Soil moisture and vegetation sensing using GNSS-R polarimetric measurement

GNSS-Reflectometry is an efficient tool for remote sensing and plays a key role in several of applications. The estimation of soil moisture and vegetation in the land field is attracting widespread interest in hydrology, climatology and carbon cycles. In order to investigate the scattering polarization properties from different types of surface environments, an airborne measurement was performed using a new 4-channel prototype for collecting the direct, reflected left-hand circular polarized (LHCP) and right-hand circular polarized (RHCP) signals. Both LHCP and RHCP reflected signals were acquired by a dual polarization antenna at the same time. A data averaging procedure was used to reduce the incoherent power of received power and the reflected data from two channels were normalized by direct signals obtained from each front-end (FE). Then three polarimetric observables were used to analyze the vegetation biomass and soil moisture fluctuations. It was concluded that polarimetric ratio is sensitive to soil moisture content (SMC), and considerably independent to roughness and vegetation biomass. The trunk component was confirmed to be the most important factor affecting the amplitude of scattering polarizations. Furthermore, the measurement results show that the PR variation between different elevation angles was affected by roughness and biomass. The results show another possibility of further geophysical parameter evaluation by using polarimetric applications in GNSS-R

Commercial graphene nanoplatelets-based tunable attenuator

A commercial graphene nanoplatelets based tunable attenuator is proposed. The attenuator comprises of a couple of microstrip lines connected through a gap where the commercial graphene nanoplatelets are drop casted. The transmitted signal through the microstrip line can be varied by an applied DC bias voltage that alters the graphene resistance. The DC resistance of commercially acquired graphene is higher than labgrown graphene and so is the resulting minimum attenuation of the transmitted signal. The proposed structure is designed to reduce both the minimum graphene resistance and the transmission. The resultant dynamic range of attenuation is more than twice as compared to other series attenuators designed with lab grown graphene nanoplatelets. The operating frequency of the attenuator is from 1 GHz to 5 GHz with maximum dynamic range of attenuation almost 10 dB

Multi-Walled Carbon Nanotubes Composites for Microwave Absorbing Applications

The response of materials to impinging electromagnetic waves is mainly determined by their dielectric (complex permittivity) and magnetic (complex permeability). In particular, radar absorbing materials are characterized by high complex permittivity (and eventually large values of magnetic permeability), Indeed, energy dissipation by dielectric relaxation and carrier conduction are principally responsible for diminishing microwave radiation reflection and transmission in non-magnetic materials. Therefore, the scientific and technological community has been investigating lightweight composites with high dielectric permittivity in order to improve the microwave absorption (i.e., radar cross-section reduction) in structural materials for the aerospace industry. Multiwalled carbon nanotubes films and their composites with different kind of polymeric resins are regarded as promising materials for radar absorbing applications because of their high permittivity. Nanocomposites based on commercial multi-wall carbon nano-tube (MWCNT) fillers dispersed in an epoxy resin matrix were fabricated. The morphology of the filler was analyzed by Field emission scanning electron microscopy (FESEM) and Raman spectroscopy, while the complex permittivity and the radiation reflection coefficient of the composites was measured in the radio frequency range. The reflection coefficient of a single-layer structure backed by a metallic plate was simulated based on the measured permittivity. Simulation achievements were compared to the measured reflection coefficient. Besides, the influence of morphological MWCNT parameters (i.e., aspect ratio and specific surface area) on the reflection coefficient was evaluated. Results verify that relatively low weight percent of MWCNTs are suitable for microwave absorption applications when incorporated into polymer matrix (i.e., epoxy resin)

IL-4 inhibits LPS-, IL-1β- and TNFα-induced expression of tissue factor in endothelial cells and monocytes

AbstractInflammatory mediators such as endotoxin, interleukin-1β(IL-1β) and tumor necrosis factors-α (TNF-α) dose-dependently increased the expression of tissue factor on the surface of cultured bovine aortic endothelial cells (ABAE), human umbilical vein endothelial cells (HUVEC) and human monocytes. In ABAE, endotoxin-, IL-1β- and TNFα-induced tissue factor expression was suppressed by interleukin-4 (IL-4) which also neutralized the pyrogenic effect of endotoxin in HUVEC and monocytes. IL-4 did not alter TNF-α-induced procoagulant changes in HUVEC and monocytes but strongly protected the monocyte surface against IL-1β-induced procoagulant changes

Clinical use of Heliox in Asthma and COPD

Heliox is a low density gas mixture of helium and oxygen commonly used in deep diving (> 6 ATM). This mixture has been also used for clinical purposes, particularly in the critical care setting. Due to of its physical proprieties, Heliox breathing reduces air flow resistances within the bronchial tree; in patients with obstructive lung diseases Heliox may also reduce the work of breathing and improve pulmonary gas exchange efficiency. Beneficial effects have been documented in severe asthma attacks and in patients with chronic obstructive pulmonary disease. A reduction in WOB during mechanical ventilation and an increase in exercise endurance capacity have also been described in COPD. Heliox has been also used in the treatment of upper airways obstruction, bronchiolitis and bronchopulmonary dysplasia. Despite the encouraging results, Heliox use in routine practice remains controversial because of technical implications and high costs

A Scalable System Architecture for High-Performance Fault Tolerant Machine Drives

When targeting mission critical applications, the design of the electronic actuation systems needs to consider many requirements and constraints not typical in standard industrial applications. One of these is tolerance to faults, as the unplanned shutdown of a critical subsystem, if not handled correctly, could lead to financial harm, environmental disaster, or even loss of life. One way this can be avoided is through the design of an electric drive systems based on multi-phase machines that can keep operating, albeit with degraded performance, in a partial configuration under fault conditions. Distributed architectures are uniquely suited to meet these challenges, by providing a large degree of isolation between the various components. This paper presents a system architecture suitable for scalable and high-performance fault tolerant machine drive systems. the effectiveness of this system is demonstrated through theoretical analysis and experimental verification on a six-phase machine

Microwave characterization of polymer composite based on Biochar: A comparison of composite behaviour for Biochar and MWCNTs

In this work, Biochar is used as a filler in Epoxy resin for composite preparation. The electrical characteristics of composites are analyzed in function of different filler percentages. Results obtained are compared with composites based on multi-wall carbon nanotubes

An Effective Land Type Labeling Approach for Independently Exploiting High-Resolution Soil Moisture Products Based on CYGNSS Data

Recently, soil moisture (SM) has been estimated using Cyclone Global Navigation Satellite System (CYGNSS) data. Machine learning (ML) algorithms for CYGNSS SM estimation can minimize unpredictable influences and help improve the accuracy of SM retrieval. However, ML-based CYGNSS SM estimation requires ancillary data from other sources, and thus, the uncertainty, internal errors, and even dependence on external parameters of this process may complicate and limit SM estimation. In this article, a simple land type (LT) digitization strategy that incorporates the idea of classification is proposed with feature optimization to achieve an effective and independent SM retrieval without any other auxiliary data. The input features are chosen from the CYGNSS data themselves, and the corresponding labels (digitized stable LTs) are used in the training stage of the SM estimation model. During the fine-tuning stage, several input features (such as the dielectric constant and incident angle) are compared and selected after optimization to achieve better results. Moreover, the CYGNSS data are gridded at 9 × 9 km to validate the enhanced soil moisture active passive mission SM products at a resolution of 9 km. Only three input variables are adopted for the SM learning model, which are directly derived from the CYGNSS data for independently estimating SM at a high spatial resolution. Powerful performance is achieved by extreme gradient boosting based on a LT digitalization strategy, with root-mean-square error (RMSE) and unbiased RMSE (ubRMSE) values of 0.063 cm3/cm3 and a correlation coefficient (R) of 0.71 for the entire dataset. The performances of different ML learning models for various LTs are presented. The mean ubRMSE and RMSE are 0.041 cm3/cm3 and 0.057 cm3/cm3, respectively. The results demonstrate the effectiveness of the proposed LT digitization strategy for retrieving SM from CYGNSS data with various ML methods and the capability of SM estimation using the CYGNSS product as a new independent source