https://www.jsstpub.com/ Journal of Space Science and Technology en daily 1 Tue, 01 Sep 2026 00:00:00 +0330 Tue, 01 Sep 2026 00:00:00 +0330 https://www.jsstpub.com/article_235317.html Integrated modelling of spacecraft roto-translational relative motion is an emerging topic of interest for multi-agent space systems design, analysis and control. Dual-quaternion is a satisfactory solution to this complex problem. Model predictive control (MPC) is, on the other hand, a powerful model-based control methodology with known superiorities over other modern control approaches used in the field of spacecraft control. Embedding the dual-quaternion model in the MPC approach for sophisticated systems, thus yields a valuable control framework. There are, however, some difficulties involved in this proposition that need to be addressed. This matter is scrutinized in the present work. To this aim, the concept of dual-quaternion and relevant tools are first reviewed. Subsequently, potential interfaces between dual-quaternion expressions and MPC framework are highlighted. Accordingly, a piecewise affine MPC scheme based on the dual-quaternion model is introduced and further developed in several aspects for space roto-translational relative control missions. First, it has been improved for zero steady-state error. Furthermore, it has been developed to explicitly deal with reaction wheels as attitude actuators, in terms of limitations as well as related costs. The efficacy of the proposed integrated scheme is assessed by comprehensive simulations. https://www.jsstpub.com/article_254604.html Ensuring a sustainable supply of food resources and effective atmospheric revitalization remains a critical challenge for long-duration space missions. This challenge is further complicated by the technical constraints and high costs associated with repeated in-orbit experimentation. In response, this study investigates and models the biological responses of Spirulina microalgae, a recognized protein-rich organism, under suborbital flight conditions, with the overarching goal of replacing conventional trial‑and‑error approaches with an intelligent predictive framework. To this end, experimental data capturing complex environmental stressors, including microgravity and distinct light wavelengths (red, white, and dark), were utilized to train an advanced neural network. Specifically, a Deep Q‑Learning (DQN) algorithm was developed, incorporating mechanisms such as experience replay to mitigate data fluctuations and enhance learning stability, and was trained over 5,000 episodes. The results demonstrate the model's outstanding performance, achieving a coefficient of determination (R²) exceeding 0.98 for all output indicators, while the root mean square error for light‑related parameters fell below 0.013, indicating an excellent agreement between predictions and actual observations. Although minor fluctuations were observed in the prediction of time‑dependent parameters, the overall convergence of the model confirms that artificial intelligence can serve as a reliable, cost‑effective, and efficient tool for the automated monitoring and optimization of biological responses in future space probes and orbital stations. This work thus represents a meaningful step toward ensuring astronaut biosafety in deep‑space exploration. https://www.jsstpub.com/article_254610.html Attitude determination is a key requirement for mission success in small satellites, particularly during orbital injection and early operational phases. However, stringent constraints on mass, power, and cost make the design of accurate and reliable attitude determination systems challenging. To address these constraints, this study proposes a low-cost sensor fusion architecture based on gyroscopes, a sun sensor, and a magnetometer. The gyroscope provides high-bandwidth angular rate measurements for short-term attitude propagation, while the sun sensor supplies periodic absolute attitude information and mitigates long-term gyro drift. During eclipse periods, when the sun sensor is unavailable, the magnetometer serves as an alternative measurement source and also complements the sun sensor to enhance estimation accuracy. Since magnetic disturbances and solar activity can degrade magnetometer performance, the proposed fusion strategy is designed to maintain both estimation accuracy and robustness under sensor degradation or partial failure. An Extended Kalman Filter is implemented to estimate spacecraft attitude. The filter propagates the attitude using the rotational kinematic model driven by gyro measurements and updates the estimate using sun sensor and magnetometer observations. Simulation results under multiple operational scenarios demonstrate that the proposed algorithm achieves accurate attitude estimation while maintaining robustness against disturbances and varying environmental conditions. https://www.jsstpub.com/article_233464.html This study applies the Random Forest (RF) machine learning (ML) algorithm to GNSS radio occultation (GNSS-RO) data for two critical space weather forecasting tasks: the prediction of ionospheric amplitude scintillation (S4 index) and the classification of geomagnetic storm occurrence. For S4 index prediction, an RF regression model was trained on a comprehensive feature set derived from interplanetary magnetic field components, geomagnetic indices, solar radio flux, and historical S4 statistics. The model's architecture is optimized, and Recursive Feature Elimination with Gini Importance (Mean Decrease Impurity) method is applied to identify the most predictive features. For storm detection, an RF classifier was trained on parameters including Total Electron Content (TEC), S4 indices, and key solar and geomagnetic variables to distinguish between storm and non-storm conditions. The optimized S4 prediction model achieved high precision with a Mean Absolute Error of 0.007263 and an R² score of 0.517352. Feature selection via Gini Importance significantly improved model efficiency, increasing the Adjusted R² by 50.5%. The geomagnetic storm classifier demonstrated a critical strength of recall of 0.99 for storm events, ensuring missing only 1% of actual storms. Analysis of feature importance confirmed the model's physical validity, correctly identifying the Kp index, F10.7 solar flux and Dst index as the primary drivers for prediction, which aligns with established solar-terrestrial physics. The results demonstrate the high potential of machine learning, specifically RFs, for precise space weather forecasting https://www.jsstpub.com/article_233903.html This article focuses on the mitigation of cavitation in fluid flow under specified velocity conditions. A Clark-Y hydrofoil with a 70 mm chord length is designed to emulate a fish-gill-inspired configuration aimed at suppressing unsteady cavitation phenomena. Jet injection with a velocity of 0.5 𝑈∞ is implemented at 15% and 60% of the chord length on the hydrofoil surface under an 8-degree angle of attack. The jet is aligned parallel to the mainstream flow. The flow is modeled using the multiphase implicit volume of fluid (VOF) method coupled with the large eddy simulation (LES) turbulence model to accurately capture the transient behavior of cavitating flow. Simulations are performed at a cavitation number of σ = 0.8 and a Reynolds number of 7 × 10⁵. The numerical findings demonstrate that jet injection effectively stabilizes the cavity shedding process and reduces the amplitude of pressure fluctuations on the hydrofoil surface. The injection at 15% chord length not only decreases cavity length but also delays the onset of cloud cavitation, resulting in a smoother pressure recovery and enhanced hydrodynamic performance. In contrast, injection at 60% chord length provides localized suppression of trailing-edge cavitation without significantly affecting leading-edge dynamics. Overall, the study highlights the capability of controlled jet injection as an efficient passive method for mitigating unsteady cavitation and improving flow stability around hydrofoils. https://www.jsstpub.com/article_233453.html This study investigates the influence of orbital parameters on the number of satellite visibility events from ground stations, with the aim of optimizing orbit design for space weather monitoring missions. A two-body dynamical model was employed to simulate the satellite trajectory over a 24 hour period, and the number of visibility passes was evaluated with respect to a network of five ground stations located across Iran. The contribution of key orbital elements—including semi-major axis, eccentricity, inclination, RAAN, argument of perigee, and mean anomaly—was quantified using the global Sobol sensitivity analysis method. The first-order Sobol indices revealed that inclination and argument of perigee exert the strongest independent influence on visibility, with maximum values of 0.45 and 0.18, respectively. In contrast, parameters such as RAAN and mean anomaly exhibited minimal first order effects, yet their total indices indicated highly significant interactive contributions, in some cases exceeding 0.9. Furthermore, scenarios corresponding to the maximum number of daily passes were identified. All five stations recorded configurations yielding up to nine passes within 24 hours. The associated orbital parameters exhibited diverse geometrical characteristics, ranging from nearly circular orbits with low eccentricity (0.0028) to more elongated orbits with higher eccentricity (0.0915), and inclinations spanning approximately 40° to 60°. These findings demonstrate the critical role of both independent and interactive effects of orbital elements in determining ground visibility, and they provide valuable insights for the multi-objective design of satellite orbits aimed at enhancing temporal coverage, maximizing pass frequency, and improving the efficiency of space weather monitoring missions. https://www.jsstpub.com/article_252100.html Orbital transfer utilizing Hall-effect thrusters (HETs) faces significant challenges due to inherent thrust instabilities and nonlinear actuator faults. This study presents a novel application of an optimized Type‑2 neuro‑fuzzy controller specifically designed to ensure robust maneuvering under severe uncertainty. Unlike prior studies that are primarily restricted to mild fault conditions and limited controller comparisons, the proposed approach is systematically evaluated against a type‑1 neuro‑fuzzy controller and a conventional linear quadratic regulator. A neural network initializes the control structure, while a genetic algorithm is used solely for fuzzy rule reduction and acceleration of the training process, thereby improving computational efficiency without direct controller tuning. Numerical simulations demonstrate that, under sustained high‑amplitude and high‑frequency fault scenarios, the proposed Type‑2 controller preserves accurate orbital tracking and system stability. In contrast, the comparative methods exhibit significant performance degradation. These results establish a reliable and fault‑tolerant control framework for low‑Earth orbit missions in which conventional robust control strategies prove inadequate. https://www.jsstpub.com/article_233459.html The application of laminated fiberous composite materials is increasing in many structures such as shell and plates especially related to applications in airospace engineering. In this paper, the three-dimensional stress distribution in an cross-ply laminated composite cylindrical shell subjected to radial loading is investigated. The formulation is based on a layer-wise, displacement-based theory. First, the displacement and strain field of a cylindrical shell is derived within the framework of the layer-wise theory. Then, by employing the principle of minimum total potential energy, the governing equations of motion and the corresponding boundary conditions for the cross-ply laminated composite cylindrical shell are obtained. An analytical solution is subsequently developed for the governing equations, and the edge boundary conditions are applied to the solution. In the numerical results section, the convergence and accuracy of obtained numerical results are examined with available results in the literature, and stress distributions in a cross-ply composite cylindrical shell which is subjected to lateral loading are computed, and various results corresponding to different loading distributions are presented., the effect of various parameter and the numerical results are investigated. https://www.jsstpub.com/article_254458.html Accurate prediction of surface pressure distributions is critical for the aerodynamic analysis and design of high-speed axisymmetric bodies, particularly for configurations such as sphere-cone and flares. Traditional computational fluid dynamics (CFD) provides reliable results but is computationally expensive, especially when evaluating numerous geometrical variations. To address this challenge, we propose a Bidirectional Long Short-Term Memory (BiLSTM) network as a data-driven surrogate model for predicting the pressure coefficient (Cp​) along axisymmetric surfaces. The BiLSTM leverages the sequential nature of surface geometry to learn both local and global flow features from a compact dataset of high-fidelity CFD simulations. Eighty geometries, each defined by seven key parameters, were simulated at Mach 2 using two-dimensional axisymmetric CFD under laminar flow assumptions. Comparative studies with feedforward neural networks (FNN) and unidirectional LSTM architectures demonstrate the superiority of the BiLSTM, achieving R² = 0.985 and a mean absolute error of 0.045. The results show that the bidirectional structure effectively captures forward and backward spatial correlations, yielding smooth and physically consistent Cp​ predictions, particularly near stagnation and flare compression zones. This framework provides a computationally efficient, high-fidelity, and generalizable approach for aerodynamic prediction, offering significant potential for rapid conceptual design and parametric studies of complex axisymmetric bodies. https://www.jsstpub.com/article_233460.html Synthetic Aperture Radar (SAR) image registration is a crucial preprocessing step for many remote sensing applications. However, the presence of speckle noise and homogeneous textures in SAR images makes this task challenging. In this study, we propose a method for SAR image registration. The proposed method, SARNet, is a lightweight and optimized deep neural network specifically designed for SAR images. It generates three distinct descriptors for each keypoint to increase the accuracy of keypoint matching. The method was evaluated on a diverse dataset including Radarsat, Sentinel-1, ALOS-PALSAR, and ERS-2 images. Experimental results show that (SARNet), along with a sub-descriptor matching algorithm, significantly establishes more accurate keypoint correspondences. The network achieves comparable accuracy to state-of-the-art deep learning methods while having the lowest number of parameters (115K) among the methods compared. https://www.jsstpub.com/article_235320.html Accurate calibration of inertial sensor biases is critical for preventing accumulated error growth in inertial navigation systems (INS). Conventional approaches, such as Kalman filtering and its variants, often involve significant computational complexity, dependence on precise noise modeling, and require extensive parameter tuning to ensure stability and convergence. To address these challenges, this paper presents a novel online algebraic calibration method that reformulates the INS error dynamics to directly isolate and express accelerometer and gyroscope bias terms in a closed-form manner. The proposed algorithm estimates biases by solving a deterministic system of linear equations constructed from the error states between a reference and a dependent navigation system, thereby completely eliminating the need for iterative filtering or probabilistic updates. Comprehensive simulation studies under both stationary and cruise flight conditions demonstrate the method’s high accuracy, rapid convergence, and robustness against measurement noise. The results further highlight its excellent numerical stability and adaptability to time-varying sensor characteristics. Overall, the proposed approach achieves precise and consistent bias estimation with minimal computational cost, providing a fast, stable, and resource-efficient alternative for real-time implementation in safety-critical applications such as aviation, autonomous vehicles, and robotics. https://www.jsstpub.com/article_234329.html In this research, a case study was conducted on a hypothetical satellite constellation based on standard 12U CubeSat satellites. Initially, the key technical specifications of the satellite, including the mass of its main subsystems, were extracted and presented in detail. The masses of subsystems such as Attitude Determination and Control System (1.845 kg), Central Processing Unit (0.716 kg), Electrical Power Supply (2.182 kg), Structure (3.965 kg), Payload (13 kg), and other components were identified, resulting in a total satellite mass of less than 24 kilograms. Subsequently, by employing the proposed cost model, a detailed cost estimation related to a single 12U satellite unit within the overall constellation program framework was provided (as per Table 3). The results of this modeling indicate that the estimated total cost per satellite amounts to $1,215,000. This total cost includes the main cost components as follows: satellite hardware and initial development ($620,100), Assembly, Integration, and Testing (AIT) costs ($899,140), program-level and management expenses ($196,140), launch operations support costs ($111,110), and ground support equipment costs ($92,320). This study demonstrates that directly linking the technical specifications of subsystems to cost components enables more accurate cost estimation and more efficient sensitivity analysis for planners and aerospace systems engineers. It significantly contributes to a better understanding of the key factors influencing the final costs of satellite constellations. https://www.jsstpub.com/article_235319.html In conventional Inertial Navigation Systems (INS), the error accumulation due to the integration process leads to drift in the actual values of position, velocity, and attitude of an unmanned aerial vehicle. In this study, to mitigate such accumulated errors, a Rotating Inertial Navigation System (RINS) is implemented around the vertical axis using low-cost Inertial Measurement Unit (IMU) sensors. First, the influence of various error sources—including bias, scale factor, and misalignment errors—over time in a static scenario is analyzed for the rotary configuration. Then, a comparative evaluation is performed against a conventional INS. Subsequently, the effect of each error source is investigated in a dynamic test through software simulation of a single-degree-of-freedom RINS, with results compared to those of the conventional INS simulation. Moreover, to validate the simulation results, experimental tests are designed and conducted to assess the accuracy of the rotary inertial navigation system under real-world conditions. Finally, the RINS is implemented on a single-degree-of-freedom rate table in the Control Systems Laboratory environment of the aerospace department of Sharif University. The results demonstrate that the RINS configuration effectively reduces bias, scale factor, and misalignment errors around the body’s x and y axes. https://www.jsstpub.com/article_234711.html In order to investigate the effect of the location of microstructured films on the aerodynamic performance of an airfoil, this paper numerically studies the aerodynamic forces resulting from the placement of V-shaped transverse microgrooves on a NACA 8-H-12 airfoil. To achieve this, microgrooves with a base and height of 150 μm were placed at eight different locations on the surface of an airfoil with a 220 mm chord. Their effects were then investigated at a velocity of 65 m/s and at angles of attack (AoA) of 0 and 4 degrees. The results indicate that the total drag force can either increase or decrease depending on the area and position of the microgrooved film on the airfoil. The maximum drag reduction observed in this study was 4.6%, which occurred with the installation of two 200 mm films at the mid-chord on both the suction and pressure sides of the airfoil at zero AoA. In all cases studied, the trend of increasing pressure drag was found to be the same as the trend of decreasing friction drag. Consequently, the greatest reduction in friction drag led to the largest increase in pressure drag, a behavior that was visible at both AoA. On the other hand, in most cases, the presence of microgrooves has led to an increase in the lift-to-drag ratio. https://www.jsstpub.com/article_235315.html Background: Long-duration space missions introduce complex physiological, psychological, and operational challenges due to microgravity, radiation, isolation, and confined environments. Ensuring astronaut health and mission success requires adaptive, evidence-based, and internationally harmonised selection protocols.Objective: This narrative synthesis evaluates current astronaut selection criteria for long-duration missions, critically examining physiological, psychological, and technical domains, identifying limitations of existing frameworks, and highlighting emerging technologies and international considerations.Methods: Peer-reviewed literature, agency standards, and technical guidance from NASA, ESA, JAXA, Roscosmos, and commercial entities were reviewed. Key physiological stressors, behavioural competencies, and skill requirements were linked to exclusion and competency criteria, with a focus on actionable recommendations, comparative analysis, and emerging AI- and digital twin–enabled assessment tools.Results: Critical selection domains include musculoskeletal and cardiovascular resilience, vestibular and immune system integrity, cognitive and emotional stability, leadership and teamwork capabilities, and advanced academic and operational competence. Comparative analysis reveals variability across agencies, highlighting gaps in mission-specific thresholds, duration-based criteria, and integration of emerging countermeasures. AI-assisted monitoring and digital twin simulations offer transformative potential for continuous risk assessment and personalised adaptation.Conclusion: Developing space programs should implement evidence-informed, adaptive selection frameworks that integrate physical, psychological, and technical competencies, harmonised international standards, and emerging technologies. Prospective evaluation, continuous monitoring, and tiered, mission-specific criteria are essential to optimise safety, performance, and operational success on long-duration missions. https://www.jsstpub.com/article_235314.html In satellite constellation design, particularly for navigation and positioning applications, system performance and associated costs are among the key factors guiding the design process. In recent years, Low Earth Orbit (LEO) satellite constellations have attracted significant attention from researchers and industry professionals due to their ability to provide wide coverage, low latency, and relatively lower manufacturing and launch costs. In this context, the present study aims to develop a design tool for the simultaneous optimization of performance and cost in LEO based navigation constellations. The proposed tool is developed by linking MATLAB and STK software environments and employs the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to explore the design space efficiently. The main performance indicator considered in this study is the Geometric Dilution of Precision (GDOP) at the Earth's surface. Additionally, to estimate the total system cost, a combination of the USCM8 and SSCM cost models is utilized. The design parameters include the number of satellites, the number of orbital planes, Walker constellation configurations, orbital elements, and transmitter power. The developed design tool is capable of conducting multi-scenario evaluations to provide a comprehensive and detailed analysis of the trade-off between coverage accuracy and system cost. The simulation results demonstrate that the tool generates reliable and realistic solutions, making it a robust framework for the conceptual design of navigation satellite constellations. This study contributes to the integration of intelligent optimization methods with advanced space simulation tools and paves the way for more advanced research in the field of space system design. https://www.jsstpub.com/article_235318.html The direct fusion of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) data using a Kalman filter is a highly effective method for enhancing navigation solution accuracy and stability. This approach distinguishes itself from indirect methods by delivering superior performance in estimation speed and precision, achieved through a simplified filter structure and reduced processing delays inherent in other integration techniques. To validate these advantages, a nonlinear Extended Kalman Filter (EKF) was implemented for a detailed analysis of parameter estimation.A comprehensive analysis of real-world flight data confirms the significant benefits of the direct fusion method. The results demonstrate a notable increase in estimation accuracy, along with enhanced stability and robustness under adverse conditions such as signal degradation or dynamic maneuvers. This approach also demonstrated faster convergence speeds, allowing the navigation solution to stabilize more quickly. Furthermore, flight test validation confirms that this direct approach accelerates the estimation of initial INS errors. This capability leads to a more rapid determination of geographic north during the alignment phase, a critical factor for rapid system deployment. Ultimately, this study provides a comprehensive empirical validation of the direct fusion method, highlighting its practical value for modern navigation challenges. https://www.jsstpub.com/article_254613.html The Inertial Navigation System (INS), as a stand-alone navigation solution, is capable of providing accurate and continuous information about a vehicle’s position, velocity, and attitude without relying on any external aiding sources. In recent years, Strapdown Inertial Navigation Systems (SINS) have increasingly replaced traditional stable platform systems due to their advantages in terms of lower cost, reduced size and weight, simpler mechanical structure, and higher reliability in harsh operational environments. These advantages make SINS particularly attractive for a wide range of civilian and military applications. However, despite these benefits, the overall performance of a SINS is highly dependent on the accuracy of its initialization process, especially the determination of the initial attitude, which plays a critical role in the subsequent navigation solution. This paper presents a novel initial alignment method for SINS that reduces hardware requirements by decreasing the number of gyroscopes from three to one and the number of accelerometers from three to two. This reduction leads to a considerable decrease in implementation cost while still maintaining acceptable accuracy in both leveling and heading determination. The proposed approach is based on analytical algorithms for coarse alignment and enables autonomous north-finding without the need for expensive equipment such as high-grade gyrocompasses. Ultimately, by reducing the number of required inertial sensors, the proposed method provides a cost-effective and practical solution while preserving reliable navigation performance in real-world applications. https://www.jsstpub.com/article_235321.html This paper presents a novel hybrid optimization approach that simultaneously employs B-spline and thin plate spline (TPS) basis functions for accurate reflector surface shaping in geostationary (GEO) satellite antennas. In this method, the reflector perturbation is modeled as the sum of expansions of these two sets of basis functions, and their unknown coefficients are determined using a minimax optimization technique. Since TPS functions minimize the bending energy and produce smoother surfaces, while B-splines provide higher precision in radiation pattern shaping, the proposed hybrid approach achieves both surface smoothness and pattern accuracy. Extensive numerical simulation results show that, although the convergence speed and accuracy of the hybrid method are about 10% lower than those of the pure B-spline method, the surface bending energy is reduced by more than 90%, resulting in a much smoother and more uniform reflector surface. Furthermore, the RMS optimization error is approximately 60% lower compared to the pure TPS-based approach. These results confirm that the proposed method preserves radiation accuracy while providing a smoother, more stable, and fabrication-friendly surface, outperforming conventional shaping methods. https://www.jsstpub.com/article_251358.html Given the increasing demand for geostationary orbital slots and operational limitations in defining them, co-locating multiple satellites in a shared slot has become an essential strategy. This paper investigates the effect of hardware errors—specifically GPS sensor errors and thruster actuator errors—on the fuel consumption of co-located geostationary satellites. First, the optimal initial positions of three satellites in the 120°E orbital slot were determined using a multi-objective particle swarm optimization algorithm, considering co-location constraints and the frequency band of the inter-satellite link. Then, differential game theory was employed to plan station-keeping maneuvers for fuel minimization. Three simulation scenarios were analyzed: an ideal error-free case, a case with simultaneous thruster and GPS sensor errors, and a case with two thruster failures and other thrusters error plus GPS error—each evaluated for two thruster configurations (10 N and 22 N). The results show that both sensor and actuator errors significantly increase the annual velocity increment, with thruster error having a more pronounced impact. Furthermore, the higher-thrust configuration (22 N) and the cooperative control strategy demonstrated greater effectiveness in mitigating error effects and maintaining mission stability. https://www.jsstpub.com/article_251365.html Reliability of the Attitude Determination and Control Subsystem (ADCS), particularly during emergency conditions and prior to entering precise pointing modes, is of critical importance. This issue can be addressed either through reliability analysis methods or by employing hardware components with low failure rates. In this paper, magnetic torquers are considered due to their simple structure, continuous operability in different mission modes, and lack of consumable resources. Accordingly, an integrated attitude determination and control system, incorporating both attitude determination and control processes simultaneously and utilizing only magnetic torquers in all operational modes, is proposed for a low-Earth-orbit microsatellite. Attitude determination is performed using sun sensor, magnetometer, and gyroscope measurements based on the multiplicative quaternion extended Kalman filter. Sensor noise and misalignment models are incorporated to evaluate estimation performance, while major environmental disturbance torques are considered to assess system robustness. Attitude control algorithms are designed for three main operational modes, including detumbling, sun-pointing, and nadir-pointing, and their stability is proven using Lyapunov theory. Numerical simulations are conducted to evaluate system requirements and performance. The results indicate that the detumbling process is completed in less than two orbital periods, while sun-pointing and nadir-pointing accuracies better than 22° and 5° are achieved, respectively. Comparison with representative references demonstrates that the proposed ADCS can effectively operate during emergency conditions and before transitioning to high-precision pointing modes. https://www.jsstpub.com/article_252049.html Satellite Constellations are used today in many missions: communications, navigation and positioning, remote sensing, earth observation, SAR radar, and the Internet of Things (IoT). One of the specific applications of the systems is the early warning mission, in which satellites detect threats using infrared radiation. In order to perform this mission, the constellation must be designed to continuously cover the target area. This paper deals with the design of the low altitude (LEO) part of a system with this mission. In these constellations, the LEO part is usually a complement to their high altitude part, which is placed in high elliptical (HEO) and geosynchronous (GEO) orbits. In this paper, the optimal configuration of the constellation is designed using evolutionary algorithms to perform the mission with the minimum number of satellites. The target area is also considered to be an area of the Earth that encompasses the entire longitude and a 130-degree wide latitude band. By running the optimization algorithm and assuming an elevation angle of 5 degrees from the horizon, an optimal constellation with 42 satellites was obtained, which has a zero revisit time at all network points, thus ensuring continuous coverage in the target area. https://www.jsstpub.com/article_254617.html In this paper, a geometric approach is proposed for the design of minimum-time Eigen-axis repointing maneuvers of symmetric spacecraft equipped with four reaction wheels in a pyramidal configuration. First, by analyzing the geometric distribution of the available control torque, an algorithm is developed to compute the maximum achievable torque along an arbitrary direction. Based on this maximum torque, the constant control coefficients required for implementing a bang–bang control law in the Eigen-axis maneuver are determined. Analytical and numerical results demonstrate that, due to the non-uniform torque distribution inherent in the pyramidal configuration, selecting an Eigen-axis perpendicular to the plane defined by the initial and final pointing directions does not necessarily yield the minimum maneuver time. Instead, the optimal Eigen-axis is located at an angle smaller than 90 degrees with respect to this plane. Furthermore, the torque distribution and maneuver performance are investigated under the condition of failure of one reaction wheel and compared with the conventional three orthogonal reaction wheel configuration. Numerical simulations show that the pyramidal configuration provides superior maneuver performance in terms of achievable torque and maneuver time, even in the presence of a single wheel failure. https://www.jsstpub.com/article_254493.html The atmospheric boundary layer height (ABLH) is a key parameter for characterizing near-surface atmospheric mixing, pollutant dispersion, and land–atmosphere exchange, yet its continuous retrieval remains challenging because different observing systems respond to different aerosol, thermodynamic, and turbulence signatures. LiDAR and ceilometer measurements provide high-resolution observations of atmospheric vertical structure, but conventional gradient-, variance-, and wavelet-based retrievals can be affected by weak aerosol contrast, clouds, residual and elevated aerosol layers, signal noise, and instrument-dependent characteristics. This study provides a structured, LiDAR-centered synthesis of recent advances in ABLH retrieval, focusing on signal processing, ABL-specific multi-source data integration, machine and deep learning, and emerging physics-informed approaches. The reviewed evidence shows that improved signal processing, automated layer tracking, and complementary thermodynamic, turbulence, surface, and model information can help reduce ambiguities that cannot be addressed from aerosol backscatter alone. Machine-learning approaches provide additional capability for automated and nonlinear retrieval, but reported performance remains dependent on reference definition, atmospheric regime, site, instrument, and validation strategy. Particular challenges include uncertainty quantification, physical layer attribution, cross-site and cross-instrument generalization, and operational robustness. Future progress therefore requires physics-informed integration, independent cross-domain validation, transparent uncertainty characterization, and systematic evaluation of failure modes rather than increasing algorithmic complexity alone. https://www.jsstpub.com/article_254494.html Accurate and reliable positioning is a fundamental necessity in today's modern world. While satellite navigation systems have found wide application in various fields, they are extremely vulnerable and are becoming unavailable. Therefore, the existence of a reliable, continuous and accurate navigation system as an alternative seems essential. By combining the principles of astronomical navigation with advanced computational tools, this approach extends beyond theoretical models to real-time applications and provides a suitable method for positioning, especially in situations where satellite navigation is not available. In this study, while examining various previous methods, a suitable algorithm that can be implemented on various available platforms is presented, in which the position can be estimated using the position of the sun, accurate time, and a model of the Earth's movement around the sun. The positioning accuracy in this method is strongly affected by the error in measuring solar angles, and for this reason, positioning based on a single solar observation usually has a large error of more than 1 km and does not provide the necessary accuracy for some applications. Therefore, forming an optimization problem for a set of consecutive observations improves the positioning accuracy to about 500 m. Given the limitations of these methods and to increase the accuracy, auxiliary and complementary sensors are required, and combining with speedometer, gyro and magnetometer that are available can further improve the positioning accuracy and, depending on the accuracy of these sensors, reach about 70 meters, which is well demonstrated by the Monte Carlo simulation results. https://www.jsstpub.com/article_254496.html کشت سوسپانسیون سلول های گیاهی از جمعت یکنواخی از سلول ها تشکیل شده است که می تواند سیگنال تنش را بطور مشابه، حتی در زمان کم میکروگراویتی دریافت نمایند. در این مطالعه برای اولین بار، اثر پرتاب زیرمداری بر مکانیسم‌های رشد سلول‌های گیاه بابونه از طریق بررسی زنده‌مانی سلولی، ملکول‌های سیگنالینگ و سیستم دفاع آنتی اکسیدانی بررسی شد. برای آزمایش پرتابی، سلول ها در پتری‌دیش‌ها کشت شده و طی پرتاب در کپسول زیستی قرار گرفتند (4 دقیقه اعمال میکروگراویتی). پس از بازیابی، سلول‌ها بعد از 5 و 10 روز جهت آنالیزهای مختلف برداشت شدند. نتایج نشان داد که پرتاب زیر‌مداری رشد سلول‌ها را کاهش داد که در ارتباط با کاهش زنده‌مانی سلولی بود. مورفولوژی سلولی از کشیده به کروی در مرحله ثابت رشد تغییر کرد که در سلول‌های پرتابی بیشتر از سلول‌های شاهد بود. مقادیر هیدروژن پراکسید (H2O2) و کلسیم بطور معنی در نمونه های پرتابی، بویژه بعد از 5 روز از پرتاب افزایش یافت (P ≤ 0.05). فعالیت آنزیم پراکسیداز یک افزایش قابل توجهی را بعد از 5 و 10 روز از پرتاب، به ترتیب 07/68 و 96/28% در مقایسه با شاهد را نشان داد. همچنین، مقدار فلاونوئید در سلول‌های پرتابی بعد از 10 روز به سطح ماکزیمم (55/1 میلی‌گرم بر گرم وزن‌تر) در مقایسه با نمونه شاهد (01/1 میلی‌گرم بر گرم وزن‌تر) رسید. یافته های این پژوهش نشان داد که ملکول های سیگنالینگ H2O2 و کلسیم نقش مهمی را در تنظیم رشد و زنده مانی سلولی بعد از پرتاب داشته که با تحریک فعالیت آنزیم پراکسیداز همراه است. https://www.jsstpub.com/article_254497.html One of the most important challenges in re-entry guidance & control is online implementation of an optimal guidance solution. Considering the existence of a pre-set optimal solution, a trajectory tracking guidance is to be designed with the capability to preserve optimal path and boundary constraints. Because of entry point uncertainties and environmental disturbances, the tracker tries to amplify or weaken the optimal guidance signal for properly guiding the vehicle to the reference trajectory. Convergence, tuning and computation burden are among important issues in design of trajectory tracking system. In the present paper, as an innovation the task is followed up by quasi-linear model predictive tracking (QLMPT) which uses MPC as a well-known linear controller in contribution with quasi-linear system of re-entry translational dynamics. It is investigated that above combination, well introduces non-linear tracking behavior. The IGC system is completed through the use of a double-loop attitude controller consisting of Proportional-Integral and Dynamic Inversion to follow guidance commands. The performance of selected IGC system, against disturbances and uncertainties is evaluated by Monte-Carlo analysis. Finally, a PIL test is done to observe hardware implementation performance. Simulation and hardware analysis, results in significant advantages for the proposed method in cases of performance, tuning & processing burden. https://www.jsstpub.com/article_254619.html Regional satellite navigation and positioning systems are increasingly important for achieving strategic autonomy and reducing infrastructure costs. This study investigates the design of a small, low-cost low Earth orbit (LEO) satellite constellation to provide navigation services over Iran. To reduce the number of required satellites, a limited operational-time concept is introduced. Accordingly, acceptable navigation performance, assessed using the Geometric Dilution of Precision (GDOP) criterion, is required for a cumulative period of one hour per day rather than continuous full-day coverage. In addition to reducing constellation size, this approach can provide an interim navigation service while larger constellations are being deployed. It may also serve as a testbed for evaluating regional navigation concepts before implementation of a full-scale system. To ensure feasibility, constraints associated with domestic launch vehicle capabilities, launch-vehicle performance limitations, and available launch windows are incorporated into the design process. First, previous studies on regional navigation constellations are reviewed. The recent development of this research field indicates a growing need for regional navigation systems, particularly LEO-based architectures. After defining the design problem and the relevant concepts, an initial orbit is selected according to the specified constraints. Sensitivity analyses are then performed for the remaining orbital and constellation design variables to identify suitable configurations. Finally, a genetic algorithm is used to optimize the constellation. The resulting design consists of two orbital planes, each containing five satellites, and is compared with the configuration obtained through sensitivity analysis