Deorbiter CubeSat mission design

This paper presents the mission design for a CubeSat-based active debris removal approach intended for transferring sizable debris objects from low-Earth orbit to a deorbit altitude of 100 km. The mission consists of a mothership spacecraft that carries and deploys several debris-removing nanosatellites, called Deorbiter CubeSats. Each Deorbiter is designed based on the utilization of an eight-unit CubeSat form factor and commercially-available components with significant flight heritage. The mothership spacecraft delivers Deorbiter CubeSats to the vicinity of a predetermined target debris, through performing a long-range rendezvous maneuver. Through a formation flying maneuver, the mothership then performs in-situ measurements of debris shape and orbital state. Upon release from the mothership, each Deorbiter CubeSat proceeds to performing a rendezvous and attachment maneuver with a debris object. Once attached to the debris, the CubeSat performs a detumbling maneuver, by which the residual angular momentum of the CubeSat-debris system is dumped using Deorbiter’s onboard reaction wheels. After stabilizing the attitude motion of the combined Deorbiter-debris system, the CubeSat proceeds to performing a deorbiting maneuver, i.e., reducing system’s altitude so much so that the bodies disintegrate and burn up due to atmospheric drag, typically at around 100 km above the Earth surface. The attitude and orbital maneuvers that are planned for the mission are described, both for the mothership and Deorbiter CubeSat. The performance of each spacecraft during their operations is investigated, using the actual performance specifications of the onboard components. The viability of the proposed debris removal approach is discussed in light of the results.     

Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: Case study; Tehran Province,Iran

In recent years, land surface temperature (LST) has become critical in environmental studies and earth science. Remote sensing technology enables spatiotemporal monitoring of this parameter on large scales. This parameter can be estimated by satellite images with at least one thermal band. Sentinel-3 SLSTR data provide LST products with a spatial resolution of 1 km. In this research, direct and indirect validation procedures were employed to evaluate the Sentinel-3 SLSTR LST products over the study area in different seasons from 2018 to 2019. The validation method was based on the absolute (direct) evaluation of this product with field data and comparison (indirect) evaluation with the MODIS LST product and the estimated LST using the non-linear split-window (NSW) algorithm. Also, two emissivity estimation methods, (1) NDVI thresholding method (NDVI-THM) and (2) classification-based emissivity method (CBEM), were used to estimate the LST using the NSW method according to the two thermal bands of Sentinel-3 images. Then, the accuracy of these methods in estimating LST was evaluated using field data and temporal changes of vegetation, which the NDVI-THM method generated better results. For indirect evaluation between the Sentinel-3 LST product, MODIS LST product, and LST estimated using NSW, four filters based on spatial and temporal separates between pairs of pixels and pixel quality were used to ensure the accuracy and consistency of the compared pairs of a pixel. In general, the accuracy results of the LST products of MODIS and Sentinel-3, and LST estimated using NSW showed a similar trend for LST changes during the seasons. With respect to the two absolute and comparative validations for the Sentinel-3 LST products, summer with the highest values of bias (?1.24 K), standard deviation (StDv = 2.66 K), and RMSE (2.43 K), and winter with the lowest ones (bias of 0.14 K, StDv of 1.13 K, and RMSE of 1.12 K) provided the worst and best results for the seasons in the period of 2018–2019, respectively. According to both absolute and comparative evaluation results, the Sentinel-3 SLSTR LST products provided reliable results for all seasons on a large temporal and spatial scale over our studied area.     

Substitution of satellite-based land surface temperature defective data using GSP method

Land surface temperature (LST) as an important environmental variable provides valuable information for earth environmental system modelling. Currently, LST is obtained through satellite thermal sensors at various spatial and temporal resolutions. Although spatially continuous satellite-based LST measurements are intended to overcome the shortcomings of sparse ground-based LST measurements, LST images often contain anomalous values due to the existence of clouds or sensor malfunctioning. The problem becomes more serious where the users deal with high spatial resolution characterized by low temporal resolution. This study examines the capability of a newly developed graph signal processing (GSP) method using two-dimensional single-date thermal data. For this purpose, four Landsat/TIRS datasets are analyzed. The data of five elliptical regions on thermal images are eliminated and then reconstructed through the GSP method and using the LST values of the enclosing rectangles containing the ellipsoids. The results indicate that the temperature variation determined by the GSP method generally conforms to the original image LST values. According to a correlation test conducted on the original image LST and those obtained through the GSP method, the values vary from 58% to 95%, which is an above-the-average rate (RMSE from 0.69 to 2.27). The statistical analysis of the original image LST in both the elliptical regions and the enclosing rectangles containing the ellipsoids indicates that an increase in the variance of LST data causes an increased error in the calculation of temperature by the GSP method, and vice versa. The results of the analysis of variance (ANOVA) and Duncan test indicated that an increase in the number of the non-zero spectral bins would result in increased RMSE values for all the dates and the regions. Moreover, the model errors were significant at the 0.05 level across all the image date and five elliptical study regions. Based on the results, the use of this method is recommended for the reconstruction of LST missing values, where dissimilarity of atmospheric conditions limits the use of other methods that depend on the time series data of various dates and a great deal of data calculation.     

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C₄ index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.     

GPS phase difference variation statistics: A comparison between phase scintillation index and proxy indices

Scintillated GPS phase observations are traditionally characterized by the phase scintillation index, derived from specialized GPS receivers usually tracking at 50 Hz. Geodetic quality GPS receivers, on the other hand, are normally tracking at frequencies up to 1 Hz. However, availability of continuously operating geodetic receivers both in time and geographical location are superior to scintillation receiver’s coverage in many parts of the world. This motivates scintillation studies using regional and global geodetic GPS networks. Previous studies have shown the usefulness of GPS estimated total electron content variations for detecting ionospheric irregularities. In this paper, collocated geodetic and scintillation receivers are employed to compare proxy indices derived from geodetic receivers with the phase scintillation index during quiet and moderately disturbed ionospheric conditions. Sensitivity of the phase scintillation indices at high latitude stations to geomagnetic activity is discussed. Global mapping of ionospheric disturbances using proxy indices from real-time 1 Hz GPS stations are also presented.     

Asymptotic theory for thrusting,spinning-up spacecraft maneuvers

We consider the problem of a spacecraft subjected to constant body-fixed forces and moments about all three axes during a spinning-up, thrusting maneuver. In applications, undesired forces and moments can arise due to thruster imbalances and misalignments and to center-of-mass offset. In previous works, approximate analytical solutions have been found for the attitude motion, and for the change in inertial velocity and inertial position. In this paper we find asymptotic and limiting-case expressions which we derive from the analytic solutions, in order to obtain simplified, practical formulas that lend insight into the motion. Specifically, we investigate how the motion evolves (1) as time grows without bound and (2) for geometric cases of the sphere, the thin rod, and the thin plate. Closed-forms or upper-bound limits are provided for angular velocities, Eulerian angles, angular momentum pointing error, transverse and axial velocities, and transverse and axial displacements. Summaries for the asymptotic limits (for zero initial conditions) are provided in tabular form. Results are verified by numerical simulations.     

Temporal extrapolation of TEC using WNN during 2007–2018 and comparison against GIM,IRI2016 and Kriging

In this paper, a new method of temporal extrapolation of the ionosphere total electron content (TEC) is proposed. Using 3-layer wavelet neural networks (WNNs) and particle swarm optimization (PSO) training algorithm, TEC time series are modeled. The TEC temporal variations for next times are extrapolated with the help of training model. To evaluate the proposed model, observations of Tehran GNSS station (35.69°N, 51.33°E) from 2007 to 2018 are used. The efficiency of the proposed model has been evaluated in both low and high solar activity periods. All observations of the 2015 and 2018 have been removed from the training step to test the proposed model. On the other hand, observations of these 2 years are not used in network training. According to the F10.7, the 2015 has high solar activity and the 2018 has quiet conditions. The results of the proposed model are compared with the global ionosphere maps (GIMs) as a traditional ionosphere model, international reference ionosphere 2016 (IRI2016), Kriging and artificial neural network (ANN) models. The root mean square error (RMSE), bias, dVTEC = |VTEC_GPS ? VTEC_Model| and correlation coefficient are used to assess the accuracy of the proposed method. Also, for more accurate evaluation, a single-frequency precise point positioning (PPP) approach is used. According to the results of 2015, the maximum values of the RMSE for the WNN, ANN, Kriging, GIM and IRI2016 models are 5.49, 6.02, 6.34, 6.19 and 13.60 TECU, respectively. Also, the maximum values of the RMSE at 2018 for the WNN, ANN, Kriging, GIM and IRI2016 models are 2.47, 2.49, 2.50, 4.36 and 6.01 TECU, respectively. Comparing the results of the bias and correlation coefficient shows the higher accuracy of the proposed model in quiet and severe solar activity periods. The PPP analysis with the WNN model also shows an improvement of 1 to 12 mm in coordinate components. The results of the analyzes of this paper show that the WNN is a reliable, accurate and fast model for predicting the behavior of the ionosphere in different solar conditions.     

A simulation study on the mode conversion process from slow Z-mode to LO mode by the tunneling effect and variations of beaming angle

For a particular angle of incidence wave, it is possible for a slow Z-mode wave incident on an inhomogeneous plasma slab to be converted into an LO mode wave. But for another wave normal angle of the incident wave, it has been considered impossible, since an evanescence region exists between two mode branches. In this case we expect that the mode conversion takes place through the tunneling effect. We investigate the effect of the spatial scale of the density gradient on the mode conversion efficiency in an inhomogeneous plasma where the mode conversion can occur only by the tunneling effect. We use the computer simulation solving Maxwell’s equations and the motion of a cold electron fluid. By considering the steepness of the density gradient, the simulation results show the efficient mode conversion could be expected even in the case that the mismatch of the refractive indexes prevents the close coupling of plasma waves. Also, we show for these cases the beaming angle does not correspond to Jones’ formula. This effect leads to the angles larger and smaller than the angle estimated by the formula. This type of mode conversion process becomes important in a case where the different plasmas form a discontinuity at their contact boundary.     

Aircraft robust multidisciplinary design optimization methodology based on fuzzy preference function

This paper presents a Fuzzy Preference Function-based Robust Multidisciplinary Design Optimization (FPF-RMDO) methodology. This method is an effective approach to multidisciplinary systems, which can be used to designer experiences during the design optimization process by fuzzy preference functions. In this study, two optimizations are done for Predator MQ-1 Unmanned Aerial Vehicle (UAV): (A) deterministic optimization and (B) robust optimization. In both problems, minimization of takeoff weight and drag is considered as objective functions, which have been optimized using Non-dominated Sorting Genetic Algorithm (NSGA). In the robust design optimization, cruise altitude and velocity are considered as uncertainties that are modeled by the Monte Carlo Simulation (MCS) method. Aerodynamics, stability and control, mass properties, performance, and center of gravity are used for multidisciplinary analysis. Robust design optimization results show 46% and 42% robustness improvement for takeoff weight and cruise drag relative to optimal design respectively.