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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]

Prior to use in operational systems, it is essential to validate ionospheric models in a manner relevant to their intended application to ensure satisfactory performance. For Over‐the‐Horizon radars (OTHR) operating in the high‐frequency (HF) band (3–30 MHz), the problem of model validation is severe when used in Coordinate Registration (CR) and Frequency Management Systems (FMS). It is imperative that the full error characteristics of models is well understood in these applications due to the critical relationship they impose on system performance. To better understand model performance in the context of OTHR, we introduce an ionospheric model validation technique using the oblique ground backscatter measurements in soundings from the Super Dual Auroral Radar Network (SuperDARN). Analysis is performed in terms of the F‐region leading edge (LE) errors and assessment of range‐elevation distributions using calibrated interferometer data. This technique is demonstrated by validating the International Reference Ionosphere (IRI) 2016 for January and June in both 2014 and 2018. LE RMS errors of 100–400 km and 400–800 km are observed for winter and summer months, respectively. Evening errors regularly exceeding 1,000 km across all months are identified. Ionosonde driven corrections to the IRI‐2016 peak parameters provide improvements of 200–800 km to the LE, with the greatest improvements observed during the nighttime. Diagnostics of echo distributions indicate consistent underestimates in model NmF2 during the daytime hours of June 2014 due to offsets of −8° being observed in modeled elevation angles at 18:00 and 21:00 UT. Plain Language Summary: Models of the ionized upper atmosphere, a region known as the ionosphere, must be validated using appropriate techniques prior to their use in operational systems. This is of greatest importance for Over‐the‐Horizon radars (OTHR) that rely on the reflection of radio waves in the 3–30 MHz band from the ionosphere for their operation. The accuracy of OTHR is largely related to the performance of the model ionosphere used to establish target positions, and so it is essential to understand how models behave under different circumstances. We introduce a new technique for validating models using measurements from the Super Dual Auroral Radar Network (SuperDARN) of research radars. Using a dominant feature present within these radar echoes, we perform an example validation of the International Reference Ionosphere (IRI) 2016 by modeling the expected path of radio waves. The performance is seen to be best during winter and typically worse in the evening. Using further information present within the measurements, we diagnose the likely cause of errors to be due to underestimates in a key model parameter. This is confirmed when we offset model parameters using direct measurements of the ionosphere and observe a significant improvement in model performance. Key Points: We introduce an ionospheric model validation technique using SuperDARN ground backscatterPerformance of the IRI‐2016 is best during the daytime of January 2014 and 2018, whilst sporadic‐E in June causes significant degradationsIRI‐2016 range errors are seen to be most significant near the terminator and during the nighttime [ABSTRACT FROM AUTHOR]

Accurate and high-speed object movement tracking systems often face significant challenges due to signal instability caused by the object's movement and rotation. To address these issues, we present STABack, a novel object tracking system using backscatter tags and accelerometer sensors, designed for high-accuracy, high-speed movement tracking. We developed an amplitude stabilization algorithm which uses envelope detection to reduce the impact of high-frequency movement on the signal, and uses dynamic threshold output to lower the BER in backscatter demodulation. Our method reduces the BER by 0.3757 compared to the regular demodulation method, resulting in a final BER of 0.07. Our evaluation of the STABack prototype shows that it achieves a median distance measurement accuracy of 6.45 cm with a standard deviation of 6.95 cm, under the condition of a speed of 120 cm. The attitude angle estimation's mean error is under 7 degrees. The system's accuracy in detecting the target object's trajectory is as high as 99%, and it can still decode with a bit error rate of no more than 0.034 at a speed of 166 cm/s. The power consumption of our system prototype is only 38.54 μW based on our experimental results. Overall, our results demonstrate that STABack can accurately estimate the movement and rotation of target objects in unstable backscatter channels. [ABSTRACT FROM AUTHOR]

High-resolution mapping of deep-sea polymetallic nodules is needed (a) to understand the reasons behind their patchy distribution, (b) to associate nodule coverage with benthic fauna occurrences, and (c) to enable an accurate resource estimation and mining path planning. This study used an autonomous underwater vehicle to map 37 km2 of a geomorphologically complex site in the Eastern Clarion–Clipperton Fracture Zone. A multibeam echosounder system (MBES) at 400 kHz and a side scan sonar at 230 kHz were used to investigate the nodule backscatter response. More than 30,000 seafloor images were analyzed to obtain the nodule coverage and train five machine learning (ML) algorithms: generalized linear models, generalized additive models, support vector machines, random forests (RFs) and neural networks (NNs). All models ML yielded similar maps of nodule coverage with differences occurring in the range of predicted values, particularly at parts with irregular topography. RFs had the best fit and NNs had the worst spatial transferability. Attention was given to the interpretability of model outputs using variable importance ranking across all models, partial dependence plots and domain knowledge. The nodule coverage is higher on relatively flat seafloor (< 3°) with eastward-facing slopes. The most important predictor was the MBES backscatter, particularly from incident angles between 25 and 55°. Bathymetry, slope, and slope orientation were important geomorphological predictors. For the first time, at a water depth of 4500 m, orthophoto-mosaics and image-derived digital elevation models with 2-mm and 5-mm spatial resolutions supported the geomorphological analysis, interpretation of polymetallic nodules occurrences, and backscatter response. [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]

Recent works have achieved considerable success in improving the concurrency of backscatter network. However, they do not optimize the balance between throughput and spectrum occupancy, both of which serve as pivotal parameters in concurrent transmissions. Moreover, these works also introduce complex components on tag thereby increasing both power consumption and deployment costs. In this article, we propose Spray, a tag-lightweight system to achieve high throughput and narrow-band occupancy with low power. The key idea is to incorporate an agile channel allocating and scheduling mechanism into the backscatter network. This approach allows for efficient spectrum utilization and concurrency without the need for energy-intensive components. To optimize throughput in the presence of the challenge of harmonic interference, we introduce a novel algorithm that determines the channels with an optimal combination of central frequencies and bandwidths. Additionally, we propose a fair scheduling strategy to ensure equitable transmission opportunities for all tags. We prototype the Spray tag using commercial off-the-shelf components and implement the excitation and receiver with software-defined radio platform. Our evaluation shows that the system supports 30 parallel tags transmitting in the bandwidth of 600 kHz and the throughput can reach more than 280 kbps. [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.

The triterpene squalene is widely used in the food, cosmetics and pharmaceutical industries due to its antioxidant, antistatic and anti-carcinogenic properties. It is usually obtained from the liver of deep sea sharks, which are facing extinction. Alternative production organisms are marine protists from the family Thraustochytriaceae, which produce and store large quantities of various lipids. Squalene accumulation in thraustochytrids is complex, as it is an intermediate in sterol biosynthesis. Its conversion to squalene 2,3-epoxide is the first step in sterol synthesis and is heavily oxygen dependent. Hence, the oxygen supply during cultivation was investigated in our study. In shake flask cultivations, a reduced oxygen supply led to increased squalene and decreased sterol contents and yields. Oxygen-limited conditions were applied to bioreactor scale, where squalene accumulation and growth of Schizochytrium sp. S31 was determined in batch, fed-batch and continuous cultivation. The highest dry matter (32.03 g/L) was obtained during fed-batch cultivation, whereas batch cultivation yielded the highest biomass productivity (0.2 g/L*h−1). Squalene accumulation benefited from keeping the microorganisms in the growth phase. Therefore, the highest squalene content of 39.67 ± 1.34 mg/g was achieved by continuous cultivation (D = 0.025 h−1) and the highest squalene yield of 1131 mg/L during fed-batch cultivation. Volumetric and specific squalene productivity both reached maxima in the continuous cultivation at D = 0.025 h−1 (6.94 ± 0.27 mg/L*h−1 and 1.00 ± 0.03 mg/g*h−1, respectively). Thus, the choice of a suitable cultivation method under oxygen-limiting conditions depends heavily on the process requirements. Key points: • Measurements of respiratory activity and backscatter light of thraustochytrids • Oxygen limitation increased squalene accumulation in Schizochytrium sp. S31 • Comparison of different cultivation methods under oxygen-limiting conditions [ABSTRACT FROM AUTHOR]

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]

Radio Frequency Identification (RFID) is a burgeoning technology pivotal for optimizing intricate supply chains. Although its potential benefits in healthcare and pharmaceuticals are substantial, RFID implementation in these sectors has been slow-moving. Surprisingly, limited research has probed RFID performance on pharmaceutical products and their packaging. This study aims to explore the influence of diverse liquid drug formulations on RFID performance, utilizing a pre-optimized Ultra High Frequency (UHF) RFID tag. Five distinct drug formulations, encompassing water, saline, and lipids, were studied, alongside deionized (DI) water and empty vials as control subjects. The products were transferred to separate vials, each affixed with a pre-optimized UHF RFID tag. Performance evaluations, including Threshold Sweep, Orientation Sweep, and Population Analysis, were conducted to assess tag readability, focusing on sensitivity, backscatter, and theoretical read range. The results underscore how the composition of different pharmaceutical drug formulations significantly impact RFID tag readability in a controlled, reflection-free environment. These findings provide valuable insights for designing intelligent packaging to enhance the readability of RFID-tagged pharmaceutical products. [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]

The ultrasonic properties of scalp may be relevant to a variety of applications including transcranial ultrasound. However, there is no information about the ultrasonic properties of scalp available in the literature. While ultrasonic studies of skin from other anatomic regions have been previously reported, scalp tissue is generally thicker with a higher density of hair follicles, blood vessels and sebaceous glands. Thus, it is unknown if the ultrasonic properties of scalp are similar to skin from other regions. The goal of this study was to measure the ultrasonic properties of human scalp. Pulse-echo measurements were performed with a 7.5 MHz ultrasound transducer to determine the speed of sound (SOS), frequency slope of attenuation (FSA) and integrated backscatter coefficient (IBC) of 32 specimens of formalin-fixed human scalp from four donors. The means ± standard deviations for these three ultrasonic quantities measured in the frequency range 2.83–7.74 MHz over all specimens were SOS = 1525 ± 16.92 m/s, FSA = 2.59 ± 0.724 dB/cm/MHz and IBC = 0.122 ± 0.0746 cm–1 Sr–1. These values are comparable to reported values for human skin from other parts of the body, but some differences in SOS and IBC exist. [ABSTRACT FROM AUTHOR]

Purpose: To compare corneal densitometry in a consecutive series of 52 endothelial keratoplasties (DMEK/DSAEK) using a Scheimpflug-based device after six months of follow-up. Methods: Corneal densitometry (CD) values of 102 eyes were divided into three main groups: 33 DMEKs, 19 DSAEKs, and 50 healthy eyes without previous ocular surgery. The CD values were then analyzed and compared between the groups. We measured three main layers in depth and four different concentric zones at 1, 3, and 6 months postoperatively. Results: In the DMEK group, total CD significantly decreased from 38.02 ± 10.21 grayscale units (GSU) to 31.13 ± 9.25 GSU (P < 0.01) between the first and the sixth month postoperative. In the DSAEK group, we found significant changes only between the first and three months after surgery (from 42.62 ± 9.31 GSU to 38.71 ± 10.53 GSU (P < 0.01). Regarding the concentric zones, CD in the DMEK group significantly decreased in the central zone from 33.55 ± 12.07 GSU to 30.63 ± 10.15 GSU (P < 0.01) and significantly increased in the periphery from 30.63 ± 10.15 GSU to 36.72 ± 9.37 GSU, (P < 0.01). The DSAEK group showed no changes in the central zone (from 36.91 ± 13.80 GSU to 36.14 ± 11.47 GSU, P = 0.52) and CD significantly increased in the periphery (41.91 ± 9.28 GSU, P < 0.01). Conclusion: When comparing CD values in DMEK versus DSAEK, we found no differences by layers or at central-paracentral concentric zones, although CD differences in the peripheral zones were statistically significant. This finding may be attributed to the thicker graft at periphery with a delayed clearance and less anatomical interphase in DSAEK. [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]

Knowing the location of polar bears (Ursus maritimus) in their winter dens is crucial for minimizing disturbance during this critical period in their life cycle. Previous research has used Forward Looking Infrared (FLIR) technology to detect bear dens but has only achieved a detection accuracy of 45% for single flights. The thermal nature of FLIR means that some bears are never detected nor are detectable using FLIR. In this paper we explore the use of Synthetic Aperture Radar (SAR) as an alternative polar bear detection technology in a simple pilot study in Churchill, Manitoba, Canada, during October 2021. In this experimental study, we focused on the detection of polar bears on the surface in the SAR images. The result of this study can inform future efforts to proceed to den-detection experiments. In this study, we achieved a polar bear identification accuracy of 66%, albeit with a small sample size. Many of the challenges we encountered involved low signal-to-noise ratios and imprecise flight paths. Concurrent research from other parties shows that neural networks and other machine learning techniques can overcome these challenges to some degree, suggesting that SAR may be a promising candidate to become an effective tool for polar bear detection, particularly when coupled with other sensors such as FLIR. [ABSTRACT FROM AUTHOR]