Dense Velocity Research Articles

Deep Volumetric Descriptor Learning for Dense Correspondence of Cone-Beam Computed Tomography via Spectral Maps.

The deep neural network has achieved great success in 3D volumetric correspondence. These methods infer the dense displacement or velocity fields directly from the extracted volumetric features without addressing the intrinsic structure correspondence, being prone to shape and pose variations. On the other hand, the spectral maps address the intrinsic structure matching in the low dimensional embedding space, remain less involved in volumetric image correspondence. This paper presents an unsupervised deep volumetric descriptor learning neural network via the low dimensional spectral maps to address the dense volumetric correspondence. The neural network is optimized by a novel criterion on descriptor alignments in the spectral domain regarding the supervoxel graph. Aside from the deep convolved multi-scale features, we explicitly address the supervoxel-wise spatial and cross-channel dependencies to enrich deep descriptors. The dense volumetric correspondence is formulated as the low-dimensional spectral mapping. The proposed approach has been applied to both synthetic and clinically obtained cone-beam computed tomography images to establish dense supervoxel-wise and up-scaled voxel-wise correspondences. Extensive series of experimental results demonstrate the contribution of the proposed approach in volumetric descriptor extraction and consistent correspondence, facilitating attribute transfer for segmentation and landmark location. The proposed approach performs favorably against the state-of-the-art volumetric descriptors and the deep registration models, being resilient to pose or shape variations and independent of the prior transformations.

Post Processing Sparse And Instantaneous 2D Velocity Fields Using Physics-Informed Neural Networks

This work tackles the problem of resolving high-resolution velocity fields from a set of sparse off-grid observations. We follow the framework of Physics-Informed Neural Networks where simple Multi-layer Perceptor (MLP) are trained to solve partial differential equations (PDEs). In contrast with other state-of-the-art methods based of Convolutional Neural Networks, these models can be applied to super-resolve sparse Lagrangian velocity measurements. Moreover, such a framework can be easily extended to output divergence-free quantities and offer simple implementation of prior physical as regularization terms. In particular, we employ a sub-grid model based on structure-functions to improve the accuracy of the super-resolved velocity fields of turbulent flows. Numerical experimentation on synthetic data shows that the proposed approach can accurately reconstruct dense Eulerian velocity fields from sparse Lagrangian velocity measurements.

Influence of protostellar jets and HII regions on the formation and evolution of stellar clusters

Context. Understanding the conditions in which stars and stellar clusters form is of great importance. In particular, the role that stellar feedback may have is still hampered by large uncertainties. Aims. We aim to investigate the role played by ionising radiation and protostellar outflows during the formation and evolution of a stellar cluster. To self-consistently take into account gas accretion, we start with clumps of tens of parsecs in size. Methods. Using an adaptive mesh refinement code, we ran magneto-hydrodynamical numerical simulations aimed at describing the collapse of massive clumps with either no stellar feedback or taking into account ionising radiation and/or protostellar jets. Results. Stellar feedback substantially modifies the protostellar cluster properties in several ways. We confirm that protostellar outflows reduce the star formation rate by a factor of a few, although the outflows do not stop accretion and, likely enough, do not modify the final cluster mass. On the other hand, once sufficiently massive stars have formed, ionising radiation efficiently expels the remaining gas and reduces the final cluster mass by a factor of several. We found that while HII radiation and jets barely change the distribution of high density gas, the latter increases the dense gas velocity dispersion again by a factor of several in a few places. As we are starting from a relatively large scale, we found that the clusters whose mass and size are, respectively, of the order of a few 1000 M⊙ and a fraction of parsec, present a significant level of rotation. Moreover, we found that the sink particles that mimic the stars themselves tend to have rotation axes aligned with the cluster’s large-scale rotation. Finally, computing the classical Q parameter used to quantify stellar cluster structure, we infer that when jets are included in the calculation, the Q values are typical of observations, while when protostellar jets are not included, the Q values tend to be significantly lower. This is due to the presence of sub-clustering that is considerably reduced by the jets. Conclusions. Both large-scale gas accretion and stellar feedback, namely HII regions and protostellar jets, appear to significantly influence the formation and evolution of stellar clusters.

Dense velocity reconstruction with VIC-based time-segment assimilation

The vortex-in-cell time-segment assimilation (VIC-TSA) method is introduced. A particle track is obtained from a finite number of successive time samples of the tracer’s position and velocity can be used for reconstruction on a Cartesian grid. Similar to the VIC + technique, the method makes use of the vortex-in-cell paradigm to produce estimates of the flow state at locations and times other than the measured ones. The working principle requires time-resolved measurements of the particles’ velocity during a finite time interval. The work investigates the effects of the assimilated length on the spatial resolution of the velocity field reconstruction. The working hypotheses of the VIC-TSA method are presented here along with the numerical algorithm for its application to particle tracks datasets. The novel parameter governing the reconstruction is the length of the time-segment chosen for the data assimilation. Three regimes of operation are identified, based on the track length and the geometrical distance between neighbouring tracks. The regime of adjacent tracks arguably provides the optimal trade-off between spatial resolution and computational effort. The VIC-TSA spatial resolution is evaluated first by a numerical exercise; a 3D sine wave lattice is reconstructed at different values of the particles concentration. The modulation appears to reduce (cut-off delay) when the time-segment length is increased. Large-scale PIV experiments in the wake of a circular cylinder at Red = 27,000 are used to evaluate the method’s suitability to real data, including noise and data outliers. Both primary vortex structures in the Kármán wake as well as interconnecting ribs are present in this complex flow field, with a typical diameter close to the average inter-particle distance. When the time-segment is increased to adjacent tracks and beyond, a more regular time dependence of local and Lagrangian properties is observed, confirming the suitability of the time-segment assimilation for accurate reconstruction of sparse velocity data.Graphical abstract

Bayesian Inference of Fault Slip and Coupling Along the Tuosuo Lake Segment of the Kunlun Fault, China

AbstractIntegrating newly‐processed Interferometric Synthetic Aperture Radar (InSAR) and available Global Positioning System (GPS) data, we obtained a dense interseismic velocity field across the Tuosuo Lake segment of the Kunlun fault. Using a Bayesian approach, we obtained the spatial distribution of fault slip and coupling. The slip rate below the locking depth, which represents contemporary steady‐state faulting, demonstrates an overall decrease, and decreases non‐monotonically toward the eastern fault tip in the range of ∼5–11 mm/a. Two strongly locked zones are identified, extending 100–120 km along‐strike and being separated by narrow sections of moderate coupling. These locked zones were likely ruptured during the 1937 M 7.5 Huashixia earthquake. The estimated moment accumulation rate provides a recurrence time of 428 ± 57 years for an earthquake similar to the 1937 event. The Maduo Mw 7.3 earthquake on 21 May 2021 imposed stress loading on the locked zones, equivalent to tectonic loading of ∼10 years, implying increased seismic hazard along the Kunlun fault.

Characterization of surface motion patterns in highly deformable soft tissue organs from dynamic MRI: An application to assess 4D bladder motion

Background and objectives: Dynamic Magnetic Resonance Imaging (MRI) may capture temporal anatomical changes in soft tissue organs with high-contrast but the obtained sequences usually suffer from limited volume coverage which makes the high-resolution reconstruction of organ shape trajectories a major challenge in temporal studies. Because of the variability of abdominal organ shapes across time and subjects, the objective of the present study is to go towards 3D dense velocity measurements to fully cover the entire surface and to extract meaningful features characterizing the observed organ deformations and enabling clinical action or decision.Methods: We present a pipeline for characterization of bladder surface dynamics during deep respiratory movements. For a compact shape representation, the reconstructed temporal volumes were first used to establish subject-specific dynamical 4D mesh sequences using the large deformation diffeomorphic metric mapping (LDDMM) framework. Then, we performed a statistical characterization of organ dynamics from mechanical parameters such as mesh elongations and distortions. Since we refer to organs as non-flat surfaces, we have also used the mean curvature change as metric to quantify surface evolution. However, the numerical computation of curvature is strongly dependant on the surface parameterization (i.e. the mesh resolution). To cope with this dependency, we employed a non-parametric method for surface deformation analysis. Independent of parameterization and minimizing the length of the geodesic curves, it stretches smoothly the surface curves towards a sphere by minimizing a Dirichlet energy. An Eulerian PDE approach is used to derive a shape descriptor from the curve-shortening flow. Intercorrelations between individuals’ motion patterns are computed using the Laplace-Beltrami Operator (LBO) eigenfunctions for spherical mapping.Results: Application to extracting characterization correlation curves for locally-controlled simulated shape trajectories demonstrates the stability of the proposed shape descriptor. Its usability was shown on MRI acquired for seven healthy participants for which the bladder was highly deformed by maximum of inspiration. As expected, the study showed that deformations occured essentially on the top lateral regions.Conclusion:Promising results were obtained, showing the organ in its 3D complexity during deformation due to strain conditions. Smooth genus-0 manifold reconstruction from sparse dynamic MRI data is employed to perform a statistical shape analysis for the determination of bladder deformation.

A rule-based model for electricity theft prevention in advanced metering infrastructure

The deployment of smart electricity meter (SEM) via the advanced metering infrastructure (AMI) has come under cyber-attacks as adversaries continue to exploit the communication links for possible evasion of electricity bill payments. Various detection models relying on energy consumption data offer a disadvantage of delayed detection and consequent huge financial losses before frauds are detected. Moreover, existing techniques mostly concentrate on detection of electricity thefts and rely on energy consumption data alone as the basis of theft perpetration whereas other potential parameters which could be exploited for electricity theft prevention exist in AMI. In this study, AMI parameters, which are indicative of electricity thefts are preselected and modelled for electricity theft prevention. First, a given AMI network is sectioned into zones with the selected parameters modelled to define security risks by formulated set of rules based on real-time scenarios. Fuzzy inference system is then employed to model the security risks to ascertain the compromised state of the monitored parameters at the defined scenarios. The result of the developed model at 50% weight of each of the modelled parameters with interdependencies show clear indications of the modelled parameters and their interactions in the determination of risks. The decisions on monitored parameters evaluated at every timestep reveal varied dense velocity behaviours for every scenario. The result is suitable for monitoring the AMI in reporting and/or disconnecting any compromised SEM within a considerable timestep before huge losses are incurred. Implementation of this scheme will contribute a significant success in the attempt to prevent electricity theft perpetration via the AMI.

Dense velocity reconstruction from particle image velocimetry/particle tracking velocimetry using a physics-informed neural network

The velocities measured by particle image velocimetry (PIV) and particle tracking velocimetry (PTV) commonly provide sparse information on flow motions. A dense velocity field with high resolution is indispensable for data visualization and analysis. In the present work, a physics-informed neural network (PINN) is proposed to reconstruct the dense velocity field from sparse experimental data. A PINN is a network-based data assimilation method. Within the PINN, both the velocity and pressure are approximated by minimizing a loss function consisting of the residuals of the data and the Navier–Stokes equations. Therefore, the PINN can not only improve the velocity resolution but also predict the pressure field. The performance of the PINN is investigated using two-dimensional (2D) Taylor's decaying vortices and turbulent channel flow with and without measurement noise. For the case of 2D Taylor's decaying vortices, the activation functions, optimization algorithms, and some parameters of the proposed method are assessed. For the case of turbulent channel flow, the ability of the PINN to reconstruct wall-bounded turbulence is explored. Finally, the PINN is applied to reconstruct dense velocity fields from the experimental tomographic PIV (Tomo-PIV) velocity in the three-dimensional wake flow of a hemisphere. The results indicate that the proposed PINN has great potential for extending the capabilities of PIV/PTV.

Updated GNSS velocity solution in the Nordic and Baltic countries with a semi-automatic offset detection method

In Fennoscandia, the Glacial Isostatic Adjustment (GIA) causes intraplate deformations that affect the national static reference frames. The GNSS-determined velocities are important data for constraining the GIA models, which are necessary for maintaining the national reference frames. The Nordic Geodetic Commission (NKG) has published a dense and consistent GNSS station velocity solution in 2019, and we present now an update of the solution covering additional 3.5 years of data. Undetected positional offsets are the main factor decreasing the accuracy of the velocity estimates. We developed a method for the semi-automatic offset detection to improve the quality of our solution. The results show that we could correctly detect 74% of the manually determined offsets, and the undetected offsets would have caused a median 0.1 mm/y bias in trend. The method pointed out some otherwise unnoticed offsets and will decrease the need for manual analysis in the future. The updated velocity solution especially improves the velocity estimates of the newly established stations and the quality of the velocity estimates in Baltic countries. The formal uncertainties estimated using the power-law plus white noise model were at a median of 0.06 and 0.15 mm/y for horizontal and vertical velocities, respectively. However, we concluded that the systematic velocity uncertainties due to the reference frame alignment were approximately at the same level.

Composition and Pressure Effects on Partitioning of Ferrous Iron in Iron-Rich Lower Mantle Heterogeneities

Both seismic observations of dense low shear velocity regions and models of magma ocean crystallization and mantle dynamics support enrichment of iron in Earth’s lowermost mantle. Physical properties of iron-rich lower mantle heterogeneities in the modern Earth depend on distribution of iron between coexisting lower mantle phases (Mg,Fe)O magnesiowüstite, (Mg,Fe)SiO3 bridgmanite, and (Mg,Fe)SiO3 post-perovskite. The partitioning of iron between these phases was investigated in synthetic ferrous-iron-rich olivine compositions (Mg0.55Fe0.45)2SiO4 and (Mg0.28Fe0.72)2SiO4 at lower mantle conditions ranging from 33–128 GPa and 1900–3000 K in the laser-heated diamond anvil cell. The resulting phase assemblages were characterized by a combination of in situ X-ray diffraction and ex situ transmission electron microscopy. The exchange coefficient between bridgmanite and magnesiowüstite decreases with pressure and bulk Fe# and increases with temperature. Thermodynamic modeling determines that incorporation and partitioning of iron in bridgmanite are explained well by excess volume associated with Mg-Fe exchange. Partitioning results are used to model compositions and densities of mantle phase assemblages as a function of pressure, FeO-content and SiO2-content. Unlike average mantle compositions, iron-rich compositions in the mantle exhibit negative dependence of density on SiO2-content at all mantle depths, an important finding for interpretation of deep lower mantle structures.

Dense conjugate initialization for deterministic PSO in applications: ORTHOinit+

This paper describes a class of novel initializations in Deterministic Particle Swarm Optimization (DPSO) for approximately solving costly unconstrained global optimization problems. The initializations are based on choosing specific dense initial positions and velocities for particles. These choices tend to induce in some sense orthogonality of particles’ trajectories, in the early iterations, in order to better explore the search space. Our proposal is inspired by both a theoretical analysis on a reformulation of PSO iteration, and by possible limits of the proposals reported in Campana et al. (2010); Campana et al. (2013). We explicitly show that, in comparison with other initializations from the literature, our initializations tend to scatter PSO particles, at least in the first iterations. The latter goal is obtained by imposing that the initial choice of particles’ position/velocity satisfies specific conjugacy conditions, with respect to a matrix depending on the parameters of PSO. In particular, by an appropriate condition on particles’ velocities, our initializations also resemble and partially extend a general paradigm in the literature of exact methods for derivative-free optimization. Moreover, we propose dense initializations for DPSO, so that the final approximate global solution obtained is possibly not too sparse, which might cause troubles in some applications. Numerical results, on both Portfolio Selection and Computational Fluid Dynamics problems, validate our theory and prove the effectiveness of our proposal, which applies also in case different neighborhood topologies are adopted in DPSO.

“Frame Wobbling” Causing Crustal Deformation Around the Ordos Block

AbstractAn accurate and dense GPS velocity field across the Ordos block is presented based on extensive observations. We utilized velocities from evenly distributed GPS stations within the block to estimate the parameters of rigid block motion and internal strain. Our results indicate that the Ordos block behaves rigidly, with a negligible anticlockwise rotation. The velocity profiles, strain, and block motion suggest that: the interior of the block undergoes no differential motion and internal strain is insignificant; relative to the Ordos, the adjacent blocks move in different directions and at different rates, and exert varying forces on it; the adjacent blocks deform the Ordos block along its boundaries of grabens and fold‐thrust fault zones. The Ordos block accommodates the forces exerted by the adjacent blocks through strain and deformation across its boundaries, which should be high strain or strain gradient zones. This pattern of deformation is illustrated by frame wobbling.

Present‐Day Kinematics in Southeastern Tibet Inferred From GPS Measurements

AbstractQuantifying the regional kinematics is critical for understanding the deformation mechanisms of the Tibetan Plateau. In this study, we construct an elastic block model using a dense GPS velocity field to derive the fault slip rates in SE Tibet. We uncover good agreement between GPS and late Quaternary slip estimates, suggesting that the GPS slip rates estimated from our model can represent the long‐term fault motions in SE Tibet. Specifically, our model shows that the slip along the Xianshuihe fault increases from west to east, which may result from the initiation of the Longriba fault. The Litang, Zhongdian, and Xiaojiang faults and the faults bounding the Lijiang block exhibit extensional motions, in agreement with the regional tectonics and earthquake focal mechanisms. In the regions north of the Red River fault whose kinematics can be described through the rotations of subblocks with small residuals, the fault motions agree with focal mechanism patterns and tectonic block models. In regions south of the Red River fault, the right‐lateral shearing among the Red River, Lancang and Sagaing faults appears to explain the first‐order deformation features. However, the relatively large residuals and complex tectonic setting suggest that various other factors, such as asthenospheric flow and the subduction of the Burma plate may also influence the deformation. Thus, a single model extended to interpret all the geodynamic features in SE Tibet, especially south of the Red River fault, may be overly simple, and the extent to which each factor contributes to this deformation requires further study.

DeGaS-MC: Dense Gas Survey in the Magellanic Clouds

Context. Understanding the star-forming processes is key to understanding the evolution of galaxies. Investigating star formation requires precise knowledge of the properties of the dense molecular gas complexes where stars form and a quantification of how they are affected by the physical conditions to which they are exposed. The proximity, low metallicity, and wide range of star formation activity of the Large and Small Magellanic Clouds (LMC and SMC) make them prime laboratories to study how local physical conditions impact the dense gas reservoirs and their star formation efficiency. Aims. The aim of the Dense Gas Survey for the Magellanic Clouds (DeGaS-MC) project is to expand our knowledge of the relation between dense gas properties and star formation activity by targeting the LMC and SMC observed in the HCO+(2−1) and HCN(2−1) transitions. Methods. We carried out a pointing survey targeting two lines toward ∼30 LMC and SMC molecular clouds using the SEPIA180 instrument installed on the Atacama Pathfinder EXperiment (APEX) telescope. We performed a follow-up mapping campaign of the emission in the same transition in 13 star-forming regions. This first paper provides line characteristic catalogs and integrated line-intensity maps of the sources. Results. HCO+(2−1) is detected in 20 and HCN(2−1) in 8 of the 29 pointings observed. The dense gas velocity pattern follows the line-of-sight velocity field derived from the stellar population. The three SMC sources targeted during the mapping campaign were unfortunately not detected in our mapping campaign but both lines are detected toward the LMC 30Dor, N44, N105, N113, N159W, N159E, and N214 regions. The HCN emission is less extended than the HCO+ emission and is restricted to the densest regions. The HCO+(2−1)/HCN(2−1) brightness temperature ratios range from 1 to 7, which is consistent with the large ratios commonly observed in low-metallicity environments. A larger number of young stellar objects are found at high HCO+ intensities and lower HCO+/HCN flux ratios, and thus toward denser lines of sight. The dense gas luminosities correlate with the star formation rate traced by the total infrared luminosity over the two orders of magnitude covered by our observations, although substantial region-to-region variations are observed.

Globally optimized cross-correlation for particle image velocimetry

We propose a global optimization method to automatically search for the correlation peak instead of computing the entire cross-correlation map throughout an interrogation window (IW) using a fast Fourier transform (FFT)-based method. The proposed method, named globally optimized cross-correlation for particle image velocimetry (GOCCPIV), minimizes an objective function consisting of a residual term for cross-correlation and a penalty term for smoothness to solve the optimal velocity field. A very small IW is adopted in GOCCPIV to obtain a dense velocity field with a high spatial resolution. The proposed method is quantitatively validated on synthetic particle image pairs with different flow patterns and is compared with the mainstream FFT-based cross-correlation method (FFTCCPIV) and physical-based optical flow (OpticalFlow). We consider the influences of the IW size, particle concentration, particle image diameter, large displacements and image noise on the velocity measurements. Error analysis indicates that GOCCPIV outperforms FFTCCPIV in resolving small-scale vortices and reducing the measurement error. Finally, the proposed method is applied to a real PIV experiment with an impinging jet. The results indicate that GOCCPIV is more suitable than FFTCCPIV for resolving high-velocity-gradient regions.

ALMA 0.″02 Resolution Observations Reveal HCN-abundance-enhanced Counter-rotating and Outflowing Dense Molecular Gas at the NGC 1068 Nucleus

Abstract We present ALMA ∼0.″02 resolution observations of the nucleus of the nearby (∼14 Mpc) type 2 active galactic nucleus NGC 1068 at HCN/HCO+/HNC J = 3–2 lines, as well as at their 13C isotopologue and vibrationally excited lines, to scrutinize the morphological, dynamical, chemical, and physical properties of dense molecular gas in the putative dusty molecular torus around a mass-accreting supermassive black hole. We confirm almost east–west-oriented dense molecular gas emission both morphologically and dynamically, which we regard as coming from the torus. Bright emission is compact (≲3 pc), and low-surface-brightness emission extends out to 5–7 pc. These dense molecular gas properties are not symmetric between the eastern and western torus. The HCN J = 3–2 emission is stronger than the HCO+ J = 3–2 emission within the ∼7 pc torus region, with an estimated dense molecular mass of (0.4–1.0) × 106 M ⊙. We interpret that HCN abundance is enhanced in the torus. We detect signatures of outflowing dense molecular gas and a vibrationally excited HCN J = 3–2 line. Finally, we find that in the innermost (≲1 pc) part of the torus, the dense molecular line rotation velocity, relative to the systemic velocity, is the opposite of that in the outer (≳2 pc) part, in both the eastern and western torus. We prefer a scenario of counter-rotating dense molecular gas with innermost almost Keplerian rotation and outer slowly rotating (far below Keplerian) components. Our high-spatial-resolution dense molecular line data reveal that torus properties of NGC 1068 are much more complicated than the simple axisymmetrically rotating torus picture in the classical active galactic nucleus unification paradigm.

Data-driven order reduction and velocity field reconstruction using neural networks: The case of a turbulent boundary layer

We present a data-driven methodology to achieve the identification of coherent structure dynamics and system order reduction of an experimental turbulent boundary layer flow. The flow is characterized using time-resolved optical flow particle image velocimetry, leading to dense velocity fields that can be used both to monitor the overall dynamics of the flow and to define as many local visual sensors as needed. A Proper Orthogonal Decomposition (POD) is first applied to define a reduced-order system. A non-linear mapping between the local upstream sensors (inputs sensors) and the full-field dynamics (POD coefficients) as outputs is sought using an optimal focused time-delay Artificial Neural Network (ANN). The choices of sensors, ANN architecture, and training parameters are shown to play a critical role. It is verified that a shallow ANN, with the proper sensor memory size, can lead to a satisfying full-field dynamics identification, coherent structure reconstruction, and system order reduction of this turbulent flow.

Large eddy simulation of turbidity currents in a narrow channel with different obstacle configurations

Turbidity currents are frequently observed in natural and man-made environments, with the potential of adversely impacting the performance and functionality of hydraulic structures through sedimentation and reduction in storage capacity and an increased erosion. Construction of obstacles upstream of hydraulic structures is a common method of tackling adverse effects of turbidity currents. This paper numerically investigates the impacts of obstacle’s height and geometrical shape on the settling of sediments and hydrodynamics of turbidity currents in a narrow channel. A robust numerical model based on LES method was developed and successfully validated against physical modelling measurements. This study modelled the effects of discretization of particles size distribution on sediment deposition and propagation in the channel. Two obstacles geometry including rectangle and triangle were studied with varying heights of 0.06, 0.10 and 0.15 m. The results show that increasing the obstacle height will reduce the magnitude of dense current velocity and sediment transport in narrow channels. It was also observed that the rectangular obstacles have more pronounced effects on obstructing the flow of turbidity current, leading to an increase in the sediment deposition and mitigating the impacts of turbidity currents.

Numerical analysis of the influence of the number of blades on the dynamic performance of a drone

The use of Unmanned Aerial Vehicles and known under the generic name of drones is increasingly present in different economic sectors, due to the versatility they offer in solving specific problems. The specific problems to be solved, however, raise the need for the technical characteristics of the drones to be optimized according to the specific usage. In the case of drones used in technological processes in agriculture, they must offer a high autonomy, that can be increased by optimizing their weight. The paper proposes the study of the dynamics of the air flow according to the configuration of the drone rotor (2 and 3 rotor respectively) by numerical analysis methods for the further development of a drone that will be used in the phytosanitary treatment process of agricultural crops. The parameters evaluated and compared are the specific parameters for characterizing the airflow: velocity and relative pressure (at different nacelle’s section, both in horizontal and vertical directions). The obtained results show that the most constant, dense and homogeneous velocity field was obtained from the 3-blade configuration of the rotor.

Spatial forecasting of seismicity provided from Earth observation by space satellite technology

Understanding the controls on the distribution and magnitude of earthquakes is required for effective earthquake forecasting. We present a study that demonstrates that the distribution and size of earthquakes in Italy correlates with the steady state rate at which the Earth’s crust moves. We use a new high-resolution horizontal strain rate (S) field determined from a very dense velocity field derived from the combination of Global Navigation Satellite System (GNSS) and satellite radar interferometry from two decades of observations. Through a statistical approach we study the correlation between the S and the magnitude of M ≥ 2.5 earthquakes that occurred in the same period of satellite observations. We found that the probability of earthquakes occurring is linked to S by a linear correlation, and more specifically the probability that a strong seismic event occurs doubles with the doubling of S. It also means that lower horizontal strain rate zone can have as large earthquakes as high horizontal strain rate zones, just with a reduced probability. The work demonstrates an independent and quantitative tool to spatially forecast seismicity.