Reflectometry Data Research Articles

CY GNSS significant wave height inversion model based on multivariate machine learning

The Cyclone Global Navigation Satellite System (CYGNSS) provides high-quality Global Navigation Satellite System Reflectometry (GNSS-R) data, which can be reliably used for the inversion of Significant Wave Height (SWH). Due to the high dynamics of CYGNSS, the received signal is easily affected by environmental factors, and the complexity of the sea conditions makes it difficult for simple models to accurately invert SWH. In order to solve the above problems, this paper proposes a multivariate SWH inversion model based on machine learning. According to the formation mechanism of waves and the correlation analysis between CYGNSS parameters and SWH, relevant parameters are selected, and three training schemes of 5 parameters, 9 parameters and 17 parameters are designed. Subsequently, the inversion model was trained and validated using Random Forest (RF) and Convolutional Neural Network (CNN), and the SWH inversion results were compared with the reference values of the European Centre for Medium-range Weather Forecasts (ECMWF). The best inversion model was the 17-parameter CNN inversion model with an RMSE of = 0.1840 m.

Development of ultra-short pulse reflectometry on the Experimental Advanced Superconducting Tokamak (EAST).

Microwave reflectometry is an invaluable diagnostic tool for measuring electron density profiles in large fusion devices. Density fluctuations near the plasma cutoff layer, particularly those that are time-varying on the timescale of the reflectometry measurement, can result in distortions in phase and/or amplitude of the reflected waveform, which present challenges to the accuracy of the reconstructed profile. The ultra-short pulse reflectometry (USPR) technique eliminates the time-varying issue in that reflectometry data are collected on a nanosecond timescale, essentially freezing the fluctuations in place. An X-mode dedicated 32-channel USPR system has been developed and installed on the EAST, covering the operation frequency range from 52 to 92GHz. This system enables high-resolution density profile measurements in the plasma pedestal and scrape-off layer, with resolutions reaching 5mm and 1 μs, respectively. Laboratory testing of the system performance has been conducted, demonstrating the potential of the USPR technique to provide accurate and high-temporal-resolution density profiles in challenging plasma environments.

Illuminating the nanostructure of diffuse interfaces: Recent advances and future directions in reflectometry techniques

Diffuse soft matter interfaces take many forms, from end-tethered polymer brushes or adsorbed surfactants to self-assembled layers of lipids. These interfaces play crucial roles across a multitude of fields, including materials science, biophysics, and nanotechnology. Understanding the nanostructure and properties of these interfaces is fundamental for optimising their performance and designing novel functional materials. In recent years, reflectometry techniques, in particular neutron reflectometry, have emerged as powerful tools for elucidating the intricate nanostructure of soft matter interfaces with remarkable precision and depth. This review provides an overview of selected recent developments in reflectometry and their applications for illuminating the nanostructure of diffuse interfaces. We explore various principles and methods of neutron and X-ray reflectometry, as well as ellipsometry, and discuss advances in their experimental setups and data analysis approaches. Improvements to experimental neutron reflectometry methods have enabled greater time resolution in kinetic measurements and elucidation of diffuse structure under shear or confinement, while innovation in analysis protocols has significantly reduced data processing times, facilitated co-refinement of reflectometry data from multiple instruments and provided greater-than-ever confidence in proposed structural models. Furthermore, we highlight some significant research findings enabled by these techniques, revealing the organisation, dynamics, and interfacial phenomena at the nanoscale. We also discuss future directions and potential advancements in reflectometry techniques. By shedding light on the nanostructure of diffuse interfaces, reflectometry techniques enable the rational design and tailoring of interfaces with enhanced properties and functionalities.

Studying Transient Phenomena in Thin Films with Reinforcement Learning.

Neutron reflectometry has long been a powerful tool to study the interfacial properties of energy materials. Recently, time-resolved neutron reflectometry has been used to better understand transient phenomena in electrochemical systems. Those measurements often comprise a large number of reflectivity curves acquired over a narrow q range, with each individual curve having lower information content compared to a typical steady-state measurement. In this work, we present an approach that leverages existing reinforcement learning tools to model time-resolved data to extract the time evolution of structure parameters. By mapping the reflectivity curves taken at different times as individual states, we use the Soft Actor-Critic algorithm to optimize the time series of structure parameters that best represent the evolution of an electrochemical system. We show that this approach constitutes an elegant solution to the modeling of time-resolved neutron reflectometry data.

Enhancing Soil Moisture Active–Passive Estimates with Soil Moisture Active–Passive Reflectometer Data Using Graph Signal Processing

The Soil Moisture Active–Passive (SMAP) mission has greatly contributed to the use of remote sensing technologies for monitoring the Earth’s land surface and estimating geophysical parameters that influence the climate system. Since the SMAP mission switched its radar receiver to allow the reception of Global Positioning System (GPS) signals, Global Navigation Satellite System Reflectometry (GNSS-R) configuration has been enabled, providing full polarimetric forward scattering measurements of the Earth’s surface, also known as SMAP Reflectometry or SMAP-R. Polarimetric GNSS-R is beneficial for sensing land surface properties, especially for more accurate estimations of soil moisture (SM) in densely vegetated areas. In this study, we explore the opportunity to enhance SMAP mission soil moisture estimates using reflected GNSS signals. We achieve this by interpolating the sparse reflectivity data with terrain information to disaggregate radiometer brightness temperatures. Our main objective is to present a novel algorithm based on Graph Signal Processing (GSP) that uses reflectometry data to enhance SMAP radiometer observations and ultimately improve SM retrievals. By implementing methods from the GSP field, we formulate the reflectivity interpolation problem as a signal reconstruction on a graph, where the weights of the edges between the nodes are chosen as a function of geophysical information. Subsequently, using the retrieved reflectivity maps, we increase the resolution of the brightness temperature data, leading to an improvement in the SM estimates. Initial findings indicate that our GSP method presents a promising alternative for analyzing sparse remote sensing observations, leveraging Earth’s surface geophysical information. This approach results in a notable improvement, with a reduced Root Mean Square Error (RMSE) of 11.8% compared to SMAP data and a reduction in unbiased RMSE (uRMSE) by 14.7% over vegetated areas.

Improved accuracy and robustness of electron density profiles from JET's X-mode frequency-modulated continuous-wave reflectometers.

JET's frequency-modulated continuous wave (FMCW) reflectometers have been operating well with the current design since 2005, and density profiles have been automatically calculated intershot since then. However, the calculated profiles had long suffered from several shortcomings: poor agreement with other diagnostics, sometimes inappropriately moving radially by several centimeters, elevated levels of radial jitter, and persistent wriggles (strong unphysical oscillations). In this research, several techniques are applied to the reflectometry data analysis, and the shortcomings are significantly improved. Starting with improving the equilibrium reconstruction that estimates the background magnetic field, adding a ripple correction in the reconstructed magnetic field profile, and adding new inner-wall reflection positions estimated through ray-tracing, these changes not only improve the agreement of reconstructed profiles to other diagnostics but also solve density profile wriggles that were present during band transitions. Other smaller but also persistent wriggles were also suppressed by applying a localized correction to the measured beat frequency where persistent oscillations are present. Finally, the burst analysis method, as introduced by Varela etal. [Nucl. Fusion 46 S693 (2006)], has been implemented to extract the beat frequency from stacked spectrograms. Due to the strong suppression of spurious reflections, the radial jitter that sometimes would span several centimeters has been strongly reduced. The stacking of spectrograms has also been shown to be very useful for stacking recurring events, like small gas puff modulations, and extracting transport coefficients that would otherwise be below the noise level.

4D time lapse tomography for monitoring cave propagation and stress distribution in Deep Mill Level Zone (DMLZ) PT Freeport Indonesia

Caving activity results in an increased induced seismicity which should be monitored to avoid massive and uncontrolled rock damage. This research was conducted at the Deep Mill Level Zone (DMLZ) underground mine, the deepest underground mine in Indonesia operated by PT Freeport Indonesia. This research aims to monitor cave propagation by using 4D tomography with a catalogue of microseismic for 57 days with a total of 14,821 events recorded by 84 stations consisting of 176,265 P phases and 133,472 S phases. The data is divided into four subsets to see the velocity evolution related to cave progress. Checkerboard Resolution Test (CRT) and Derivative Weight Sum (DWS) are used to assess the resolution of the inversion. 3D initial velocity model is constructed based on geological information and coring data. We have succeeded in identifying the interpreted cave propagation of a 60 m extension to the NW at around 100 m above undercut level based on 4D changes in velocity tomogram validate by Time Domain Reflectometry data. The decrease of Vp and Vs in subset 3 is interpreted due to the fracturing processes as the cave progresses. Furthermore, we observe a stress redistribution along with the progress of the cave, which is characterized by high velocities (Vp and Vs) due to compensation for changes in low velocity values ​​in the area in front of the cave, which is starting to collapse. We suggest that a considerable change in the velocity tomogram as an indicator of impending caving.

Validation of short-pulse reflectometry turbulence measurements with a synthetic diagnostic

Experimental measurements of the turbulence amplitude utilizing a short pulse reflectometry method are presented. Two discharges with shaped plasma possessing opposite signs of triangularity are considered and a higher turbulence amplitude is found in the positive triangularity case. To confirm this result, a synthetic short pulse reflectometry diagnostic is developed. Local gyrokinetic modeling with the GENE code is carried out to produce turbulence relevant to the experimental conditions. This turbulence is then used as an input for the full-wave CUWA code to produce synthetic short pulse reflectometry signals. By matching synthetic and experimental reflectometry data, the difference between turbulence amplitudes in the two cases is confirmed. Additionally, the capability of the diagnostic to also measure the frequency spectrum of the turbulence is demonstrated.

High temporal resolution of pedestal dynamics via machine learning on density diagnostics

At the Joint European Torus, the reference diagnostic to measure electron density is Thomson scattering. However, this diagnostic has a low sampling rate, which makes it impractical to study the temporal dynamics of fast processes, such as edge localized modes. In this work, we use machine learning to predict the density profile based on data from another diagnostic, namely reflectometry. By learning to transform reflectometry data into Thomson scattering profiles, the model is able to generate the density profile at a much higher sampling rate than Thomson scattering, and more accurately than reflectometry alone. This enables the study of pedestal dynamics, by analyzing the time evolution of the pedestal height, width, position and gradient. We also discuss the accuracy of the model when applied on experimental campaigns that are different from the one it was trained on.

Daily Soil Moisture Retrieval by Fusing CYGNSS and Multi-Source Auxiliary Data Using Machine Learning Methods.

The Cyclone Global Navigation Satellite System (CYGNSS), a publicly accessible spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data, provides a new alternative opportunity for large-scale soil moisture (SM) retrieval, but with interference from complex environmental conditions (i.e., vegetation cover and ground roughness). This study aims to develop a high-accuracy model for CYGNSS SM retrieval. The normalized surface reflectivity calculated by CYGNSS is fused with variables that are highly related to the SM obtained from optical/microwave remote sensing to solve the problem of the influence of complicated environmental conditions. The Gradient Boost Regression Tree (GBRT) model aided by land-type data is then used to construct a multi-variables SM retrieval model with six different land types of multiple models. The methodology is tested in southeastern China, and the results correlate very well with the existing satellite remote sensing products and in situ SM data (R = 0.765, ubRMSE = 0.054 m3m-3 vs. SMAP; R = 0.653, ubRMSE = 0.057 m3 m-3 vs. ERA5 SM; R = 0.691, ubRMSE = 0.057 m3m-3 vs. in situ SM). This study makes contributions from two aspects: (1) improves the accuracy of the CYGNSS retrieval of SM based on fusion with other auxiliary data; (2) constructs the SM retrieval model with multi-layer multiple models, which is suitable for different land properties.

Lake Altimetry Using Long Coherent Integration Carrier Phase Measurements from Airborne GNSS Reflectometry

Today, land and ocean observations are crucial for monitoring climate change. The method of GNSS reflectometry is an opportunistic way to provide low-cost observations of many geophysical parameters. However, although this method has been the subject of numerous research studies, work is still in progress to improve its possibilities and fields of application. This paper focuses on GNSS reflectometry using carrier phase measurements for water altimetry measurements. The difficulties in implementing such a method lie in the need to collect a coherent signal and to solve the integer ambiguity value. In this context, the implementation of innovative signal processing is described, including the correlation of the reflected signals in dedicated software and the prolongation of the coherent integration time to enhance signal coherency. These processes were applied to data collected over Carcans-Hourtin Lake in France to compute the height of the reflection surface which was then compared to in situ GNSS buoy height measurements. The results show that at 300 ft (91.44 m), the differences between the lake heights measured with the buoy and with the reflectometry data can reach less than 1 cm for the L1, E1 and E5 GNSS signals. In addition, the slope of the geoid estimated with the reflectometry data is very consistent with that of the RAF20 geoid model, with a difference of up to less than 2 mm/km.

Application of the 2D Code Tamic Analyzer Based on the Method of Impedance Analog of the Sestroretskii Electromagnetic Space for Problems of Plasma Diagnostics in Tokamak Fusion Facilities

Applications of the two-dimensional full-wave electromagnetic code Tamic Analyzer, developed by B.V. Sestroretskii to study the limiting possibilities of diagnostics of reflectometry in tokamak plasma. It is shown that by comparing experimental reflectometry data with calculations using this code, it is possible to determine the structure of density fluctuations in a tokamak, but the experimental radial correlation lengths can be significantly overestimated. Simulation using the Taimic Anylyser code made it possible to determine the limiting level of density fluctuations at which it is possible to measure the plasma density profile and observe Alfven fluctuations in the International Experimental Thermonuclear Reactor of the tokamak type (ITER) under construction. Possible ways to increase the speed and counting field of the code are considered.

Magnetic Phase Transitions in Ultrathin YFeO3 Films According to Synchrotron Mössbauer Reflectometry Data

The magnetic properties of ultrathin single-crystal Y57FeO3 orthoferrite films have been studied by Mössbauer reflectometry. Mössbauer spectra were measured using the ESRF synchrotron in the reflection geometry. As the temperature changes from 3.6 K to about 773 K, the splitting of the Zeeman sextet in the spectra successively decreases and, simultaneously, a quadrupole doublet appears in them, which indicates the development of a magnetic phase transition. From the temperature dependences of the magnetic hyperfine field Bhf for the main orthorhombic Y57FeO3 phase, Néel temperatures equal to TN ≈ 593 K, 562 K, and 567 K and the critical parameter values equal to β ≈ (0.28–0.3) ± 0.02 are determined in films with thicknesses of 28, 6.5, and 4 nm, respectively. An analysis of changing the ratio of the line intensities in the Zeeman sextet with temperature makes it possible to trace the successive rotation of the direction of the antiferromagnetic axes in Y57FeO3 toward the surface plane with an increase in the temperature and a decrease in the film thickness.

Magnetic Phase Transitions in Ultrathin YFeO3 Films According to Synchrotron Mössbauer Reflectometry Data

Magnetic Phase Transitions in Ultrathin YFeO3 Films According to Synchrotron Mössbauer Reflectometry Data

Characterization of PillarHall test chip structures using a reflectometry technique

Thin film samples where one of the thin layers consists of a vacuum or air are called PillarHalls due to their support structure in silicon wafers. Custom PillarHall samples were provided by Chipmetrics Ltd and characterized by reflectometry with a Cary 7000 spectrometer. Data at 8° of angle of incidence were collected with p-polarization of the incident light within the wavelength range of 550–1800 nm. These data were then analyzed with a dedicated MATLAB code, using fitting software accompanying the transfer matrix method for calculation of the reflectance spectrum. Layer thicknesses and unknown refractive indices were chosen as fitted parameters. The oscillating reflectance spectrum of the PillarHall test chip yielded an air gap thickness of 86 nm with an estimated standard uncertainty of 5 nm. This is close to the nominal value of 100 nm. The results demonstrate that reflectometry data are sensitive to the thickness of the thin air layer deep inside the silicon structure.

Joint Retrieval of Sea Surface Rainfall Intensity, Wind Speed, and Wave Height Based on Spaceborne GNSS-R: A Case Study of the Oceans near China

In this paper, a method for joint sea surface rainfall intensity (RI), wind speed, and wave height retrieval based on spaceborne global navigation satellite system reflectometry (GNSS-R) data is proposed, which especially considers the effects between these two parameters. A method of rainfall detection (RD) according to different wind speed ranges is also proposed by mitigating the impact of swell and wind speed. The results, with data collected over the oceans near Southeast Asia, show that the RD method has a detection accuracy of up to 81.74%. The RI retrieval accuracy can reach about 2 mm/h by simultaneously correcting the effects of wind speed and swell. The accuracy of wind speed retrieval is improved by about 5% after removing rainfall interference through RD in advance. After considering the influence of wind speed and eliminating rainfall interference, the retrieval accuracy of significant wave height (SWH) is improved by about 18%. Finally, the deep convolutional neural network (DCNN) model is built to estimate the SWH of the swell. The results show that the retrieval accuracy of the swell height is better than 0.20 m after excluding rainfall interference. The proposed joint retrieval method provides an important reference for the future acquisition of multiple high-precision marine geophysical parameters by spaceborne GNSS-R technology.

High-order coupling of shear and sonic continua in JET plasmas

A recent model coupling the shear-Alfvén and acoustic continua, which depends strongly on the equilibrium shaping and on elongation in particular, is employed to explain the properties of Alfvénic activity observed on JET plasmas below but close to the typical frequency of toroidicity-induced Alfvén eigenmodes (TAEs). The frequency gaps predicted by the model result from high-order harmonics of the geodesic field-line curvature caused by plasma shaping (as opposed to lower-order toroidicity) and give rise to high-order geodesic acoustic eigenmodes (HOGAEs), their frequency value being close to one-half of the TAEs one. The theoretical predictions of HOGAE frequency and radial location are found to be in fair agreement with measurements in JET experiments, including magnetic, reflectometry and soft x-ray data. The stability of the observed HOGAEs is evaluated with the linear hybrid magnetohydrodynamic/drift-kinetic code CASTOR-K, taking into account the energetic-ion populations produced by neutral beam injection and ion cyclotron resonance heating systems. Wave-particle resonances, along with drive/damping mechanisms, are also discussed in order to understand the conditions leading to HOGAEs destabilisation in JET plasmas.

In Situ Measurement of Buried Electrolyte–Electrode Interfaces for Solid State Batteries with Nanometer Level Precision

Many technologies including high-energy solid-state batteries rely on high quality solid–solid interfaces. Solid–solid interfaces are particularly difficult to study as their nature requires the interface to be buried. In this work we demonstrate the use of a combination of neutron reflectometry and in situ electrochemistry to measure the interface between Li metal and the solid electrolyte LiPON across an 8 cm2 area. Neutron reflectometry measurements confirm the interphase to be less than 7 nm thick. The neutron reflectometry data further emphasize that the interphase that forms is a chemical gradient consisting of a Li-rich layer that gradually decreases in Li content until it blends into pure LiPON. Experimental confirmation that we can make ideal solid–solid interphases less than 10 nm thick will help facilitate the adoption of high efficiency next generation solid state batteries. Further this combination of complementary techniques provides a more general methodology for studying buried solid–solid interfaces across applications.

Neutral polymers partial exclusion in ionic solutions near charged membranes.

Neutral polymers have become a useful tool in biophysics. They are currently used in a variety of single-channel conductance measurements, to change solution conductivity and viscosity, to induce osmotic stress in polymer-excluded regions, or simply as crowding agents. Many experiments involve the proximity of neutral polymers to charged membranes or their confinement in aqueous pores formed by protein channels with charged residues. A well-known phenomenon relevant to this problem is the polarization of a neutral particle with dielectric properties different from the surrounding medium under the effect of an electric field. This polarization causes a dielectrophoretic force when inhomogeneous electric fields act on the particle and it has been extensively used in many biomedical applications involving particle trapping and cell separation, among others. In addition, whenever a neutral particle perturbs the equilibrium electric double layer near a charged surface, the osmotic pressure gradient acts as a net repulsive force that pushes the particle away from that surface. Both the dielectrophoretic force and the hydrostatic force contribute to exclude a neutral particle from a charged surface, provided the polarizability is lower in the particle than in the solution. Here we report analytical predictions and simulations of the exclusion of polyethylene glycol (PEG) molecules of different molecular weight from charged surfaces. Molecular dynamics of PEG near charged DPPS membranes and neutral DPPC membranes show a region of PEG exclusion near the membrane-solution interface for charged membranes. The simulations also point to a preferential alignment of PEG molecules near the charged membrane. The simulations are confirmed by preliminary neutron reflectometry data, which are consistent with an exclusion region of concentrated PEG solutions near the highly charged silicon dioxide/water interface.

A Bagged-Tree Machine Learning Model for High and Low Wind Speed Ocean Wind Retrieval From CYGNSS Measurements

This paper presents two empirical models, the low wind bagged trees (LWBT) and high wind bagged trees (HWBT) ensemble models to estimate ocean surface wind speed using spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data. The models are empirically trained using NASA’s Cyclone GNSS (CYGNSS) mission level 1 data (version 2.1). The truth label for the LWBT model is the wind speed product derived from European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 and Global Data Assimilation System (GDAS), while the label for the HWBT model is wind speed measurements from Stepped Frequency Microwave Radiometer (SFMR). Testing results show that the LWBT and HWBT models achieved global wind speed retrieval root-mean-square-error (RMSE) of ~1.5 m/s and ~1.4 m/s, respectively, corresponding to an improvement of 29% and 65% with respect to the CYGNSS Level 2 standard wind speed product. The maximum bias is reduced by 65% and 60% for LWBT and HWBT over the Level 2 wind speeds, respectively. Two typhoon case studies are presented to corroborate the model performances and their retrieved wind speeds are consistent with reports from World Meteorological Organization (WMO) and with the measurement provided by the Huangmao Zhou (HMZ) weather station.