esa-sceva/satcom-chunk-collection huggingface dataset:
# Perspectives on the Structural Health Monitoring of Bridges by Synthetic Aperture Radar
## 2 Methodology
### Processing Framework  
Based on the models and tools described in the previous subsections, here the processing framework to estimate the micro-motion of the infrastructure is introduced. In order to analyze the vibrational anomalies, we use the estimated shifts \(\mathbf{D}_{\text{tot}_{(c,D)}}^{i,j}\) in conjunction with (13) as follows  
\[\mathbf{D}^{i,j}_{\text{tot}_{(c,D)}}=\mathbf{H}(\omega)\mathbf{ f}. \tag{15}\]  
Equation (15) gives an operational solution to the vibrational model (13) using SAR data. A flow diagram of the processing framework is represented in Figure 2. As it can be observed, the framework is composed by 8 processing stages: the first two consist in the selection of the single image for the analysis followed by a 2D-DFT of the selected image. The third stage is a band-pass filtering operation generating the sub-images centered at the different Doppler frequencies. These sub-images are the input for the successive stages: the SAR focusing, the coregistration, the Pixel Tracking, and the Modal Analysis which solves (15). In particular, the all stages address the following tasks:  
* Selection of a single SLC-SAR image observing bridges.
* Computation of the bi-dimensional spectrum via 2D-DFT.
* Band-pass filtering according to the small-frequency baseline strategy of Figure 3. The output of the third block consists in multiple images centered at the consecutive different Doppler frequencies.  
* Computation of the inverse 2D-DFT for retrieving the SLC SAR image with lower azimuth spatial resolution.
* Sub-pixel coregistration;
* Two dimensional vibration estimation by pixel tracking;
* Two-dimensional modal analysis for infrastructure crack extraction.  
Figure 3: Bandwidth allocation strategy.  
Figure 2: Computational map.
# The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation
This model is a fast, radiative transfer model for nadir-viewing passive visible, infrared (IR), and microwave satellites [3]. The CAMEL product is integrated into this model via an IR emissivity module which, like the CAMEL HSR algorithm, creates a high spectral resolution emissivity on 417 infrared spectral channels. A single year of CAMEL data from 2007 is used by the RTTOV Version 12 IR emissivity module. This year is selected for use because it has a lack of observation degradation in terms of calibration from MODIS and is optimal for representing the MODIS record. The use of the CAMEL product within RTTOV's IR emissivity module is an update to the previously used UWIREMIS product, which is available with and leverages the UW-Madison UW BF dataset [4]. This study details the methodology for the CAMEL uncertainty product calculation, and it provides an assessment of the CAMEL emissivity dataset. Comparison of CAMEL with lab data from samples collected at ground sites provides the most important form of validation. However, because this form of validation is only available for case sites and not globally, other forms are used as well. Comparisons with an 8-year average of Infrared Atmospheric Sounding Interferometer (IASI) land surface emissivity derived from measurements are made for specific case sites as well as over the globe [5; 6]. In addition, this study makes use of the RTTOV model CAMEL HSR emissivity module to provide an assessment of the CAMEL product. Comparisons of RTTOV simulated IASI hyperspectral IR radiances are made to observations for four focus days. Simulations of IASI radiances are performed using the European Center for Medium Range Weather Forecasting's (ECMWF) forecast model as input into the RTTOV model. Simulations are made using both the CAMEL RTTOV emissivity module as well as a constant 0.98 emissivity, which is a typical simplification in global NWP models. These simulations are then compared to observed radiances, which provide a chance to assess the impact of the difference between the 0.98 constant emissivity and the CAMEL HSR estimate. Statistics are then grouped according to the International Geosphere-Biosphere Programme (IGBP) land classification scheme [7; 8] and are used to assess the CAMEL product's accuracy over different surface types. The paper is structured as follows: Section 2 describes the CAMEL emissivity product inputs. Section 3 outlines the CAMEL uncertainty estimation method in detail and shows results for global maps and lab validation case sites. Section 4 contains the CAMEL product assessments made from comparisons to independent sources and the RTTOV simulated IASI radiances. Section 5 holds the discussion of results and conclusions.
## 3 Activation Functions
### Learning/Adaptive Activation Functions
#### 3.4.3 MeLU  
The mathematical basis of the Mexican ReLU (MeLU) [61] activation function can be described as follows. Given the real numbers \(a\) and \(\lambda\) and letting \(\phi^{a,\ \lambda}(x)=\max(\lambda-|x-a|,0)\) be a so-called Mexican hat type of function, then when \(|x-a|>\lambda\), the function \(\phi^{a,\ \lambda}(x)\) is null but increases with a derivative of \(1\) and \(a\) between \(a-\lambda\) and decreases with a derivative of \(-1\) between \(a\) and \(a+\lambda\).  
Considering the above, MeLU is defined as  
\[y_{i}=ReLU(x_{i})=\ PReLU^{c_{0}}(x_{i})+\sum_{j=1}^{k-1}c_{j}\ \phi^{a_{j}\lambda_{j}}(x_{i}), \tag{25}\]  
where \(k\) is the number of learnable parameters for each channel, \(c_{j}\) are the learnable parameters, and \(c_{0}\) is the vector of parameters in PReLU.  
The parameter \(k\) (\(k=4\) or \(8\) here) has one value for PReLU and \(k-1\) values for the coefficients in the sum of the Mexican hat functions. The real numbers \(a_{j}\) and \(\lambda_{j}\) are fixed (see Table 1) and are chosen recursively. The value of \(maxInput\) is set to \(256\). The first Mexican hat function has its maximum at \(2\cdot maxInput\) and is equal to zero in \(0\) and \(4\cdot maxInput\). The next two functions are chosen to be zero outside the interval \([0,2\cdot maxInput]\) and \([2\cdot maxInput,4\cdot maxInput]\), with the requirement being they have their maximum in \(maxInput\) and \(3\cdot maxInput\).  
The Mexican hat functions on which MeLU is based are continuous and piecewise differentiable. Mexican hat functions are also a Hilbert basis on a compact set with the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**J** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline \(a_{j}\) & 512 & 256 & 768 & 128 & 384 & 640 & 896 \\ \(\lambda_{j}\) & 512 & 256 & 256 & 128 & 128 & 128 & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fixed parameters of MeLU with \(maxInput=256\) (these are the same values as in [61]).  
\(L^{2}\) norm. As a result, MeLU can approximate every function in \(L^{2}([0,\ 1024])\) as k goes to infinity.  
When the \(c_{i}\) learnable parameters are set to zero, MeLU is identical to ReLU. Thus, MeLU can easily replace networks pre-trained with ReLU. This is not to say, of course, that MeLU cannot replace the activation functions of networks trained with Leaky ReLU and PReLU. In this study, all \(c_{i}\) are initialized to zero, so start off as ReLU, with all its attendant properties.  
MeLU's hyperparameter ranges from zero to infinity, producing many desirable properties. The gradient is rarely flat, and saturation does not occur in any direction. As the size of the hyperparameter approaches infinity, it can approximate every continuous function on a compact set. Finally, the modification of any given parameter only changes the activation on a small interval and only when needed, making optimization relatively simple.
## 4 Mobile Head CT Scanner and Rotorcraft Retrofitting
### Design Considerations for Retrofitting Head CT Scanners into Rotorcraft
#### 4.2.4 CereTom and AgustaWestland AW189  
Currently, CereTom by Neurologica is one of the most popular commercial off-the-shelf portable CT scanners and is reported to be used in multiple road MSU programs across the world [11,15,31,32]. This has been attributed to its small and lightweight nature. However, the CereTom still poses a relatively heavy (438 kg) single piece of equipment for an air ambulance, and its weight needs to be further reduced for improved aircraft weight distribution and flight performance. [PERSON] et al. illustrated the cabin layout of a fixed-wing (Pilatus PC-24) and rotor-wing (NH90) aircraft attached with a CT scanner; however, the illustrations served as a preliminary concept and lacked details on how the CT scanner is attached to the aircraft's interior [22].  
In this case study, CereTom by NeuroLogica along with the search and rescue helicopter AgustaWestland 189 are selected according to the project requirements provided by our collaborating partners and manufacturers for the retrofitting and design process. In collaboration with Australian Stroke Alliance and Melbourne Brain Centre, the current CereTom model used in Australia's first-ever road MSU will be acquired and will form the basis to initiate the design process, subsequently extending the applicable principles onto the Air-MSU concept. The aim of this research is to explore a methodology in designing a lightweight CT scanner structure that can be retrofitted into the proposed search-and-rescue helicopter model--the AW189 model.
## 5 Analysis of Experimental Results
### Effects of Steel Bars on the Detectability of Delaminations  
Figure 17 shows the influence of steel bars in terms of SNR. It is noted that delaminations at 8 cm depth were undetectable, so they are not included in Figure 17. Linear regression is used to fit the data. In Figure 17, \"D5 cm\" represents the delamination at 5 cm depth without steel bars, while \"D5 cm-S\" represents the case having steel bars. For the depths of 6 and 7 cm, they indicate the same manner as the depth of 5 cm. The results show that with the same depth, heating time, and test conditions, the SNR obtained in cases without reinforcement is higher than cases with steel bars. This means that the delaminations under reinforced steel can be observed less clearly than those without the effect of reinforcement. Besides, the heating time provided for detecting delaminations requires a longer duration due to the influence of steel bars. For example, the delaminations at 5 and 6 cm depth without the effect of rebars can be detected under only 20 and 25 min heating regimes, respectively, while those placed below rebars are undetectable corresponding to the SNRs of \(-2.24\) and \(-1.40\) for 5 and 6 cm depths, respectively. Even for heating up to 40 min, the delamination at 7 cm depth below rebars was still undetectable.  
Figure 16: Comparison between the real and predicted depths of delaminations in the case of \(\alpha=7.5\times 10^{-7}\) m\({}^{2}\) s\({}^{-1}\).  
Figure 17: Comparison of the SNR of cases without and with steel bars.
## 2 Materials and Methods
### QSAR Studies  
The physico-chemical properties of the FLP in correlation to biological activity were studied using QSAR modeling on the Hyper Chem (Professional 8.0.3 program, New Delhi, India). The structure of each of the FLP was optimized with a semi-empirical PM3 method using a (MM+) force field, while a Fletcher-Reeves conjugate gradient algorithm was used for the energy minimization. A range of QSAR parameters, including hydration energy, free energy, refractivity, total energy, surface area, polarizability, volume, dipole moment, RMS gradient, mass, and partition coefficient (log P), were calculated.
# Bodewadt Flow Over a Permeable Disk with Homogeneous-Heterogeneous Reactions: A Numerical Study
## 3 Numerical Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(A\)} & \multicolumn{2}{c}{\(\sqrt{F}\) (0)\({}^{2}\)+\(G\) (0)\({}^{2}\)} & \multicolumn{2}{c}{\(H\) (\(\infty\))} & \multicolumn{2}{c}{\(\phi\) (0)} & \multicolumn{2}{c}{\(\phi^{\prime}\) (0)} \\ \hline
1 & 1.312385 & \(-\)0.268272 & 0.7205807 & 0.360290 \\ \hline
1.5 & 1.810597 & \(-\)1.023830 & 0.848774 & 0.424387 \\ \hline
2 & 2.533371 & \(-\)1.703410 & 0.886562 & 0.443281 \\ \hline
2.5 & 3.525320 & \(-\)2.316386 & 0.908228 & 0.454114 \\ \hline
3 & 4.800394 & \(-\)2.883492 & 0.922698 & 0.461349 \\ \hline
3.5 & 6.35416 & \(-\)3.423185 & 0.933146 & 0.466573 \\ \hline
5 & 12.616804 & \(-\)4.972426 & 0.952344 & 0.476172 \\ \hline
6 & 18.081658 & \(-\)5.983917 & 0.959984 & 0.479992 \\ \hline
10 & 50.0295589 & \(-\)9.996503 & 0.975608 & 0.487804 \\ \hline \end{tabular}
\end{table} 
Table 3: Values of wall skin friction, volumetric flow rate, wall concentration, and wall concentration gradient for different values of \(A\) when \(K\,=\,0.5\,=\,K_{\rm s}\) and \(Sc\,=\,2\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(A\)} & \multicolumn{2}{c}{\(-F\) (0)} & \multicolumn{2}{c}{\(G^{\prime}\) (0)} \\ \cline{2-5}  & **Present** & **[12]** & **Present** & **[12]** \\ \hline
0 & 0.9419 & 0.9420 & 0.7728 & 0.7729 \\ \hline
0.5 & 0.9033 & 0.9033 & 1.0650 & 1.0652 \\ \hline
1 & 0.8351 & 0.8351 & 1.3883 & 1.3885 \\ \hline
2 & 0.6468 & 0.6468 & 2.1560 & 2.1560 \\ \hline
3 & 0.4800
## 4 Experiment
### Exciting Frequency Selection  
Firstly, a mode-tuning experiment [7] is carried out to select the optimal excitation frequency that could maximize the magnitude/amplitude responses of the guided waves. The sweep ranges of central frequency are determined by considering the local resonance characteristics of PZT-5A. As a result, the driving frequencies are swept from 110 kHz to 330 kHz in steps of 20 kHz. Then, it is applied to one of the actuators to generate guided waves and the rest of the sensors are used for receiving. At each excitation frequency, the magnitude signals of ultrasonic guided waves are recorded. The local resonance frequency of PZT-5A measured by a WK-6500B impedance instrument is 265 kHz, as shown in Figure 3, and the resonance frequency [29] calculated by theoretical Equation (11) is 304 kHz. The central frequency error between theoretical calculation and experimental measurement is less than 13%, and the difference can be negligible. The frequency near the resonance frequency of PZT-5A is selected as the central excitation frequency of the experimental because the signals acquired at this frequency have a high stability and a high signal-to-noise ratio. Therefore, the excitation frequency of 270 kHz is chosen to excite guided waves in the rest of the studies. Figure 4 shows the excitation signal of five-cycle Hanning-windowed sinusoidal tone bursts at 270 kHz, it can also be seen that the amplitude of the central frequency from the spectrogram is maximum at 270 kHz.  
\[\mathrm{f_{res-PZT}}=\frac{\mathrm{n_{n}}}{2\mathrm{\pi r}}\sqrt{\frac{1}{ \rho\mathrm{E}(1-\mathrm{\
u}^{2})}} \tag{11}\]  
\(\mathrm{n_{n}}=\mathrm{n_{n}}(\mathrm{\
u})\), in which n generally equals 1, \(\mathrm{\
u_{PZT}}=0.35\), \(\mathrm{n_{1}}=2.0795\), r refers to the radius of PZT-5A, and E stands for the elastic modulus.  
Figure 4: The 270 kHz excitation signals of five-cycle sinusoidal tone bursts modulated by Hanning-windowed **(a)** Excitation signal; **(b)** Excitation spectrum signal.  
Figure 3: The measured impedance of PZT-5A by a WK-6500B impedance instrument.
## 3 Classification and Synthesis of Bimetallic Nanoparticles
### Stabilization Strategies  
Due to their strong van der Waals interactions, metallic nanoparticles must be stabilized either electrostatically or sterically using coordinating (capping) agents that prevent the nanoparticles from aggregating. Electrostatic stabilization involves the columbic repulsion of the electrical double layer formed by ions adsorbed at the particle surface and between neighboring particles. When two particles are far apart or the distance between the particles is larger than the thickness of two electric double layers, the electrostatic repulsion between two particles is zero [147]. However, when two particles move closer, the two electric double layers overlap, and a repulsive force is developed. For example, the preparation of gold nanoparticles by [PERSON]'s method uses the reduction of [AuCl\({}_{4}\)]\({}^{-}\) by sodium citrate. The particles formed are surrounded by an electric double layer arising from adsorbed citrate and chloride ions and by the corresponding cations [148]. Similarly, [PERSON] et al. prepared silver nanoparticles using the Tollens process without adding any external stabilizing agents. Under appropriate conditions, mixing of the reagents generates a stable dispersion of silver nanoparticles. The nanoparticles with dimension ranging from 20-30 nm were found to be stable for at least one year via electrostatic stabilization [149].  
Steric stabilization, also called polymeric stabilization, is a method commonly used to stabilize metal nanoparticles. Polymer layers adsorbed on the surface of the nanoparticles also serve as a diffusion barrier to the growth of nuclei. The diffusion-limited growth gives rise to small but uniform initial nuclei, which lead to monodisperse nanoparticles [150]. Figure 10 shows a schematic representation of electrostatic stabilization and steric stabilization. The polymer layer can play three roles here: (1) it can prevent aggregation of the nanoparticles sterically, (2) it can enhance the formation of monodisperse particles via diffusion-limited growth, and (3) it can provide control of the morphology of the nanoparticles. For example, [PERSON] et al. synthesized Au and Ag nanorods with aspect ratios of 5:25 in the presence of cetyltrimethylammonium bromide (CTAB) [151]. Furthermore, [PERSON] et al. demonstrated the large-scale synthesis of cubic silver nanoparticles with controllable shapes by using the polyol process [152]. The latter reaction involved the reduction of a precursor by ethylene glycol at elevated temperatures in the presence of poly(vinyl pyrrolidone) (PVP). In separate studies, [PERSON] et al. demonstrated a convenient and effective \"thiol-frozen\" approach to stabilize triangular silver nanoplates with bis(2-ethylhexyl) sulfosuccinate, which is also known as Aerosol OT or AOT [153].
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acronym** & **Description** \\ \hline ACO & Ant Colony Optimization \\ AODV & Ad hoc On-demand Distance Vector \\ ARE & Average Residual Energy \\ AR-SC & Adjustable Range Set Covers \\ BACA & Binary Ant Colony Algorithm \\ BS & Base Station \\ BSTS & Bulk Service a Time Scheme \\ CCA & Clear Channel Assessment \\ CCBE & Cross-layer Cluster-Cased Energy-efficient \\ CEE & Cross-layer Energy Efficiency \\ CGA & Chaotic Genetic Algorithm \\ CH & Clusterhead \\ CL-MAC & Cross-Layer MAC \\ CREC & Cross-layer, Reliable, and Efficient Communication protocol \\ CSMA/CA & Carrier Sense Multiple Access with Collision Avoidance \\ DLC & Data Link Control \\ DV & Distance Vector \\ DWEHC & Distributed Weight-Based Energy-Efficient Hierarchical Clustering \\ EAP-CMAC & Energy Aware Physical-layer Network Cooperative MAC \\ ECC & Error Correction Codes \\ ECCA & Enhanced Clear Channel Assessment \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of acronyms.
## 2 Localization Methods  
### Localization Methods of Unknown Emitters  
There are two main types of methods for estimating the location of a radio transmission source: an active method in which the target terminal receives radio waves emitted by a beacon whose absolute location is known and estimates its own location and a passive method in which a sensor receives radio waves transmitted by the target terminal and the system estimates the terminal's location. A well-known example of the former is the Global Positioning System (GPS), but it is not suitable for estimating the location of illegal radio wave sources because of the hardware limitation of a GPS chip non-necessarily equipped in the target terminal and the need for cooperation between the localization system and the terminal to be estimated. For this reason, the monitoring of illegal radio waves often employs the latter method in which the location is estimated by the system using information on radio waves emitted by the target terminals.  
The radio wave information used for location estimation includes the received signal strength indicator (RSSI), time difference of arrival (TDOA), and angle of arrival (AOA). Localization using the AOA is called triangulation, which is also used in DEURAS-D, one of the above-mentioned DEURAS [2] programs. However, such a geometric method assumes that the distance between the terminal and the sensor is in an LoS condition, but in environments with many scattering objects, e.g., urban areas, it is easy to become NLoS and the accuracy of localization is greatly degraded [7].  
Therefore, this paper uses the location fingerprinting method, i.e., a statistical location estimation method, to enable location estimation even in NLoS environments. The next section provides an overview of the fingerprint-based localization.
# Estimation of Gross Primary Productivity (GPP)
## 2 Materials and Methods
### Statistical Analysis  
To define which smoothing function or threshold of VIs was most suitable for detecting SOS and EOS of GPP, two statistical analyses were applied for SOS and EOS, separately. First, the perpendicular distance of each threshold (first derivative, percentile 10%, percentile 20%, percentage 10% and percentage 20%) and smoothing methods (SavGol, HANTS, Polyfit) to the 1:1 line was calculated. The least mean distance of threshold and smoothing methods for each year was derived for SOS and EOS, separately. Through this analysis we obtained which threshold, or smoothing method, and which VI-based estimates of the date of SOS/EOS had the least difference with the date of SOS/EOS derived from GPP. For the second method, the mean difference between the SOS/EOS of GPP and the SOS/EOS of each VI for all threshold methods and smoothing functions were calculated for each year, separately. A positive difference illustrated an earlier SOS/EOS derived from VI time-series, while a negative difference was indicative of a delay in SOS/EOS.  
Finally, a correlation analysis was performed to investigate the relationship between GPP and VIs. This analysis allowed us to understand if the coppicing in 2017 and drought in 2018 have an effect on the VI-GPP relationships and which VI can explain better this effect. We assumed the existence of a strong linear relationship between VIs and GPP as reported for different ecosystems [44; 58]. Furthermore, this analysis was performed using original, not fitted, time-series data of both GPP and VIs to exclude the effect of filtering methods on the VI-GPP relationship. The goodness of the linear model was tested by the following statistics: the coefficient of determination (R\({}^{2}\)) and root mean square error (RMSE).