esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Synthetic aperture radar (SAR) equipped on the hypersonic air vehicle in near space has many advantages over the conventional airborne SAR. However, its high-speed maneuvering characteristics with curved trajectory result in serious range migration, and exacerbate the contradiction between the high resolution and wide swath. To solve this problem, this paper establishes the imaging geometrical model matched with the flight trajectory of the hypersonic platform and the multichannel azimuth sampling model based on the displaced phase center antenna (DPCA) technology. Furthermore, based on the multichannel signal reconstruction theory, a more efficient spectrum reconstruction model using discrete Fourier transform is proposed to obtain the azimuth uniform sampling data. Due to the high complexity of the slant range model, it is difficult to deduce the processing algorithm for SAR imaging. Thus, an approximate range model is derived based on the minimax criterion, and the optimal second-order approximate coefficients of cosine function are obtained using the two-population coevolutionary algorithm. On this basis, aiming at the problem that the traditional Omega-K algorithm cannot compensate the residual phase with the difficulty of Stolt mapping along the range frequency axis, this paper proposes an Exact Transfer Function (ETF) algorithm for SAR imaging, and presents a method of range division to achieve wide swath imaging. Simulation results verify the effectiveness of the ETF imaging algorithm.  
hypersonic air vehicle; azimuth multichannel sampling; DPCA; high resolution wide swath (HRWS); ETF imaging algorithm; near space; curved trajectory
## 3 Materials and Methods  
### Rationale of the MOUNTS System  
Global volcano monitoring has the aim to assemble and provide timely information of physical and geochemical parameters at a large number of (or all) volcanoes worldwide. The information can in turn be useful to both the scientific and operational communities, to understand volcanic processes and mitigate volcanic hazards. The rationale behind MOUNTS is based on the following three requisites:  
(1) To analyze the multi-sensor dataset offered by the Sentinel missions to provide the most complete and comprehensive understanding of volcanic processes (Figure 1). Specifically, the datasets considered are: (i) SAR imagery from Sentinel-1 (S1) to monitor surface deformation, morphological changes, and reflectivity changes, (ii) SWIR imagery from Sentinel-2 (S2) to monitor surface thermal anomalies, and (iii) TROPOMI SO\({}_{2}\) data from Sentinel-5P (S5P) to monitor volcanic SO\({}_{2}\) gas plumes. Ground-based data is also incorporated, by querying global earthquake catalogues (GEOFON and USGS) for earthquakes recorded in the vicinity of the volcano. The volcanic processes which can be apprehended from the integration of these datasets is summarized in Figure 1, and those flagged with an asterisk are illustrated in the paper.  
(2) To disseminate the results of the analysis as: (i) geocoded images, and (ii) time series of key parameters extracted from these, so as to have a comprehensive understanding of their evolution through time. The results are published on an open-access website (www.mounts-project.com), with a design similar to MIROVA, whereby each monitored volcano is assigned a webpage following a standard template. Dissemination of these results is achieved in NRT, typically within \(<\)1-6 h following the availability of the source product on ESA's Sentinel Data Hub. The availability of such products after sensing varies depending on the data type and processing level: typically \(<\)24 h from sensing for Sentinel-1 SLC (Single Look Complex) products, between 2 and 12 h from sensing for Sentinel-2 L1C (Level-1 C) products, and \(<\)3 h from sensing for Sentinel-5P NRTI (Near Real Time) SO\({}_{2}\) products.  
(3) To implement a modular architecture, allowing easy incorporation of (i) new data types to analyze, (ii) new targets to monitor (i.e., new volcanoes, or new regions of interests for a given volcano depending on the activity, e.g., Kilauea \(10\times 10\) km extent during regular volcanic activity, and/orKilauea East Rift Zone 40 \(\times\) 60 km extent during flank eruption), and (iii) new algorithms to process the data (i.e., new processing chains, or trained neural networks to solve specific tasks).  
MOUNTS is the first system to integrate all these components on a unique platform, with open-access, and global coverage capability.
# Advances in Improving Energy Efficiency of Fiber-Wireless Access Networks: A Comprehensive Overview
## 6 Implementation of SDN-Based Energy Conservation Concepts in the FiWi Networks  
In recent years, traditional access networks have become difficult to manage due to the constant increase in the number of users and network devices, which is followed by the increased complexity of the network structure. Compared with traditional communication networks, networks exploiting the SDN concept introduce flexible, dynamic and programmable networking concepts. The implementation of SDN results in easier network management and improved overall network performance [10]. The SDN allows for the virtualization of network functions (NFV) using applications that run on top of the SDN controller. These applications use a programming language that simplifies the rapid deployment of new services and capabilities. The SDN and its NFV applications can be used to manage the PON devices of different standards in a coordinated and dynamic manner [98].
# Performance Assessment of NAMI DANCE in Tsunami Evolution and Currents Using a Benchmark Problem
## 4 Discussion  
Nonlinear depth-averaged shallow water equations are commonly used to simulate the evolution and propagation of long waves offshore, near shore, and on the ground. These equations are solved using an explicit numerical scheme in which the staggered grid method in space and the leapfrog method in time are adopted. NAMI DANCE is one of many numerical tools that solve two-dimensional depth-averaged nonlinear shallow water equations to estimate the long wave behavior depending on bathymetry and topography. It is still controversial whether these equations predict tsunami currents accurately or not. In this study, the competence of depth-averaged NSW equations was tested using a benchmark problem. The results were analyzed in two aspects; free surface elevation and velocity  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Free Surface Elevation (FSE)** & **\% NRMSE** & **\% MAX** \\ \hline Gage 1 & 0.60 & 0.02 \\ Gage 2 & 7.30 & 19.30 \\ Gage 3 & 15.00 & 17.60 \\ Gage 4 & 2.60 & 2.60 \\ Gage 5 & 7.20 & 18.80 \\ Gage 6 & 8.70 & 8.20 \\ Gage 7 & 4.50 & 7.30 \\ Gage 8 & 5.00 & 6.80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Error statistics of numerical results for free surface elevation time series recorded at each gage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Horizontal Velocity** & **\% NRMSE** & **\% MAX** \\ \hline Gage 2, U & 8.25 & 18.90 \\ Gage 2, V & 16.00 & 27.90 \\ Gage 9, U & 10.75 & 15.00 \\ Gage 9, V & 16.00 & 28.60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Error statistics of numerical results for horizontal velocity time series recorded at each gage.  
time series. It is revealed that depth-averaged NSW equations satisfactorily predict the evolution, propagation, and amplification of long waves over complex bathymetry when turbulence effect is negligible. In the opposite case, the numerical results and observed data show a similar trend; however, the propagation and amplification of long waves are not accurately predicted. The turbulence and shoaling effects in the vicinity of the island, as well as the limitations of depth-averaged NSW equations, are considered to play a role in the underestimation of maximum wave amplitudes. The predicted and measured velocity components in x-direction are in good agreement with each other. The maxima of the velocity components in x-direction are reached. On the other hand, even though the predicted and measured velocity components in y-direction show a similar trend, the maximum velocity values are underestimated. Therefore, the prediction of the velocity components in y-direction needs improvement. Depth-averaged NSW equations may be a sufficient tool while developing tsunami mitigation strategies. However, long wave currents need further analysis in closed basins such as harbors and ports where they become a prominent parameter for the impact and drag on marine vessels, and also the resilience and endurance of coastal structures.
## 3 Methodology
### Experimental Set-Up
#### 3.3.2 Deep Learning Models  
To implement recurrent neural networks (RNNs), a high level neural network API called Keras with Tensorflow back end was used. We tried different parameter settings to design each DL model by changing various parameters, such as number of neurons, number of layers, optimizing function, and learning rate, to obtain the best DL model which not only performs well on the train data but also gives good prediction results on the unseen test data. We used 2-4 layers for constructing each RNN model and ran the model by selecting the number of neuron in each layer ranging as 50, 70, 100, or 150 and found that, by using two layers and keeping the number of neurons in each layer as 70, our RNN models give the best performance by minimizing the problem of overfitting and reducing model complexity. To compare RNNs, we used the same number of epochs, batch size, dropout, and loss function. Hyperparameter settings for GRU, LSTM, and BiULSTM were kept the same for comparison. During model construction process, we used dropout [38], which is a common way to prevent overfitting in neural networks. The number of neurons or units in RNN, dropout rate, and other parameters in each layer from top to bottoms are given as:  
* No. of cells in each layer: [70; 70]
* dropout rate of 20% has been used in the second layer of these three models.
* Activation Function: ReLU
* Dense layer unit:1
* For CNNLSTM model, the parameter settings for each layer from top to bottom are as follows:
* No. of filters in CONV1D layer: 32, Kernel size: 3, stride:1
* Maxpooling layer: Pool size:3
* LSTM layer cells: 32, dropout rate: 30%
* Activation Function: ReLU
* Dense layer unit:1  
Each DL model was trained using mini batch size of 32; early stopping [39] technique was also utilized to prevent the model from overfitting. Call backs were used to save best weights for each model. To optimize the models, we used Rmsprop [40], which is an unpublished optimization algorithm introduced by [PERSON] and designed for neural networks.  
Customized search method was adopted to find the best learning rate for DL models, and 0.0001 were observed to be appropriate, while'mean absolute error' was used as the loss function to monitor the loss during training process.
## 5 Fifth-Order Power Series Expansion  
It is important to bear in mind that the predictions obtained in the above examples consider a truncated power series to replace the nonlinear restoring forces, \(f_{i}=(x_{1},x_{2})\), by uncoupled cubic polynomial expressions, which, if inaccurate, would cause the conjecture to be false. In an attempt to avoid this situation, the modal system (4) was next replaced by a truncated fifth-order power series expansion that contained nonlinear damping terms [13; 38; 51; 52; 53]. As usual, driving forces were assumed to remain constant during the transformation approach. The uncoupled dynamic equations of motion are now written as  
Figure 14: Bifurcation diagrams computed from the numerical integration solutions of Equations (29) and (43). The parameter values used to obtain these curves were \(m_{1}=10\) kg, \(m_{2}=0.6\) kg, \(c_{1}=0.1\) Ns/m, \(c_{2}=0.08\) Ns/m, \(k_{1}=44\) N/m, \(k_{2}=8\) N/m\({}^{3}\), \(k_{3}=9.016\), \(k_{4}=-0.65\) N/m\({}^{3}\), and \(f_{0}=0.37\) N, with \(\omega_{n2}=2\omega_{n1}\). Here, the black line represents the bifurcation diagram computed from the numerical integration solutions of Equation (43), while the red line describes the one obtained from Equation (29). Figure 15: Bifurcation diagrams computed from the numerical integration solutions of Equations (29) and (43). The parameter values used to obtain these curves were \(m_{1}=10\) kg, \(m_{2}=0.6\) kg, \(c_{1}=0.1\) Ns/m, \(c_{2}=0.08\) Ns/m, \(k_{1}=44\) N/m, \(k_{2}=8\) N/m\({}^{3}\), \(k_{3}=20.828\) N/m, \(k_{4}=-0.65\) N/m\({}^{3}\), and \(f_{0}=0.37\) N, with \(\omega_{n2}=3\omega_{n1}\). Here, the black line represents the bifurcation diagram computed from the numerical integration solutions of Equation (43), while the red line describes the one obtained from Equation (29). \[ \begin{split}\ddot{u}_{1}+\omega_{nl}^{2}u_{1}+\
u_{1}\dot{u}_{1}+ \
u_{2}\dot{u}_{2}+\varphi_{1}u_{1}^{3}+\varphi_{2}u_{1}^{2}u_{2}+\varphi_{3}u_ {1}u_{2}^{2}+\varphi_{4}u_{2}^{3}\\ \equiv\ddot{u}_{1}+a_{1}u_{1}+a_{2}u_{1}^{3}+a_{2}u_{1}^{5}+2\mu_ {1}\dot{u}_{1}+\mu_{2}u_{1}\dot{u}_{1}^{2}+\mu_{3}u_{1}^{2}\dot{u}_{1}+\cdots=P _{1}(t),\\ \ddot{u}_{2}+\omega_{n2}^{2}u_{2}+\
u_{2}\dot{u}_{1}+\
u_{3}\dot{u} _{2}+\varphi_{5}u_{1}^{3}+\varphi_{6}u_{1}^{2}u_{2}+\varphi_{7}u_{1}u_{2}^{2}+ \varphi_{8}u_{1}^{3}\\ \equiv\ddot{u}_{2}+b_{1}u_{2}+b_{2}u_{2}^{3}+b_{3}u_{2}^{5}+2\mu_ {4}\dot{u}_{2}+\mu_{5}u_{2}\dot{u}_{2}^{2}+\mu_{6}u_{2}^{2}\dot{u}_{2}+\cdots=P _{2}(t).\end{split}  \tag{53}\]  
The Expressions (53) and (54) provide equivalent decoupled representation forms, in the sense of Lyapunov, of the original equations of motion whose accuracy depends on the unknown coefficients: \(a_{i}\), \(b_{i}\), and \(\mu_{i}\).
# CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks
## 3 Congestion-Aware Routing Algorithm
### Route Evaluation Parameters  
The CARA algorithm proposed in this article is a distributed routing protocol. Each node calculates routing parameters based on existing information when forwarding a data packet and then selects the best neighbor node to receive the data packet according to the steps in Section 3.2. The CARA algorithm defines four routing evaluation parameters that evaluate nodes from the three scales of distance, processing capacity, and congestion. The \"forward rate\" expresses the distance relationship between nodes and between the nodes and sink. The \"load factor\" reflects the node's data packet processing capability. \"The cache remaining rate\" and \"the forward average cache remaining rate\" describe the node's perception of the degree of congestion in itself and its surroundings. These four parameters are described in detail below.
###### Abstract  
This retrospective study was undertaken to determine survival rates and aesthetic outcomes of immediate placement of multiple implants at anterior maxilla sites. One hundred and eighteen implants placed in 39 patients (21 women and 18 men; average age 58.3 years) were immediately restored (24-72 h after placement). Aesthetic assessment, radiographic bone loss, and biological and prosthetic complications were evaluated. Data collection between 12 and 84 months (mean 32.2 \(\pm\) 18) after final prosthetic installation revealed that no implants were lost, and that 106/118 (89.8%) implants had no more than 1.5 mm of bone loss by the end of the first year and an additional 0.2 mm for each successive year. The marginal bone loss was higher for extractions due to periodontitis compared to extractions due to caries (mean mesial loss of 1.37 mm vs. 1.01 mm, respectively, and mean distal loss of 1.37 mm and 0.99 mm, respectively, \(p\) = 0.001). The mesial papilla was present in 83/118 implants (70.3%), while the distal papilla was present in 76/118 implants (64.4%). The cervical metallic part of the abutment was exposed in 16/118 (13.5%) implants. There was a higher ratio of recessions and missing papilae in patients in whom the extractions were performed due to periodontal reasons. Within the limitations of the present study, aesthetic and radiographic parameters support immediate restoration of partially edentulous maxillae.  
immediate restoration; aesthetic assessment; allograft 2018 8, 377; doi:10.3390/app8030377
## 5 Experimental Results  
### Preliminary Test Exploiting SPR-TPOFs Sensors with and without a Buffer Layer  
At an early stage, we tested two SPR-TPOF sensor configurations, one with and one without the photoresist buffer layer, to determine if the use of this intermediate layer leads to a sensitivity enhancement similar to the SPR platform based on non-tapered POFs [17]. Both SPR-TPOF sensors had the same taper ratio of about 1.8 and had been tested with different water-glycerin mixtures in contact with the gold film (the refractive index ranged from 1.332 to 1.371).  
Figure 5a,b shows the SPR spectra, normalized to the reference spectrum (obtained with air as the surrounding medium) [17] at different water-glycerin solutions for the configurations with and without buffer layer. Figure 5c presents the resonance wavelength variations (\(\Delta\lambda\)), calculated with respect to the water (1.332) as a function of the refractive index (n), along with the linear fitting to the data for both the configurations (with and without buffer layer). The linear fitting of the data can be used to approximate the sensitivity of the sensor. In Figure 5c, each experimental value is the average of five subsequent measurements, and the respective standard deviations (error bars) are shown too.  
Figure 4: Experimental setup used to test SPR-TPOF sensors.  
Figure 5: _Cont._  
In fact, if an alteration of the refractive index (\(\delta\)n) produces a variation of the resonance wavelength (\(\delta\lambda\)), sensitivity at a fixed refractive index (n) can be defined as [28,29]:  
\[S(n)=\frac{\delta\lambda}{\delta n}\left[\frac{nm}{RIU}\right] \tag{1}\]  
So, from Equation (1) and Figure 5c, the sensitivity can be approximated with the slope of the linear fitting, and it is approximately equal to 1800 nm/RIU for both configurations. Therefore, in terms of sensitivity, these results establish that the buffer layer is not necessary for the SPR-TPOF sensors because it does not improve the performances even if the shape of the resonance gets worse. The shapes of these SPR curves are relative to the high value of the taper ratio (about 1.8). As will be shown in the next section (Section 5.2), the full width at half maximum of the SPR curves increases when the taper ratio increases.  
Through these preliminary experimental results, we verified that the simple sensor configuration without the buffer layer can be used to study how several taper ratios produce different performances.
# Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging
## 2 Materials and Methods
### Methods for Adapting Medical Imaging  
#### 2.2.1 Designing a Medical Imaging Data Exchange System  
As this study focuses on proposing a reference model regarding medical imaging, a data exchange system was created using a HAPI FHIR server according to HL7 FHIR standards. HAPI FHIR supports the XML and JSON data formats with system compatibility confirmed by sending randomly generated medical data in XML format to the server. The medical data used were made by resources according to HL7 parameters, and a resource called Phantom was added to the pre-existing resources (ImagingStudy, Patient, Endpoint) used. As the Phantom resource is not a resource in pre-existing HL7 FHIR, this study will discuss this matter in detail in the section regarding measurement uncertainty designs. The Patient resource handles any data relating to the patient while the ImagingStudy resource focuses on data relating to medical imaging. The actual medical imaging does not exist within this resource and instead includes Unique identifier (UID) values such as Series UID and Instance UID. The Endpoint resource handles the address in which the actual medical image is saved. In Figure 1, the diagram shows the relationship among other resources based on the ImagingStudy resource.
## 2 Identification Method
### Identification of Vehicle Speed  
For the convenience of explanation, a segmental model of an orthotropic bridge deck is selected as an example, as shown in Figure 2. Two strain gauges are installed at the lower edge of the same U-rib (named as N7 and N8) to measure the structural response when the vehicle across the deck. The longitudinal positions of the sensors are arranged in the adjacent segmental span (B-B and C-C sections in the figure), and the distance between measurement points is \(d\).  
Assuming that the vehicle passes across the bridge deck at a uniform speed, the strain responses measured by the two measurement points are \(P_{7}(t)\) and \(P_{8}(t)\), and the sampling frequency is \(f_{8}\). As a result of the local stress characteristic at the measurement points, their strain responses are similar, and the cross-correlation function can be calculated as follows:  
\[R(\tau)=\int P_{7}(t)P_{8}(t+\tau)dt, \tag{1}\]  
where \(R(\tau)\) is the correlation function of the response of measurement points N7 and N8, \(\tau\) is the time shift, and the extreme value of \(R(\tau)\) corresponding to \(\tau_{\text{max}}\) is the time difference between the vehicle passing the two measurement points, as shown in Figure 3.  
Figure 1: The whole flowchart of the identification method.  
Figure 2: The segmental model of an orthotropic bridge deck and layout of the measurement points.  
The vehicle's speed can be estimated by the following equation:  
\[v=\frac{d\times f_{s}}{\tau_{\max}}, \tag{2}\]  
It should be noted that the vehicle speed can be estimated directly based on the difference between x coordinates for the peak of N7 and N8. However, the peak is susceptible to noise interference and the time of peak is unstable. It is inaccurate to directly estimate the vehicle speed based on peaks. The proposed cross-correlation function determines the speed by analyzing two entire responses instead of a few peak points, which make the results more sable and ensure better anti-noise performance.