Miracle-dz/UniFAR-FTUs huggingface dataset:
3D continuum radiative transfer in complex dust configurations. II. 3D structure of the dense molecular cloud core ρ Oph D

Constraints on the density and thermal 3D structure of the dense molecular cloud core ρ Oph D are derived from a detailed 3D radiative transfer modeling. Two ISOCAM images at 7 and 15 µm are fitted simultaneously by representing the dust distribution in the core with a series of 3D Gaussian density profiles. Size, total density and position of the Gaussians are optimized by simulated annealing to obtain a 2D column density map. The projected core density has a complex elongated pattern with two peaks. We propose a new method to calculate an approximate temperature in an externally illuminated complex 3D structure from a mean optical depth. This "Tτ"-method is applied to a 1.3 mm map obtained with the IRAM 30m telescope to find the approximate 3D density and temperature distribution of the core ρ Oph D. The spatial 3D distribution deviates strongly from spherical symmetry. The elongated structure is in general agreement with recent gravo-turbulent collapse calculations for molecular clouds. We discuss possible ambiguities of the background determination procedure, errors of the maps, the accuracy of the Tτ-method and the influence of the assumed dust particle sizes and properties.
Role of superoxide dismutase in the photochemical response of cultured RPE cells to laser exposure at 413 nm

Thresholds for photochemical damage were performed in RPE cell lines (artificially pigmented) taken from either human (hTERT-RPE1), wild type (wt) mouse, or transgenic mice deficient (+/-) in either superoxide dismutase 1 (SOD1) or SOD2. The four cell lines were characterized by immunohistochemical and immunoblot analyses to determine relative abundance of the SOD proteins. There was no difference in sensitivity between the human, murine wt and murine SOD1-deficient cells, whereas there was a dramatic (2 fold) increase in threshold irradiance value for the murine SOD2-deficient cells. Possible explanations for the unexpected result are provided.
Statistical Inference in Orthogonal Regression for Three-Part Compositional Data Using a Linear Model with Type-II Constraints

Orthogonal regression is a proper tool to analyze relations between two variables when three-part compositional data, i.e., three-part observations carrying relative information (like proportions or percentages), are under examination. When linear statistical models with type-II constraints (constraints involving other parameters besides the ones of the unknown model) are employed for estimating the parameters of the regression line, approximate variances and covariances of the estimated line coefficients can be determined. Moreover, the additional assumption of normality enables to construct confidence domains and perform hypotheses testing. The theoretical results are applied to a real-world example.
Heat capacity measurements of two-dimensional self-assembled hexadecanethiol monolayers on polycrystalline gold

The melting characteristics of hexadecanethiol two-dimensional self-assembled monolayers (SAMs) grown on polycrystalline Au surfaces are obtained via heat capacity measurements using nanocalorimetry at scanning rates of ∼30 000°C∕s. The analyzed amount of alkanethiol in the form of SAMs is typically in the 10−11 mol range. Melting occurs over a broad (full width on the half height ∼100°C) temperature range with a melting temperature of about 70°C, if the alkanethiol is partially desorbed from the Au surface during thermal treatment. SAMs in their as-deposited state without desorption have melting temperatures above 100°C. The heat of fusion is about 20kJ∕mol. Partial desorption of the alkanethiol is directly observed. The melting characteristics of three-dimensional SAMs on Au nanoparticles are comparable to earlier studies using conventional calorimetry.
Boundary integral equation technique defined on a spherical surface with different applications in electromagnetics and acoustics

A boundary integral equation technique is presented which is defined on a spherical surface. This technique can be used in electromagnetics to determine the singular behaviour of the fields at the common tip of a number of bi-isotropic cones with arbitrary cross-section. The technique is also applicable for electromagnetic conical waveguides and acoustical horns with hard walls with arbitrary cross-section. The Green's function in the integral equation is an associated Legendre function. The integral equation is solved with the method of moments combined with the method of Galerkin.
A Spark-Based Parallel Implementation of Compressed Hyperspectral Image Reconstruction and Anomaly Detection

Joint reconstruction and anomaly detection(JRAD) is an effective method for hyperspectral image reconstruction and anomaly detection that uses the tensor kernel norm to characterize the spectral-spatial correlation information, which can better separate abnormal pixels while reconstructing the image. However, with the increasing scale of remote sensing data, JRAD algorithm would suffer from large amount of computation load and high resource consumption on single-machine platforms. To address these limitations, this paper proposes a Spark-based parallel implementation for JRAD algorithm. Combining with the characteristics of hyperspectral image data represented by tensors, we propose a parallel processing flow for compressed sensing which is a major procedure in JRAD. In addition, a method for parallel iterative execution of complex tensor groups is also developed for accelerating the reconstruction and anomaly detection process on Spark. Experimental result demonstrate that, compared with the serial JRAD algorithm, the parallel JRAD implemented in this work reduces memory consumption and enhances the computational efficiency in terms of a considerable speedup on Spark. The Spark-based implementation also guarantees image reconstruction and anomaly detection accuracy and related performance metrics.
High Gain Circularly Polarized Fabry-Pérot Antenna for S-band CubeSat Applications

This paper presents a high gain circular polarized Fabry-Pérot antenna for CubeSat applications. A key idea is the implementation of cavity using a $5\times 5$ Metasurface ground surface to improve the total gain of the proposed antenna design. The two opposite corners of the radiating patch elements are truncated to achieve a right-hand circular polarisation (RHCP). The presented simulation results show that the proposed antenna provides a high gain of 9.4 dBi, −10 dB bandwidth 12% (2.42-2.73 GHz) with a reflection coefficient of −15.6 dB at 2.5GHz and a −3dB axial ratio bandwidth of 1.6% at 2.5GHz.
Efficient Monitoring of Dynamic Spectrum Access for Robust and Reliable Detection of Unauthorized Access

Success of dynamic spectrum access depends on automated enforcement of spectrum access policies. To this end, we undertake a crowdsourced approach to spectrum monitoring. In this paper, we focus on robust and reliable detection of unauthorized spectrum access over prolonged intervals of time. For this purpose, we propose a framework that is primarily driven by efficient spectrum sampling and reputation management of crowdsourced spectrum monitoring agents. This is to ensure robustness and success in detecting and catching intruders consistently. Simulation results indicate that our proposed scheme performs successfully to catch intruders across all the spectrum monitoring epochs, against varying intruder profiles, sensing failure rates and trustworthiness of crowdsourced monitoring agents.
5G Physical Layer Resiliency Enhancements with NB-IoT Use Case Study

5G has received significant interest from commercial as well as defense industries. However, resiliency in 5G remains a major concern for its use in military and defense applications. In this paper, we explore physical layer resiliency enhancements for 5G and use narrow-band Internet of Things (NB-IoT) as a study case. Two physical layer modifications, frequency hopping, and direct sequence spreading, are analyzed from the standpoint of implementation and performance. Simulation results show that these techniques are effective to harden the resiliency of the physical layer to interference and jamming. A discussion of protocol considerations for 5G and beyond is provided based on the results.
Mobile 3D scanning and mapping for freely rotating and vertically descended LiDAR

Situational awareness in search and rescue missions is key to successful operations, e.g., in collapsed buildings, underground mine shafts, construction sites, and underwater caves. LiDAR sensors in robotics play an increasingly important role in this context, as do robust and application-specific algorithms for simultaneous localization and mapping (SLAM). In many of these scenarios mapping requires the utilization of a vertically descended scanning system. This work presents a mobile system designed to solve this task, including a SLAM approach for descended LiDAR sensors with small field of view (FoV), which are in uncontrolled rotation. The SLAM approach is based on planar polygon matching and is not limited to the presented scenario. We test the system by lowering it from a crane inside a tall building at a fire-fighter school, applying our offline SLAM approach, and comparing the resulting point clouds of the environment with ground truth maps acquired by a terrestrial laser scanner (TLS). We also compare the SLAM approach to a state-of-the-art approach with respect to runtime and accuracy of the resulting maps. Our solution achieves comparable mapping accuracy at 0.2% of the runtime.
Investigating Bluetooth Low Energy Advertisements in Urban Public Space for Street-level Crowd Density Sensing

Technologies for sensing crowd density have a potential to make our society smarter, and such technologies have been used to help social distancing in the context of COVID-19 pandemic. We have developed a method to sense and forecast street-level crowd density by observing public Bluetooth Low Energy (BLE) advertisements from popular contact tracing applications in Japan. We have deployed our methods in several locations in Tokyo and published the estimated street-level crowd density level on our website as well as a television program. In this paper, we report the status of our project, focusing on the result of experiments to verify the potential of our method after the contact tracing applications stop working. Through an experiment in an urban public space in Tokyo, we have shown that BLE advertisements are almost occupied with contact tracing applications and manufacture specific data from a few companies. In addition, by monitoring different types of BLE advertisements in several locations in Japan, we have clarified that those containing manufacture specific data with a certain company identifier have almost the same trend as those from contact tracing applications, with the average correlation coefficient of 0.990.
Analysis and Design of Buck Converter $R^{2}P^{2}$ with Interleaved Function

Numerous of applications use power conversion apply DC/DC converters with wide transformation ranges and the capacities to provide high current or voltage levels. Low voltage applications have been standardized to 48V, 24V, 12V and 5V values for different applications such as LED lighting, battery banks and power supplies for telecommunication system. Some requirements that DC/DC converters must meet are high transformation ratios, high power density, high efficiency and low current and voltage ripple at the input and output. In this work, the analysis and design of a Quadratic Buck Converter based on the concept of $R^{2}P^{2}$ with interleaved function is proposed for low voltage and high current applications. The operating modes of the converter during the switching process of the switches are presented. The average models are also presented as well as some comparative aspects with some topologies reported in the literature.
Wb-MSF: A Large-scale Multi-source Information Diffusion Dataset for Social Information Diffusion Prediction

Recently, a large number of social network studies focus on the diffusion of information posted by individual users, which consequently brings in a strong demand for social network datasets. Nevertheless, most of the available datasets have been published for nearly a decade, and their scale is not large enough. Moreover, they ignore the multiple posts originated by different users spontaneously under the same topic, these posts form a kind of multi-source information. This paper presents Wb-MSF, a large-scale dataset that contains multi-source information cascades and user followership. Different from existing datasets used in information diffusion tasks, Wb-MSF is the first multi-source information dataset, and further provides a followership network. Wb-MSF is crawled from a famous social platform Sina-Weibo and contains tens of millions of followership edges and tens of thousands of information cascades formed by millions of users. It can support information diffusion prediction problem. In this paper, our discussions and experiments including carrying out a statistical analysis of the dataset, and examining the difference between single-source and multi-source information and the effect of the followership network are based on this problem.
Metasurface-Driven Beam Steering Antenna for Satellite Communications

A metasurface-driven 3D beam-scanning (elevation, azimuth or both) antenna solution is presented in this paper. A pair of novel phase gradient metallic metasurfaces (PGMMs) is designed using the near-electric field phase transformation method operating in the Ku-band. Rotating PGMMs independently atop a static, fixed beam base antenna will enable wide-angle beam-steering in both azimuth and elevation planes. A prototype is made and tested to validate the predicted results. The measured results exhibit excellent beam scanning performance with the highest elevation angle of ±38° and a full 360° in the azimuth. This beam-steering approach does not rely on active radio-frequency components. Moreover, the proposed metasurface obviates costly dielectrics, reducing additional cost and weight, and is suitable for stressed environmental conditions such as high-power systems and inter-satellite or deep-space communication systems.
5G-Enabled Defence-in-Depth for Multi-domain Operations

In many civil and military use cases, 5G can be a segment of a more complex end-to-end solution. In such a configuration, properly using the 5G security mechanisms to achieve defence-in-depth and compliance with zero-trust architecture is challenging. In our paper, we discuss the existing 5G security mechanisms related to the connection of trusted and untrusted non-3GPP access networks (wireless and wired), 5G roaming, and the 3GPP approach to support Uncrewed Aerial Services, which provides authentication between the mobile terminal and an external server. The identified use cases show the need and opportunities for reusing 5G security mechanisms beyond the 5G network domain. We discuss the options for end-to-end defence-in-depth security provisioning in multi-domain networks, and we present two approaches to providing end-to-end defence-in-depth mechanisms. The first relies on the alignment of security policies in multiple domains and is based on security proxies. The second is based on the multi-domain network slices.
Numerical simulations of pulsating detonations: I. Nonlinear stability of steady detonations

Very-long-time numerical simulations of an idealized pulsating detonation with one irreversible reaction having an Arrhenius form are performed using a hierarchical adaptive second-order Godunov-type scheme. The initial data are given by the steady solution and the truncation error produces the perturbation to trigger the instability. The detonation is allowed to run for thousands of half-reaction times of the underlying steady wave to ensure that the final amplitudes and periods of the nonlinear oscillations are achieved. Thorough resolution studies are performed for various representative regimes of the instability. It is shown that to obtain quantitatively good solutions over 50 numerical grid points in the half-reaction length of the steady detonation are required, while to obtain a converged solution over 100 points are required, even near the stability boundary. This is much higher resolution than has generally been used in previous papers in either one or two dimensions. Resolutions of less than approximately 20 points per half-reaction length give very poor predictions of the periods and amplitudes near the stability boundary or entirely spurious solutions for more unstable detonations. The evolution of the converged solutions as the activation energy increases, and the detonation becomes more unstable, is also investigated.
Multi-Robot System for Autonomous Cooperative Counter-UAS Missions: Design, Integration, and Field Testing

With the rapid development of technology and the proliferation of uncrewed aerial systems (UAS), there is an immediate need for security solutions. Toward this end, we propose the use of a multi-robot system for autonomous and cooperative counter-UAS missions. In this paper, we present the design of the hardware and software components of different complementary robotic platforms: a mobile uncrewed ground vehicle (UGV) equipped with a LiDAR sensor, an uncrewed aerial vehicle (UAV) with a gimbal-mounted stereo camera for air-to-air inspections, and a UAV with a capture mechanism equipped with radars and camera. Our proposed system features 1) scalability to larger areas due to the distributed approach and online processing, 2) long-term cooperative missions, and 3) complementary multimodal perception for the detection of multirotor UAVs. In field experiments, we demonstrate the integration of all subsystems in accomplishing a counter-UAS task within an unstructured environment. The obtained results confirm the promising direction of using multi-robot and multi-modal systems for C-UAS.
Adaptive ECG Leads Selection for Low-Power ECG Monitoring Systems Using Multi-class Classification

The computer-aided interpretation of ECG signals has become a pivotal tool for physicians in the clinical assessment of cardiovascular diseases during the last decade. Therefore, computerized diagnosis systems depend heavily on machine learning and deep learning models to guarantee high classification accuracy. However, a large amount of power is consumed due to the need for heavy computations to handle the classification tasks which act as a barrier to maintain continuous ECG monitoring. Hence, this work targets energy saving in the constrained embedded environment on a Texas Instruments CC2650 Micro-controller Unit (MCU). We provide a new approach to support energy-efficient ECG monitoring in real-time through the adaptive selection of ECG leads after applying multi-class classification on the raw ECG signals. We deploy two different CNN model scenarios on MIT-BIH and CODE-test datasets, and adjust the number of ECG streamed channels to 1,4, and 8, based on the detected cardiac abnormalities, such as arrhythmias and heart blocks. The adaptive selection of ECG channels achieves 77.7% power saving in the normal cardiac condition and up to 55.5% for the heart blocks, sinus bradycardia, and sinus tachycardia.
Smart home power monitoring based on Internet of Things paradigm

Recently, the Internet of Things (IoT) is a massive worldwide system infrastructure of Internet-enabled things with net services thanks to its main information and communication data treatment with Smart Home (SH). This paper deals with the relationship between IoT and SH, the necessities for interconnected IoT in SH integrating a flexible number of Plug-in Electric Vehicles (PEVs) and power supervision challenges for domestic energy management. In this context, a problem formulation for smart home power management is developed for residential-based IoT applications. This control strategy aims to fulfill the equilibrium between the energy demand and supply warranting therefore a flattened power demand profile. Simulation results are discussed to highlight how IoT-based smart home energy management can be achieved for ensuring a valley-filled and peak-shaved daily power profile thanks to PEV storage/feedbacking power penetration level, reducing the total load power variance and avoiding therefore stress on smart network
A Multistage Vortex Visualization Method

In order to understand complex vortical flows in large data sets, we must be able to detect and visualize vortices in an automated fashion. Additionally, an informative vortex visualization should provide more information that just the location of the vortex core. In this paper, we describe a multistage vortex visualization algorithm that attempts to exploit the availability of additional information to convey vortex characteristics such as core position, extent, strength, etc. The results produced by a traditional vortex detection algorithm are enhanced using a set of postprocessing tools. The multistage visualization method can use the results of any field-based vortex detection technique in which the scalar field has an extrema in the vortex core or can use the results of any method which produces a continuous vortex core line. This flexibility allows the user to select a vortex detection algorithm appropriate for the problem at hand. Our method works on unstructured meshes as well as structured grids and avoids the use of the computationally expensive and potentially nonsmooth gradient during core line extraction and characterization. Its ability to discern both weak and strong vortices as well as vortices in close spatial proximity to each other is another advantage which comes from the use of the local extrema method. Further, our method obtains subcell resolution of the vortex core line through the use of function fitting. We demonstrate the efficacy of our approach by applying it to several test cases. These test cases highlight the strengths and weaknesses of the method. We describe how the algorithm can be improved and draw conclusions based on the results presented herein.
Structure of a strongly coupled large polaron

We use the translationally invariant Bogolyubov–Tyablikov method to propose a polaron theory. We present calculations of autolocalised electron states for different types of interaction. The structure of these states is shown to be strongly related to the structure and details of the local phonon spectrum. We calculate this spectrum in the strong-coupling limit. Applications of the large polaron model and possibilities of experimental tests are considered for the strong coupling. We generalise the Bogolyubov–Tyablikov treatment to the strongly coupled bipolaron and give criteria of the stability and formation of the bipolaron states.
Effects of Vertical Wind Shear on the Predictability of Tropical Cyclones

AbstractThrough cloud-resolving simulations, this study examines the effect of vertical wind shear and system-scale flow asymmetry on the predictability of tropical cyclone (TC) intensity during different stages of the TC life cycle. A series of ensemble experiments is performed with varying magnitudes of vertical wind shear, each initialized with an idealized weak TC-like vortex, with small-scale, small-amplitude random perturbations added to the initial conditions. It is found that the environmental shear can significantly affect the intrinsic predictability of tropical cyclones, especially during the formation and rapid intensification stage. The larger the vertical wind shear, the larger the uncertainty in the intensity forecast, primarily owing to the difference in the timing of rapid intensification.In the presence of environmental shear, initial random noise may result in changes in the timing of rapid intensification by as much as 1–2 days through the randomness (and chaotic nature) of moist conve.