Prolonged Thermal Relaxation of the Thermosetting Polymers

The rigidity of structures made of polymer composite materials, operated at elevated temperatures, is mainly determined by the residual rigidity of the polymer binder (which is very sensitive to elevated temperatures); therefore, the study of ways to increase the rigidity of polymer materials under heating (including prolonged heating) is relevant. In the previous research, cured thermosetting polymer structure’s non-stability, especially under heating, is determined by its supra-molecular structure domain’s conglomerate character and the high entropy of such structures. The polymer elasticity modeling proved the significance of the entropy factor and layer (EPL) model application. The prolonged heating makes it possible to release adsorptive inter-layer bonds and volatile groups. As a result, the polymer structure is changing, and inner stress relaxation occurs due to this thermo-process, called thermo-relaxation. The present study suggests researching thermo-relaxation’s influence on polymers’ deformability under load and heating. The research results prove the significant polymer structure modification due to thermo-relaxation, with the polymer entropy parameter decreasing, the glassing onset temperature point (Tg) increasing by 1.3–1.7 times, and the modulus of elasticity under heating increasing by 1.5–2 times.

Detection of Actionable Oil using RADARSAT-2 and Simulated RADARSAT Constellation Mission Data

688970 For oil spill response, one of the key parameters is detection of actionable oil. Actionable oil (AO), which tends to be thick, emulsified oil, refers to oil that can be cleaned-up versus non-actionable oil (sheen) which cannot be readily cleaned-up. Previous studies by MDA of a controlled oil spills in the North Sea have shown the capability of RADARSAT-2 quad polarized data to detect AO using the entropy parameter (H) derived from the Cloude-Pottier decomposition. H → 0 for oil-free water and H → 1 in the presence of an oil slick. To further test the detection of AO, RADARSAT-2, ASTER, WorldView imagery and in situ measurement were acquired April 25, 2017 at the MC20 site off Louisiana. Five oil thickness classes ranging from 1 micron to 100 microns were derived from a maximum likelihood classifier based on the ASTER and WorldView images and in situ samples. For oil-free water, the average H was 0.13 and 0.62 for the slick. There was good correlation between the variability of H and the oil thickness classes. Specifically, larger H was correlated with oil thickness in the 50 – 200 micron range and smaller H was correlated with oil in the 1 micro range. Not surprising there was overlap in H for area were the oil thickness was ~ 1 micron and the area deemed to be slick-free. The results indicate that actionable oil can be discriminated from non-actionable oil based on the relative difference of H. Although these results are encouraging, one of the operational limitations is based on the use of the relatively small swath-width (25 km – 50 km) of the RADARSAT-2 quad polarized mode. The impact of the swath-width can be mitigated with data from the RADARSAT Constellation Mission (RCM). The RCM has a compact polarimetry (CP) mode that provides more polarimetric information than dual polarized modes, less than quad polarized modes, but is available for swath widths up to 500 km. Analysis of simulated SC50 RCM data (50 m resolution, 350 km swath width) derived from the aforementioned RADARSAT-2 image shows similar oil-slick variability that was observed in the RADARSAT-2 image and hence the capability to detect AO.

AN ANALYSIS OF THE STANDARD DEVIATION OF THE ENTROPY PARAMETER FOR TARGET DETECTION ON THE BACKGROUND CLUTTER

The paper proposes a method to improve the performance of target detection on the background clutter using the standard deviation of polarization entropy H parameter. The performance of target detection is analyzed with different types of target, on the background clutter models Rayleigh, Weibull and Laplace. Also examined the special case when the target has the same H parameter as the background clutter. The results showed that the performance of the method using the standard deviation of H in the target detection on the background clutter has increased significantly compared to the case where only the H parameter is used.

Determining Levels of Biological Geometric Information at Polygonal Shape Patterns

Based on a measuring system to determine the statistical heterogeneity of individual polygons we propose a method to use polygonal shape patterns as a source of data in order to determine the Shannon entropy of biological organizations. In this research, the term entropy is a particular amount of data related with levels of spatial heterogeneity in a series of different geometrical meshes and sets of random polygons. We propose that this notion of entropy is important to measure levels of information in units of bits, measuring quantities of heterogeneity in geometrical systems. In fact, one important result is that binarization of heterogeneity frequencies yields a supported metric to determine geometrical information from complex configurations. Thirty-five geometric aggregates are tested; biological and non-biological, in order to obtain experimental results of their spatial heterogeneity which is verified with the Shannon entropy parameter defining low particular levels of geometrical information in biological samples. Geometrical aggregates (meshes) include a spectrum of organizations ranging from cell meshes to ecological patterns. Experimental results show that a particular range (0.08 and 0.27) of information is intrinsically associated with low rates of heterogeneity. We conclude it as an intrinsic feature of geometrical organizations in multi-scaling biological systems.

Novel Clustering Schemes for Full and Compact Polarimetric SAR Data: An Application for Rice Phenology Characterization

<div>Information on rice phenological stages from Synthetic Aperture Radar (SAR) images is of prime interest for in-season monitoring. Often, prior in-situ measurements of phenology are not available. In such situations, unsupervised clustering of SAR images might help in discriminating phenological stages of a crop throughout its growing period. Among the existing unsupervised clustering techniques using full-polarimetric (FP) SAR images, the eigenvalue-eigenvector based roll-invariant scattering-type parameter, and the scattering entropy parameter are widely used in the literature. In this study, we utilize a unique target scattering-type parameter, which jointly uses the Barakat degree of polarization and the elements of the polarimetric coherency matrix. Likewise, we also utilize an equivalent parameter proposed for compact-polarimetric (CP) SAR data. These scattering-type parameters are analogous to the Cloude-Pottier's parameter for FP SAR data and the ellipticity parameter for CP SAR data. Besides this, we also introduce new clustering schemes for both FP and CP SAR data for segmenting diverse scattering mechanisms across the phenological stages of rice. In this study, we use the RADARSAT-2 FP and simulated CP SAR data acquired over the Indian test site of Vijayawada under the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative. The temporal analysis of the scattering-type parameters and the new clustering schemes help us to investigate detailed scattering characteristics from rice across its phenological</div><div>stages.</div>

Novel Clustering Schemes for Full and Compact Polarimetric SAR Data: An Application for Rice Phenology Characterization

<div>Information on rice phenological stages from Synthetic Aperture Radar (SAR) images is of prime interest for in-season monitoring. Often, prior in-situ measurements of phenology are not available. In such situations, unsupervised clustering of SAR images might help in discriminating phenological stages of a crop throughout its growing period. Among the existing unsupervised clustering techniques using full-polarimetric (FP) SAR images, the eigenvalue-eigenvector based roll-invariant scattering-type parameter, and the scattering entropy parameter are widely used in the literature. In this study, we utilize a unique target scattering-type parameter, which jointly uses the Barakat degree of polarization and the elements of the polarimetric coherency matrix. Likewise, we also utilize an equivalent parameter proposed for compact-polarimetric (CP) SAR data. These scattering-type parameters are analogous to the Cloude-Pottier's parameter for FP SAR data and the ellipticity parameter for CP SAR data. Besides this, we also introduce new clustering schemes for both FP and CP SAR data for segmenting diverse scattering mechanisms across the phenological stages of rice. In this study, we use the RADARSAT-2 FP and simulated CP SAR data acquired over the Indian test site of Vijayawada under the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative. The temporal analysis of the scattering-type parameters and the new clustering schemes help us to investigate detailed scattering characteristics from rice across its phenological</div><div>stages.</div>

Tsallis Entropy for Cross-Shareholding Network Configurations

In this work, we develop the Tsallis entropy approach for examining the cross-shareholding network of companies traded on the Italian stock market. In such a network, the nodes represent the companies, and the links represent the ownership. Within this context, we introduce the out-degree of the nodes—which represents the diversification—and the in-degree of them—capturing the integration. Diversification and integration allow a clear description of the industrial structure that were formed by the considered companies. The stochastic dependence of diversification and integration is modeled through copulas. We argue that copulas are well suited for modelling the joint distribution. The analysis of the stochastic dependence between integration and diversification by means of the Tsallis entropy gives a crucial information on the reaction of the market structure to the external shocks—on the basis of some relevant cases of dependence between the considered variables. In this respect, the considered entropy framework provides insights on the relationship between in-degree and out-degree dependence structure and market polarisation or fairness. Moreover, the interpretation of the results in the light of the Tsallis entropy parameter gives relevant suggestions for policymakers who aim at shaping the industrial context for having high polarisation or fair joint distribution of diversification and integration. Furthermore, a discussion of possible parametrisations of the in-degree and out-degree marginal distribution—by means of power laws or exponential functions— is also carried out. An empirical experiment on a large dataset of Italian companies validates the theoretical framework.