The fundamental observable in Global Navigation Satellite System Reflectometry (GNSS-R) is the so-called Delay-Doppler Map (DDM), which is derived from the cross-correlation of the reflected signal with either the direct signal or a locally generated replica of the transmitted signal as implemented in our algorithm. Efficient coherent integration is essential for enhancing the signal-to-noise ratio (SNR) and improving the quality of the retrieved data products. This work investigates the limitations of coherent integration in conventional GNSS-R (cGNSS-R) and proposes a novel blind compensation technique to extend integration time by mitigating phase differences caused by navigation bit changes and propagation effects. The maximum length of coherent integration depends on the application; however, achieving a higher SNR within the same integration time enhances the quality of DDMs. Simulation results obtained from high-dynamic synthetic data demonstrate the effectiveness of the proposed technique. Additionally, real data from the UK-DMC reflectometry mission is used to validate the approach.

This study presents the implementation of an onboard Image Classification Unit (ICU) for the AlAinSat-1 CubeSat, aiming to enhance its autonomy and data processing capabilities. The focus is on integrating a trained CNN model onto AlAinSat-1’s STM32 microcontroller.The designed system employs TensorFlow models trained for image classification tasks relevant to CubeSat missions, such as target accuracy detection, to determine if the image captures the required target, and image quality assessment to estimate cloud cover percentage.The integration process onto the STM32 microcontroller involves addressing the resource constraints inherent in CubeSat platforms. The paper details the optimization techniques applied to adapt the model to the STM32 architecture, ensuring efficient execution within the available hardware resources.Key aspects covered in the study include hardware-software co-design considerations, addressing memory and computational limitations, and optimizing mission duration for power consumption efficiency. Additionally, the development of a reliable communication interface between the onboard Image Classification Unit (ICU) and CubeSat’s main control system is discussed to facilitate seamless integration into the overall satellite architecture.The presented implementation enables CubeSats to perform onboard image classification tasks, reducing the need for constant communication with ground stations and enabling quicker response times for mission decisions. This research contributes to the growing field of embedded machine learning applications in spaceborne remote sensing systems, showcasing the feasibility and benefits of incorporating image processing capabilities on resource-constrained platforms.

Radars have been implemented using Software Defined Radios (SDR) for more than a decade. SDRs are convenient in terms of hardware development since the signal generation, conditioning, and post-processing can easily be implemented in hardware. However, signal synchronization remains a major issue, and it can seriously impact the final performance. In this study, the synchronization and the calibration of the transmitted signals in an SDR-based radar transmitting PRN modulated pulse is studied. Time synchronization and jitter are compensated using a "Filter Delay" block, and a peak detector in a reference channel. The amplitude and phase of the peak of the cross-correlation of a sample of the transmitted signal is used to calibrate the received echoes allowing to compensate on a pulse-by-pulse basis the amplitude and phase drifts.

Earth Observation (EO) using Signals of Opportunity (SoOp), the advent of high-performance and customizable Software Defined Radios (SDRs), and small satellites are revolutionizing today many Remote Sensing techniques. One of the most widely used SoOp are the Global Navigation Satellite System signals used for Radio Occultations (GNSS-RO) and Reflectometry (GNSS-R). In this last one, the reflected GNSS signals acquire properties of the surface where they are reflected, and when these signals are compared to the direct ones, one can infer surface roughness and dielectric constant information. Nowadays SDR-based GNSS-R instruments are becoming more cost-effective, power-efficient, and small enough to be adopted as CubeSats payloads. In GNSS-R receivers, all the information resides in the so-called Delay Doppler Map (DDM), which is the cross-correlation between the reflected signal and the direct one (interferometric GNSS-R or iGNSS-R), or a locally generated replica of the direct one (conventional GNSS-R or cGNSS-R) for different delays and Doppler frequency cuts. Producing these DDMs is computationally intensive due to the large number of Fast Fourier Transforms (FFTs) involved. To develop an efficient GNSS-R instrument, this work presents the development of such an instrument— the first of its kind in the United Arab Emirates (UAE)— and introduces its first operational version. Also, it explores the capture of raw GPS L1 signal, and its processing, having as auxiliary or reference information of the PRN codes of the satellites in view, and their central Doppler frequencies. The coherent and incoherent integration times will also be traded off to enhance the spatial resolution.

The rapid development of spacecrafts has led to the necessity of using high-performance computer systems on-board space missions. This requires a higher level of command and data handling specifications to satisfy those needs. As they regulate and synchronize different activities of a satellite or spacecraft, command and data handling systems (CDHS) are crucial parts of space missions. The CDHS’ modular architecture provides flexibility, which is significant when developing satellite systems. This paper’s objective is to introduce a hardware architecture for a modular, multi-layered command and data handling system. The on-board computer (OBC), which will launch on AlAinSat-1 and is based on the PC-104 platform, is the central component of the proposed CDHS architecture. In this paper, we also discuss the fully developed architecture which uses the cPCI Serial Space standard and other standard interfaces, such as PCIe. Next, we describe the benefits of flying the PC/104 computing layer of the CDHS architecture within AlAinSat-1. Finally, we present the results of the functional test and thermal vacuum testing that were done on the PC/104 computing layer of the CDHS architecture. In the future, we aim to complete the full testing of the four layers including the cPCI standard layer.

The Remote sensing and Interference detector with radiome-Try and vegetation Analysis (RITA Payload) [1] is a development of the UPC NanoSat Lab, aimed at Earth Observation (EO). It is one of the winners of the Second IEEE GRSS Student Grand Challenge, and will fly on-board AlainSat-1, a 3U CubeSat developed by the National Space Science and Technology Center (NSSTC) of the United Arab Emirates.RITA hosts three experiments. An L-band microwave radiometer (MWR) will gather data of soil moisture and sea ice thickness and concentration, aided with a Radio-frequency Interference (RFI) detection algorithm. A LoRa transceiver will perform on-demand execution of the EO experiments [2]. Finally, a Near-Infrared (NIR) Hyperspectral Camera will gather data for vegetation monitoring, agriculture applications, hydrology and coastal and inland waters mapping, among others [3].This work is focused on the calibration and validation of the Hyperspectral imager, at optical, electronic and spectral levels, as well as in the verification of its performance to measure Normalized Difference Vegetation Index (NDVI).

This paper presents a workflow for implementing structural analysis on CubeSats using NX software. Four fundamental analyses, namely quasi-static, modal, random vibration, and buckling analyses, are demonstrated as a guide to simulate these tests for such CubeSats. AlAinSat-1 frame structure is used to apply the analyses using NX. The purpose of this paper is to guide beginners on how to use the software and perform such analyses. The methodology used in this study involves creating three types of FEM models that vary in the number of elements, conducting the four structural analyses, and evaluating the different techniques of the FEM models. To achieve accurate results using FEA, the element size of the meshing should be as small as possible. A convergence analysis shows that the results of models 2 and 3 are convergent. Therefore, the model 2 approach could be used to get acceptable results while reducing the computation time.

The Remote sensing and Interference detector with radiome-Try and vegetation Analysis (RITA) payload was selected in 2019 the 2nd GRSS Student Grand Challenge to fly onboard AlainSat-1, a 3U CubeSat developed at the National Space Science and Technology Center (NSSTC) in United Arab Emirates. RITA payload includes a passive Microwave Radiometer (MWR), a LoRa transceiver, and an hyper-spectral camera. The objective of the payload is to retrieve Earth Observation (EO) parameters such as: soil moisture, sea-ice thickness and concentration, among others. A Radio Frequency Interference (RFI) detection algorithm is also included to the payload to create interference maps in the received signals.

AlainSat-1 is an educational and scientific nanosatellite project that was initiated in late 2019 by the IEEE Geoscience and Remote Sensing Society (GRSS) along with National Space Science and Technology Center (NSSTC) of UAE University in the frame of the 2nd Student Grand Challenge [1]. The project involves close collaboration between four international universities to design, build, test and launch a remote sensing CubeSat.The spacecraft is a 3U CubeSat that has a mass of around 4 Kgs. The spacecraft has an active 3-axis control system capable of attitude determination and control to less than one degree. Two communications systems will be used on-board: a UHF System and an S-Band System. The project has passed the Critical Design Review (CDR) stage and is currently in the assembly and integration phase. The satellite is currently planned for launch to a sun-synchronous orbit on-board a Falcon 9 rocket in the second quarter of 2024.

This paper presents the development and implementation of control algorithms for the Attitude Determination and Control Subsystem (ADCS) of AlAinSat-1, a 3U CubeSat. It focuses on detumbling, sun pointing, nadir-pointing, and ground contact modes. Furthermore, it discusses the mathematical models of different sources of disturbance, such as gravity gradient, magnetic torque, atmospheric drag, and solar radiation pressure. The successful implementation of the B-dot algorithm for detumbling and a PID controller for nadir-pointing is demonstrated through numerical simulations. The simulation results indicate a final spinning rate of 1 deg/s in detumbling mode, confirming the efficacy of the algorithm. The implemented PID controller effectively regulates the attitude, resulting in a 1-degree pointing accuracy during the nadir-pointing mode.

CubeSats are cost-effective nanosatellites constructed using commercial-off-the-shelf (COTS) components, which are not specifically designed for space applications. Given the severe space environment where CubeSats are exposed to multiple heat sources such as solar radiation, albedo, and earth infrared radiation (IR), pre-launch testing of these components is of paramount importance. This study presents a simulation of the thermal behavior of AlAinSat-1 under extreme temperatures, utilizing Siemens NX’s 3D Space Systems Thermal module. The simulation process, which spanned from idealization to the assignment of appropriate assumptions and boundary conditions for each case, was conducted under both steady state and transient conditions. The results suggest that AlAinSat-1 can withstand the extreme thermal environment during its mission. However, these findings, being heavily reliant on theoretical assumptions and calculations, necessitate validation through actual experimental testing. Consequently, a series of thermal tests using a thermal vacuum chamber (TVAC) are scheduled in to confirm AlAinSat-1's survivability in the harsh space environment. These tests are crucial to prevent any potential in-orbit failures.

In 2019, the IEEE 2nd GRSS Student Grand Challenge selected the Remote sensing and Interference detector with radiomeTry and vegetation Analysis (RITA) payload to fly on board of AlainSat-1, a 3U CubeSat from the United Arab Emirates' National Space Science and Technology Center (NSSTC). The RITA payload is equipped with a Commercial Off- The-Shelf (COTS) Software Defined Radio (SDR) for the RF signal conditioning and a System on a Chip (SoC) processor in charge of controlling the execution and processing of each experiment that conforms the payload. RITA includes an L-band microwave radiometer (MWR), a Radio Frequency Interference (RFI) detector, a LoRa transceiver, and a hyper-spectral camera. A multi-level acquisition strategy joining several sensors for the aforementioned experiments' allows this payload retrieve parameters such as: soil moisture derived from the MWR, Normalized Difference Vegetation Index (NVDI) obtained from the hyper-spectral camera, improved acquisition of vegetation-related measurements through the LoRa transceiver, or even the compilation of worldwide interference maps. This manuscript will focus on the design, implementation, verification, and testing of the MWR on board the RITA payload, a successor of previous CubeSat missions such as FSSCat, which carried the FMPL-2 payload, or 3Cat-4 carrying FMPL-1 payload.

Global change and sea level rise are increasing the interest in monitoring oceans, and specially the Arctic. L-band microwave radiometers can be used for thin sea ice thickness and oceans monitoring, and these maps are being routinely generated from SMOS [1] and SMAP [2], which if combined with imagers provide a higher resolution. The CubeSat-based payload RITA (RadIomeTry and vegetation Analysis) plans to address those monitoring needs by including an IoT enabled payload, a hyperspectral imager and an L-band microwave radiometer with RFI detection and mitigation [3]. The IoT payload implements an SDR-based LoRa modulation and it will be used to perform On-Demand executions of the radiometer and the imager payloads on-board RITA. As part of this work, an in-depth description of the objectives, concept of operations and hardware and software architecture and implementation of the IoT LoRa module will be provided.

The Remote sensing and Interference detector with radiomeTry and vegetation Analysis (RITA) payload is one of the Remote Sensing payloads selected by the 2nd GRSS Student Grand Challenge in 2019 to fly on board of a 3U satellite that is being developed at the National space Science and Technology Center (NSSTC), United Arab Emirates University. RITA has been designed as an academic mission with a strong focus on Earth Observation techniques and technologies. This payload is equipped with a Software-Defined Radio for microwave radiometry and RFI detection, a hyperspectral camera (25 bands from 600 to 975 nm), and a LoRa transceiver, which will work in tandem to produce vegetation-related measurements with improved accuracy. Soil moisture measurements, for example, will be derived from a Total Power Microwave Radiometer working at L-band and Normalized Difference Vegetation Index (NDVI) measurements in a technique known as Pixel Downscaling. In this work, recent advances in the payload design will be presented, as well as a more in depth mission analysis.

This paper describes the concept of operation and science data processing chain for the MeznSat mission, a 3U CubeSat for greenhouse gas monitoring. The mission aims to use a shortwave infrared spectrometer operating in the Short-Wave Infra-Red (SWIR) range of 1000-1650 nm to find the concentrations of atmospheric gases, namely water vapor (H2O), Carbon Dioxide (CO2), Oxygen (O2), and Methane (CH4) above the UAE. The satellite also carries an RGB camera that is used throughout the mission to validate the spectrometer angle and orientation and provide geolocation accuracy. In this work, a description of the orbital analysis and the scheme followed to activate the payloads throughout the mission is discussed, along with the process followed to acquire the data from the satellite and retrieve the count data. The concentrations of the gases are then estimated using a retrieval process that uses a forward radiative transfer model to create a lookup table to map the gas concentrations to their corresponding synthetic spectra produced by the forward model.

There is a continuing trend in the space community to reduce costs involved in space missions. Miniaturization has been the primary method that was deployed for reducing space mission costs. This is now changing, due to the recent advancements in technology that enable advanced mission architectures namely, distributed spacecraft missions. Several spacecraft flying in close formations would collaboratively achieve the mission aims at lower costs and with enhanced reliability compared to larger single platform missions. Distributed algorithms have been proposed for use on-board these missions for coordination and control purposes. No work has been done to implement a distributed system at the lower levels of abstraction. This paper proposes the deployment of distributed computing on-board close formation flying missions. Two distributed computing paradigms namely, client-server and mobile agent, are analytically compared in view of close formation flying missions. As a result the mobile agent paradigm is proposed for the application in hand.