[GSoC’19|VisMa|Mayank] Week 1 to 3

This blog post is intended to pen down my experience of working with aerospaceresearch.net and on VisMa – Visual Math as a part of Google Summer of Code 2019. A part of this blog post will be about how it feels to be working here & other parts will focus on technicalities of the project (about how VisMa has been improved over the past three weeks).

Firstly, working with aerospaceresearch.net has been a great experience. The mentors of the project Shantanu, Manfred & Siddharth are very helpful. We almost regularly have chat on Zulip (& sometimes on WhatsApp) wherein we discuss the plans for the project and about implementation. Owing to these healthy discussions the project development is going at a fast rate (which is indeed a good thing!). The code base of VisMa is very well written and the complex task of simplifying mathematical equations has been beautifully coded. About the workload, I work regularly for 5 – 6 hours, but sometimes when a crucial bug finds its way in, the work time has to be increased. Overall it’s a Fun & Learn experience.

Now let’s talk about what we (the VisMa Team) have achieved so far. During the community bonding period, I spend my time to get equipped with basics of PyQt (on which the GUI of VisMa is based) and spend the remaining time studying the tokenizing and simplify modules. Honestly, these modules were hard to understand. But the effort turned fruitful during the coding period. Also, I completed the task of simultaneous equation solver during this time. The logic was easy to frame, but the inclusion of comments and animations was tricky and involved the entire redesigning of the LaTex generator module (the part which created the final latex to be displayed in GUI).

Over to the first week of the coding period, the task which was decided was to re-design all the simplify module in an object-oriented fashion, which before coding period I believed would be most difficult to write (after all more than 2000 lines of complex Pythonic logic, to be honest, was scary). But, efforts to understand module during the Community Bonding Period turned out to be fruitful, and I was happy to achieve this task in the desired time. Like any other programming project, there are some minor bugs out there, which will be fixed in the upcoming time!

Coming to week second, we decided to work on Expression Simplification. Now, what expression is, briefly (& vaguely) any part of inputted equation enclosed in brackets can be termed as an Expression. So like when we solve an equation by hand, we solve the bracket part first, our task was to make visma do same! Earlier it didn’t support Expression simplification. For that, we decided to go for a complex recursive logic (solving the inner expression first then the outer expressions & so on). Recursive logic was complex & time consuming to implement, taking care of all comments & animations during recursion was a challenge. It took somewhat 9 days to come up with something fruitful. But yes it was worth it, VisMa could easily solve Expressions now. A happy moment indeed! 🙂

Next phase was to include a new module to VisMa. It was decided it has to be a Discrete Math module (‚cause why the heck not!). We decided to go for Combinatorics stuff first, thus implementing Combinations & Permutation stuff. These modules require factorial of a number, I got this idea why not to implement factorial as a separate module and then use in Permutations & Combinations stuff. This added another factorial feature to VisMa.

For now, I am working of adding triple degree equation solvers to project. We as of now have decided to do so using Cardano Algorithm. If this turns out well it will also be done for four-degree equations. After this will be done, VisMa will be capable of solving & showing detailed steps for up to 4-degree equations.

Below is a short GIF showing some of new features in action:

Hope you enjoyed reading Chapter One of my Developer Diary. From now on, I will write these blogs bi-weekly.

Chears to Code, Space & Maths!

[GSoC2019|PlaneSpotting|Shoumik] Introduction: Self – Synchronisation

Hi there!

My name is Shoumik Dey. I am currently a second year student at Manipal Institute of Technology, India. I preserve strong interest in aviation, aeromodelling and maybe this is the reason that I have chosen to work on this project, this summer, for Google Summer of Code 2019.

You can also find me here:

This is the first blog post for this project. This post will describe the work done so far, the current outcomes, hurdles faced and also they were/can be be solved.

Introduction

‚ADS-B‘ stands for Automatic Dependent Surveillance–Broadcast. The aircraft automatically brodcasts each frame on the 1090MHz frequency periodically which contains navigational data, such as, location, altitude, airspeed etc.

All ADS-B frames do not contain location information in them, therefore the ultimate goal of this project can be described in two parts:

  • Self-syncronisation of the ground stations in focus without an external agent.
  • Determining the location of the aircraft when location data is not received using multi-lateral positioning

Self-synchronisation

The current and widely used method of synchronising the clock of any ground based station is by using GNSS(Global Navigation Satellite System)-receiver interrogation. The local clocks gets aligned with the atomic clocks in the satellite.

But in this case, the synchronisation takes place by calculating the actual point of time when an aircraft broadcasts a message. This serves as the reference time at all ground stations, and since this time value is same all over, hence all the stations become self-synchronised.

Data required for self synchronisation:

  • Time of arrival of the message at the ground station
  • Time difference of arrival at that station.

Calculating the time difference of arrival(TDOA):

  1. The receiver provides the time at which the frame(with location) is received.
  2. The time of travel of the frame is calculated by using simple math and that is subtracted from the time of arrival.
sqrt((x2-x1)^2 + (y2 - y1)^2 + (z2 - z1)^2)

where x1, y1, z1: Latitude, longitude and altitude of ground station; x2, y2, z2: Latitude, longitude and altitude reported by aircraft.

Data that we have for self-synchronisation:

We have decided on using dump1090 for generating the ADS-B messages from the IQ stream as recorded by the SDR radio. Dump1090 provides the mlat(multilateration) data in avr format. The first 6 bytes of the message provides the sample position of the last bit of that message.

Example:

Raw: @0000000929E28e3ff6e6990c4684000011548194;
Sample Position: 0000000929E2
Message: 8e3ff6e6990c4684000011548194

Next, we shift the sample position record from the last bit of the message to the first bit.

Typical recording of a 112 bit ADS-B message
Typical recording of a 56 bit ADS-B message

As we can see, the length of the messages is exactly half of each other.

The sample position reported by dump1090 is exactly 240 samples ahead of the preamble (You can see the preamble at the beginning, by 4 spikes in the signal).

So, the sample position is taken back by 240 samples for both the cases to reach the beginning of the preamble.

Now, data in hand:

  • Start time of the stream
  • Sample position of the beginning of the message in the stream.

Therefore the time of arrival(TOA) can thus be calcualted.

Work done so far

  • A plugin to import the mlat output of dump1090 (ADS-B raw) has been created, which imports the message and the sample position into the main program
  • The raw data is reorganised in our suitable format and terms.
  • With each frame present;( for 112 bit frames as of now)
    • Determination of the type of frame and it’s content
    • Decoding those data present in that frame
  • Output dump file which contains the frame and the data contained in it. (Only extraction, no calcualation)

Next up….

The next blog post will contain the implementation of the calculation layer of the project. In this layer, the data in the frames will be processed and information such as position, velocity, altitude will be covered.

The post will also contain the unusual findings and irregularities that were found in the stream and how we have decided to deal with it.

[GSoC2019|DirectDemod|Vladyslav M] BigImage and BigGlobe.

This blog post will cover the process of visualization of NOAA images, specifically we will talk about general visualization pipeline and different ways to visualize satellite imagery, including creating interactive web maps and virtual globes. 

Visualization pipeline

We shall start from the very beginning. NOAA satellites constantly scan the Earth’s surface and then transmit it as a radio wave signal. This signal is captured by SDR (Software-defined radio system) and saved in wav format. Next step is dedicated to decoding this signal, i. e. finding sync signals, extracting and decoding an image line-by-line.

The decoded image includes both sync signals and 2 variants of the scanned image (in different color schemes), therefore it requires one more preprocessing step, where it is being cropped and rotated. Next two images are the results of this step, they will be used as an example for visualization.

Figure 1. Example of decoded image.

Figure 2. Example of decoded image.

Next step is georeferencing, which has already been described in previous post [1]. On this step, the coordinate system will be assigned to the image, every pixel will have a corresponding geographic coordinates. Main computation is done with gdal library.

Merging images

The goal of the project is to primarily create tools, that could visualize a set of satellite images. It means that we must find a way to combine several images into one standalone raster, which we could then display as a web map or as a virtual globe. 

Using tools from image analysis (image stitching) would be a bad approach, because of two reasons:

  • these methods are approximate in their’s nature
  • do not take in account image distortions

A better approach would be to use pixel’s coordinates. We know the coordinates of each pixel, thus we could tell exactly where it will be located on the new image. For overlapping areas, for each pixel we will have one of 3 cases:

  • no pixels mapped to this pixel
  • 1 pixel is mapped is mapped to this pixel
  • several pixels mapped to this pixel

If no pixels have been map to the position, then it will be filled with special no-data value. If there is one pixel, then it’s value will be assigned to the corresponding pixel on new image. If there are several candidates for certain pixel, we will compute new pixel value from them based on some strategy, for example taking max or average. In this example we’ll use averaging for overlapping regions. Below is an example of merging 2 images above, country boundaries added for clarity.

Figure 3. Example of merged satellite images.

As you may see, there is still a contrast present between the images and the overlapping part, the reason for this are different lightning conditions for images. Nevertheless, image quality with this method outperforms other methods, like taking maximum for example.

Creating TMS

On this step our raster will be transformed to yet another representation, which will be used for further visualization.

TMS (Tile Map Service) – is a protocol for serving maps as tiles i. e. splitting the map up into a pyramid of images at multiple zoom levels [2]. See the image to better understand the concept.

Figure 4. Tiles pyramid.

So for each level zoom level a set of tiles is generated, each tile has it’s own resolution and will be displayed at specific position at specific zoom. To generate this set of tiles gdal2tiles.py program is used, it transforms an input raster into a directory tree with tiles [3].

It should be noted that TMS is not the only choice here, another alternative is WMS (Web Map Service) [4]. 

Interactive web-maps

Finally, having TMS tiles computed we could create a web map. To do this we will use leaflet.js, which is a library for web-mapping applications. [5] We will combine two layers, the first one is our tiled raster, which will be loaded from local directories and the second one is Open Street Map layer which will be loaded from OSM site. The code for map is contained in the map.html file and could be opened directly in browser. You can see a live example here https://caballeto.github.io/index.html, or in the iframe below.

Virtual globe

Another way to visualize satellite images is of course to plot them on virtual globe. Once again we could use already computed TMS tiles here. To render the globe we will use webglearth2 library, which is created exactly for this purpose. Similarly to a web map, we will add an OSM layer as a background layer. To open it locally you will have to start an http server, and then open the globe.html via http. Running commands (run from directory where globe.html is stored): 

  • python -m http.server 8000
  • go to browser and connect to localhost:8000/globe.html.

Or you can simply take a look at an online version below, better view at https://caballeto.github.io/globe.html.  

Software

To create presented visualizations for a raster, you can use generate_map.py tool from directdemod package. It will automatically create tiles from your raster and then will generate corresponding map.html and globe.html files.

Conclusion

In this post we’ve looked at different ways and tools for raster visualization, specifically we’ve analysed the visualization pipeline for satellite imagery. Presented types of visualizations are compatible with many different mapping applications (leaflet, openlayers etc.) and could be easily published and used on the Internet. 

References

  1. https://aerospaceresearch.net/?p=1248
  2. https://wiki.openstreetmap.org/wiki/TMS
  3. https://gdal.org/programs/gdal2tiles.html
  4. https://en.wikipedia.org/wiki/Web_Map_Service
  5. https://leafletjs.com

[GSoC2019|DirectDemod|Vladyslav M] Installing DirectDemod. Map overlays.

This post will cover the installation process of DirectDemod package, we’ll also talk on how to create pretty map overlays using the directdemod.georeferencer module.

Installing DirectDemod

Recently, the installation process for DirectDemod has been updated (currently on Vinay_dev and Vladyslav_dev dev branches only). You must have anaconda or miniconda distribution installed. From here do the following:

  1. Clone repository:
    • git clone https://github.com/aerospaceresearch/DirectDemod
  2. Change branch to Vinay_dev
    • git checkout -b Vinay_dev
  3. Create environment
    • conda env create -f environment.yml -n my_env
  4.  Activate the environment
    • conda activate my_env
  5. Add directdemod module to your PYTHONPATH.
  6. Test installation with pytest.

Create fancy map overlays

Before creating map overlays we need to have a georeferenced image, let’s make one. samples/decoded directory contains 2 already decoded and preprocessed NOAA images ready for georeferencing. Create an empty file – SDRSharp_20190521_170204Z_137500000Hz_IQ.wav in samples directory, it will serve as a placeholder; the image was already extracted but the name of SDR file contains valuable information needed for georeferencing. 

To georeference the image, we will first extract information from the name of SDR file. Go to directdemod directory and type the following command.

python misc.py -f ../samples/SDRSharp_20190521_170204Z_137500000Hz_IQ.wav -i ../samples/decoded/SDRSharp_20190521_170204Z_137500000Hz.png

misc.py extracts data about satellite’s orbit and position, and then embeds it in json format into created tif file.

 Now we will use georefencer.py CLI interface to georeference the image and create a map overlay.  -m or –map option tells the georeferencer to overlay the image with map boundaries after georeferencing.

python georeferencer.py -m -i ../samples/decoded/SDRSharp_20190521_152538Z_137500000Hz.tif

Finally, the georeferenced image.

Figure 1. Georeferenced image combined with country boundaries.

Note on shapefiles

A shapefile is a vector data storage format for storing geographical features [1]. In the context of this application, country boundaries are being represented as a shapefile. Inside the shapefile points may be stored as polygons or as polylines, overlaying an image with polylines will have an effect of overlaying country frontiers, while overlaying an image with polygons will have an effect of overlaying country areas. 

Figure 2. Georeferenced image combined with polygons shapefile.

Further work

The next blog post will discuss merging NOAA satellite images, including different techniques for handling overlapping areas cases.

References

  1. https://en.wikipedia.org/wiki/Shapefile

[GSoC2019|DirectDemod|Vladyslav M] Introduction – Georeferencing NOAA images

Hello everyone!

Let me introduce myself, my name is Vladyslav Mokrousov, I am a second year student at software engineering department of Kyiv Polytechnical Institute, Ukraine. This summer I will be working on Google Summer of Code (GSoC) project for AerospaceResearch.net

Please check my github and twitter.

This blog will briefly describe the goal of the project, and then will proceed to describe what has been done so far.

BigImage and BigGlobe Projection

During this summer I will work on creating software for visualization of satellite imagery, mainly images from NOAA satellites. The goal of the project, as my proposal states:

Current software provides possibility for decoding APT signals received by RTL-SDR receivers, but the task of understanding the extracted data is hard, because an open-source programs for proper visualization of these images do not yet exist. This project aims to create an open-source software to target that issue, so more developers and enthusiasts will receive an efficient tool to help them in their research. The software would provide functionality for creating different types of interactive visualizations of satellite weather images, along with their georeferencing.

Particularly, I am working on software, which will combine many satellite images into one big picture (BigImage). The software will handle cases of overlapping images using different merging strategies. Another main task of the software will be to provide accurate image georeferencing [1].

Image georeferencing

Image georeferencing refers to the process of associating image coordinate system with geographical coordinate system, i. e. each pixel of the image will be paired with certain geocoordinates. This will allow combining these images with maps or with each other.

Figure 1. Georeferenced image combined with map.

An example above shows how the georeferenced image can be used for visualization. The process of georeferencing is divided in two parts:

  1. Computing set of Ground Control Points (GCPs).
  2. Fitting the image to match these GCPs.

To compute GCPs we will use the TLE file, which describes the orbit parameters of the satellite. Using pyorbital module we consistently calculate the position coordinates of satellite and match them with relative position of pixels on the image. The 2 part is carried out by gdal [2] library, which is called via it’s Python API.

Performance

The aim for created software is to produce visualizations of big amount of image data, therefore the implementation should be fast and should handle memory expensive cases. To satisfy this requirement gdal library is used. It is an open source software designed for raster and vector data manipulations. gdal is written in C++, that’s why it is extremely fast. Manual memory management ensures the efficiency of memory use. The implementation makes use of gdal Python bindings, which provide interface for using gdal functionality in Python.

Further work

Image georeferencing is the first step required to produce good looking projections of satellite images, especially for creating image overlays. In the next blog we will talk about map overlays and techniques for merging overlapping parts of NOAA images.

References

  1. https://en.wikipedia.org/wiki/Georeferencing
  2. https://gdal.org/

Call for ESA Summer of Code in Space 2019! Be our Summer Student and code your open-source space projects (stipends of up to 4000€)

this summer will purely be a summer of code! We are not only offering you Google Summer of Code (GSOC 2019, still open until April 9th), but also the European Space Agency’s Summer of Code in Space 2019 (ESA SOCIS).

Now it is up to you to decide if you want to code open-source space with us during ESA Summer of Code in space 2019? You should!
Be our Summer Student and code your open-source space projects. You get stipends of up to 4000€. The Call for ESA SOCIS 2019 is now open!

Together, we as AerospaceResearch.net, Ksat-Stuttgart (University of Stuttgart) and EP2Lab (Carlos III University of Madrid) are proud to be selected as official mentoring organizations for the ESA Summer of Code in Space 2019.
And we are now looking for students to spend their summers coding on great open-source space software, getting paid by ESA, releasing scientific papers about their projects and supporting the open-source space community by useful programmes.
Are you a student? You have time until May 4, 2019, to apply for various coding ideas to work on, and be part of out team!

More information and registration here.

ESA Summer of Code in Space

ESA Summer of Code in Space (SOCIS) is a program run by the European Space Agency that focuses on bringing student developers into open source software development for space applications. Students work with a mentoring organization on a 3 month programming project during their summer break.

Through SOCIS, accepted student applicants are paired with a mentor or mentors from the participating organizations and with experts from ESA (where available), thus gaining exposure to open source software development and insights from ESA. In turn, the participating organizations are able to prototype new open source projects and possibly bring in new developers to work on relevant topics for space.

SOCIS is inspired by (but not affiliated or related in any way to) Google’s Summer of Code initiative, and is designed with the following objectives in mind:

  • raise the awareness of open source projects related to space, especially among students;
  • raise awareness of ESA within the open source community;
  • improve existing space-related open source software.

For further information, please chat with us on https://aerospaceresearch.zulipchat.com

Call for Google Summer of Code 2019! Be our Summer Student and code your open-source space projects (stipends of up to $6600)

Again for the 5th time, AerospaceResearch.net[0] is proud to be selected as an official mentoring organization for the Summer of Code 2019 (GSOC) program run by Google[1].
And we are now looking for students to spend their summers coding on great open-source space software, getting paid up to 6600 USD by Google, releasing scientific papers about their projects and supporting the open-source space community.

Until 9. April 2019, students can apply for an hands on experience with applied space programs. As an umbrella organisation, AerospaceResearch.net, KSat-Stuttgart e.V. and ep2lab of Carlos III University of Madrid are offering you various coding ideas[2] to work on:

  • The Distributed Ground Station Network – global tracking and communication with small-satellites[2][4]
  • KSat-Stuttgart – the small satellite society at the Institute of Space Systems / University of Stuttgart[2]
  • ep2lab of Carlos III University of Madrid[2]
  • or your very own proposal![2]
„Call for Google Summer of Code 2019! Be our Summer Student and code your open-source space projects (stipends of up to $6600)“ weiterlesen

We have a ZulipChat now. Join us there!

During the Google Summer of Code 2018 Mentors Summit, many projects used the open-source Zulip Chat for discussing their projects and planning their next steps.

It is a fine little chat in the spirit of Slack, but fully open-source.

We started using it for our projects and for our general discussions. So if you are looking for us, want to onboard the projects or have an idea to propose, we welcome you to join our ZulipChat channel. We are looking to meet you on

AerospaceResearch.ZulipChat.com

Space Lightning Talks! Start of the radion workshop week “FUNKWOCHE”

Our event in Jena will be special because it will be the first and currently only location that is a „Makerspace“. Makerspaces are places were people can work on creative solutions for global or private problems. And this kind of free „maker“ spirit is what will also help solve problems in space exploration. So you have the unique chance to join other creative people during the #SpaceTalks and also to take part in a full week of workshops. Because #SpaceTalks will initiate the FunkWoche during which you can come here and work on everything related to communications, be it radio frequencies, lasers, amateur radio,…. and also satellite communications. So we will bring #SpaceTalks to Jena under this flag of „FunkWoche 2018/2 and ESA’s European Spacetalks – space and even more radio! / FunkWoche 2018/2 und ESA’s European Spacetalks – Raumfahrt und noch mehr Funk“.

The event will take place at the Jena Hackspace, will be hybrid with Lightning Talks (short 15-20 minute talks of you!) for #SpaceTalks and as the starting event for the hamradio workshop week FunkWoche – and be on Monday, 19 November 2018, 2000hrs to 2130hrs. Talks and discussions may be in German or in English. Everyone is welcome.

Lightning Talks:

  • Intro: Satelliten Communication – what flies where and what can I do with it, and why to uwe and my build an antenna rotator?! (Working Title), Andreas
  • Status of the Distributed Ground Station Network for Tracking Cubesats, Andreas
  • (TBC) GroundStations, but safe/secure!, lowl3v3l
  • Your Space Topic, YOU!

Due to limited places, please register here or on meetup and RVSP this in your calendars! Further details tbc in the next weeks. We are inviting you to give a lightning talk. Just send us your title at contact@aerospaceresearch.net.

„Space Lightning Talks! Start of the radion workshop week “FUNKWOCHE”“ weiterlesen

FunkWoche 2018/2 und ESA’s European Spacetalks – Raumfahrt und noch mehr Funk (19.-24.11.2018 ab 19:00 Uhr, im Hackspace Jena e.V.)

Es ist wieder Zeit sich mit Funk zu beschäftigen und dieses mal mit mehr Raumfahrt.
Daher rufen wir nun die „FunkWoche 2018/2 und ESA’s European Spacetalks – Raumfahrt und noch mehr Funk“ aus.
Das Konzept ist wie letztes mal, dass wir jeden Tag in dieser Woche an Funkthemen arbeiten, reden, diskutieren, auslegen, oder anderweitig kreativ umsetzen werden. Und mit „wir“ meinen wir auch euch!. Mögliche Themen sammeln wir bereits hier[0] und Uwe und ich werden eine Motorsteuerung aus Aliexpressteilen bauen, mit der wir Satelliten verfolgen und damit kommunizieren wollen. Wir laden euch ein dabei mitzumachen!
Dazu passend werden wir ESA’s European Spacetalks[0] nach Jena holen und kurze Vorträge über Raumfahrt halten.
Spacetalks sind so: „We are all concerned by space activities because they make a difference to our lives on a daily basis and correspond to one of humanity’s greatest challenges. In November 2018, members of the European Space family will be sharing their passion in a series of talks presenting a vast array of space-related topics.“Zur zeit sind es Europaweit 170 Talks, und wir sind dabei. Ihr seht also, jeder kann, soll und darf da mitmachen!

„FunkWoche 2018/2 und ESA’s European Spacetalks – Raumfahrt und noch mehr Funk (19.-24.11.2018 ab 19:00 Uhr, im Hackspace Jena e.V.)“ weiterlesen