Anisotropic superconducting properties of aligned Sm0.95_{0.95}La0.05_{0.05}FeAsO0.85_{0.85}F0.15_{0.15} microcrystalline powder

The Sm$_{0.95}$La$_{0.05}$FeAsO$_{0.85}$F$_{0.15}$ compound is a quasi-2D layered superconductor with a superconducting transition temperature T$_c$ = 52 K. Due to the Fe spin-orbital related anisotropic exchange coupling (antiferromagnetic or ferromagnetic fluctuation), the tetragonal microcrystalline powder can be aligned at room temperature using the field-rotation method where the tetragonal $\it{ab}$-plane is parallel to the aligned magnetic field B$_{a}$ and $\it{c}$-axis along the rotation axis. Anisotropic superconducting properties with anisotropic diamagnetic ratio $\chi_{c}/\chi_{ab}\sim$ 2.4 + 0.6 was observed from low field susceptibility $\chi$(T) and magnetization M(B$_{a}$). The anisotropic low-field phase diagram with the variation of lower critical field gives a zero-temperature penetration depth $\lambda_{c}$(0) = 280 nm and $\lambda_{ab}$(0) = 120 nm. The magnetic fluctuation used for powder alignment at 300 K may be related with the pairing mechanism of superconductivity at lower temperature.Comment: 4 pages, 6 figure

SegTHOR: Segmentation of Thoracic Organs at Risk in CT images

In the era of open science, public datasets, along with common experimental protocol, help in the process of designing and validating data science algorithms; they also contribute to ease reproductibility and fair comparison between methods. Many datasets for image segmentation are available, each presenting its own challenges; however just a very few exist for radiotherapy planning. This paper is the presentation of a new dataset dedicated to the segmentation of organs at risk (OARs) in the thorax, i.e. the organs surrounding the tumour that must be preserved from irradiations during radiotherapy. This dataset is called SegTHOR (Segmentation of THoracic Organs at Risk). In this dataset, the OARs are the heart, the trachea, the aorta and the esophagus, which have varying spatial and appearance characteristics. The dataset includes 60 3D CT scans, divided into a training set of 40 and a test set of 20 patients, where the OARs have been contoured manually by an experienced radiotherapist. Along with the dataset, we present some baseline results, obtained using both the original, state-of-the-art architecture U-Net and a simplified version. We investigate different configurations of this baseline architecture that will serve as comparison for future studies on the SegTHOR dataset. Preliminary results show that room for improvement is left, especially for smallest organs.Comment: Submitted to a journal in december 201

A Sequential Circuit-Based IP Watermarking Algorithm for Multiple Scan Chains in Design-for-Test

In Very Large Scale Integrated Circuits (VLSI) design, the existing Design-for-Test(DFT) based watermarking techniques usually insert watermark through reordering scan cells, which causes large resource overhead, low security and coverage rate of watermark detection. A novel scheme was proposed to watermark multiple scan chains in DFT for solving the problems. The proposed scheme adopts DFT scan test model of VLSI design, and uses a Linear Feedback Shift Register (LFSR) for pseudo random test vector generation. All of the test vectors are shifted in scan input for the construction of multiple scan chains with minimum correlation. Specific registers in multiple scan chains will be changed by the watermark circuit for watermarking the design. The watermark can be effectively detected without interference with normal function of the circuit, even after the chip is packaged. The experimental results on several ISCAS benchmarks show that the proposed scheme has lower resource overhead, probability of coincidence and higher coverage rate of watermark detection by comparing with the existing methods

Risk assessment of critical time to renewable operation with steady-state security region

PS10: Risk Assessment in Power System Operation: ID 371Uncertain and variable characteristics of renewable energy resources introduce challenges to power system operation. A normal operating point might be drifted towards an unreliable operating point due to stochastic outputs od renewables. This paper proposes a novel method for estimating critical time to unreliable operating point with steady-state constraints. In this work, a stochastic differential equation is employed to describe the distribution of renewables with predictable tendency and stochastic errors of prediction; meanwhile, the DC power flow based steady-state security region is used to restrict the injected space. To find the critical time that uncontrollable renewables leave the security region, according the flexibility requirements defined by NERC, the uncontrollable region is identified with the Fourier-Motzkin elimination first. And then, by solving the Chebychev center problem, the critical distance for variable renewable outputs is obtained. Finally, an analytical solution of expected exit-time for renewable outputs leaving the security region is given with the Martingale stopping theorem. The proposed method can be used to construct the condition-driven risk indicators. An illustrative example is employed to demonstrate and validate the proposed method.postprin