esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Simplified Vulnerability Assessment Methods for Masonry and RC Building Structures
### Methods Belonging to Group A
#### 2.1.2 S&C Method  
Sandoli and [PERSON] [9] assign a vulnerability class to existing buildings based on structural typology, building age, and typology of horizontal structures (floors). Despite the few qualitative parameters necessary to assign the class, a range of peak ground acceleration (PGA) corresponding to the building collapse, i.e., a capacity in term of PGA (referred to as PGA\({}_{\text{c}}\)) is provided too.  
For masonry buildings, the following three classes and the corresponding values of \(PGA_{c}\) are provided:  
* Class A: high vulnerability class, with PGA\({}_{\text{c}}\leq 0.05\) g;
* Class B: medium vulnerability class, with \(0.05\) g \(<\) PGA\({}_{\text{c}}\leq 0.10\) g;
* Class C: low vulnerability class, with \(0.10\) g \(<\) PGA\({}_{\text{c}}\leq 0.15\) g.  
Class A is divided into a further two subclasses:  
* MUR 1--'old buildings' made of masonry walls, with vaulted floors or wooden/iron plan floors without effective connections to the walls and without diffuse systems of ties;
* MUR 5--'modern buildings not realized in according to the codes' made of masonry walls, with plan floors without effective connections to the walls and without curbs.  
Class B is divided into further two subclasses:  
* MUR 2--'old buildings with interventions', made of masonry walls, vaulted floors, or wooden/iron plan floors without effective connections to walls, but with diffuse systems of ties at all levels;
* MUR 4--'semi-modern buildings' made of masonry walls, with plan floors connected to the walls through reinforced concrete curbs, but without diffuse systems of chains/ties.  
Finally, class C refers to:  
* MUR 3--'modern buildings' made of masonry walls, with plan floors connected with reinforced concrete curbs to the walls at each level.  
For RC buildings, two ranges of maximum PGA capacity and four vulnerability classes are provided, mainly based on the building age and on the technical code evolution:  
* 0.10 g \(<\) PGA\({}_{\text{c}}\leq 0.15\) g:  
* CA1--buildings realized before 1939: structures designed for only gravity loads, the presence of frames in only one direction with deep beams, and heavy infill walls;
* CA2--buildings realized between 1939 and 1970: structures designed for only gravity loads, the presence of frames in only one direction with deep beams in perimeter frames, deep and flat beams in internal frames, and heavy or light infill walls;
* CA3--buildings realized between 1970 and enactment of seismic codes: structures designed for only gravity loads and built with higher quality and certified materials, the presence of frames in only one direction with deep or flat beams, and mainly light infill walls.
* 0.15 g \(<\) PGA\({}_{\text{c}}\leq 0.35\) g:  
* CA4--buildings realized after enactment of seismic codes: structures designed according to seismic codes, the presence of frames in both directions with deep or flat beams, and mainly light infill walls.
## 3 Methodology  
We selected training and validation smoke samples over fire-prone regions for the development of the new SSDA algorithm and tested the algorithm at a global scale. The training samples were selected visually and manually based on true-color images listed in Table 2, which had 47,363, 8871, 44,933, and 84,184 pixels for clouds, bare soil, smoke, and dust, respectively. The spectral and spatial information of the training samples were then applied to design multi-criteria for the SSDA. By combining the multi-criteria with additional datasets from VIIRS active fire and cloud mask products, we firstly eliminated non-smoke classes and then determined the smoke class. The SSDA smoke was evaluated both qualitatively and quantitatively using VIIRS true-color image, VIIRS ADP, TROPOMI smoke, previously published smoke results, and manually-delineated smoke (see detail in Section 2.6).
## 2 Description and Challenges of a Customized Biomedical Implantable Device
### Research Challenges in Implantable Devices
#### 2.2.3 The Nano-Biosensor  
Special attention must be focused on nanobiosensors [92]: they need to combine accuracy, reliability, precision, life span, manufacturing and scalability, as well as address wealth and environmental risks, in order to overcome technological and market bottlenecks. A nanobiosensor or nanosensor is generally defined as a nanometre size scale measurement system comprising a probe with a sensitive biological recognition element, or bio-receptor, a physicochemical detector component, and a transducer in between. Two types of nanosensors with potential medical applications are cantilever array sensors and nanotube/nanowire sensors and nanobiosensors, which can be used to test nanolitres or less of blood for a wide range of biomarkers.  
Then, a biosensor is a measurement system for the detection of an analyte that combines a biological component with a physicochemical detector. The general function of a biosensor is to convert binding events between biological receptors and target agents into a signal thanks to a transducer which can be based on an optical, a thermal, a gravimetric or an electrochemical detection (see Figure 4). This last category has gained increasing attention in the last few years. The high sensitivity, low cost and easy miniaturization of the electronic detection taken in conjunction with the wide range of applications, has resulted in these devices becoming a perfect analytical tool in different fields, such as diagnosis of genetic diseases, detection of infectious agents, study of genetic predisposition, development of personalized medicine, detection of differential genetic expression, drug screening, _etc_.  
The development of highly sensitive and low-cost sensors in the nanoscale, and its combination with nano-microfluidics solutions [94], based on micro-channels, micromixers and microvalves, are increasing the interest in the implementation of multi-parametric point-of-care devices, as a portable and low-cost solution to enhance diagnostics methods. In summary, Figure 5 shows principal technologies, challenges and materials for multipurpose implantable sensors.  
Figure 4: Generic function of several types of biosensor [93].
## Materials and Methods
### System's Description
#### 2.1.1 Pre-Processing and Feature Extraction  
The acquired ECG data follows the scheme in Figure 2. As referenced in Reference [10], to delete noise, the ECG signals pass through a fifth-grade Butterworth bandpass filter from 1 to 35 Hz. Once the signal is filtered; the goal is detecting the R peaks to help in further signal segmentation. The filtered signal gets differentiated, obtaining first and second differentiations, called Non-Differentiation (ND), First Differentiation (FD), and Second Differentiation (SD) respectively. FD and SD help in the R peak detection because they provide information about changes in ND. R peaks are the local maxima in ND and translate into local minima in FD. These local minima are easier to detect than the original R peaks because they are more prominent. In the case there are outliers, two adjacent segments get compared by correlation. SD finally helps to check that the final segments have the corresponding shape.  
After the R peak references are calculated, the feature extraction consists of segmenting the QRS complex in ND, FD, and SD independently, obtaining 3 different types of signals, as seen in Figure 2. The segmentation is given by two variables: mg1 determines the number of signal points to the left of the reference point and mg2 the samples to the right including the reference point itself. This implies that the segments have a length of \(rng1+rng2\) points, which are the selected feature points. N peaks specifies the number of peaks selected to form the matrix, selected by appearing order, which is the samples.
# Interferometric DEM-Assisted High Precision Imaging Method for ArcSAR
## 3 Analyzing the Error Caused by ArcSAR Imaging on the Reference Plane  
In this section, the error model of ArcSAR imaging on the reference plane is first built. Then, the height difference threshold for determining whether the ArcSAR image will severely defocus is calculated. We also explore the relationship between height difference threshold and system parameters.
## 2 Materials and Methods
### Experiment Design
#### 2.3.2 Experiment Setting  
Four sets of experiments, namely, Exp2017, Exp2018, Exp2019, and Exp2020, were carried out to evaluate the CO flux, XCO, and surface-CO concentrations over western Europe in four consecutive winter seasons since 2017. Each set of experiments consisted of three parallel experiments of DA, SIM, and SIM_anthro.  
The DA experiments inverted the posterior CO fluxes by assimilating MOPITT observations on the basis of the prior CO fluxes. The final CO concentration results of the DA experiments were generated by applying the posterior CO fluxes to the WRF-Chem model once again and without the assimilation, similar to the approach used in the CarbonTracker [98]. The SIM and SIM_anthro experiments used the prior CO fluxes and the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Prior CO Fluxes (kiloton)**} & \multicolumn{2}{c}{**Prior CO Fluxes**} \\ \cline{2-5}
**Experiment** & **Anthro** & **Biogenic** & **Biomass** & **Total** & **(mol km\({}^{-2}\) h\({}^{-1}\))** \\  & **(EDGARV)** & **(MEGAN)** & **(FINN)** & & \\ \hline Exp2017 & 6018.59 & 126.14 & 140.93 & 6285.66 & 14.55 \\ Exp2018 & 6018.59 & 121.26 & 45.84 & 6185.69 & 14.31 \\ Exp2019 & 6018.59 & 130.58 & 210.82 & 6359.99 & 14.72 \\ Exp2020 & 6018.59 & 141.97 & 101.10 & 6261.66 & 14.49 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prior CO fluxes from anthropogenic, biogenic, and biomass-burning for each experiment.  
prior anthropogenic CO fluxes to simulate the CO concentrations by the WRF-Chem model under the same experimental conditions as the DA experiments, respectively. There was no assimilation process in the SIM and SIM_anthro experiments.  
The duration of each experiment was from 1 December to 28 February of the following year. The spin-up phase of each DA experiments occured two weeks before 1 December. The simulation cycle was 6-h at 00:00, 06:00, 12:00, and 18:00 UTC, respectively, and the advancing time step was 150 s.  
The size of ensemble members of the DA experiments was set to 20, the same as in [65; 66; 99]. A three-dimensional Gaspari-Cohn localization function [100] with a horizontal half-width of 0.1 radian and a vertical half-width of 200 hPa was used to compensate the under-sampling errors caused by the limited size of ensemble members, according to [101].
## 2 Materials and Methods  
### EC Fluxes and Meteorological Data  
The data from 11 EC flux sites from around the world with a measurement duration of at least two years (Table 1) were used for this study. The towers were located in four different crop (paddy rice, winter wheat, soybean and maize) fields. Hourly measured and derived EC fluxes as well as meteorological data over the growing seasons (Figure 1) were obtained from the Fluxnet 2015 Dataset ([[http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/](http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/)]([http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/](http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/)) accessed on 10 October 2019), including downward shortwave radiation (R\({}_{\mathrm{g}}\), W m\({}^{-2}\)), net solar radiation (R\({}_{\mathrm{n}}\), W m\({}^{-2}\)), air temperature (T\({}_{\mathrm{a}}\), \({}^{\circ}\)C), vapor pressure deficit (VPD, hPa), precipitation (mm), latent heat flux (LE, W m\({}^{-2}\)), sensible heat flux (H, W m\({}^{-2}\)) and gross primary production (GPP, g C m\({}^{-2}\) d\({}^{-1}\)[29; 30]). Data associated with equipment failures and stable boundary layer conditions (fraction velocity u\({}_{*}\) < 0.25 m/s in this study) were removed [31; 32]; then, the data gaps (\(\leq\)2 h) were filled with linear interpolation while larger gaps (\(>\)2 h) were filled using mean diurnal variation (MDV). In the MDV method, a missing observation is replaced by the mean for that time period (half-hour) based on previous and subsequent days. Data windows of days 7 and 14 were usually chosen for averaging within the application [25]. Ultimately, hourly data was aggregated to daily data. Besides, data from rainy days (the amount of daily precipitation \(>\) 0.0 mm) were removed when performing path analysis and correlation analysis (see Section 2.4).
## 3 A Hybrid Propagation Channel Model
### The Theory of the Ray-Splitting Technique  
The ray-splitting method is used to study double scattering. Sampling is performed around the direction of specular reflection to simulate the spatial distributions of the scattered ray energy. The number of samples is positively correlated with the accuracy of the calculation. Therefore, the greater the number of scattered rays, the more accurate and time-consuming the calculation is. It is therefore necessary to consider the tradeoff between computational accuracy and time. An optimal solution is to reduce the number of scattered rays as much as possible while maintaining a certain degree of computational accuracy. As the spatial distribution of the energy of the scattered ray is not uniform, we can improve the sampling efficiency by appropriately increasing the sampling density with a higher scattered energy density.  
When sampling the scattered rays, the local coordinate system of the surface is established. In Figure 1, the intersection position of the incident rays and the triangular surface is set as the origin \(O^{\prime}\) of the coordinate system, and the unit normal vector of the triangular surface is set as \(\hat{n}\). The direction of specular reflection is the \(z^{\prime}\)-axis, and there is no fixed rule for the selection of the \(x^{\prime}\)-axis and the \(y^{\prime}\)-axis directions, though they should satisfy the right-hand rule. In this study, the cross product of the unit normal vector of the triangular surface and the specular reflection ray is set as the \(y^{\prime}\)-axis, and the \(z^{\prime}\)-axis is finally determined according to the right-hand rule such that \(\hat{y}^{\prime}\) is \(\hat{n}\times\hat{z}^{\prime}\) and \(\hat{x}^{\prime}=\hat{y}^{\prime}\times\hat{z}^{\prime}\).  
The scattering angle \(\theta\) and the scattering azimuth angle \(\varphi\) can thus be obtained as  
\[\begin{split}\theta&=\cos^{-1}[1-\zeta_{1}\cdot(1- \cos\theta_{1})]\\ \varphi&=2\pi\zeta_{2}\end{split}\, \tag{26}\]  
where \(\theta\) and \(\varphi\) are both random numbers between 0 and 1.
# Experimental Demonstration of Single-Channel EEG Signal Using 32 x 32 Pixel OLED Screen and Camera
## 2 Related Works
### Recent Work on Visible Light Communication for EEG  
VLC has been extensively used in areas such as vehicle-to-vehicle communication, Light -Fidelity (Li-FI), underwater applications, and military applications, but the research work of VLC for EEG in healthcare has just begun over the past few years [5]. In healthcare applications, specifically EEG, due to low cost, being free from EMI, and reliability, several researchers have moved to VLC from RF technology. In the VLC system, the transmitter can be LED, or OLED, and the receiver can be a photodiode or a camera. Due to the low data rate limit of cameras, photodiodes are deployed as receivers in VLC systems; however, this option does require hardware modifications. Several researchers have proposed their VLC system for EEG signal transmission comprising of both types of transmitters and receivers.  
In [3], a VLC system deploying a LED/PD pair of RGB (red, green and blue) achieved a data rate of 4 kbps at a distance of 50 cm for EEG signal transmission. However, an array of three transmitters and three photodiodes were used to transmit and receive the EEG signal, hence added towards the cost and hardware modifications. The authors in [39] proposed a brain health monitoring system based on Support Vector Machine (SVM) for daily monitoring using Bluetooth, an RF protocol suffering from EMI and not applicable in RF sensitive areas. The authors in [40] achieved a data rate of 2 kbps using VL-OCC for EEG signal transmission; however, the distance achieved was 50 cm. In [41], authors demonstrated a wearable EEG system using a single electrode based on a mobile phone VLC, achievinga data rate of 2.4 kbps using a modulation scheme of OOK. The EEG signal was successfully transmitted over 4 m using a mobile as the receiver and LED as the transmitter.  
The authors in this paper proposed a LOS, single-channel, EEG data transmission system using an OLED screen as the transmitter and a camera as the receiver. The VL-OCC system architecture and applications are listed in the next section.
## 2 Materials and Methods  
### Geometry of the Finite-Element Model  
Two finite-element models were employed in this study, namely a partial mandible model and a complete mandible model. The complete mandibular model included the cortical and cancellous bones, all teeth except the first premolar, and the PDLs for all teeth except for the first premolar. A bracket was fixed to a canine crown, and an orthodontic miniscrew was placed between the first and second molar roots. The partial mandible model included the jawbone from the right mandibular lateral incisor to the second molar. Similar to the complete mandibular model, the partial mandibular model included the cortical bone, cancellous bone, the PDL, an orthodontic bracket, and a miniscrew. For both the partial and complete mandible models, the contours of the cortical bone, cancellous bone, and teeth were added to a dental cone beam computed tomography image of a human skull by using Mimics 15.0 (Materialise, Leuven, Belgium) before the image was segmented and saved in stereolithography (STL) format. Subsequently, the STL file was input into Geomagic Design X (3D Systems, ICP, Rock Hill, SC, USA) and transformed into a solid model. Since the PDL was excessively thin, we expanded the tooth model by 0.25 mm and performed a Boolean operation with the teeth and mandible to obtain a solid PDL model. Subsequently, we used SolidWorks (Swanson Analysis, Canonsburg, PA, USA) to illustrate the brackets and miniscrews (Figure 1). Finally, the finite-element package of ANSYS Workbench (Swanson Analysis, Canonsburg, PA, USA) was used to establish the material properties, meshing, boundary conditions, and loading conditions for all components of the solid model, thereby enabling the development of solutions. The numbers of elements and nodes in the partial mandible model were 44,827 and 71,548, respectively. The numbers of elements and nodes in the complete mandible model were 151,296 and 235,603, respectively. The interfaces between the model components were assumed to have perfect bonding.
## 4 Performance Analysis of the BOC Signal and BPSK Signal
### Tracking Performance for the BOC Signal  
The tracking performance of the BOC signal is mainly reflected in the ranging error and velocity measurement error. The ranging result error of the code tracking loop is shown in Equation (16):  
\[\sigma_{\mathrm{DLL}}=[\frac{d\cdot B_{DF}}{2\cdot C/N_{0}}(1+\frac{1}{T_{coh} \cdot C/N_{0}})]^{0.5}\cdot\lambda_{c} \tag{16}\]  
Here, \(\lambda_{c}\) represents the wavelength of the BOC signal. \(B_{DF}\) is the bandwidth of the code tracking loop. \(C/N_{0}\) is the carrier-to-noise ratio of the BOC signal. \(d\) represents the chip interval of the delay locked loop (DLL). \(T_{coh}\) represents the integration time [35].  
The velocity measurement error can be equivalent to the Doppler frequency measurement error, which is determined by the third-order phase locked loop (PLL) in the system. The dynamic stress error and thermal noise jitter error of the third-order PLL are shown in (17):  
\[\sigma_{\mathrm{PLL}}=\sigma_{\mathrm{IPLL}}+\frac{1}{3}\theta_{e}=[\frac{B_{ PF}}{C/N_{0}}(1+\frac{1}{2T_{coh}\cdot C/N_{0}})]^{0.5}+\frac{\overset{\cdot}{f} \cdot 2\pi}{3(1.2B_{PF})^{3}} \tag{17}\]  
The Doppler frequency can be calculated as (18).  
\[\sigma_{\mathrm{Doppler}}=\frac{\sqrt{2}\sigma_{\mathrm{PLL}}}{2\pi\cdot \Delta t} \tag{18}\]  
Figure 9: Single acquisition success probability under different situations.  
Here, \(\sigma_{\text{IPLL}}\) represents the thermal noise jitter error, and \(\theta_{e}\) represents the dynamic stress error. \(B_{PF}\) is the bandwidth of the PLL. \(\bar{f}\) is the system acceleration. \(\Delta t\) is the refresh interval time of the Doppler measurement, which is equal to 0.2 ms in the system [36; 37].  
Figure 10 shows the Doppler frequency measurement error and ranging error of the composite signal under different combination modes of BOC and BPSK signals through Monte Carlo simulation. When \(\alpha:\beta\geq 0.3:0.7\), the degradation of the Doppler measurement performance is mainly due to the reduction in the power occupied by the BOC signal in the composite signal. In addition, when \(\alpha:\beta<0.3:0.7\), the improvement of the BPSK signal power will reduce the ranging and Doppler frequency measurement performance. However, when the \(C/N_{0}\) is higher than 65 dB-Hz, the performance of the BOC signal has the approximate measurement performance of the pure BOC signal, even if the power of the BOC signal only occupies 10% of the composite signal.  
Figure 10: Performance of the composite signal and pure signal: (**a**) Doppler frequency measurement results of the composite signal and pure BOC signal, and (**b**) ranging results of the composite signal and pure BOC signal.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 4 Discussion
Example satellite image costs are shown in Table 11.  
To compare the cost of surveying the network by satellite, to manually survey with a person in a car, the length of the supply networks for a number of rectangular areas within Figure 1 was determined. The length of channels was found to be roughly proportional to the square root of the area: length \(\approx 25\sqrt{\text{area}}\). Thus, for a 500 km\({}^{2}\) area, the length of channel is about 550 km. If 1.5 m resolution satellite images are determined to be sufficient, the cost for an image will be around 500 \(\times\) 6 = $3000 (Table 11). If a manual survey is done, assuming a 10 km/h driving speed, the cost is the hourly rate for a driver/surveyor multiplied by the time, plus the mileage costs for the car. Assuming an hourly rate (including tax, superannuation and insurance) of $100/h, and mileage costs of $1/km, the cost to manually survey the area would be 100 \(\times\) 550/10 + 550 = $6050, approximately twice that of the satellite image. This simple analysis shows that replacing manual monitoring with satellite monitoring is an attractive option. Satellite monitoring also provides additional benefits such as quantifiable weed area calculations and regular mapping of the region. Additional value could be generated from the purchased images by using them for other purposes, such as finding NDVI to determine the total area of irrigated land in a given season. Future work will involve investigating the benefits that hyperspectral data may bring to the task of weed identification. Hyperspectral data has more bands and therefore may be able to separate the classes more precisely, particularly considering the different reflectance signatures of weed and land vegetation species (K