Determination Of Velocity Research Articles

Technical and Economic Feasibility Investigation for the Treatment of Microplastic-Contaminated Marine Sediments Through an Environmentally Sustainable Separation Process

This work provides a comprehensive study of a density separation treatment through sucrose solution for the removal of microplastics (MPs) from marine sediments. The theoretical determination of flotation velocities for 1.0 mm diameter spherical MPs with a density of 1.3 g/cm3 at various solution temperatures and sucrose contents was performed. An optimal velocity of 1.03 m/h was observed with a 70% sucrose solution at 50 °C. The validation of theoretical velocities was carried out through experimental tests at optimal operating conditions for polypropylene (PP), high-density polyethylene (HDPE), polylactic acid (PLA), and polyvinyl chloride (PVC) as target MPs. The results showed an experimental floating velocity slightly lower than the theoretical predictions for PP, HDPE, and PLA. PVC, instead, characterized by a higher density than the separation solution, showed a settling velocity 42% lower than the theoretical one. Further tests were performed to assess the solid-to-liquid (S/L) ratio effect on MPs’ separation efficiency. The results showed an optimal S/L of 75 kg/m3 with 80% PVC removal and total PP, HDPE, and PLA removal. Finally, the design and cost optimization of a longitudinal settling tank were proposed for the pilot/real-scale treatment. The observed outcomes provided in-depth details useful for the development of an environmentally sustainable treatment for the preservation of marine areas.

Longitudinal wave propagation determination in concrete specimen under impact loading by ultrahigh-speed camera image sequence and strain gauge data analysis

Abstract To demonstrate the potential of an ultrahigh-speed camera with frame rates beyond a million images per second for high dynamic material analysis, cylindrical concrete specimens are tested under compression at strain rates up to 164.3 s-1 in a split Hopkinson pressure bar facility. The evaluation includes the determination of the actual longitudinal wave propagation velocity and the induced longitudinal deformations. The results obtained from image sequence analysis are validated with those obtained by the well-established strain gauge measuring technique that estimate the specimen deformation in the same facility. Data analysis for both measuring techniques is based on the one-dimensional wave theory. Image quality challenges affecting the material analysis are overcome by applying a Bézier fitting filter to the image sequences. Using the image-sequence-based approach to select stress-wave-induced motion points, different wave characteristics are acquired. Subsequently, selecting the points in compression during the first stress wave transit yields longitudinal wave propagation velocities up to 2995 m/s with a standard deviation of 265 m/s. From the validation results, first, an observed bilateral complementarity of both techniques enables a comprehensive analysis of the specimen deformation. Second, individual discrepancies between the material properties obtained from both techniques up to ultimate compressive strength for deformation, velocity, strain, and stress of 20.8 %, 14.4 %, 8.3 %, and 10.0 %, respectively, are achieved.

State-of-the-Art Navigation Systems and Sensors for Unmanned Underwater Vehicles (UUVs)

Researchers are currently conducting several studies in the field of navigation systems and sensors. Even in the past, there was a lot of research regarding the field of velocity sensors for unmanned underwater vehicles (UUVs). UUVs have various services and significance in the military, scientific research, and many commercial applications due to their autonomy mechanism. So, it’s very crucial for the proper maintenance of the navigation system. Reliable navigation of unmanned underwater vehicles depends on the quality of their state determination. There are so many navigation systems available, like position determination, depth information, etc. Among them, velocity determination is now one of the most important navigational criteria for UUVs. The key source of navigational aids for different deep-sea research projects is water currents. These days, many different sensors are available to monitor the UUV’s velocity. In recent times, there have been five primary types of sensors utilized for UUV velocity forecasts. These include Doppler Velocity Logger sensors, paddlewheel sensors, optical sensors, electromagnetic sensors, and ultrasonic sensors. The most popular sensing sensor for estimating velocity at the moment is the Doppler Velocity Logger (DVL) sensor. DVL sensor is the most fully developed sensor for UUVs in recent years. In this work, we offer an overview of the field of navigation systems and sensors (especially velocity) developed for UUVs with respect to their use with tidal current sensing in the UUV setting, including their history, evolution, current research initiatives, and anticipated future.

Reliable velocity determination through GNSS TDCP/Doppler combination using an improved FGO framework

Velocity is of great significance for navigation and controlling. GNSS has become the mainstream outdoor velocity determination method by its global, all-weather, high-precision, and fast response characteristics, with time-differenced carrier phase (TDCP) and Doppler regarded as two commonly used methods for precise velocity determination using stand-alone GNSS receivers. However, TDCP aims to estimate the average velocity and is susceptible to cycle slip, while Doppler is limited by the accuracy of Doppler observations. Both methods have certain fragility in complex environments. In this contribution, a method based on factor graph optimization (FGO) using TDCP/Doppler combination is proposed. First, the proposed method considers TDCP measurements as a factor to connect Doppler measurements. This method combines the accuracy of TDCP with the usability of Doppler in complex environments, obtaining the merits of both observations. Second, an improved FGO estimator is used to obtain continuous and accuracy velocity. On one hand, the robust function of dynamic covariance estimation (DCE) avoids the need to adjust parameters for different data. On the other hand, the error envelope is generated based on the minimum detectable bias (MDB) which also proves its stronger internal reliability. Finally, kinematic tests are carried out to verify the proposed method. In urban environment, compared with Doppler and TDCP only, the estimation accuracy and usability are improved by 83.67% and 4.41%, respectively. Meanwhile, compared with WLS and EKF, the estimation accuracy is improved by 63.10% and 40.99%, respectively. It is demonstrated that the proposed method can effectively improve the velocity performance in urban environment.

A robust time synchronization algorithm for GNSS/IMU integrated navigation in urban environments

Abstract Global navigation satellite systems (GNSS) are often integrated with inertial measurement units (IMU) for navigation in urban environments. The ability of these integrated systems to achieve precise positioning and navigation is highly dependent on accurate time synchronization. While the current time synchronization algorithms are capable of achieving millisecond-level synchronization in open environments, their performance deteriorates significantly in complex urban environments due to the impact on GNSS measurements of non-line-of-sight (NLOS) signals and multipath effects. Here we propose a loosely coupled GNSS/IMU integration time synchronization error modeling and compensation algorithm to tackle this problem in the context of urban vehicle navigation. In the algorithm, the time synchronization error is augmented as a state variable in the Robust Extended Kalman Filter (REKF), with the robustness factor threshold being dynamically adjusted based on the time synchronization error. Furthermore, we have designed a system time synchronization detection scheme based on two detection factors, with the aim of achieving high-precision GNSS/IMU time synchronization in complex urban environments. Experimental results show that the proposed algorithm synchronizes data time to the millisecond level in urban positioning environments. This represents an improvement of more than 90% in time synchronization precision compared to the traditional EKF-based time synchronization algorithm, and an improvement of more than 60% compared to the REKF-based time synchronization algorithm. Furthermore, compared to the EKF-based GNSS/IMU fusion algorithm, the proposed algorithm enhances the accuracy of the determination of both position and velocity in the horizontal and 3D directions by more than 50% and heading accuracy by 85%.

Long short-term memory (LSTM) neural networks for in situ particle velocity determination in material strength experiments under ramp wave compression

In the experiments of measuring the strength of materials under ramp compression, accurately determining in situ particle velocity is crucial for calculating material sound speed during loading–unloading path and materials strength under high pressure. This paper proposes a machine learning approach that utilizes Long Short-Term Memory (LSTM) neural networks and Bayesian optimization algorithms to enhance the analysis of data from ramp compression strength measurement experiments. This method leverages LSTM neural networks to uncover the complex relationship between the rear interface velocity of the sample and the in situ particle velocity in numerical simulations. By using a well-trained network model, it enables direct interpretation of experimental data, leading to accurate predictions of key physical quantities along the loading and unloading paths in ramp compression experiments. A comparative analysis between theoretical curves from numerical simulations and LSTM neural network predictions shows a high degree of consistency. This approach is applied to ramp compression experiments on Ta and CuCrZr materials, demonstrating superior accuracy over the free-surface approximation and incremental impedance matching methods. Additionally, this method relies solely on the equation of state during numerical computations, eliminating the need for the complex constitutive equations required by the transfer function method, thus enhancing data processing efficiency and practicality.

Use of shear wave velocity to assess Champlain marine clay fabric anisotropy

This paper investigates the anisotropic characteristics of Champlain marine clay soil using a combination of laboratory techniques. A modified oedometer cell with a piezoelectric ring actuator technique was used to measure shear wave velocity during consolidation stages. The axisymmetric design of the oedometer allowed for the determination of shear wave velocity in both the vertical and horizontal planes. The preliminary findings reveal that the sensitive marine clay is inherently anisotropic, with lower preconsolidation pressure for horizontally consolidated specimens and faster propagation of shear waves in the plane parallel to the bedding layer. High-precision strain gauges integrated into the consolidation ring were used to evaluate horizontal stress during the one-dimensional consolidation test. The ability to determine mean effective stress enables the normalization of shear wave velocities using this stress, providing more coherent empirical correlations in terms of shear wave velocity. Scanning electron microscopy was used to examine the microstructure of clay specimens, providing qualitative and quantitative insight into the restructuring and reorientation of clay platelets under consolidation stress. The consistency of the results through both micro and macro-scale analyses confirms the reliability of the experimental approach, highlighting its potential for future studies on the anisotropy of Champlain marine clay fabrics.

Multiphase Gas Nature in the Sub-parsec Region of the Active Galactic Nuclei. III. Eddington Ratio Dependence on the Structures of Dusty and Dust-free Outflows

We investigated the influence of the Eddington ratio on sub-parsec-scale outflows in active galactic nuclei (AGNs) with supermassive black holes (SMBHs) masses of 107 M ⊙ using two-dimensional radiation hydrodynamics simulations. When the range of the Eddington ratio is γ Edd > 10−3, the radiation force exceeds the gas pressure, leading to stronger outflows and larger dust sublimation radius. Although the sub-parsec-scale outflows are a time-dependent phenomenon, our simulations demonstrated that the radial distributions can be well explained by the steady solutions of the spherically symmetric stellar winds. The dynamic structure of sub-parsec-scale outflows is influenced by the dust sublimation radius and the critical radii determined by the dynamical equilibrium condition. Although significantly affecting the outflow velocity, the Eddington ratio exerts minimal effects on temperature and number density distribution. Furthermore, our analytical solutions highlight the importance of the dust sublimation scale as a crucial determinant of terminal velocity and column density in dusty outflows. Through comparisons of our numerical model with the obscuring fraction observed in nearby AGNs, we reveal insights into the Eddington ratio dependence and the tendency toward the large obscuring fraction of the dusty and dust-free gases. The analytical solutions are expected to facilitate an understanding of the dynamical structure and radiation structures along the line of sight and their viewing angles from observations of ionized outflows.

Advancing the C[formula omitted] low-temperature oxidation chemistry through species measurements in a rapid compression machine, Part A: 1-Butene

Alkene chemistry plays a crucial role in the autoignition and oxidation of larger hydrocarbons. Unlike its other isomers, 1-butene is characterized by a two-stage ignition process. Various previous studies of 1-butene oxidation have used experimental techniques, including the measurement of ignition delay times in rapid compression machines (RCM) and in shock tubes, the determination of flame velocities, and the measurement of species concentrations in flames and in jet-stirred reactors (JSR). JSR studies provide an important insight into intermediate species formation at low temperatures but are constrained to low pressures and/or highly diluted conditions. To bridge the gap between JSR and engine-relevant conditions, this study presents species concentration measurements during the oxidation of 1-butene at 733 K and 30 bar under stoichiometric ’air-like’ conditions in an RCM, complemented by IDT measurements in the temperature range of 680–910 K. We designed an innovative 2-valve sampling setup to reduce quantitative uncertainties and the time required for species measurements. Our results indicate that existing 1-butene models fail to accurately predict the IDTs and the formation of the key oxidation intermediates. In response, potential optimizations for an improved kinetic model based on NUIGMech1.3 are discussed. Rate parameters for predominantly fuel consumption pathways, along with other reactions and thermochemical properties in the Waddington mechanism, have been altered within expected uncertainty limits to reflect the experimentally observed IDTs and species concentrations of this study and other validation data from the literature. However, the refined model does not predict the formation of 2-ethenyloxirane and ethene, indicating a gap in our understanding of the chemistry of these components. Overall, this study demonstrates the importance of measuring intermediates under the same conditions as IDTs to accurately address deficiencies in current kinetic mechanisms, and represents the first phase of a comprehensive investigation advancing the understanding of C4 oxidation chemistry.Novelty and significance statementThe novelty of this research lies in the design of an innovative sampling system for RCM species measurements, lowering the time for experimental execution and the uncertainties of the measurements. This enabled first-time species measurements during the oxidation of butene isomers in an RCM at high pressure and low level of dilution, contributing to the refinement of the 1-butene sub-mechanism within the NUIGMech1.3 framework. This research contributes to the understanding of the oxidation of alkenes, an important class of intermediates in gasoline and biofuel combustion. It emphasizes the need to measure intermediate species at the same conditions as ignition delay times, which are essential for understanding oxidation pathways under engine-relevant conditions. This research is part of a broader investigation of C4 oxidation chemistry, along with our companion work on n-butane. The resulting kinetic model is capable of reproducing most of the available n-butane and 1-butene validation targets.

Integrating downhole seismic testing into dynamic probing light (DPL): first results

Dynamic probing light (DPL) is available among a wide range of dynamic penetrometers. This equipment can be used in locations where conventional sounding equipment may have limited access. Here, the shear wave velocity (Vs) determination by the downhole seismic technique is incorporated to the DPL to develop a hybrid test, the seismic DPL test, for the preliminary characterization of tropical soils. The corresponding equipment, test procedure and data interpretation of the seismic DPL (S-DPL) test are presented and discussed. S-DPL test campaigns were carried out at two experimental research sites where crosshole seismic tests were conducted to provide reference Vs profiles. The Vs profiles determined with the S-DPL test are in accordance with those determined with the crosshole test for both sites. The proposed hybrid test (S-DPL) has the potential to serve applications in preliminary site characterization, particularly at sites including unsaturated tropical soil profiles.

Numercial modeling for enhanced heat transfer efficiency of spiral coils with supercritical fluid flow under different operating conditions

This research investigates the thermohydraulic performance and exergy destruction associated with the flow of supercritical carbon dioxide (sCO2) within spirally coiled mini tubes. The study examines the impact of different cross-sectional geometries. The primary objective of the study is to examine the impact of critical parameters, including shape, hydraulic diameter, inlet temperature, mass flux, and operating pressure, on important variables such as friction factor, heat transfer coefficient, and exergy efficiency. The computational simulation employs the RNG k−ε model. The Coupled algorithm was utilized for the determination of velocity and pressure fields, utilizing second-order discretization for domain partitioning and first-order discretization for other terms. The carbon dioxide (CO2) was conceptualized as a compressible gas with complex thermophysical attributes that are contingent upon variations in temperature and pressure. The thermophysical properties of carbon dioxide are evaluated within a defined range of operating conditions (298. 15 K < T < 455 K and 8 MPa < p < 10 MPa). The observed trends in HTC (heat transfer coefficient) demonstrate a correlation with specific heat, showing a peak at lower temperatures under increased operating pressures. Elevated operational pressure results in a reduction of the maximum HTC. The augmentation of mass flux results in an increase in heat transfer coefficient, thereby indicating an improvement in system efficiency. An augmentation in hydraulic diameter yields diminished heat transfer coefficients, mitigated pressure loss, and heightened exergy destruction.

Measuring River Surface Velocity Using UAS‐Borne Doppler Radar

AbstractUsing Unoccupied Aerial Systems (UAS) equipped with optical RGB cameras and Doppler radar, surface velocity can be efficiently measured at high spatial resolution. UAS‐borne Doppler radar is particularly attractive because it is suitable for real‐time velocity determination, because the measurement is contactless, and because it has fewer limitations than image velocimetry techniques. In this paper, five cross‐sections (XSs) were surveyed within a 10 km stretch of Rönne River in Sweden. Ground‐truth surface velocity observations were retrieved with an electromagnetic velocity sensor (OTT MF Pro) along the XS at one m spacing. Videos from a UAS RGB camera were analyzed using both Particle Image Velocimetry (PIV) and Space‐Time Image Velocimetry (STIV) techniques. Furthermore, we recorded full waveform signal data using a Doppler radar at multiple waypoints across the river. An algorithm fits two alternative models to the average amplitude curve to derive the correct river surface velocity based on Gaussian models with: (a) one peak, and (b) two peaks. Results indicate that river flow velocity and propwash velocity caused by the drone can be found in XS where the flow velocity is low, while the drone‐induced propwash velocity can be neglected in fast and highly turbulent flows. To verify the river flow velocity derived from Doppler radar, a mean PIV value within the footprint of the Doppler radar at each waypoint was calculated. Finally, quantitative comparisons of OTT MF Pro data with STIV, mean PIV and Doppler radar revealed that UAS‐borne Doppler radar could reliably measure the river surface velocity.

The Role of 2, 4, and 5-dimensional Cardiac Flow MRI for Evaluation of Valvulopathies: A Literature Review.

Two-dimensional phase-contrast magnetic resonance imaging (2D flow MRI) and its multidimensional alternatives, 4D and 5D flow MRI, measure blood flow in the heart and great vessels. While 2D flow MRI is the standard technique, it has limitations regarding need for precise image plane prescribing and long scan time. In contrast, 4D and 5D flow MRI acquire 3D volumes, enabling retrospective assessment of all vessels. This review evaluates these three techniques for quantification of blood flow of the aortic and pulmonary valves in congenital heart disease. A systematic literature search was conducted in August 2024 using the PUBMED database, including articles comparing 2D, 4D, and 5D flow MRI. Fifteen articles comparing 2D and 4D, one comparing 2D and 5D and three articles comparing 4D and 5D flow MRI were included. No study compared all three techniques. 2D, 4D and 5D flow MRI demonstrated a good agreement for flow quantification. 4D flow MRI, however, tends to present a better accuracy and internal consistency than 2D flow MRI for determination of peak velocities and flow in stenotic lesions, particularly when comparing velocities to echocardiography. 4D and 5D flow MRI are associated with shorter scan times than 2D flow MRI. 4D and 5D flow MRI appear to offer promising alternatives to 2D flow MRI with the advantage of reduced scan times. Larger and prospective studies including echocardiography are needed to evaluate the potential of 4D and 5D to replace 2D flow MRI for flow quantification and peak velocity determination.

Determination of Velocities of Thread Axis Points When Interacting with a Direct Large Curvature Taking into Account the Deformation in the Contact Zone

The theoretical study of the process of the movement of the thread, which is deformed in the contact zone with the guide, from the point of view of determining the speed, is of great importance for solving a number of specific applied problems. When determining the speed, it was assumed that the start of the Lagrangian and Euler coordinates coincided. Taking into account that the crumpling of the thread in the contact zone occurs at forces much lower than the forces required to stretch the thread, the thread was considered as non-extensible. To determine the law of the distribution of the speed of the points of the thread, an independent vector, which is always connected to the main triangle, was introduced into consideration. The relationship between the vector of total curvature and the vector of absolute angular velocity is obtained. The obtained values of the speed at an arbitrary point of the axis of the thread as a function of the deformation vector, which determines the degree of twisting of the thread in the contact zone. The obtained results can be used to study various technological processes in the sewing, knitting, and textile industries, where the movement of the thread takes place along the guide surface of great curvature.

Enhancing convergence speed with feature enforcing physics-informed neural networks using boundary conditions as prior knowledge

This research introduces an accelerated training approach for Vanilla Physics-Informed Neural Networks (PINNs) that addresses three factors affecting the loss function: the initial weight state of the neural network, the ratio of domain to boundary points, and the loss weighting factor. The proposed method involves two phases. In the initial phase, a unique loss function is created using a subset of boundary conditions and partial differential equation terms. Furthermore, we introduce preprocessing procedures that aim to decrease the variance during initialization and choose domain points according to the initial weight state of various neural networks. The second phase resembles Vanilla-PINN training, but a portion of the random weights are substituted with weights from the first phase. This implies that the neural network’s structure is designed to prioritize the boundary conditions, subsequently affecting the overall convergence. The study evaluates the method using three benchmarks: two-dimensional flow over a cylinder, an inverse problem of inlet velocity determination, and the Burger equation. Incorporating weights generated in the first training phase neutralizes imbalance effects. Notably, the proposed approach outperforms Vanilla-PINN in terms of speed, convergence likelihood and eliminates the need for hyperparameter tuning to balance the loss function.

Determination of Initial Data in the Time-Fractional Pseudo-Hyperbolic Equation

We examine a time-fractional pseudo-hyperbolic equation involving positive operators. We explore the determination of initial velocity and perturbation. It is demonstrated that these initial inverse problems are ill posed. Additionally, we prove that under certain conditions, the inverse problems exhibit well-posedness properties. Our focus is on developing a theoretical framework for these initial inverse problems associated with time-fractional pseudo-hyperbolic equations, laying the groundwork for future studies on numerical algorithms to solve these problems. This investigation is crucial for understanding the fundamental behavior of the equations under various initial conditions and perturbations. By establishing a rigorous theoretical framework, we pave the way for future research to focus on practical numerical methods and simulations. Our results provide a deeper insight into the mathematical structure of time-fractional pseudo-hyperbolic equations, ensuring that future computational approaches are built on a solid theoretical foundation.

Effect of temperature on water evaporation coefficient (E) in a thermobalance: A solar-driven steam generation approach

During this investigation, the variation of the water evaporation phenomenon with the defined drying temperature and mass of water was analyzed, five levels were studied (50, 60, 70, 80 and 90 ℃), finally a correlation between temperature and evaporation rate was generated. With the study carried out, it was defined that the water evaporation velocity can be calculated with an initial mass of 35 g at 90 ℃, while the necessary time for the determination was 120 min. In addition, it was determined that the evaporation velocity follows a quadratic behavior with temperature, according to the experiments carried out with the Sartorius MA-100 balance, while the maximum deviation recorded was 0.349 mmol/m2s for a temperature of 80 ℃. It is concluded that the determination of the water evaporation velocity is highly dependent on the temperature and mass of water. Furthermore, this study can be used as a basis for future studies aimed at improving the efficiency of processes such as steam and electricity cogeneration.

Gaussian Process Regression as a precrash velocity determination method–subcompact vehicle class

The following paper presents an innovative approach to determining vehicle precrash velocity when hitting an immovable obstacle facing forward. Precrash velocity is necessary in order to perform a crash reconstruction. It is needed for the time-space analysis of the events, as well as to assesscrash mitigation and to evaluate drivers’ technique and tactics. For this task, the authors are using Gaussian Process Regression (GPR). Such an approach offers a number of advantages over the currently used methods that prove to be outdated when considering modern vehicles. The mathematical model was trained on a database shared by the National Highway Traffic Safety Administration.. This database covers a large number of crash tests of different kind, however authors focus on frontal collisions of the subcompact car class. Due to low accuracy of linear methods used up till now, Authors developed an innovative approach to determine the EES parameter utilizing Gaussian process regression. The newly developed method is an effective and accurate way to determine the vehicle’s velocity and shows promising results, as is demonstrated in this paper.

Improved determination of the S-wave velocity of rocks in dry and saturated conditions: Application of machine-learning algorithms

The determination of S-wave velocity (Vs) is of significant importance in various engineering disciplines, including mining, civil, and geotechnical engineering. It is beneficial to indirectly determine Vs under both dry and saturated conditions and to understand its relationship with influencing input variables: coring depth (H), durability index (DI), water content (Wa), dry density (ρd), saturated density (ρs), and porosity (n). In this study, we evaluate these relationships using three multiple machine-learning algorithms (MLAs): artificial neural network (ANN), fuzzy inference system (FIS), and gene expression programming (GEP), alongside a linear regression method (LRM) and predict both dry S-wave velocity (Vs-dry) and saturated S-wave velocity (Vs-sat) of rocks. The research involves the analysis of 90 datasets derived from samples of schist, phyllite, and sandstone rocks collected from Azad and Bakhtiari dam sites in Iran. The diversity of these datasets is a key advantage of this study, providing a solid foundation for models training and testing while enhancing the models’ generalizability. Model optimization techniques are employed in the Python, MATLAB, GenXProTools, and SPSS environments to identify the most effective versions of ANN, FIS, GEP, and LRM models, respectively. The prediction performance analysis reveals that all applied models yield acceptable levels of accuracy for predicting Vs-dry and Vs-sat. However, GEP emerges as the best model for predicting both Vs-dry and Vs-sat. The ANN and FIS models also achieve high levels of accuracy, while LRM performs comparatively less well. Additionally, sensitivity analysis conducted using the cosine amplitude method (CAM) highlights the influence of different variables on Vs-dry and Vs-sat. The ρd is found to be the most influential parameter on Vs-dry, whereas DI exhibits the least impact. Conversely, the ρs significantly affects Vs-sat, while Wa shows the lowest impact. The exceptional performance of these proposed MLAs confirms their applicability in real-world rock engineering and geotechnics projects, offering precise determination of Vs. The diversity of studied rock types and datasets, along with the use of cost-effective and easy measurable inputs, the determination of Vs in both dry and saturated status, and the application of robust MLAs for Vs determination are the main novelties of this study. However, further researches involving additional datasets and more rock types are required to validate these findings.

Framework for UAV-based river flow velocity determination employing optical recognition

The determination of river velocity is important for hydromorphological analyses and river monitoring systems. Indirect measurements of river velocity using videos recorded by unmanned aerial vehicles (UAV) allow fast and cost-effective processing of information about the river stretch. This paper presents a method for computing flow velocity of the river surface using deep supervised model RAFT to determine the optical flow in combination with image pre-processing by convolutional operations. Moreover, the windiness coefficients and variance score were proposed to evaluate reliability of the collected data and the obtained results of optical flow detection. Various image pre-processing techniques were applied, namely the selection of the analysed area and the number of convolutional operations to select the one with the lowest variance score. This score represents the consistency of the river flow velocity during the video and can be used to filter out unreliable results. The numerical experiments were performed using the videos and directly measured velocity values of 4 shallow rivers in Lithuania collected during the field surveys. The optical velocity estimation method showed good correspondence to the directly measured values for the velocity range from 0 m/s to 0.8 m/s in the points with low variance score up to 0.192 that represents the first quartile of the variance. The optical flow method tends to underestimate the velocity up to 0.5 m/s for the quartiles with the higher variance scores. It was shown that in most cases the lowest variance score value was obtained using pre-processing techniques without convolutional operations. However, the need to analyse various pre-processing techniques arises from the different origin of the objects moving on the river surface.