{"id":422,"date":"2017-08-26T10:18:51","date_gmt":"2017-08-26T08:18:51","guid":{"rendered":"https:\/\/aerospaceresearch.net\/?p=422"},"modified":"2017-08-28T16:36:44","modified_gmt":"2017-08-28T14:36:44","slug":"gsoc2017-siqnal-cubesat-tracking-using-iq-stream-data","status":"publish","type":"post","link":"https:\/\/aerospaceresearch.net\/?p=422","title":{"rendered":"[GSOC2017] SiqNAL: CubeSat tracking using IQ stream data"},"content":{"rendered":"<h2><span style=\"font-weight: 400;\">Introduction<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">CubeSats is a class of nanosatellites that uses a standard size and form factor. The standard CubeSat size is <strong>one unit<\/strong> \u00a0or <strong>1U<\/strong> \u00a0measuring 10x10x10 cms and is extendable to larger sizes; 1.5, 2, 3, 6, and even 12U. Originally developed in 1999 by California Polytechnic State University of San Luis Obispo (Cal Poly) and Stanford University to provide a platform for education and space exploration. The development of CubeSats has expanded its industry, academy, and industry with ever-increasing capabilities. CubeSats now provides a cost-effective platform for science investigations, new technology demonstrations and advanced mission concepts using constellations, swarms disaggregated systems.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">While CubeSats came into the picture for it&#8217;s low cost of manufacturing with many small to medium sized individuals. So through this project.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Project Requirements<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">This project is made using python-3.x or python 2.7. Python libraries used for this project are<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">scipy-0.18.1<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">matplotlib-2.0.0<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Numpy-1.12.0<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">PyQt4-4.11.4<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Except for PyQt4 all other libraries can be installed using python package manager. Installation of PyQt4 depends on the distribution. Detailed guide for installation of requirements is available\u00a0 <a href=\"https:\/\/goo.gl\/aeK3Fc\">here<\/a> .\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Overview of Project<\/span><\/h2>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"size-full wp-image-423 aligncenter\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/SiqNAL-Overview.png\" alt=\"\" width=\"2625\" height=\"966\" \/><\/p>\n<p><!--more--><\/p>\n<p>This project aims to provide a reliable open-sourced cubesats tracking following different mechanisms and protocols for transmitting signals that too using very low end receiver i.e Software Defined Radio (SDR) and under limited computation power\u00a0for which no reliable satellite tracking facilities are provided.<\/p>\n<p>As an input it takes output file of sdr (both .dat or .wav) plots waterfall displaying of the signal file showing signal present in different bands of frequency. Further, it applies bandpass filtering to remove undesired frequency and based upon the transmission mechanism used by the transponder on the satellite it follows appropriate tracking pipeline. Right now transponders following Automatic Packet Reporting System (APRS), Beacons or Automatic Picture Transmission (APT) is supported.<\/p>\n<p><span style=\"font-weight: 400;\">A bird&#8217;s eye view is shown in the above flow chart. Our whole pipeline can be sub-divided into 4 major units:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">SDR Recording Import<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Waterfall Diagram<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Formulating frequency bands &amp; complex bandpass filtering<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Signal Detection<\/span><\/li>\n<\/ol>\n<h3><span style=\"font-weight: 400;\">I) SDR Recording Import<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Our project supports SDR IQ signal recording in both file formats (.dat) as well as (.wav). File import can be done in two ways<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\">\n<h4><b>Single File Analysis<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">Single file is select for signal detection.<img decoding=\"async\" loading=\"lazy\" class=\"size-full wp-image-424 aligncenter\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/ImportFile.png\" alt=\"\" width=\"397\" height=\"323\" \/><\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li>\n<h4><b>Folder Import<\/b><\/h4>\n<\/li>\n<\/ul>\n<p>This is the server version of this project. To import metadata with file JSON file with the same name as file is required. Any new file added to the folder is also imported and analyzed automatically. Below image shows how to select<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"size-full wp-image-425 aligncenter\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/SelectFolder.png\" alt=\"\" width=\"396\" height=\"325\" \/><\/p>\n<h3><span style=\"font-weight: 400;\">II) Waterfall Diagram<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">For each signal file analyzed a waterfall image is saved which shows visually presence of signals in different frequency bands as depicted in images below.<\/span><\/p>\n<figure id=\"attachment_426\" aria-describedby=\"caption-attachment-426\" style=\"width: 585px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-426\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/iss_zoom.png\" alt=\"\" width=\"585\" height=\"335\" \/><figcaption id=\"caption-attachment-426\" class=\"wp-caption-text\">A1) ISS Signal Waterfall (Zoomed Spectrum)<\/figcaption><\/figure>\n<figure id=\"attachment_429\" aria-describedby=\"caption-attachment-429\" style=\"width: 585px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-429\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/noaa_zoom.png\" alt=\"\" width=\"585\" height=\"333\" \/><figcaption id=\"caption-attachment-429\" class=\"wp-caption-text\">B1) NOAA Signal Waterfall (Zoomed Spectrum)<\/figcaption><\/figure>\n<figure id=\"attachment_437\" aria-describedby=\"caption-attachment-437\" style=\"width: 585px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-437\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/funcube_zoom.png\" alt=\"\" width=\"585\" height=\"331\" \/><figcaption id=\"caption-attachment-437\" class=\"wp-caption-text\">C1) Funcube Signal Waterfall (Zoomed Spectrum)<\/figcaption><\/figure>\n<p>In the plots A1 one can see transmission made by ISS to it\u2019s ground centre with time region of no transmission. In plot B1 we can see the presence of NOAA-15 frequency with doppler shifting. In plot C1 beacon type signal can be observed at small time intervals.<\/p>\n<h3><span style=\"font-weight: 400;\">III) Formed frequency bands &amp; complex bandpass filtering<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">After importing signal file along with the required meta-data, the program is used in JSON format, does doppler correction and selects those satellites whose frequencies can be present in the signal file. Based on the frequency bands of satellite complex bandpass filter for each band is constructed.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">IV) Signal Detection<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">we calculate threshold values at each cell based on values from leading &amp; lagging cells with some guard cells in between to prevent mixing of signal. Summation of both parts are calculated and sent to arithmetic unit which does further analysis. Then the output is multiplied by a constant &amp; the logical unit detects the presence of signal. Mathematical expression used for this is,<\/span><\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"size-full wp-image-442 aligncenter\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/CFAR_Equation.png\" alt=\"\" width=\"447\" height=\"148\" \/><\/p>\n<figure id=\"attachment_443\" aria-describedby=\"caption-attachment-443\" style=\"width: 644px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-443 size-full\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/CFAR.png\" alt=\"\" width=\"644\" height=\"376\" \/><figcaption id=\"caption-attachment-443\" class=\"wp-caption-text\">Overview of Signal Detection<\/figcaption><\/figure>\n<p><span style=\"font-weight: 400;\">Using the above technique we found the presence of APRS signal &amp; beacon signal in file. Only difference lying in Arithmetic unit is explained below:<\/span><\/p>\n<ul>\n<li><b>Automatic Packet Reporting System(APRS):\u00a0<\/b>We subtracted summation of leading cells from lagging cells &amp; looked for unusual sharp minima &amp; based on that detector confirms presence of APRS signals. Below graphs shows performance of our model in recordings having low as well as high noise levels.<b><br \/>\n<\/b><\/p>\n<figure id=\"attachment_444\" aria-describedby=\"caption-attachment-444\" style=\"width: 537px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" class=\"size-full wp-image-444\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/d6_iss_4.png\" alt=\"\" width=\"537\" height=\"398\" \/><figcaption id=\"caption-attachment-444\" class=\"wp-caption-text\">APRS signal in low SNR<\/figcaption><\/figure>\n<p><figure id=\"attachment_445\" aria-describedby=\"caption-attachment-445\" style=\"width: 544px\" class=\"wp-caption alignnone\"><img decoding=\"async\" loading=\"lazy\" class=\"size-full wp-image-445\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/508_iss_52.png\" alt=\"\" width=\"544\" height=\"397\" \/><figcaption id=\"caption-attachment-445\" class=\"wp-caption-text\">APRS signal in high SNR<\/figcaption><\/figure><\/li>\n<\/ul>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><strong>Beacon:\u00a0<\/strong><span style=\"font-weight: 400;\">We took mean of leading cells and lagging cells &amp; looked for points crossing the threshold &amp; based on that detector confirms presence of beacon signals. Below graphs shows performance of our model around peaks surrounded by low as well as high noise.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<figure id=\"attachment_447\" aria-describedby=\"caption-attachment-447\" style=\"width: 536px\" class=\"wp-caption alignnone\"><img decoding=\"async\" loading=\"lazy\" class=\"size-full wp-image-447\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/funcube_0.png\" alt=\"\" width=\"536\" height=\"398\" \/><figcaption id=\"caption-attachment-447\" class=\"wp-caption-text\">Beacon Signal in low noise<\/figcaption><\/figure>\n<figure id=\"attachment_448\" aria-describedby=\"caption-attachment-448\" style=\"width: 532px\" class=\"wp-caption alignnone\"><img decoding=\"async\" loading=\"lazy\" class=\"size-full wp-image-448\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/funcube_21.png\" alt=\"\" width=\"532\" height=\"398\" \/><figcaption id=\"caption-attachment-448\" class=\"wp-caption-text\">Beacon Signal in higher noise<\/figcaption><\/figure>\n<figure id=\"attachment_449\" aria-describedby=\"caption-attachment-449\" style=\"width: 532px\" class=\"wp-caption alignnone\"><img decoding=\"async\" loading=\"lazy\" class=\"size-full wp-image-449\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/funcube_45.png\" alt=\"\" width=\"532\" height=\"395\" \/><figcaption id=\"caption-attachment-449\" class=\"wp-caption-text\">Beacon Signal in higher noise<\/figcaption><\/figure>\n<ul>\n<li><b>Automatic Picture Transmission(APT) <\/b><span style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">signals mostly used by NOAA satellites. These signal transmission is not discrete instead it is continuously transmitted by the satellites and whenever ground station is in range of satellite it can receive the signal. The first graph shows frequency band in which APT signals are present while the second\u00a0one contains absence of APT signal.<\/span><\/span><\/li>\n<\/ul>\n<figure id=\"attachment_461\" aria-describedby=\"caption-attachment-461\" style=\"width: 585px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-461\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/noaa_present1.png\" alt=\"\" width=\"585\" height=\"443\" \/><figcaption id=\"caption-attachment-461\" class=\"wp-caption-text\">Frequency Band with presence of APT signals<\/figcaption><\/figure>\n<figure id=\"attachment_465\" aria-describedby=\"caption-attachment-465\" style=\"width: 585px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-465\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/noaa_notpresent.png\" alt=\"\" width=\"585\" height=\"429\" \/><figcaption id=\"caption-attachment-465\" class=\"wp-caption-text\">Frequency Band without APT signal<\/figcaption><\/figure>\n<p><span style=\"font-weight: 400;\">We can conclude that presence of APT signal due to continuous nature increases mean and median of the signal when taken in short time intervals as well as full signal file. Also standard deviation in the presence as well as absence of APT signals is small as in both cases deviation from mean is quite low. But standard deviation is almost comparable to mean in absence of APT signal as mean is itself small. Hence we calculated values for small time intervals of signal using formula below<\/span><\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"size-full wp-image-452 aligncenter\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/APT_formula.png\" alt=\"\" width=\"254\" height=\"37\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Where threshold was calculated by,<\/span><\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"size-full wp-image-453 aligncenter\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/APT_formula2.png\" alt=\"\" width=\"276\" height=\"19\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Then around this threshold based upon distribution of points it is classified whether APT signals are present or not.<\/span><\/p>\n<h2>Outputs<\/h2>\n<p><span style=\"font-weight: 400;\">Below are the screenshots of output on terminal of Funcube, iss &amp; NOAA respectively. Only that frequency band is include in which presence of signal is detected.<\/span><\/p>\n<figure id=\"attachment_454\" aria-describedby=\"caption-attachment-454\" style=\"width: 585px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-454\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/Output_ISS.png\" alt=\"\" width=\"585\" height=\"75\" \/><figcaption id=\"caption-attachment-454\" class=\"wp-caption-text\">Detection of ISS signals<\/figcaption><\/figure>\n<figure id=\"attachment_455\" aria-describedby=\"caption-attachment-455\" style=\"width: 585px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-455\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/Output_Funcube.png\" alt=\"\" width=\"585\" height=\"104\" \/><figcaption id=\"caption-attachment-455\" class=\"wp-caption-text\">Detection of Funcube signals<\/figcaption><\/figure>\n<figure id=\"attachment_456\" aria-describedby=\"caption-attachment-456\" style=\"width: 585px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-456\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/Output_NOAA.png\" alt=\"\" width=\"585\" height=\"79\" \/><figcaption id=\"caption-attachment-456\" class=\"wp-caption-text\">Detection of NOAA signals<\/figcaption><\/figure>\n<h2>Future Work<\/h2>\n<p><span style=\"font-weight: 400;\">This project can be taken further ahead by usage of normalized cross-correlation of signals captured using different SDR\u2019s. We tried this approach but the major problem we faced was colored gaussian noise instead of expected white gaussian noise. Also clocks of all SDR\u2019s need to be synchronized so that we will perform correlation without using time lagging signals.<\/span><\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-457\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/Normalized-Cross-Correlation.png\" alt=\"\" width=\"585\" height=\"301\" \/><\/p>\n<p><span style=\"font-weight: 400;\">The above graph is obtained after doing normalized cross correlation on APRS signal &amp; the points we got after all where lying in between zones where signal was present but due to presence of colored noise as well as asynchronous clocks it was difficult to find point of start of signal.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">Another approach which can be used for the preamble of preamble. Following graph shows the result of normalized cross-correlation search result &amp; zoomed.<\/span><\/p>\n<figure id=\"attachment_458\" aria-describedby=\"caption-attachment-458\" style=\"width: 585px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-458\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/Correlation_Signal.png\" alt=\"\" width=\"585\" height=\"309\" \/><figcaption id=\"caption-attachment-458\" class=\"wp-caption-text\">Time domain plot of signals<\/figcaption><\/figure>\n<figure id=\"attachment_459\" aria-describedby=\"caption-attachment-459\" style=\"width: 585px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-459\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/Correlation_Value.png\" alt=\"\" width=\"585\" height=\"297\" \/><figcaption id=\"caption-attachment-459\" class=\"wp-caption-text\">Normalized cross-correlation with known preamble<\/figcaption><\/figure>\n<figure id=\"attachment_460\" aria-describedby=\"caption-attachment-460\" style=\"width: 585px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-460\" src=\"https:\/\/aerospaceresearch.net\/wp-content\/uploads\/2017\/08\/Correlation_Result.png\" alt=\"\" width=\"585\" height=\"299\" \/><figcaption id=\"caption-attachment-460\" class=\"wp-caption-text\">Start of signal detected<\/figcaption><\/figure>\n<h2><span style=\"font-weight: 400;\">Acknowledgment<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">At the end I would like to thank <\/span><a href=\"http:\/\/aerospaceresearch.net\/\"><span style=\"font-weight: 400;\">Aerospace Research<\/span><\/a><span style=\"font-weight: 400;\"> for giving me opportunity to work with them in <\/span><a href=\"https:\/\/summerofcode.withgoogle.com\/\"><span style=\"font-weight: 400;\">Google Summer of Code 2017<\/span><\/a><span style=\"font-weight: 400;\"> and I would also like to thank <\/span><a href=\"https:\/\/github.com\/hornig\"><span style=\"font-weight: 400;\">Andreas Hornig<\/span><\/a><span style=\"font-weight: 400;\"> for being the mentor of this project &amp; Extending his help whenever needed.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Links<\/span><\/h2>\n<ul>\n<li style=\"font-weight: 400;\"><a href=\"https:\/\/goo.gl\/xLXGsH\">Project Detailed Explanation<\/a><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\"><a href=\"https:\/\/github.com\/aerospaceresearch\/SiqNAL\">GitHub Repo<\/a><\/span><\/li>\n<li style=\"font-weight: 400;\"><a href=\"https:\/\/github.com\/aerospaceresearch\/SiqNAL\/commits\/master?author=jay-krishna\"><span style=\"font-weight: 400;\">GitHub Master Branch commits<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\"><a href=\"https:\/\/goo.gl\/8dddPf\"><span style=\"font-weight: 400;\">Project Documentation<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\"><a href=\"https:\/\/goo.gl\/9a76To\">Project Demonstration<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Introduction CubeSats is a class of nanosatellites that uses a standard size and form factor. The standard CubeSat size is one unit \u00a0or 1U \u00a0measuring 10x10x10 cms and is extendable to larger sizes; 1.5, 2, 3, 6, and even 12U. Originally developed in 1999 by California Polytechnic State University of San Luis Obispo (Cal Poly) &hellip; <a href=\"https:\/\/aerospaceresearch.net\/?p=422\" class=\"more-link\"><span class=\"screen-reader-text\">\u201e[GSOC2017] SiqNAL: CubeSat tracking using IQ stream data\u201c<\/span> weiterlesen<\/a><\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/aerospaceresearch.net\/index.php?rest_route=\/wp\/v2\/posts\/422"}],"collection":[{"href":"https:\/\/aerospaceresearch.net\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aerospaceresearch.net\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aerospaceresearch.net\/index.php?rest_route=\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/aerospaceresearch.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=422"}],"version-history":[{"count":12,"href":"https:\/\/aerospaceresearch.net\/index.php?rest_route=\/wp\/v2\/posts\/422\/revisions"}],"predecessor-version":[{"id":574,"href":"https:\/\/aerospaceresearch.net\/index.php?rest_route=\/wp\/v2\/posts\/422\/revisions\/574"}],"wp:attachment":[{"href":"https:\/\/aerospaceresearch.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=422"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aerospaceresearch.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=422"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aerospaceresearch.net\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=422"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}