esa-sceva/satcom-chunk-collection huggingface dataset:
# Going Deeper into OSNR Estimation with CNN
## 3 Experimental Result and Analysis
### Training Methodology  
The number of data with a size of \(4\times 1024\)\((channels\times length)\) is 76,800. Within the dataset, 25% was classed as the test dataset and 75% served as the training dataset. In order to investigate monitoring performance under different chromatic dispersions (CD), we made optical signals travel different distances over the recirculation loop. Part of these data were included in the training dataset for generalization. We trained OptInception using the TensorFlow library based on graphics processing units (GPU) [41]. The mean square error was to be minimized after the forward propagation in every mini-batch training loop. The update of weights depends on the Adam optimization algorithm [42], where the individual adaptive learning rate is computed for different parameters and brings better training performance, higher computation efficiency, and lower computation resource occupation. In order to enhance generalization ability, batch normalization [43] was deployed inside convolutional layers while the dropout was put before a fully connected network. Bayesian optimization was used to find the best hyperparameters, such as learning rate and batch size [44,45]. A combination of a batch size of 40 and learning rate of 0.0005 seemed to be suitable in our training.
## 3 ISS Orbit Implications for Sampling of Diurnal Variability of Aerosol Vertical Distributions
Two of these regions, Southeast Asia shown in Figure 6 and Northwestern North America (125\({}^{\circ}\) W-100\({}^{\circ}\) W; 40\({}^{\circ}\) N-51\({}^{\circ}\) N), feature peak July, August, and September aerosol detections in the afternoon and evening hours. These afternoon hours coincide with peak fire activity in Southeast Asia [65] and Western North America, e.g., [66; 67]. In these regions, the CALIOP sampling frequency would miss the peak aerosol loading hours, highlighting the value of Earth-observing instruments in inclined orbits. Figure 8: In 2015–2017, July, August, September, vertical aerosol detection frequencies (Aerosol CAD Score > 1=51) for the Southeast Asia region represented in Figure 6 (**left**) and for Northwestern North America (125\({}^{\circ}\) W-100\({}^{\circ}\) W; 40\({}^{\circ}\) N-51\({}^{\circ}\) N) (**right**). Blue shaded region denotes the daily range of hourly observations, black hatching for the range of observations in the peak aerosol local hours (16:00–19:00 Southeast Asia and 18:00–21:00 for Northwestern North America). Solid lines denote the hours corresponding to the A-Train crossing (green morning, purple afternoon).
# Advances in Graphene Field Effect Transistors (FETs) for Annine Neurotransmitter Sensing
## 2 Problem Outline
### GFET Characterisation  
Defects, contaminants, and functional groups bound to graphene's surface can all affect its electrical and mechanical properties. As a result, understanding the surface structure and chemistry has a significant impact on its behaviour during surface modification and functionalisation with amine NTs.
## 3 Results
### Physicochemical and Color Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Traditional Honey**} & \multicolumn{3}{c}{**Non-Traditional Honey**} \\ \hline  & **H1** & **H2** & **H3** & **H4** & **H5** & **H6** \\ \hline  & \multirow{2}{*}{Rape honey} & \multirow{2}{*}{Lime honey} & Meadow and & \multirow{2}{*}{Lime honey} & Buckwheat & Honey \\  & & & & marsh honey & & honey \\ Moisture (\%) & 16.80 \({}^{\text{c}}\)\(\pm\) 0.14 & 15.40 \({}^{\text{a}}\)\(\pm\) 0.14 & 16.35 \({}^{\text{b}}\)\(\pm\) 0.07 & 17.80 \({}^{\text{d}}\)\(\pm\) 0.14 & 18.15 \({}^{\text{d}}\)\(\pm\) 0.21 & 15.35 \({}^{\text{a}}\)\(\pm\) 0.21 \\ Electrical conductivity & 0.17 \({}^{\text{a}}\)\(\pm\) 0.00 & 0.63 \({}^{\text{b}}\)\(\pm\) 0.01 & 0.70 \({}^{\text{f}}\)\(\pm\) 0.01 & 0.35 \({}^{\text{e}}\)\(\pm\) 0.00 & 0.24 \({}^{\text{c}}\)\(\pm\) 0.00 & 0.89 \({}^{\text{d}}\)\(\pm\) 0.00 \\ (mS/cm) & & & & & & \\ Acidity (meq/kg) & 13.3 \({}^{\text{a}}\)\(\pm\) 0.6 & 13.0 \({}^{\text{a}}\)\(\pm\) 1.7 & 30.7 \({}^{\text{b}}\)\(\pm\) 1.2 & 8.3 \({}^{\text{c}}\)\(\pm\) 0.6 & 24.3 \({}^{\text{d}}\)\(\pm\) 0.6 & 31.3 \({}^{\text{b}}\)\(\pm\) 1.5 \\ pH & 3.55 \({}^{\text{b}}\)\(\pm\) 0.00 & 4.49 \({}^{\text{c}}\)\(\pm\) 0.08 & 2.95 \({}^{\text{c}}\)\(\pm\) 0.03 & 3.37 \({}^{\text{a}}\)\(\pm\) 0.03 & 3.28 \({}^{\text{a}}\)\(\pm\) 0.01 & 3.09 \({}^{\text{d}}\)\(\pm\) 0.02 \\ HMF (mg/kg) & 0.7 \({}^{\text{a}}\)\(\pm\) 0.3 & 0.4 \({}^{\text{a}}\)\(\pm\) 0.2 & 4.2 \({}^{\text{b}}\)\(\pm\) 0.2 & 12.7 \({}^{\text{c}}\)\(\pm\) 0.8 & 11.8 \({}^{\text{c}}\)\(\pm\) 2.3 & 4.5 \({}^{\text{b}}\)\(\pm\) 0.8 \\ Color (mm Pfund) & 5.2 \({}^{\text{a}}\)\(\pm\) 2.0 & 37.2 \({}^{\text{b}}\)\(\pm\) 2.5 & 73.7 \({}^{\text{d}}\)\(\pm\) 0.8 & 5.6 \({}^{\text{a}}\)\(\pm\) 1.9 & 105.4 \({}^{\text{e}}\)\(\pm\) 2.8 & 60.0 \({}^{\text{c}}\)\(\pm\) 0.4 \\ Color & Water white & \multicolumn{2}{c}{Extra light} & \multicolumn{2}{c}{Light amber} & Water white & \multicolumn{1}{c}{Amber} & Light amber \\ \hline  & \multicolumn{2}{c}{Different letters (a–\(\ell\)) in the same line are significantly different at the 95\% level (\(p\)\(\leq\) 0.05).} & & & \\ \end{tabular}
\end{table} 
Table 1: Physicochemical parameters and color of honeys (average values and standard deviation). color of honey varied from water white to amber, that means that the intensity of color values ranged from 5.2 to 105.4 mm. The sample H5 (non-traditional buckwheat honey) was darker in comparison to samples H1 (traditional rape honey) and H4 (non-traditional lime honey), which were the brightest among all the analyzed honey samples.
# A Modified Compact Flexible Vivaldi Antenna Array Design for Microwave Breast Cancer Detection
## 2 Materials and Methods
### Parametric Study of Modified Antenna  
To achieve the highest possible bandwidth of the antenna and enhance the reflection coefficient, the DGS parameters and location were varied. Moreover, the dimensions and size of the parasitic element were studied. The modified antenna achieves the best possible result. The studies detailed below show the effects of changing each parameter.
# Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks
## 5 Numerical Simulation Results
Limited by the numerical errors of the iterative approach, the interference may not be perfectly aligned into the orthogonal subspace. However, the maximum percentage of interference leakage is less than 3% when it is feasible, which does not reduce the effectiveness of the proposed cognitive IA algorithm. If more strict interference-free transmissions are needed in the practical implementation, one or two degrees of freedom can be reserved to achieve more precise interference alignment.
# Optimization of Uplift Piles for a Base Plate Considering Local Anti-Floating Stability
## 3 Verification Analysis
### Numerical Models  
The frame structure with a base plate of 46 m \(\times\) 30 m \(\times\) 0.5 m is selected as an example. The drawback of the BESO process is that the optimization result will be affected by the initial design, and the local optimal solution may be obtained. The proposed process takes the pile length as the design variable. However, for the design parameters of the piles, in addition to the pile lengths, the pile diameters and the pile spacings should also be considered. Therefore, based on the same initial pile lengths and pile elements volume, two designs with different pile diameters and pile spacings are selected for optimization. The positions of piles and column loads in the two cases are shown in Figure 4.  
For Case 1, the uplift piles are designed with large diameter and sparse distribution (LS). The pile spacing is 4.2 m, which is 1/2 column load net spacing. The pile diameter is 0.7 m. For Case 2, the uplift piles are designed with small diameter and dense distribution (SD). The pile spacing is 2.8 m, which is 1/3 column load net spacing. The pile diameter is 0.486 m. The initial pile length for both cases is 16 m. Two three-dimensional numerical models are established as shown in Figure 5. The concentrated force of 8400 kN is applied  
Figure 3: The flow chart of the BESO process.  
to the base plate instead of the single column load. The radial mesh system is used for both cases. Due to mesh size having a significant impact on the variation in the pile length in the proposed process, meshes are equally divided in the vertical direction, and the vertical size of each mesh is 0.5 m.  
The Mohr-Coulomb constitutive model is used for the soil, and the linear elastic constitutive model is used for the base plate and piles. The interface element is used to simulate the slip between the pile and soil. The constitutive parameters of the soil, base  
Figure 4: Initial designs of uplift piles: **(a)** Case 1: large, sparse (LS); **(b)** Case 2: small, dense (SD).  
Figure 5: Numerical model: **(a)** Case 1 (LS); **(b)** Case 2 (SD); **(c)** A’-A sectional view.  
plate, and uplift piles are presented in detail in Table 1. Parameters of the interface element are derived from the user manual in the FLAC3D help document. A uniform load of 120 kPa upwards is applied to the base plate instead of water pressure (i.e., hydraulic head = 12 m).
# Enhanced Linearity in Intracranial Pressure Monitoring System Through Sample Isolation Bridge ROIC
## 2 System Design of the ROIC
The isolation circuitry reduces the impact of noise on the entire signal link, thus improving the signal integrity and measurement accuracy of the system. The use of isolation techniques such as optical or magnetic coupling reduces the noise and interference introduced by the ground loop. This helps to improve the noise performance of the system, especially in the case of high-precision measurements and low noise requirements. Isolated  
Figure 3: ROIC using (**a**) conventional SAR ADC, (**b**) SAR ADC with a RC filter, and (**c**) proposed Sampled-Data Isolation Based SAR ADC. sampling switches allow signals to be transmitted between different power domains or grounds, which improves system design flexibility and avoids the possibility of noise interference from multiple sources. Figure 3c uses a sample isolation module instead of an RC filter, so the issue of how quickly the ADC input charge is stabilized in the injection transient is considered. The RC filter causes some degree of attenuation of the signal, including a reduction in signal amplitude and an increase in phase shift. This may be less desirable in some high-fidelity and high-precision applications. RC filters are susceptible to PCB layout and external electromagnetic interference, especially in high-frequency applications. Parasitic inductance and capacitance may affect the desired performance of the filter. Sample isolation techniques are suitable for applications that require high accuracy, high immunity to interference, and the need to transmit signals between different power domains or grounds. Despite the higher cost and complexity, it may be necessary for certain mission-critical and safety-critical systems. RC filters are more suitable for cost-sensitive, simple-to-implement applications with moderate bandwidth requirements. It provides a cost-effective noise suppression solution and is a common tool in many economical measurement and data acquisition systems.
# CFD-Simulink Modeling of the Inflatable Solar Dryer for Drying Paddy Rice
## 2 Materials and Methods
### Mathematical Modeling of the ISD
#### 2.3.8 Mass Balance  
The moisture evaporating from the crop in the drying area is absorbed by the air. This causes a change in the enthalpy of the air, which can be described by the energy and mass balance. The rate of moisture transfer between the product and the surrounding air is expressed as:  
\[\rho_{air}.\delta_{air}.v_{air}.\frac{\partial H}{\partial x}.dx.dt=-\rho_{ pad}.dx.\frac{\partial M}{\partial t}.dt \tag{19}\]  
where \(\partial H/\partial x\) is the humidity ratio and \(\partial M/\partial t\) is the drying rate gradient in the direction of the airflow. The drying rate is calculated as:  
\[\frac{\partial M}{\partial t}=-k.\left(M-M_{e}\right) \tag{20}\]  
where \(M_{e}\) is the equilibrium moisture content and \(k\) is the drying constant. The calculation of \(M_{e}\) was obtained from the Chung-Pfost equation using the coefficients given by [PERSON] and [PERSON] [36]:  
\[M_{e}=\left(-\frac{1}{0.179}\right).ln\left(\left(-\frac{\left((T_{air}-273.15 )+16.912\right)}{277.091}\right).ln(\varphi)\right) \tag{21}\]  
where \(\varphi\) is the relative humidity of the drying air.  
The drying constant was obtained from paddy rice drying experiments conducted under controlled conditions. This experiment was necessary as no other paper on the subject had been reported. The drying experiments were carried out using the overflow chamber of a high-precision laboratory dryer from the Institute of Agricultural Engineering, University of Hohenheim in Stuttgart (Germany). The experimental system has been previously described in detail by [PERSON], et al. [37]. The drying experiments were conducted at air temperatures of 30, 40, 50, and 60 \({}^{\circ}\)C and humidity ratio of 0.020 kg kg\({}^{-1}\)The paddy rice was filled in a tray of 0.30 \(\times\) 0.30 \(\times\) 0.04 m having a layer thickness of 0.04 m. The paddy rice was dried to the equilibrium moisture content. The experiments were performed per triplicate for each drying condition.  
The drying equation was built following three steps. The first step corresponds to the computation of the moisture ratio from the drying experiments; the second step was to obtain the drying coefficient (\(k\)) per experiment attributed to the Newton model. The third step was to fit the drying coefficient of the different experimental conditions by using an Arrhenius type equation:  
\[k=4758\cdot e^{-\frac{2987}{T_{air}}} \tag{22}\]  
The goodness of fit for Equation (22) resulted in a coefficient of determination (R\({}^{2}\)) of 0.87 and a root mean square error (RMSE) of 0.10 h\({}^{-1}\).
## 3 Results
### Numerical Experiments  
Figure 11 shows the results of the numerical model in terms of surface and bottom water currents, with no more variables than the force of the wind pushing the water. SIMU 1 (up-bay wind) indicated an expected water circulation pattern for the homogeneous flow (surface water following the wind direction and return flow at the bottom). However, when a varying bathymetry was considered (SIMU 2), the surface flow in the central axis reduced significantly in comparison to the shallower shore where the bottom friction is very relevant due to its shallowness. Tests have been made with different bottom roughness and it was found that the magnitude varies but the circulation pattern does not. In this case, an outward flow was observed in the central axis of the estuary in the bottom layers, opposite to the direction of the wind, as noted in the observations of the campaigns in the previous section. A homogeneous upward flow is observed at the shore. This pattern was reproduced for upward and smaller winds (SIMU 3). For the case down-bay wind, the picture differed significantly: downward flow towards the shore and upward flow in the axis and the bottom layers (SIMU 4). Therefore, the numerical experiments revealed transversal variability in flow direction due to the bathymetry. SIMU 5, 6 and 7 reproduced the previous cases but a stretching at the bay entrance is added. In this case, the pictures do not significantly differ in relation of the upward/downward flow in the shore/central axis for bottom layers in relation to the open mouth. The Coriolis effect was negligible in idealized cases when comparing SIMU 1 and SIMU 8. Finally, sensitivity test of the bottom friction parametrization has not shown relevant influence on the water circulation patterns affecting mainly the water speed.  
Focussing on the impact of the wind on water circulation in the bay entrance, the transversal barotropic velocities are shown in Figure 12. This figure shows how the laterally variable bathymetry induces axially symmetric transverse structure (see inshore velocities near the boundaries and offshore in the central section of the bay entrance for SIMU 2; note that barotropic velocities for SIMU 1 its near to zero). Opposite water circulation pattern occurs for downwind simulation (SIMU 4). Bay mouth stretching (SIMU 6) has a similar pattern than SIMU 4 but increasing the spatial gradients between inflow and outflow (not shown). In this case, the magnitude of the transversal barotropic velocity is lower, reducing also the flow exchange expected between the bay and the open sea if the width mouth is reduced.  
Figure 11: Representation of the simulations: (**a**) SIMU 1, surface and bottom; (**b**) SIMU 2, surface and bottom; (**c**) SIMU 3, surface and bottom; (**d**) SIMU 4, surface and bottom; (**e**) SIMU 5, surface and bottom; (**f**) SIMU 6, surface and bottom; (**g**) SIMU 7, surface and bottom; (**h**) SIMU 8, surface and bottom. Blue arrows show the wind direction with the intensity wind and black arrows show the water current intensity.
# Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies
## 3 Mesh in IEEE 802.11 Networks  
### Standard: IEEE 802.11s  
#### 3.1.1 IEEE 802.11s Basics  
In the last decade, a growing interest in mesh networking, from both academic and industrial entities, brought to the definition of an IEEE 802.11 standard amendment [8], specifically addressed to IEEE 802.11 mesh networks, denoted as IEEE 802.11s [9; 10]. The physical layers of IEEE 802.11s and IEEE 802.11 standards are the same: it introduces new routing procedures that are performed at the Medium Access Control (MAC) layer, rather than at the network layer. To have an efficient routing, the nodes must have an accurate knowledge of the wireless links connecting them to their 1-hop neighbors. This leads to a seamless routing for protocols of the higher layer. In an IEEE 802.11s mesh network, also named as Mesh Basic Service Set (MBSS), there are different logical components, as shown in Figure 1. The main ones are the mesh stations (mesh STAs) that can participate to the formation of the MBSS, in which each node has the same level of complexity and there is no hierarchical structure. The mesh STAs, moreover, participate to the path selection and forwarding, leading to a very simple self-organizing network. In case of integration with other type of networks, such as the \"traditional\" IEEE 802.11 infrastructure BSS, or if the MBSS has to access external networks,other logical components are needed; the one that guarantees the access to the mesh network for \"traditional\" IEEE 802.11 stations is named as Mesh APs (MAPs). A MAP, however, does not enable the communication between a mesh STA and a non-mesh STA. In fact, the logical component that enables the integration between mesh BSS and infrastructure BSS--thus enabling the communication between mesh STAs and non-mesh STAs--is the mesh gate. Furthermore, in order to enable also the communication between the mesh BSS and non-IEEE 802.11 Local Area Networks (LANs), such as wired LAN, other logical components are used, namely the Mesh Portal Points (MPPs), which enable the communication with an external entities.  
Compared with a traditional IEEE 802.11 network, the IEEE 802.11s mesh solution--this is valid for wireless mesh networks in general--has some advantages: (i) it enables rapid and low-cost back-haul deployment; (ii) it provides coverage in scenarios hard to wire; (iii) it is self-organizing and extensible--the addition of a node simply extends the network and, thanks to the routing algorithm, the network can quickly adapt to the new conditions; and (iv) it can have a greater coverage, exploiting multi-hop forwarding. At the opposite, there are also some drawbacks: (i) the data rate decreases with the number of hops, and (ii) the wireless medium is in general less reliable than the wired medium. Thus, when deploying an IEEE 802.11, all these aspects must be taken into account. In general, since our view is IoT-oriented, the mesh solution is preferable, since a WSN can have advantages in terms of scalability, flexibility, costs and simplicity of deployment of a WMN.
## 4 Results
### Validation of the S-GWR Models  
#### 4.2.1 Validation of the Spatial Non-Stationary  
In order to validate the spatial non-stationary of the S-GWR model, we explore the spatial distribution of the regression coefficients between four principal components and landslide susceptibility. Results are shown in Figure 5. The coefficients estimated by S-GWR vary greatly with districts in space. This result implies a noticeable spatial non-stationarity of the relationship between four principal components and landslide susceptibility. The regression coefficients of each component have positive and negative values, which indicates that the relevance between each influencing factor and landslide susceptibility has varying direction and strength in space.  
Spatial distribution of the regression coefficient of geological factors is approximately from northeast to southwest, similar to the distribution of actual lithology features and fault zones. The coefficient values of geological factors tend to decrease with the increase of the distance to fault zone and the large absolute values are mainly distributed in the metasandstones, phyllite, and sandy slates area. The distribution area with large absolute values of the coefficients of slope shape factor consist of the actual landslide points. In most of the eastern part of the study area, slope shape factor is positively correlated with landslide susceptibility, but the opposite effect exists in some central regions. The majority of the coefficient of hydrographic factor is negative, indicating that there is a significant negative correlation between the distance-to-stream and landslide susceptibility in most regions. This correlation is quite obvious in the central area, where most of the landslides occurred, such as Chaoba township, Courtyard Hui township, and the northeast area near the Bailong river. The aspect coefficient distribution map shows that the absolute value in the eastern region is larger than that in the western region. Compared with the first three principal components, the difference between the maximum and minimum values of aspect factor coefficients is the smallest.  
Figure 4: Landslide susceptibility mapping (LSM) by the proposed S-GWR model.
# Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning
## 1 Introduction
To enhance the robustness of the dictionary updating process against noise and outliers and improve the inpainting effect of HSIs, we use more robust \(l_{1}\) loss as the data fitting term in the objective function when performing the dictionary learning. The contents of this paper are divided into five parts. Section 1 briefly introduces the background and current status of the research topic. In Section 2, we present the mathematical inpainting model and the inpainting mechanism. In Section 3, the robust spectral dictionary learning algorithm and the robust sparse coding approach are explained. Section 4 formally outlines INORSDL, an inpainting approach based on online robust dictionary learning and sparse coding in the spectral domain. In Section 5, we present the experimental results of the proposed approach, compared with the state of art HSI inpainting algorithms. The final section concludes this paper.