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Comprehensive electrical and electromagnetic tests have been successfully completed on the full‐scale electrical engineering version of the GIOTTO spacecraft in the Electromagnetic Compatibility Test Facility at British Aerospace Dynamics Group at Bristol. GIOTTO will intercept Halley's Comet when it approaches the ecliptic plane of Earth in 1986.

For fixed-wing micro air vehicles, the attitude determination is usually produced by the horizon/Global Navigation Satellite System (GNSS) in which the GNSS provides yaw estimates, while roll and pitch are computed using horizon sensors. However, the attitude determination has been independently obtained from the two sensors, which will result in insufficient usage of data. Also, when implementing attitude determination algorithms on embedded platforms, the computational resources are highly restricted. This paper aims to propose a computationally efficient linear Kalman filter to solve the problem.

The observation model is in the form of a least-square optimization composed by GNSS and horizontal measurements. Analytical quaternion solution along with its covariance is derived to significantly speed up on-chip computation.

The reconstructed attitude from Horizon/GNSS is integrated with quaternion kinematic equation from gyroscopic data that builds up a fast linear Kalman filter. The proposed filter does not involve coupling effects presented in existing works and will be more robust encountering bad GNSS measurements.

Electronic systems are designed on a real-world fixed-wing plane. Experiments are conducted on this platform that show comparisons on the accuracy and computation execution time of the proposed method and existing representatives. The results indicate that the proposed algorithm is accurate and much faster computation speed in studied scenarios.

Part two of this article looks at the TELECOM 2, HISPASAT communications satellites and SOHO the scientific satellite. It also examines Matra's involvement in the ARIANES project.

This paper aims to identify the role of internet of things (IoT) in water supply chain management and helps to understand its future path from the junction of computer science and resource management.

The current research was studied through bibliometric review and content analysis, and various contributors and linkages were found. Also, the possible directions and implications of the field were analyzed.

The paper’s key findings include the role of modern computer science in water resource management through sensor technology, big data analytics, IoT, machine learning and cloud computing. This, in turn, helps in understanding future implications of IoT resource management.

A more extensive database can add up to more combinations of linkages and ideas about the future direction. The implications and understanding gained by the research can be used by governments and firms dealing with water management of smart cities. It can also help find ways for optimizing water resources using IoT and modern-day computer science.

This study is one of the very few investigations that highlighted IoT’s role in water supply management. Thus, this study helps to assess the scope and the trend of the case area.

This paper investigates the feasibility of using the emission intensity of low‐pressure argon and nitrogen gas discharges as the sensing mechanism for a microwave electric field optical sensor probe in microwave resonant cavities. The emission is coupled to a photodiode for detection through an optical fibre due to the difficulty in using conventional optoelectronic devices in close proximity to microwave cavities. The discharge emission intensity is monitored at a range of different input powers to the cavity. The proposed designs for the electric field sensing probe are also included.

The purpose of this paper is to develop user friendly software with the minimum error and maximum performance in a form of remote sensing satellites evaluation software for estimation of weights and ranks of the remote sensing satellite plans, to decrease risk of management decisions.

The analytic hierarchy process (AHP) as a comprehensive framework for strategic decision making is used to arrive at the weights of criteria and sub‐criteria of remote sensing satellites. The Ms‐Access software is written to compute the ranks of the remote sensing satellite plans based on the relative weights of inputs and then, the outputs from AHP are shown as a numerical graph and generates the Ms‐Access database.

One of the main objectives of this paper is an attempt to access this skill that compare several remote sensing satellite plans on quantity and quality point of view by several effective criteria such as mass, power consumption and cost of satellites, in addition to the remote sensing subsystem, communication subsystem, telemetry, tracking and control subsystem, attitude determination control subsystem and their own sub‐criteria.

It is hard in just one paper, to gather lots of information about remote sensing satellite systems, use a new methodology that is unknown for aerospace engineering, and talk about an innovative software.

This paper provides helpful evaluating software which has a data bank that it is very useful and impartial advice for space strategy's managing organization to compare several plans.

This study provides low cost, time‐saving, and high‐performance remote sensing satellite evaluation software and gives valuable information and guidelines which help management decisions of aerospace organization.

Autonomous orbit determination using geomagnetic measurements is an important backup technique for safe spacecraft navigation with a mere magnetometer. The geomagnetic model is used for the state estimation of orbit elements, but this model is highly nonlinear. Therefore, many efforts have been paid to developing nonlinear filters based on extended Kalman filter (EKF) and unscented Kalman filter (UKF). This paper aims to analyze whether to use UKF or EKF in solving the geomagnetic orbit determination problem and try to give a general conclusion.

This paper revisits the problem and from both the theoretical and engineering results, the authors show that the EKF and UKF show identical estimation performances in the presence of nonlinearity in the geomagnetic model.

While EKF consumes less computational time, the UKF is computationally inefficient but owns better accuracy for most nonlinear models. It is also noted that some other navigation techniques are also very similar with the geomagnetic orbit determination.

The intrinsic reason of such equivalence is because of the orthogonality of the spherical harmonics which has not been discovered in previous studies. Thus, the applicability of the presented findings are not limited only to the major problem in this paper but can be extended to all those schemes with spherical harmonic models.

The results of this paper provide a fact that there is no need to choose UKF as a preferred candidate in orbit determination. As UKF achieves almost the same accuracy as that of EKF, its loss in computational efficiency will be a significant obstacle in real-time implementation.

Modern, complex control systems for specific application domains often display common system design architectures with similar subsystem functionality and interactions. The similarities between these subsystems in most spacecraft can be exploited to create a model‐driven system development environment and then transformed into software or hardware either manually or automatically. Modifications to software and hardware during operations can be similarly made in the same controlled way. The approach is illustrated using a spacecraft attitude determination and control subsystem, but applies equally to other types of aerospace systems.

The purpose of this paper is to propose an identification algorithm to obtain generalized attitude model (GAM) of satellites in on‐orbit environment, which includes missing attitude data and multi‐noise. The identified GAM and noise model are the basis of attitude control and state estimation on‐orbit.

To cope with noises contaminating both input and output of attitude model, the errors‐in‐variables model is transformed into a traditional Box‐Jenkins model according to the attitude control loop. The wavelet denoising (WD) technique is helpful to predict the missing output data using the identified GAM.

By the numerical simulation, it is verified that the proposal accompanied with WD has a faster prediction capability than that of the algorithm without WD. As a result, the proposed approach is suitable to attitude model identification of on‐orbit satellites.

This identification algorithm can deal with two kinds of on‐orbit conditions and has a fast parameter convergent rate. Therefore, it has a practical application value in on‐orbit environment.

This paper aims to present the development and performance evaluation of an attitude and rate estimation algorithm using an extended Kalman filter structure based on a body‐referenced representation of the state.

The algorithm requires only geomagnetic field data and can be used as a low‐cost alternative or as a back‐up estimator in the case of attitude sensor failures. The satellite rate is estimated as a part of the filter state and thus no gyroscope is necessary. The assessment of the algorithm performance is realized through a Monte Carlo simulation using a low‐Earth orbit, nadir‐pointing satellite.

Given some attitude and rate error requirements, the range of admissible initial errors on the filter state and the effect of un‐modelled disturbance torque are determined, along with the achievable attitude and rate accuracies.

Because the simulation set‐up is clearly stated, the results of this evaluation can be used as a benchmark for other estimation algorithms.

The necessary assumptions and approximations used to derive the filter equations are explicitly pointed out for the benefit of the readers. Well‐defined filter initial conditions are used in an extensive series of tests resulting into a unique set of findings.