esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Auxiliary Data  
To better understand the global spatial distribution of SMAP/CYGNSS \(\beta\) values, we compared \(\beta\) values with mean annual precipitation, mean NDVI, topographic roughness, and landcover class. We describe the data, processing, and upscaling methodology used to calculate each below.  
We calculated global mean annual precipitation using monthly 0.1 \(\times\) 0.1-degree CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and RFE2 (National Oceanic and Atmospheric Administration Climate Prediction Center African Rainfall Estimation Algorithm v2) rainfall data from 2001-2020, archived by FLDAS (Famine Early Warning Systems Network Land Data Assimilation System) [24]. We then upscaled the results to match our coarse-scale grid (33 km boxes, gridded at 9 km) using a linear averaging method.  
We calculated mean NDVI using monthly 0.05 \(\times\) 0.05-degree MODIS/Terra (Moderate Resolution Imaging Spectroradiometer) NDVI data from 2001-2020 [25]. We then upscaled  
Figure 5: Regional maps [25–37N, 85–99W] of all the parameters in the SMAP/CYGNSS TB algorithm. Each map is an aggregation of data from 1 June through 15 July 2020. (**a**) 33 km SMAP enhanced TB (gridded at 9 km), \(TB_{C}\). (**b**) Slope of the linear regression between SMAP emissivity and CYGNSS reflectivity, \(\beta_{C}\). (**c**) Effective surface temperature, \(T_{S,C}\). (**d**) 33 km CYGNSS reflectivity (gridded at 9 km), \(\Gamma_{C}\). (**e**) 3 km CYGNSS reflectivity, \(\Gamma_{F}\). (**f**) 3 km SMAP/CYGNSS TB, \(TB_{F}\).  
the results to match our coarse-scale grid (33 km boxes, gridded at 9 km) using a linear averaging method.  
We calculated topographic roughness using the root-mean-square (RMS) surface height, derived from 90 m Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) data [26]. We calculated RMS surface height values to match our coarse-scale grid (33 km boxes, gridded at 9 km) using the following equation:  
\[topographic\ roughness=\sqrt{\frac{1}{N}\sum_{n=1}^{N}|x_{n}-\overline{x}|^{2}}. \tag{4}\]  
Here, \(x_{n}\) is any given surface height value in the 33 km box, N is the total number of surface height values in the 33 km box, and \(\overline{x}\) is the mean value of surface heights within the 33 km box.  
We used annual 0.05 \(\times\) 0.05-degree MODIS IGBP (International Geosphere-Biosphere Programme) landcover class data [27]. We upscaled the MODIS IGBP data to match our coarse-scale grids (33 km boxes, gridded at 9 km) using an areal-percent weighted average. For each 33 km box, we calculated the areal-percent of overlap for each 0.05 \(\times\) 0.05-degree grid that overlapped or fell within the box. We multiplied the areal-percent weight by the percent-coverage of the dominant landcover class for the 0.05 \(\times\) 0.05-degree grid and added it to a running sum for that landcover class. The largest landcover class running sum was assigned as the dominant landcover class of the 33 km box.
## 4 Case Studies
Moreover, the use of Ma-MIMO antennas and higher frequency bands has completely changed the network's coverage and management requirements. RF-EMF will only be present when and where it is needed. In parallel to measurements, computations will be performed on simulated and real scenarios to analyze the effect of Ma-MIMO antennas and transmitter positions on field distribution. However, there are concerns that this technology increases the risks of carcinogenesis. The scope of this case study (Figure 6) is to assess the urban planning and exposure management for 5G NR location design architecture and to examine the possibility of cell site distribution in an urban environment to optimize the exposure and to analyze field distribution over the territory due to Ma-MIMO antennas. Real standard measurement periods, in compliance with ICNIRP guidelines, and long-term (hours to days) acquisitions will be carried out to evaluate 5G RF-EMF exposure by resorting to maximum power extrapolation (MPE) techniques applied to the field level measured in real traffic and environmental conditions, both in line-of-sight and non-line-of-sight conditions. Statistical models will be developed and validated in real scenarios to consider such variability in time and space of the distribution, coverage, and design antenna mapping. In this respect, the measurement procedures for coverage quality evaluation and exposure assessment need to be rethought and reviewed to implement effective and efficient measurement methodologies for validating developed models in optimal planning. Urban planning, based on ray-tracing methodologies, will be performed on simulated and real scenarios to analyze transmitter positioning on field distribution and levels. The RF-EMF field distribution over scenarios due to the new technologies (e.g., 5G and Ma-MIMO) and such statistical models will be evaluated in terms of the exposure characterization for different purposes, such as new methodologies for exposure assessment. In addition, biological investigations will be carried out in vitro (neuroblastoma cells and human keratinocytes) and ex vivo (human lymphocytes) to gain information on potential health risks under realistic exposure conditions in 5G frequencies as used in existing 5G design architectures. Selected realistic conditions will be tested to investigate biological responses in the presence and absence of other agents (combined exposures). An umbrella review of epidemiological studies of cancer risks in relation to near-field and far-field RF-EMF exposure and risk assessment approaches on cancer will be performed.
## 4 Results for a Medium-to-Strong Black Box Rock
### (Micro-)Fracturing for the Three Basic Types of Stress Paths
Figure 7: Variation of the contact failure modes (i.e., tensile only, shear only, and mixed mode) as a function of the stress path, i.e., from the initial isotropic stress state until the load level of failure, corresponding to 50% failed contacts. The three types of stress paths are presented for the case, starting in the uniaxial loading experiment with a confining pressure of 10 MPa (see Figure 5). The initial and final failure loads in absolute values are presented at the top of each graph. (**a**) Uniaxial loading, S1(loa),S3(=), major principal stress levels are presented; (**b**) uniaxial unloading, S1(=),S3(unl), minor principal stress levels are presented; (**c**) simultaneous loading and unloading, S1(loa),S3(unl), major principal stress levels are presented. For all uniaxial unloading simulations, i.e., S1(=)S3(unl), a typical characteristic in comparison to the previous type of simulations is that the (micro-)fracturing started later (Figure 7b). For the first 20% of the total stress path until failure, no contact elements were activated. This clearly was the effect of the large confining stress, which was present at the start of each experiment (i.e., large initial isotropic stress state). Similar to the previous type of stress paths, the first contact activations also were in tension. For the situation of Figure 7b, only tensile activation took place until about 50% of the total load interval. At that moment, about 11% of all contacts had failed. For the other initial isotropic stress states, the first shear activation occurred at lower unloading percentages (between 35% and 45%). During further unloading of the minor principal stress, shear failure continued, but the pure shear activation remained limited in comparison to the uniaxial loading simulations. For the case of Figure 7b, about 22% of all failed contact elements had failed in shear only mode at the moment of the macro-failure (Table 2 (b)). At that moment, 32% of all failed contact elements had undergone a pure tensile activation. The remaining 46% of all failed contact elements were characterised by a mixed mode failure. For the other cases of uniaxial unloading, these percentages were similar (Table 2 (b)). These results seemed to be realistic, certainly when taking into account the simplicity of the model. As illustrated in Figure 2, extension fractures were observed during the unloading phase and before the full failure or macro-failure was reached [13; 22]. Finally, for the stress path, where simultaneous loading and unloading took place, i.e., S1(loa),S3(unl), observations of both previous types of stress paths were observed (Figure 7c). For the purple case in Figure 5, the first contact failed in tension only, and this at about 30% of the total load interval until failure. The first activation in shear took place at 55% of the total load interval. At that moment, about 4% of all contact elements had
# Evaluating the Effect of Intensity Standardisation
## 2 Materials and Methods
### Considered Intensity Standardisation Techniques
#### 2.3.3 Gaussian Mixture Model-Based Standardisation  
The Gaussian mixture model-based standardisation leverages supervised clustering to segment the white matter. This clustering technique assumes that the histogram of intensities can be represented using a mixture of Gaussian distributions for each region of interest. Intensities belong to a cluster depending on the number of standard deviations from the intensity value to the mean. Given a set of voxels \(x_{i}\in I\), clustering consists of finding \(K\) Gaussian distributions maximising the following loss function:  
\[\operatorname*{arg\,max}_{(\pi,\mu,\sigma)}\sum_{x_{i}\in I}\ln\sum_{k=1}^{K} \pi_{j}\cdot\mathcal{N}(x_{i}\,|\,\mu_{k},\sigma_{k}), \tag{4}\]  
where the \(k\)-th Gaussian distribution of the mixture is represented by its mean \(\mu_{k}\) and standard deviation \(\sigma_{k}\) and has a weight \(\pi_{k}\) in the mixture. Note that \(\sum_{j=1}^{K}\pi_{j}=1\). Similarly to the fuzzy c-mean standardisation, the method divides all voxel intensities by the mean intensity of the segmented white matter [18].
## 5 Examples of Forward-Inverse Modeling
### Atmospheric Input & Output
#### 5.4.2 The OC-SMART Multilayer Neural Network Approach  
In open ocean areas where the water IOPs are correlated with pigmented particles, standard atmospheric correction (AC) algorithms seem to work reasonably well. However, in turbid coastal water areas the IOPs of suspended inorganic particles, and colored dissolved organic matter (CDOM) may vary independently of pigmented particles. Therefore, in turbid coastal waters standard AC algorithms often exhibit large inaccuracies that may lead to highly uncertain and frequently negative water-leaving radiances (\(L_{\text{w}}\)) or remote sensing reflectances (\(R_{\text{rs}}\) values). To address this problem, [PERSON] et al. [109] introduced a new atmospheric correction algorithm for coastal water areas based on a multilayer neural network (MLNN) method. A coupled atmosphere-ocean radiative transfer model (AccuRT) was used to simulate the Rayleigh-corrected radiance (\(L_{\text{rc}}\)) at the TOA and the \(R_{\text{rs}}\) just above the surface simultaneously, and to train a MLNN to derive the aerosol optical depth (AOD) and \(R_{\text{rs}}\) values directly from the TOA \(L_{\text{rc}}\). The method was validated using both a synthetic dataset and Aerosol Robotic Network-Ocean Color (AERONET-OC) measurements.  
Extensive testing has shown that this MLNN approach has several advantages [109]:  
1. It significantly improved the quality of retrieved remote sensing reflectances (compared to the SeaDAS NIR algorithm) by reducing the average percentage difference (APD) between MODIS retrievals and ground-truth (AERONET-OC) validation data.
2. In highly absorbing coastal water, such as the Baltic Sea, it provides reduction of the APD by more than 60%, and in highly scattering water, such as the Black Sea, it provides reduction of the APD by more than 25%.
3. It is robust and resilient to contamination due to sunlight and adjacency effects of land and cloud edges.
4. It is applicable in extreme conditions such as those encountered for heavily polluted continental aerosols, extreme turbid water, and dust storms.
5. It does not require shortwave infrared (SWIR) bands, and is therefore suitable for all ocean color sensors.
6. It is very fast and suitable for operational use.  
Recent addition of training data to make it representative for a variety of water types has shown that this approach can produce a seamless (smooth) transition between turbid coastal water areas and clean open ocean areas.  
Figure 7: MODIS image comparison between OC-SMART (**top**) and standard SeaDAS (**bottom**) retrievals on 18 April 2014 over a Norwegian coastal area [107]. From left to right: \(\tau_{869}\), \(t\), CHL, CDOM and \(b_{bp}\), respectively.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 7 Evaluation and Discussion
### Proof of Concept Demonstration of Data Plane Forwarding
#### 7.1.1 Discussion of Architectural Challenges  
This section discusses a subset of the most important challenges that relate to the proposed architecture. The selected topics relate to the control plane and the service plane implementation challenges and discuss the constraints in the architecture.
## 3 Performance Analysis  
### Analysis for AWGN without Narrow Band Interference  
We first consider a simple case, where the ultra-wide band signal is transmitted in the AWGN channel without narrow band interference. The received signal over the AWGN channel with \(N_{u}\) ultra-wide band users can be expressed as:  
\[r_{a}(t)=\sum_{q=1}^{N_{u}}\sqrt{\frac{E_{[q]}}{N_{f}}}\sum_{i=0}^{\infty}\Xi _{[q]}\left(\left\lfloor\frac{i}{N_{f}}\right\rfloor\right)d_{[q]}(i)p_{\text{ rx}}\Big{(}t-iT_{f}-c_{[q]}^{\text{TH}}(i)T_{c}-\tau_{[q]}\Big{)}+ \sigma_{n}^{2}n(t), \tag{3}\]where \(p_{\rm rx}(t)\) represents the received ultra-wide band impulse, \(\tau_{[q]}\) denotes the propagation time delay of the \(q\)-th user and \(n(t)\) denotes the normalized white noise.  
In AWGN channel, the ultra-wide band wireless receiver commonly consists of a correlator structure or matched filter (MF), where the received hybrid signals are correlated with the local template at the receiver. The local template of the \(k\)-th information bit for the \(p\)-th user can be expressed as:  
\[s_{\rm temp,[p]}(t)=\sum_{i=kN_{f}}^{(k+1)N_{f}-1}d_{[p]}(i)p_{ \rm rx}\Big{(}t-iT_{f}-c_{[p]}^{\rm TH}(i)T_{c}-\varsigma_{[p]}\Big{)}, \tag{4}\]  
where the \(p\)-th user is assumed to be the primary user.  
The output of the local correlator for the \(p\)-th user can be written as:  
\[y_{\rm mf}=\int r_{a}(t)s_{\rm temp,[p]}(t)dt=\sqrt{\frac{E_{[p] }}{N_{f}}}\Xi_{[p]}\sum_{i=kN_{f}}^{(k+1)N_{f}-1}R_{\rm rx}(\varsigma_{[p]}(i ))+y_{\rm mai}+n, \tag{5}\]  
where \(R_{\rm rx}(x)=\int_{-\infty}^{+\infty}p_{\rm rx}(t)p_{\rm rx}(x-t)dt\) is the autocorrelation function of the received ultra-wide band pulse waveform, \(y_{\rm mai}\) denotes the multi address interferences (MAIs) from other ultra-wide band users and \(n\) denotes the output noise, which can be modeled as a normal distribution \(n\sim\mathcal{N}(0,N_{f}\sigma_{n}^{2})\).  
Commonly, the second term MAIs can be rewritten as the sum of the multi-access interference terms from each user:  
\[y_{\rm mai}=\sum_{q=1}^{p-1}\sqrt{\frac{E_{[q]}}{N_{f}}}y_{[q]}+ \sum_{q=p+1}^{N_{\rm tr}}\sqrt{\frac{E_{[q]}}{N_{f}}}y_{[q]}, \tag{6}\]  
where each interference term is in turn the summation of interference to the pulses of the desired user. Considering the symbol synchronous and chip synchronous code division case, \(y_{[q]}\) is asymptotically distributed as [49]:  
\[y_{[q]}\sim\mathcal{N}(0,\frac{N_{f}}{N}\{\mathrm{E}\{R_{\rm rx} ^{2}(\varsigma_{[q]})\}+\mathrm{E}\{R_{\rm rx}^{2}(T_{c}-|\varsigma_{[q]}|)\}\}). \tag{7}\]  
Thus, the unconditional bit error probability under AWGN channel can be expressed approximately as:  
\[P_{e}\approx Q\left(\frac{\sqrt{E_{1}}\mathrm{E}\{R_{\rm rx}^{2} (\varsigma_{[p]})\}}{\sqrt{\frac{1}{N}\left(\sum_{q=1}^{p-1}E_{[q]}\mathrm{Y} _{[q]}+\sum_{q=p+1}^{N_{\rm tr}}E_{[q]}\mathrm{Y}_{[q]}\right)+\sigma_{n}^{2} }}\right), \tag{8}\]  
where \(\mathrm{Y
## 3 Results and Discussion  
### Characterization of Bio-Coagulant of Deshelled Carica Papaya Seeds  
#### 3.1.1 Protein Content  
The active coagulant agents from grounded solid samples can be extracted from deshelled seeds by water, or even other extractants such as salt solutions, buffer solutions or organic solvents [17, 21]. In this paper, the extraction efficiency of distilled water was studied to determine the influence of water as a solvent. Obtained extracts were analyzed and protein content (mg/mL) was determined.  
Protein in the extract of deshelled _Carica papaya_ seeds was present in comparable concentration with other established bio-coagulants (Table 3). The recorded protein content of deshelled _Carica papaya_ seeds is 0.363 mg/mL, slightly lower than the protein in the extract of _Quercus robur_ (0.540 mg/mL). The protein content of deshelled _Carica papaya_ seeds was also comparable to the study by [PERSON] et al. [22] that obtained protein extract from an established bio-coagulant of _Moringa oleifera_ with an average of 0.371 mg/mL. The only difference is the protein from _Moringa oleifera_ was extracted using organic solvent while for the deshelled _Carica papaya_ seeds, the protein was extracted using distilled water, without influence of chemical addition. This makes the extraction method employed in this study, eco-friendlier and more cost-effective. These findings suggested that the deshelled _Carica papaya_ seeds could possibly have higher concentration of natural polyelectrolyte (which in this case is protein) if further extraction is conducted by using chemical extractants. Nonetheless, the deshelled _Carica papaya_ seeds still pose sufficient coagulation ability as other established bio-coagulant viz. _Moringa oleifera_.
# ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform
## 2 Methods
### Feature Extraction  
After the 2D-GFT, spectral features can be extracted.  
Finding the decisive features of the signals is a crucial step in the procedure. In the following subsections, spectral features are explained in detail. They are extracted from the spectral modes obtained using the bispectrum and 2D-GFT. Different spectral features, including spectral flatness, spectral brightness and spectral roll-off, were extracted from divided single heart beat [25].  
Spectral flatness measures the distribution of power in all spectral bands. It is the ratio of the geometric mean to the arithmetic mean of the spectrum. In this paper, it has been extended into the two-dimensional case. Thus, the ratio is determined by the arithmetic and geometric mean both in rows and columns.  
\[SF=\frac{\sqrt[N/2]{N/2}\sqrt[N/2]{\prod_{j=1}^{N/2}\bigl{|}s(i,j)\bigr{|}}}{ \frac{1}{N/2}\frac{1}{N/2}\sum_{i=1}^{N/2}\bigl{|}s(i,j)\bigr{|}}, \tag{11}\]  
where \(|\cdot|\) is the absolute value.  
The spectral flatness ranges from 0 to 1. The spectral flatness close to 0 means an extremely narrow band, while close to 1 means extraordinarily flat.  
The spectral brightness is the ratio of sum of magnitudes of spectrum above a boundary \(F\) to the total sum of all the magnitudes in the spectrum. Once \(F\) determined, a big spectral brightness indicates the concentration of power in the band between \(F\) and \(N/2\). The definition has also been expanded into a 2-D case, similar to the spectral flatness.  
\[SB=\frac{\sum_{i=F}^{N/2}\sum_{j=F}^{N/2}\bigl{|}s(i,j)\bigr{|}}{\sum_{i=1}^{N /2}\bigl{|}s(i,j)\bigr{|}}, \tag{12}\]  
Spectral Roll-off is also a function corresponding to the boundary \(F\). Spectral roll-off can be used as the skewness which measures the non-uniformity of the signal about its mean value. In 2D-domain, the definition can be described as  
\[SR=\text{min}F:\sum\
olimits_{j=1}^{F}\sum\
olimits_{i=1}^{F}s(i.j)\geq\beta \sum\
olimits_{j=1}^{N/2}\sum\
olimits_{i=1}^{N/2}s(i.j), \tag{13}\]  
where \(\beta\) represents the coefficient.
## 2 Basics Theory
### Model Parameterization  
A spherical partition approach is applied to discretize the Earth into a set of spherical homogeneous layers (for instance \(k\)), with a half-space layer at the bottom as shown in Figure 1. Thus, the parameters of the model \(\mathbf{m}(k,z,\mathbf{\omega})\) consist of depths of the \(k\)-1 interfaces \(\mathbf{z}=(z_{1},z_{2},\ldots,z_{k\!\!-\!1})\) and the conductivities \(\mathbf{\omega}=(\sigma_{1},\sigma_{2},\ldots,\sigma_{k})\) of the \(k\) layers. Both the interface depths and the conductivities are represented in the log scale. However, for convenience, we will refer to them as interface depths and layer conductivities in this paper. In general, the layer number is unknown and can be determined by the data information. The priors applied are considered to be uniform on the bounded range \([z_{min},z_{max}]\) for interface depth (100 km is used for \(z_{\min}\) considering the frequencies used for GDS) and \([\sigma_{\min},\sigma_{\max}]\) for layer conductivities. The interface number is drawn from the uniform distribution over bounded range of \([k_{\min},k_{\max}]\). To avoid generating an extreme thin layer, the minimum thinness for the new generated layer is set to \(h_{\min}=50\) km.
## 2 NSAS
### Governing Equation  
The preliminary analysis of the NSAS was for a single-degree-of-freedom (SDOF) structure (Figure 2), consisting of mass \(m\), a spring with stiffness \(k\), and a viscous dashpot with damping coefficient \(c\). Subject to base excitation \(a_{g}\), an NSAS is equipped with the primary structure to mitigate the oscillating motion. The mechanical system is described in nondimensional form, including damping ratio \(\xi=c_{d}/2\sqrt{mk}\), negative-stiffness ratio \(\chi=k_{n}/k\) and stiffness ratio \(\kappa=k_{d}/k\). The primary structure is characterized by circular frequency \(\omega_{0}=\sqrt{k/m}\) and inherent damping ratio \(\zeta=c/2\sqrt{mk}\). In the ultimate condition, where \(\zeta\)= 0, the structural spring is in parallel with the proposed NSAS. The governing equation established for the NSAS-SDOF structure was  
\[\mathbf{M}\ddot{\mathbf{u}}(t)+\mathbf{C}\ddot{\mathbf{u}}(t)+\mathbf{Ku}(t)= \mathbf{D}_{\mathbf{s}}a_{g}(t) \tag{1}\]  
where the overdot signifies the derivative with respect to time \(t\). Displacement vector \(\mathbf{u}(t)\), excitation-location vector \(\mathbf{D}_{\mathbf{s}}\), the NSAS structure matrices of mass \(\mathbf{M}\), stiffness \(\mathbf{K}\) and damping \(\mathbf{C}\) are given in detail as  
\[\mathbf{u}(t)=\left[\begin{array}{c}u(t)\\ u_{d}(t)\end{array}\right]\mathbf{D}_{\mathbf{s}}=\left[\begin{array}{c}-1 \\ 0\end{array}\right]\mathbf{M}=\left[\begin{array}{cc}1&0\\ 0&0\end{array}\right]\mathbf{,\ }\mathbf{C}=2\omega_{0}\left[\begin{array}{cc} \zeta&0\\ 0&\xi\end{array}\right]\mathbf{,\ }\mathbf{K}=\omega_{0}^{2}\left[\begin{array}{cc}1+ \chi&-\chi\\ -\chi&\kappa+\chi\end{array}\right] \tag{2}\]  
where \(u\) is the displacement of the primary structure relative to the ground, and \(u_{d}\) is the inner deformation of the dashpot (equal to the deformation of the spring with positive stiffness). In the state-space form, where the state vector is \(\mathbf{z}(t)=\left[\begin{array}{c}\mathbf{u}(t)\\ \dot{\mathbf{u}}(t)\end{array}\right]\), the governing equations can be rewritten as  
\[\dot{\mathbf{z}}(t)=\mathbf{A}\mathbf{z}(t)+\mathbf{D}a_{g}(t) \tag{3}\]  
where system matrix \(\mathbf{A}\) and the excitation-location vector in state-space \(\mathbf{D}\) can be determined as  
\[\mathbf{A}=\left[\begin{array}{cc}0&\mathbf{I}\\ -\mathbf{M}^{-1}\mathbf{K}&-\mathbf{M}^{-1}\mathbf{C}\end{array}\right]\mathbf{, \ }\mathbf{D}=\left[\begin{array}{c}0\\ \mathbf{M}^{-1}\mathbf{D}_{\mathbf{s}}\end{array}\right] \tag{4}\]  
The analytical solution to Equation (4) from time \(t_{1}\) to \(t_{2}\) is  
\[\mathbf{z}(t_{2})=e^{\mathbf{A}(t_{2}-t_{1})}\mathbf{z}(t_{1})+\int_{t_{1}}^{ t_{2}}e^{\mathbf{A}(t_{2}-t)}\mathbf{D}a_{g}(t)\mathrm{d}t \tag{5}\]  
Figure 1: Physical realization and mechanical model of a negative-stiffness amplification system (NSAS).