb{E}[|\xi_{k}x_{k}|^{2}]}{\mathbb{E}[|\eta_{k}|^{2}]}. \tag{22}\]
## 2 Materials and Methods
### Material Characterizations  
The surface morphological feature was characterized by a scanning electron microscope (SEM, SU8230, Hitachi, Tokyo, Japan) operated at 10 kV. The crystalline structure was investigated via X-ray powder diffraction (XRD, EQUINOX 1000, ThermoFisher, Waltham, America) with Cu K\(\alpha\) radiation (\(\lambda\) = 0.15406 nm). X-ray photoelectron spectroscopy (XPS, ESCALAB 250 Xi, ThermoFisher, Waltham, America) under monochromatized Al K\(\alpha\) excitation was adopted to reveal the chemical status.
# An Energy-Efficient Coverage Algorithm for Macrocell--Small Cell Network Systems
## 2 Related Work
Meanwhile, [PERSON] proposed a number of transmission algorithms that could ensure that data-rate needs were met, even under a variety of SBS deployment strategies, while simultaneously allowing the total amount of energy consumed by the transceivers of all the BSs to be minimized [19; 20]. Furthermore, in a 2014 study, a method for ascertaining the minimum cost of network deployment with random deployment was suggested by [PERSON] et al. [21], while in a more recent paper, [PERSON] et al. [22] proposed a continuous-time Markov chain model that could be used to quantify the survivability of a network subjected to extensive failures resulting from, for example, security attacks, natural disasters, and common mode software and hardware failures. Meanwhile, a number of other recent studies (such as [23; 24; 25]) have focused on systems involving the simultaneous activity of multiple mobile network operators. [PERSON] et al. [23], for example, suggested a framework for improved energy efficiency and throughput in such systems based on intra-operator spectrum sharing, while an auction-based offloading and switching off algorithm, that could be used to conserve energy and costs by spurring the operators of mobile networks to offload their traffic and turn off any redundant MBSs, was proposed by [PERSON] et al. [24]. Relatedly, a 2017 study by [PERSON] et al. [25] investigated the cost efficiency and energy efficiency of a network resulting from the cooperative activity of numerous mobile network operators. By providing various alternatives for enhancing specific aspects of system performance, all of these studies (that is, [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]) have made important contributions to furthering the development of macrocell--small cell two-layer network systems. Nonetheless, none of them managed to provide a comprehensive investigation of potential means of minimizing the total amount of power consumed by the transceivers of all the BSs while simultaneously ensuring that network users are provided with complete wireless signal coverage under various conditions; in particular, none of them adequately addressed the specific problem of coverage holes. Moreover, as network service needs continue to rapidly grow, the importance of enhancing the ability of BSs to provide comprehensive network coverage, while avoiding any waste of power during the operation of their transceivers, will also continue to grow. Further research into this subject is therefore required.
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 4 Application of Composite PAD Solution  
Generally, the passive methods of islanding detection are relatively difficult to set-up the relay exactly, no matter the DG types. The existing ROCOF relay and VVS relay are normally set-up based on the engineering experience. There are three main settings for this new composite PAD solution \(\Delta Ang\_set\), \(\sum Ang\_set\), and \((df/dt)\_set\). This section will introduce the calculation of the proper settings applied to differentiate the islanding condition from the variable system events under grid-connected mode. However, the applicatioin guide for setting calculation could be improved step by step with more engineering experience from the field tests.