One of the primary objectives of the Double Star mission is the accurate measurement of the magnetic field vector along the orbits of the two spacecraft. The magnetic field is an essential parameter for the understanding of space plasma processes and is also required for the effective interpretation of data from the other instruments on the spacecraft. We present the design of the magnetic field instrument onboard both of the Double Star spacecraft and an overview of the performance as measured first on-ground and then in-orbit. We also report the results of in-flight calibration of the magnetometers, and the processing methods employed to produce the final data products which are provided to Double Star investigators, and the wider community in general. Particular attention is paid to the techniques developed for removing magnetic interference generated by the solar arrays on the first (equatorial orbiting) spacecraft. Results from the first year of operations are reviewed in the context of combined observations by Double Star and Cluster, and examples given from the different regions visited by the spacecraft to date.

/pdf/the-double-star-magnetic-field-investigation-instrument-3fshs7cmq4.pdf

The Double Star magnetic field investigation: instrument design, performance and highlights of the first year's observations

We have developed a mission concept that uses 3-unit cubesats to perform new measurements of lunar magnetic fields, less than 100 meters above the Moon’s surface. The mission calls for sending the cubesats on impact trajectories to strongly magnetic regions on the surface, and transmitting measurements in real-time to a nearby spacecraft, or directly to the Earth, up until milliseconds before impact. The cubesats and their instruments are partly based on the NSF-funded CINEMA cubesat now in Earth orbit. Two methods of reaching the Moon as a secondary payload are discussed: 1) After launching into geostationary transfer orbit with a communication satellite, a small mother-ship travels into lunar orbit and releases the cubesats on impact trajectories, and 2) The cubesats travel to the Moon using their own propulsion after release into geosynchronous orbit. This latter version would also enable other near-Earth missions, such as constellations for studying magnetospheric processes, and observations of close-approaching asteroids.

/pdf/lunar-magnetic-field-measurements-with-a-cubesat-nj9pgxue5q.pdf

Lunar magnetic field measurements with a cubesat

Modified gradiometer technique applied to Double Star (TC-1)

http://ir.nssc.ac.cn/bitstream/122/4466/1/2014103273.pdf

Radial distribution of magnetic field in earth magnetotail current sheet

Accurate prediction of solar irradiation, particularly Diffuse Horizontal Irradiance (DHI), is essential for optimizing solar energy systems. This paper presents a hybrid framework that integrates satellite imagery and satellite-derived data to improve the precision of DHI forecasts. Two key datasets were employed: ground-based Solar Irradiation Measurement (SRM) and satellite-based Solar Irradiation (SSR). The proposed methodology utilizes machine learning models along two paths. The first path processes satellite imagery using advanced techniques, including pixel imputation with a modified Random Forest (RF) and Generative Adversarial Network (GAN), noisy region detection with Self-Organizing Maps (SOM), and noise removal through a Latent Diffusion Model (LDM). The second path handles tabular data through a diffusion probabilistic model designed for missing data imputation. These features are then merged and fed into a Long Short-Term Memory (LSTM) network, enhanced to capture seasonality and trends for accurate DHI predictions. The paper makes significant contributions by introducing a robust hybrid framework that leverages both satellite imagery and tabular data, incorporating novel preprocessing methods such as GAN-based pixel imputation and diffusion-based noise removal. The LSTM model is further adapted to handle seasonal and trend components, resulting in enhanced DHI forecasting accuracy. The proposed model demonstrates superior performance, achieving a Mean Squared Error (MSE) of 8.170 W/m2, Root Mean Squared Error (RMSE) of 1.749 W/m2, Mean Absolute Error (MAE) of 0.693 W/m2, and an R-squared value of 2.574, significantly outperforming conventional methods such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), and traditional LSTM models. Additionally, the model records a low Mean Absolute Percentage Error (MAPE) of 0.8183% and a computation time of 35.4 s, highlighting its efficiency. These results contribute to more accurate and reliable solar energy resource management by offering enhanced DHI forecasts across various spatial and temporal scales.

A hybrid deep learning framework for solar irradiation prediction based on regional satellite images and data

The Double Star magnetic field investigation: Overview of instrument performance and initial results

Increasingly frequent wildﬁres signiﬁcantly aﬀect solar energy production as the atmospheric aerosols generated by wildﬁres diminish the incoming solar radiation to the earth. Atmospheric aerosols are measured by Aerosol Optical Depth (AOD), and AOD data streams can be retrieved and monitored by geostationary satellites. However, multi-source remote-sensing data streams often present heterogeneous characteristics, including diﬀerent data missing rates, measurement errors, systematic biases, and so on. To accurately estimate and predict the underlying AOD propagation process, there exist practical needs and theoretical interests to propose a physics-informed statistical approach for modeling wildﬁre AOD propagation by simultaneously utilizing, or fusing, multi-source heterogeneous satellite remote-sensing data streams. Leveraging a spectral approach, the proposed approach integrates multi-source satellite data streams with a fundamen-tal advection-diﬀusion equation that governs the AOD propagation process. A bias correction process is included in the statistical model to account for the bias of the physics model and the truncation error of the Fourier series. The proposed approach is applied to California wildﬁres AOD data streams obtained from the National Oceanic and Atmospheric Administration. Comprehensive numerical examples are provided to demonstrate the predictive capabilities and model interpretability of the proposed approach. Computer code has been made available on GitHub.

Physics-Informed Statistical Modeling for Wildfire Aerosols Process Using Multi-Source Geostationary Satellite Remote-Sensing Data Streams

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The Double Star magnetic field investigation: Overview of instrument performance and initial results

Physics-Informed Statistical Modeling for Wildfire Aerosols Process Using Multi-Source Geostationary Satellite Remote-Sensing Data Streams