https://mrcne.github.io/space-radio-foti/

https://clipchamp.com/watch/NEzQZob0tzY

What exactly does it do?

It provides a web UI for viewing ionospheric electron density as described by different datasets and methodologies.

How does it work?

The diagram below provides an overview of the system.

What benefits does it have?

The properties of the ionosphere at any given time and point are dictated by a number of factors, the most significant being solar activity. The resulting variable states of the region of our upper atmosphere can have some truly remarkable effects on radio signals within the HF portion of the spectrum, typically 3-30MHz.  

When conditions are right, the highest part of the ionosphere known as the F Layer can refract radio signals back down to earth, enabling them to propagate great distances beyond what would otherwise be possible. Radio HAM's/Amateurs use this phenomenon to their advantage to make long distance contacts often up to thousands of kilometers, using the ionosphere as what you might call “natural satellite”.

Although this “party-trick” of our atmosphere is a magnificent tool for HAM's (worth noting that it's also still used by some companies as a method of data transmission), it's equally as unpredictable as it's ever changing.

This segment of our project aims to harness the rich databases of the HAM radio reporting networks, in order to produce mapping layers depicting current HF propagation conditions.

There are several such databases open to public queries; namely the WSPR network, PSK Reporter and reverse beacon network.

Due to the time constraints associated with this challenge we opted to focus on just one of these platforms. The WSPR Network (Weak Signal Propagation Reporter).

We know that HF communication links, particularly long distance ones, interact with the ionosphere in some capacity, and so it's safe to say that global data from these links will help us understand a little about the state of the ionosphere.

Our concept is to query the WSPR database, download a set of station links recorded over a period of time, and process them before forwarding onto a mapping server.

In order to better understand the WSPR network we set up a live WSPR station at our team base, as can be seen below.

Our original idea (see sketch below) was to take any given link aka “spot”, and apply the following process...

• Calculate the natural free space path loss between stations
• Assign a matrix value to the link with the following formula:

Tx power – FSPL + Rx Signal level x 10% of distance

• Identify any paths this link crossed, and the coordinates of the intersect point(s)
• In the event of a path intersect, assign the sum of the path's matrix values at that point.
• Transpose these points to a map with colours representing matrix values (ignoring line of sight links below the radio horizon).

Using this model we had hoped that the resultant heat map would have shown areas of high electron count, and thus the areas of good HF propagation conditions.

In the interest of time due to the complexities associated with this approach we decided to tackle the task from a different angle. (However please note we did complete the data acquisition and processing code for this option). 

The revised idea involves taking any given link aka “spot”, and applying the following process...

• Calculate the geographical mid-point.
• Resolve frequency to a band
• Append this information along with timestamp to a file and ingest to the mapping server.

When we plot these midpoints on the map, we get an indication of common points of signal path, and the assumption is made that these areas are higher in electron count.

Using a rudimentary midpoint between stations does not take into consideration some propagation occurrences such as chordal hop or multiple reflections. However, with a bulk dataset, results show that the output is smoothed to illustrate reliable, historic and live HF propagation conditions. As can be seen below for the 20 and 40 metre bands..

Given more time, we would amalgamate data from other HAM reporter networks such as PSK reporter and the reverse beacon network. This would increase the resolution of our map whilst smoothing out less accurate values.

Code for this segment of the project: HFdata_Matrix.py & HFdata_Midpoint.py

Introduction:

●   International Space Station (ISS) is a multinational collaborative project by NASA (United States), Roscosmos (Russia), JAXA (Japan), ESA (Europe), and CSA (Canada).

●   The core purpose of ISS is to act as a research laboratory in the areas of astrobiology, astronomy, meteorology, physics, and other fields.

●    ISS holds a Floating Potential Measurement Unit (FPMU).

●    FPMU is a collection of 4 probes that are used to measure the ISS floating potential as well as the electron density and temperature of the local plasma environment.

Physical Importance:

“With respect to space weather, the electron density information from the ISS would greatly serve the purpose of better understanding the local morphology of the ionosphere and thereby help in developing space weather prediction algorithms.”

Key Advantage for the ISS:

Top-side Electron Density Profile (from ~400 km to ~450 km)

How to use ISS data from the developed MATLAB program:

1)   Download the datasets from here:

https://spdf.gsfc.nasa.gov/pub/data/international_space_station_iss/sp_fpmu/ 

2)   keep the program in your data directory:

3)   To read the fpmu data which were in CDF format a special MATLAB patch file should be downloaded to your system from the below link:

https://cdf.gsfc.nasa.gov/html/matlab_cdf_patch.html

4)   The CDF files can now be ready to use.

5)   Paste the MATLAB program into your data directory

This program is a part of NASA Space Apps Challenge 2022 developed by “Fellowship of the Ionosphere Team”

6)   Program to run the ISS files fpmu datasets downloaded from SPDF NASA:   Final_ISS_program.m

The github link is given below: 

mrcne/space-radio-foti (github.com)

Sample Outputs from ISS FPMU observations:

Fig.1. Ionospheric Height versus the Electron density plot

Fig.1. depicts the electron density variations in the ionosphere F layer peaking at ~406 km to ~408 km for the day of 1 January 2015. The maximum electron density is noticed to be around 3.4X1012 el/m3. Also, the electron density peak height varies with solar and geomagnetic conditions. There electron density structures depend on the hourly, daily, seasonal, semi-annual, annual and solar cycle variations. So, it suggests that better ionospheric climatology could be understood with the electron density obtained from ISS FPMU probes.  

Fig.2. ISS daily orbital trajectory for the 1 January 2015.

The x-label represents the geographic longitude and the y-axis represents the geographic latitude. The colorbar signifies the electron density variations.

In general, it is well-known that the electron density is higher over the equatorial region because of the Equatorial Ionization Anomaly (EIA). There is a plasma fountain effect (Appleton Anomaly) that happens day-to-day over the Equatorial latitudes because of the EXB drift that transports the plasms from the geomagnetic equator towards the higher latitudes. With respect to altitude, it peaks at 350-400 km and may change based on the background ionospheric conditions. Fig.2. represents such electron density variations over the Equatorial region. Electron density information from the ISS FPMU sensor is successful in understanding the EIA phenomenon in a significant manner. The observation data from ISS could be useful for better prediction of space weather impacts.

Enhancing NeQuickG Model with real world observations

The aim of the “Calling All Radio Enthusiasts” challenge is to make use of observations from ground based HAM network and space based ISS broadcasts. The data ingestion of these both could possibly provide a good solution in the prediction of space weather events priorly. 

In this 21st century, most of the technological systems depend on trans-ionospheric signal propagation. It includes HF and satellite propagation. The Ionosphere acts as a medium for reliable communication. Understanding the nature of the ionosphere is necessary for better signal propagation.

Our proposed solution is as given below.

1. Utilize what we know from the global ionospheric model
2. Improve the model with real-time observations
3. Optimization, using Particle Swarm Optimization algorithm, in the proposed framework that could lead to a space weather forecast approach and better detection for locations with maximum electron density profile.

 Utilize what we know from the global ionospheric model:

1. NeQuickG Model: 

NeQuickG model is a global ionospheric model developed by International Center for Theoretical Physics (ICTP) and University of Gruz as a single frequency model to provide ionospheric corrections for the GNSS user community. Now, the European Space Agency officially approves the NeQuickG model as a signal of service for the Galileo Users. 

The advantage of the NeQuick G is It utilizes the inputs from solar and geomagnetic indices and decides three different coefficients for a day that can better represent the state of the ionosphere. It helps in providing better spatial and temporal resolution.

1. Improve the model with real-time observations:

Ingeneral, 

• Why is it important?

Severe space weather conditions could completely disrupt the communications systems around the globe. Several developed countries have included space weather prediction as crucial tasks in their disaster management registers. So, better understanding of the space weather is necessary to keep our Earth safe from severe geomagnetic storms. Ionosphere is a key layer that can sense the space weather impact through the variations of the electron density. 

As a quote mentions that “When everything fails HAM’s are on their duty”. Now, the HAM broadcasts could be useful even in the case of better space weather forecasts. The amateur networks now serve the purpose of understanding various disturbances (from large-scale to small-scale) happening in the ionosphere. Some of them are the Reverse Beacon Network (RBN), WSPR Netwerk and PSKR Network (correct me here). The multiple reflections from the transmitter-receiver links of these amateur networks are greatly useful to understand the bottom side behavior of the ionosphere. 

International Space Station (ISS) has a floating potential measurement unit (fpmu) which is a combination of four probes that measures the electron density information of the top-side ionosphere from the ISS would greatly serve the purpose of better understanding the local morphology of the ionosphere and thereby help in developing space weather prediction algorithms.”

The observations from HAM broadcasts and ISS concurrently serve to image the electron density of the ionosphere from top-to-bottom. This could solve many challenges currently open in the scientific research community. 

Some of them are listed below.

1. Understanding the multi-scale ionospheric irregularities and their prediction
2. Developing reliable metrics in understanding the space weather impacts
3. Enhancing the accuracy of the HF propagation channels
4. Imaging the Three-dimensional Ionosphere
5. Estimation of critical frequency parameters for better ground network signal propagation 

OPTIMIZATION for LOCATIONS to MAXIMIZE ELECTRON DENSITY:

Our project uses the ionosphere physical model for both referencing and optimization purposes. The used technique is a bio-inspired optimization algorithm that mimics the behavior of ants amid finding the lowest position for their logistic needs[1]. 

based on the findings in ants and birds 

means of communication. Each member 

of the colony broadcasts to its colleagues

if it finds a better solution that the global

one already broadcasted. After several 

iterations, they finally reach the place that has their goal being their objective of finding.

Our project quotes this nice behavior to find the location across the globe that enjoys the highest electron density ever. The objective function we are going to maximize is the electron density, while we are still searching for the latitude and longitude across the globe that has this maximum electron density.

The code is applied to the physical electron density which is supplied by the European Union Agency for the Space Program [2]. Due to problem complexity, there have been many techniques proposed to reach a reasonable solution, one of which is the parameter relaxation in [7]. Such a technique relaxes some of the parameters able to be guessed in turn of searching the catchy ones. In our problem, the time backs to January 1st, 2017 at 00:00 UT. Accordingly, the optimization problem converges for the latitude and longitude.

An instance of date, January 1st, 2017, is chosen as a reference which is supplied by one of the dates in [8], and the ele   

physical-electron-density-vertical-profile

In this Python code, we impose the physical electron density profile as a function of time and location.

We inherited the methodology presented in NequickG package which is supplied by [2]

Some reviews were covered to reach a more understanding of coding parameters, as published in[6]

Our code hinges on the knowing of three main parameters, latitude, longitude and time.

NequickG is used to set the used parameters as follows:

NEQTime: The time when the ionosphere electron density is modeled in Year, Month and Hour.

Position: Set by the known latitude and longitude.

resolution_lat: The latitude resolution, initially set to be 2.5 degrees.

resolution_lon: The longitude resolution, initially set to be 5 degrees.

required_height_of_study: The required height of study interest, initially set to be 400 Kms

GalileoBroadcast: This is the coefficients reference from which the physical model is fed with. For instance, the coefficients are as follows:

## Gallileo Coefficients backing to January 1st, 2017 from [3]

a0 = 4.4000e+01

a1 = 3.8281e-01

a2 = -1.8616e-03

The output is shown in a .csv file that comprises: Latitudes; Longitudes and Electron Density.

Usage:

Run the main Python script "physical-electron-density-vertical-profile" which calls the required classes.

[1] International space station data

https://spdf.gsfc.nasa.gov/pub/data/international_space_station_iss/sp_fpmu/

[2] https://hamsci.org/

[3] European Union Agency for the Space Program.https://www.gsc-europa.eu/support-to-developers/ionospheric-correction-algorithms/nequick-g-source-code

[4] Source: National Aeronautics and Space Administration: https://cddis.nasa.gov/archive/gps/data/daily/2019/brdc/

We began our journey as a group of strangers from distant lands (🇪🇬🇵🇱🇮🇳🇬🇧🇹🇷🇬🇧) and ended up as Fellowship of the Ionosphere.

6 different people, 6 different worlds, 6 different professionals. In the spirit of this year's edition motto "There’s always space for one more", we've decided to invite maximum number of participants to our team. After that we couldn't figure out how to unlock additional space beyond 6 ;)

List all of the data, resources, and tools used in your project. Resources should include any code, text, and images (even if they are open source or freely available) that you used when creating your solution. If you are using any copyrighted materials, make sure you have permission to use them. 

[1] M. R. Bonyadi and Z. Michalewicz, "Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review," in Evolutionary Computation, vol. 25, no. 1, pp. 1-54, March 2017, doi: 10.1162/EVCO_r_00180.Abstract: This paper reviews recent studies on the Particle Swarm Optimization (PSO) algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7869491&isnumber=7869486

[6] https://hamsci.org/publications/hamsci-distributed-array-small-instruments-personal-space-weather-station-dasi-psws.

[7] https://www.researchgate.net/publication/349599769_Visible_Light_Communications_Localization_Error_Enhancement_using_Parameter_Relaxation

DATA

[4] https://spdf.gsfc.nasa.gov/pub/data/international_space_station_iss/sp_fpmu/ 

for cdf patch 

[5] CDF Patch for MATLAB for Version R2007a and later (nasa.gov)

ham, radion, iss, model