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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]

Methods to assess ultrasound backscatter anisotropy from clinical array transducers have recently been developed. However, they do not provide information about the anisotropy of microstructural features of the specimens. This work develops a simple geometric model, referred to as the secant model, of backscatter coefficient anisotropy. Specifically, we evaluate anisotropy of the frequency dependence of the backscatter coefficient parameterized in terms of effective scatterer size. We assess the model in phantoms with known scattering sources and in a skeletal muscle, a well-known anisotropic tissue. We demonstrate that the secant model can determine the orientation of the anisotropic scatterers, as well as accurately determining effective scatterer sizes, and it may classify isotropic versus anisotropic scatterers. The secant model may find utility in monitoring disease progression as well as characterizing normal tissue architectures. [ABSTRACT FROM AUTHOR]

The traditional convolutional neural network (CNN)-based methods usually use the spatial information in the amplitude of complex synthetic aperture radar (SAR) images. Several studies have started to concentrate on merging the unique physical properties of SAR images, such as DSN-v1, extracting the backscattering characteristic from the frequency domain. Although DSN-v1 has obtained impressive classification ability, there is some room for improvement. In this letter, DSN-v2 is proposed to boost the classification ability of man-made and natural objects in SAR images. The improvement is reflected in two aspects. First, a multiscale subband feature extraction (MSFE) component is designed for natural objects. Since we observe their multiscale subband spectrum is significantly different, multiple encoders are used to extract effective features. Second, the additive angular margin (AAM) loss is introduced to distinguish man-made objects more clearly by manually adding a margin to the decision boundary. The experimental results on the Sentinel-1 (S1) dataset show that DSN-v2 achieves superior classification performance and model training speed compared with DSN-v1. [ABSTRACT FROM AUTHOR]

In recent years, the application of deep learning for water-body detection in synthetic aperture radar (SAR) images has seen extensive development. However, a significant proportion of these works primarily concentrate on enhancing and optimizing the model structure, with inadequate exploration of the potential impact of hyperparameter settings, a critical determinant of model performance. Thus, to fully exploit the power of deep learning in water-body detection from SAR images, this letter presents a diversified optimization strategy that revolves around the dung beetle optimizer-convolutional neural network (DBO-CNN) model, complemented by characteristic fusion and decision-level fusion. The DBO-CNN model employs the DBO algorithm to search for the optimal hyperparameter of the CNN model for bolstering the performance of water-body detection in SAR images. To further enhance the performance, the DBO-CNN model uses unique input data which is constructed by integrating the polarimetric characteristic obtained from $H/\alpha $ and model-based (MB) polarization decomposition methods with backscatter characteristics. Finally, two decision-level fusion methods are proposed to optimize detection results, enhancing the recall and intersection over union (IoU) to 96.5% and 91.5%, respectively. In summary, Spaceborne SAR images, with the application of polarization decomposition and neural network, provide new insights and in-depth understanding for detecting water-body. [ABSTRACT FROM AUTHOR]

Reliable and accurate rice mapping using synthetic aperture radar (SAR) in cloudy and rainy areas is essential for achieving the United Nations Sustainable Development Goal 2 of 2030. An interpretable deep learning SAR rice area mapping method is proposed in this letter to suppress the interference of wetlands and other land covers to multitemporal SAR rice area mapping and improve the accuracy and confidence of the “black box” deep learning model results. Combining the temporal backscattering coefficients and interferometric coherence, three interpretable temporal features are extracted to effectively distinguish rice. Then, the explainable feature-aware network (XFANet), which can provide the learned importance weights of the normalization methods as self-interpretation, is constructed, and the pixel-wise gradient-weighted class activation mapping (PGCAM) post-hoc interpretation method is introduced to interpret the feature variation within the model. The experimental results in the Kampong Chhang and Kampong Chham provinces of Cambodia show that the proposed three interpretable features well suppressed the wetland disturbance to rice. With high interpretability, the overall accuracy (OA) of XFANet reaches 93.43%. [ABSTRACT FROM AUTHOR]

Heavy rain is associated with strong winds and extreme waves in a tropical cyclone (TC). In this letter, a practical algorithm for rain rate retrieval in TCs is proposed through 24 dual-polarized [vertical-vertical (VV) and vertical-horizontal (VH)] Sentinel-1 (S-1) synthetic aperture radar (SAR) images acquired in interferometric-wide (IW) swath mode, in which 13 images are collocated with the observations from stepped-frequency microwave radiometers (SFMRs). TC winds are directly obtained from VH-polarized images utilizing the geophysical model function (GMF) S-1 IW mode wind speed retrieval model after noise removal (S1IW.NR). The normalized radar cross section (NRCS) at VV-polarization channel is simulated using GMF CMOD5N and VH-polarized SAR wind. It is found that the difference between the simulated NRCSs and measurements from SAR is linearly related to the rain rate and oscillates with the incidence angle. Following this finding, an empirical algorithm for SAR rain rate retrieval is developed, denoted as CRAIN2_S1, which considers the influence of the radius of the maximum wind speed. The proposed algorithm is applied to 11 images in the dataset, and the validation of the rain rate (up to 35 mm/hr) against the products from global precipitation measurements (GPMs) has a root mean square error (RMSE) of 1.74 mm/hr, a correlation coefficient of 0.92 and a scatter index (SI) of 0.29. Collectively, it is concluded that the algorithm CRAIN2_S1 can be practically applied for dual-polarized SAR rain rate retrieval without any external information. [ABSTRACT FROM AUTHOR]

The accuracy of soil moisture estimated from synthetic aperture radar (SAR) backscatter data at high resolutions is limited by speckle. A common practice to mitigate speckle is to multilook the data prior to retrieving soil moisture. While multilooking indeed reduces speckle, it also decreases the spatial resolution and removes possibly useful high resolution information from the data. We, therefore, hypothesized that using higher resolution backscatter data for soil moisture retrieval would lead to higher retrieval accuracies. A high resolution field study combined with a synthetic experiment showed that calculating soil moisture prior to multilooking to the final target resolution calculate-then-average (CtA) has substantial advantages over the average-then-calculate (AtC) approach. Currently, the AtC strategy is most often applied in soil moisture studies, mainly due to its computational advantage compared to the CtA approach. We show that by making use of a higher source resolution backscatter data than the target resolution, we could improve soil moisture retrieval over an agricultural field. [ABSTRACT FROM AUTHOR]

Forest fire not only seriously affects the stability of the forest ecosystem but also threatens the safety of human life and property. The previously burned areas extracting method mainly focuses on optical images, susceptible to cloud and fog objective environmental factors. Although there are related studies on the threshold segmentation of single SAR feature types, further information mining for SAR image data, e.g., backscattering intensity, polarization decomposition, texture, and other features, is still insufficient. Therefore, this letter proposes a burned area extracting method using polarization and texture features of Sentinel-1A images to combine and comprehensively mine various SAR feature change information caused by forest fires with a random forest (RF) model. For validation purposes, we compared the burned areas’ extracted results with the reference data acquired based on Sentinel-2A optical imagery. The comparative results show that the SAR extraction results highly agree with the reference data, with an accuracy of 87.12%, and the commission and omission errors were 20.44% and 12.88%, respectively. The proposed machine learning method helps extract fire areas covered by thick smoke or persistent clouds and provides a reference to related research. [ABSTRACT FROM AUTHOR]

This letter describes a model-based algorithm for estimating tree height and other bio-physical land parameters from time series of synthetic aperture radar (SAR) interferometric coherence and backscatter supported by sparse lidar data. The random-motion-over-ground model (RMoG) is extended to time series and revisited to capture the short- and long-term temporal coherence variability caused by motion of the scatterers and changes in the soil and canopy backscatter. The proposed retrieval algorithm estimates first the spatially slow-varying RMoG model parameters using sparse lidar data, and subsequently the spatially fast-varying model parameters such as tree height. The recently published global Sentinel-1 (S-1) interferometric coherence and backscatter data set and sparse spaceborne GEDI lidar data are used to illustrate the algorithm. Results obtained for a small region over Spain show that the temporal coherence and backscatter time series have the potential to be used for global, model-based land parameter estimation. [ABSTRACT FROM AUTHOR]

To test the anisotropy of human tendons in conventional B-mode ultrasound, we prospectively performed ultrasound scans of 40 normal patella tendons and 24 patella tendons with chronic tendinopathy in adults. We scanned all tendons in longitudinal orientation (parallel to tendon fibers) using a linear array transducer (8.5 MHz) with beam steering at 0°, 5°, 10°, 15° and 20°. We used ImageJ histogram analysis to process B-mode images offline for assessing backscatter as a function of angle, known as backscatter anisotropy, between normal tendons and the subcutaneous tissues and between normal tendons and tendons with tendinopathy. We compared the results using the slopes of linear regression lines drawn through the angle-dependent data, and we concluded that the tissue anisotropy was significantly different if the 95% confidence intervals of the line slopes for different tissues did not overlap. We observed significant differences between normal tendons and both the adjacent subcutaneous tissues and tendons with tendinopathy. However, the difference in the regression slopes between tendons with tendinopathy and the adjacent subcutaneous soft tissues was not significant. It appears that changes in anisotropic backscatter could be used to detect tendon abnormalities and in assessing the significance of disease and the effectiveness of therapy. [ABSTRACT FROM AUTHOR]

The measurement of suspended sediments from acoustic backscatter was originally developed in marine science for monitoring near‐bottom suspensions usually composed of sand particles. In the last decade, there has been a growing interest in adapting these techniques to rivers. The so‐called "solid particle theory" developed in oceanography mainly applies to suspensions of non‐cohesive solid particles producing incoherent backscatter signal. So far, this theory has been used for interpreting river backscatter even if it relies on assumptions that are not obviously met in rivers. This study uses a set of measurements made on the Rhône River in France to discuss the typical issues which challenge the interpretation of sound backscattering for monitoring suspended sediments in rivers. Large discrepancies between model outputs and measurements for frequencies lower than 2.5 MHz suggest that other scattering processes including flocculation and air micro‐bubbles may have a large impact on acoustic backscatter and attenuation. Deviations of the backscatter echo distribution from Rayleigh statistics were observed, suggesting that the assumption of incoherent backscattering is not always met. This work calls for the development of a more complete theory for interpreting river backscatter. Key Points: Modeling the acoustic backscatter of suspended sediments in rivers often results in large errorsVarious sources of error may arise from the diversity and complexity of scatterers in riversSpecific experiments and theoretical developments are required to identify and reduce modeling errors [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]

Supply chain management aims to achieve both efficiency and low cost. Backscatter technology provides a low-energy consumption approach for critical links in the supply chain, such as warehouse management and cargo identification. Traditional backscatter systems achieve tag data transmission through dedicated hardware or controlled transmission sources. An additional access point (AP) can be used to ensure that the original data are always known in tag data decoding. These requirements increase the deployment costs and are not suitable for large-scale applications. To address these challenges, we introduce CRCScatter, a backscatter system based on a cyclic redundancy check (CRC) reverse algorithm, with an uncontrolled source and a single-AP receiver. The CRCScatter decoder at the receiver uses the constraints within 802.11b WiFi packets to recover the original packet and decode tag data from the backscatter packet. Our Matlab simulation results show that CRCScatter is effective in the low signal-to-noise ratio (SNR) regime, and its average decoding time is independent of the length of tag data. By appending redundant bits in tag data, the decoding accuracy of CRCScatter can be improved. In summary, CRCScatter presents a backscatter communication mode based on ambient WiFi signals with fewer hardware requirements and low deployment costs. Furthermore, the decoding idea of calculating unknown data based on the packet constraints has the potential to expand to different types of excitation packages. [ABSTRACT FROM AUTHOR]

The multi-scale interaction between combustion and turbulence is of great importance in modifying the small-scale flame structure and kinetic energy, especially in swirling flames under practical conditions. In the present study, direct numerical simulation of swirling partially premixed flame is conducted within a model combustor under gas turbine conditions. The reactive flow is compared to the corresponding non-reactive one to investigate the influence of combustion on the scaled kinetic energy transport. Kinetic energy spectra demonstrate that the turbulent kinetic energy is reduced in the dissipative subrange while enhanced in the energetic one by the flame. The critical scale is located in the inertial subrange and close to the estimated turbulent flame thickness. Filtering analyses show that the resolved-scale kinetic energy is augmented by the increased large-scale pressure-gradient work in the reactive flow, while the subgrid-scale kinetic energy is attenuated by the enhanced small-scale viscous dissipation. The backscatter prevails in the heat release regions when the filter size is larger than the laminar flame thickness, and this effect decreases with the swirling flow developing downstream. The interaction between the kinetic energy flux and the local dilatation as well as the subgrid-scale pressure-gradient work is also investigated to achieve a comprehensive understanding about the effects of combustion on the backscatter. [ABSTRACT FROM AUTHOR]

Non‐orthogonal multiple access (NOMA) technique introduces spectrum cooperation among different users and devices, which improves spectrum efficiency significantly. Energy‐limited devices benefit from the backscatter (BAC) technique to transmit signals without extra energy consumption. The combination of NOMA and BAC provides a promising solution for Internet of Things (IoT) networks, where massive devices simultaneously transmit and receive signals. This study investigates a system model with two NOMA downlink users and an uplink device. The aim is to maximise the data rate of the uplink device by optimising the power allocation coefficient and the backscattering coefficient. Meanwhile the quality of service requirements of two NOMA users are guaranteed. The closed‐form solution of two optimisation variables is derived, and an alternating algorithm is also proposed to solve the formulated optimisation problem efficiently. The proposed system verifies the feasibility of IoT devices being added into existing networks and provides a promising solution for wireless communication networks in the future. [ABSTRACT FROM AUTHOR]

Accurately estimating wheat yield is crucial for informed decision making in precision agriculture (PA) and improving crop management. In recent years, optical satellite-derived vegetation indices (Vis), such as Sentinel-2 (S2), have become widely used, but the availability of images depends on the weather conditions. For its part, Sentinel-1 (S1) backscatter data are less used in agriculture due to its complicated interpretation and processing, but is not impacted by weather. This study investigates the potential benefits of combining S1 and S2 data and evaluates the performance of the categorical boosting (CatBoost) algorithm in crop yield estimation. The study was conducted utilizing dense yield data from a yield monitor, obtained from 39 wheat (Triticum spp. L.) fields. The study analyzed three S2 images corresponding to different crop growth stages (GS) GS30, GS39-49, and GS69-75, and 13 Vis commonly used for wheat yield estimation were calculated for each image. In addition, three S1 images that were temporally close to the S2 images were acquired, and the vertical-vertical (VV) and vertical-horizontal (VH) backscatter were calculated. The performance of the CatBoost algorithm was compared to that of multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) algorithms in crop yield estimation. The results showed that the combination of S1 and S2 data with the CatBoost algorithm produced a yield prediction with a root mean squared error (RMSE) of 0.24 t ha−1, a relative RMSE (rRMSE) 3.46% and an R2 of 0.95. The result indicates a decrease of 30% in RMSE when compared to using S2 alone. However, when this algorithm was used to estimate the yield of a whole plot, leveraging information from the surrounding plots, the mean absolute error (MAE) was 0.31 t ha−1 which means a mean error of 4.38%. Accurate wheat yield estimation with a spatial resolution of 10 m becomes feasible when utilizing satellite data combined with CatBoost. [ABSTRACT FROM AUTHOR]

To clarify the dosimetric characteristics of a real-time variable shape rubber-containing tungsten (STR) bolus in a clinical plan and investigate the efficacy of the STR bolus in photon radiotherapy for keloids and other superficial tumors. A 5 mm gel bolus or 1 mm STR bolus was placed on a solid water phantom. Tangential irradiation was performed using a TomoTherapy Radixact-X9 and 6 MV X-ray flattening-filter-free beam, and the surface dose was measured with radiochromic film. Clinical-like plans (TomoDirect; TD and TomoHelical; TH) were applied with the same geometry and the dose distributions were measured. The increase in surface dose by the build-up effect and backscatter was 37.7% and 8.0% for the gel bolus, and 40.5% and 26.4% for the STR bolus, respectively. In the TD and TH plans, the increase in surface dose was 27.4% and 48.3% for the gel bolus, and 39.0% and 63.2% for the STR bolus. Similary, changes in the sagittal plane dose were − 3.9% and 6.1% for the gel bolus, and − 6.3% and 6.9% for the STR bolus. The STR bolus effectively increased the surface dose by the build-up effect and backscatter in photon radiotherapy for keloids and other superficial tumors. [ABSTRACT FROM AUTHOR]

This study contributes to a better understanding of geogenic reef distribution in the southern Baltic Sea and highlights the implications of survey-related factors on automated boulder classification when utilizing data from multiple surveys. The distribution of hard grounds and reefs is needed as a baseline for geological and biological studies, but also for offshore construction, navigation and coastal management. In this study we provide maps of the distribution of geogenic reefs for about 750 km2 in the southern Baltic Sea, at the sites Wismar Bay, Darss Sill and Plantagenet Ground. The maps are based on full-coverage backscatter surveys with different side scan sonar and multibeam echo sounder systems. The distribution and number of boulders in the backscatter maps was determined using a convolutional neural network combined with quality control by human experts. The extent of the geogenic reefs was calculated on the basis of the number of boulders in 50 m x 50 m grid cells. We compare the results with previous reef maps based on point sampling, which show reefs of either biogenic or geogenic origin. According to the earlier maps, 11% of the Plantagenet Ground seabed was classified as reef habitat type. This is similar to the result of our study (12%), although we only considered reefs of geogenic origin. In the Darss Sill, geogenic reefs are larger in this study than in previous maps (30% versus 23%). In both regions, the spatial distribution of reefs differs significantly between old and new maps. For Wismar Bay, previous maps classify 3% of the seafloor as habitat type reef, whereas this study classifies 35% as geogenic reef. The use of automated classification during seafloor mapping allowed large areas to be interpreted in a few days. It also provided more information on the distribution of boulders within the geogenic reef. However, the boulder distribution maps show the negative effects of survey geometry, frequency and environmental conditions on automated boulder classification when data from different surveys are combined.

To facilitate the conservation of seafloor habitats and planning of offshore activities, there is a growing need for mapping marine benthos in an effective and efficient way. Acoustic data acquired by multi-beam echosounders (MBES) have been extensively used for large-scale and high-resolution seafloor characterization. A deeper understanding of the relationship between backscatter data and sediment compositions can help to identify the benthos occurrence from the MBES data. With two multi-spectral MBES datasets collected near the western Wadden Sea islands in the North Sea, we investigated the potential of mapping marine benthos through backscatter classification. Two unsupervised classification methods, i.e., Bayesian classification, which mainly exploits the backscatter strength from incident angles larger than 20°, and hierarchical clustering of the backscatter strength at different angular ranges, were employed and the results were compared. The classification results from both methods showed a good correspondence with sediment properties such as the median grain size. Moreover, based on a principal component analysis of bottom sample properties, the hierarchical clustering results indicated a better distinction between contributions from the gravel content and benthos occurrence, e.g., sand mason worm density, than Bayesian classification, through involving the backscatter angular variations. Classification for multiple frequencies, on the other hand, showed little difference regarding the relationship with bottom sample properties. Although the backscatter difference between frequencies was also found to positively correlate with certain sample properties, using multi-spectral features for acoustic classification in this study did not reveal additional information compared to single-frequency classification results.

Lidar profiling of the atmosphere provides information on existence of cloud and aerosol layers and the height and structure of those layers. Knowledge of feature boundaries is a key input to assimilation models. Moreover, identifying feature boundaries with minimal latency is essential to impact operational assimilation and real-time decision making. Using advanced convolution neural network algorithms, we demonstrate real-time determination of atmospheric feature boundaries using an airborne backscatter lidar. Results are shown to agree well with traditional processing methods and are produced with higher horizontal resolution than the traditional method. Demonstrated using airborne lidar, the algorithms and process are extendable to real-time generation of data products from a future spaceborne sensor.

A passive keyless entry and start (PKES) system is an electronic lock for an automobile that provides the great convenience of opening the door when the user is in proximity. However, the system suffers from relay attacks. Recent studies revealed that relayed signals result in valid packets that are sufficient to unlock doors. In particular, the adversary causes proximity errors by injecting a certain time delay before relaying to manipulate the phase rotation in the response signal. To this end, we present a novel relay-resilient proximity detection solution, BackProx, which uses pseudo-random frequency hopping with the assistance of a reference backscattering device. Since the relay adversary transmits the relayed signals from the key fob at long distances, the signals should propagate over longer distances, resulting in inevitable significant phase rotation with different frequencies. Inspired by this finding, BackProx uses an additional backscattering device to ensure the proximity of the key fob using the invariant characteristics of radio frequency signals in the physical layer (i.e., phase rotation). Our evaluation demonstrates the effectiveness of BackProx in resisting three types of relay attacks. The results show that it achieved a 98% true positive rate at close range and a 0.3% false positive rate at long range. [ABSTRACT FROM AUTHOR]

The classical Smagorinsky model's solution is an approximation to a (resolved) mean velocity. Since it is an eddy viscosity model, it cannot represent a flow of energy from unresolved fluctuations to the (resolved) mean velocity. This model has recently been corrected to incorporate this flow and still be well‐posed. Herein we first develop some basic properties of the corrected model. Next, we perform a complete numerical analysis of two algorithms for its approximation. They are tested and proven to be effective. [ABSTRACT FROM AUTHOR]

Advances 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. [ABSTRACT FROM AUTHOR]

Soil moisture plays an important role in agriculture, hydrology, plants’ seasonal growth cycle, and their sustainability under climate change. This letter presents a novel method for regional mapping of soil moisture with partial vegetation cover by using dual-polarized Sentinel-1 C-band SAR data. Model-based retrieval of soil moisture from radar backscatter under partial vegetation cover first necessitates the separation of the backscatter of the underlying soil from total radar backscatter and then the implementation of an inversion technique to estimate its soil moisture content from the backscattering coefficient of the underlying soil. The regional mapping of soil moisture was verified with in situ measurements along a climatic gradient (650–100 mm/year) in central Israel. A good agreement between the estimated soil moisture and the measured from ground samples was found, with an $R^{2}$ of 0.793 with an RMSE of 0.047. [ABSTRACT FROM AUTHOR]

The temporal and spatial resolutions of spaceborne lidar are insufficient, and traditional 1064-/532-/355-nm wavelength lidar devices have difficulty in performing horizontal observations in urban areas due to the eye safety problems. Therefore, there is no mature method of horizontal pollution observation that satisfies hyperfine environmental management and control in urban areas. Recently, 1.5- $\mu \text{m}$ eye-safe lidars were used to build a Jinan quantum lidar network (JQLN), which can observe horizontal aerosol pollution with a radius of 6 km and with high spatial and temporal resolutions of 15 m and 8 min, respectively. In this study, sudden particulate matter (PM) pollution occurred in Jinan from February 21 to 22, 2021. Spaceborne lidar was used to perform a large-scale analysis, and the detailed transport path of pollution was measured by lidar. Pollution transport path forecasting within half an hour and 180 km2 was performed. The forecasting results were in good agreement with the 11 national monitoring stations (NMSs) distributed within the region. The forecasting errors were within minutes and hundreds of meters, and the maximum errors of the pollution arrival time and distribution distance were less than 3 min and 200 m, respectively. This study and the JQLN are promising methods for hyperfine environmental management and control in urban areas. [ABSTRACT FROM AUTHOR]

Forests' ecosystems are an essential part of the global carbon cycle with vast carbon storage potential. These systems are currently under external pressures showing increasing change due to climate change. A better understanding of the biophysical properties of forests is, therefore, of paramount importance for research and monitoring purposes. While there are many biophysical properties, the focus of this study is on the in-depth analysis of the connection between the C-band Copernicus Sentinel-1 SAR backscatter and evapotranspiration (ET) estimates based on in situ meteorological data and the FAO-based Penman–Monteith equation as well as the well-established global terrestrial ET product from the Terra and Aqua MODIS sensors. The analysis was performed in the Free State of Thuringia, central Germany, over coniferous forests within an area of 2452 km2, considering a 5-year time series (June 2016–July 2021) of 6- to 12-day Sentinel-1 backscatter acquisitions/observations, daily in situ meteorological measurements of four weather stations as well as an 8-day composite of ET products of the MODIS sensors. Correlation analyses of the three datasets were implemented independently for each of the microwave sensor's acquisition parameters, ascending and descending overpass direction and co- or cross-polarization, investigating different time series seasonality filters. The Sentinel-1 backscatter and both ET time series datasets show a similar multiannual seasonally fluctuating behavior with increasing values in the spring, peaks in the summer, decreases in the autumn and troughs in the winter months. The backscatter difference between summer and winter reaches over 1.5 dB, while the evapotranspiration difference reaches 8 mm/day for the in situ measurements and 300 kg/m2/8-day for the MODIS product. The best correlation between the Sentinel-1 backscatter and both ET products is achieved in the ascending overpass direction, with datasets acquired in the late afternoon, and reaches an R2-value of over 0.8. The correlation for the descending overpass direction reaches values of up to 0.6. These results suggest that the SAR backscatter signal of coniferous forests is sensitive to the biophysical property evapotranspiration under some scenarios. [ABSTRACT FROM AUTHOR]

Evaluation of crop growth parameters is crucial to understand the crop phenology and its relation with Synthetic Aperture Radar (SAR) scattering mechanism. The study explores the potential of dual polarization Sentinel–1 backscatter parameters to investigate its sensitivity with various biophysical parameters of paddy crop. Temporal profile of crop growth parameters such as fresh biomass, dry biomass, vegetation water content (VWC) and plant height of paddy crop are assessed with respect to SAR parameters such as VV, VH, VH/VV and Radar Vegetation Index (RVI). Temporal Sentinel–1A SAR data during June to November 2018 was considered for this study. Regression analysis using linear and logarithmic models reveal that the sensitivity of Sentinel–1A backscatter parameters with crop biophysical parameters is best when the crop is at early vegetation stage i.e. between tillering to booting stages with maximum R2 value of 0.68 for liner regression with VH and 0.69 for multiple regression for crop height followed by other biophysical parameters. At later vegetation stage i.e. ripening stage maximum R2 value of 0.52 for liner regression with VH and multiple regression for crop biomass is observed. There is no significant correlation for peak vegetation stage. The results are indicative for the potential use of dual-polarimetric SAR parameters as input parameters for multiple regression models to improve the prediction of biophysical parameters at different phenolgical stages. [ABSTRACT FROM AUTHOR]

We present the design of a passive wireless communication method that does not rely on ambient or generated RF sources. Instead, the method modulates the Johnson (thermal) noise of a resistor to transmit information bits wirelessly. By selectively connecting or disconnecting a matched resistor to an antenna, the system can achieve data rates of up to 26 bps and distances of up to 7.3 m. This communication method operates at very low power, similar to that of an RFID tag, with the advantage of not requiring a preexisting RF signal to reflect. [ABSTRACT FROM AUTHOR]

Self-sustainable energy harvesting for Internet of Things devices is challenging since ambient energy may be sporadic and unpredictable. This situation leads to frequent power failures that lead to intermittent operations, which prevent the reliability of data communications. This article presents fundamental hardware circuitry that enables reliable intermittent communications over wireless batteryless node networks. We emphasize two main mechanisms that ensure energy awareness and reliability: energy status-sharing and synchronized operation. We introduce novel low-power and self-sustainable plug-and-play circuits to support these mechanisms. [ABSTRACT FROM AUTHOR]

The low-power radio technologies open up many opportunities to facilitate Internet-of-Things (IoT) into our daily life, while their minimalist design also makes IoT devices vulnerable to many active attacks. Recent advances use an antenna array to extract fine-grained physical-layer signatures to identify the attackers, which adds burdens in terms of energy and hardware cost to IoT devices. In this paper, we present ShieldScatter, a lightweight system that attaches low-cost tags to single-antenna devices to shield the system from active attacks. The key insight of ShieldScatter is to intentionally create multi-path propagation signatures with the careful deployment of tags. These signatures can be used to construct a sensitive profile to identify the location of the signals’ arrival, and thus detect the threat. In addition, we also design a tag-random scheme and a multiple receivers combination approach to detect a powerful attacker who has the strong priori knowledge of the legitimate user. We prototype ShieldScatter with USRPs and tags to evaluate our system in various environments. The results show that even when the powerful attacker is close to the legitimate device, ShieldScatter can mitigate 95 percent of attack attempts while triggering false alarms on just 7 percent of legitimate traffic. [ABSTRACT FROM AUTHOR]

A long-standing vision of backscatter networks is to provide long-range connectivity and high-speed transmissions for batteryless Internet-of-Things (IoT). Recent years have seen major innovations in designing backscatter networks toward this goal. Yet, they either operate at a very short range, or experience extremely low throughput. This paper takes one step further towards breaking this stalemate, by presenting PolarScatter that exploits channel polarization in long-range backscatter networks. We transform backscatter channels into nearly noiseless virtual channels through channel polarization, and convey bits with extremely low error probability. Specifically, we propose a new polar code scheme that automatically adapts itself to different channel quality, and design a low-cost encoder to accommodate polar codes on resource-constrained backscatter tags. Furthermore, we devise a new metric to calculate log-likelihood ratio for accurate decoding, and present a stopping criterion of iterations to reduce decoding latency. We build a prototype PCB tag, and our experiments show that it achieves up to 11.5× throughput improvement over the state-of-the-art long-range backscatter solution. We also simulate an IC design in TSMC 65 nm LP CMOS process. Compared with traditional encoders, our encoder reduces storage overhead by three orders of magnitude, and lowers the power consumption to tens of microwatts. [ABSTRACT FROM AUTHOR]

The inter-scale transfer of kinetic energy is analyzed in fuel-lean turbulent premixed swirl flames with varying equivalence ratio, velocity, and swirl number. Measurements are made using tomographic particle image velocimetry (TPIV) and formaldehyde planar laser induced fluorescence (PLIF) to measure three-dimensional velocity fields and deduce planar flame properties, respectively. It first is demonstrated that velocity measurements that spatially filter the true velocity fields at the TPIV interrogation box size can be used to deduce inter-scale energy transfer at scales sufficiently above the TPIV resolution. Mean small-to-large scale transfer of kinetic energy – i.e. mean backscatter or an inverse cascade – occurs across all conditions and length-scales studied, in contrast to the classical production-driven forward cascade described by Kolmogorov's equilibrium concepts. The magnitude of backscatter increases with increasing turbulence kinetic energy and equivalence ratio (in these lean flames), but decreased with increasing swirl number. Normalization by a dimensionless thermal expansion parameter that relates the pressure difference and kinetic energy difference over the flame (O'Brien et al., (2017)) collapses the data across equivalence ratios at a given flow condition. Further normalization by a characteristic parameter describing the swirl-induced radial pressure gradient largely collapses the data across swirl numbers and flow rates. The existence of the inverse cascade at the studied conditions and the relationship with the swirl-induced pressure gradient indicate that large-scale, geometry-driven pressure fields play an important role in turbulent kinetic energy dynamics for practical systems. Novelty and Significance Statement This paper presents a novel extension of previous analyzes of the inverse turbulent kinetic energy cascade in turbulent premixed flames to high Karlovitz numbers and large spatial scales. It provides the first demonstration – either experimentally or computationally – that energy backscatter reaches to scales beyond the turbulent flame brush thickness and occurs for Ka > 100. By studying multiple swirl numbers, it provides novel insight regarding the influence of geometry-scale pressure fields on kinetic energy dynamics in flames. It also demonstrates new analytical techniques for experimentally quantifying backscatter. The manuscript is significant because it provides some of the most compelling evidence to-date that standard models for turbulence and turbulence-flame interactions – which are based on Kolmogorov's equilibrium hypothesis – are missing key physical phenomena. It, therefore, motivates development and use of more accurate LES closure models. [ABSTRACT FROM AUTHOR]