A conventional virtual flight test generally refers to a 3-DOF dynamic flight test in a wind tunnel. In the wind tunnel test, the model aircraft is connected to the strut through a 3-DOF rotation mechanism and installed in the wind tunnel test section so that the model displacement is constrained but has 3 degrees of angular motion freedom. Open-loop and closed-loop control of the aircraft model is achieved by directly driving the rudder surface or by using commands from the flight control system.


Data for neural networks.

Magnetic flux intensity - input

The real pose of a single magnet - output


The dataset consists of three parts, the first part consists of single notes and playing technique samples, and the second includes the triple viewed video, steoro-microphone recordings and 4 track optical vibration recordings in raw file for famous Chinese Folk music ‘Jasmine Flower’ and the first section of ‘Ambush from ten sides’. The third part concerns about the source separated tracks from optical recordings and expressive annotation files are included in the annotation files.


We collect IMU measurements under three different patterns: Fixing a smartphone in front of his chest (chest), swing a smartphone while holding it in his hand (swing), and putting a smartphone in his pocket (pocket). We use Google Pixel 3XL for the pattern of chest and Google Pixel 3a for the patterns of swing and pocket. The sampling frequency of each measurement is fixed to 15Hz. We collect the measurement of 111 paths in total, categorized into 4 types. We partition them into 84 and 27 paths, used for training and testing, respectively. It takes 10 hours to collect all datasets.


Document layout analysis (DLA) plays an important role for identifying and classifying the different regions of digital documents in the context of Document Understanding tasks. In light of this, SciBank seeks to provide a considerable amount  of data from text (abstract, text blocks, caption, keywords, reference, section, subsection, title), tables, figures and equations (isolated equations and inline equations) of 74435 scientific articles pages. Human curators validated that these 12 regions were properly labeled.

  1. Datasheet_for_SciBank_Dataset.pdf. The Datasheet for this Dataset includes all the relevant details of the composition, collection, preprocessing, cleaning and labeling process used to construct SciBank.
  2. METADATA_FINAL.csv. Each row represent the metadata for every region according to the following fields
    1. Folder: the name of the folder within the main folder PAPER_TAR
    2. Page: png filename of the image where the region is located
    3. Height_Page, Width_Page: dimensions in pixels of the png image page
    4. CoodX, CoodY, Width, Height: coordinates of the region in pixels 
    5. Class: region label
    6. Page_in_pdf: page number within the PDF containing the page of the region
  3. PAPER_TAR folder includes the PNG images from all paper pages and the PDF papers in hierarchical subdirectories, both referenced by METADATA_FINAL.csv. 



Understanding the properties of grain boundaries in polycrystalline superconductors is essential for optimizing their critical current density. Here, we provide computational simulations of 2D Josephson junctions (JJs) in low magnetic fields using time--dependent Ginzburg--Landau theory, since they can be considered a proxy for a grain boundary between two grains.


<p>The proliferation of efficient edge computing has enabled a paradigm shift of how we monitor and interpret urban air quality. Coupled with the dense spatiotemporal resolution realized from large-scale wireless sensor networks, we can achieve highly accurate realtime local inference of airborne pollutants. In this paper, we introduce a novel Deep Neural Network architecture targeted at latent time-series regression tasks from continuous, exogenous sensor measurements, based on the Transformer encoder scheme and designed for deployment on low-cost power-efficient edge processors.


Simulation results from EVI-OnDemand estimating need for infrastructure to support electric ride-hailing vehicles in the US.


Skeleton datasets for Normal, Antalgic, Stiff legged, Lurching, Steppage, and Trendelenburg gaits.


Sequential skeleton and average foot pressure data for normal and five pathological gaits (i.e., antalgic, lurching, steppage, stiff-legged, and Trendelenburg) were simultaneously collected. The skeleton data were collected by using Azure Kinect (Microsoft Corp. Redmond, WA, USA). The average foot pressure data were collected by GW1100 (GHIWell, Korea). 12 healthy subjects participated in data collection. They simulated the pathological gaits under strict supervision. A total of 1,440 data instances (12 people x 6 gait types x 20 walkings) were collected.