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Senate Subcommittee on Government Research -- Correspondence, 1966-67 -- Consulting -- U.S. Government -- letter, 1967-04-06
No full textLetter from Miller, Benjamin F. to Sabin, Albert B. dated 1967-04-06.Sabin Collection Fair Use Policy</a
1956 -- Correspondence, Unsorted -- letter, 1956-08-01
No full textLetter from Miller, Benjamin F. to Sabin, Albert B. dated 1956-08-01.Sabin Collection Fair Use Policy</a
ASTRIANet Data for: Cosmos-1408
No full text Overview
This dataset contains observations along the expected track of the COSMOS-1408 satellite on Nov. 16, 2021. Objects are observed which have low relative angular velocity with respect to the expected pre-breakup track, possibly having originated from the COSMOS-1408 breakup event.
ASTRIANet
The ASTRIANet robotic telescope network collects raw optical imagery, unfiltered and uncorrected, stored in Flexible Image Transport System (FITS) standard format to provide both contiguous single-exposure data and associated metadata. ASTRIANet systems operate in rate-tracking mode following publicly available predicted two-line elements (TLEs) provided through Space Track and/or CelesTrack. Note that this ASTRIANet data is completely unprocessed, and the RA_OBJ/DEC_OBJ coordinates provided in the FITS headers are predicted propagations from available TLEs used for telescope pointing, and should not be confused with detections.
This dataset contains observations collected by both the ASTRIANet02 CCD telescope based in the New Mexico Skies Observatory, USA (NMS) and the ASTRIANet03 CMOS telescope based in El Sauce Observatory, Chile (ELS). Both systems operate at 300ms exposure for this dataset. The ASTRIANet02/NMS system provides ~1.76deg field-of-view and the ASTRIANet03/ELS system provides ~0.25deg field-of-view.
ASTRIANet02 at the New Mexico Skies Observatory, USA is a shared resource with the Keldysh Institute of Applied Mathematics in Moscow, Russia.
Data
The curated set of data included in this submission is as follows:
FULLPASS_NMS_20211116_13552_cosmos_1408.tar.gz: A packaged collection of optical data for the COSMOS-1408 satellite for the entire pass collected on Nov. 16, 2021 by the ASTRIANet02 telescope. This compressed archive contains 86 FITS files for an ~400s contiguous observation. The telescope tracks an older pre-breakup TLE. Several spot-like features appearing across the frames are not streaked, indicating a low relative angular velocity with respect to the expected track, and thus these features may have been generated by the COSMOS-1408 breakup. The middle frames of this pass suffer from strong lunar illumination and other artifacts.
VISUAL_FIRST12_NMS_20211116_13552_cosmos_1408.gif: An video visual of the first 12 frames of the ASTRIANet02 data, before onset of strong lunar illumination, showing several bright features.
FULLPASS_ELS_20211116_13552_cosmos_1408.tar.gz: A packaged collection of optical data for the COSMOS-1408 satellite for the entire pass collected on Nov. 16, 2021 by the ASTRIANet03 telescope. This compressed archive contains 43 FITS files for an ~150s contiguous observation. The telescope tracks an older pre-breakup TLE.
SELECTED_ELS_20211116_13552_cosmos_1408.tar.gz: A packaged collection of optical data for the COSMOS-1408 satellite for a selected sample of the pass collected on Nov. 16, 2021 by the the ASTRIANet03 telescope. This compressed archive contains 10 FITS files for an ~30s contiguous observation, selected to show a single bright object which has a very low relative angular velocity with respect to the expected pre-breakup TLE.
ELS_20211116080905629_13552.fit: A selected single frame from the COSMOS-1480 observation from Nov. 16, 2021 by the the ASTRIANet03 telescope. This frame is selected to show several bright objects which only occur on this single frame. The telescope tracks a pre-breakup TLE, and the lack of streaking on these features over the exposure suggest a low relative angular velocity with respect to the expected track, and thus these features may have been generated by the COSMOS-1408 breakup.
VISUAL_SELECTED_ELS_20211116_13552_cosmos_1408.mp4: An video visual of the selected ASTRIANet03 data showing a single bright object over several frames.
VISUAL_ELS_20211116080905629_13552.jpg: A still visual of the ASTRAINet03 data showing several bright objects.
Documentation
Documentation on the sensor technical specifications and tracking information used to produce the dataset is included as follows:
Questionnaire_sensor_NMSkies.docx: Technical specifications for the ASTRIANet02/NMS telescope used in collecting this dataset, including sensor details and other station information.
Questionnaire_sensor_Chile_ElSauce.docx: Technical specifications for the ASTRIANet03/ELS telescope used in collecting this dataset, including sensor details and other station information.
Usage
These datasets can be reused by policymakers, by industry, by researchers and by the public interested in compliance with international regulations for ASOs
ASTRIANet Data for: Python Computational Inference from Structure (PyCIS)
No full text Usage and Overview
This data set contains FITS-formatted files of annotated image data collected by the ASTRIANet telescope network, along with a technical specification document on the telescope sensor and an Excel-formatted table of example image annotations. Two full passes of data are provided as packaged tar.gz files containing contiguous observations of one LEO (Starlink-1422) and one GEO (Navstar-48) satellite over a 5-minute and 11-minute period respectively, and GIF-format video visualizations of each observation are provided. Individual zipped FITS files of single-frame data are also provided for the Starlink satellite. This data is provided as an example of typical ASTRIANet sensor capabilities, and may be used to run demonstrations of the related PyCIS object detection software. For more details on this software, the ASTRIANet telescope network, and the provided data and documents, please see the descriptions provided below.
ASTRIANet
ASTRIANet is a robotic telescope network operated by ASTRIA, designed to record time-series of optical observations of Anthropogenic Space Objects (ASOs) from all orbital regimes LEO-to-GEO. The network supports the Space Domain Awareness (SDA) mission of ASTRIAGraph by closing the information lifecycle from raw observation to trajectory data fusion. Per its operational strategy, the network collects electro-optical observations with significant imaging and tracking noise, and does not collect traditional calibration frames. Accurate ASO detection and tracking algorithms are required to enable Orbit Determination (OD) and bridge the ASTRIANet telescope network with the ASTRIAGraph database by delivering actionable trajectory information.
PyCIS Algorithm Details: A-Contrario Detection and Tracking
Under the so-called "a-contrario" paradigm, structures in data are considered "meaningful" if they are unlikely to occur by chance according to a background noise model. This "Helmholtz Principle" is formalized by a "Number of False Alarms" (or NFA) function which probabilistically defines a structure's “meaningfulness” given both a noise model assumption and some measurement function [2]. We use this dataset to demonstrate the feasibility of detecting ASOs from 16-Megapixel visual imagery observations under the “a-contrario” paradigm.
Our motivation in implementing an “a-contrario” detection scheme is two-fold. Firstly, we seek to avoid traditional CCD sensor reductions (e.g. background subtraction, flat-fielding) which can reduce precision as well as operational observation time. Secondly, we seek to avoid making discrete point detections on each image fame, in contrast to many implementations of so-called “track-before-detect” schemes and other three-dimensional “a-contrario” algorithms.
Our "Python Computation Inference from Structure" (PyCIS) algorithm [1] builds upon existing low-level implementations of the “a-contrario” principle, which detect edge features in 2D image data by probabilistically detecting meaningful regions of gradient vector field alignments [3, 4]. We have extended these implementations to extract centerline trajectories of of stars and other space objects moving over time. Maintaining the “a-contrario” framework, we then classify meaningful trajectory populations such as stars and sensor noise and identify potential anthropogenic space objects by rejection. For further details, please reference the related software.
Data
The curated set of data included in this submission is as follows:
20201220_45696_starlink-1422/*.zip: The raw optical image data from the ASTRIANet02 telescope (New Mexico Skies Observatory), stored as separate Flexible Image Transport System (FITS)-format files containing single Header/Data Units (HDUs) of single-exposure image frames and associated metadata. This header and image data can be processed using the Astropy module for Python (see the PyCIS GitHub for details). The ASTRIANet02 system follows a target track from publicly available two-line elements (TLEs) and collects unfiltered and uncorrected optical CCD data (see attached documents). Upon data collection, locally stored data is curated for quality and uploaded to the Corral storage system of the Texas Advanced Computing Center (TACC). This observation set includes 66 frames of data for a Starlink satellite collected on December 20, 2020 over a 5-minute observation, chosen for use in PyCIS demonstrations due to ASO visibility and motion patterns in the image plane.
FULLPASS_20201220_45696_starlink-1422.tar.gz: A packaged collection of optical data of the Starlink-1422 LEO satellite collected on December 20, 2020 from the ASTRIANet02 telescope containing 66 individual FITS files over a 5-minute observation. While individual frames of FITS data may be downloaded for analysis (see above), this package is provided for users interested in the full time-series data.
FULLPASS_20201224_26407_navstar-48.tar.gz: A packaged collection of optical data of the Navstar-48 GEO satellite collected on December 24, 2020 from the ASTRIANet02 telescope containing 93 individual FITS files over an 11-minute observation. This package is provided for users interested in full time-series data, and inspection of individual frames will require downloading the full pass.
VISUAL_*.gif: Video visualizations of each FULLPASS data set. For visual comprehension, the videos use 4-pixel binning to reduced 1024x1024 resolution, are clipped and normalized to 5 standard deviations from the mean for improved contrast, and are animated at 5 frames per second.
Documentation
Documentation on the sensor technical specifications and tracking information used to produce the dataset is included as follows:
example_header.xlsx: Extracted header data from the 20201220013535730_45696.zip data file. Standard sensor information is provided, along with details on the object being tracked and the celestial environment, auto-populated by the telescope software during collection. The TLEs are sourced from Space Track or CelesTrak (for Starlink satellites). The RA_OBJ/ DEC_OBJ coordinates are predictions from the associated TLE using SGP4, for use in validating detections. The header design complies with the Electro-Optical Space Situational Awareness (EOSSA) data product format [5].
Questionnaire_sensor_NMSkies.docx: Technical specifications of the ASTRIANet telescope used to collect the observational data, including sensor details and station information.
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 PyCIS algorithm and demonstrations which utilize this data set can be found at https://github.com/ut-astria/PyCIS [1].</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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