Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison of the Transfer Functions and Flow Fields of Flames With Increasing Swirl Number

The frequency response of premixed swirled flames is investigated by comparing their Transfer Function (FTF) between velocity and heat release rate fluctuations. The equivalence ratio and flow velocity are kept constant and four different swirling injectors are tested with increasing swirl numbers. The first injector features a vanishing low swirl number S = 0.20 and produces a flame anchored by the recirculating flow in the wake of a central bluff body. The three other swirling injectors produce highly swirled flows (S > 0.6) leading to a much larger internal recirculation region, which size increases with the swirl level. When operating the burner at S = 0.20, the FTF gain curve smoothly increases to reach a maximum and then smoothly decreases towards zero. For the highly swirled flames (S > 0.6), the FTF gain curve shows a succession of valleys and peaks attributed to interferences between axial and azimuthal velocity fluctuations at the injector outlet. The FTF phase-lag curves from the vanishing low and highly swirled flames are the same at low frequencies despite their large differences in flame length and flame aspect ratio. Deviations between the FTF phase lag curves of the different swirled flames start above the frequency corresponding to the first valley in the FTF gain of the highly swirled flames. Phase averaged images of the axial flow fields and of the flame chemiluminescence are used to interpret these features. At forcing frequencies corresponding to peak FTF gain values, the cold flow response of all flames investigated is dominated by large coherent vortical structures shed from the injector lip. At forcing frequencies corresponding to a valley in the FTF gain curve of the highly swirled flames, the formation of large coherent structures is strongly hindered in the cold flow response. These observations contrast with previous interpretations of the mechanisms associated to the low FTF response of swirled flames. It is finally found that for flames stabilized with a large swirl number, heat release rate fluctuations result both from large flame luminosity oscillations and large flame volume oscillations. For conditions leading to a small FTF gain value, both the flame luminosity and flame volume fluctuations are suppressed confirming the absence of strong perturbations within the flow at these frequencies. The experiments made in this work reveal a purely hydrodynamic mechanism at the origin of the low response of swirling flames at certain specific frequencies.
Co-Design of Finite-Time Convergence and Noise Suppression: A Unified Neural Model for Time Varying Linear Equations With Robotic Applications

Computing time-varying linear systems is widely encountered in engineering practice and scientific computation. Dynamic neural networks, as a class of modeling approaches, have been intensively explored in recent decades for solving linear equations. The time-varying nature of this problem and the noisy workspace for many engineering practice require two features of practical design: 1) fast convergence in time and 2) robustness against noises and disturbance. Existing solutions usually decouple the problem into two steps by designing a fast-convergent neural controller and then topped with an additional low-pass filter to reach noise robustness. However, due to the interplay of the mentioned two dynamical parts, the overall system may lose stability if the parameters are not well tuned. In this paper, we establish the first dynamical neural model for simultaneously achieving fast-convergence, particularly finite-time convergence, and noise-robustness, with the capability to reject the unknown noise when it is constant or varies slowly. To do so, a superior design formula activated by noise-tolerant nonlinear functions is proposed to enhance the capability of zeroing neural networks (ZNNs), achieving denoising and finite-time convergence in a unified design. According to this design formula, a novel recurrent neural network (RNN) with finite-time convergence and inherently noise-suppression performance [thus termed the finite-time robust RNN (FTRRNN)] is developed and applied to robotic motion tracking illustrated via time-varying linear equation system solving. Furthermore, theoretical analyses on the global stability, the finite-time convergence and the denoising ability of the proposed design formula and the corresponding FTRRNN model are presented in details. The upper bound on the convergence time is also analytically derived. A numerical example is supplied to verify the superior property of the FTRRNN model to the ZNN model according to the results of computing time-varying linear equation system in the presence of additive noises. Finally, an application to robotic motion tracking is presented to show that the presented FTRRNN model can successfully realize the ellipse-path tracking control of a planar two-link manipulator in front of the external disturbances, while the conventional ZNN model fails under the same conditions.
Broadband circularly polarized dipole look‐like F‐shaped antenna with symmetrically inverted F‐shaped modified ground plane

A microstrip antenna consists of F‐shaped radiating structure with symmetrical F‐shaped modified or defected ground plane look‐like dipole antenna featured for broad axial ratio bandwidth (ARBW) is presented. Modified/defected ground plane comprises of an optimized rectangular ground plane united with symmetrically inverted F‐shaped structure printed at the bottom side of substrate. Furthermore, a narrow rectangular slit was imprinted from the modified ground plane to obtain broad ARBW. Proposed antenna optimized, fabricated, and experimentally examined. Measured results confirm that it can yield an fractional impedance bandwidth of approximately ≈37.5% and a 3‐dB ARBW of ≈31.7%. The measured gain range from 2 dB to 4 dB within the 3‐dB ARBW with maximum gain of 4 dBic. The cross polar rejection was observed better than 15 dB alongwith wide −3 dB beamwidth of 85°.
Robust Blind Image Fusion for Misaligned Hyperspectral Imaging Data

The low spatial resolution of hyperspectral imaging can be significantly improved by fusing the hyperspectral image with a high resolution photograph. In most practical cases, however, the exact alignment between the fused images is not known a priori. In this work, we study how including a blind deconvolution approach in the mathematical model can help resolve translational misalignments. In particular, we investigate the influence of different initialization strategies. The efficiency of the proposed model is validated by numerical experiments using both simulated and real remote sensing data.
Privacy Transformation Planner for User-centric Data Privacy Systems

In recent years, we have witnessed a significant increase in interest and adoption of strict data privacy controls across organizations. This development is primarily attributed to a general shift in public perception of privacy and the pervasiveness of data misuse and breaches. New data privacy systems are emerging and progressively find their way into existing data systems. These privacy developments raise a new fundamental challenge in existing data systems, namely privacy management. Privacy presents some unique constraints, and this includes dealing with a heterogeneous set of privacy policies (in all usercentric systems and, e.g., due to different jurisdictions) and managing limited, non-replenishable resources (what we call a ”privacy budget”). Hence, this necessitates new solutions that are tailored to privacy. In this thesis, we address the following challenges in the privacy management lifecycle: • Designing generic privacy controls and mapping them to a set of allowed privacy transformations based on data type and application. • Developing methods to manage privacy resources that arise from associated privacy controls. In this work, we propose a privacy management system that offers controls for both users and service providers to design privacy policies. As different parts of data are tagged with different privacy controls, the issue of how this data can still be combined arises. We want to serve queries operating on this data to respect these privacy controls and ensure that privacy resources are optimized for the longevity of data use. In other words, we want to serve as many queries as possible (perhaps with some prioritization among those queries, e.g., if some queries are more important than others) while still respecting all privacy constraints. This task boils down to a matching problem: matching queries and data subjects in a way that respects all constraints and optimizes a target function. Towards solving this problem, we present a unified model that integrates all privacy constraints and objectives regarding data usage. Using this model, we present a greedy, heuristic approach that quickly produces good assignments with low memory usage. However, for more complicated settings, this approach may suffer from suboptimal management of privacy resources. We then introduce an integer linear program (ILP) approach to address this issue, which allows finding near-optimal solutions (with a modifiable bound on suboptimality, e.g., 1% or 0.1%) for scenarios where latency and memory requirements are of less concern. Produced assignments (by
Certifiably Vulnerable: Using Certificate Transparency Logs for Target Reconnaissance

The Web PKI ecosystem provides an underlying layer of security to many Internet protocols used today. By relying on Certificate Authorities (CAs), communication can be authenticated and encrypted based on a chain of trust. Unfortunately, this chain of trust has been broken in the past. For instance, in 2011, adversaries managed to issue fraudulent certificates on behalf of the DigiNotar CA, resulting in a loss of trust in DigiNotar. To better detect fraudulent certificates, Google introduced the concept of Certificate Transparency (CT), which is based on append-only logs that allow one to monitor and detect wrongly issued X.509 certificates.In this work, we investigate the potential of these logs as a data source for target reconnaissance. Concretely, we divide our study into two parts: First, we deploy several honeypot web servers over a period of 200 days to study the effect on incoming scanning traffic after pushing a certificate to one or more CT logs. We find that adding a certificate to a CT log leads to incoming network probes, just seconds after publishing the entry. This suggests that CT logs are used as input for web scans. In the IPv6 address space, our web server received 2,700 packets after pushing our certificate to a CT log, compared to 0 packets in our control group.Second, we use large-scale active measurements to find potentially vulnerable domains from CT log data. Using certificate issuance and renewal patterns, we identify websites that are either at the beginning or at the end of their life cycle. Our results show that freshly deployed websites are not more likely to contain a known CVE compared to websites that just renewed their certificate. On the other side of the spectrum, however, we find that websites with an expired certificate, yet still deployed in the wild, tend to contain more outdated software, and hence more known CVEs. As such, CT logs can indeed function as a data source for target reconnaissance.
The MiniCity: A 1/10th Scale Evaluation Platform for Testing Autonomous Urban Perception and Planning

A major impediment to the adoption of autonomous vehicles (AVs) is the need to fully evaluate and test the full autonomous vehicle hardware and software stack in realistic traffic scenarios. This is especially challenging for perception tasks, such as object detection and localization, which impact various components of the full AV stack and depend heavily on sensor configuration. Recent work [1], [2] highlight the need for evaluating perception algorithms in the context of both the whole autonomous system and specific downstream tasks, such as obstacle avoidance, which is difficult to accomplish using existing datasets or simulators. In this work, we propose the MiniCity, a miniature autonomous vehicle platform for evaluating perception algorithms in a city-wide, multi-vehicle scale. In the MiniCity, 1/10th scale vehicles are equipped with full-scale hardware – Lidar, stereo cameras, and IMUs – and a full autonomy software stack – allowing researches to evaluate their perception algorithm in isolation and the impact to the vehicle’s quality of autonomous driving. Current tools for evaluating autonomous vehicle software and hardware consists of datasets, simulation, or full-scale vehicles. The high-cost and inherent safety risk of full-scale vehicles mean that most full-scale testing is limited to closed course testing or tasks with limited interactions with other vehicles. Increasingly, researchers are relying on datasets or simulators for benchmarking the performance of their algorithms. Datasets [3], [4], [5], [6], [7], [8] provide highfidelity sensor recordings and are thus a popular choice for evaluating perception tasks such as object pose estimation and lane detection; however, they can not evaluate the impact on downstream modules such as trajectory planning or collision avoidance. Simulators [9], [10], [11], [12] can allow for evaluating the full AV stack; however, they fall victim to the sensor sim-to-real gap and incur high computational cost for simulating multi-vehicle interactions. Miniature robot platforms [13], [14], [15], [16], [17] provide a middle ground in evaluation platforms, providing researchers a lower cost
Absorbed dose and noise in intensity-modulated dental computed tomography

For a dental cone-beam computed tomography (CBCT) with a reduced patient dose, we have considered the tube-current modulation, and tube-current and voltage, or beam intensity, modulation techniques. For various modulation scenarios to the dental CBCT with various fields of view (FOVs), we assessed the absorbed dose using the Monte Carlo method. We also applied the scenarios to a laboratory bench-top CBCT system and evaluated the voxel noise. While the modulation techniques reduced the effective dose, more effectively as increasing FOV, they increased the voxel noise. The gain from their combinations was therefore none or slightly less, compared to the CBCT with the conventional uniform power scanning. We used the filtered-backprojection and the simultaneous algebraic reconstruction technique for image reconstructions, and we ignored the correction for data truncation in smaller FOVs. Therefore, there is enough room for the enhancement of image quality performance using advanced reconstruction methods. In addition, the modulation scenarios considered only the field around the cervical spine. Nevertheless we have found meaningful dose reductions in dental CBCTs in corporation with the modulation techniques.
SECOMP2CHERI: Securely Compiling Compartments from CompCert C to a Capability Machine

Undefined behavior is endemic in the C programming language: buffer overflows, use after frees, double frees, invalid type casts, various concurrency bugs, etc., cause mainstream C compilers to produce code that can behave completely arbitrarily. This leads to devastating security vulnerabilities that are often remotely exploitable, and both Microsoft and Chrome report that around 70% of their high severity security bugs are caused by memory safety issues alone [6, 15, 18]. We study how compartmentalization can mitigate this problem by restricting the scope of undefined behavior both (a) spatially to just the compartments that encounter undefined behavior [12], and (b) temporally by still providing protection to each compartment up to the point in time when it encounters undefined behavior [1]. While our past work has focused on formally secure compilation of compartmentalized code for toy languages with buffers and procedures [1, 12], in this talk we report on our ongoing work on scaling up these ideas to a realistic C compiler, based on CompCert [14]. While in prior work [1] we used software-fault isolation (SFI) or a tagged architecture [4] to enforce compartmentalization at the lowest level, in this talk we will focus on a new secure compilation backend for CompCert targeting a variant of the CHERI capability machine [21].
The QMOM Method to Solve The PBE Equations of the Urban Haze

Mega and medium-sized cities in China are facing the increasingly serious aerosol pollution, among them Xi’an is one of the typical cities. During the early stage of ash haze, aerosol concentration is rising so fast that the simulation of the process is difficult. This article uses the population balance equation (PBE) describing the distribution of fine particles, and solves the changing process of particle size distribution by the moment method. Population balance equation, a non-linear hyperbolic equation about number density function, is employed to describe micro-behaviors (aggregation, breakage, growth) of disperse phase and resulting variation of particle size distribution. By expressing PBEs for both batch and continuous operations in a general form, and using weighted residual method to derive the moment equations, different moments can be tracked directly. Three examples of simulation results show that compared with the analytical solution, relative error of this method is within 10−5 and has the characteristics of high precision and low computation amount. In early 2016, the early stage of PM2.5 pollution in Xi’an is simulated and the simulation results and experimental results are highly consistent.
An edge-preserving iterative back-projection method for image super-resolution

As an effective super-resolution reconstruction technique, the original iterative back-projection (IBP) suffers from jaggy artifacts around the edge caused by the initial estimation of high-resolution image obtained with traditional interpolation methods like bilinear interpolation. In this paper, we present an edge-preserving iterative back-projection method for image super-resolution, which employs an effective edge-preserving interpolation method called iterative curvature based interpolation (ICBI) method for up-sampling to get the initial estimation of high-resolution image. The proposed method combines the IBP algorithm with the ICBI method which is a two-step grid filling and an iterative correction of the interpolated pixels obtained by minimizing an objective function depending on the second-order directional derivatives of the image intensity. Experiment results increases the PSNR compared to other existing algorithms and also improves edge quality of the image considerably.
Quantum enhanced metrology: quantum mechanical correlationsand uncertainty relations

The foundational theory of quantum enhanced metrology for parameter estimation is of fundamental importance to the progression of science and technology as the scientific method is built upon empirical evidence, the acquisition of which is entirely reliant on measurement. Quantum mechanical properties can be exploited to yield measurement results to a greater precision (lesser uncertainty) than that which is permitted by classical methods. This has been mathematically demonstrated by the derivation of theoretical bounds which place a fundamental limit on the uncertainty of a measurement. Furthermore, quantum metrology is of immediate interest in the application of quantum technologies since measurement plays a central role. This thesis focuses on the role of quantum correlations and uncertainty relations which govern the precision bounds. We show how correlations can be distributed amongst limited resources in realistic scenarios, as permitted by current experimental capabilities, to achieve higher precision measurements than current approaches. This is extended to the setting of multiparameter estimation in which we demonstrate a more technologically feasible method of correlation distribution than those previously posited which perform as well as, or worse than, our scheme. Furthermore, a quantum metrology protocol is typically comprised of three stages: probe state preparation, sensing and then readout, where the time required for the first and last stages is usually neglected. We consider the more realistic sensing scenario of time being a limited resource which is divided amongst the three stages and demonstrate the most efficient use of this resource. Additionally, we take an information theoretic approach to quantum mechanical uncertainty relations and derive a one-parameter class of uncertainty relations which supplies more information about the quantum mechanical system of interest than conventional uncertainty relations. Finally, we demonstrate how we can use this class of uncertainty relations to reconstruct information of the state of the quantum mechanical system.
"Creepy Towards My Avatar Body, Creepy Towards My Body": How Women Experience and Manage Harassment Risks in Social Virtual Reality

Online harassment against women - particularly in gaming and virtual worlds contexts - remains a salient and pervasive issue, and arguably reflects the systems of offline structural oppression to control women's bodies and rights in today's world. Harassment in social Virtual Reality (VR) is also a growing new frontier of research in HCI and CSCW, particularly focusing on marginalized users such as women. Based on interviews with 31 women users of social VR, our findings present women's experiences of harassment risks in social VR as compared to harassment targeting women in pre-existing, on-screen online gaming and virtual worlds, along with strategies women employ to manage harassment in social VR with varying degrees of success. This study contributes to the growing body of literature on harassment in social VR by highlighting how women's marginalization online and offline impact their perceptions of and strategies to mitigate harassment in this unique space. It also provides a critical reflection on women's mitigation strategies and proposes important implications to rethink social VR design to better prevent harassment against women and other marginalized communities in the future metaverse.
A Comparative Study of Job Satisfaction among female teachers at secondary level

Teachers are the special human beings given by the nature. They can help to frame everyone’s future. A teacher has many qualities; they are just like a bag that is full of knowledge, but this knowledgeable bag is not an ordinary bag, it has many miracle powers because whenever we want to empty the knowledge of this bag, every time the bag become full. Some teachers are strict, some are soft hearted, some behave like a friend and some behave like foe but the aim of all is same, they always want to develop a good and responsible citizen in their students. This study aims to investigate the job satisfaction level among government and private secondary school female teachers. 100 respondents from private secondary schools and 100 from government secondary schools. Data analyzed with descriptive statics using SPSS version21. The study was found that job satisfaction of government secondary schools teachers are high compare to private secondary schools teachers.
Modeling and Control of Propellant Slosh Dynamics in Observation Spacecraft with Actuator Saturations

In this paper, contributions to the modeling and mitigation of propellant sloshing disruptive torques, arising in particular during attitude maneuvers, are presented. The perturbation is modeled as an uncertain LPV system whose time-varying parameters are identified using CFD data. Relying on this model, the mitigation strategy consists in the design of a robust parameter-varying sloshing torque observer by means of the structured multi-model H∞ framework. The estimated torque is then used to improve a satisfying attitude controller initially designed on a dry spacecraft. Finally the system reference is modified, using the reference governor framework, to ensure that the actuator capabilities are not exceeded.