Contact person: Business Client

Phone:Show

Email:Show

Location: India

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"Redesign Prototype for OrbitIQ (3D Earth + Advanced UI)

Project Overview:
We’ve developed a working Streamlit prototype (OrbitIQ) that models and predicts satellite clock and orbital errors using ML. We now want to redesign the UI/UX to make it more visually appealing, futuristic, and interactive, suitable for showcasing in a national-level innovation competition.
You may also use react js to design and develop it.
here are some youtube links that you can refer to develop.

Main Requirement: I want our product to look much better than these youtube links and possibly deployed online.

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Key Requirements:

Full UI revamp in Streamlit with a black / dark tech theme (space-tech look).

Integration of a 3D Earth visualization (via PyDeck, Plotly, or [login to view URL] wrapper).

Add interactive tabs for:

Clock error visualization

Orbit error evolution

Predicted trajectory for Day 8

Uncertainty graph (GPR output)

Include dynamic charts and real-time animation of orbit movement.

Ensure fast loading and mobile-friendly responsiveness.

integrate TensorFlow prediction outputs into visual graphs.

Deliverables:

Streamlit .py app file with updated UI

Deployed version on Streamlit Cloud / Hugging Face Spaces

Short guide on how to update data and retrain models


Problem statement title: To develop AI/ML based models to predict time-varying patterns of the error build up between uploaded and modelled values of both satellite clock and ephemeris parameters of navigation satellites

Background
The accuracy of Global Navigation Satellite Systems (GNSS) is fundamentally limited by errors in satellite clock biases and ephemeris (satellite orbit) predictions. These errors, if not accurately modeled and predicted, can lead to significant deviations in positioning and timing solutions. This challenge tasks participants with developing and applying generative Artificial Intelligence (AI) and Machine Learning (ML) methods to model and predict the differences between uploaded (broadcast) and ICD based modelled values. The goal is to produce highly accurate error predictions for future time intervals, enhancing the reliability and precision of GNSS applications.

Detailed Description
Participants will be provided with a seven-day dataset containing recorded clock and ephemeris errors between uploaded and modeled values from GNSS satellites in both GEO/GSO and MEO. The models must be capable of predicting these errors at 15-minute intervals for an eighth day that is not included in the training data. Evaluation will focus on the accuracy of these predictions over various validity periods: 15 minutes, 30 minutes, 1 hour, 2 hours, and up to 24 hours into the future from the last known data point. Competitors are encouraged to explore a wide range of generative AI/ML techniques, including but not limited to:
Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), for time-series forecasting.
Generative Adversarial Networks (GANs) for synthesizing realistic error patterns.
Transformers for capturing long-range dependencies in the data.
Gaussian Processes for probabilistic modeling of errors.

Expected Solution
• Successful models will demonstrate robust performance across all prediction horizons and provide insights into the underlying dynamics of GNSS errors.
• The error distribution from the proposed model will be evaluated in terms of closeness to the normal distribution. Closer the error distribution to the normal distribution, better will be the performance." (client-provided description)