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Ocean mesoscale eddies are often poorly represented in climate models, and therefore, their effects on the large scale circulation must be parameterized. Traditional parameterizations, which represent the bulk effect of the unresolved eddies, can be improved with new subgrid models learned directly from data. Zanna and Bolton (2020), https://doi.org/10.1029/2020gl088376 (ZB20) applied an equation‐discovery algorithm to reveal an interpretable expression parameterizing the subgrid momentum fluxes by mesoscale eddies through the components of the velocity‐gradient tensor. In this work, we implement the ZB20 parameterization into the primitive‐equation GFDL MOM6 ocean model and test it in two idealized configurations with significantly different dynamical regimes and topography. The original parameterization was found to generate excessive numerical noise near the grid scale. We propose two filtering approaches to avoid the numerical issues and additionally enhance the strength of large‐scale energy backscatter. The filtered ZB20 parameterizations led to improved climatological mean state and energy distributions, compared to the current state‐of‐the‐art energy backscatter parameterizations. The filtered ZB20 parameterizations are scale‐aware and, consequently, can be used with a single value of the non‐dimensional scaling coefficient for a range of resolutions. The successful application of the filtered ZB20 parameterizations to parameterize mesoscale eddies in two idealized configurations offers a promising opportunity to reduce long‐standing biases in global ocean simulations in future studies. Plain Language Summary: This research focuses on improving the accuracy of ocean models by addressing the challenges of representing the mesoscale eddies on coarse grids. These eddies play a crucial role in the Earth's climate system, but traditional climate models struggle to capture their effects. Here, we implemented a new data‐driven parameterization simulating the physics of the mesoscale eddies into the state‐of‐the‐art ocean model. The parameterization is interpretable and captures key physical processes related to the mesoscale eddies known as energy backscatter. We tested this parameterization in two idealized ocean scenarios and found that it significantly improves the biases in the representation of the mean state and energetics. We propose new filtering schemes which improve the physical and numerical properties of the parameterization. Accurate representation of the mesoscale eddies by the present scheme has the potential to resolve long‐standing biases present in global ocean models and thus allow for more reliable climate simulations. Key Points: A data‐driven mesoscale eddy parameterization is implemented and evaluated in different configurations of the GFDL MOM6 ocean modelWe introduce filtering schemes to reduce the generation of grid‐scale noise and enhance the large‐scale backscatterThe subgrid parameterization improves the representation of the energy distributions and the climatological mean state [ABSTRACT FROM AUTHOR]

Abstract Backscatter communications, which originated from World War II, have been widely applied in the logistics domain, and recently attract emerging interest from both academic and industrial circles. Here, the backscatter communication systems equipped with retrodirective arrays that can re‐transmit the impinging signals back toward the direction of incidence are studied so as to reduce the power loss of the signals. Specifically, the authors consider the tag is equipped with retrodirective arrays to improve reliability and enhance communication range. The probability density function of channel coefficients is then derived. Next, a channel estimator based on Bayesian theory is proposed to acquire the modulus values of channel parameters and calculate its Bayesian Cramer–Rao Lower Bound. Finally, simulation results are provided to corroborate these theoretical studies.

Measuring the size of suspended particles in water is crucial in related fields such as environmental engineering, marine engineering, and hydraulic engineering. Considering the size distribution of suspended particles in real rivers, the amplitude (Amp) of the backscattering of particles with sizes ranging from 0.006 mm to 0.030 mm was analysed in this research using a lightweight ultrasonic meter developed by the authors and four probes with bandwidths ranging from 3.0 MHz to 30.0 MHz. In the analysis of Amp measurements for different particle sizes, using 0.008 mm as the reference particle size and converting to the rest of the particle sizes, if the Rayleigh scattering condition is satisfied between the particle sizes and the probe frequency, the conversion error value will be between −10% and 10%. This verifies the correctness of the theoretically derived particle size-Amp theory (the positive power relationship between the particle sizes and the Amp) and reflects the validity constraints of designing this experimental setup. The measurement method based on particle backscattering Amp analysis utilised in this study will help to achieve real-time measurements of suspended particles at river sites. [ABSTRACT FROM AUTHOR]

Snow cover and runoff play an important role in the Arctic environment, which is increasingly affected by climate change. Over the past 30 years, winter temperatures in northern Sweden have risen by 2 °C, accompanied by an increase in precipitation. This has led to a higher incidence of thaw–freeze and rain-on-snow events. Snow properties, such as the snow depth and longevity, and the timing of snowmelt in spring significantly impact the alpine tundra vegetation. The emergent vegetation at the edge of the snow patches during spring and summer constitutes an essential nutrient supply for reindeer. We have used Sentinel-1 synthetic aperture radar (SAR) to determine the onset of the surface melt and the end of the snow cover in the core reindeer grazing area of the Laevás Sámi reindeer-herding community in northern Sweden. Using SAR data from March to August during the period 2017 to 2021, the start of the surface melt is identified by detecting the season's backscatter minimum. The end of the snow cover is determined using a threshold approach. A comparison between the results of the analysis of the end of the snow cover from Sentinel-1 and in situ measurements, for the years 2017 to 2020, derived from an automatic weather station located in Laevásvággi reveals a 2- to 10-day difference in the snow-free ground conditions, which indicates that the method can be used to investigate when the ground is free of snow. VH data are preferred to VV data due to the former's lower sensitivity to temporary wetting events. The outcomes from the season backscatter minimum demonstrate a distinct 25-day difference in the start of the runoff between the 5 investigated years. The backscatter minimum and threshold-based method used here serves as a valuable complement to global snowmelt monitoring. [ABSTRACT FROM AUTHOR]

Backscatter communications, which originated from World War II, have been widely applied in the logistics domain, and recently attract emerging interest from both academic and industrial circles. Here, the backscatter communication systems equipped with retrodirective arrays that can re‐transmit the impinging signals back toward the direction of incidence are studied so as to reduce the power loss of the signals. Specifically, the authors consider the tag is equipped with retrodirective arrays to improve reliability and enhance communication range. The probability density function of channel coefficients is then derived. Next, a channel estimator based on Bayesian theory is proposed to acquire the modulus values of channel parameters and calculate its Bayesian Cramer–Rao Lower Bound. Finally, simulation results are provided to corroborate these theoretical studies. [ABSTRACT FROM AUTHOR]

Mesoscale eddies modulate the stratification, mixing, tracer transport, and dissipation pathways of oceanic flows over a wide range of spatiotemporal scales. The parameterization of buoyancy and momentum fluxes associated with mesoscale eddies thus presents an evolving challenge for ocean modelers, particularly as modern climate models approach eddy‐permitting resolutions. Here we present a parameterization targeting such resolutions through the use of a subgrid mesoscale eddy kinetic energy budget (MEKE) framework. Our study presents two novel insights: (a) both the potential and kinetic energy effects of eddies may be parameterized via a kinetic energy backscatter, with no Gent‐McWilliams along‐isopycnal transport; (b) a dominant factor in ensuring a physically‐accurate backscatter is the vertical structure of the parameterized momentum fluxes. We present simulations of 1/2° and 1/4° resolution idealized models with backscatter applied to the equivalent barotropic mode. Remarkably, the global kinetic and potential energies, isopycnal structure, and vertical energy partitioning show significantly improved agreement with a 1/32° reference solution. Our work provides guidance on how to parameterize mesoscale eddy effects in the challenging eddy‐permitting regime. Plain Language Summary: Ocean eddies evolving on horizontal lengthscales of order 10–100 km are not sufficiently resolved in modern global ocean models that have horizontal resolutions of about 25–100 km. The under‐representation of such eddies leads to inaccuracies in the modeled ocean state, including weakened current systems, incorrect stratification, and erroneous distributions of physical and biological ocean tracers. Here we develop a novel approach to mimicking the unresolved eddy effects by artificially energizing the flow in a way that is consistent with eddy dynamics, specifically their vertical structure. We find that our approach is able to correct for a variety of unresolved eddy effects when employed in a coarse‐resolution ocean model and compared against a high‐resolution reference case. Our work provides new insights on how to account for unresolved eddies in the next generation of climate models. Key Points: We propose a parameterization for mesoscale eddies targeting ocean models at eddy‐permitting resolutionsWe find that both the potential and kinetic energy effects of eddies on the flow may be parameterized via a kinetic energy backscatterThe novel approach of our backscatter scheme is its use of vertical structure, that is, backscattering onto the equivalent barotropic mode [ABSTRACT FROM AUTHOR]

With the proliferation of smart devices, the Internet of Things (IoT) is rapidly expanding. This study proposes a miniaturized controllable metamaterial with low control voltage for achieving low-power and compact designs in IoT node devices. Operating at a target frequency of 2.4 GHz, the proposed metamaterial requires only a 3.3 V control voltage and occupies approximately one-third of the wavelength in size. Experimental validation demonstrates its excellent reflective control performance, positioning it as an ideal choice for low-power IoT systems, particularly in the context of miniaturized and low-power IoT node applications. [ABSTRACT FROM AUTHOR]

Abstract Ocean mesoscale eddies are often poorly represented in climate models, and therefore, their effects on the large scale circulation must be parameterized. Traditional parameterizations, which represent the bulk effect of the unresolved eddies, can be improved with new subgrid models learned directly from data. Zanna and Bolton (2020), https://doi.org/10.1029/2020gl088376 (ZB20) applied an equation‐discovery algorithm to reveal an interpretable expression parameterizing the subgrid momentum fluxes by mesoscale eddies through the components of the velocity‐gradient tensor. In this work, we implement the ZB20 parameterization into the primitive‐equation GFDL MOM6 ocean model and test it in two idealized configurations with significantly different dynamical regimes and topography. The original parameterization was found to generate excessive numerical noise near the grid scale. We propose two filtering approaches to avoid the numerical issues and additionally enhance the strength of large‐scale energy backscatter. The filtered ZB20 parameterizations led to improved climatological mean state and energy distributions, compared to the current state‐of‐the‐art energy backscatter parameterizations. The filtered ZB20 parameterizations are scale‐aware and, consequently, can be used with a single value of the non‐dimensional scaling coefficient for a range of resolutions. The successful application of the filtered ZB20 parameterizations to parameterize mesoscale eddies in two idealized configurations offers a promising opportunity to reduce long‐standing biases in global ocean simulations in future studies.

The increase in Earth observations from space in recent years supports improved quantification of carbon storage by terrestrial vegetation and fosters studies that relate satellite measurements to biomass retrieval algorithms. However, satellite observations are only indirectly related to the carbon stored by vegetation. While ground surveys provide biomass stock measurements to act as reference for training the models, they are sparsely distributed. Here, we addressed this problem by designing an algorithm that harnesses the interplay of satellite observations, modeling frameworks and field measurements, and generated global estimates of above-ground biomass (AGB) density that meet the requirements of the scientific community in terms of accuracy, spatial and temporal resolution. The design was adapted to the amount, type and spatial distribution of satellite data available around the year 2020. The retrieval algorithm estimated AGB annually by merging estimates derived from C- and L-band Synthetic Aperture Radar (SAR) backscatter observations with a Water Cloud type of model and does not rely on AGB reference data at the same spatial scale as the SAR data. This model is integrated with functions relating to forest structural variables that were trained on spaceborne LiDAR observations and sub-national AGB statistics. The yearly estimates of AGB were successively harmonized using a cost function that minimizes spurious fluctuations arising from the moderate-to-weak sensitivity of the SAR backscatter to AGB. The spatial distribution of the AGB estimates was correctly reproduced when the retrieval model was correctly set. Over-predictions occasionally occurred in the low AGB range (300 Mg ha−1). These errors were a consequence of sometimes too strong generalizations made within the modeling framework to allow reliable retrieval worldwide at the expense of accuracy. The precision of the estimates was mostly between 30% and 80% relative to the estimated value. While the framework is well founded, it could be improved by incorporating additional satellite observations that capture structural properties of vegetation (e.g., from SAR interferometry, low-frequency SAR, or high-resolution observations), a dense network of regularly monitored high-quality forest biomass reference sites, and spatially more detailed characterization of all model parameters estimates to better reflect regional differences.

The accurate mapping of seafloor substrate types plays a major role in understanding the distribution of benthic marine communities and planning a sustainable exploitation of marine resources. Traditionally, this activity has relied on the efforts of marine geology experts, who accomplish it manually by examining information from acoustic data along with the available ground- truth samples. However, this approach is challenging and time-consuming. Hence, it is important to explore automatic methods to replace this manual process. In this study, we investigated the potential of deep learning (U-Net) for classifying the seabed as either "bedrock" or "non-bedrock" using bathymetry and/or backscatter data, acquired with multibeam echosounders (MBES). Slope and hillshade data, derived from the bathymetry, were also included in the experiment. Several U-Net models, taking as input either one of these datasets or a combination of them, were trained using an expert delineated map as reference. The analysis revealed that U-Net has the ability to map bedrock and non-bedrock areas reliably. On our test set, the models using either bathymetry or slope data showed the highest performance metrics and the best visual match with the reference map. We also observed that they often identified topographically rough features as bedrock, which were not interpreted as such by the human expert. While such discrepancy would typically be considered an error of the model, the scale of the expert annotations as well as the different methods used by the experts to manually generate maps must be considered when evaluating the predictions quality. While encouraging results were obtained here, further research is necessary to explore the potential of deep learning in mapping other seabed types and evaluating the models' generalization capabilities on similar datasets but different geographical locations. [ABSTRACT FROM AUTHOR]

Earth observation satellite-04 or EOS-04 (Radar imaging satellite-1A or RISAT-1A) provides the opportunity for characterization of surface features using C-band fully-polarimetric, hybrid-polarimetric and dual-polarimetric radar data from same platform in high to moderate resolution (2–50 m depending on mode) and varied incidence angle (12°–55°). These unique EOS-04 radar datasets can be processed using Microwave Data Analysis Software (MIDAS) tool, to derive radar polarimetric parameters. MIDAS is capable of carrying out synthetic aperture radar (SAR) data processing comprising radiometric and majority of the polarimetric processing of EOS-04 datasets. In addition, MIDAS has the functionality to orthorectify and geocode EOS-04 polarimetric products, which is missing in other softwares. Further, MIDAS contains module for bulk-processing of timeseries EOS-04 products. We showcase results obtained from EOS-04 datasets and describe the scattering behaviour of various targets. Further, we also demonstrate the temporal variation in the backscatter values from different target features obtained from EOS-04 time-series stack, which has strong applicability in land use/land cover and agricultural applications. Thus, the radiometric and polarimetric products can be highly beneficial for characterization of physical properties of the scatterers based on their radar scattering behaviour and further classification of the surface features. [ABSTRACT FROM AUTHOR]

The use of multiple synthetic aperture radar polarizations can improve biomass estimations compared to using a single polarization. In this study, we compared predictions of aboveground biomass change from ALOS-2 PALSAR-2 backscatter using linear regression based on (1) the cross-polarization channels, (2) the co- and cross-polarizations from fully polarimetric SAR, (3) Freeman–Durden polarimetric decomposition, and (4) the polarimetric radar vegetation index (RVI). Additionally, the impact of forest structure on the sensitivity of the polarimetric backscatter to AGB and AGB change was assessed. The biomass consisted of mainly coniferous trees at the hemi-boreal test site Remningstorp, located in southern Sweden. We found some improvements in the predictions when quad-polarized data (RMSE = 79.4 tons/ha) were used instead of solely cross-polarized data (RMSE = 84.9 tons/ha). However, when using Freeman–Durden decomposition, the prediction accuracy improved further (RMSE = 69.7 tons/ha), and the highest accuracy was obtained with the radar vegetation index (RMSE = 50.4 tons/ha). The corresponding R2 values ranged from 0.45 to 0.82. The bias was less than 1 t/ha for all models. An analysis of forest variables showed that the sensitivity to AGB was reduced for high values of basal-area-weighted mean height, basal area, and stem density when predicting absolute AGB, but the best change prediction model was sensitive to changes larger than the apparent saturation point for AGB state estimates. We conclude that by using fully polarimetric SAR images, forest biomass changes can be estimated more accurately compared to using single- or dual-polarization images. The results were improved the most (in terms of RMSE and R2) by using the Freeman–Durden decomposition model or the RVI, which captured especially the large changes better. [ABSTRACT FROM AUTHOR]

BLE-based (Bluetooth Low Energy-based) backscatter has received considerable attention, as it aims to communicate with everyday smart devices such as smartphones, smartwatches, and tablets in passive IoT. The state-of-the-art BLE backscatter systems enable communication using a specialized continuous wave (CW) generator or entirely using commodity BLE 4.0 radios as an RF source. However, the existing BLE communication systems suffer from several key issues, including a short carrier length and a large frequency shift. This paper presents a passive BLE (PBLE) backscatter communication system that utilizes commodity BLE 5.0 radios. The system uses a BLE 5.0 extended advertising packet with partial single tones as excitations transmitting on the secondary advertising channel of BLE 5.0, and the BLE backscatter tag produces bandpass frequency-shift keying modulation at 1 Mb/s, which enables compatibility with BLE advertising channels. The prototype is implemented using an NRF52832 BLE 5.0 commodity chip, smart devices, and tags with FPGAS and chips. In FPGA board-level verification, when the downlink distance is 0.5 m, the uplink distance can reach 10 m. In chip testing, the uplink distance can reach 7 m when the downlink distance is 1 m. The baseband power consumption is 2 μW, with a total power consumption of 10 μW. This system eliminates the need for expensive and costly specialized RF sources, unlike the BLE backscatter communication system that uses a specialized CW generator. Compared to the BLE backscatter communication system that uses commodity BLE 4.0 radios, this system reduces the minimum frequency shift from 24 MHz to 2 MHz and increases the length of the single tones as a CW by a factor of about seven, from 31 bytes to 254 bytes. [ABSTRACT FROM AUTHOR]

Electron beams are often used to treat superficial lesions of the lip, cheek,nose, and ear.Lead is frequently used to block distal structures.It is customary to place an internal bolus of low atomic number in between the tissue and the lead to reduce electron backscatter from the lead. Space for the lead and the internal bolus is quite limited.A previous method for estimating the thickness of the lead plus internal bolus is not self-consistent and leads to a larger than necessary thickness. A new method is described here to provide a quick, accurate, and self-consistent estimate of the minimum necessary thickness of the internal bolus and the lead for incident electron beam energies of 4, 6, 8, 9, and 10 MeV as a function of the thickness of the overlying tissue. This method limits the dose enhancement at the tissue/bolus interface due to the underlying lead to 10%.Measurements made with gafchromic film validate this methodology. [ABSTRACT FROM AUTHOR]

Abstract Mesoscale eddies modulate the stratification, mixing, tracer transport, and dissipation pathways of oceanic flows over a wide range of spatiotemporal scales. The parameterization of buoyancy and momentum fluxes associated with mesoscale eddies thus presents an evolving challenge for ocean modelers, particularly as modern climate models approach eddy‐permitting resolutions. Here we present a parameterization targeting such resolutions through the use of a subgrid mesoscale eddy kinetic energy budget (MEKE) framework. Our study presents two novel insights: (a) both the potential and kinetic energy effects of eddies may be parameterized via a kinetic energy backscatter, with no Gent‐McWilliams along‐isopycnal transport; (b) a dominant factor in ensuring a physically‐accurate backscatter is the vertical structure of the parameterized momentum fluxes. We present simulations of 1/2° and 1/4° resolution idealized models with backscatter applied to the equivalent barotropic mode. Remarkably, the global kinetic and potential energies, isopycnal structure, and vertical energy partitioning show significantly improved agreement with a 1/32° reference solution. Our work provides guidance on how to parameterize mesoscale eddy effects in the challenging eddy‐permitting regime.

Snow avalanches constitute a dangerous natural hazard in snow-clad mountain regions. Spaceborne synthetic aperture radar (SAR) has emerged as an effective tool for monitoring avalanche activity also in remote areas due to its all-weather capabilities and sensitivity to the presence of avalanche debris. The objective of this study was the application of a novel polarimetric change detection approach to the task of identifying avalanche debris candidate areas and characterizing the impact of factors, such as avalanche size and topography, on its performance. We applied polarimetric change vectors (PCVs) for change detection between pairs of Sentinel-1 SAR images acquired over the Swiss Alps during a two-month period characterized by episodes of exceptionally high avalanche activity. PCV were classified using a two-step mixture analysis of their magnitude and direction components. Candidates for avalanche debris were matched against a reference database of $>$ 16 000 avalanche outlines. The detected changes were largely attributed to snow processes, such as transitions from dry to wet snow and vice versa, as well as the occurrence of avalanches. 23% of all reference avalanche polygons could be matched to retrieved avalanche debris patches. The matching rate strongly depended on avalanche size, reaching $>$ 33% for very large (size 4) avalanches and, to a lesser degree, on the orientation of the avalanche relative to the look direction of the sensor. Apart from constituting a standalone approach for detecting avalanche debris candidate areas, the PCV method has potential for integration into automatic avalanche detection workflows based on machine learning methods.

Soil Moisture (SM) is a key geophysical variable that enables a better understanding of the Earth’s hydrological processes. Missions like Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) have been the primary sources of SM estimations for years. The NASA-ISRO Synthetic Aperture Radar (NISAR) mission, planned to launch in 2024, will bring L-band and S-band SAR measurements that will be used for SM estimation. Consequently, investigations to link SMAP with NISAR to produce accurate SM retrievals at improved spatial resolutions will be a focus to many research initiatives. This investigation aims to set the basis to use polarimetric Global Navigation Satellite System-Reflectometry (GNSS-R) products to enhance the temporal resolution of NISAR’s L-band SAR data through its typical 12-day period. As a proof of concept, we model SMAP radar backscatter measurements from SMAP-Reflectometry (SMAP-R) measurements, using the 3 months of data collected by the SMAP radar while it was operational. The model is based on the sensitivity of polarimetric GNSS-R to roughness, vegetation, and SM, as well as on the complementary sensitivity existing between forward and backward scatter. Different regression models are implemented using single-, dual-, and full-polarization GNSS-R measurements synthetized from SMAP-R data. This study highlights the importance of the new constellations of polarimetric GNSS-R being built and shows how those frequent measurements can serve L-band SAR missions to improve their time resolution.

The forest is an important part of carbon resources. Forest growing stock volume (GSV) is an important parameter of forest. The Water Cloud Model (WCM) is a simple equation that describes the interaction between ground objects and electromagnetic waves. It has also been applied in the estimation of forest GSV. When estimating GSV, the WCM equation parameters are usually calculated using least squares, but the least squares method relies on field reference data. The subsequent WCM development algorithm BIOMASAR uses a sliding window method that does not rely on measured data. However, the sliding window method is inefficient and can easily lead to missing pixels. We designed the backscatter/fractional vegetation coverage (FVC) feature space based on WCM and BIOMASAR to estimate forest GSV. Comparing with the national forest inventory (NFI) reference dataset and the BIOMASAR algorithm results in the study area, the method is evaluated from three aspects: accuracy, efficiency, and texture. The results show that this method does not rely on actual reference data, and the efficiency is increased from 1661s in the sliding window to 663s. The correlation with the NFI reference data is 0.45, the RMSE is 116.2327 m3/ha, and the RRMSE is 64.86%. The accuracy is better than the BIOMASAR sliding window GSV results in this study area, and compared with Google Earth images of the same period, it is also more consistent with the field texture. In short, the backscatter/FVC feature space can efficiently obtain forest GSV estimates more consistent with field conditions without relying on measured data.

Harmonic transponders are passive wireless devices that hold great promise for a variety of long-term tracking and sensing applications. These nonlinear devices receive an interrogation signal at one frequency $(f)$ and backscatter harmonics (typically, $2f$ is of interest). The device’s conversion loss is the change in power from what is received and what is transmitted. We show herein that conversion loss is dependent jointly on interrogation power, interrogation frequency, and interrogation angle. This coupled nature of the device’s behavior necessitates performance metrics that capture these characteristics. We present a methodology to generically test these devices and propose metrics that capture the noted dependencies.

This paper proposes to combat active eavesdropping using intelligent reflecting surface (IRS) backscatter techniques. To be specific, the source (Alice) sends the confidential information to the intended user (Bob), while the eavesdropper (Willie) transmits a jamming signal to interrupt the transmission for more data interception. To enhance the secrecy, an IRS is deployed and connected with Alice through fiber to transform the jamming signal into the confidential signal by employing backscatter techniques. Based on the considered model, an optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio (SINR) at Bob under the constraints of the transmit power at Alice, the reflection vector at the IRS, and the allowable maximum the SINR at Willie. To address the optimization problem, an alternate optimization algorithm is developed. The simulation results verify the achievable secrecy gain of the proposed scheme. The proposed scheme is effective in combating active eavesdropping. Furthermore, the deployment of large-scale IRS significantly enhances the secrecy rate. [ABSTRACT FROM AUTHOR]

Parkinson, F.; Douglas, K.; Li, Z.; Meijer, A.; Stacey, C.D.; Kung, R., and Podhorodeski, A., 2024. A generalized semiautomated method for seabed geology classification using multibeam data and maximum likelihood classification. Journal of Coastal Research, 40(1), 1–16. Charlotte (North Carolina), ISSN 0749-0208. This paper presents a GIS-based model to perform semiautomated seabed classification that can act as a first-pass, pseudoclassified surficial geological map. The user can then edit the output into a finalized map in less time than by manual classification. The model uses maximum likelihood classification with unsupervised classification through iterative self-organizing clusters. This model is fully contained within the ArcGIS software suite as a ModelBuilder workflow composed of geoprocessing tools and Python script tools. Model inputs tested include different combinations of multibeam echosounder–derived data: slope, backscatter, and terrain ruggedness. Furthermore, to test the assumption of Gaussian distribution of input data required for maximum likelihood classification, Box–Cox power transformations were applied to slope and backscatter data and were used as model inputs. To illustrate the performance of the model, two locations are highlighted as case studies: Milbanke Sound and Spiller Channel, located on the central coast of British Columbia, Canada. Association between model outputs and ground-truth classes was generally weak to moderate when measured using Cramér's V association scores. Overall, the slope and backscatter parameter model had the highest scores of association. Results from an overlay analysis comparing model outputs with user-confirmed polygons show that the slope and backscatter model performs best in regions with distinct changes in the hardness of sediments but that in fjord regions dominated geologically by steeper bathymetric change, the slope parameter model may perform better. However, all model outputs had difficulty delineating bedrock units. The model has the flexibility to identify certain seabed habitat features as well, including glass sponge reefs—biologically active bioherms that have led to marine protected area designations in other areas of British Columbia. [ABSTRACT FROM AUTHOR]

Ongoing backscatter communications and localisation research have been able to obtain incredibly accurate results in controlled environments. The main issue with these systems is faced in complex RF environments. This paper investigates concurrent localization and ambient radio frequency (RF) energy harvesting using backscatter communication systems for Internet of Things networks. Dynamic real-world environments introduce complexity from multipath reflection and shadowing, as well as interference from movements. A machine learning framework leveraging K-Nearest Neighbors and Random Forest classifiers creates robustness against such variability. Historically, received signal measurements construct a location fingerprint database resilient to perturbations. The Random Forest model demonstrates precise localization across customized benches with programmable shuffling of chairs outfitted with RF identification tags. Average precision accuracy exceeds 99% despite deliberate placement modifications, inducing signal fluctuations emulating mobility and clutter. Significantly, directional antennas can harvest over −3 dBm, while even omnidirectional antennas provide −10 dBm—both suitable for perpetually replenishing low-energy electronics. Consequently, the intelligent backscatter platform localizes unmodified objects to customizable precision while promoting self-sustainability. [ABSTRACT FROM AUTHOR]

Abstract Backscatter communication (BackCom), the underlying technology for modern‐day Radio Frequency Identification, has been studied as a promising solution for future ultra‐low power Internet‐of‐Things (IoT) applications. Its development has pushed the performance boundaries significantly, in terms of communication distance, data transmission rate, and power consumption. An up‐to‐date review of one branch of BackCom systems, namely Ambient BackCom, which utilises the already available ambient signals, instead of a dedicated radio frequency carrier in most of the BackCom works, to establish BackCom links is conducted. This further reduces the cost and complexity of the system and opens an opportunity for mass deployment.

Passive internet of things (IoT) is the fundamentally enabling technology of promoting digital transformation and upgrading of industries.In recent years, passive IoT communication technology such as radio frequency identification has been widely used in asset management fields due to its advantages of low cost, zero power consumption, and easy deployment.In this context, passive IoT communication technology and the latest research progress were introduced, the potential evolution prospects of passive IoT were analyzed.Based on that, development bottlenecks and challenges of passive IoT communication were given, a novel passive IoT communication system with deep fusion of the cellular system was presented, and the key technology research directions of this field were proposed.It is aimed to inspire new thoughts for the development of passive IoT communication technology.

Passive backscatter communication can free wireless sensors from limited-life batteries and heavy manual maintenance, and will also reduce their hardware cost.Passive backscatter communication is one of the key technologies to realize the passive internet of things (IoT).Due to the limited resources of passive sensor nodes, how to use coding technology to improve system reliability has become a challenging problem for passive IoT.The coding and decoding technologies of passive nodes in the passive IoT were focused on.Firstly, the development history of traditional channel coding and the existing RFID channel coding and decoding schemes were systematically expounded.Then, three suitable coding schemes were selected for passive backscatter communication technology.Finally, the performance of these three encoding schemes were compared by simulation and the advantages and disadvantages were summarized.

This document is a corrigendum for an article titled "Seabed classification of multibeam echosounder data into bedrock/non-bedrock using deep learning." The corrigendum addresses minor inaccuracies in the article, specifically regarding metrics values for multiple-input models and misclassification values in tables. The corrections have been made to the tables and corresponding confusion matrices. The authors apologize for the errors and state that they do not change the scientific conclusions of the article. [Extracted from the article]

Measuring the size of suspended particles in water is crucial in related fields such as environmental engineering, marine engineering, and hydraulic engineering. Considering the size distribution of suspended particles in real rivers, the amplitude (Amp) of the backscattering of particles with sizes ranging from 0.006 mm to 0.030 mm was analysed in this research using a lightweight ultrasonic meter developed by the authors and four probes with bandwidths ranging from 3.0 MHz to 30.0 MHz. In the analysis of Amp measurements for different particle sizes, using 0.008 mm as the reference particle size and converting to the rest of the particle sizes, if the Rayleigh scattering condition is satisfied between the particle sizes and the probe frequency, the conversion error value will be between −10% and 10%. This verifies the correctness of the theoretically derived particle size-Amp theory (the positive power relationship between the particle sizes and the Amp) and reflects the validity constraints of designing this experimental setup. The measurement method based on particle backscattering Amp analysis utilised in this study will help to achieve real-time measurements of suspended particles at river sites.

Snow cover and runoff play an important role in the Arctic environment, which is increasingly affected by climate change. Over the past 30 years, winter temperatures in northern Sweden have risen by 2 °C, accompanied by an increase in precipitation. This has led to a higher incidence of thaw–freeze and rain-on-snow events. Snow properties, such as the snow depth and longevity, and the timing of snowmelt in spring significantly impact the alpine tundra vegetation. The emergent vegetation at the edge of the snow patches during spring and summer constitutes an essential nutrient supply for reindeer. We have used Sentinel-1 synthetic aperture radar (SAR) to determine the onset of the surface melt and the end of the snow cover in the core reindeer grazing area of the Laevás Sámi reindeer-herding community in northern Sweden. Using SAR data from March to August during the period 2017 to 2021, the start of the surface melt is identified by detecting the season’s backscatter minimum. The end of the snow cover is determined using a threshold approach. A comparison between the results of the analysis of the end of the snow cover from Sentinel-1 and in situ measurements, for the years 2017 to 2020, derived from an automatic weather station located in Laevásvággi reveals a 2- to 10-day difference in the snow-free ground conditions, which indicates that the method can be used to investigate when the ground is free of snow. VH data are preferred to VV data due to the former’s lower sensitivity to temporary wetting events. The outcomes from the season backscatter minimum demonstrate a distinct 25-day difference in the start of the runoff between the 5 investigated years. The backscatter minimum and threshold-based method used here serves as a valuable complement to global snowmelt monitoring.

The soil freeze–thaw (FT) cycle is a critical component of the terrestrial cryosphere and plays a significant role in hydrological, ecological, climatic, and biogeochemical processes within permafrost landscapes. The FT states can be monitored with in-situ field measurements, but these procedures are costly and limited to single chosen sites. Remote sensing data provides the opportunity to collect information repeatedly across extensive geographical areas. To explore a more effective way to monitor the FT states in the terrestrial cryosphere, in this study, we used microwave and optical remote sensing data and introduced the Deep Learning approach to simulate the soil FT states in the western part of Nunavik, Canada.Two networks, Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN), were trained and tested with over 35,000 and approximately 54,000 randomly selected data samples, respectively. The trained CNN networks outperformed the MLP networks, achieving the highest testing accuracy of 95.67% and the highest validation accuracy of 87.28% based on ground truth data from 32 measurement stations from all seasons across the year. This study proposed the reference periods concept for convenient labeling in data preparation and tested different combinations of influence variables to achieve better transferability of the method for future studies. Our findings offer a more effective way to monitor FT states in the terrestrial cryosphere, offering valuable insights into the consequences of climate change on permafrost landscapes. Moreover, the suggested deep learning approach can be easily expanded when additional input sources are accessible. This expansion has the potential to further improve the model's performance for the FT retrieval.

The accurate mapping of seafloor substrate types plays a major role in understanding the distribution of benthic marine communities and planning a sustainable exploitation of marine resources. Traditionally, this activity has relied on the efforts of marine geology experts, who accomplish it manually by examining information from acoustic data along with the available ground-truth samples. However, this approach is challenging and time-consuming. Hence, it is important to explore automatic methods to replace this manual process. In this study, we investigated the potential of deep learning (U-Net) for classifying the seabed as either “bedrock” or “non-bedrock” using bathymetry and/or backscatter data, acquired with multibeam echosounders (MBES). Slope and hillshade data, derived from the bathymetry, were also included in the experiment. Several U-Net models, taking as input either one of these datasets or a combination of them, were trained using an expert delineated map as reference. The analysis revealed that U-Net has the ability to map bedrock and non-bedrock areas reliably. On our test set, the models using either bathymetry or slope data showed the highest performance metrics and the best visual match with the reference map. We also observed that they often identified topographically rough features as bedrock, which were not interpreted as such by the human expert. While such discrepancy would typically be considered an error of the model, the scale of the expert annotations as well as the different methods used by the experts to manually generate maps must be considered when evaluating the predictions quality. While encouraging results were obtained here, further research is necessary to explore the potential of deep learning in mapping other seabed types and evaluating the models’ generalization capabilities on similar datasets but different geographical locations.

ABSTRACTAdvances in sonar technology have revolutionized our ability to map the seafloor, however, differences between legacy and modern data pose challenges when analysing multi-source datasets. Acoustic backscatter recorded via multibeam echosounder is commonly used to characterize the seafloor, but a lack of standardized calibration often yields relative rather than absolute backscatter measurements, hindering comparison between surveys. ‘Bulk shift’ methods have been developed for harmonizing legacy backscatter datasets using overlapping survey areas for relative statistical calibration. This becomes increasingly difficult, though, given many datasets collected over extensive time periods. Backscatter data were collected in the Bay of Fundy, Canada, using multiple sonar systems and vessels over an 18-year period. Here, we propose a reproduceable strategy for harmonizing this large volume of disparate backscatter data using the bulk shift method. A final, harmonized map is presented for the entire Bay of Fundy and is validated using in situ observations from seafloor imagery.

Backscatter communication (BackCom), the underlying technology for modern‐day Radio Frequency Identification, has been studied as a promising solution for future ultra‐low power Internet‐of‐Things (IoT) applications. Its development has pushed the performance boundaries significantly, in terms of communication distance, data transmission rate, and power consumption. An up‐to‐date review of one branch of BackCom systems, namely Ambient BackCom, which utilises the already available ambient signals, instead of a dedicated radio frequency carrier in most of the BackCom works, to establish BackCom links is conducted. This further reduces the cost and complexity of the system and opens an opportunity for mass deployment. [ABSTRACT FROM AUTHOR]

With the advent of the 5G era, radio frequency identification (RFID) has been widely applied in various fields as one of the key technologies for the Internet of Things (IoT) to realize the Internet of Everything (IoE). In recent years, RFID-based motion sensing has emerged as an important research area with great potential for development. In this paper, an RFID backscatter sport motion sensing scheme is proposed, which effectively solves the multi-classification problem by using the received signal strength (RSS) of the backscattered RFID and the error correcting output coding (ECOC)-based support vector machine (SVM). We conduct extensive experiments to validate the effectiveness of the proposed scheme, in which the signal intensities of different types of action poses are collected and the SVM is used as the classification algorithm to achieve high classification accuracies. [ABSTRACT FROM AUTHOR]

The retrieval of the biophysical parameters and subsequent estimation of the above-ground biomass (AGB) of vegetation stands are made possible by the simulation of the extinction and scattering components from the canopy layer using vector radiative transfer (VRT) theory-based scattering models. With the use of such a model, this study aims to evaluate and compare the potential of dual-pol, multi-frequency SAR data for estimating above-ground biomass. The data selected for this work are L-band dual polarized (HH/HV) ALOS-2 data, S-band dual polarized (HH/HV) NovaSAR data, and C-band dual polarized (VV/VH) Sentinel-1 data. The two key biophysical parameters, tree height, and trunk radius are retrieved using the proposed methodology, applying the frequencies independently. A general allometric equation with vegetation-specific coefficients is used to estimate the AGB from the retrieved biophysical parameters. The retrieval results are validated using ground truth measurements collected from the study area. The L-band, with the coefficient of determination ( R 2 ) of 0.73 and the root mean square error (RMSE) of 35.90 t/ha, has the best correlation between the modeled and field AGBs, followed by the S-band with an R 2 of 0.37 and an RMSE of 63.37 t/ha, and the C-band with an R 2 of 0.25 and an RMSE of 72.32 t/ha. The L-band has yielded improved estimates of AGB in regression analysis as well, with an R 2 of 0.48 and an RMSE of 50.02 t/ha, compared to the S- and C-bands, which have the R 2 of 0.12 and 0.03 and the RMSE of 70.98 t/ha and 80.84 t/ha, respectively. [ABSTRACT FROM AUTHOR]

With the proliferation of smart devices, the Internet of Things (IoT) is rapidly expanding. This study proposes a miniaturized controllable metamaterial with low control voltage for achieving low-power and compact designs in IoT node devices. Operating at a target frequency of 2.4 GHz, the proposed metamaterial requires only a 3.3 V control voltage and occupies approximately one-third of the wavelength in size. Experimental validation demonstrates its excellent reflective control performance, positioning it as an ideal choice for low-power IoT systems, particularly in the context of miniaturized and low-power IoT node applications.

The use of multiple synthetic aperture radar polarizations can improve biomass estimations compared to using a single polarization. In this study, we compared predictions of aboveground biomass change from ALOS-2 PALSAR-2 backscatter using linear regression based on (1) the cross-polarization channels, (2) the co- and cross-polarizations from fully polarimetric SAR, (3) Freeman–Durden polarimetric decomposition, and (4) the polarimetric radar vegetation index (RVI). Additionally, the impact of forest structure on the sensitivity of the polarimetric backscatter to AGB and AGB change was assessed. The biomass consisted of mainly coniferous trees at the hemi-boreal test site Remningstorp, located in southern Sweden. We found some improvements in the predictions when quad-polarized data (RMSE = 79.4 tons/ha) were used instead of solely cross-polarized data (RMSE = 84.9 tons/ha). However, when using Freeman–Durden decomposition, the prediction accuracy improved further (RMSE = 69.7 tons/ha), and the highest accuracy was obtained with the radar vegetation index (RMSE = 50.4 tons/ha). The corresponding R2 values ranged from 0.45 to 0.82. The bias was less than 1 t/ha for all models. An analysis of forest variables showed that the sensitivity to AGB was reduced for high values of basal-area-weighted mean height, basal area, and stem density when predicting absolute AGB, but the best change prediction model was sensitive to changes larger than the apparent saturation point for AGB state estimates. We conclude that by using fully polarimetric SAR images, forest biomass changes can be estimated more accurately compared to using single- or dual-polarization images. The results were improved the most (in terms of RMSE and R2) by using the Freeman–Durden decomposition model or the RVI, which captured especially the large changes better.

Bottom trawling is a fishing method that involves towing of nets along the seafloor to catch demersal species. The dragging of trawling gears along the seafloor results in scraping and ploughing of the surficial sediments, leading to the formation of turbid plumes of resuspended sediments and causing measurable changes in the seabed morphology. High-resolution multibeam data, side scan sonar, sediment grain size and vessel tracking data have been used to investigate the impact of bottom trawling on the seafloor morphology and surficial sediments of the northern Catalan continental shelf (NW Mediterranean), providing new insights into the impact of this anthropogenic activity on the seafloor. Multibeam data evidenced the occurrence of large-scale erosive features as a consequence of repeated scouring by fishing gears in localized areas. They are characterized by elongated (70-300 m wide and up to 8 km long) channelized areas of high backscatter with variable incision (from 0.2 m to 1.2 m). The spatial distribution of these morphologies shows a similar pattern to that observed in the fishing intensity, with maximum values in the areas of increased trawling intensity, corresponding to the main fishing grounds. Side scan sonar data also shows higher densities of trawl marks in these areas than in the surroundings. Sediment cores collected on these features show an upward-coarsening trend in the first 4-5 cm of the core, suggesting that part of the finer fraction resuspended by trawling is winnowed, increasing the sand content of the surface sediment. The identification of such large erosive morphologies in the main fishing grounds evidences that repeated trawling over the same fishing ground during decades can result in deep excavation of the seafloor, leading to permanent large-scale morphological changes. Furthermore, the evolution of these erosive features over a 13-year interval points towards long recovery periods after the cessation of trawling activities.

Concepts for two cooperative active reflector tags (ARTs) for joint ranging and communication in a linear frequency modulated continuous wave secondary radar system are presented along with corresponding theoretical derivations. To prove the concepts, the ARTs operating from 24 to 24.5 GHz are implemented using custom integrated superregenerative amplifiers (SRAs) designed in a 130 nm SiGe BiCMOS technology. The SRAs have a measured minimum detected input power greater than −80 dBm, and an output power greater than 3.2 dBm after wire‐bonding. The ARTs also feature roll invariance using bandwidth enhanced microstrip patch antenna diversity and a novel quench frequency shift keying based low data rate simplex communication. Indoor ranging experiments done to prove the concepts and the theoretical considerations show a maximum distance of 77 m in the ranging mode and a data rate of 0.5 symbols per second in the data transfer mode. For distances less than 5 m, the characterised accuracy is better than 12 cm and the precision is better than 2 cm. To the best of authors' knowledge this is one of the first reported scientific works leading to a proof of concept for integrated superregenerative amplifier based joint ranging and quench frequency shift keying. The combination of maximum range and precision are also one of the best reported among similar works in the literature. [ABSTRACT FROM AUTHOR]

Satellite image analysis is a potentially powerful tool for monitoring coastal shoreline positions. This study explores the use of multi-temporal, dual-polarised Sentinel-1 GRD synthetic aperture radar (SAR) imagery with a spatial resolution of 10 m for delineating shorelines. It was conducted in a data-deficient and complex environment (the Niger delta of Nigeria), in a developing country with a cloud-heavy climate. The study focuses on exploring and testing the capability of using multitemporal waterlines from SAR images to derive shoreline positions at high and low tidal states. From 54 Sentinel-1 images recorded in 2017, the study selected 12 images to represent both high and low tidal states. These were spread across the wet and dry seasons in order to account for seasonal differences. Shoreline positions were obtained by identifying the land–water boundary via segmentation using histogram-minimum thresholding, vectorizing and smoothing that boundary, and averaging its position over multiple waterlines. The land–water segmentation had an overall accuracy of 95–99%. It showed differences between wet and dry season shoreline positions in areas dominated by complex creek networks, but similarities along open coasts. The SAR-derived shorelines deviated from the reference lines by a maximum of 43 m (approximately four pixels), and often less than 10 m (one pixel) in most locations (open coast, estuarine, complex creek networks) at high and low tides, except low tide lines in areas with extensive inter-tidal flats at shorelines 70 m to 370 m from the reference lines. However, for applications such as coastal vulnerability assessment, the high tide shoreline is of greater importance. Thus, depending on the application of interest, problems with low tide shoreline delineation may be irrelevant. Despite limitations, notably the relatively small number of images available that were recorded at high or low tide, the method provides a simple, objective, and cost-effective approach to monitoring shorelines at high and low tide. [ABSTRACT FROM AUTHOR]

Currently, climate change requires the quantification of carbon stored in forest biomass. Synthetic aperture radar (SAR) data offers a significant advantage over other remote detection measurement methods in providing structural and biomass-related information about ecosystems. This study aimed to develop non-parametric Random Forest regression models to assess the changes in the aboveground forest biomass (AGB), basal area (G), and tree density (N) of Mediterranean pine forests by integrating ALOS-PALSAR, Sentinel 1, and Landsat 8 data. Variables selected from the Random Forest models were related to NDVI and optical textural variables. For 2015, the biomass models with the highest performance integrated ALS-ALOS2-Sentinel 1-Landsat 8 data (R2 = 0.59) by following the model using ALS data (R2 = 0.56), and ALOS2-Sentinel 1-Landsat 8 (R2 = 0.50). The validation set showed that R2 values vary from 0.55 (ALOS2-Sentinel 1-Landsat 8) to 0.60 (ALS-ALOS2-Sentinel 1-Landsat 8 model) with RMSE below 20 Mg ha−1. It is noteworthy that the individual Sentinel 1 (R2 = 0.49). and Landsat 8 (R2 = 0.47) models yielded equivalent results. For 2020, the AGB model ALOS2-Sentinel 1-Landsat 8 had a performance of R2 = 0.55 (validation R2 = 0.70) and a RMSE of 9.93 Mg ha−1. For the 2015 forest structural variables, Random Forest models, including ALOS PAL-SAR 2-Sentinel 1 Landsat 8 explained between 30% and 55% of the total variance, and for the 2020 models, they explained between 25% and 55%. Maps of the forests' structural variables were generated for 2015 and 2020 to assess the changes during this period using the ALOS PALSAR 2-Sentinel 1-Landsat 8 model. Aboveground biomass (AGB), diameter at breast height (dbh), and dominant height (Ho) maps were consistent throughout the entire study area. However, the Random Forest models underestimated higher biomass levels (>100 Mg ha−1) and overestimated moderate biomass levels (30–45 Mg ha−1). The AGB change map showed values ranging from gains of 43.3 Mg ha−1 to losses of −68.8 Mg ha−1 during the study period. The integration of open-access satellite optical and SAR data can significantly enhance AGB estimates to achieve consistent and long-term monitoring of forest carbon dynamics. [ABSTRACT FROM AUTHOR]

The past few years have witnessed a growing interest in wireless and batteryless implants, due to their potential in long-term biomedical monitoring of in-body conditions, such as internal organ movements, bladder pressure, and gastrointestinal health. Early proposals for batteryless implants relied on inductive near-field coupling and ultrasound harvesting, which require direct contact between the external power source and the human body. To overcome this near-field challenge, recent research has investigated the use of RF backscatter in wireless micro-implants because of its ability to communicate with wireless receivers that are placed at a distance outside the body $(\sim 0.5$ m), allowing a more seamless user experience. Unfortunately, existing far-field backscatter designs remain limited in their functionality: they cannot perform biometric sensing or secure data transmission; they also suffer from degraded harvesting efficiency and backscatter range due to the impact of variations in the surrounding tissues. In this article, we present the design of a batteryless, wireless and secure system-on-chip (SoC) implant for in-body strain sensing. The SoC relies on four features: 1) employing a reconfigurable in-body rectenna which can operate across tissues adapting its backscatter bandwidth and center frequency; 2) designing an energy efficient 1.37 mmHg strain sensing front-end with an efficiency of 5.9 mmHg $\cdot $ nJ/conversion; 3) incorporating an AES-GCM security engine to ensure the authenticity and confidentiality of sensed data while sharing the ADC with the sensor interface for an area-efficient random number generation; 4) implementing an over-the-air closed-loop wireless programming scheme to reprogram the RF front-end to adapt for surrounding tissues and the sensor front-end to achieve faster settling times below 2 s.

In this article, a compact chipless RFID tag with a small size and high data density is proposed. The tag consists of nested octagonal loops arranged in a concentric configuration. Each two neighboring resonating elements are decentrally coupled by two connector lines at obverse sides. The small size of the tag is achieved through embedding multiple octagonal elements in a nested arrangement, which share the same side, and through connecting the neighboring elements. The tag configuration is designed and optimized to improve the resonating modes with respect to the number of resonating elements. The number of generated resonating modes (N) is mathematically obtained by N=2n-1, such (n) is the resonating elements number. An 11-bits data capacity is presented in the proposed tag within a physical dimension of $10\times10$ mm2, resulting high data density of 11 b/cm2. The proposed tag is realized on Rogers RT/Duroid 5880 substrate having substrate height h=0.508 mm, relative permittivity $\epsilon _{r}=$ 2.2, and loss tangent $\delta = 0.0009$ . The proposed tag is designed and simulated using CST Studio Suite ®. The tag’s performance is initially evaluated through simulation and analyzed for effectiveness. The tag is then fabricated and measured, and the results are compared to ensure agreement between simulation and measurement. The tag advantages are compact size, high data density, and improved resonating modes. The operating frequency range of the proposed tag is 7.5 GHz-15 GHz.

Radio frequency (RF) energy harvesting and wireless power transmission (WPT) technologies —both near-field and far-field—have attracted significant interest for wireless applications and RFID systems. We already utilize near-field WPT products in our life and it is expected that RF EH and far-field WPT systems can drive the future low-power wireless systems. In this article, we initially present a brief historical overview of these technologies. The main technical challenges of rectennas and WPT transmitters are discussed. Furthermore, this paper presents the recent advances on the development of these technologies, including the possibility of powering RFID systems through the millimeter wave power from 5G networks, the trends in flexible rectennas design and the technological developments on the simultaneous wireless information and power transfer (SWIPT).

Geophysical and geological data from the North Mozambique Channel acquired during the 2020–2021 SISMAORE oceanographic cruise reveal a corridor of recent volcanic and tectonic features 200 km wide and 600 km long within and north of Comoros Archipelago. Here we identify and describe two major submarine tectono-volcanic fields: the N’Droundé province oriented N160°E north of Grande-Comore Island, and the Mwezi province oriented N130°E north of Anjouan and Mayotte Islands. The presence of popping basaltic rocks sampled in the Mwezi province suggests post-Pleistocene volcanic activity. The geometry and distribution of recent structures observed on the seafloor are consistent with a current regional dextral transtensional context. Their orientations change progressively from west to east (${\sim }$N160°E, ${\sim }$N130°E, ${\sim }$EW). The volcanism in the western part appears to be influenced by the pre-existing structural fabric of the Mesozoic crust. The 200 km-wide and 600 km-long tectono-volcanic corridor underlines the incipient Somalia–Lwandle dextral lithospheric plate boundary between the East-African Rift System and Madagascar.

Internal wave adalah gelombang laut yang terjadi di lapisan dalam di perairan laut dangkal dan dalam, di mana kemunculannya hanya dapat terjadi jika kolom air terstratifikasi akibat adanya perbedaan densitas massa air. Penelitian ini bertujuan untuk mendeteksi peristiwa internal wave, serta mengukur nilai hambur balik akustik dari internal wave yang terjadi di Selat Lombok. Data yang digunakan adalah hasil pengukuran instrumen broadband echosounder SIMRAD EK80 frekuensi 200 kHz tipe pulsa continuous wave (CW) dengan data pendukung elevasi muka air, suhu, salinitas, dan densitas dari Conductivity Temperature Depth (CTD) “Yoyo” selama 24 jam. Nilai hambur balik didapatkan dengan analisis menggunakan perangkat lunak Sonar5-Pro. Echogram menunjukkan adanya pembentukan nonlinear internal wave disertai dengan adanya turbulensi yang terjadi pada saat gelombang pasang membawa massa air dengan densitas berbeda melewati topografi kasar di Selat Lombok. Nilai backscatter hasil segmentasi berentang dari -75 dB sampai -59 dB.

The upper part of the Krka River estuary and Prokljan Lake are a specific example of a well-stratified estuarine environment in a submerged river canyon. Here, we reconstructed the geomorphological evolution of the area and classified the data gathered in the study, integrating multibeam echosounder data, backscatter echosounder data, side-scan sonar morpho-bathymetric surveys, and acoustic sub-bottom profiling, with the addition of ground-truthing and sediment analyses. This led to the successful classification of the bottom sediments using the object-based image analysis method. Additional inputs to the multibeam echosounder data improved the segmentation of the seafloor classification, geology, and morphology of the surveyed area. This study uncovered and precisely defined distinct geomorphological features, specifically submerged tufa barriers and carbonate mounds active during the Holocene warm periods, analogous to recent tufa barriers that still exist and grow in the upstream part of the Krka River. Fine-grained sediments, classified as estuarine sediments, hold more organic carbon than coarse-grained sediments sampled on barriers. A good correlation of organic carbon with silt sediments allowed the construction of a prediction map for marine sedimentary carbon in this estuarine/lake environment using multibeam echosounder data. Our findings highlight the importance of additional inputs to multibeam echosounder data to achieve the most accurate results. [ABSTRACT FROM AUTHOR]