Systematic examination of the geomagnetic storm sudden commencement using multi resolution analysis

Geomagnetic storm sudden commencements (SSC) contain a wealth of information which is useful in many applications. It is important to point out that the SSCs used in this study are sampled at the rate of one sample per second in order get use of such high resolution data. In this paper, two studies are made on the geomagnetic SSC and an SSC onset automatic detection algorithm is introduced. The first study is about finding the relationship between the SSC rise time and its amplitude. Where it is found that there is a positive correlation between the amplitude and the amplitude gradient which is the amplitude divided by rise time. The second study is checking the spectrum of the SSC, starting from its onset until the end of the SSC rise time. This check had proved that the SSC contains both low and high frequency regions. This led us to introduce a new term, namely the SSC variation rate (VR). This VR is defined as the maximum rate of change of the field in the higher-frequency region of the SSC. These two studies were the guide to build an SSC automatic detector of one sample per second data using multi resolution analysis (MRA) of the discrete wavelet transform (DWT). The data set contains 134 SSCs with different VRs that were collected from the Circum-pan Pacific Magnetometer Network (CPMN). It is found that the standard deviation of the detection error is 41 s and that the average error is 9 s. From the calculated error distribution function, it is found that the detection error is within the range of −1.5 to 3 min. The detection process, as will be shown in the article, takes 70 s for one station and 3 min if the decision is related to the detection(s) of other stations. These results demonstrate the superiority of the proposed algorithm over other algorithms in which the detection error ranges between −8 and 5 min and the detection process takes 2–10 min. In addition to being faster and more accurate than the other algorithms, the proposed algorithm is the first algorithm that automatically detects the SSC onset times from high-resolution data unlike previous studies that focused on determining the SSC times automatically using one-minute resolution data.     

含中央分层缺陷层合板的压缩性能研究

通过考察层合板厚度(含铺层形式)、分层位置与大小等因素对压缩强度的影响,并采用局部屈曲、分层扩展以及软化夹杂等3种模型对含分层层合板的压缩强度进行了计算和破坏机理分析。得出如下结果:厚板对分层缺陷不敏感,中等厚度和薄层合板比较敏感;缺陷的位置和大小对层合板压缩强度的影响没有明显的规律可循;3种模型中软化夹杂模型效果较好,说明大部分含缺陷层合板的压缩过程具有分层扩展、基体开裂等损伤交杂在一起的特点,这导致了试件的破坏。     

Automatic detection of geomagnetic sudden commencement via time–frequency clusters

One of global processes in ionosphere–thermosphere–magnetosphere system is the geomagnetic storms. It is of great importance to develop an algorithm that auto-detects sudden commencement because it could be an indicator of onset of the geomagnetic storm. Automatic detection of geomagnetic sudden commencement is based on time–frequency clusters generated by spectrogram. Proposed algorithm is tested on data set collected from stations belong to the international real-time magnetic observatory network (INTERMAGNET). Maximum standard deviation of algorithm detection times is observed to be one minute of the corresponding arrival times published by National Geophysical Data Center (NGDC).     

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.     

Detection and mapping vegetation cover based on the Spectral Angle Mapper algorithm using NOAA AVHRR data

Satellite data, taken from the National Oceanic and Atmospheric Administration (NOAA) have been proposed and used for the detection and the cartography of vegetation cover in North Africa. The data used were acquired at the Analysis and Application of Radiation Laboratory (LAAR) from the Advanced Very High Resolution Radiometer (AVHRR) sensor of 1 km spatial resolution. The Spectral Angle Mapper Algorithm (SAM) is used for the classification of many studies using high resolution satellite data. In the present paper, we propose to apply the SAM algorithm to the moderate resolution of the NOAA AVHRR sensor data for classifying the vegetation cover. This study allows also exploiting other classification methods for the low resolution. First, the normalized difference vegetation index (NDVI) is extracted from two channels 1 and 2 of the AVHRR sensor. In order to obtain an initial density representation of vegetal formation distribution, a methodology, based on the combination between the threshold method and the decision tree, is used. This combination is carried out due to the lack of accurate data related to the thresholds that delimit each class. In a second time, and based on spectral behavior, a vegetation cover map is developed using SAM algorithm. Finally, with the use of low resolution satellite images (NOAA AVHRR) and with only two channels, it is possible to identify the most dominant species in North Africa such as: forests of the Liege oaks, other forests, cereal’s cultivation, steppes and bar soil.     

Impact of local and global factors and meteorological parameters in temporal variation of atmospheric potential gradient

In this research work, we have performed comparative diurnal variations of atmospheric Potential Gradient (PG) of fair-weather days by using the data of three stations installed in Northern, Pakistan for the year 2018. We investigated the impact of both local and global factors and meteorological parameters in the diurnal variation of atmospheric Potential Gradient on the annual and seasonal time scale. We observed two peaks, primary and secondary. This is because of the land-based measurements of annual and seasonal variations. The annual average curve of Potential Gradient of all three stations: Islamabad (CES), Muzaffarabad (MZF), and Balakot (BKT) demonstrated a notable deviation from the standard oceanic Carnegie curve. The atmospheric Potential Gradient variations are due to numerous meteorological factors e.g., air pollution, humidity, aerosol particles, fog, and temperature. Among three stations, the MZF station is located in highland (mountainous) and it demonstrated a higher atmospheric Potential Gradient. We further differentiate the results of our three stations with global results for authenticity and observed coherence between them. In addition, a positive correlation of fair-weather Potential Gradient is observed with temperature and a notable correlation between relative humidity and atmospheric Potential Gradient for all the three observatories.     

Optimal Precession Control of Randomly Perturbed Spin-Stabilized Satellite

Dynamics of the satellite angular momenta is modeled by ordinary differential equations with additive white noise. A feedback controller er is desired to maintain tolerable satellite momenta and to economize ze the fuel consumption. The stochastic optimal control problem is transformed into an equivalent deterministic optimization problem involving a parabolic partial differential equation. Necessary optimality y conditions are used to develop a computation algorithm. Results regarding optimal controls, expected costs, and confinement probabilities in different noisy environments are presented.     

Spatial and temporal monitoring of drought conditions using the satellite rainfall estimates and remote sensing optical and thermal measurements

Drought is an important natural disaster that causes devastating impacts on the ecosystem, livestock, environment, and society. So far, various remote-sensing methods have been developed to estimate drought conditions, each of which has advantages and restrictions. This study aims to monitor the real-time drought indices at the field scales via the integration of various earth observations. Our proposed method consists of two steps. In the first step, the relationships between long-term standardized precipitation indices (SPI) derived from PERSIANN-CDR rainfall data and two drought-dependent parameters derived from MODIS products, including normalized NDVI and soil-air temperature gradient, are obtained at the spatial resolution of PERSIANN-CDR grid (approximately 25 km). As the next step, the corresponding relationships are applied to estimate the drought index maps at the spatial resolution of MODIS products (1 km). Numerous analyses are carried out to evaluate the proposed method. The results revealed that, from various drought indices, including SPIs of different timescales (1, 3, 6, and 12-months), SPI-3 and SPI-6 are more appropriate to the proposed method in terms of correlation with temperature and vegetation parameters. The findings also demonstrate the competency of the proposed method in estimating SPI indices with average RMSE 0.67 and the average correlation coefficient of 0.74.     

Hyper Heuristic Approach for Design and Optimization of Satellite Launch Vehicle

Satellite launch vehicle lies at the cross-road of multiple challenging technologies and its design and optimization present a typical example of multidisciplinary design and optimization(MDO) process.The complexity of problem demands highly effi-cient and effective algorithm that can optimize the design.Hyper heuristic approach(HHA) based on meta-heuristics is applied to the optimization of air launched satellite launch vehicle(ASLV).A non-learning random function(NLRF) is proposed to con-trol low-level meta-heuristics(LLMHs) that increases certainty of global solution,an essential ingredient required in product conceptual design phase of aerospace systems.Comprehensive empirical study is performed to evaluate the performance advan-tages of proposed approach over popular non-gradient based optimization methods.Design of ASLV encompasses aerodynamics,propulsion,structure,stages layout,mass distribution,and trajectory modules connected by multidisciplinary feasible design approach.This approach formulates explicit system-level goals and then forwards the design optimization process entirely over to optimizer.This distinctive approach for launch vehicle system design relieves engineers from tedious,iterative task and en-ables them to improve their component level models.Mass is an impetus on vehicle performance and cost,and so it is considered as the core of vehicle design process.Therefore,gross launch mass is to be minimized in HHA.     

Simulation-based evaluation of a cold atom interferometry gradiometer concept for gravity field recovery

The prospects of future satellite gravimetry missions to sustain a continuous and improved observation of the gravitational field have stimulated studies of new concepts of space inertial sensors with potentially improved precision and stability. This is in particular the case for cold-atom interferometry (CAI) gradiometry which is the object of this paper. The performance of a specific CAI gradiometer design is studied here in terms of quality of the recovered gravity field through a closed-loop numerical simulation of the measurement and processing workflow. First we show that mapping the time-variable field on a monthly basis would require a noise level below $5\text{mE}/\sqrt{\text{Hz}}$. The mission scenarios are therefore focused on the static field, like GOCE. Second, the stringent requirement on the angular velocity of a one-arm gradiometer, which must not exceed ${10}^{-6}$?rad/s, leads to two possible modes of operation of the CAI gradiometer: the nadir and the quasi-inertial mode. In the nadir mode, which corresponds to the usual Earth-pointing satellite attitude, only the gradient $V_{\mathit{yy}}$, along the cross-track direction, is measured. In the quasi-inertial mode, the satellite attitude is approximately constant in the inertial reference frame and the 3 diagonal gradients $V_{\mathit{xx}}\text{,}{V}_{\mathit{yy}}$ and $V_{\mathit{zz}}$ are measured. Both modes are successively simulated for a 239?km altitude orbit and the error on the recovered gravity models eventually compared to GOCE solutions. We conclude that for the specific CAI gradiometer design assumed in this paper, only the quasi-inertial mode scenario would be able to significantly outperform GOCE results at the cost of technically challenging requirements on the orbit and attitude control.