Strong Scatterers Research Articles

Strong-Scattering Multiparameter Reconstruction Based on Elastic Direct Envelope Inversion and Full-Waveform Inversion with Anisotropic Total Variation Constraint

Strong-scattering medium can usually form a good sealing medium for oil and gas resources. However, conventional elastic full-waveform inversion (EFWI) methods are difficult to build reliable velocity models under the condition of lacking low-frequency information. The elastic direct envelope inversion (EDEI) method has been proven to be able to model large-scale Vp and Vs structures of strong-scattering media. The successive use of EDEI and EFWI can obtain fine structures of the strong scatterers and their shielding areas. However, the inversion effects of inner velocity and bottom boundaries of strong scatterers by the existing methods need to be improved. In this paper, we propose the elastic direct envelope inversion with anisotropic total variation constraint (EDEI-ATV). The anisotropic total variation (ATV) constraint has the advantage of making the velocity more uniform inside the layer and sharper on boundaries, which can be used to improve the inversion results of EDEI. During the iterations, the ATV constraint is directly applied to the update of Vp and Vs, and the alternately iterative algorithm can achieve good results. After obtaining reliable large-scale Vp and Vs structures, the EFWI with anisotropic total variation constraint (EFWI-ATV) is performed to obtain high-precision Vp and Vs structures. Numerical examples verify the effectiveness of the proposed method.

Exploring Integrability-Chaos Transition with a Sequence of Independent Perturbations.

A gas of interacting particles is a paradigmatic example of chaotic systems. It is shown here that, even if all but one particle are fixed in generic positions, the excited states of the moving particle are chaotic. They are characterized by the number of principal components (NPC)-the number of integrable system eigenstates involved into the nonintegrable one, which increases linearly with the number of strong scatterers. This rule is a particular case of the general effect of an additional perturbation on the system chaotic properties. The perturbation independence criteria supposing the system chaoticity increase are derived here as well. The effect can be observed in experiments with photons or cold atoms as the decay of observable fluctuation variance, which is inversely proportional to NPC and, therefore, to the number of scatterers. This decay indicates that the eigenstate thermalization is approached. The results are confirmed by numerical calculations for a harmonic waveguide with zero-range scatterers along its axis.

Two-Stage Time-Varying Vibration Compensation for Coherent LiDAR Based on the Adaptive Differential Evolution Method

Coherent LiDAR, which is based on triangular frequency modulated continuous wave (T-FMCW), is a promising technology for high-resolution imaging, because it can achieve long-range and high-precision measurements using low power. However, owing to the short wavelength of laser, the system is extremely sensitive to time-varying vibration. Most vibration compensation methods perform well in the scenes that contain strong scatterers using enough data. However, they lose validity in scenes without strong scatterers and sufficient observation time. To address this problem, we develop a second-order vibration error model for coherent LiDAR that divides the Doppler frequency shift caused by time-varying vibrations into quadratic and primary errors. Based on the vibration error model, we propose a two-stage time-varying vibration compensation method using the adaptive differential evolution (ADE) algorithm. In the first stage, we iteratively determine the optimal quadratic vibration coefficients that minimize the spectral entropy of the compensated dechirp signals using the ADE algorithm. Subsequently, we compensate for the quadratic errors using the estimated quadratic vibration coefficients. In the second stage, we use the frequency spectrum cross-correlation method to estimate the primary vibration coefficients and compensate for the primary errors. We perform experiments in the scene containing a point target and the scenes containing multi-component targets and distributed targets without strong scatterers. The results indicate that the proposed method can effectively compensate for time-varying vibration errors using only one-period T-FMCW data in different scenes. Further, the results of the proposed method are more accurate and stable than the Doppler frequency shift method.

Segmental Aperture Imaging Algorithm for Multirotor UAV-Borne MiniSAR

This article takes on the challenges of synthetic aperture radar (SAR) imaging for miniaturized SAR (MiniSAR) onboard a multirotor unmanned aerial vehicle (UAV). Several unique challenges are systematically analyzed, and a corresponding analytical phase error model is established, which accurately models the effects of both translational and rotational motions of UAVs. A segmental aperture imaging (SAI) algorithm, an autofocus algorithm based on strong scatterers, is proposed. It simply divides the platform trajectory into uneven segments, which are first independently focused with motion compensation and then stitched together to form a complete SAR image. Both the theoretical derivation of the signal model and the implementation of the imaging algorithm are presented. A simulation analysis with actual UAV trajectory and attitude data is conducted, which demonstrates the efficacy and performance of the proposed imaging algorithm. It shows that the ideal focusing effect can be achieved as evaluated by various metrics, and the proposed algorithm has superior performance compared to the subaperture phase gradient autofocus (PGA) and minimum entropy autofocus (MEA) methods. Finally, the multirotor-borne MiniSAR system FUSAR-Ku is used for experiments to verify the proposed algorithm. Experimental results show that the proposed algorithm can achieve the theoretical decimeter-resolution imaging performance as measured by various metrics.

High thermal conductivity in wafer-scale cubic silicon carbide crystals

High thermal conductivity electronic materials are critical components for high-performance electronic and photonic devices as both active functional materials and thermal management materials. We report an isotropic high thermal conductivity exceeding 500 W m−1K−1 at room temperature in high-quality wafer-scale cubic silicon carbide (3C-SiC) crystals, which is the second highest among large crystals (only surpassed by diamond). Furthermore, the corresponding 3C-SiC thin films are found to have record-high in-plane and cross-plane thermal conductivity, even higher than diamond thin films with equivalent thicknesses. Our results resolve a long-standing puzzle that the literature values of thermal conductivity for 3C-SiC are lower than the structurally more complex 6H-SiC. We show that the observed high thermal conductivity in this work arises from the high purity and high crystal quality of 3C-SiC crystals which avoids the exceptionally strong defect-phonon scatterings. Moreover, 3C-SiC is a SiC polytype which can be epitaxially grown on Si. We show that the measured 3C-SiC-Si thermal boundary conductance is among the highest for semiconductor interfaces. These findings provide insights for fundamental phonon transport mechanisms, and suggest that 3C-SiC is an excellent wide-bandgap semiconductor for applications of next-generation power electronics as both active components and substrates.

Deep learning for hetero–homo conversion in channel-domain for phase aberration correction in ultrasound imaging

Echo imaging in ultrasound computed tomography (USCT) using the synthetic aperture technique is performed with the assumption that the speed of sound is constant in the system. However, tissue heterogeneity causes a mismatch between the predicted arrival time and the actual arrival time of the echo signal, which will result in phase aberration, leading to the quality degradation of the reconstructed B-mode image. The conventional correction methods that use the correlation of each different channel require the presence of strong point scatterers and involve the problem of local solutions due to excessive correction. In this study, we propose a novel approach to correcting the signal distortion due to sound speed heterogeneity using a deep neural network (DNN). The DNN was trained to convert the distorted radio frequency (RF) inputs for the heterogeneous medium to the distortion-free RF outputs for the homogeneous medium. The network with U-net architecture using ResNet-34 as a backbone was trained using the hetero–homo corresponding channel-domain RF data generated via numerical simulations. The trained network performed phase aberration correction in the channel-domain RF, with the B-mode images reconstructed with the corrected RF demonstrating a higher contrast and an improved resolution compared with uncorrected cases. It was also demonstrated that the DNN model is robust to both varied reflection intensities and varied sound speed heterogeneities. The successful results demonstrated that the proposed DNN-based method is effective for phase aberration correction in US imaging.

HA-Unet: A Modified Unet Based on Hybrid Attention for Urban Water Extraction in SAR Images

Urban water plays a significant role in the urban ecosystem, but urban water extraction is still a challenging task in automatic interpretation of synthetic aperture radar (SAR) images. The influence of radar shadows and strong scatters in urban areas may lead to misclassification in urban water extraction. Nevertheless, the local features captured by convolutional layers in Convolutional Neural Networks (CNNs) are generally redundant and cannot make effective use of global information to guide the prediction of water pixels. To effectively emphasize the identifiable water characteristics and fully exploit the global information of SAR images, a modified Unet based on hybrid attention mechanism is proposed to improve the performance of urban water extraction in this paper. Considering the feature extraction ability and the global modeling capability in SAR image segmentation, the Channel and Spatial Attention Module (CSAM) and the Multi-head Self-Attention Block (MSAB) are both introduced into the proposed Hybrid Attention Unet (HA-Unet). In this work, Resnet50 is adopted as the backbone of HA-Unet to extract multi-level features of SAR images. During the feature extraction process, CSAM based on local attention is adopted to enhance the meaningful water features and ignore unnecessary features adaptively in feature maps of two shallow layers. In the last two layers of the backbone, MSAB is introduced to capture the global information of SAR images to generate global attention. In addition, two global attention maps generated by MSAB are aggregated together to reconstruct the spatial feature relationship of SAR images from high-resolution feature maps. The experimental results on Sentinel-1A SAR images show that the proposed urban water extraction method has a strong ability to extract water bodies in the complex urban areas. The ablation experiment and visualization results vividly indicate that both CSAM and MSAB contribute significantly to extracting urban water accurately and effectively.

A 3-D Full Convolution Electromagnetic Reconstruction Neural Network (3-D FCERNN) for Fast Super-Resolution Electromagnetic Inversion of Human Brain.

Three-dimensional (3-D) super-resolution microwave imaging of human brain is a typical electromagnetic (EM) inverse scattering problem with high contrast. It is a challenge for the traditional schemes based on deterministic or stochastic inversion methods to obtain high contrast and high resolution, and they require huge computational time. In this work, a dual-module 3-D EM inversion scheme based on deep neural network is proposed. The proposed scheme can solve the inverse scattering problems with high contrast and super-resolution in real time and reduce a huge computational cost. In the EM inversion module, a 3-D full convolution EM reconstruction neural network (3-D FCERNN) is proposed to nonlinearly map the measured scattered field to a preliminary image of 3-D electrical parameter distribution of the human brain. The proposed 3-D FCERNN is completely composed of convolution layers, which can greatly save training cost and improve model generalization compared with fully connected networks. Then, the image enhancement module employs a U-Net to further improve the imaging quality from the results of 3-D FCERNN. In addition, a dataset generation strategy based on the human brain features is proposed, which can solve the difficulty of human brain dataset collection and high training cost. The proposed scheme has been confirmed to be effective and accurate in reconstructing the distribution of 3-D super-resolution electrical parameters distribution of human brain through noise-free and noisy examples, while the traditional EM inversion method is difficult to converge in the case of high contrast and strong scatterers. Compared with our previous work, the training of FCERNN is faster and can significantly decrease computational resources.

Signatures of anharmonic phonon transport in ultrahigh thermal conductance across atomically sharp metal/semiconductor interface

The scatterings among the heat carriers at metal/semiconductor heterojunctions are important for the heat dissipation process in quantum information science systems. Phonon-phonon coupling at interfaces is accepted to be one of the main channels for thermal energy across the metal/semiconductor interface. Here, we show the thermal conductance across the metal/semiconductor interface, i.e., Al/Si interface, at room temperature is around 350 MW/m2K, which is 1.43 times higher than the harmonic limit and stems from the strong anharmonic phonon-phonon scatterings at the interface. The anharmonic phonon-phonon scatterings are found to benefit the interfacial thermal conductance from two mechanisms, i.e., the increase of the phonon occupation and inelastic phonon scattering channels. Our modal analysis suggests that the thermal conductance contributed by the interfacial vibrational modes is negligible, and the interfacial thermal transfer is dominated by the extended vibrational modes. By comparing our calculated thermal conductance with the experimental measurements, we find that electron-phonon coupling contributes ∼9% to the total interfacial thermal conductance, and the phonon-phonon coupling dominates the thermal transport across the interface. Our findings here have provided guidance for the thermal management design in the related quantum information electronics.

A New Correction to the Rytov Approximation for Strongly Scattering Lossy Media

We propose a correction to the conventional Rytov approximation (RA) and investigate its performance for predicting wave scattering under strong scattering conditions. An important motivation for the correction and investigation is to help in the development of better models for inverse scattering. The correction is based upon incorporating the high frequency theory of inhomogeneous wave propagation for lossy media into the RA formulation. We denote the technique as the extended Rytov approximation for lossy media (xRA-LM). xRA-LM significantly improves upon existing non-iterative linear scattering approximations such as RA and the Born approximation (BA) by providing a validity range for the permittivity of the objects of up to 50 times greater than RA. We demonstrate the technique by providing results for predicting wave scattering from piece-wise homogeneous scatterers in a two-dimensional (2D) region. Numerical investigation of the performance of xRA-LM for solving direct problem show that xRA-LM can accurately predict wave scattering by electrically large, low-loss scatterers with high complex permittivity ($\epsilon_r> 50+5j$). To the best of our knowledge, this is the first non-iterative, linear approximate wave scattering model which has a large validity range in terms of both permittivity and electrical size.

Deep Unfolding Contrast Source Inversion for Strong Scatterers via Generative Adversarial Mechanism

To alleviate the extremely intrinsical ill-posedness and nonlinearity of electromagnetic inverse scattering under high contrast and low signal to noise ratio (SNR), we propose a deep unfolding network based on generative adversarial network (GAN) under contrast source inversion (CSI) framework, termed UCSI-GAN. The method solves inverse scattering problems (ISPs) using end-to-end generating confrontation way by incorporating a physical model together with its iterative updating formulation into the internal architecture of GAN. First, the nonlinear iterative scheme is extended to a deep unfolding generator network, and the contrast source and contrast updates are mapped to each module of the generator network. Second, to stabilize the imaging process, we add a refinement network to each variable update. Finally, the discriminator network is employed to ensure the authenticity of reconstructed images. The generator network and discriminator network are alternately trained with a generative adversarial learning strategy to reconstruct the properties of the medium object. Numerical experiments demonstrated that the performance of UCSI-GAN is better than traditional CSI and state-of-the-art learning approaches under high contrast and low SNR condition.

Feasibility of a Novel 3D Dosimetry System for Commissioning of Single Isocenter Multitarget SRS Treatment

<h3>Purpose/Objective(s)</h3> Verification of Single Isocenter Multitarget SRS treatments (SIM-SRS) is uniquely challenging due to the combination of high dose, steep dose gradients and the requirement for very high spatial accuracy. Here we demonstrate the feasibility of a novel 3D dosimetry system for uniquely comprehensive dosimetry validation and commissioning of this exceptionally challenging technique. <h3>Materials/Methods</h3> The 3D dosimetry system utilizes a new commercially available radiochromic gel dosimeter and a state-of-art in-house telecentric optical-CT readout system DLOS (Duke Large Field of View telecentric Optical CT Scanner). The system was used to quantify the accuracy in 3D and in high resolution (isotropic 1mm) of two SIM-SRS treatments: a simpler two target treatment, and a more complex 5 target treatment. The SIM-SRS plans were created on patient anatomy and then recalculated on a CT of a 15cm diameter dosimeter, with isocenter placed such that each target was located within the dosimeter. Both treatments were delivered to the dosimeters which are gellan gum based radiochromic dosimeters containing a water-soluble tetrazolium salt which reduces into an insoluble formazan dye (with associated color change) under ionizing radiation. Optical-CT readout of the dosimeters pre and post irradiation enabled a high-resolution comprehensive evaluation of the accuracy of delivery. The DLOS is a bi-telecentric system which enables highest accuracy optical-CT readout through strong scatter and stray light rejection. The measured optical density distribution was spatially registered to the dose from the planning system and converted to dose via a calibration curve. Independent verification of treatment accuracy was also performed with a commercial Monte Carlo dose calculation algorithm. <h3>Results</h3> For the 2-target plan, the measured dose agreed with the planning system with a Gamma-Index passing rate of 99.88%, 99.27%, and 92.75%, using a 3%/3mm, 3%/2mm, 5%/1mm criteria, respectively. For the 5-target plan, the Gamma-Index passing rate was 99.59%, 97.35%, and 93.82%, using a 3%/3mm, 3%/2mm, 5%/1mm criteria, respectively. In comparison, the dose calculated using Monte Carlo agreed with the planning system with a Gamma-Index passing rate of 100.0% using 3%/1mm for both the 2 target and 5 target plans. Mean dose for the PTVs of the 2-target plan was within –0.71% and –1.53%. Mean dose for the PTVs of the 5-target plan was within (1.08±0.97)%. <h3>Conclusion</h3> This study demonstrates the feasibility of a new 3D dosimetry tool for verification of advanced radiation treatments. Application to SIM-SRS treatments has demonstrated excellent agreement between measured and predicted dose in the phantom. The combination of DLOS optical-CT with Clearview dosimeters was found to be important in obtaining accurate 3D dosimetry. The method introduced here is anticipated to be applicable to a wide range of clinically challenging treatment techniques.

Phase Stability, Strong Four-Phonon Scattering, and Low Lattice Thermal Conductivity in Superatom-Based Superionic Conductor Na3OBH4.

Superatom-based superionic conductors are of current interest due to their promising applications in solid-state electrolytes for rechargeable batteries. However, much less attention has been paid to their thermal properties, which are vital for safety and performance. Motivated by the recent synthesis of superatom-based superionic conductor Na3OBH4 consisting of superhalogen cluster BH4, we systematically investigate its lattice dynamics and thermal conductivity using the density functional theory combined with a self-consistent phonon approach. We reveal the bonding hierarchy features by studying the electron localization function and potential energy surface and further unveil the rattling effect of the BH4 superatom, which introduces strong quartic anharmonicity and induces soft phonon modes in low temperatures by assisting Na displacements, thus calling for the necessity of quartic renormalization and four-phonon scattering in calculating the lattice thermal conductivity. We find that the contribution of four-phonon processes to the lattice thermal conductivity increases from 13 to 32% when the temperature rises from 200 to 400 K. At room temperature (300 K), the phonon scattering phase space is enlarged by 133% due to the four-phonon interactions, and the lattice thermal conductivity is evaluated to be 5.34 W/mK, reduced by 24% as compared with a value of 6.99 W/mK involving three-phonon scattering only. These findings provide a better understanding of the lattice stability and thermal transport properties of superionic conductor Na3OBH4, shedding light on the role of strong quartic anharmonicity played in superatom-based materials.

Chicken antibodies are highly suitable for particle enhanced turbidimetric assays.

Antibody-based assays are commonly used in clinical laboratories for analyzing plasma, serum and other samples for particular protein markers. Although such assays have been traditionally based on antibodies raised in mammals (e.g., mice, rabbits, goats), there are several advantages of using avian antibodies (IgY) raised in chickens, including production volumes, costs, and ethical/animal welfare considerations. A further disadvantage of using mammalian IgG in such assays is the potential for agglutination when exposed to rheumatoid factor (RF) in serum. However, when used in the free form the immune complexes formed with avian antibodies have been reported to have less ability than those formed with mammalian antibodies to cause the light scatter which are used for instrument measurement. In addition, when the amount of antigen exceeds the maximum precipitating point in relation to the amount of antibody, there is a rapid decline in the absorbance values of the immune complexes (antigen excess) when IgY is used. However, when avian antibodies are conjugated to a substrate and used in particle enhanced turbidimetric assays (PETIA), these problems are avoided. Here we investigated three clinical assays using chicken antibodies, one using free (unbound) IgY and two with IgY-based PETIA. The IgY PETIA demonstrated a strong scatter response, even at high antigen concentrations in contrast to the steep decline seen with free IgY antibodies. IgY PETIA reagents can provide test results with low coefficient of variation (<1% for duplicate samples). We also investigated the effect of RF on agglutination of mammalian antibodies (IgG from mouse, rabbit, sheep, and human) and chicken antibodies. Whereas agglutination was observed with all the mammalian antibodies in the presence of RF, this was not observed at all with chicken IgY. Our results support the growing body of evidence that chicken egg yolks can thus be a valuable source of antibodies for use in PETIA in clinical laboratories.

Patch-Like Reduction (PLR): A SAR Offset Tracking amplitude filter for deformation monitoring

As complementary to Synthetic Aperture Radar (SAR) Differential Interferometry (DInSAR), SAR Offset Tracking (OT) is an efficient tool for large ground deformation monitoring in situations when DInSAR cannot work. However, SAR images are affected by speckle noise and some strong point-like scatters which can cause what is known as Patch Like (PL), a kind of errors that can be seen as homogeneous patches of almost constant deformation in the results. These errors are clearly visible in the results as non-consistent deformations along time, but they are difficult to detect with the traditional metrics that evaluate the cross-correlation results, like the Signal to Noise Ratio (SNR). This paper addresses this problem and proposes a simple amplitude filter to reduce PL named as Patch Like Reduction (PLR). The main idea is to find a sensor and scene independent threshold to remove the high amplitude pixels prone to cause PL. Five different SAR data sets and in-field GPS measurements are used to determine the optimal threshold and evaluate the performance of the proposed method. The results show that PL effects can be reduced with the proposed amplitude filter. The processing parameters of the improved OT processing chain are optimized as well to preserve the results resolution as much as possible. • The causes of Patch Like errors in Offset Tracking results have been investigated. • An amplitude filter (PLR) has been proposed to reduce PLs. • The filter threshold is automatically defined from images amplitude distributions. • The filter improves OT results by reducing the changes of PL appearance. • Template sizes and tapering windows have been optimized in terms of resolution.

Ballistic Imaging through Strongly Scattering Media by Using a Combination of Supercontinuum Illumination and Fourier Spatial Filtering

Imaging through turbid media is important but has created challenging issues for a long time. Previous research studies have shown that the object hidden in a turbid medium might be seen just by effectively suppressing the speckles by using low-coherent light sources, such as random laser and supercontinuum. However, the image contrast was seriously degraded due to the background noise of scattered photons. In this work, we demonstrate ballistic imaging for an object hidden behind strongly scattering media, in which the speckles are suppressed by the supercontinuum (SC) illumination and lots of scattered photons are simultaneously filtered by the Fourier spatial gate. Compared with speckle-free imaging from using SC illumination and ballistic imaging by Fourier spatial filtering, this method combines their advantages and shows a degree of synergism. When the optical depth of the scattering medium reaches 14, the image using this combined method is able to increase the image identifiability and the relative image contrast by about two times and four times compared to the method using only SC illumination, respectively. Our work offers a way for direct imaging through strongly turbid media without a complex image process.

Offshore Bridge Detection in Polarimetric SAR Images Based on Water Network Construction Using Markov Tree

It is difficult to detect bridges in synthetic aperture radar (SAR) images due to the inherent speckle noise of SAR images, the interference generated by strong coastal scatterers, and the diversity of bridge and coastal terrain morphologies. In this paper, we present a two-step bridge detection method for polarimetric SAR imagery, in which the probability graph model of a Markov tree is used to build the water network, and bridges are detected by traversing the graph of the water network to determine all adjacent water branch pairs. In the step of the water network construction, candidate water branches are first extracted by using a region-based level set segmentation method. The water network is then built globally as a tree by connecting the extracted water branches based on the probabilistic graph model of a Markov tree, in which a node denotes a single branch and an edge denotes the connection of two adjacent branches. In the step of the bridge detection, all adjacent water branch pairs related to bridges are searched by traversing the constructed tree. Each bridge is finally detected by merging the two contours of the corresponding branch pair. Three polarimetric SAR data acquired by RADARSAT-2 covering Singapore and Lingshui, China, and by TerraSAR-X covering Singapore, are used for testing. The experimental results show that the detection rate, the false alarm rate, and the intersection over union (IoU) between the recognized bridge body and the ground truth are all improved by using the proposed method, compared to the method that constructs a water network based on water branches merging by contour distance.

Optical Efficiency Improvement of Chip-on-Board Design LEDs with TiO2/Silicone Packaging Coating

Since TiO2 nanoparticles and silicon composites include a strong scatter influence, they are well-known for improving scattered lighting in LED packets. To improve the optic quality of LEDs packaged with chip-on-board (COB), a thin layer made of high-concentration TiO2 and silicon glue is added to the primary packing layer. COB LEDs’ light extraction efficiency (LEE) rises up to 65% when the key encapsulation includes just silicone, according to the findings of experiments. As a coating of TiO2 and silicone is added, however, the increase in LEE is dependent on the TiO2 concentration. The LEE can be increased from 6% to 24% as the concentration of nanoparticles drops to 0.035 g/cm3. Furthermore, at a mean correlated color temperature (CCT) of around 8500 K, the TiO2/silicone compounds layer will assist in lowering the angular correlated color temperature (CCT) variance between 900 and 470 K within the -90° to 90° observing angle range.

Enhanced Machine Learning Approach for Accurate and Fast Resolution of Inverse Scattering Problem in Breast Cancer Detection

An improved machine learning approach is presented in this paper to guarantee the fast convergence of the Born Iterative Method, even in the presence of strong scatterers, by assuming a single operating frequency and a reduced number of antennas in the scattering setup. The initial estimation of the dielectric profile, provided by the Born Iterative Method, was processed by a specific convolutional neural network to improve the reconstruction using a fast machine learning approach. To ensure rapid convergence, a proper choice of the initial guess in terms of the minimum permittivity value over the entire domain was also made. Numerical validations on realistic breast phantoms were illustrated, demonstrating an average error of 2.4% and an accuracy greater than 96% for all considered tests, even when considering a single operating frequency and a reduced amount of training data.

Flow properties of powdery or granular filling substances of dangerous goods packagings—Comparison of the measurement of the angle of repose and the determination of the Hausner ratio

To ensure safety when transporting dangerous goods, it is important to specify the flow properties of the respective solid filling substance of the packagings. For this purpose, the angle of repose is currently used for the UN approvals in Germany. Measurements were carried out on 12 powdery or granular substances applying the angle of repose measuring methods customary in the test centres. The results of the methods differ significantly from each other. In addition, some of the techniques cannot be applied for very cohesive or coarse‐grained materials. The results for the angle of repose show a strong scatter for some constellations (coefficient of variation more than 20%). Safety during transport of dangerous goods cannot be guaranteed with this currently practised system of measuring the angle of repose. As a consequence, an alternative parameter to characterize the flow properties of bulk materials should be used in the recognized test centers for dangerous goods packagings, such as the Hausner ratio. This approach leads to more precise test results for the substances examined (maximum coefficient of variation 2.8%). It also has advantages in terms of applicability and occupational safety. Since the flow properties are safety‐relevant, both in terms of mechanical safety and safety against the release of dangerous substances, the testing practice in the recognized test labs should be improved and standardized.