Search Results (72)

This research proposes a tailored Systems Engineering (SE) design process for the development of Attitude and Orbit Control Systems (AOCS) for small satellites operating in formation. These missions, known as Distributed Spacecraft Missions (DSMs), involve groups of satellites—commonly referred to as satellite constellations—whose primary objective is to maintain controlled relative positioning in three dimensions. In these configurations, each satellite may serve a specific role. For instance, one may act as a navigation reference, while another functions as a communication relay. These roles support synchronized control and ensure mission cohesion. To achieve precise relative positioning, the system must integrate specialized sensors and maintain continuous inter-satellite communication. This capability enables precise navigation across both the space and ground segments, while ensuring high control accuracy. As such, the development of AOCS must be approached as a complex systems challenge, involving the coordinated behavior of multiple autonomous elements working toward a shared mission objective. This study tailors the SE process using the ISO/IEC 15288 standard and incorporates a Model-Based Systems Engineering (MBSE) approach to enhance traceability, consistency, and architectural coherence throughout the system lifecycle. As a result, it proposes a customized SE process for AOCS development that begins in the mission’s conceptual phase and addresses the specific functional and operational demands of formation flying. A conceptual example illustrates the proposed process. It focuses on subsystem coordination, communication needs, and the architecture required to support an AOCS for autonomous satellite formations. Full article

A novel satellite architecture and deployment method is proposed to reduce the logistical cost and complexity of launching and dispersing satellite constellations. The architecture consists of a primary satellite that separates into multiple smaller units, which are subsequently dispersed using differential drag. An algorithm is developed to determine the required disengagement velocities and optimal timing for separation maneuvers. Two case studies with orbital simulations demonstrate the feasibility of this approach for constellation deployment and phasing. The results indicate that while mission-specific factors influence deployment dynamics, informed selection of the disengagement velocities is crucial for minimizing phase times and mitigating potential delays. The findings confirm the feasibility of the proposed architecture and dispersal method, offering a cost-effective alternative to traditional deployment strategies for future satellite constellations. Full article

This article presents the decision feedback equalizer (DFE), the maximum likelihood detection (MLD), and the radius-directed equalization (RDE) algorithms designed in MATLAB-R2018a to equalize the received signal in a dispersive optical link up to 120 km. DFE is essential for improving signal quality in several communication systems, including WiFi networks, cable modems, and long-term evolution (LTE) systems. Its capacity to mitigate inter-symbol interference (ISI) and rapidly adjust to channel variations renders it a flexible option for high-speed data transfer and wireless communications. Conversely, MLD is utilized in applications that require great precision and dependability, including multi-input–multi-output (MIMO) systems, satellite communications, and radar technology. The ability of MLD to optimize the probability of accurate symbol detection in complex, high-dimensional environments renders it crucial for systems where signal integrity and precision are critical. Lastly, RDE is implemented as an alternative algorithm to the CMA-based equalizer, utilizing the idea of adjusting the amplitude of the received distorted symbol so that its modulus is closer to the ideal value for that symbol. The algorithms are tested using a converged 5G mm-wave analog radio-over-fiber (A-RoF) system at 60 GHz. Their performance is measured regarding error vector magnitude (EVM) values before and after equalization for different optical fiber lengths and modulation formats (QPSK, 16-QAM, 64-QAM, and 128-QAM) and shows a clear performance improvement of the output signal. Moreover, the performance of the proposed algorithms is compared to three commonly used algorithms: the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA), and the adaptive median filtering (AMF), demonstrating superior results in both QPSK and 16-QAM and extending the transmission distance up to 120 km. DFE has a significant advantage over LMS and AMF in reducing the inter-symbol interference (ISI) in a dispersive channel by using previous decision feedback, resulting in quicker convergence and more precise equalization. MLD, on the other hand, is highly effective in improving detection accuracy by taking into account the probability of various symbol sequences achieving lower error rates and enhancing performance in advanced modulation schemes. RDE performs best for QPSK and 16-QAM constellations among all the other algorithms. Furthermore, DFE and MLD are particularly suitable for higher-order modulation formats like 64-QAM and 128-QAM, where accurate equalization and error detection are of utmost importance. The enhanced functionalities of DFE, RDE, and MLD in managing greater modulation orders and expanding transmission range highlight their efficacy in improving the performance and dependability of our system. Full article

The space-division multiplexed (SDM) transmission technique based on uncoupled multi-core fibers (MCF) shows great implementation potential due to its huge transmission capacity and compatibility with existing transceivers. In this paper, we demonstrate a real-time single-span 106 km field trial over deployed 4-core MCF cable using commercial 800 Gb/s optical transport network (OTN) transceivers. The transceivers achieved a modulation rate of 130 Gbaud with the optoelectronic multiple-chip module (OE-MCM) packaging technique, which enabled the adoption of a highly noise-tolerant probability constellation shaping a 16-array quadrature amplitude modulation (PCS-16QAM) modulation format for 800 Gb/s OTN transceivers, and could realize unrepeated long-span transmission. The 4-core 800 Gb/s transmission systems achieved a real-time transmission capacity of 256 Tb/s with fully loaded 80-wavelength channels over the C+L band. The performance of different kinds of 800 G OTN transceivers with different modulation formats under this long-span unrepeated optical transmission system is also estimated and discussed. This field trial demonstrates the feasibility of applying uncoupled MCF with 800 Gb/s OTN transceivers in unrepeated long-span transmission scenarios and promotes its field implementation in next-generation high-speed optical interconnection systems. Full article

The Earth’s time-variable gravity field holds significant research and application value. However, satellite gravimetry missions such as GRACE and GRACE-FO face limitations in spatial resolution when detecting monthly gravity fields, while traditional radar altimeters lack the observational efficiency needed for monthly gravity anomaly inversion. These limitations hinder further exploration and application of the Earth’s time-variable gravity field. Leveraging its advantages, such as rapid global coverage, high revisit frequency, and low cost for constellation formation, spaceborne GNSS-R technology holds the potential to address the observational efficiency gaps of traditional radar altimeters. This study presents the first assessment of the capability of spaceborne GNSS-R interferometric altimetry for high spatial resolution monthly marine gravity anomaly inversion through simulations. The results indicate that under the PARIS Operational scenario of a single GNSS-R satellite (a spaceborne GNSS-R interferometric altimetry scenario proposed by Martin-Neira), a 30′ grid resolution marine gravity anomaly can be inverted with an accuracy of 4.93 mGal using one month of simulated data. For a dual-satellite constellation, the grid resolution improves to 20′, achieving an accuracy of 4.82 mGal. These findings underscore the promise of spaceborne GNSS-R interferometric altimetry technology for high spatial resolution monthly marine gravity anomaly inversion. Full article

Terrestrial and satellite communications, tactical data links, positioning, navigation, and timing (PNT), as well as distributed sensing will continue to require precise timing and the ability to synchronize and disseminate time effectively. However, the supply of space-qualified clocks that meet Global Navigation Satellite Systems (GNSS)-level performance standards is limited. As the awareness of potential disruptions to GNSS due to adversarial actions grows, the current reliance on GNSS-level timing appears costly and outdated. This is especially relevant given the benefits of developing robust and stable time scale references in orbit, especially as various alternatives to GNSS are being explored. The onboard realization of clock ensembles is particularly promising for applications such as those providing the on-demand dissemination of a reference time scale for navigation services via a proliferated Low-Earth Orbit (pLEO) constellation. This article investigates potential inter-satellite network architectures for coordinating time and frequency across pLEO platforms. These architectures dynamically allocate radio resources for clock data transport based on the requirements for pLEO time scale formations. Additionally, this work proposes a model-based control system for wireless networked timekeeping systems. It envisions the optimal placement of critical information concerning the implicit ensemble mean (IEM) estimation across a multi-platform clock ensemble, which can offer better stability than relying on any single ensemble member. This approach aims to reduce data traffic flexibly. By making the IEM estimation sensor more intelligent and running it on the anchor platform while also optimizing the steering of remote frequency standards on participating platforms, the networked control system can better predict the future behavior of local reference clocks paired with low-noise oscillators. This system would then send precise IEM estimation information at critical moments to ensure a common pLEO time scale is realized across all participating platforms. Clock steering is essential for establishing these time scales, and the effectiveness of the realization depends on the selected control intervals and steering techniques. To enhance performance reliability beyond what the existing Linear Quadratic Gaussian (LQG) control technique can provide, the minimal-cost-variance (MCV) control theory is proposed for clock steering operations. The steering process enabled by the MCV control technique significantly impacts the overall performance reliability of the time scale, which is generated by the onboard ensemble of compact, lightweight, and low-power clocks. This is achieved by minimizing the variance of the chi-squared random performance of LQG control while maintaining a constraint on its mean. Full article

Urban shrinkage is a significant challenge to sustainable urban development. To date, the existing research has yet to analyze urban shrinkage at a fine-scale level. This study addresses this gap by employing nighttime light (NTL) data, which, due to its strong correlation with human activity and high spatial–temporal resolution, offers a robust approach for micro-scale population estimation. This paper aims to explore the characteristics and formation mechanisms of urban shrinkage spaces in Guangzhou, using NTL data and applying ordinary least squares (OLS) and geographically weighted regression (GWR) models. The correlational analysis reveals a marked improvement in model fit with GWR (R² = 0.91) compared with OLS (R² = 0.63), confirming the predictive power of NTL-based GWR for population mapping and the spatial delineation of urban shrinkage. We demonstrate that urban shrinkage spaces in Guangzhou are predominantly distributed in the outer suburbs, while urban growth is concentrated within the urban core area and inner suburbs. The formation of urban shrinkage in Liwan District examined as a case study, is primarily influenced by market factors, government actions, and regulatory constraints—a constellation of factors likely generalizable with other contexts of urban shrinkage. A comprehensive understanding of urban shrinkage at a fine-scale level is imperative for policy makers to optimize urban land use planning and mitigate the factors contributing to shrinkage space, thereby promoting sustainable urban development. Full article

Contrails, or condensation trails, left by aircraft, significantly contribute to global warming by trapping heat in the Earth’s atmosphere. Despite their critical role in climate dynamics, the environmental impact of contrails remains underexplored. This research addresses this gap by focusing on the use of CubeSats for real-time contrail monitoring, specifically over major air routes such as the Europe–North Atlantic Corridor. The study proposes a 3 × 3 CubeSat constellation in highly eccentric orbits, designed to maximize coverage and data acquisition efficiency. Simulation results indicate that this configuration can provide nearly continuous monitoring with optimized satellite handovers, reducing blackout periods and ensuring robust multi-satellite visibility. A machine learning-based system integrating space-based humidity and temperature data to predict contrail formation and inform flight path adjustments is proposed, thereby mitigating environmental impact. The findings emphasize the potential of CubeSat constellations to revolutionize atmospheric monitoring practices, offering a cost-effective solution that aligns with global sustainability efforts, particularly the United Nations Sustainable Development Goal 13 (Climate Action). This research represents a significant step forward in understanding aviation’s non-CO₂ climate impact and demonstrates the feasibility of real-time contrail mitigation through satellite technology. Full article

Cancer discovery is directed at the identification of a specific cancer type which allows for specific therapeutic interventions. Background/Objectives: Recently, similar immune checkpoint therapeutics have been applied with success across several cancer types, opening the field for other immune disruptive interventions that have practical applications. Methods: We have discovered an innate immune system (InImS) biomarker that allows for the characterization of allied cancer subtypes and outliers that might aid with diagnosis, treatment, and prognostication. Results: These InImS biomarkers are related to PD-L1 treatment outcomes and can be potentially manipulated by dietary means. Conclusions: The FERAD (ferritin–fecal p87) and absolute neutrophil/lymphocyte (aNLR) ratios are two such InImS biomarkers and we show herein, that they allow for the discovery of diagnosis and prognostication patterns, as demonstrated by this study. Full article

The development of hierarchical structures of unmanned aerial vehicles (UAVs) increases the efficiency of unmanned aerial systems. The grouping of UAVs increases the region of recognition or force of assault. Achieving these requirements is possible through a UAV formation. The UAVs in the formation must be controlled and managed by a commander, but the commander cannot control individual UAVs. The UAVs within the formation have assigned specific individual tasks, so is possible to achieve the flight of the formation with minimum collisions between UAVs and maximized equipment utilization. This paper aims to present a method of formation control for multiple UAVs that allows dynamic changes in the constellations of UAVs. The article includes the results of tests and research conducted in real-world conditions involving a formation capable of adapting its configuration. The results are presented as an element of research for the autonomy swarm, which can be controlled by one pilot/operator. The control of a swarm consisting of many UAVs (several hundred) by one person is now a current problem. The article presents a fragment of research work on high-autonomy UAV swarms. Here is presented a field test that focuses on UAV constellation control. Full article

Recent advances in laser scanning systems have enabled the acquisition of 3D point cloud representations of scenes, revolutionizing the fields of Architecture, Engineering, and Construction (AEC). This paper presents a novel pipeline for the automatic generation of 3D semantic models of multi-level buildings from indoor point clouds. The architectural components are extracted hierarchically. After segmenting the point clouds into potential building floors, a wall detection process is performed on each floor segment. Then, room, ground, and ceiling extraction are conducted using the walls 2D constellation obtained from the projection of the walls onto the ground plan. The identification of the openings in the walls is performed using a deep learning-based classifier that separates doors and windows from non-consistent holes. Based on the geometric and semantic information from previously detected elements, the final model is generated in IFC format. The effectiveness and reliability of the proposed pipeline are demonstrated through extensive experiments and visual inspections. The results reveal high precision and recall values in the extraction of architectural elements, ensuring the fidelity of the generated models. In addition, the pipeline’s efficiency and accuracy offer valuable contributions to future advancements in point cloud processing. Full article

A disruptive Electric Propulsion system is proposed for next-generation Low-Earth-Orbit (LEO) small satellite constellations, utilizing an RF-powered Helicon Plasma Thruster (HPT). This system is built around a Magnetically Enhanced Inductively Coupled Plasma (MEICP) reactor, which enables acceleration of quasi-neutral plasma through a magnetic nozzle. The MEICP reactor features an innovative design with a multi-dipole magnetic confinement system, generated by neodymium iron boron (NdFeB) permanent magnets, combined with an azimuthally asymmetric half-wavelength right (HWRH) antenna and a variable-section ionization chamber. The plasma reactor is followed by a solenoid-free magnetic nozzle (MN), which facilitates the formation of an ambipolar potential drop, enabling the conversion of electron thermal energy into ion beam energy. This study explores the impact of an inhomogeneous magnetic field on the heating mechanism of the HPT and highlights its multi-mode operation within a pulsed power range of 200 to 500 W of RF. The discharge state, characterized by high-energy electron-excited ions and low-energy excited neutral particles in the plasma plume, was analyzed using optical emission spectroscopy (OES). The experimental testing campaign, conducted under pulsed power excitation, reveals that, as RF input power increases, the MEICP reactor transitions from inductive (H-mode) to wave coupling (W-mode) discharge modes. Spectrograms, electron temperature, and plasma density measurements were obtained for the Helicon Plasma Thruster within its operational envelope. Based on OES data, the ideal specific impulse was estimated to exceed 1000 s, highlighting the significant potential of this technology for future LEO/VLEO space missions. Full article

In the near future, international space agencies have planned to achieve significant milestones in investigating the utilization of Global Navigation Satellite Systems (GNSS) within and beyond the current space service volume up to their application to lunar missions. These initiatives aim to demonstrate the feasibility of GNSS navigation at lunar altitudes. Based on the outcomes of such demonstrations, dozens of lunar missions will likely be equipped with a GNSS receiver to support autonomous navigation in the lunar proximity. Relying on non-invasive, consolidated differential techniques, GNSS will enable baseline estimation, thus supporting a number of potential applications to lunar orbiters such as collaborative navigation, formation flight, orbital manoeuvers, remote sensing, augmentation systems and beyond. Unfortunately, the large dynamics and the geometry of such differential GNSS scenarios set them apart from current terrestrial and low-earth orbit use cases. These characteristics result in an increased sensitivity to measurements time misalignment among orbiters. Hence, this paper offers a review of baseline estimation methods and characterizes the divergences and limitations w.r.t. to terrestrial applications. The study showcases the estimation of the baseline length between a lunar CubeSat mission, VMMO, and the communication relay Lunar Pathfinder mission. Notably, real GNSS measurements generated by an Engineering Model of the NaviMoon receiver in the European Space Agency (ESA/ESTEC) Radio Navigation Laboratory are utilized. A radio-frequency constellation simulator is used to generate the GNSS signals in these hardware-in-the-loop tests. The performed analyses showed the invalidity of common terrestrial differential GNSS ranging techniques for space scenarios due to the introduction of significant biases. Improved ranging algorithms were proposed and their potential to cancel ranging errors common to both receivers involved was confirmed. Full article

Recently, various deep learning methods have been applied to Modulation Format Identification (MFI). The interpretability of deep learning models is important. However, this interpretability is challenged due to the black-box nature of deep learning. To deal with this difficulty, we propose an Adaptive Mask-Based Interpretable Convolutional Neural Network (AMI-CNN) that utilizes a mask structure for feature selection during neural network training and feeds the selected features into the classifier for decision making. During training, the masks are updated dynamically with parameters to optimize feature selection. The extracted mask serves as interpretable weights, with each weight corresponding to a feature, reflecting the contribution of each feature to the model’s decision. We validate the model on two datasets—Power Spectral Density (PSD) and constellation phase histogram—and compare it with three classical interpretable methods: Gradient-Weighted Class Activation Mapping (Grad-CAM), Local Interpretable Model-Agnostic Explanations (LIME), and Shapley Additive exPlanations (SHAP). The MSE values are as follows: AMI-CNN achieves the lowest MSE of 0.0246, followed by SHAP with 0.0547, LIME with 0.0775, and Grad-CAM with 0.1995. Additionally, AMI-CNN achieves the highest PG-Acc of 1, whether on PSD or on constellation phase histogram. Experimental results demonstrate that the AMI-CNN model outperforms compared methods in both qualitative and quantitative analyses. Full article

Communication signal reconstruction technology represents a critical area of research within communication countermeasures and signal processing. Considering traditional OFDM signal reconstruction methods’ intricacy and suboptimal reconstruction performance, a dual discriminator CGAN model incorporating LSTM and Transformer is proposed. When reconstructing OFDM signals using the traditional CNN network, it becomes challenging to extract intricate temporal information. Therefore, the BiLSTM network is incorporated into the first discriminator to capture timing details of the IQ (In-phase and Quadrature-phase) sequence and constellation map information of the AP (Amplitude and Phase) sequence. Subsequently, following the addition of fixed position coding, these data are fed into the core network constructed based on the Transformer Encoder for further learning. Simultaneously, to capture the correlation between the two IQ signals, the VIT (Vision in Transformer) concept is incorporated into the second discriminator. The IQ sequence is treated as a single-channel two-dimensional image and segmented into pixel blocks containing IQ sequence through Conv2d. Fixed position coding is added and sent to the Transformer core network for learning. The generator network transforms input noise data into a dimensional space aligned with the IQ signal and embedding vector dimensions. It appends identical position encoding information to the IQ sequence before sending it to the Transformer network. The experimental results demonstrate that, under commonly utilized OFDM modulation formats such as BPSK, QPSK, and 16QAM, the time series waveform, constellation diagram, and spectral diagram exhibit high-quality reconstruction. Our algorithm achieves improved signal quality while managing complexity compared to other reconstruction methods. Full article