AlphaRadio

https://github.com/DartiLila/Ionomap

https://drive.google.com/file/d/1oYh3-1Mp32Qu1bzcBTQMf6GrfBfhfZjt/view?usp=sharing

IonoMap, is a visualization data-analysis map, that tracks the density of electrons in the Ionosphere in accordance with an electron measurement ratio we created, which our team will call the AlphaRadio Electron Ratio or AER. AER can be calculated as the following:

 AER = | 1 - (D / AERc) | * 100%

where D would be the electron density in m^-3 that can be measured in Plasma Probing Frequencies (see 1.6, 2. in references for more details) and AERc or the AER constant is the reference value 9*10^12 m^-3, the choosing of which can be explained on our hackathon journey. 

The main map was created using the Basemap and Matplotlib Library of Python, which is connected to a database that can make the data* display possible over a world map. The data was gathered from the NASA tool Panopoly to be visualized by our program's database. 

The connection to the website was created with the DASH Library. 

Upon entering the website, the user can see the average AER on a specific region for all time. (Upcoming features will be able to filter by region and time range) They can also see some pivotal point locations of the major partnering space stations on the map.

According to the map legend, different colors will display a wide range of percentages over Earth. The larger the percentage, the denser on electrons the area. By analyzing our intuitive map, each user will have an understanding of the densities around the globe.

However, our main goal was to help the International Space Station predict the best spot for transmitting Amateur Radio Waves (AM Frequencies) for communication. 

In future features, the map will be able to display coordinates of less and more dense areas, so it will be easier to extract relevant information so radio centers and the ISS can use this data more easily. 

The usage of AM frequencies is very important due to power consumption preservation. In contrast to satellite frequencies or FM, AM frequencies require less power and are more nature-friendly, and can promote a sustainable future!**

 AM frequencies have a hard time passing through the dense ionosphere, as the wave can refract on the layer. Locating less dense areas increases our chances of AM waves passing through the ionosphere and back to Earth.

Our map aspires to be an essential tool for navigating inter atmosphere communication. It creates a solution for better ham radio broadcasts, and solves the “Calling all radio enthusiasts!” challenge on the NASA Space Apps Hackathon!

*Note: Due to time constraints and non-granted access, the data shown on the demo may not be of the highest accuracy (More on Hackathon Journey and Space Agency Data).

**Environmental issues of FM (Nepal Study)

One of the first problems we faced was how the electrons in the Ionosphere were measured, and what did it mean for our data collection. We looked through the NASA Technical Reports Server (https://ntrs.nasa.gov), and found this overview of an article.

This was the first usage of the NASA data, the next being this document, showing techniques and real-life data of the electron density of the ionosphere layer. 

Lastly, the raw data portrayed and connected to the database for visualization purposes on the prototype was accessed through the help of the Panopoly tool by NASA.

Our Hackathon Journey began around late September 2022. Upon finding out about this competition we decided to join right away, as all of us members are friends and we share an interest in Science and Technology. 

On October first, we gathered on a Discord call, to start the challenge! Upon opening the Challenges tab we clicked on the “Calling all radio enthusiasts!” topic without thinking much, as we are Electronics students in a technical high school and we definitely have a knack for frequency waves! :D

After reading through the description, and doing some ‘google-ing’ on terms we didn’t understand, we decided to create an interactive map in Python to show the density of the ionosphere. 

Unfortunately, this was only the easy part, as neither of us understood how the ionosphere could be measured, upon being confused as to what data should be displayed on the map to make it understandable to anyone viewing.

Due to time constraints, we separated into two: 

• Darti Lila would set up a Python Map using the Basemap library.
• Martina Karalliu would roam through databases to try and find the best way to portray the ionosphere density in data, while also completing documentation (~Hello!)

The first useful information we found was a NASA Technical Report that showed Plasma Frequency Probing could be used to measure electrons on the Ionosphere (see: 2.3 in references). Although we didn’t have access to the full document, it gave us an inside of what we should search up next, plasma probing and results (see: 2. and 1.6 in references). 

One particular document was the 19660017366 in the NASA Technical Report files or nr. 2.2 in references. Particular tables on pages nr. 39 and 50, showed: nr. of electron density in the measurement m^-3. So we decided to use this as one of the key points in the data we would display on the map.

However, with such high values, we decided to compare these values to a constant and create a ratio to display percentages on the map. Upon researching average values to compare them to the ones on the tables, Martina stumbled upon a physics problem that stated that the Average Ionosphere electron density was 9*10^12 m^-3 (see: 3 in references). 

Funnily enough, we decided to use this value as our much-needed constant, creating AER (AlphaRadio Electron Ratio), which can be found below as:

AER = | 1 - (D / AERc) | * 100%

D - electron Data

AERc - constant: 9*10^12 m^-3

Let's start with the application. Darti started researching the best libraries to use for data visualization and analyses in python. He found out about two useful modules, Basemap and Matplotlib. With Basemap he was able to visualize the data in a map of our planet while with Matplotlib he was able to compute the data.

The data used in this project comes from https://water.weather.gov/ahps/, where precipitation data was used due to the lack of public data about the ions in the ionosphere(the data was also used to have a better visualization because it is similar to that of ion density). The raw data was accessed through the NASA tool Panopoly and it was visualized by Basemap.

Upon coding the map and exporting it into the Python library Dash to create a website, Darti attached a live map that changes based on data input to the program and the rest is history!

1) Ionosphere research:

1.1) The Ionosphere

1.2)Understanding the Ionosphere (spread)

1.3) NASA Heliophysics

1.4) Digital Ionospheric Sounding System

1.5) MIT Lecture on Ionosphere probing

1.6) Ionosphere electrons

2) Plasma Probing:

2.1)Plasma Probe Lecture

2.2) Probing Techniques

2.3)Measuring Ionospheric Density Through Plasma Frequency Probe (Overview)

3)Homework in Ionosphere electrons

#ionosphere #physics #ions #software #python #FM #AM #radio #ISS #mapping #Electrons #matplotlib #basemap