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Emulsion fuels have the potential to reduce both particulate matter and NOx emissions and can potentially improve the efficiency of combustion engines. However, their limited stability remains a critical barrier to practical use as an alternative fuel. In this study, we explore the evaporation behavior of thermodynamically stable water-in-oil microemulsions. The water-in-oil microemulsion droplets prepared from different types of oil were acoustically levitated and heated using a continuous laser at different irradiation intensities. We show that the evaporation characteristics of these microemulsions can be controlled by varying water-to-surfactant molar ratio ({\omega}) and volume fraction of the dispersed phase ({\phi}). The emulsion droplets undergo three distinct stages of evaporation, namely pre-heating, steady evaporation, and unsteady evaporation. During the steady evaporation phase, increasing {\phi} reduces the evaporation rate for a fixed {\omega}. It is observed that the evaporation of microemulsion is governed by the complex interplay between its constituents and their properties. We propose a parameter ({\eta}) denoting the volume fraction ratio between volatile and non-volatile components, which indicates the cumulative influence of various factors affecting the evaporation process. The evaporation of microemulsions eventually leads to the formation of solid spherical shells, which may undergo buckling. The distinction in the morphology of these shells is explored in detail using SEM imaging., Comment: 42 pages, 11 figures

Polymer droplets subjected to a heated environment have significance in several fields ranging from spray drying and powder formation to surface coating. In the present work, we investigate the evaporation of a high viscoelastic modulus aqueous polymeric droplet in an acoustically levitated environment. Depending on the laser irradiation intensity, we observe nucleation of a bubble in the dilute regime of polymer concentration, contrary to the previously observed bubble nucleation in a semi-dilute entangled regime for low viscoelastic modulus polymer droplets. After the bubble nucleation, a quasi steady bubble growth occurs depending on the laser irradiation intensity and concentrations. Our scaling analysis reveals that bubble growth follows Plesset-Zwick criteria independent of the viscoelastic properties of the polymer solution. Further, we establish that the onset of bubble growth has an inverse nonlinear dependence on the laser irradiation intensity. At high concentrations and laser irradiation intensities, we report the expansion and collapse of polymer membrane without rupture, indicating the formation of an interfacial skin with significant strength. The droplet oscillations are primarily driven by the presence of multiple bubbles and, to some extent, by the rotational motion of the droplet. Finally, depending on the nature of bubble growth, different types of precipitate form contrary to the different modes of atomization observed in low viscoelastic modulus polymer droplets.

In this paper, an adaptive super-twisting controller is designed for an agile maneuvering quadrotor unmanned aerial vehicle to achieve accurate trajectory tracking in the presence of external disturbances. A cascaded control architecture is designed to determine the desired accelerations using the proposed controller and subsequently used to compute the desired orientation and angular rates. The finite-time convergence of sliding functions and closed-loop system stability are analytically proven. Furthermore, the restrictive assumption on the maximum variation of the disturbance is relaxed by designing a gain adaptation law and low-pass filtering of the estimated equivalent control. The proper selection of design parameters is discussed in detail. Finally, the effectiveness of the proposed method is evaluated by high-fidelity software-in-the-loop simulations and validated by experimental studies., Comment: We are working on a new version of this paper and revising some technical parts. We will replace the new version as soon as it is carefully revised

In this paper, we investigate requirements to prepare an application for Specific Operations Risk Assessment (SORA), regulated by European Union Aviation Safety Agency (EASA) to obtain flight authorization for Unmanned Aerial Vehicles (UAVs) operations and propose some perspectives to automate the approach based on our successful application. Preparation of SORA requires expert knowledge as it contains technicalities. Also, the whole process is an iterative and time-consuming one. It is even more challenging for higher-risk operations, such as those in urban environments, near airports, and multi- and customized models for research activities. SORA process limits the potential socio-economic impacts of innovative UAV capabilities. Therefore, in this paper, we present a SORA example, review the steps and highlight challenges. Accordingly, we propose an alternative workflow, considering the same steps, while addressing the challenges and pitfalls, to shorten the whole process. Furthermore, we present a comprehensive list of preliminary technical procedures, including the pre/during/post-flight checklists, design and installation appraisal, flight logbook, operational manual, training manual, and General Data Protection Regulation (GDPR), which are not explicitly instructed in SORA manual. Moreover, we propose the initial idea to create an automated SORA workflow to facilitate obtaining authorization, which is significantly helpful for operators, especially the scientific community, to conduct experimental operations.

This paper presents an integrated approach that combines trajectory optimization and Artificial Potential Field (APF) method for real-time optimal Unmanned Aerial Vehicle (UAV) trajectory planning and dynamic collision avoidance. A minimum-time trajectory optimization problem is formulated with initial and final positions as boundary conditions and collision avoidance as constraints. It is transcribed into a nonlinear programming problem using Chebyshev pseudospectral method. The state and control histories are approximated by using Lagrange polynomials and the collocation points are used to satisfy constraints. A novel sigmoid-type collision avoidance constraint is proposed to overcome the drawbacks of Lagrange polynomial approximation in pseudospectral methods that only guarantees inequality constraint satisfaction only at nodal points. Automatic differentiation of cost function and constraints is used to quickly determine their gradient and Jacobian, respectively. An APF method is used to update the optimal control inputs for guaranteeing collision avoidance. The trajectory optimization and APF method are implemented in a closed-loop fashion continuously, but in parallel at moderate and high frequencies, respectively. The initial guess for the optimization is provided based on the previous solution. The proposed approach is tested and validated through indoor experiments.

The experimental mass data from the Atomic Mass Compilation - 2012 (AMC12) has been analyzed for two-neutron separation energies (S$_{2n}$), two-proton separation energies (S$_{2p}$), double-beta decay energies (Q$_{2\beta^-}$), and four-beta decay energies (Q$_{4\beta^-}$) and plotted against neutron number and mass number, respectively. A new weighted slope method of extrapolation, tested for known and new mass measurements, has been used to obtain the extrapolated mass values with better precision for more than 1100 nuclei far from the valley of stability, out of which more than 100 are being reported for the first time. A comparison has been made with five of the popular mass models with reference to experimental extrapolated masses from the present work and the Atomic Mass Evaluation 2016 (AME16). The extrapolated experimental atomic mass data will be very useful for both experimentalists and mass-model theoreticians, as well as in simulations of astrophysical r-processes., Comment: Tables/data sets 2-5 in the article are available in spreadsheet (.xlsx) format through Figshare (reference 22 in the article)

Intensity fluctuations in supercontinuum generation are studied in polarization-maintaining (PM) and non-PM all-normal dispersion tellurite photonic crystal fibers. Dispersive Fourier transformation is used to resolve the shot-to-shot spectra generated using 225 fs pump pulses at 1.55 {\mu}m, with experimental results well reproduced by vector and scalar numerical simulations. By comparing the relative intensity noise for the PM and non-PM cases, supported by simulations, we demonstrate the advantage of the polarization-maintaining property of the PM fibers in preserving low-noise dynamics. We associate the low-noise in the PM case with the suppression of polarization modulation instability., Comment: Shreesha Rao D. S., Tanvi Karpate, Amar Nath Ghosh, Iv\'an B. Gonzalo, Mariusz Klimczak, Dariusz Pysz, Ryszard Buczy\'nski, Cyril Billet, Ole Bang, John M. Dudley, and Thibaut Sylvestre, "Noise in supercontinuum generated using PM and non-PM tellurite glass all-normal dispersion fibers". Optics Letters 47.10 (May 2022), pp. 2550-2553

The photonic modes mediated absorption dynamics at femtosecond time scales along with the control of spontaneous emission tunability are investigated all-dielectric optical microcavity having BaTiO3 (BTO) as defect layer. The cavity-enhanced transient absorption reveals the dominant excited state absorption (ESA) of both photonic and electronic modes due to strong third-order optical nonlinearity influence. Photoluminescence of BTO is found to be guided and tuned by the photonic cavity mode. Such active photonic structures can be envisaged as a potential candidate in nonlinear optics and photonic device applications., Comment: 7 pages, 4 figures, iNEEBA 2021 Conference, Paper ID: OP-ESC-07

In this paper, we propose a photorealistic style transfer network to emphasize the natural effect of photorealistic image stylization. In general, distortion of the image content and lacking of details are two typical issues in the style transfer field. To this end, we design a novel framework employing the U-Net structure to maintain the rich spatial clues, with a multi-layer feature aggregation (MFA) method to simultaneously provide the details obtained by the shallow layers in the stylization processing. In particular, an encoder based on the dense block and a decoder form a symmetrical structure of U-Net are jointly staked to realize an effective feature extraction and image reconstruction. Besides, a transfer module based on MFA and "adaptive instance normalization" (AdaIN) is inserted in the skip connection positions to achieve the stylization. Accordingly, the stylized image possesses the texture of a real photo and preserves rich content details without introducing any mask or post-processing steps. The experimental results on public datasets demonstrate that our method achieves a more faithful structural similarity with a lower style loss, reflecting the effectiveness and merit of our approach.

Droplet-droplet interactions is ubiquitous in various applications ranging from medical diagnostics to enhancing and optimizing liquid jet propulsion. We employ an experimental technique where the laser pulse interacts with a micron-sized droplet and causes optical breakdown. The synergy of a nanosecond laser pulse and an isolated spherical droplet is accurately controlled and manipulated to influence the deformation and fragmentation of an array of droplets. We elucidate how the fluid dynamic response (such as drop-drop and shock-drop interactions) of an arrangement of droplets can be regulated and optimally shaped by laser pulse energy and its interplay with the optical density of liquid target. A new butterfly type breakup is revealed, which is found to result in controlled and efficient fragmentation of the outer droplets in an array. The spatio-temporal characteristics of a laser-induced breakdown dictate how shock wave and central droplet fragments can influence outer droplets. The incident laser energy and pulse width employed in this work are representative of diverse industrial applications such as surface cleaning, nano-lithography, microelectronics, and medical procedures such as intraocular microsurgery.

One-dimensional (1D) metal-dielectric (MD) periodic structures take advantage of large refractive index contrast between metal and dielectrics to invoke extremely high nonlinear ultrafast responses of metal. These structures are also special due to their extremely high laser damage threshold. The Bragg like 1D MD structure (Ag/SiO2)4 enables strong optical field confinement with much enhanced nonlinear features as compared to simple metal or single (Ag/SiO2)1 structure. In the present work, the ultrafast nonlinear optical responses of the above structures are investigated via femtosecond broadband optical pump-probe technique. The enhanced nonlinear optical absorption is of reverse saturation of absorption (RSA) nature, resulted due to free-carrier absorption (FCA) and excited-state absorption (ESA) processes. The spectral nonlinearities are closely related to the pump-induced modification of the metal's dielectric functions, which are qualitatively visualized by transfer matrix and two-temperature models. The ultrafast temporal evolution of nonlinear absorption clearly demonstrated enhanced optical nonlinearity, disentangled by the electron-electron and electron-phonon dynamic interactions at picosecond time scales. A phenomenological pulse propagation model is employed that incorporates the experimentally obtained nonlinear absorption coefficients and different nonlinear effects exhibited by the system. Nonlinearity plays a crucial role in controlling the ultrafast pulse propagation and could open a new window for many nonlinear device applications. The findings of these new optical materials could possibly pave the way for promising applications in ultrafast photonics., Comment: (23 main text + 9 supplementary) pages, (7 main text + 8 supplementary) figures

We present the first demonstration of shot-noise limited supercontinuum-based spectral domain optical coherence tomography (SD-OCT) with axial resolution of 5.9 $\mu$m at a center wavelength of 1370 nm. Current supercontinuum-based SD-OCT systems cannot be operated in the shot-noise limited detection regime because of severe pulse-to-pulse relative intensity noise of the supercontinuum source. To overcome this disadvantage we have developed a low-noise supercontinuum source based on an all-normal dispersion (ANDi) fiber, pumped by a femtosecond laser. The noise performance of our 90 MHz ANDi supercontinuum source is compared to that of two commercial sources operating at 80 and 320 MHz repetition rate. We show that the low noise of the ANDi supercontinuum source improves the OCT images significantly in terms of both higher contrast, better sensitivity, and improved penetration. From SD-OCT imaging of skin, retina, and multi-layer stacks we conclude that supercontinuum-based SD-OCT can enter the domain of shot-noise limited detection., Comment: 14 pages, 6 combined images

We experimentally investigate the dissolution of microscale sessile alcohol droplets in water under the influence of impermeable vertical confinement. The introduction of confinement suppresses the mass transport from the droplet to bulk medium in comparison with the non-confined counterpart. Along with a buoyant plume, flow visualization reveals that the dissolution of a confined droplet is hindered by a newly identified mechanism - levitated toroidal vortex. The morphological changes in the flow due to the vortex-induced impediment alters the dissolution rate, resulting in enhancement of droplet lifetime. Further, we have proposed a modification in the key non-dimensional parameters (Rayleigh number $Ra^{'}$ (signifying buoyancy) and Sherwood number $Sh^{'}$ (signifying mass flux)) and droplet lifetime $\tau_{c}^{'}$, based on the hypothesis of linearly stratified droplet surroundings (with revised concentration difference $\Delta C^{'}$), taking into account the geometry of the confinements. We show that experimental results on droplet dissolution under vertical confinement corroborate universal scaling relations $Sh^{'} \sim Ra^{' 1/4}$ and $\tau_{c}^{'} \sim \Delta C^{'-5/4}$. We also draw attention to the fact that the revised scaling law incorporating the geometry of confinements proposed in the present work can be extended to other known configurations such as droplet dissolution inside a range of channel dimensions, as encountered in a gamut of applications such as micro-fluidic technology and biomedical engineering., Comment: 10 pages, 5 figures

We delineate and examine the distinct breakup modes of evaporating water-in-oil emulsion droplets under acoustic levitation. The emulsion droplets consist of decane/dodecane/tetradecane as oil, while the water concentration is varied from 10% to 30% (v/v). The droplets were heated under different laser irradiation intensities and were observed to exhibit three broad breakup mechanisms, viz., breakup through bubble growth, sheet breakup, and catastrophic breakup. The occurrence of these modes of the breakup is found to be primarily dependent on the volatility differential among the components. Early nucleation in water/decane emulsions results in the growth of vapor bubble, which is characterized by intricate patterns of wave propagation on the droplet surface. The formation of these patterns suggests that the short time scale and length scale of wave patterns is the manifestation of Faraday instability, triggered on the droplet surface by the acoustic field induced resonance. A sheet-like breakup, on the contrary, occurs predominantly in emulsions comprising of components with relatively high volatility difference (water/dodecane emulsions) due to the breakup of an indiscernible small sized bubble. Intense catastrophic breakup occurs for emulsions with significantly vast volatility difference (water/tetradecane emulsions) where the droplet undergoes prompt fragmentation into fine secondary droplets., Comment: 36 pages, 19 figures

Spin bath polarization is the key to enhancing the sensitivity of quantum sensing and information processing. Significant effort has been invested in identifying the consequences of quantumness and its control for spin-bath polarization. Here, by contrast, we focus on the adverse role of quantum correlations (entanglement) in a spin bath that can impede its cooling in many realistic scenarios. We propose to remove this impediment by modified cooling schemes, incorporating probe-induced disentanglement via alternating, non-commuting probe-bath interactions, so as to suppress the buildup of quantum correlations in the bath. The resulting bath polarization is thereby exponentially enhanced. The underlying thermodynamic principles have far-reaching implications for quantum technological applications, Comment: 5 pages, 3 figures

Machine Learning models, extensively used for various multimedia applications, are offered to users as a blackbox service on the Cloud on a pay-per-query basis. Such blackbox models are commercially valuable to adversaries, making them vulnerable to extraction attacks to reverse engineer the proprietary model thereby violating the model privacy and Intellectual Property. Here, the adversary first extracts the model architecture or hyperparameters through side channel leakage, followed by stealing the functionality of the target model by training the reconstructed architecture on a synthetic dataset. While the attacks proposed in literature are empirical, there is a need for a theoretical framework to measure the information leaked under such extraction attacks. To this extent, in this work, we propose a novel probabilistic framework, Airavata, to estimate the information leakage in such model extraction attacks. This framework captures the fact that extracting the exact target model is difficult due to experimental uncertainty while inferring model hyperparameters and stochastic nature of training to steal the target model functionality. Specifically, we use Bayesian Networks to capture uncertainty in estimating the target model under various extraction attacks based on the subjective notion of probability. We validate the proposed framework under different adversary assumptions commonly adopted in literature to reason about the attack efficacy. This provides a practical tool to infer actionable details about extracting blackbox models and help identify the best attack combination which maximises the knowledge extracted (or information leaked) from the target model.

Artificial Intelligence systems require a through assessment of different pillars of trust, namely, fairness, interpretability, data and model privacy, reliability (safety) and robustness against against adversarial attacks. While these research problems have been extensively studied in isolation, an understanding of the trade-off between different pillars of trust is lacking. To this extent, the trade-off between fault tolerance, privacy and adversarial robustness is evaluated for the specific case of Deep Neural Networks, by considering two adversarial settings under a security and a privacy threat model. Specifically, this work studies the impact of the fault tolerance of the Neural Network on training the model by adding noise to the input (Adversarial Robustness) and noise to the gradients (Differential Privacy). While training models with noise to inputs, gradients or weights enhances fault tolerance, it is observed that adversarial robustness and fault tolerance are at odds with each other. On the other hand, ($\epsilon,\delta$)-Differentially Private models enhance the fault tolerance, measured using generalisation error, theoretically has an upper bound of $e^{\epsilon} - 1 + \delta$. This novel study of the trade-off between different elements of trust is pivotal for training a model which satisfies the requirements for different pillars of trust simultaneously.

Deep Learning Accelerators are prone to faults which manifest in the form of errors in Neural Networks. Fault Tolerance in Neural Networks is crucial in real-time safety critical applications requiring computation for long durations. Neural Networks with high regularisation exhibit superior fault tolerance, however, at the cost of classification accuracy. In the view of difference in functionality, a Neural Network is modelled as two separate networks, i.e, the Feature Extractor with unsupervised learning objective and the Classifier with a supervised learning objective. Traditional approaches of training the entire network using a single supervised learning objective is insufficient to achieve the objectives of the individual components optimally. In this work, a novel multi-criteria objective function, combining unsupervised training of the Feature Extractor followed by supervised tuning with Classifier Network is proposed. The unsupervised training solves two games simultaneously in the presence of adversary neural networks with conflicting objectives to the Feature Extractor. The first game minimises the loss in reconstructing the input image for indistinguishability given the features from the Extractor, in the presence of a generative decoder. The second game solves a minimax constraint optimisation for distributional smoothening of feature space to match a prior distribution, in the presence of a Discriminator network. The resultant strongly regularised Feature Extractor is combined with the Classifier Network for supervised fine-tuning. The proposed Adversarial Fault Tolerant Neural Network Training is scalable to large networks and is independent of the architecture. The evaluation on benchmarking datasets: FashionMNIST and CIFAR10, indicates that the resultant networks have high accuracy with superior tolerance to stuck at "0" faults compared to widely used regularisers., Comment: MobiQuitous 2020

Atomization of emulsion droplets is ubiquitous across a variety of application domains ranging from NextGen combustors to fabrication of biomedical implants. An understanding of the atomization mechanism in emulsions can result in a paradigm shift in customized designs of efficient systems, be it in energy or biotechnology sectors. In this paper, we specifically study the breakup mechanism of an evaporating contact-free (acoustic levitation) emulsion droplet (water-oil) under external heating. Three distinct regimes are observed during the lifespan of the evaporating droplet. Initially, the droplet diameter regresses linearly with time, followed by vapor bubble nucleation due to a significant difference in the boiling temperature among the components of the emulsion. The collapse of this bubble results in a high-intensity breakup of the droplet leading to the propulsion of residual liquid in the form of a crown-like sheet. The area of the expanding crown varies linearly with the square of the time. It is hypothesized that the expansion of the liquid sheet centrifuges the larger water sub-droplets towards the edge, resulting in unique spatial segregation. Subsequently, we report the first observation of complex patches (representing water sub-droplets) and the rupture of the thin sheet adjacent to patches into holes (with hole growth rate ranging from 1.2 to 1.4 m/s) in the context of an evaporating isolated emulsion droplet. The hole formation results in the creation of ligaments which undergo breakup into secondary droplets with Sauter mean diameter (SMD) ~ 50 {\mu}m., Comment: 22 pages and 8 figures

In this paper we numerically study supercontinuum generation by pumping a silicon nitride waveguide, with two zero-dispersion wavelengths, with femtosecond pulses. The waveguide dispersion is designed so that the pump pulse is in the normal-dispersion regime. We show that because of self-phase modulation, the initial pulse broadens into the anomalous-dispersion regime, which is sandwiched between the two normal-dispersion regimes, and here a soliton is formed. The interaction of the soliton and the broadened pulse in the normal-dispersion regime causes additional spectral broadening through formation of dispersive waves by non-degenerate four-wave mixing and cross-phase modulation. This broadening occurs mainly towards the second normal-dispersion regime. We show that pumping in either normal-dispersion regime allows broadening towards the other normal-dispersion regime. This ability to steer the continuum extension towards the direction of the other normal-dispersion regime beyond the sandwiched anomalous-dispersion regime underlies the directional supercontinuum notation. We numerically confirm the approach in a standard silica microstructured fiber geometry with two zero-dispersion wavelengths.