Miracle-dz/UniFAR-FTUs huggingface dataset:
The Coronal Mass Ejection of 1998 April 20: Direct Imaging at Radio Wavelengths

We observed the fast coronal mass ejection (CME) of 1998 April 20 with the radioheliograph at Nançay, France, between 164 and 432 MHz. Spectroscopic data were obtained between 40 and 800 MHz by the spectrometer at Tremsdorf, Germany, and between 20 kHz and 14 MHz with the WAVES instrument on board the Wind spacecraft. Energetic particle data were obtained from the Wind 3D Plasma and Energetic Particle experiment. The CME was observed in white light by the Large-Angle Spectrometric COronagraph experiment on board the Solar and Heliospheric Observatory spacecraft. For the first time, the expanding CME loops are imaged directly at radio wavelengths. We show that the radio-emitting CME loops are the result of nonthermal synchrotron emission from electrons with energies of ~0.5-5 MeV interacting with magnetic fields of ~0.1 to a few gauss. They appear nearly simultaneously with the onset of an associated type II radio burst, shock-accelerated type III radio bursts, and the initiation of a solar energetic particle event. We suggest possible sources of the energetic electrons responsible for this "radio CME" and point out diagnostic uses for synchrotron emission from CME loops.
LCD masks for spatial augmented reality

One aim of Spatial Augmented Reality is to visually integrate synthetic objects into real-world spaces amongst physical objects, viewable by many observers without 3D glasses, head-mounted displays or mobile screens. In common implementations, using beam-combiners, scrim projection, or transparent self-emissive displays, the synthetic object’s and real-world scene’s light combine additively. As a result, synthetic objects appear low-contrast and semitransparent against well-lit backgrounds, and do not cast shadows. These limitations prevent synthetic objects from appearing solid and visually integrated into the real-world space. We use a transparent LCD panel as a programmable dynamic mask. The LCD panel displaying the synthetic object’s silhouette mask is colocated with the object’s color image, both staying aligned for all points-of-view. The mask blocks the background providing occlusion, presents a black level for high-contrast images, blocks scene illumination thus casting true shadows, and prevents blow-by in projection scrim arrangements. We have several implementations of SAR with LCD masks: 1) beam-combiner with an LCD mask, 2) scrim projection with an LCD mask, and 3) transparent OLED display with an LCD mask. Large format (80” diagonal) and dual layer volumetric variations are also implemented.
Photothermolysis by laser-induced microbubbles generated around gold nanorod clusters selectively formed in leukemia cells

In an effort of developing clinical LANTCET (laser-activated nano-thermolysis as cell elimination technology) we achieved selective destruction of individual tumor cells through laser generation of vapor microbubbles around clusters of light absorbing gold nanorods (GNR) selectively formed in target tumor cells. Among all gold nanoparticles, nanorods offer the highest optical absorption in the near-infrared. We applied covalent conjugates of gold nanorods with targeting vectors such as monoclonal antibodies CD33 (specific for Acute Myeloid Leukemia), while GNR conjugates with polyethylene-glycol (PEG) were used as nonspecific targeting control. GNR clusters were formed inside the tumor cells at 37 °C due to endocytosis of large concentration of nanorods accumulated on the surface of tumor cells targeted at 4 °C. Formation of GNR clusters significantly reduces the threshold of tumor cell damage making LANTCET safe for normal cells. Appearance of GNR clusters was verified directly with optical resonance scattering microscopy. LANTCET was performed in vitro with living cells of (1) model myeloid K562 cells (CD33 positive), (2) primary human bone marrow CD33-positive blast cells from patients diagnosed with acute myeloid leukemia. Laser-induced microbubbles were generated and detected with a photothermal microscope equipped with a tunable Ti-Sa pulsed laser. GNT cluster formation caused a 100-fold decrease in the threshold optical fluence for laser microbubble generation in tumor cells compared with that in normal cells under the same targeting and irradiation conditions. Combining imaging based on resonance optical scattering with photothermal imaging of microbubbles, we developed a method for detection, image-guided treatment and monitoring of LANTCET. Pilot experiments were performed in flow mode bringing LANTCET closer to reality of clinical procedure of purging tumor cells from bone marrow grafts.
04 - Reconnaissance de chiffres manuscrits par modèle de Markov pseudo-2D

We present a new methodfor recognising handwritten characters based on pseudo– 2D Markov models. This method belongs to a class of techniques which attemp t to recover the prototype shape of a character starting from a distorted image . By contrast with popular approaches in this field, our method does not need an y explicit feature extraction and it outputs recognition scores which are estimate s of Bayes probabilities. It also allows an automatic training. One of its distinctive characteristics is the use of a truely bi–dimensional but causal model for th e estimation of probabilities . Lastly, we show how synthetic images of character s fitted to the statistical distribution of shapes in the training set can be generate d by this model.
Design and Implementation of Reference Current Source Based on Deep Submicron Technology

The reference current source has the advantages of low temperature coefficient and is not affected by the supply voltage. It is widely used in various analog circuits. Therefore, the study of reference current source is of great significance for the field of integrated circuits. In this paper, a reference current source with low temperature coefficient is designed and fabricated based on 0. 18μm BCD process. The reference current source is based on the structure of the current summation mode, and the current with a positive temperature coefficient and the current with a negative temperature coefficient are summed and output, which improves its temperature coefficient. The test results show that when the operating voltage is 2.5 V, the quiescent current of the reference current source is 250 μ A. When the temperature is 25 °C, its output reference current is 10.84 μ A. The reference current source can generate a reference current with a temperature coefficient of $2.76\times 10^{\mathbf{-4}}~\mu{\mathrm {A}}$/°C in the temperature range of −20°C to 85°C.
Implementation of Novel Reduced-Order H∞ Filter for Simultaneous Detection and Mitigation of FDI-Attacks in AGC Systems

Recent developments in power systems to build a smart grid depend critically on communication networks which provide unprecedented advantages for comprehensive system visibility, monitoring, protection, and control. However, it increases the system complexity and vulnerability to cyberattacks that could jeopardize the seamless system operation. The attack must be detected effectively, and then proper measures to mitigate the effect quickly to restore normality. This work implements a reduced-order $H_{\infty }$ filter to simultaneously estimate the state variable and unknown inputs such as noise or attack signals in the automatic generation control (AGC) system. Further, the knowledge of the disturbances is utilized to mitigate the attack by appropriately compensating the sensor readings, such that the control decisions are based on the correct states, not the corrupted signals generated in the attack scenario. The stability criterion is provided based on Lyapunov functions to check the estimation error in the presence of the exogenous disturbance. Further, the proposed $H_{\infty }$ estimator performance is evaluated by comparing it with the Kalman filter to verify its effectiveness. The estimator’s performance is validated by developing a cosimulation platform using the server–client architecture of data exchange. Further, the performance is validated in a real-time digital simulator (RTDS) platform used as the power system interacting with an estimator deployed on Raspberry Pi board sensing the attacks.
A Study on Traffic Matrix Estimation Techniques in Software-Defined Networks

Today the demand for internet services is increasing which leads to an exponential growth of internet traffic. In this paper, the Traffic Matrix Estimation (TME) problem is considered. The TME is one of the critical problems to be solved in the area of Software Defined Networks. TME problem can be solved by determining the traffic demand matrix by using load measurements. Traffic matrix estimation involves finding the most informative flows in a network. To accurately estimate the Traffic Matrix (TM) there are different approaches. This article surveys the various approaches for estimating the TM. Each method follows a different procedure to solve the problem of TME. The various methods to solve the traffic matrix estimation problem are considered followed by the comparison of various TM estimation methods. Also, the algorithm used in each of those methods are depicted very clearly for understanding. Finally, the challenges and opportunities were discussed which provides the guideline to go ahead with the TME problem.
Continuous Diagnostic Device for Pin Insulators

The paper describes the pin insulators diagnostic device controlling its leakage current and signalizing if a defect occurred. The insulator condition is assessed under operating voltage using leakage current monitoring. A laboratory device sample has been made. The device compares input and reference (regulated) voltage signals using a comparator. Besides, the scheme contains a diode bridge protecting a comparator from overvoltage. Several experiments were performed. Different types of insulators were mounted on a shunt and high alternating voltage was applied to the insulators. Leakage current was taken from the shunt structure. The monitored value of the leakage current corresponds to the value of the insulation normal resistance limit. Insulators with different defects were used in the tests: aluminum powder, soot, dust and artificial defect in the form of graphite line. As a result of the work, the operability of the developed device is confirmed. Probably, the next step of the work is the development of the diagnostic device signal transmission system connecting to substation for the personnel control.
Control of light propagation in one-dimensional quasi-periodic nonlinear photonic lattices

We investigate light localization in quasi-periodic nonlinear photonic lattices (PLs) composed of two periodic component lattices of equal lattice potential strength and incommensurate spatial periods. By including the system parameters from the experimentally realizable setup, we confirm that the light localization is a threshold determined phenomenon in a limit of negligible nonlinearity. In addition, we show that self-trapping can affect the localized light in the established setup only in the presence of strong nonlinearity. Guided by these findings we consider the possibility of governing light propagation by proposing a composite lattice system comprising alternating quasi-periodic parts with different potential depths and nonlinearity strengths.
Using Beam and Fulcrum Displays to Explore Data

Abstract The beam-and-fulcrum display is a useful complement to the boxplot. It displays the range, mean, standard deviation, and studentized range. It reveals the existence of outliers and permits some assessment of shape. Embellishments to the beam-and-fulcrum diagram can show the item frequency, and/or a confidence interval for the mean. Its intuitive simplicity makes the beam-and-fulcrum an attractive tool for exploratory data analysis (EDA) and classroom instruction.
When the CSI from Alice to Bob is Unavailable: What Can Eve Do to Eliminate the Artificial Noise?

Artificial noise elimination (ANE) has arisen as a possible countermeasure for mitigating the influence of artificial noise (AN) at the eavesdropper (Eve). However, conventional ANE schemes require the attainable channel state information (CSI) between the transmitter (Alice) and legitimate receiver (Bob), which reduces the feasibility of this proposal. In this paper, we investigate the issue of ANE without the CSI of Alice-Bob link by minimizing the artificial-noise-to-signal ratio (ANSR). Moreover, the detailed minor component analysis (MCA) algorithm is presented, and the computational complexity is quantified. Simulation results demonstrate that MCA can effectively degrade the influence of AN without the knowledge of CSI.
Performance Evaluation over DL-Based Channel Prediction Algorithm on Realistic CSI

With the development of smart connected automated guided vehicles (AGVs) and robots, many new services and applications occur, which require flexible wireless end-to-end communication, high data transmission, and intensive computation. To achieve such a high demanding communication system, it is very important to predict the wireless channel parameters, which can help schedule the system resource management and optimize the system performance in advance, such as throughput and transmission efficiency. In this paper, we present our efforts towards proposing a deep learning-based channel prediction algorithm, which is then evaluated on the data set measured with different system state report frequencies from our implemented software-defined radio platform in different indoor environments. Results showed that the proposed channel predictor has a convincing ability on the real-world channel prediction.
An Analysis of Field Weakening Mode in DTC Based Induction Drive for Control Influences Forming

The article deals with DTC-based induction drives. DTC systems provide the same regulated properties as AC electric drives like DC electric drives. To expand the speed-range ratio in a DTC-based induction motor and provide a motor work with speed above its synchronous speed, it is necessary to weaken the induction-motor field. At the same time, DTC-based induction-motor work in the field weakening zone requires improvement. Therefore, the task of control-strategy synthesis in the field weakening zone of the DTC-based induction motor is very important. To ensure satisfactory dynamic and static characteristics of DTC-based induction drive in the zone of flux weakening, it is advisable to change the flux linkage in inverse proportion to the change in speed. The field-weakening mode in DTC-based induction motor in order to determine a control influence in case of two-zone speed regulation is analyzed.
Content Delivery Methods Considering Both Emergency and Demand for Neighbor Cooperative Offloading System

We have considered the offload methods to alleviate the load on carrier networks using neighbor communication. It is better to select the contents with high demands to be delivered with high priority. However, we have to take into account the expiration time or emergency of the contents to distribute them in case of disasters. In this paper, an adaptive content distribution method to count both the demand and the emergency of each content is evaluated and discussed. As a result, our proposal is reported to be effective to balance the demand and the emergency to some extent.
Using Semi-supervised Transfer Learning for Classification of Solar Lentigo, Lentigo Maligna, and Lentigo Maligna Melanoma

Skin cancer is the most frequent malignancy worldwide, with the number of new cases increasing yearly. Computer-aided diagnosis from skin images has recently become a critical procedure to detect early melanoma stages before becoming metastasis. This study intended to classify three stages of skin cancer: solar lentigo (SL), lentigo maligna (LM), and lentigo maligna melanoma (LMM) using transfer learning and semi-supervised transfer learning of a convolutional neural network algorithm based on VGG-16 and VGG-19. Skin images were obtained from various databases, including labeled and unlabeled data, and were preprocessed using hair removal software and a data balancing technique. The image data were then trained in ten experiments: supervised learning, supervised transfer learning, and semi-supervised transfer learning using VGG-16 and VGG-19 with and without augmentation. The results show that supervised learning gives an accuracy of 0.47. Based on VGG-16 and VGG19 which are comparable in performance, the accuracies increase to 0.72 and 0.72 for supervised transfer learning, and 0.92 and 0.98 for semi-supervised transfer learning, respectively. However, when applying augmentation, the accuracies decrease. Therefore, the use of semi-supervised transfer learning based on VGG-19 gives the best prediction in our study.
Smart Moisture Sensor-Based Irrigation System

Suitable state of environment is the utmost requirement for efficient plant growth, efficient use of, improved crop yields and other resources. Automation of data gaining process of soil condition allows growing of plants with less struggle. In today's existing system, the user is kept intimated about the condition of the field. Such system is expensive, arduous to maintain and less acknowledged by the technically unskilled workers. The idea of this paper is to create an easily installable and a simple microcontroller-based circuit to scrutinize and document the amount of moisture content in soil and fertilizer level that are regularly changed and controlled to increase their efficiency in order to achieve utmost plant yield and growth. The deployed microcontroller consumes less power and is a cost-efficient chip by ATMEL. It contacts the sensor modules contemporarily in order to manage the irrigation process economically inside a field by actuating a motor as per the needed circumstance of the crops. To display the accumulated real-time data from the sensor and the position of different components, a liquid crystal display is installed. Further, the cost is cut short by the utilization of easily accessible devices. The software used can be replaced at any instance due to its flexible design. Therefore, enabling it to match a specific need as per requests of the users. This leads to a proficient structure for optimization of yield with minimum use of water. This system is also cost effective, handy and user friendly.
Cosmological Formation of Quasar Black Holes

Massive black holes (≥10 5 M ○ .) can form in a quasi-spherical collapse of gas clouds, out of nonlinear density perturbations above the Jeans mass after the cosmological recombination [200≤(1+z)≤1400]. Angular momentum and energy are extracted efficiently from the electrons through Compton friction with the cosmic background radiation. Due to the radial Compton drag, the dark-matter mass enclosed within a baryonic shell changes as a function of time. Most of the collapse time is spent near turnaround at low gas densities. During that phase, the cloud can be reionized by a sub-Eddington internal luminosity
Stalker Ads: A study to Understand the Consumer Awareness and Perception towards Targeted Advertising

Advertisers today give personalized ad messages for each customer based on personal information to break out of the congested ad environment. They use this targeting strategy to improve the efficiency of their ads, but it also raises information security and privacy concerns as consumers are often unaware of the practices that lead to the functioning of these advertisements. Apart from this, consumer interest and the credibility of the advertisement content also play a vital role in deducing a consumer perception. The current study uses an online questionnaire-based survey to look at the impact on the effectiveness of the targeted advertisement based on consumer perception, which is derived from multiple factors like consumer interest, content credibility, security concern and awareness, and value delivery. It is observed that consumer awareness causes privacy concerns, which leads to a decrease in the value delivery of these ads. On the other hand, it is also observed that content credibility hardly affects value delivery. Moreover, the findings indicated that the value delivery from these targeted advertisements has a significant positive impact on consumer perception, which ultimately relates to the overall effectiveness of these targeted advertisements. The study adds to the theoretical understanding of the factors that impact customer perceptions of targeted advertising. Also, the analysis and findings from this study will help the advertisers understand the consumer concerns related to these targeted ads and guide them to take the required efforts to gain consumer trust and improve the overall transparency and efficiency of the targeted advertisements.
Bundle Adjustment-Based Sonar-Inertial Odometry for Underwater Navigation

A sonar-inertial odometry (SIO) algorithm based on bundle adjustment is proposed in this study for underwater navigation. To improve the precision of vehicle state estimation, SIO fuses the measurements of two sensors that are tightly cou-pled, that is the acoustic observations from a forward-looking sonar (FLS) and the inertial measurements from an inertial measurement unit (IMU). In particular, SIO can establish the relative pose constraints of multi-frame sonar measurements by employing bundle adjustment without the prior assumption about the environment. The initial pose used to estimate the sonar inter-frame motion is dead reckoning with inertial measurements. Combined with factor graph optimization, the two factors are elegantly integrated into the SIO framework: (1) Inertial measurement factor with preintegration, is calculated for the relative constraints between sonar frames only, thereby reducing the computing resources consumption. (2) Sonar measurement factor using reprojection errors, is created by investigating the relationship between the sonar imaging and the motion transformation, which corrects for drift in dead reckoning via feature perception. Furthermore, the analytical form of the sonar measurement factor is completely developed, and the simulation shows that the proposed SIO outperforms dead reckoning in terms of accuracy.
Opportunities and Challenges of Switched Reluctance Motors for Traction Applications

The recent changes in transportation requirements led to the standardization of electric drives for various traction applications. Sustainability being one of the main purposes, subjective evaluation and performance optimization of the electric drives are of prime importance. Most of the traction motors are fundamentally designed based on the Torque-Speed and Speed-Time curves. Because of higher power density distributions, permanent magnet synchronous motor (PMSM) and brushless DC motor (BLDC) are having a better performance when compared to the other electric motors. Hence, they are the predominant choice in traction applications. But the field poles of the rotors of these motors are made of permanent magnets. This makes the rotor construction frail. Also, the uncertainty in the cost of rare earth metals made the manufacturers look for permanent magnet free alternative. Based on all these, a higher efficient and higher power density switched reluctance motor (SRM) technology forms an attractive and reasonable substitute for most of the applications. This paper presents the primary opportunities and some of the basic challenges like torque ripple generation which produces noise and vibrations and control system design complexity, which are involved in designing a switched reluctance motor for traction applications. Accordingly, various advantages and disadvantages are also emphasized.