NASA Projects Research Articles

India’s Transition Towards Becoming a Carbon Neutral Nation

The escalating impacts of climate change and the imperative to decarbonize global energy systems have spurred a concerted effort toward exploring and adopting renewable energy technologies. Among all these options, wind energy stands out as a top choice. It has a lot of potential to meet the growing need for energy and help the environment simultaneously. In this study, we comprehensively assess wind energy potential in five key cities across India: Udupi, Kollam, Surat, Visakhapatnam, and Jamnagar. Leveraging two decades of wind data sourced from reputable repositories such as the Iarc Power Project of NASA, we employ rigorous statistical analysis, including the application of the Weibull distribution, to elucidate the probability distribution of wind speeds and assess the viability of wind energy utilization in each city. We have also focused on other ways for decarbonizing India using new and advanced technologies such as Carbon Capture, Utilization and Storage (CCUS), and green hydrogen. Our analysis extends beyond theoretical considerations to encompass practical dimensions such as infrastructure requirements, cost implications, and technological advancements. Through meticulous evaluation, we aim to provide actionable insights that can inform policy formulation, infrastructure development, and investment strategies aimed at accelerating the adoption of wind energy technologies in India. Our research suggests a solution for implementing and policy framework for CCUS to make it more effective in achieving India’s carbon-neutral goals. We have also discussed green hydrogen its types, cost of implementation, and its current application. By fostering dialogue and collaboration, our research seeks to contribute to the ongoing discourse surrounding renewable energy transitions, facilitating informed decision-making towards a more sustainable energy future.

Comparison of artificial neural networks and multiple linear regression model in the prediction of near-earth objects minimum distance to the earth

Comparison of artificial neural networks and multiple linear regression model in the prediction of near-earth objects minimum distance to the earth

Small satellite Extracurricular projects – Implementing a context‐driven SysEng engine for improved adaptability and student agency

AbstractUniversity student projects usually attempt to follow, and often fail to follow, industry frameworks and standards to ensure the success of their development effort. The typical student satellite project follows ESA ECSS or NASA project frameworks, prescribing document artefacts, project reviews, the project life cycle, and typical project processes. In the case of Orbit NTNU, this was done without understanding the underlying dependencies and supporting connections between artefacts, design steps, and project phases. This paper presents a framework for increased understanding of the purpose of common design artefacts and their interdependencies by applying model‐centric system thinking and systemigrams as a tool for highlighting relationships and dependencies within the SysEng discipline, thus enabling the students to complete well‐informed and planned design iterations that build a solid and coherent system understanding throughout the project lifecycle. The method is built to be adapted to the needs of the project team and can readily be used by 1st year university students with one semester of SysEng experience.

Evaluation of new and net community production estimates by multiple ship-based and autonomous observations in the Northeast Pacific Ocean

New production (NP) and net community production (NCP) measurements are often used as estimates of carbon export potential from the mixed layer of the ocean, an important process in the regulation of global climate. Diverse methods can be used to measure NP and NCP, from research vessels, autonomous platforms, and remote sensing, each with its own set of benefits and uncertainties. The various methods are rarely applied simultaneously in a single location, limiting our ability for direct comparisons of the resulting measurements. In this study, we evaluated NP and NCP from thirteen independent datasets collected via in situ, in vitro, and satellite-based methods near Ocean Station Papa during the 2018 Northeast Pacific field campaign of the NASA project EXport Processes in the Ocean from RemoTe Sensing (EXPORTS). Altogether, the datasets indicate that carbon export potential was relatively low (median daily averages between −5.1 and 12.6 mmol C m−2 d−1), with most measurements indicating slight net autotrophy in the region. This result is consistent with NCP estimates based on satellite measurements of sea surface temperature and chlorophyll a. We explored possible causes of discrepancies among methods, including differences in assumptions about stoichiometry, vertical integration, total volume sampled, and the spatiotemporal extent considered. Results of a generalized additive mixed model indicate that the spatial variation across platforms can explain much of the difference among methods. Once spatial variation and temporal autocorrelation are considered, a variety of methods can provide consistent estimates of NP and NCP, leveraging the strengths of each approach.

DeepOCL: A deep neural network for Object Constraint Language generation from unrestricted nature language

AbstractObject Constraint Language (OCL) is one kind of lightweight formal specification, which is widely used for software verification and validation in NASA and Object Management Group projects. Although OCL provides a simple expressive syntax, it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first‐order logic, which is approximately half accurate at the first stage of development. A deep neural network named DeepOCL is proposed, which takes the unrestricted natural language as inputs and automatically outputs the best‐scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule‐based generation approaches. To demonstrate the validity of our proposed approach, ablation experiments were conducted on a new sentence‐aligned dataset named OCLPairs. The experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation, scored 74.30 on BLEU, and greatly outperformed experienced developers by 35.19%. The proposed approach is the first deep learning approach to generate the OCL expression from the natural language. It can be further developed as a CASE tool for the software industry.

Investigation of the influence of space weather on the occurrence of geoinduction currents in the middle latitudes

The study of physical processes based on geomagnetic induction currents among the geophysical problems of the last decade is one of the main problems of fundamental research. Consideration of geomagnetic-induced current as a threatening "shock" aspect originally took place in the NASA project "life with a star", and one of the main results of this working group was that the problems of geomagnetic-induced current must be solved in combination with science and methods. In the proposed work, calculations were carried out using a model of the same type, the model of which is based on the fundamental laws. The retrospective analysis of the geophysical situation for 2016-2021 was carried out on the basis of the data of the Alma-Ata geomagnetic observatory. For all the considered events, the GIC values were calculated and the peculiarities of the occurrence of GIC at middle latitudes were studied. Of all the events studied for 2016-2021, 25 events were identified. Among these events: 1 large geomatnetic storm, 1 medium geomagnetic storm, 5 substorms and 18 weak geomagnetic storms. For these events, the following was identified. During a large geomagnetic storm, the GIC amplitude value was I=0.4mA. During an average magnetic storm I=0.14mA. During a storm with a sudden onset induced current amplitude I=0.75mA. During weak magnetic storms, the amplitude values varied within (0.06 - 0.15)mA. In general, it can be seen, that the GIC amplitude values depend on the intensity of the geomagnetic storm. However, there are some exceptions. For example, a storm with a sudden onset according to the planetary index refers to weak geomagnetic storms, however, the GIC value turned out to be greater than the GIC induced during a large geomagnetic storm. At the same time, we also considered substorms. During substorms, currents of the order of 0.07-0.13mA were induced.

Student Space Missions—Facilitating Pathways to Success for Next Generation Professionals in Space

The Undergraduate Student Instrumentation Project (USIP) was a NASA program created to engage undergraduates in rigorous scientific research for the purpose of developing the next generation of professionals in space research. It is now run by the University of Houston using local resources. The development of next generation space professionals is addressed by using inquiry- based learning. Students are guided through the process of selecting a question of interest to them from disciplines such as heliophysics; atmospheric physics, chemistry, and biology; and geoscience. The students are then guided through the process of developing an experimental investigation to address their question. This student-led project is executed by the students from initial ideation of research objectives to the design, testing, and deployment of scientific payloads. The 5E Instructional model places the student at the center of knowledge building, while instructors facilitate interaction with content and guide the inquiry process. The project is designed to integrate engineering, technology, physics, material science, and earth and atmospheric sciences as an important opportunity for the students to gain access to cross-disciplinary experiential research. In addition to classroom engagement, the students build their own payloads and ground instruments. This project increases students’ command of essential skills such as teamwork, problem solving, communication, innovation, and leadership. For the students, this formative experience continues to encourage the development of a broader range of technical skills than is typically offered within an undergraduate degree. These skills include project management, systems engineering, balloon payload design, and balloon flight operations. More specifically, we teach sensor and instrument design, avionics, circuit, and power systems design, payload mechanical and thermal design, and telemetry and navigation. The students are also taught to prepare and present standard NASA project review materials, such as Preliminary Design Review, Critical Design Review and Mission Readiness Review presentations. Furthermore, the time and energy that students commit to this project promotes professional responsibility and emphasizes the necessity of coherent teamwork. Not only do students make connections with each other during this process, but also to the broader space science community. They often work with professionals from outside of the USIP structure, and regularly attend and present at conferences and student competitions throughout the project. Student projects included subjects ranging from atmospheric trace gas chemistry, ground penetrating radar and thermal infrared imaging coupled with multiwavelength LiDAR study of surface topography and chemistry, auroral electron precipitation, quantitative multi-wave- length airglow studies, search for stratospheric microplastics, monitoring auroral radio emissions, and stratospheric conductivity. This program is a for-credit course of two to three years duration.

Determining flame temperature by broadband two color pyrometry in a flame spreading over a thin solid in microgravity

Fire spread inside a spacecraft is a constant concern in space travel. Understanding how the fire grows and spreads, and how it can potentially be extinguished is critical for planning future missions. The conditions inside a spacecraft can greatly vary from those encountered on earth, including microgravity, low velocity flows, reduced ambient pressure and high oxygen, and thus affecting the combustion processes. In microgravity, the contributions of thermal radiation from gaseous species and soot can play a critical role in the spread of a flame and the problem has not been fully understood yet. The overall objective of this work is to address this by studying the soot temperature of microgravity flames spreading over a thin solid in microgravity. The experiments presented here were performed as part of the NASA project Saffire IV, conducted in orbit on board the Cygnus resupply vehicle before it re-entered the Earth’s atmosphere. The fuel considered is a thin fabric made of cotton and fiberglass (Sibal) exposed to a forced flow of 20 cm/s in a concurrent flow configuration. Reconstruction of the flame temperature fields is extracted from two color broadband emission pyrometry (B2CP) as the flame propagates over the solid fuel. A methodology, relevant assumptions and its applicability to other microgravity experiments are discussed here. The data obtained shows that the technique provides an acceptable average temperature around ∼1300 K, which remains relatively constant during the spread with an error value smaller than 117 K. The data presented in this work provides a methodology that could be applied to other microgravity experiments to be performed by NASA. It is expected that the results will provide insight for what is to be expected in different conditions relevant for fire safety in future space facilities.

Coca-Cola bottler backs sugar research

A large bottler of Coca-Cola products is funding work at the University of California, Berkeley, lab of chemist Peidong Yang that is aimed at converting carbon dioxide into sugar via artificial photosynthesis. Coca-Cola Europacific Partners says the technology would use less land and energy than growing crops to produce sugar. The company hopes the approach, also used in a NASA project to make sugar in space, could be used to create biobased plastic packaging.

Growing the NASA safety and mission assurance (SMA) workforce of tomorrow

The NASA Safety Center (NSC) was established in 2006 in response to Columbia Accident Investigation Board (CAIB) recommendations to strengthen NASA's safety program. The NSC supports all NASA centers and facilities. The NSC fosters world-class Safety and Mission Assurance (SMA) support for NASA programs and projects through professional development activities and the advancement of the following SMA technical disciplines:•Aviation Safety•Operational Safety•Quality Engineering•Reliability and Maintainability•SMA Technical Leadership•Software Assurance•System Safety

Diversity based imbalance learning approach for software fault prediction using machine learning models

The Software fault prediction (SFP) target is to distinguish between faulty and non-faulty modules. The prediction model’s performance is vulnerable to the class imbalance issue in SFP. The existing oversampling approaches generate relatively identical synthetic data, which results in over-generalization and less diverse data. Moreover, many undesirable noisy modules are introduced while generating synthetic data. In this study, we propose the Weighted Average Centroid based Imbalance Learning Approach (WACIL), an effective synthetic over-sampling technique to mitigate the imbalance issue. The WACIL first finds borderline instances, then generates pseudo-data of them through a weighted average centroid concept and filters out inappropriate noise data through a filtration process. We conducted experiments on 24 PROMISE and NASA projects and compared them with some of the existing sampling approaches using K-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) and Deep Neural Network (DNN) as classification models. WACIL achieves superior results in terms of Fall Out Rate (FOR), F-measure and Area Under Curve (AUC) and obtains comparable results in terms of Recall and G-mean compared to the competitive approaches. The statistical analysis indicates that WACIL’s ability to outperform the other over-sampling techniques is significant under the statistical Wilcoxon signed rank test and matched pairs rank biserial correlation coefficient effect size. Hence, WACIL is advisable as a competent choice to deal with the imbalance issue in SFP.

Fuzzy Adaptive Teaching Learning-Based Optimization for Solving Unconstrained Numerical Optimization Problems

Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. It has successfully addressed several real-world optimization problems, but it may still be trapped in local optima and may suffer from the problem of premature convergence in the case of solving some challenging optimization problems. To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. The proposed fuzzy adaptive teaching learning-based optimization algorithm uses three measures from the search space, namely, quality measure, diversification measure, and intensification measure. As the 50-50 probabilities for exploitation and exploration in the basic teaching learning-based optimization algorithm may be counterproductive, the Mamdani-type fuzzy inference system of the new algorithm takes these measures as a crisp inputs and generates selection as crisp output to choose either exploitation or exploration based on the current search requirement. This fuzzy-based adaptive selection helps to adequately balance global search or exploration and local search or exploitation operations during the search process as these operations are intrinsically dynamic. The performance of the fuzzy adaptive teaching learning-based optimization is evaluated against other metaheuristic algorithms including basic teaching learning-based optimization on 23 unconstrained global test functions. Moreover, adaptive teaching learning-based optimization is used to search for near-optimal values for the four parameters of the COCOMO II model, which are then tested for validity on a software project of NASA. Analysis and comparison of the obtained results indicate the efficiency and competitiveness of the proposed algorithm in addressing unconstrained continuous optimization tasks.

Large-Scale Crop Production for the Moon and Mars: Current Gaps and Future Perspectives

In this perspectives paper, we identify major challenges for space crop production: altered convection in the microgravity environment, scheduling and logistics, crew time and the need for advanced automation, robotics, modeling, and machine learning. We provide an overview of the existing space crop production gaps identified by the Kennedy Space Center (KSC) space crop production team and discuss efforts in current development in NASA projects to address these gaps. We note that this list may not be exhaustive but aims to present the baseline needs for space crop production implementation and a subset of current solutions to the greater scientific community in order to foster further ingenuity.

Software Quality Evaluation by Cocomo II With NN and SVM

Cost, time and quality projection are the crucial aspects in software development process. Incorrect estimations can cause losses which in turn may lead to irreversible damage. It is generally perceived that a imperfectly estimated project always results in a substandard quality due to the efforts being wrongly directed. Firstly Effort Estimation is calculated by actual effort and proposed Effort. That Effort evaluation of 500 NASA projects, after that evaluation is done by four parameters Standard Error, Standard Deviation, Mean Absolute Error, Root Mean Square Error. The author amalgamated the robustness of COCOMO-II with that of Neural Network NN and Support Vector Machine SVM .Quality Which we evaluate that is quality Evaluation of Semantic Web Application. In the last checks the majority of all four parameters for software quality assessment.

Multisensory stimulation

Multisensory stimulation

A Regression Analysis Based Model for Defect Learning and Prediction in Software Development

The development of software undergoes multiple regression phases to deliver quality software. Therefore, to minimize the development effort, time and cost it is very important to understand the probable defects associated with the designed modules. It is possible that occurrence of a range of defects may impact the designed modules which need to be predicted in advance to have a close inter-association with the depended modules. Most of the existing defect prediction classifier mechanisms are derived from the past project data learning, but it is not sufficient for new project defect predicting as the new design may have a different kind of parameters and constraints. This paper recommends Regression Analysis (RA) based defect learning and prediction Defect Prediction (RA-DP) mechanism to support the defective or non-defective prediction for quality software development. The RA-DP approach provides two methods to perform this prediction analysis. It initially presents an association learning through RA to construct the regression rules from the learned knowledge required for the defect prediction. The constructed regression rules are used for defect prediction and analysis. To measure the performance of the RA-DP a regression experimental evaluation is performed over the defect-prone PROMISE dataset from NASA project. The outcome of the results is analyzed through measuring the prediction Accuracy, Sensitivity and Specificity to demonstrate the improvisation and effectiveness of the proposal in comparison to a few existing classifiers.

GIS-based rockfall hazard zones modeling along the coastal Gulf of Aqaba Region, Egypt

Rockfall is a natural hazard in mountainous areas not to be underestimated. Mass activities differing in rock volume may cause considerable economic damage. Accomplishing qualitative appraisal of high-potential zones for rockfall is a first step towards implementing mitigation strategies. Nowadays, Geographical Information Systems (GIS) are the state-of-the-art tool for a fast and economic approach of identifying potential hazard zones rather than using conventional mapping with in-situ field data. Primarily, current research focuses on designing and implementing user-friendly tools delineating potential rockfall hazard zonation (RHZ). The constructed model examines triggering factors like slope, aspect, elevation, lithology, structural lineament, rainfall intensity, and seismic activity focal depth of a mountainous coastal region (Gulf of Aqaba, Egypt). The extracted geomorphological parameters were based on a high-resolution TanDEM-X Digital Elevation Model. The enhanced Landsat ETM + 7 was used to generate the lithological and structural lineament parameters, while the rainfall data were collected from NASA project tool. The zonation model was implemented by means of ESRI’s ArcGIS Pro ModelBuilder. Google Earth Pro orthophotos compared with the generated rockfall hazard zonation map indicate the potential RHZ with high reliability. The achieved results show that 15 % of the study area qualifies as a high rockfall hazard zone. As the RHZs generated by the model depend on the input data and the selected rating scores and weights, obtaining ground truth is essential to get a trustworthy result. Finally, this study recommends employing the built RHZ model on similar terrains worldwide to support decision-makers involving any sustainable development projects.

Extended COCOMO: robust and interpretable neuro-fuzzy modelling

Prediction of software development efforts is one of the crucial activities in software project management. Still, search for the perfect model for software cost estimation has become most difficult task of the organisations dealing in software development. This paper presents the extended version of COCOMO, which is done with the help of two very popular methods, i.e., artificial neural networks (ANN) and fuzzy logic. Firstly, the expert judgement about model is used for validation, and overpowers the common software engineering 'black box' problem that arises widely in ANN-based solutions. Moreover, we choose the best combination of one of the three membership functions for continuous-rating values, which reduce the variance while estimating the cost of similar projects. The validation, using 93 NASA projects dataset, shows that the model significantly improves the estimation accuracy in terms of mean magnitude of relative error (MMRE) by 10.104% relative to other known estimation models.

A new quantum model of the magnetic field of the Hot Earth, Moon and terrestrial planets

Distribution of Pand Swaves velocities in the Earth’s inner core suggests that its matter has been quantum entangled since the origin of the Earth and the Solar system. This assumption we made allows us to develop the quantum model of the geomagnetic field evolution from its start to its disappearance. Unlike the generally accepted dynamo our model provides an obvious source of energy which is a phase transition and the thermal, mechanical and electrical energy released during it. The latter generates a double electric layer which rotation gives rise to the initial dipole field. Changing its direction the phase transition causes a reversal of the magnetic field. Magnetic and paleomagnetic data on the Earth, Moon, Mercury and Mars analyzed in the frameworks the Hot Earth model and features of their gravitation recorded at NASA project offered as conditions for the planets formation and evolution and so predictions for the further evolution of the Earth and its magnetic field.

Software defect prediction using K‐PCA and various kernel‐based extreme learning machine: an empirical study

Predicting defects during software testing reduces an enormous amount of testing effort and help to deliver a high-quality software system. Owing to the skewed distribution of public datasets, software defect prediction (SDP) suffers from the class imbalance problem, which leads to unsatisfactory results. Overfitting is also one of the biggest challenges for SDP. In this study, the authors performed an empirical study of these two problems and investigated their probable solution. They have conducted 4840 experiments over five different classifiers using eight NASA projects and 14 PROMISE repository datasets. They suggested and investigated the varying kernel function of an extreme learning machine (ELM) along with kernel principal component analysis (K-PCA) and found better results compared with other classical SDP models. They used the synthetic minority oversampling technique as a sampling method to address class imbalance problems and k-fold cross-validation to avoid the overfitting problem. They found ELM-based SDP has a high receiver operating characteristic curve over 11 out of 22 datasets. The proposed model has higher precision and F-score values over ten and nine, respectively, compared with other state-of-the-art models. The Mathews correlation coefficient (MCC) of 17 datasets of the proposed model surpasses other classical models' MCC.