2875 research outputs found
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Rain Types for Formation of Nocturnal Offshore Rainfall near the West Coast of Sumatra
No full textSupporting radar data set for the publication cited her
Egok360 A 360 Egocentric Kinetic Human Activity Video Dataset
No full textRecently, there has been a growing interest in wearable sensors which provides new research perspectives for 360 ° video analysis. However, the lack of 360 ° datasets in literature hinders the research in this field. To bridge this gap, in this paper we propose a novel Egocentric (first-person) 360° Kinetic human activity video dataset (EgoK360). The EgoK360 dataset contains annotations of human activity with different sub-actions, e.g., activity Ping-Pong with four sub-actions which are pickup-ball, hit, bounce-ball and serve. To the best of our knowledge, EgoK360 is the first dataset in the domain of first-person activity recognition with a 360° environmental setup, which will facilitate the egocentric 360 ° video understanding. We provide experimental results and comprehensive analysis of variants of the two-stream network for 360 egocentric activity recognition. The EgoK360 dataset can be downloaded from https://egok360.github.io/
Statistical Families of the Trackable Earth-Orbiting Anthropogenic Space Object Population in Their Specific Orbital Angular Moment Space
No full text Motivation
Our motivation for this analysis is to create an efficient and transparent system to categorize anthropogenic space objects (ASO's) based on orbital behavior, namely angular momentum. Doing so, we hope to lay the groundwork for future studies that involve trackability, detection, and identification. Through the creation of "orbital zip-codes", and characterization of these clusters of ASOs in angular momentum space, we can better understand their orbital behavior to a greater extent and identify migration patterns. Objects that moved from one cluster to another were considered to be "nomadic" objects. In theory, ASOs should have a constant or near-constant angular momentum value. Objects that exhibit significant movement between clusters require keen investigation as to why they exhibit erratic orbital migration patterns. The Data
The 12 datasets included in this submission are JSON files that contain orbital information regarding ASOs over a 12-month period in 2019. Each dataset is a snapshot of the objects on the first day of each month. The orbital information includes angular momentum, semi-major axis, eccentricity, mean anomaly, longitude of the ascending node, equinoctial elements, and inclination.
The data was acquired from ASTRIAGraph. Python scripts were written to convert the data from JSON format to a clean, manipulatable Pandas data frame. The angular momentum vectors in the x, y, and z axes were isolated and fed into K-means and spectral clustering models were implemented after conversion to polar coordinates. From here, plotting can be done if one wishes to observe the created clusters. The elbow method was used to determine the optimal number of clusters for the month of January. This number was kept constant for each run of the model on a different dataset to maintain consistency.
One issue encountered was the redundant naming of the ASO objects inherent in the dataset. To discover and track nomadic objects over the one-year period, it may be helpful to handle these redundancies through renaming. Additionally, the month of January was instantiated as a reference point. The data in other months was compared to that of January to identify nomadic objects. The clusters can be characterized through object density or population. The 3-dimensional volume of a cluster could be roughly calculated by using 3D shape formulas. The object density of a cluster can be obtained by sampling points iteratively from one cluster in a constrained volume and dividing by said volume. Usage
These datasets can be reused by policymakers, by industry, by researchers and by the public interested in clustering of ASO
CtQ Assessment Instruments
No full textAssessment Instruments for Curiosity to Question GEO 391/371T Research Design, Data Analysis and Visualization. Instruments included modified URSSA and Network Survey in Word (.doc) and Qualtrics (.qtf) formats
Replication Data for: Transparency and Accountability in Space Domain Awareness: Demonstrating ASTRIAGraph's Capabilities with the United Nations Registry Data
No full textUnited Nations Objects Launched into Outer Space Register Data
Under the purview of the Office for Outer Space Affairs UNOOSA, The United Nations maintains a Register of Objects Launched into Outer Space (a.k.a. Anthropogenic Space Objects or ASOs) for purposes of assessing peaceful uses of outer space. Maintained since 1962, it was established as a Convention on Registration of Objects Launched into Outer Space in 1976, by which abiding States and international intergovernmental organizations have to implement their own national registries and contribute information to the United Nations Register. UNOOSA is required to publicly disseminate the information provided by States and international intergovernmental organizations about their space objects. In tandem with international space law, the register is useful to identify which States bear "international responsibility and liability" for ASO/s. We use this dataset to implement a case study related to the themes of transparency and accountability.
The United Nations Registration Data Case Study in ASTRIAGraph
Our motivation for this case study is to provide data solutions to trans-disciplinary problems in space safety, security, and sustainability. We measure our progress against these solutions by assessing to what extent our research makes space more transparent, predictable, and develops a body of evidence upon which space actors can be held accountable for their behavior(s). Our ASO digital library of the UN space object registry and index contributes to transparency and accountability.
Within ASTRIAGraph we maintain an up-to-date dictionary of ASOs registered and reflected in the UN registry and index websites. The data is gathered, curated, ingested, and organized within ASTRIAGraph in relation to questions we need to answer. Among other inquiries, using this data we can visualize registered space objects, identify ASO's Launch States’ liability, and assess trends in the registration patterns of these Launch States. Using different fields from the registration information, we calculate the ranking of Launch States in relation to registration promptness.
The Data
The two curated sets of data included in this submission are:
A) allUNregisteredObjects.json: Is the integrated UN register data which includes information from the Online Index, and data extracted from the pdf documents submitted to the registry by States. To acquire the data in this format, we devised and instantiated a process of data retrieval and scrubbing. We began with the acquisition of the space object data catalogued on the UNOOSA website index, along with acquiring the PDFs for the registration documents submitted by the States. Once the files have been gathered, we employ python code to extract all of the raw data from the PDFs into "convertedJSONs". However, the documents have a large variety of formats, data labels, and even different subsets of data, and so post-processing is necessary to declutter and group the data into a few more consistent categories, as well as judge the quality of the data extraction process, while also coupling this data with that we had extracted earlier from the UNOOSA website. Once this post-processing is completed, we have "standardizedJSONs" which are then appended to the master-list of data that we call "allUNregisteredObjects.json".
B) weighted_ranking.csv: The weighted Launch State registration rates derive from the curated allUNregisteredObjects.json file. It establishes a ranking of Launch States in relation to registration promptness. These rankings, set between one and five stars, are first given to individual objects, but countries with many registered ASOs often have outliers with large registration lag. As such, the rankings are scaled higher for states which register larger numbers of ASOs to prevent undue punishment of these countries for their compliance.
Documentation
Included are documentation about the process by which the two data files were produced, and a presentation with results from analyses performed on them.
Usage
These datasets can be reused by policymakers, by industry, by researchers and by the public interested in compliance with international regulations for ASOs.
The demonstration queries for this study case can be found at: http://astriaservices.tacc.utexas.edu/compliance
To visualize ASOs registered with the UNOOSA in ASTRIAGraph, go to: http://astriaservices.tacc.utexas.edu/astriagraph_uno/ </p
EEG Analysis Matlab Scripts
No full textThese are the analysis scripts (+ electrode files) used for the EEG data. Requires EEGLAB toolbox and Matlab 2019b or greater
Replication Data for: Characterization of grain-size distribution, thermal conductivity, and gas diffusivity in variably saturated binary sand mixtures
No full textReplication Data for: Characterization of grain-size distribution, thermal conductivity, and gas diffusivity in variably saturated binary sand mixture
Biaxial Mechanical Data (Young and Aged Mice, Ventral and Dorsal)
No full textThis data set contains the biaxial tension data of mouse skin. It contains 111 total .csv files that provide information on how the data was collected. All data is with respect to the in vitro unloaded reference configuration (see Figure 1 in the description file). The file name provides details on the sample including subject number, sample number, subject age (i.e., young = 12 weeks, aged = 52 weeks), and sample location (i.e., dorsal or ventral). Each data sets contains three .csv files, each labeled and representing one of the three loading configurations – Equibiaxial (1:1 stretch ratio between 11 and 22 direction), OffbiaxialX (1:2 stretch ratio), and OffbiaxialY (2:1 stretch ratio), determined by rake-to-rake displacement in a displacement-controlled test. Every .csv file contains 4 columns. Columns one and two contain the DIC acquired stretches (unitless) of the tissue in the 11 and 22 direction, respectively. Columns three and four contain the Cauchy stresses (in MPa) in the 11 and 22 direction, respectively. Note, the anatomical lateral direction is the 11 direction and anatomical cranial-caudal direction is the 22 direction. These data represent the experimental downstroke data after 5 cycles of preconditioned loading, at a strain rate of approximately 1.25%/s (approximated during testing by rake-to-rake distance), in 37°C 1xPBS. Thickness of tissues for stress calculations were determined via transverse sections in optimal cutting temperature medium post-testing. For more details, we refer the reader to our referenced manuscript
